From b5b62a51dcff96a6cabb4f7765d6a5943dc9bdca Mon Sep 17 00:00:00 2001
From: titusquah <46580668+titusquah@users.noreply.github.com>
Date: Fri, 31 Jul 2020 16:39:54 -0600
Subject: [PATCH] Updated examples to include basic, filtered, and Pitzer fit
 methods.

---
 .idea/workspace.xml                           |  48 +-
 data/csvs/labeled_zeroes_removed_ce.csv       |  21 +
 data/csvs/labeled_zeroes_removed_dy.csv       |  21 +
 data/csvs/labeled_zeroes_removed_la.csv       |  21 +
 data/csvs/labeled_zeroes_removed_nd.csv       |  51 ++
 data/csvs/labeled_zeroes_removed_pr.csv       |  33 +
 data/csvs/labeled_zeroes_removed_sm.csv       |  21 +
 data/csvs/labeled_zeroes_removed_y.csv        |  21 +
 data/xmls/PC88A_HCL_Ce.xml                    |   4 +-
 data/xmls/PC88A_HCL_Ce_w_pitzer.xml           | 338 ++++++++++
 data/xmls/PC88A_HCL_Dy.xml                    | 481 ++++++++++++++
 data/xmls/PC88A_HCL_Dy_w_pitzer.xml           | 495 ++++++++++++++
 data/xmls/PC88A_HCL_La.xml                    | 324 ++++++++++
 data/xmls/PC88A_HCL_La_w_pitzer.xml           | 339 ++++++++++
 data/xmls/PC88A_HCL_Nd.xml                    |   7 +-
 data/xmls/PC88A_HCL_NdPrCeLaDySmY.xml         | 606 ++++++++++++++++++
 .../xmls/PC88A_HCL_NdPrCeLaDySmY_w_pitzer.xml |  58 +-
 data/xmls/PC88A_HCL_Nd_w_pitzer.xml           | 341 ++++++++++
 data/xmls/PC88A_HCL_Pr.xml                    | 325 ++++++++++
 data/xmls/PC88A_HCL_Pr_w_pitzer.xml           | 341 ++++++++++
 data/xmls/PC88A_HCL_Sm.xml                    | 483 ++++++++++++++
 data/xmls/PC88A_HCL_Sm_w_pitzer.xml           | 498 ++++++++++++++
 data/xmls/PC88A_HCL_Y.xml                     | 481 ++++++++++++++
 data/xmls/PC88A_HCL_Y_w_pitzer.xml            | 496 ++++++++++++++
 docs/Examples/2_basic_fit_method.ipynb        | 477 ++++++++++++++
 docs/Examples/3_filtered_fit_method.ipynb     | 594 +++++++++++++++++
 docs/Examples/4_Pitzer_fit_method.ipynb       | 441 +++++++++++++
 27 files changed, 7316 insertions(+), 50 deletions(-)
 create mode 100644 data/csvs/labeled_zeroes_removed_ce.csv
 create mode 100644 data/csvs/labeled_zeroes_removed_dy.csv
 create mode 100644 data/csvs/labeled_zeroes_removed_la.csv
 create mode 100644 data/csvs/labeled_zeroes_removed_nd.csv
 create mode 100644 data/csvs/labeled_zeroes_removed_pr.csv
 create mode 100644 data/csvs/labeled_zeroes_removed_sm.csv
 create mode 100644 data/csvs/labeled_zeroes_removed_y.csv
 create mode 100644 data/xmls/PC88A_HCL_Ce_w_pitzer.xml
 create mode 100644 data/xmls/PC88A_HCL_Dy.xml
 create mode 100644 data/xmls/PC88A_HCL_Dy_w_pitzer.xml
 create mode 100644 data/xmls/PC88A_HCL_La.xml
 create mode 100644 data/xmls/PC88A_HCL_La_w_pitzer.xml
 create mode 100644 data/xmls/PC88A_HCL_NdPrCeLaDySmY.xml
 create mode 100644 data/xmls/PC88A_HCL_Nd_w_pitzer.xml
 create mode 100644 data/xmls/PC88A_HCL_Pr.xml
 create mode 100644 data/xmls/PC88A_HCL_Pr_w_pitzer.xml
 create mode 100644 data/xmls/PC88A_HCL_Sm.xml
 create mode 100644 data/xmls/PC88A_HCL_Sm_w_pitzer.xml
 create mode 100644 data/xmls/PC88A_HCL_Y.xml
 create mode 100644 data/xmls/PC88A_HCL_Y_w_pitzer.xml
 create mode 100644 docs/Examples/2_basic_fit_method.ipynb
 create mode 100644 docs/Examples/3_filtered_fit_method.ipynb
 create mode 100644 docs/Examples/4_Pitzer_fit_method.ipynb

diff --git a/.idea/workspace.xml b/.idea/workspace.xml
index 79caa72..a7ad1c4 100644
--- a/.idea/workspace.xml
+++ b/.idea/workspace.xml
@@ -2,18 +2,23 @@
 <project version="4">
   <component name="ChangeListManager">
     <list default="true" id="f4439dc0-6756-4612-8f7d-596d8949f300" name="Default Changelist" comment="">
-      <change afterPath="$PROJECT_DIR$/data/csvs/no_formiga_or_5_oa_PC88A_HCL_NdPrCeLaDySmY.csv" afterDir="false" />
-      <change afterPath="$PROJECT_DIR$/data/xmls/test_PC88A_HCL_NdPrCeLaDySmY_w_pitzer.xml" afterDir="false" />
-      <change afterPath="$PROJECT_DIR$/docs/Examples/outputs/multi_only_iterative_fitter_output.csv" afterDir="false" />
-      <change afterPath="$PROJECT_DIR$/docs/Examples/test_eval.py" afterDir="false" />
+      <change afterPath="$PROJECT_DIR$/data/csvs/labeled_zeroes_removed_ce.csv" afterDir="false" />
+      <change afterPath="$PROJECT_DIR$/data/csvs/labeled_zeroes_removed_dy.csv" afterDir="false" />
+      <change afterPath="$PROJECT_DIR$/data/csvs/labeled_zeroes_removed_la.csv" afterDir="false" />
+      <change afterPath="$PROJECT_DIR$/data/csvs/labeled_zeroes_removed_nd.csv" afterDir="false" />
+      <change afterPath="$PROJECT_DIR$/data/csvs/labeled_zeroes_removed_pr.csv" afterDir="false" />
+      <change afterPath="$PROJECT_DIR$/data/csvs/labeled_zeroes_removed_sm.csv" afterDir="false" />
+      <change afterPath="$PROJECT_DIR$/data/csvs/labeled_zeroes_removed_y.csv" afterDir="false" />
+      <change afterPath="$PROJECT_DIR$/data/xmls/PC88A_HCL_Dy.xml" afterDir="false" />
+      <change afterPath="$PROJECT_DIR$/data/xmls/PC88A_HCL_La.xml" afterDir="false" />
+      <change afterPath="$PROJECT_DIR$/data/xmls/PC88A_HCL_Pr.xml" afterDir="false" />
+      <change afterPath="$PROJECT_DIR$/data/xmls/PC88A_HCL_Sm.xml" afterDir="false" />
+      <change afterPath="$PROJECT_DIR$/data/xmls/PC88A_HCL_Y.xml" afterDir="false" />
+      <change afterPath="$PROJECT_DIR$/docs/Examples/2_basic_fit_method.ipynb" afterDir="false" />
       <change beforePath="$PROJECT_DIR$/.idea/workspace.xml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/workspace.xml" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/data/xmls/PC88A_HCL_Ce.xml" beforeDir="false" afterPath="$PROJECT_DIR$/data/xmls/PC88A_HCL_Ce.xml" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/data/xmls/PC88A_HCL_Nd.xml" beforeDir="false" afterPath="$PROJECT_DIR$/data/xmls/PC88A_HCL_Nd.xml" afterDir="false" />
       <change beforePath="$PROJECT_DIR$/data/xmls/PC88A_HCL_NdPrCeLaDySmY_w_pitzer.xml" beforeDir="false" afterPath="$PROJECT_DIR$/data/xmls/PC88A_HCL_NdPrCeLaDySmY_w_pitzer.xml" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/docs/Examples/iterative_fitter.py" beforeDir="false" afterPath="$PROJECT_DIR$/docs/Examples/iterative_fitter.py" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/docs/Examples/iterative_fitter_eval_grapher.py" beforeDir="false" afterPath="$PROJECT_DIR$/docs/Examples/iterative_fitter_eval_grapher.py" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/docs/Examples/iterative_fitter_monitor.py" beforeDir="false" afterPath="$PROJECT_DIR$/docs/Examples/iterative_fitter_monitor.py" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/docs/Examples/outputs/temp.xml" beforeDir="false" afterPath="$PROJECT_DIR$/docs/Examples/outputs/temp.xml" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/llepe/llepe.py" beforeDir="false" afterPath="$PROJECT_DIR$/llepe/llepe.py" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/llepe/optimizers.py" beforeDir="false" afterPath="$PROJECT_DIR$/llepe/optimizers.py" afterDir="false" />
     </list>
     <option name="SHOW_DIALOG" value="false" />
     <option name="HIGHLIGHT_CONFLICTS" value="true" />
@@ -466,7 +471,14 @@
       <option name="project" value="LOCAL" />
       <updated>1595905947460</updated>
     </task>
-    <option name="localTasksCounter" value="40" />
+    <task id="LOCAL-00040" summary="Changed iterative fit to only fit to multicomponent data.">
+      <created>1596211535974</created>
+      <option name="number" value="00040" />
+      <option name="presentableId" value="LOCAL-00040" />
+      <option name="project" value="LOCAL" />
+      <updated>1596211535974</updated>
+    </task>
+    <option name="localTasksCounter" value="41" />
     <servers />
   </component>
   <component name="Vcs.Log.Tabs.Properties">
@@ -531,11 +543,11 @@
       <screen x="-1920" y="2" width="1920" height="1040" />
     </state>
     <state x="-1213" y="379" key="ANALYSIS_DLG_com.intellij.analysis.BaseAnalysisAction$1/0.0.1536.824/-1920.2.1920.1040@-1920.2.1920.1040" timestamp="1590787657711" />
-    <state x="-1364" y="115" key="CommitChangelistDialog2" timestamp="1595905945326">
-      <screen x="-1920" y="0" width="1920" height="1040" />
+    <state x="-1364" y="117" key="CommitChangelistDialog2" timestamp="1596235285970">
+      <screen x="-1920" y="2" width="1920" height="1040" />
     </state>
     <state x="-1364" y="115" key="CommitChangelistDialog2/0.0.1536.824/-1920.0.1920.1040@-1920.0.1920.1040" timestamp="1595905945326" />
-    <state x="-1364" y="117" key="CommitChangelistDialog2/0.0.1536.824/-1920.2.1920.1040@-1920.2.1920.1040" timestamp="1595009194979" />
+    <state x="-1364" y="117" key="CommitChangelistDialog2/0.0.1536.824/-1920.2.1920.1040@-1920.2.1920.1040" timestamp="1596235285970" />
     <state x="662" y="155" key="CommitChangelistDialog2/0.0.1920.1040@0.0.1920.1040" timestamp="1594913509145" />
     <state x="-1828" y="94" width="1736" height="856" key="DiffContextDialog" timestamp="1591048879404">
       <screen x="-1920" y="2" width="1920" height="1040" />
@@ -619,11 +631,11 @@
     </state>
     <state x="-1368" y="256" key="Vcs.Push.Dialog.v2/0.0.1536.824/-1920.2.1920.1040@-1920.2.1920.1040" timestamp="1592321242949" />
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-    <state x="-1341" y="299" width="607" height="353" key="com.intellij.ide.util.TipDialog" timestamp="1596210496525">
+    <state x="-1341" y="299" width="607" height="353" key="com.intellij.ide.util.TipDialog" timestamp="1596227393154">
       <screen x="-1920" y="2" width="1920" height="1040" />
     </state>
     <state x="-1657" y="182" key="com.intellij.ide.util.TipDialog/0.0.1536.824/-1920.0.1920.1040@-1920.0.1920.1040" timestamp="1596062175691" />
-    <state x="-1341" y="299" width="607" height="353" key="com.intellij.ide.util.TipDialog/0.0.1536.824/-1920.2.1920.1040@-1920.2.1920.1040" timestamp="1596210496525" />
+    <state x="-1341" y="299" width="607" height="353" key="com.intellij.ide.util.TipDialog/0.0.1536.824/-1920.2.1920.1040@-1920.2.1920.1040" timestamp="1596227393154" />
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@@ -655,6 +667,10 @@
           <line>9</line>
           <option name="timeStamp" value="1" />
         </line-breakpoint>
+        <line-breakpoint enabled="true" suspend="THREAD" type="python-line">
+          <url>file://$PROJECT_DIR$/../../anl_box/Box Sync/titus/pitzer_parameter_explore/code/pitzer_lin_param_fit.py</url>
+          <option name="timeStamp" value="2" />
+        </line-breakpoint>
       </breakpoints>
       <default-breakpoints>
         <breakpoint type="python-exception">
diff --git a/data/csvs/labeled_zeroes_removed_ce.csv b/data/csvs/labeled_zeroes_removed_ce.csv
new file mode 100644
index 0000000..7c63232
--- /dev/null
+++ b/data/csvs/labeled_zeroes_removed_ce.csv
@@ -0,0 +1,21 @@
+label,Feed H+[M],Feed H+[M].1,PC88A[M],PC88A[M].1,Feed Ce[M],Xaq Ce,D Ce
+Li (1987) Ce,0.01,0.01,1,1,0.05080080000000001,0.0305,0.6656
+Li (1987) Ce,0.01,0.01,1,1,0.09999878,0.0763,0.3106
+Li (1987) Ce,0.01,0.01,1,1,0.15009456,0.1232,0.2183
+Li (1987) Ce,0.01,0.01,1,1,0.20000679999999998,0.1708,0.171
+Li (1987) Ce,0.01,0.01,1,1,0.30001216,0.2672,0.1228
+Li (1987) Ce,0.03,0.03,1,1,0.0499975,0.035,0.4285
+Li (1987) Ce,0.03,0.03,1,1,0.10000115,0.0785,0.2739
+Li (1987) Ce,0.03,0.03,1,1,0.1500032,0.128,0.1719
+Li (1987) Ce,0.03,0.03,1,1,0.20000832,0.1752,0.1416
+Li (1987) Ce,0.03,0.03,1,1,0.29999816,0.2728,0.0997
+Li (1987) Ce,0.05,0.05,1,1,0.05010115,0.0355,0.4113
+Li (1987) Ce,0.05,0.05,1,1,0.10000382,0.0826,0.2107
+Li (1987) Ce,0.05,0.05,1,1,0.1499967,0.1305,0.1494
+Li (1987) Ce,0.05,0.05,1,1,0.20000079999999998,0.17800000000000002,0.1236
+Li (1987) Ce,0.07,0.07,1,1,0.05,0.04,0.25
+Li (1987) Ce,0.07,0.07,1,1,0.10029675,0.0865,0.1595
+Li (1987) Ce,0.07,0.07,1,1,0.15000153,0.1351,0.1103
+Li (1987) Ce,0.07,0.07,1,1,0.20054418,0.1838,0.0911
+Li (1987) Ce,0.07,0.07,1,1,0.29999441,0.2753,0.0897
+Li (1987) Ce,0.07,0.07,1,1,0.29998992,0.2811,0.0672
diff --git a/data/csvs/labeled_zeroes_removed_dy.csv b/data/csvs/labeled_zeroes_removed_dy.csv
new file mode 100644
index 0000000..50b4275
--- /dev/null
+++ b/data/csvs/labeled_zeroes_removed_dy.csv
@@ -0,0 +1,21 @@
+label,Feed H+[M],Feed H+[M].1,PC88A[M],PC88A[M].1,Feed Dy[M],Xaq Dy,D Dy
+Li (1987) Dy,0.1,0.1,1,1,0.10000029,0.0221,3.5249
+Li (1987) Dy,0.1,0.1,1,1,0.15000025,0.0575,1.6087
+Li (1987) Dy,0.1,0.1,1,1,0.19999582,0.1037,0.9286
+Li (1987) Dy,0.1,0.1,1,1,0.25000596,0.1494,0.6734
+Li (1987) Dy,0.1,0.1,1,1,0.30000931,0.1997,0.5023
+Li (1987) Dy,0.3,0.3,1,1,0.050000199999999995,0.0106,3.717
+Li (1987) Dy,0.3,0.3,1,1,0.09999641,0.0409,1.4449
+Li (1987) Dy,0.3,0.3,1,1,0.14999806,0.0802,0.8703
+Li (1987) Dy,0.3,0.3,1,1,0.19999448,0.1252,0.5974
+Li (1987) Dy,0.3,0.3,1,1,0.29999394,0.2206,0.3599
+Li (1987) Dy,0.5,0.5,1,1,0.04999904,0.0221,1.2624
+Li (1987) Dy,0.5,0.5,1,1,0.09999996,0.0573,0.7452
+Li (1987) Dy,0.5,0.5,1,1,0.15000096,0.0984,0.5244
+Li (1987) Dy,0.5,0.5,1,1,0.19999616,0.1414,0.4144
+Li (1987) Dy,0.5,0.5,1,1,0.30001016,0.2369,0.2664
+Li (1987) Dy,0.7,0.7,1,1,0.04799855,0.0313,0.5335
+Li (1987) Dy,0.7,0.7,1,1,0.1000035,0.071,0.4085
+Li (1987) Dy,0.7,0.7,1,1,0.15000453,0.1131,0.3263
+Li (1987) Dy,0.7,0.7,1,1,0.19980428,0.1586,0.2598
+Li (1987) Dy,0.7,0.7,1,1,0.30000123,0.2517,0.1919
diff --git a/data/csvs/labeled_zeroes_removed_la.csv b/data/csvs/labeled_zeroes_removed_la.csv
new file mode 100644
index 0000000..62d63a2
--- /dev/null
+++ b/data/csvs/labeled_zeroes_removed_la.csv
@@ -0,0 +1,21 @@
+label,Feed H+[M],Feed H+[M].1,PC88A[M],PC88A[M].1,Feed La[M],Xaq La,D La
+Li (1987) La,0.0001,0.0001,1,1,0.04999962,0.0363,0.3774
+Li (1987) La,0.0001,0.0001,1,1,0.09999712,0.0844,0.1848
+Li (1987) La,0.0001,0.0001,1,1,0.1499944,0.1345,0.1152
+Li (1987) La,0.0001,0.0001,1,1,0.19999665,0.1835,0.0899
+Li (1987) La,0.0001,0.0001,1,1,0.24999486,0.2329,0.0734
+Li (1987) La,0.0001,0.0001,1,1,0.29998637,0.2833,0.0589
+Li (1987) La,0.01,0.01,1,1,0.0499991,0.0395,0.2658
+Li (1987) La,0.01,0.01,1,1,0.10000231,0.0881,0.1351
+Li (1987) La,0.01,0.01,1,1,0.15000091,0.1367,0.0973
+Li (1987) La,0.01,0.01,1,1,0.20009536,0.1856,0.0781
+Li (1987) La,0.01,0.01,1,1,0.40540686,0.3846,0.0541
+Li (1987) La,0.03,0.03,1,1,0.09999756,0.0897,0.1148
+Li (1987) La,0.03,0.03,1,1,0.15000544,0.1391,0.0784
+Li (1987) La,0.03,0.03,1,1,0.19999343,0.1883,0.0621
+Li (1987) La,0.03,0.03,1,1,0.29999853,0.2881,0.0413
+Li (1987) La,0.04,0.04,1,1,0.05030076,0.0444,0.1329
+Li (1987) La,0.04,0.04,1,1,0.09999639,0.0919,0.0881
+Li (1987) La,0.04,0.04,1,1,0.15000408,0.1413,0.0616
+Li (1987) La,0.04,0.04,1,1,0.20049903,0.1917,0.0459
+Li (1987) La,0.04,0.04,1,1,0.29948604,0.2883,0.0388
diff --git a/data/csvs/labeled_zeroes_removed_nd.csv b/data/csvs/labeled_zeroes_removed_nd.csv
new file mode 100644
index 0000000..552d2de
--- /dev/null
+++ b/data/csvs/labeled_zeroes_removed_nd.csv
@@ -0,0 +1,51 @@
+label,Feed H+[M],Feed H+[M],PC88A[M],PC88A[M],Feed Nd[M],Xaq Nd,D Nd
+Lyon (2016) Nd,0.01,0.01,1,1,0.08666112,0.05352756,0.619
+Lyon (2016) Nd,0.05,0.05,1,1,0.08666112,0.060900295,0.423
+Lyon (2016) Nd,0.1,0.1,1,1,0.08666112,0.067916239,0.276
+Lyon (2016) Nd,0.3,0.3,1,1,0.08666112,0.084878668,0.021
+Lyon (2016) Nd,0.01,0.01,1,1,0.042983916,0.015124531,1.842
+Lyon (2016) Nd,0.05,0.05,1,1,0.042983916,0.020410216,1.106
+Lyon (2016) Nd,0.1,0.1,1,1,0.042983916,0.026354332,0.631
+Lyon (2016) Nd,0.3,0.3,1,1,0.042983916,0.039290599,0.094
+Lyon (2016) Nd,0.01,0.01,1,1,0.005546312,3.16E-05,174.668
+Lyon (2016) Nd,0.05,0.05,1,1,0.005546312,0.000451912,11.273
+Lyon (2016) Nd,0.1,0.1,1,1,0.005546312,0.001558828,2.558
+Lyon (2016) Nd,0.3,0.3,1,1,0.005546312,0.005400498,0.027
+Lee (2005) Nd,0.251,0.25,1,1,0.1,0.087622932,0.141253754
+Lee (2005) Nd,0.251,0.25,1.5,1.5,0.1,0.07955199,0.257039578
+Lee (2005) Nd,0.251,0.25,2,2,0.1,0.074253556,0.34673685
+Lee (2005) Nd,0.251,0.25,2.5,2.5,0.1,0.071525275,0.398107171
+Lee (2005) Nd,0.251,0.25,3,3,0.1,0.066613942,0.501187234
+Lee (2005) Nd,0.134,0.13,1,1,0.1,0.075124079,0.331131121
+Lee (2005) Nd,0.0835,0.08,1,1,0.1,0.065582051,0.52480746
+Lee (2005) Nd,0.0285,0.03,1,1,0.1,0.059662917,0.676082975
+Lee (2005) Nd,0.262,0.26,2,2,0.1,0.079923999,0.251188643
+Lee (2005) Nd,0.134,0.13,2,2,0.1,0.061313682,0.630957344
+Lee (2005) Nd,0.0835,0.08,2,2,0.1,0.051151089,0.954992586
+Lee (2005) Nd,0.0285,0.03,2,2,0.1,0.044837645,1.230268771
+Lee (2005) Nd,0.0114,0.01,2,2,0.1,0.041450132,1.412537545
+Lee (2005) Nd,0.262,0.26,3,3,0.1,0.074253556,0.34673685
+Lee (2005) Nd,0.134,0.13,3,3,0.1,0.048273748,1.071519305
+Lee (2005) Nd,0.0835,0.08,3,3,0.1,0.040337083,1.479108388
+Lee (2005) Nd,0.0285,0.03,3,3,0.1,0.033900091,1.9498446
+Lee (2005) Nd,0.0114,0.01,3,3,0.1,0.030387123,2.290867653
+Li (1987) Nd,0.01,0.01,1,1,0.05000119,0.0239,1.0921
+Li (1987) Nd,0.01,0.01,1,1,0.09999803,0.0683,0.4641
+Li (1987) Nd,0.01,0.01,1,1,0.1500057,0.117,0.2821
+Li (1987) Nd,0.01,0.01,1,1,0.200004,0.168,0.1905
+Li (1987) Nd,0.01,0.01,1,1,0.30001149,0.2637,0.1377
+Li (1987) Nd,0.05,0.05,1,1,0.03453582,0.0201,0.7182
+Li (1987) Nd,0.05,0.05,1,1,0.09996185,0.0757,0.3205
+Li (1987) Nd,0.05,0.05,1,1,0.19999308,0.1722,0.1614
+Li (1987) Nd,0.1,0.1,1,1,0.04999817,0.0389,0.2853
+Li (1987) Nd,0.1,0.1,1,1,0.1499974,0.133,0.1278
+Li (1987) Nd,0.1,0.1,1,1,0.19999876,0.1807,0.1068
+Li (1987) Nd,0.1,0.1,1,1,0.2500032,0.232,0.0776
+Li (1987) Nd,0.1,0.1,1,1,0.30001216,0.2807,0.0688
+Li (1987) Nd,0.3,0.3,1,1,0.01996974,0.0189,0.0566
+Li (1987) Nd,0.3,0.3,1,1,0.04997968,0.0478,0.0456
+Li (1987) Nd,0.3,0.3,1,1,0.15000144,0.1464,0.0246
+Li (1987) Nd,0.3,0.3,1,1,0.29998765,0.2957,0.0145
+Li (1987) Nd,0.5,0.5,1,1,0.04999786,0.0482,0.0373
+Li (1987) Nd,0.5,0.5,1,1,0.10000096,0.0976,0.0246
+Li (1987) Nd,0.5,0.5,1,1,0.20000784,0.1968,0.0163
diff --git a/data/csvs/labeled_zeroes_removed_pr.csv b/data/csvs/labeled_zeroes_removed_pr.csv
new file mode 100644
index 0000000..908e9a9
--- /dev/null
+++ b/data/csvs/labeled_zeroes_removed_pr.csv
@@ -0,0 +1,33 @@
+label,Feed H+[M],Feed H+[M],PC88A[M],PC88A[M],Feed Pr[M],Xaq Pr,D Pr
+Lyon (2016) Pr,0.03,0.03,1,1,0.09296866,0.06556323,0.418
+Lyon (2016) Pr,0.05,0.05,1,1,0.09296866,0.066835844,0.391
+Lyon (2016) Pr,0.1,0.1,1,1,0.09296866,0.080076365,0.161
+Lyon (2016) Pr,0.3,0.3,1,1,0.09296866,0.091957131,0.011
+Lyon (2016) Pr,0.03,0.03,1,1,0.047548856,0.02373882,1.003
+Lyon (2016) Pr,0.05,0.05,1,1,0.047548856,0.025646632,0.854
+Lyon (2016) Pr,0.1,0.1,1,1,0.047548856,0.034988121,0.359
+Lyon (2016) Pr,0.3,0.3,1,1,0.047548856,0.045371046,0.048
+Lyon (2016) Pr,0.01,0.01,1,1,0.00638716,5.54E-05,114.332
+Lyon (2016) Pr,0.05,0.05,1,1,0.00638716,0.000669233,8.544
+Lyon (2016) Pr,0.1,0.1,1,1,0.00638716,0.002264147,1.821
+Lyon (2016) Pr,0.3,0.3,1,1,0.00638716,0.006323921,0.01
+Li (1987) Pr,0.01,0.01,1,1,0.04999995,0.0259,0.9305
+Li (1987) Pr,0.01,0.01,1,1,0.09999925,0.0725,0.3793
+Li (1987) Pr,0.01,0.01,1,1,0.20000715,0.1673,0.1955
+Li (1987) Pr,0.01,0.01,1,1,0.29999192,0.2648,0.1329
+Li (1987) Pr,0.03,0.03,1,1,0.05000109,0.0307,0.6287
+Li (1987) Pr,0.03,0.03,1,1,0.10009624,0.0776,0.2899
+Li (1987) Pr,0.03,0.03,1,1,0.20000456,0.1736,0.1521
+Li (1987) Pr,0.03,0.03,1,1,0.2999997,0.273,0.0989
+Li (1987) Pr,0.05,0.05,1,1,0.05002915,0.0349,0.4335
+Li (1987) Pr,0.05,0.05,1,1,0.09997622,0.0809,0.2358
+Li (1987) Pr,0.05,0.05,1,1,0.14999553,0.1299,0.1547
+Li (1987) Pr,0.05,0.05,1,1,0.25001088,0.2272,0.1004
+Li (1987) Pr,0.07,0.07,1,1,0.04999995,0.0385,0.2987
+Li (1987) Pr,0.07,0.07,1,1,0.09999936,0.0864,0.1574
+Li (1987) Pr,0.07,0.07,1,1,0.14998624,0.1343,0.1168
+Li (1987) Pr,0.07,0.07,1,1,0.24999474,0.2334,0.0711
+Li (1987) Pr,0.1,0.1,1,1,0.0499992,0.0415,0.2048
+Li (1987) Pr,0.1,0.1,1,1,0.10000199,0.0901,0.1099
+Li (1987) Pr,0.1,0.1,1,1,0.1999998,0.189,0.0582
+Li (1987) Pr,0.1,0.1,1,1,0.30000544,0.2888,0.0388
diff --git a/data/csvs/labeled_zeroes_removed_sm.csv b/data/csvs/labeled_zeroes_removed_sm.csv
new file mode 100644
index 0000000..f8d1d7b
--- /dev/null
+++ b/data/csvs/labeled_zeroes_removed_sm.csv
@@ -0,0 +1,21 @@
+label,Feed H+[M],Feed H+[M].1,PC88A[M],PC88A[M].1,Feed Sm[M],Xaq Sm,D Sm
+Li (1987) Sm,0.01,0.01,1,1,0.050050300000000006,0.0115,3.3522
+Li (1987) Sm,0.01,0.01,1,1,0.09997919999999999,0.048,1.0829
+Li (1987) Sm,0.01,0.01,1,1,0.1500016,0.0944,0.589
+Li (1987) Sm,0.01,0.01,1,1,0.24999968,0.1864,0.3412
+Li (1987) Sm,0.01,0.01,1,1,0.30000263,0.2371,0.2653
+Li (1987) Sm,0.05,0.05,1,1,0.05032248,0.0156,2.2258
+Li (1987) Sm,0.05,0.05,1,1,0.10000216,0.0556,0.7986
+Li (1987) Sm,0.05,0.05,1,1,0.15000255,0.1035,0.4493
+Li (1987) Sm,0.05,0.05,1,1,0.30392343,0.1997,0.5219
+Li (1987) Sm,0.05,0.05,1,1,0.30000411,0.2471,0.2141
+Li (1987) Sm,0.1,0.1,1,1,0.04999989,0.0219,1.2831
+Li (1987) Sm,0.1,0.1,1,1,0.09999773,0.0623,0.6051
+Li (1987) Sm,0.1,0.1,1,1,0.15000375,0.1105,0.3575
+Li (1987) Sm,0.1,0.1,1,1,0.2500038,0.2073,0.20600000000000002
+Li (1987) Sm,0.1,0.1,1,1,0.30061536,0.2572,0.1688
+Li (1987) Sm,0.3,0.3,1,1,0.0502452,0.0408,0.2315
+Li (1987) Sm,0.3,0.3,1,1,0.09999592,0.0842,0.1876
+Li (1987) Sm,0.3,0.3,1,1,0.14999496,0.1334,0.1244
+Li (1987) Sm,0.3,0.3,1,1,0.1999935,0.1814,0.1025
+Li (1987) Sm,0.3,0.3,1,1,0.30001088,0.2782,0.0784
diff --git a/data/csvs/labeled_zeroes_removed_y.csv b/data/csvs/labeled_zeroes_removed_y.csv
new file mode 100644
index 0000000..5d2aa29
--- /dev/null
+++ b/data/csvs/labeled_zeroes_removed_y.csv
@@ -0,0 +1,21 @@
+label,Feed H+[M],Feed H+[M].1,PC88A[M],PC88A[M].1,Feed Y[M],Xaq Y,D Y
+Li (1987) Y,0.1,0.1,1,1,0.1500005,0.047,2.1915
+Li (1987) Y,0.1,0.1,1,1,0.19999809,0.0857,1.3337
+Li (1987) Y,0.1,0.1,1,1,0.2999996,0.1772,0.693
+Li (1987) Y,0.5,0.5,1,1,0.05,0.016,2.125
+Li (1987) Y,0.5,0.5,1,1,0.10000039999999999,0.0445,1.2472
+Li (1987) Y,0.5,0.5,1,1,0.15000048,0.0828,0.8116
+Li (1987) Y,0.5,0.5,1,1,0.19999436,0.1214,0.6474
+Li (1987) Y,0.5,0.5,1,1,0.29999366,0.2083,0.4402
+Li (1987) Y,1.0,1.0,1,1,0.04999995,0.0333,0.5015
+Li (1987) Y,1.0,1.0,1,1,0.09999846,0.0694,0.4409
+Li (1987) Y,1.0,1.0,1,1,0.15000104,0.1108,0.3538
+Li (1987) Y,1.0,1.0,1,1,0.19999756,0.1526,0.3106
+Li (1987) Y,1.0,1.0,1,1,0.29999472,0.2416,0.2417
+Li (1987) Y,1.0,1.0,1,1,0.25000384,0.1984,0.2601
+Li (1987) Y,1.5,1.5,1,1,0.050001300000000005,0.0405,0.2346
+Li (1987) Y,1.5,1.5,1,1,0.09999854,0.0838,0.1933
+Li (1987) Y,1.5,1.5,1,1,0.19999977,0.1753,0.1409
+Li (1987) Y,2.0,2.0,1,1,0.09999804,0.0908,0.1013
+Li (1987) Y,2.0,2.0,1,1,0.19992651,0.1851,0.0801
+Li (1987) Y,2.0,2.0,1,1,0.30001335,0.2821,0.0635
diff --git a/data/xmls/PC88A_HCL_Ce.xml b/data/xmls/PC88A_HCL_Ce.xml
index b70fc2a..d9fc1f7 100644
--- a/data/xmls/PC88A_HCL_Ce.xml
+++ b/data/xmls/PC88A_HCL_Ce.xml
@@ -33,7 +33,7 @@
       <thermo>
         <const_cp Tmax="300.0" Tmin="298.0">
            <t0 units="K">298.14999999999998</t0>
-	   <h0 units="J/mol" updated="Updated at 8:33 6-16-2020">-130251.85800000011</h0>  
+	   <h0 units="J/mol" updated="Updated at 15:10 7-31-2020">-1460300.0</h0>  
 	   <s0 units="J/mol/K"> 558.9824 </s0>  
            <cp0 units="J/mol/K"> 0.0</cp0>
         </const_cp>
@@ -50,7 +50,7 @@
         <const_cp Tmax="300.0" Tmin="298.0">
            <t0 units="K">298.14999999999998</t0>
 	    
-	   <h0 units="J/mol" updated="Updated at 8:33 6-16-2020">-1190252.4083322566</h0> 
+	   <h0 units="J/mol" updated="Updated at 15:10 7-31-2020">-5177400.0</h0> 
 	   <s0 units="J/mol/K"> 1117.965 </s0>  
            <cp0 units="J/mol/K">0.0</cp0>
         </const_cp>
diff --git a/data/xmls/PC88A_HCL_Ce_w_pitzer.xml b/data/xmls/PC88A_HCL_Ce_w_pitzer.xml
new file mode 100644
index 0000000..e46659f
--- /dev/null
+++ b/data/xmls/PC88A_HCL_Ce_w_pitzer.xml
@@ -0,0 +1,338 @@
+<ctml>
+  <validate reactions="yes" species="yes" />
+                   
+ 
+  <phase dim="3" id="PC88A_liquid">
+    <elementArray datasrc="elementz.xml">
+        Cl O H C P Ce dummy 
+    </elementArray>
+    <speciesArray datasrc="#species_PC88A_liquid">
+      (HA)2(org) dodecane Ce(H(A)2)3(org)
+    </speciesArray>
+    <state>
+      <temperature units="K"> 298.15 </temperature>
+      <pressure units="Pa"> 100000.0 </pressure>
+      <soluteMolalities>
+        (HA)2(org): 0.25
+      </soluteMolalities>
+    </state>
+    <thermo model="IdealSolidSolution">
+       <standardConc model="molar_volume" />
+    </thermo>
+    <standardConc model="molar_volume" />
+    <transport model="None" />
+    <kinetics model="none" />
+  </phase>
+
+ 
+  <speciesData id="species_PC88A_liquid">
+
+    
+    <species name="(HA)2(org)">
+      <atomArray> C:16 H:35 O:3 P:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+           <t0 units="K">298.14999999999998</t0>
+	   <h0 units="J/mol" updated="Updated at 16:15 7-31-2020">-1460300.0</h0>  
+	   <s0 units="J/mol/K"> 558.9824 </s0>  
+           <cp0 units="J/mol/K"> 0.0</cp0>
+        </const_cp>
+      </thermo>
+     <standardState model="constant_incompressible">
+         <molarVolume updated="yep">0.32</molarVolume>
+      </standardState>
+    </species>
+
+    
+    <species name="Ce(H(A)2)3(org)">
+      <atomArray> C:48 H:102 O:9 P:3 Ce:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+           <t0 units="K">298.14999999999998</t0>
+	    
+	   <h0 units="J/mol" updated="Updated at 16:15 7-31-2020">-5177400.0</h0> 
+	   <s0 units="J/mol/K"> 1117.965 </s0>  
+           <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+     <standardState model="constant_incompressible">
+	     <molarVolume updated="yep">0.94</molarVolume>   
+      </standardState>
+    </species>
+
+     
+    
+    <species name="dodecane">
+      <atomArray> dummy:1  </atomArray>  
+      
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+	   <t0 units="K">298.14999999999998</t0> 
+	   <h0 units="J/mol"> 0.0 </h0> 
+	   <s0 units="J/mol/K"> 0.0 </s0>  
+           <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+     <standardState model="constant_incompressible">
+	     <molarVolume> 0.227113 </molarVolume>  
+      </standardState>
+    </species>
+
+  </speciesData>
+
+
+
+  <phase dim="3" id="HCl_electrolyte">
+    <speciesArray datasrc="#species_waterSolution">
+      H2O(L) H+ OH- Cl- Ce+++
+    </speciesArray>
+
+    <state>
+      <temperature units="K"> 298.15 </temperature>
+      <pressure units="Pa"> 100000.0 </pressure>
+      <soluteMolalities>
+        Cl-: 1.0E-7
+        H+: 1.0E-7
+      </soluteMolalities>
+    </state>
+
+    <thermo model="HMW">
+      <standardConc model="solvent_volume" />
+      <activityCoefficients TempModel="complex1" model="Pitzer">
+        
+        <A_Debye model="water" />
+        <ionicRadius default="3.042843" units="Angstroms">
+        </ionicRadius>
+        <binarySaltParameters anion="Cl-" cation="H+">
+          <beta0> 0.177000779, 0.000125778, 0.0, -33.4777082, -0.262214535 </beta0>
+          <beta1> 0.292922504, -0.027938838, 0.0, 3402.47027, 19.7936248 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi>  0.000362, -0.00003036, 0.0, -2.91038E-11, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 12 </Alpha2>
+          <source>
+            refit of Holmes, H.F., Busey, J.M., Simonson, J.M., Mesmer, R.E.,
+            Archer, D.G., and Wood, R.H., 1987, The enthalpy of dilution of HCl(aq)
+            to 648 K and 4p MPa.  Thermodynamic properties, Journal of Chemical
+            Thermodynamics, v. 19, p. 863-890.
+          </source>
+        </binarySaltParameters>
+
+        
+
+        <binarySaltParameters anion="NO3-" cation="H+">
+          <beta0> 0.123757018, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1> 0.31055417, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi>  -0.005539018, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 12 </Alpha2>
+          <source>
+            02mar (Table 3)
+            Marion, G. M.  (2002) "A molal-based model for strong acid chemistry at low
+            temperatures (less than 200 to 298 K)", Geochimica et Cosmochimica Acta, Vol. 66,
+            No. 14, pp. 2499-2516. These parameters are derived from the mole fraction model of
+            Clegg and Brimblecombe (1990) but were parameterized by Marion (2002) for the Pitzer
+            molality-based model.
+          </source>
+        </binarySaltParameters>
+		<binarySaltParameters anion="Cl-" cation="Ce+++">
+          <beta0 updated="Updated at 16:15 7-31-2020"> 0.6005, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1 updated="Updated at 16:15 7-31-2020"> 4.91, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi updated="Updated at 16:15 7-31-2020"> -0.026189999999999998, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 0 </Alpha2>
+          <source>
+            Table S1: Pitzer parameters for osmotic and activity coeff Supp Info.
+            May, P. M.;  Rowland, D.;  Hefter, G.;  Konigsberger, E. 
+			A generic and updatable Pitzer characterization of aqueous binary electrolyte 
+			solutions at 1 bar and 25 C.
+			Journal of Chemical and Engineering Data 2011,56, 5066&#8211;5077.
+          </source>
+        </binarySaltParameters>
+      </activityCoefficients>
+      <solvent> H2O(L) </solvent>
+    </thermo>
+    <elementArray datasrc="elements.xml"> O H Ce C Fe Si N Na Cl Ca P E </elementArray>
+    <kinetics model="none">
+    </kinetics>
+  </phase>
+
+  <speciesData id="species_waterSolution">
+
+    <species name="H2O(L)">
+      
+      <atomArray>H:2 O:1 </atomArray>
+      <thermo>
+        <NASA P0="100000.0" Tmax="600.0" Tmin="273.14999999999998">
+          <floatArray name="coeffs" size="7">
+            7.255750050E+01,  -6.624454020E-01,   2.561987460E-03,  -4.365919230E-06,
+            2.781789810E-09,  -4.188654990E+04,  -2.882801370E+02
+          </floatArray>
+        </NASA>
+      </thermo>
+      <standardState model="waterPDSS">
+        
+        <molarVolume> 0.018068 </molarVolume>
+      </standardState>
+    </species>
+
+
+    <species name="OH-">
+      <speciesChemFormula> OH- </speciesChemFormula>
+      <atomArray> O:1 H:1 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -37595 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -54977 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -2.56 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> 0.12527 </a1>
+        <a2 units="cal/gmol"> 7.38 </a2>
+        <a3 units="cal-K/gmol/bar"> 1.8423 </a3>
+        <a4 units="cal-K/gmol"> -27821 </a4>
+        <c1 units="cal/gmol/K"> 4.15 </c1>
+        <c2 units="cal-K/gmol"> -103460 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 172460 </omega_Pr_Tr>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+    
+    <species name="NO3-">
+      <speciesChemFormula> NO3- </speciesChemFormula>
+      <atomArray> N:1 O:3 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -26507 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -49429 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> 35.12 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> 0.73161 </a1>
+        <a2 units="cal/gmol"> 678.24 </a2>
+        <a3 units="cal-K/gmol/bar"> -4.6838 </a3>
+        <a4 units="cal-K/gmol"> -30594 </a4>
+        <c1 units="cal/gmol/K"> 7.7 </c1>
+        <c2 units="cal-K/gmol"> -67250 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 109770 </omega_Pr_Tr>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    <species name="Na+">
+      <speciesChemFormula> Na+ </speciesChemFormula>
+      <atomArray> Na:1 </atomArray>
+      <charge> 1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -62591 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -57433 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> 13.96 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> 0.1839 </a1>
+        <a2 units="cal/gmol"> -228.5 </a2>
+        <a3 units="cal-K/gmol/bar"> 3.256 </a3>
+        <a4 units="cal-K/gmol"> -27260 </a4>
+        <c1 units="cal/gmol/K"> 18.18 </c1>
+        <c2 units="cal-K/gmol"> -29810 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 33060 </omega_Pr_Tr>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+    
+    
+    <species name="Cl-">
+      <speciesChemFormula> Cl- </speciesChemFormula>
+      <atomArray> Cl:1 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="273.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -31379 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_TR units="cal/gmol"> -39933 </DH0_f_Pr_TR>
+          <S0_Pr_Tr units="cal/gmol/K"> 13.56 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/mol/bar"> 0.4032 </a1>
+        <a2 units="cal/mol"> 480.1 </a2>
+        <a3 units="cal-K/mol/bar"> 5.563 </a3>
+        <a4 units="cal-K/mol"> -28470 </a4>
+        <c1 units="cal/mol/K"> -4.4 </c1>
+        <c2 units="cal-K/mol"> -57140 </c2>
+        <omega_Pr_Tr units="cal/mol"> 145600 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> 17.79 </molarVolume>
+      </standardState>
+      <source> ref:G9 </source>
+    </species>
+
+    <species name="H+">
+      <speciesChemFormula> H+ </speciesChemFormula>
+      <atomArray> H:1 </atomArray>
+      <charge> +1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="273.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> 0 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_TR units="cal/gmol"> 0 </DH0_f_Pr_TR>
+          <S0_Pr_Tr units="cal/gmol/K"> 0 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/mol/bar"> 0 </a1>
+        <a2 units="cal/mol"> 0 </a2>
+        <a3 units="cal-K/mol/bar"> 0 </a3>
+        <a4 units="cal-K/mol"> 0 </a4>
+        <c1 units="cal/mol/K"> 0 </c1>
+        <c2 units="cal-K/mol"> 0 </c2>
+        <omega_Pr_Tr units="cal/mol"> 0 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> 0 </molarVolume>
+      </standardState>
+      <source> ref:G9 </source>
+    </species>
+
+
+    <species name="Ce+++">
+      <speciesChemFormula> Ce+++ </speciesChemFormula>
+      <atomArray> Ce:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -161600 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -167400 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -49 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.29292 </a1>
+        <a2 units="cal/gmol"> -1493.38 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.6196 </a3>
+        <a4 units="cal-K/gmol"> -21616 </a4>
+        <c1 units="cal/gmol/K"> 4.0445 </c1>
+        <c2 units="cal-K/gmol"> -108974 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 222510 </omega_Pr_Tr>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+
+  </speciesData>
+
+</ctml>
\ No newline at end of file
diff --git a/data/xmls/PC88A_HCL_Dy.xml b/data/xmls/PC88A_HCL_Dy.xml
new file mode 100644
index 0000000..d180d44
--- /dev/null
+++ b/data/xmls/PC88A_HCL_Dy.xml
@@ -0,0 +1,481 @@
+<ctml>
+  <validate reactions="yes" species="yes" />
+                   
+ 
+  <phase dim="3" id="PC88A_liquid">
+    <elementArray datasrc="elementz.xml">
+        Cl O H C P Nd Pr Ce La Dy Y Sm dummy 
+    </elementArray>
+    <speciesArray datasrc="#species_PC88A_liquid">
+      (HA)2(org) dodecane Dy(H(A)2)3(org)
+    </speciesArray>
+    <state>
+      <temperature units="K"> 298.15 </temperature>
+      <pressure units="Pa"> 100000.0 </pressure>
+      <soluteMolalities>
+        (HA)2(org): 0.25
+      </soluteMolalities>
+    </state>
+    <thermo model="IdealSolidSolution">
+       <standardConc model="molar_volume" />
+    </thermo>
+    <standardConc model="molar_volume" />
+    <transport model="None" />
+    <kinetics model="none" />
+  </phase>
+
+ 
+  <speciesData id="species_PC88A_liquid">
+
+
+    <species name="(HA)2(org)">
+      <atomArray> C:16 H:35 O:3 P:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+          <t0 units="K">298.14999999999998</t0>
+          <h0 units="J/mol" updated="Updated at 15:11 7-31-2020">-1460300.0</h0>
+          <s0 units="J/mol/K"> 558.9824 </s0>
+          <cp0 units="J/mol/K"> 0.0</cp0>
+        </const_cp>
+      </thermo>
+      <standardState model="constant_incompressible">
+        <molarVolume updated="yep">0.32</molarVolume>
+      </standardState>
+    </species>
+
+
+
+    <species name="Dy(H(A)2)3(org)">
+      <atomArray> C:48 H:102 O:9 P:3 Dy:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+          <t0 units="K">298.14999999999998</t0>
+
+          <h0 units="J/mol" updated="Updated at 15:11 7-31-2020">-5177400.0</h0>
+          <s0 units="J/mol/K"> 1117.965 </s0>
+          <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+      <standardState model="constant_incompressible">
+        <molarVolume updated="yep">0.94</molarVolume>
+      </standardState>
+    </species>
+
+    
+    
+    <species name="dodecane">
+      <atomArray> dummy:1  </atomArray>  
+      
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+	   <t0 units="K">298.14999999999998</t0> 
+	   <h0 units="J/mol"> 0.0 </h0> 
+	   <s0 units="J/mol/K"> 0.0 </s0>  
+           <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+     <standardState model="constant_incompressible">
+	     <molarVolume> 0.227113 </molarVolume>  
+      </standardState>
+    </species>
+
+  </speciesData>
+
+
+
+  <phase dim="3" id="HCl_electrolyte">
+    <speciesArray datasrc="#species_waterSolution">
+      H2O(L) H+ OH- Cl- Dy+++
+    </speciesArray>
+
+    <state>
+      <temperature units="K"> 298.15 </temperature>
+      <pressure units="Pa"> 100000.0 </pressure>
+      <soluteMolalities>
+        Cl-: 1.0E-7
+        H+: 1.0E-7
+      </soluteMolalities>
+    </state>
+
+    <thermo model="HMW">
+      <standardConc model="solvent_volume" />
+      <activityCoefficients TempModel="complex1" model="Pitzer">
+        
+        <A_Debye model="water" />
+        <ionicRadius default="3.042843" units="Angstroms">
+        </ionicRadius>
+        <binarySaltParameters anion="Cl-" cation="H+">
+          <beta0> 0.177000779, 0.000125778, 0.0, -33.4777082, -0.262214535 </beta0>
+          <beta1> 0.292922504, -0.027938838, 0.0, 3402.47027, 19.7936248 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi>  0.000362, -0.00003036, 0.0, -2.91038E-11, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 12 </Alpha2>
+          <source>
+            refit of Holmes, H.F., Busey, J.M., Simonson, J.M., Mesmer, R.E.,
+            Archer, D.G., and Wood, R.H., 1987, The enthalpy of dilution of HCl(aq)
+            to 648 K and 4p MPa.  Thermodynamic properties, Journal of Chemical
+            Thermodynamics, v. 19, p. 863-890.
+          </source>
+        </binarySaltParameters>
+
+        
+
+        
+
+      </activityCoefficients>
+      <solvent> H2O(L) </solvent>
+    </thermo>
+    
+    <elementArray datasrc="elementz.xml"> O H Nd Pr Ce La Dy Y C N Cl P E </elementArray>
+    <kinetics model="none">
+    </kinetics>
+  </phase>
+
+  <speciesData id="species_waterSolution">
+
+    <species name="H2O(L)">
+      
+      <atomArray>H:2 O:1 </atomArray>
+      <thermo>
+        <NASA P0="100000.0" Tmax="600.0" Tmin="273.14999999999998">
+          <floatArray name="coeffs" size="7">
+            7.255750050E+01,  -6.624454020E-01,   2.561987460E-03,  -4.365919230E-06,
+            2.781789810E-09,  -4.188654990E+04,  -2.882801370E+02
+          </floatArray>
+        </NASA>
+      </thermo>
+      <standardState model="waterPDSS">
+        
+        <molarVolume> 0.018068 </molarVolume>
+      </standardState>
+    </species>
+
+
+    <species name="OH-">
+      <speciesChemFormula> OH- </speciesChemFormula>
+      <atomArray> O:1 H:1 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -37595 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -54977 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -2.56 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> 0.12527 </a1>
+        <a2 units="cal/gmol"> 7.38 </a2>
+        <a3 units="cal-K/gmol/bar"> 1.8423 </a3>
+        <a4 units="cal-K/gmol"> -27821 </a4>
+        <c1 units="cal/gmol/K"> 4.15 </c1>
+        <c2 units="cal-K/gmol"> -103460 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 172460 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -4.18 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+    <species name="NO3-">
+      <speciesChemFormula> NO3- </speciesChemFormula>
+      <atomArray> N:1 O:3 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -26507 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -49429 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> 35.12 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> 0.73161 </a1>
+        <a2 units="cal/gmol"> 678.24 </a2>
+        <a3 units="cal-K/gmol/bar"> -4.6838 </a3>
+        <a4 units="cal-K/gmol"> -30594 </a4>
+        <c1 units="cal/gmol/K"> 7.7 </c1>
+        <c2 units="cal-K/gmol"> -67250 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 109770 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> 29.0 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    
+
+    
+    
+    <species name="Cl-">
+      <speciesChemFormula> Cl- </speciesChemFormula>
+      <atomArray> Cl:1 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="273.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -31379 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_TR units="cal/gmol"> -39933 </DH0_f_Pr_TR>
+          <S0_Pr_Tr units="cal/gmol/K"> 13.56 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/mol/bar"> 0.4032 </a1>
+        <a2 units="cal/mol"> 480.1 </a2>
+        <a3 units="cal-K/mol/bar"> 5.563 </a3>
+        <a4 units="cal-K/mol"> -28470 </a4>
+        <c1 units="cal/mol/K"> -4.4 </c1>
+        <c2 units="cal-K/mol"> -57140 </c2>
+        <omega_Pr_Tr units="cal/mol"> 145600 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> 17.79 </molarVolume>
+      </standardState>
+      <source> ref:G9 </source>
+    </species>
+
+    <species name="H+">
+      <speciesChemFormula> H+ </speciesChemFormula>
+      <atomArray> H:1 </atomArray>
+      <charge> +1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="273.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> 0 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_TR units="cal/gmol"> 0 </DH0_f_Pr_TR>
+          <S0_Pr_Tr units="cal/gmol/K"> 0 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/mol/bar"> 0 </a1>
+        <a2 units="cal/mol"> 0 </a2>
+        <a3 units="cal-K/mol/bar"> 0 </a3>
+        <a4 units="cal-K/mol"> 0 </a4>
+        <c1 units="cal/mol/K"> 0 </c1>
+        <c2 units="cal-K/mol"> 0 </c2>
+        <omega_Pr_Tr units="cal/mol"> 0 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> 0 </molarVolume>
+      </standardState>
+      <source> ref:G9 </source>
+    </species>
+
+
+    <species name="Nd+++">
+      <speciesChemFormula> Nd+++ </speciesChemFormula>
+      <atomArray> Nd:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -160600 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -166500 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -49.5 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.33707 </a1>
+        <a2 units="cal/gmol"> -1454.52 </a2>
+        <a3 units="cal-K/gmol/bar"> 8.3211 </a3>
+        <a4 units="cal-K/gmol"> -21777 </a4>
+        <c1 units="cal/gmol/K"> 1.6236 </c1>
+        <c2 units="cal-K/gmol"> -118344 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 225500 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -43.1 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+    <species name="Pr+++">
+      <speciesChemFormula> Pr+++ </speciesChemFormula>
+      <atomArray> Pr:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -162600 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -168800 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -50 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.32406 </a1>
+        <a2 units="cal/gmol"> -1419.98 </a2>
+        <a3 units="cal-K/gmol/bar"> 8.1257 </a3>
+        <a4 units="cal-K/gmol"> -21920 </a4>
+        <c1 units="cal/gmol/K"> -1.1975 </c1>
+        <c2 units="cal-K/gmol"> -127511 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 223500 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -42.1 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    <species name="Ce+++">
+      <speciesChemFormula> Ce+++ </speciesChemFormula>
+      <atomArray> Ce:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -161600 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -167400 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -49 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.29292 </a1>
+        <a2 units="cal/gmol"> -1493.38 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.6196 </a3>
+        <a4 units="cal-K/gmol"> -21616 </a4>
+        <c1 units="cal/gmol/K"> 4.0445 </c1>
+        <c2 units="cal-K/gmol"> -108974 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 222510 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -39.8 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    <species name="La+++">
+      <speciesChemFormula> La+++ </speciesChemFormula>
+      <atomArray> La:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -164000 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -169600 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -52 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.2788 </a1>
+        <a2 units="cal/gmol"> -1438.24 </a2>
+        <a3 units="cal-K/gmol/bar"> 10.9602 </a3>
+        <a4 units="cal-K/gmol"> -21844 </a4>
+        <c1 units="cal/gmol/K"> 4.2394 </c1>
+        <c2 units="cal-K/gmol"> -106122 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 215720 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -38.6 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+
+    <species name="Dy+++">
+      <speciesChemFormula> Dy+++ </speciesChemFormula>
+      <atomArray> Dy:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -158700 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -166500 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -55.2 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.30003 </a1>
+        <a2 units="cal/gmol"> -1510.74 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.6879 </a3>
+        <a4 units="cal-K/gmol"> -21545 </a4>
+        <c1 units="cal/gmol/K"> 9.5076 </c1>
+        <c2 units="cal-K/gmol"> -94919 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 237920 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -40.7 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    <species name="Y+++">
+      <speciesChemFormula> Y+++ </speciesChemFormula>
+      <atomArray> Y:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -163800 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -170900 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -60 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.30140 </a1>
+        <a2 units="cal/gmol"> -1514.08 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.7010 </a3>
+        <a4 units="cal-K/gmol"> -21531 </a4>
+        <c1 units="cal/gmol/K"> 7.1634 </c1>
+        <c2 units="cal-K/gmol"> -103067 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 237920 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -40.8 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+    
+    
+
+    <species name="Sm+++">
+      <speciesChemFormula> Sm+++ </speciesChemFormula>
+      <atomArray> Sm:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -159100 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -165200 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -50.7 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.32065 </a1>
+        <a2 units="cal/gmol"> -1561.08 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.8857 </a3>
+        <a4 units="cal-K/gmol"> -21337 </a4>
+        <c1 units="cal/gmol/K"> 1.9385 </c1>
+        <c2 units="cal-K/gmol"> -118548 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 229550 </omega_Pr_Tr>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+
+    <species name="Gd+++">
+      <speciesChemFormula> Gd+++ </speciesChemFormula>
+      <atomArray> Gd:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -158600 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -164200 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -49.2 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.29771 </a1>
+        <a2 units="cal/gmol"> -1505.06 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.6656 </a3>
+        <a4 units="cal-K/gmol"> -21568 </a4>
+        <c1 units="cal/gmol/K"> 6.5606 </c1>
+        <c2 units="cal-K/gmol"> -103474 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 232650 </omega_Pr_Tr>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+    
+    
+    
+    
+  </speciesData>
+
+</ctml>
\ No newline at end of file
diff --git a/data/xmls/PC88A_HCL_Dy_w_pitzer.xml b/data/xmls/PC88A_HCL_Dy_w_pitzer.xml
new file mode 100644
index 0000000..e1b2fe9
--- /dev/null
+++ b/data/xmls/PC88A_HCL_Dy_w_pitzer.xml
@@ -0,0 +1,495 @@
+<ctml>
+  <validate reactions="yes" species="yes" />
+                   
+ 
+  <phase dim="3" id="PC88A_liquid">
+    <elementArray datasrc="elementz.xml">
+        Cl O H C P Nd Pr Ce La Dy Y Sm dummy 
+    </elementArray>
+    <speciesArray datasrc="#species_PC88A_liquid">
+      (HA)2(org) dodecane Dy(H(A)2)3(org)
+    </speciesArray>
+    <state>
+      <temperature units="K"> 298.15 </temperature>
+      <pressure units="Pa"> 100000.0 </pressure>
+      <soluteMolalities>
+        (HA)2(org): 0.25
+      </soluteMolalities>
+    </state>
+    <thermo model="IdealSolidSolution">
+       <standardConc model="molar_volume" />
+    </thermo>
+    <standardConc model="molar_volume" />
+    <transport model="None" />
+    <kinetics model="none" />
+  </phase>
+
+ 
+  <speciesData id="species_PC88A_liquid">
+
+
+    <species name="(HA)2(org)">
+      <atomArray> C:16 H:35 O:3 P:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+          <t0 units="K">298.14999999999998</t0>
+          <h0 units="J/mol" updated="Updated at 16:18 7-31-2020">-1460300.0</h0>
+          <s0 units="J/mol/K"> 558.9824 </s0>
+          <cp0 units="J/mol/K"> 0.0</cp0>
+        </const_cp>
+      </thermo>
+      <standardState model="constant_incompressible">
+        <molarVolume updated="yep">0.32</molarVolume>
+      </standardState>
+    </species>
+
+
+
+    <species name="Dy(H(A)2)3(org)">
+      <atomArray> C:48 H:102 O:9 P:3 Dy:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+          <t0 units="K">298.14999999999998</t0>
+
+          <h0 units="J/mol" updated="Updated at 16:18 7-31-2020">-5177400.0</h0>
+          <s0 units="J/mol/K"> 1117.965 </s0>
+          <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+      <standardState model="constant_incompressible">
+        <molarVolume updated="yep">0.94</molarVolume>
+      </standardState>
+    </species>
+
+    
+    
+    <species name="dodecane">
+      <atomArray> dummy:1  </atomArray>  
+      
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+	   <t0 units="K">298.14999999999998</t0> 
+	   <h0 units="J/mol"> 0.0 </h0> 
+	   <s0 units="J/mol/K"> 0.0 </s0>  
+           <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+     <standardState model="constant_incompressible">
+	     <molarVolume> 0.227113 </molarVolume>  
+      </standardState>
+    </species>
+
+  </speciesData>
+
+
+
+  <phase dim="3" id="HCl_electrolyte">
+    <speciesArray datasrc="#species_waterSolution">
+      H2O(L) H+ OH- Cl- Dy+++
+    </speciesArray>
+
+    <state>
+      <temperature units="K"> 298.15 </temperature>
+      <pressure units="Pa"> 100000.0 </pressure>
+      <soluteMolalities>
+        Cl-: 1.0E-7
+        H+: 1.0E-7
+      </soluteMolalities>
+    </state>
+
+    <thermo model="HMW">
+      <standardConc model="solvent_volume" />
+      <activityCoefficients TempModel="complex1" model="Pitzer">
+        
+        <A_Debye model="water" />
+        <ionicRadius default="3.042843" units="Angstroms">
+        </ionicRadius>
+        <binarySaltParameters anion="Cl-" cation="H+">
+          <beta0> 0.177000779, 0.000125778, 0.0, -33.4777082, -0.262214535 </beta0>
+          <beta1> 0.292922504, -0.027938838, 0.0, 3402.47027, 19.7936248 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi>  0.000362, -0.00003036, 0.0, -2.91038E-11, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 12 </Alpha2>
+          <source>
+            refit of Holmes, H.F., Busey, J.M., Simonson, J.M., Mesmer, R.E.,
+            Archer, D.G., and Wood, R.H., 1987, The enthalpy of dilution of HCl(aq)
+            to 648 K and 4p MPa.  Thermodynamic properties, Journal of Chemical
+            Thermodynamics, v. 19, p. 863-890.
+          </source>
+        </binarySaltParameters>
+
+        <binarySaltParameters anion="Cl-" cation="Dy+++">
+          <beta0 updated="Updated at 16:18 7-31-2020"> 0.6129, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1 updated="Updated at 16:18 7-31-2020"> 5.359, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi updated="Updated at 16:18 7-31-2020"> -0.0197, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 0 </Alpha2>
+          <source>
+            Table S1: Pitzer parameters for osmotic and activity coeff Supp Info.
+            May, P. M.;  Rowland, D.;  Hefter, G.;  Konigsberger, E. 
+			A generic and updatable Pitzer characterization of aqueous binary electrolyte 
+			solutions at 1 bar and 25 C.
+			Journal of Chemical and Engineering Data 2011,56, 5066&#8211;5077.
+          </source>
+        </binarySaltParameters>
+
+        
+
+      </activityCoefficients>
+      <solvent> H2O(L) </solvent>
+    </thermo>
+    
+    <elementArray datasrc="elementz.xml"> O H Nd Pr Ce La Dy Y C N Cl P E </elementArray>
+    <kinetics model="none">
+    </kinetics>
+  </phase>
+
+  <speciesData id="species_waterSolution">
+
+    <species name="H2O(L)">
+      
+      <atomArray>H:2 O:1 </atomArray>
+      <thermo>
+        <NASA P0="100000.0" Tmax="600.0" Tmin="273.14999999999998">
+          <floatArray name="coeffs" size="7">
+            7.255750050E+01,  -6.624454020E-01,   2.561987460E-03,  -4.365919230E-06,
+            2.781789810E-09,  -4.188654990E+04,  -2.882801370E+02
+          </floatArray>
+        </NASA>
+      </thermo>
+      <standardState model="waterPDSS">
+        
+        <molarVolume> 0.018068 </molarVolume>
+      </standardState>
+    </species>
+
+
+    <species name="OH-">
+      <speciesChemFormula> OH- </speciesChemFormula>
+      <atomArray> O:1 H:1 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -37595 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -54977 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -2.56 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> 0.12527 </a1>
+        <a2 units="cal/gmol"> 7.38 </a2>
+        <a3 units="cal-K/gmol/bar"> 1.8423 </a3>
+        <a4 units="cal-K/gmol"> -27821 </a4>
+        <c1 units="cal/gmol/K"> 4.15 </c1>
+        <c2 units="cal-K/gmol"> -103460 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 172460 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -4.18 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+    <species name="NO3-">
+      <speciesChemFormula> NO3- </speciesChemFormula>
+      <atomArray> N:1 O:3 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -26507 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -49429 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> 35.12 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> 0.73161 </a1>
+        <a2 units="cal/gmol"> 678.24 </a2>
+        <a3 units="cal-K/gmol/bar"> -4.6838 </a3>
+        <a4 units="cal-K/gmol"> -30594 </a4>
+        <c1 units="cal/gmol/K"> 7.7 </c1>
+        <c2 units="cal-K/gmol"> -67250 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 109770 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> 29.0 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    
+
+    
+    
+    <species name="Cl-">
+      <speciesChemFormula> Cl- </speciesChemFormula>
+      <atomArray> Cl:1 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="273.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -31379 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_TR units="cal/gmol"> -39933 </DH0_f_Pr_TR>
+          <S0_Pr_Tr units="cal/gmol/K"> 13.56 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/mol/bar"> 0.4032 </a1>
+        <a2 units="cal/mol"> 480.1 </a2>
+        <a3 units="cal-K/mol/bar"> 5.563 </a3>
+        <a4 units="cal-K/mol"> -28470 </a4>
+        <c1 units="cal/mol/K"> -4.4 </c1>
+        <c2 units="cal-K/mol"> -57140 </c2>
+        <omega_Pr_Tr units="cal/mol"> 145600 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> 17.79 </molarVolume>
+      </standardState>
+      <source> ref:G9 </source>
+    </species>
+
+    <species name="H+">
+      <speciesChemFormula> H+ </speciesChemFormula>
+      <atomArray> H:1 </atomArray>
+      <charge> +1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="273.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> 0 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_TR units="cal/gmol"> 0 </DH0_f_Pr_TR>
+          <S0_Pr_Tr units="cal/gmol/K"> 0 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/mol/bar"> 0 </a1>
+        <a2 units="cal/mol"> 0 </a2>
+        <a3 units="cal-K/mol/bar"> 0 </a3>
+        <a4 units="cal-K/mol"> 0 </a4>
+        <c1 units="cal/mol/K"> 0 </c1>
+        <c2 units="cal-K/mol"> 0 </c2>
+        <omega_Pr_Tr units="cal/mol"> 0 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> 0 </molarVolume>
+      </standardState>
+      <source> ref:G9 </source>
+    </species>
+
+
+    <species name="Nd+++">
+      <speciesChemFormula> Nd+++ </speciesChemFormula>
+      <atomArray> Nd:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -160600 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -166500 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -49.5 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.33707 </a1>
+        <a2 units="cal/gmol"> -1454.52 </a2>
+        <a3 units="cal-K/gmol/bar"> 8.3211 </a3>
+        <a4 units="cal-K/gmol"> -21777 </a4>
+        <c1 units="cal/gmol/K"> 1.6236 </c1>
+        <c2 units="cal-K/gmol"> -118344 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 225500 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -43.1 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+    <species name="Pr+++">
+      <speciesChemFormula> Pr+++ </speciesChemFormula>
+      <atomArray> Pr:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -162600 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -168800 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -50 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.32406 </a1>
+        <a2 units="cal/gmol"> -1419.98 </a2>
+        <a3 units="cal-K/gmol/bar"> 8.1257 </a3>
+        <a4 units="cal-K/gmol"> -21920 </a4>
+        <c1 units="cal/gmol/K"> -1.1975 </c1>
+        <c2 units="cal-K/gmol"> -127511 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 223500 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -42.1 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    <species name="Ce+++">
+      <speciesChemFormula> Ce+++ </speciesChemFormula>
+      <atomArray> Ce:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -161600 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -167400 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -49 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.29292 </a1>
+        <a2 units="cal/gmol"> -1493.38 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.6196 </a3>
+        <a4 units="cal-K/gmol"> -21616 </a4>
+        <c1 units="cal/gmol/K"> 4.0445 </c1>
+        <c2 units="cal-K/gmol"> -108974 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 222510 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -39.8 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    <species name="La+++">
+      <speciesChemFormula> La+++ </speciesChemFormula>
+      <atomArray> La:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -164000 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -169600 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -52 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.2788 </a1>
+        <a2 units="cal/gmol"> -1438.24 </a2>
+        <a3 units="cal-K/gmol/bar"> 10.9602 </a3>
+        <a4 units="cal-K/gmol"> -21844 </a4>
+        <c1 units="cal/gmol/K"> 4.2394 </c1>
+        <c2 units="cal-K/gmol"> -106122 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 215720 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -38.6 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+
+    <species name="Dy+++">
+      <speciesChemFormula> Dy+++ </speciesChemFormula>
+      <atomArray> Dy:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -158700 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -166500 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -55.2 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.30003 </a1>
+        <a2 units="cal/gmol"> -1510.74 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.6879 </a3>
+        <a4 units="cal-K/gmol"> -21545 </a4>
+        <c1 units="cal/gmol/K"> 9.5076 </c1>
+        <c2 units="cal-K/gmol"> -94919 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 237920 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -40.7 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    <species name="Y+++">
+      <speciesChemFormula> Y+++ </speciesChemFormula>
+      <atomArray> Y:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -163800 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -170900 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -60 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.30140 </a1>
+        <a2 units="cal/gmol"> -1514.08 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.7010 </a3>
+        <a4 units="cal-K/gmol"> -21531 </a4>
+        <c1 units="cal/gmol/K"> 7.1634 </c1>
+        <c2 units="cal-K/gmol"> -103067 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 237920 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -40.8 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+    
+    
+
+    <species name="Sm+++">
+      <speciesChemFormula> Sm+++ </speciesChemFormula>
+      <atomArray> Sm:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -159100 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -165200 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -50.7 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.32065 </a1>
+        <a2 units="cal/gmol"> -1561.08 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.8857 </a3>
+        <a4 units="cal-K/gmol"> -21337 </a4>
+        <c1 units="cal/gmol/K"> 1.9385 </c1>
+        <c2 units="cal-K/gmol"> -118548 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 229550 </omega_Pr_Tr>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+
+    <species name="Gd+++">
+      <speciesChemFormula> Gd+++ </speciesChemFormula>
+      <atomArray> Gd:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -158600 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -164200 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -49.2 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.29771 </a1>
+        <a2 units="cal/gmol"> -1505.06 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.6656 </a3>
+        <a4 units="cal-K/gmol"> -21568 </a4>
+        <c1 units="cal/gmol/K"> 6.5606 </c1>
+        <c2 units="cal-K/gmol"> -103474 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 232650 </omega_Pr_Tr>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+    
+    
+    
+    
+  </speciesData>
+
+</ctml>
\ No newline at end of file
diff --git a/data/xmls/PC88A_HCL_La.xml b/data/xmls/PC88A_HCL_La.xml
new file mode 100644
index 0000000..1edaa0f
--- /dev/null
+++ b/data/xmls/PC88A_HCL_La.xml
@@ -0,0 +1,324 @@
+<ctml>
+  <validate reactions="yes" species="yes" />
+                   
+ 
+  <phase dim="3" id="PC88A_liquid">
+    <elementArray datasrc="elementz.xml">
+        Cl O H C P La dummy 
+    </elementArray>
+    <speciesArray datasrc="#species_PC88A_liquid">
+      (HA)2(org) dodecane La(H(A)2)3(org)
+    </speciesArray>
+    <state>
+      <temperature units="K"> 298.15 </temperature>
+      <pressure units="Pa"> 100000.0 </pressure>
+      <soluteMolalities>
+        (HA)2(org): 0.25
+      </soluteMolalities>
+    </state>
+    <thermo model="IdealSolidSolution">
+       <standardConc model="molar_volume" />
+    </thermo>
+    <standardConc model="molar_volume" />
+    <transport model="None" />
+    <kinetics model="none" />
+  </phase>
+
+ 
+  <speciesData id="species_PC88A_liquid">
+
+    
+    <species name="(HA)2(org)">
+      <atomArray> C:16 H:35 O:3 P:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+           <t0 units="K">298.14999999999998</t0>
+	   <h0 units="J/mol" updated="Updated at 15:10 7-31-2020">-1460300.0</h0>  
+	   <s0 units="J/mol/K"> 558.9824 </s0>  
+           <cp0 units="J/mol/K"> 0.0</cp0>
+        </const_cp>
+      </thermo>
+     <standardState model="constant_incompressible">
+         <molarVolume updated="yep">0.32</molarVolume>
+      </standardState>
+    </species>
+
+    
+    <species name="La(H(A)2)3(org)">
+      <atomArray> C:48 H:102 O:9 P:3 La:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+           <t0 units="K">298.14999999999998</t0>
+	    
+	   <h0 units="J/mol" updated="Updated at 15:10 7-31-2020">-5177400.0</h0> 
+	   <s0 units="J/mol/K"> 1117.965 </s0>  
+           <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+     <standardState model="constant_incompressible">
+	     <molarVolume updated="yep">0.94</molarVolume>   
+      </standardState>
+    </species>
+
+     
+    
+    <species name="dodecane">
+      <atomArray> dummy:1  </atomArray>  
+      
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+	   <t0 units="K">298.14999999999998</t0> 
+	   <h0 units="J/mol"> 0.0 </h0> 
+	   <s0 units="J/mol/K"> 0.0 </s0>  
+           <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+     <standardState model="constant_incompressible">
+	     <molarVolume> 0.227113 </molarVolume>  
+      </standardState>
+    </species>
+
+  </speciesData>
+
+
+
+  <phase dim="3" id="HCl_electrolyte">
+    <speciesArray datasrc="#species_waterSolution">
+      H2O(L) H+ OH- Cl- La+++
+    </speciesArray>
+
+    <state>
+      <temperature units="K"> 298.15 </temperature>
+      <pressure units="Pa"> 100000.0 </pressure>
+      <soluteMolalities>
+        Cl-: 1.0E-7
+        H+: 1.0E-7
+      </soluteMolalities>
+    </state>
+
+    <thermo model="HMW">
+      <standardConc model="solvent_volume" />
+      <activityCoefficients TempModel="complex1" model="Pitzer">
+        
+        <A_Debye model="water" />
+        <ionicRadius default="3.042843" units="Angstroms">
+        </ionicRadius>
+        <binarySaltParameters anion="Cl-" cation="H+">
+          <beta0> 0.177000779, 0.000125778, 0.0, -33.4777082, -0.262214535 </beta0>
+          <beta1> 0.292922504, -0.027938838, 0.0, 3402.47027, 19.7936248 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi>  0.000362, -0.00003036, 0.0, -2.91038E-11, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 12 </Alpha2>
+          <source>
+            refit of Holmes, H.F., Busey, J.M., Simonson, J.M., Mesmer, R.E.,
+            Archer, D.G., and Wood, R.H., 1987, The enthalpy of dilution of HCl(aq)
+            to 648 K and 4p MPa.  Thermodynamic properties, Journal of Chemical
+            Thermodynamics, v. 19, p. 863-890.
+          </source>
+        </binarySaltParameters>
+
+        
+
+        <binarySaltParameters anion="NO3-" cation="H+">
+          <beta0> 0.123757018, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1> 0.31055417, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi>  -0.005539018, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 12 </Alpha2>
+          <source>
+            02mar (Table 3)
+            Marion, G. M.  (2002) "A molal-based model for strong acid chemistry at low
+            temperatures (less than 200 to 298 K)", Geochimica et Cosmochimica Acta, Vol. 66,
+            No. 14, pp. 2499-2516. These parameters are derived from the mole fraction model of
+            Clegg and Brimblecombe (1990) but were parameterized by Marion (2002) for the Pitzer
+            molality-based model.
+          </source>
+        </binarySaltParameters>
+
+      </activityCoefficients>
+      <solvent> H2O(L) </solvent>
+    </thermo>
+    <elementArray datasrc="elements.xml"> O H La C Fe Si N Na Cl Ca P E </elementArray>
+    <kinetics model="none">
+    </kinetics>
+  </phase>
+
+  <speciesData id="species_waterSolution">
+
+    <species name="H2O(L)">
+      
+      <atomArray>H:2 O:1 </atomArray>
+      <thermo>
+        <NASA P0="100000.0" Tmax="600.0" Tmin="273.14999999999998">
+          <floatArray name="coeffs" size="7">
+            7.255750050E+01,  -6.624454020E-01,   2.561987460E-03,  -4.365919230E-06,
+            2.781789810E-09,  -4.188654990E+04,  -2.882801370E+02
+          </floatArray>
+        </NASA>
+      </thermo>
+      <standardState model="waterPDSS">
+        
+        <molarVolume> 0.018068 </molarVolume>
+      </standardState>
+    </species>
+
+
+    <species name="OH-">
+      <speciesChemFormula> OH- </speciesChemFormula>
+      <atomArray> O:1 H:1 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -37595 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -54977 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -2.56 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> 0.12527 </a1>
+        <a2 units="cal/gmol"> 7.38 </a2>
+        <a3 units="cal-K/gmol/bar"> 1.8423 </a3>
+        <a4 units="cal-K/gmol"> -27821 </a4>
+        <c1 units="cal/gmol/K"> 4.15 </c1>
+        <c2 units="cal-K/gmol"> -103460 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 172460 </omega_Pr_Tr>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+    
+    <species name="NO3-">
+      <speciesChemFormula> NO3- </speciesChemFormula>
+      <atomArray> N:1 O:3 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -26507 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -49429 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> 35.12 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> 0.73161 </a1>
+        <a2 units="cal/gmol"> 678.24 </a2>
+        <a3 units="cal-K/gmol/bar"> -4.6838 </a3>
+        <a4 units="cal-K/gmol"> -30594 </a4>
+        <c1 units="cal/gmol/K"> 7.7 </c1>
+        <c2 units="cal-K/gmol"> -67250 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 109770 </omega_Pr_Tr>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    <species name="Na+">
+      <speciesChemFormula> Na+ </speciesChemFormula>
+      <atomArray> Na:1 </atomArray>
+      <charge> 1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -62591 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -57433 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> 13.96 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> 0.1839 </a1>
+        <a2 units="cal/gmol"> -228.5 </a2>
+        <a3 units="cal-K/gmol/bar"> 3.256 </a3>
+        <a4 units="cal-K/gmol"> -27260 </a4>
+        <c1 units="cal/gmol/K"> 18.18 </c1>
+        <c2 units="cal-K/gmol"> -29810 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 33060 </omega_Pr_Tr>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+    
+    
+    <species name="Cl-">
+      <speciesChemFormula> Cl- </speciesChemFormula>
+      <atomArray> Cl:1 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="273.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -31379 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_TR units="cal/gmol"> -39933 </DH0_f_Pr_TR>
+          <S0_Pr_Tr units="cal/gmol/K"> 13.56 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/mol/bar"> 0.4032 </a1>
+        <a2 units="cal/mol"> 480.1 </a2>
+        <a3 units="cal-K/mol/bar"> 5.563 </a3>
+        <a4 units="cal-K/mol"> -28470 </a4>
+        <c1 units="cal/mol/K"> -4.4 </c1>
+        <c2 units="cal-K/mol"> -57140 </c2>
+        <omega_Pr_Tr units="cal/mol"> 145600 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> 17.79 </molarVolume>
+      </standardState>
+      <source> ref:G9 </source>
+    </species>
+
+    <species name="H+">
+      <speciesChemFormula> H+ </speciesChemFormula>
+      <atomArray> H:1 </atomArray>
+      <charge> +1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="273.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> 0 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_TR units="cal/gmol"> 0 </DH0_f_Pr_TR>
+          <S0_Pr_Tr units="cal/gmol/K"> 0 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/mol/bar"> 0 </a1>
+        <a2 units="cal/mol"> 0 </a2>
+        <a3 units="cal-K/mol/bar"> 0 </a3>
+        <a4 units="cal-K/mol"> 0 </a4>
+        <c1 units="cal/mol/K"> 0 </c1>
+        <c2 units="cal-K/mol"> 0 </c2>
+        <omega_Pr_Tr units="cal/mol"> 0 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> 0 </molarVolume>
+      </standardState>
+      <source> ref:G9 </source>
+    </species>
+
+
+    <species name="La+++">
+      <speciesChemFormula> La+++ </speciesChemFormula>
+      <atomArray> La:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -164000 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -169600 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -52 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.2788 </a1>
+        <a2 units="cal/gmol"> -1438.24 </a2>
+        <a3 units="cal-K/gmol/bar"> 10.9602 </a3>
+        <a4 units="cal-K/gmol"> -21844 </a4>
+        <c1 units="cal/gmol/K"> 4.2394 </c1>
+        <c2 units="cal-K/gmol"> -106122 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 215720 </omega_Pr_Tr>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+
+  </speciesData>
+
+</ctml>
\ No newline at end of file
diff --git a/data/xmls/PC88A_HCL_La_w_pitzer.xml b/data/xmls/PC88A_HCL_La_w_pitzer.xml
new file mode 100644
index 0000000..1cf4c02
--- /dev/null
+++ b/data/xmls/PC88A_HCL_La_w_pitzer.xml
@@ -0,0 +1,339 @@
+<ctml>
+  <validate reactions="yes" species="yes" />
+                   
+ 
+  <phase dim="3" id="PC88A_liquid">
+    <elementArray datasrc="elementz.xml">
+        Cl O H C P La dummy 
+    </elementArray>
+    <speciesArray datasrc="#species_PC88A_liquid">
+      (HA)2(org) dodecane La(H(A)2)3(org)
+    </speciesArray>
+    <state>
+      <temperature units="K"> 298.15 </temperature>
+      <pressure units="Pa"> 100000.0 </pressure>
+      <soluteMolalities>
+        (HA)2(org): 0.25
+      </soluteMolalities>
+    </state>
+    <thermo model="IdealSolidSolution">
+       <standardConc model="molar_volume" />
+    </thermo>
+    <standardConc model="molar_volume" />
+    <transport model="None" />
+    <kinetics model="none" />
+  </phase>
+
+ 
+  <speciesData id="species_PC88A_liquid">
+
+    
+    <species name="(HA)2(org)">
+      <atomArray> C:16 H:35 O:3 P:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+           <t0 units="K">298.14999999999998</t0>
+	   <h0 units="J/mol" updated="Updated at 16:16 7-31-2020">-1460300.0</h0>  
+	   <s0 units="J/mol/K"> 558.9824 </s0>  
+           <cp0 units="J/mol/K"> 0.0</cp0>
+        </const_cp>
+      </thermo>
+     <standardState model="constant_incompressible">
+         <molarVolume updated="yep">0.32</molarVolume>
+      </standardState>
+    </species>
+
+    
+    <species name="La(H(A)2)3(org)">
+      <atomArray> C:48 H:102 O:9 P:3 La:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+           <t0 units="K">298.14999999999998</t0>
+	    
+	   <h0 units="J/mol" updated="Updated at 16:16 7-31-2020">-5177400.0</h0> 
+	   <s0 units="J/mol/K"> 1117.965 </s0>  
+           <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+     <standardState model="constant_incompressible">
+	     <molarVolume updated="yep">0.94</molarVolume>   
+      </standardState>
+    </species>
+
+     
+    
+    <species name="dodecane">
+      <atomArray> dummy:1  </atomArray>  
+      
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+	   <t0 units="K">298.14999999999998</t0> 
+	   <h0 units="J/mol"> 0.0 </h0> 
+	   <s0 units="J/mol/K"> 0.0 </s0>  
+           <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+     <standardState model="constant_incompressible">
+	     <molarVolume> 0.227113 </molarVolume>  
+      </standardState>
+    </species>
+
+  </speciesData>
+
+
+
+  <phase dim="3" id="HCl_electrolyte">
+    <speciesArray datasrc="#species_waterSolution">
+      H2O(L) H+ OH- Cl- La+++
+    </speciesArray>
+
+    <state>
+      <temperature units="K"> 298.15 </temperature>
+      <pressure units="Pa"> 100000.0 </pressure>
+      <soluteMolalities>
+        Cl-: 1.0E-7
+        H+: 1.0E-7
+      </soluteMolalities>
+    </state>
+
+    <thermo model="HMW">
+      <standardConc model="solvent_volume" />
+      <activityCoefficients TempModel="complex1" model="Pitzer">
+        
+        <A_Debye model="water" />
+        <ionicRadius default="3.042843" units="Angstroms">
+        </ionicRadius>
+        <binarySaltParameters anion="Cl-" cation="H+">
+          <beta0> 0.177000779, 0.000125778, 0.0, -33.4777082, -0.262214535 </beta0>
+          <beta1> 0.292922504, -0.027938838, 0.0, 3402.47027, 19.7936248 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi>  0.000362, -0.00003036, 0.0, -2.91038E-11, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 12 </Alpha2>
+          <source>
+            refit of Holmes, H.F., Busey, J.M., Simonson, J.M., Mesmer, R.E.,
+            Archer, D.G., and Wood, R.H., 1987, The enthalpy of dilution of HCl(aq)
+            to 648 K and 4p MPa.  Thermodynamic properties, Journal of Chemical
+            Thermodynamics, v. 19, p. 863-890.
+          </source>
+        </binarySaltParameters>
+
+        
+
+        <binarySaltParameters anion="NO3-" cation="H+">
+          <beta0> 0.123757018, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1> 0.31055417, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi>  -0.005539018, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 12 </Alpha2>
+          <source>
+            02mar (Table 3)
+            Marion, G. M.  (2002) "A molal-based model for strong acid chemistry at low
+            temperatures (less than 200 to 298 K)", Geochimica et Cosmochimica Acta, Vol. 66,
+            No. 14, pp. 2499-2516. These parameters are derived from the mole fraction model of
+            Clegg and Brimblecombe (1990) but were parameterized by Marion (2002) for the Pitzer
+            molality-based model.
+          </source>
+        </binarySaltParameters>
+		
+		<binarySaltParameters anion="Cl-" cation="La+++">
+          <beta0 updated="Updated at 16:16 7-31-2020"> 0.593, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1 updated="Updated at 16:16 7-31-2020"> 5.277, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi updated="Updated at 16:16 7-31-2020"> -0.02434, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 0 </Alpha2>
+          <source>
+            Table S1: Pitzer parameters for osmotic and activity coeff Supp Info.
+            May, P. M.;  Rowland, D.;  Hefter, G.;  Konigsberger, E. 
+			A generic and updatable Pitzer characterization of aqueous binary electrolyte 
+			solutions at 1 bar and 25 C.
+			Journal of Chemical and Engineering Data 2011,56, 5066&#8211;5077.
+          </source>
+        </binarySaltParameters>
+      </activityCoefficients>
+      <solvent> H2O(L) </solvent>
+    </thermo>
+    <elementArray datasrc="elements.xml"> O H La C Fe Si N Na Cl Ca P E </elementArray>
+    <kinetics model="none">
+    </kinetics>
+  </phase>
+
+  <speciesData id="species_waterSolution">
+
+    <species name="H2O(L)">
+      
+      <atomArray>H:2 O:1 </atomArray>
+      <thermo>
+        <NASA P0="100000.0" Tmax="600.0" Tmin="273.14999999999998">
+          <floatArray name="coeffs" size="7">
+            7.255750050E+01,  -6.624454020E-01,   2.561987460E-03,  -4.365919230E-06,
+            2.781789810E-09,  -4.188654990E+04,  -2.882801370E+02
+          </floatArray>
+        </NASA>
+      </thermo>
+      <standardState model="waterPDSS">
+        
+        <molarVolume> 0.018068 </molarVolume>
+      </standardState>
+    </species>
+
+
+    <species name="OH-">
+      <speciesChemFormula> OH- </speciesChemFormula>
+      <atomArray> O:1 H:1 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -37595 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -54977 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -2.56 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> 0.12527 </a1>
+        <a2 units="cal/gmol"> 7.38 </a2>
+        <a3 units="cal-K/gmol/bar"> 1.8423 </a3>
+        <a4 units="cal-K/gmol"> -27821 </a4>
+        <c1 units="cal/gmol/K"> 4.15 </c1>
+        <c2 units="cal-K/gmol"> -103460 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 172460 </omega_Pr_Tr>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+    
+    <species name="NO3-">
+      <speciesChemFormula> NO3- </speciesChemFormula>
+      <atomArray> N:1 O:3 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -26507 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -49429 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> 35.12 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> 0.73161 </a1>
+        <a2 units="cal/gmol"> 678.24 </a2>
+        <a3 units="cal-K/gmol/bar"> -4.6838 </a3>
+        <a4 units="cal-K/gmol"> -30594 </a4>
+        <c1 units="cal/gmol/K"> 7.7 </c1>
+        <c2 units="cal-K/gmol"> -67250 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 109770 </omega_Pr_Tr>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    <species name="Na+">
+      <speciesChemFormula> Na+ </speciesChemFormula>
+      <atomArray> Na:1 </atomArray>
+      <charge> 1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -62591 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -57433 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> 13.96 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> 0.1839 </a1>
+        <a2 units="cal/gmol"> -228.5 </a2>
+        <a3 units="cal-K/gmol/bar"> 3.256 </a3>
+        <a4 units="cal-K/gmol"> -27260 </a4>
+        <c1 units="cal/gmol/K"> 18.18 </c1>
+        <c2 units="cal-K/gmol"> -29810 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 33060 </omega_Pr_Tr>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+    
+    
+    <species name="Cl-">
+      <speciesChemFormula> Cl- </speciesChemFormula>
+      <atomArray> Cl:1 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="273.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -31379 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_TR units="cal/gmol"> -39933 </DH0_f_Pr_TR>
+          <S0_Pr_Tr units="cal/gmol/K"> 13.56 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/mol/bar"> 0.4032 </a1>
+        <a2 units="cal/mol"> 480.1 </a2>
+        <a3 units="cal-K/mol/bar"> 5.563 </a3>
+        <a4 units="cal-K/mol"> -28470 </a4>
+        <c1 units="cal/mol/K"> -4.4 </c1>
+        <c2 units="cal-K/mol"> -57140 </c2>
+        <omega_Pr_Tr units="cal/mol"> 145600 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> 17.79 </molarVolume>
+      </standardState>
+      <source> ref:G9 </source>
+    </species>
+
+    <species name="H+">
+      <speciesChemFormula> H+ </speciesChemFormula>
+      <atomArray> H:1 </atomArray>
+      <charge> +1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="273.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> 0 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_TR units="cal/gmol"> 0 </DH0_f_Pr_TR>
+          <S0_Pr_Tr units="cal/gmol/K"> 0 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/mol/bar"> 0 </a1>
+        <a2 units="cal/mol"> 0 </a2>
+        <a3 units="cal-K/mol/bar"> 0 </a3>
+        <a4 units="cal-K/mol"> 0 </a4>
+        <c1 units="cal/mol/K"> 0 </c1>
+        <c2 units="cal-K/mol"> 0 </c2>
+        <omega_Pr_Tr units="cal/mol"> 0 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> 0 </molarVolume>
+      </standardState>
+      <source> ref:G9 </source>
+    </species>
+
+
+    <species name="La+++">
+      <speciesChemFormula> La+++ </speciesChemFormula>
+      <atomArray> La:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -164000 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -169600 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -52 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.2788 </a1>
+        <a2 units="cal/gmol"> -1438.24 </a2>
+        <a3 units="cal-K/gmol/bar"> 10.9602 </a3>
+        <a4 units="cal-K/gmol"> -21844 </a4>
+        <c1 units="cal/gmol/K"> 4.2394 </c1>
+        <c2 units="cal-K/gmol"> -106122 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 215720 </omega_Pr_Tr>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+
+  </speciesData>
+
+</ctml>
\ No newline at end of file
diff --git a/data/xmls/PC88A_HCL_Nd.xml b/data/xmls/PC88A_HCL_Nd.xml
index 9924336..f49f41b 100644
--- a/data/xmls/PC88A_HCL_Nd.xml
+++ b/data/xmls/PC88A_HCL_Nd.xml
@@ -33,7 +33,7 @@
       <thermo>
         <const_cp Tmax="300.0" Tmin="298.0">
            <t0 units="K">298.14999999999998</t0>
-	   <h0 units="J/mol" updated="Updated at 10:52 6-16-2020">-852842.5769865816</h0>  
+	   <h0 units="J/mol" updated="Updated at 15:8 7-31-2020">-1460300.0</h0>  
 	   <s0 units="J/mol/K"> 558.9824 </s0>  
            <cp0 units="J/mol/K"> 0.0</cp0>
         </const_cp>
@@ -50,7 +50,7 @@
         <const_cp Tmax="300.0" Tmin="298.0">
            <t0 units="K">298.14999999999998</t0>
 	    
-	   <h0 units="J/mol" updated="Updated at 10:52 6-16-2020">-3358931.9094941025</h0> 
+	   <h0 units="J/mol" updated="Updated at 15:8 7-31-2020">-5177400.0</h0> 
 	   <s0 units="J/mol/K"> 1117.965 </s0>  
            <cp0 units="J/mol/K">0.0</cp0>
         </const_cp>
@@ -135,7 +135,8 @@
             Clegg and Brimblecombe (1990) but were parameterized by Marion (2002) for the Pitzer
             molality-based model.
           </source>
-        </binarySaltParameters>
+		</binarySaltParameters>
+
 
       </activityCoefficients>
       <solvent> H2O(L) </solvent>
diff --git a/data/xmls/PC88A_HCL_NdPrCeLaDySmY.xml b/data/xmls/PC88A_HCL_NdPrCeLaDySmY.xml
new file mode 100644
index 0000000..cf8377b
--- /dev/null
+++ b/data/xmls/PC88A_HCL_NdPrCeLaDySmY.xml
@@ -0,0 +1,606 @@
+<ctml>
+  <validate reactions="yes" species="yes" />
+                   
+ 
+  <phase dim="3" id="PC88A_liquid">
+    <elementArray datasrc="elementz.xml">
+        Cl O H C P Nd Pr Ce La Dy Y Sm dummy 
+    </elementArray>
+    <speciesArray datasrc="#species_PC88A_liquid">
+      (HA)2(org) dodecane Nd(H(A)2)3(org) Pr(H(A)2)3(org) Ce(H(A)2)3(org) La(H(A)2)3(org) Dy(H(A)2)3(org) Sm(H(A)2)3(org) Y(H(A)2)3(org)
+    </speciesArray>
+    <state>
+      <temperature units="K"> 298.15 </temperature>
+      <pressure units="Pa"> 100000.0 </pressure>
+      <soluteMolalities>
+        (HA)2(org): 0.25
+      </soluteMolalities>
+    </state>
+    <thermo model="IdealSolidSolution">
+       <standardConc model="molar_volume" />
+    </thermo>
+    <standardConc model="molar_volume" />
+    <transport model="None" />
+    <kinetics model="none" />
+  </phase>
+
+ 
+  <speciesData id="species_PC88A_liquid">
+
+
+    <species name="(HA)2(org)">
+      <atomArray> C:16 H:35 O:3 P:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+          <t0 units="K">298.14999999999998</t0>
+          <h0 units="J/mol" updated="Updated at 12:9 7-31-2020">-1059541.4005512903</h0>
+          <s0 units="J/mol/K"> 558.9824 </s0>
+          <cp0 units="J/mol/K"> 0.0</cp0>
+        </const_cp>
+      </thermo>
+      <standardState model="constant_incompressible">
+        <molarVolume updated="Updated at 21:02:32">0.320974226079</molarVolume>
+      </standardState>
+    </species>
+
+
+    <species name="Nd(H(A)2)3(org)">
+      <atomArray> C:48 H:102 O:9 P:3 Nd:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+          <t0 units="K">298.14999999999998</t0>
+
+          <h0 units="J/mol" updated="Updated at 12:9 7-31-2020">-3978649.6160132056</h0>
+          <s0 units="J/mol/K"> 1117.965 </s0>
+          <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+      <standardState model="constant_incompressible">
+        <molarVolume updated="Updated at 21:02:32">0.973500071223</molarVolume>
+      </standardState>
+    </species>
+
+
+    <species name="Pr(H(A)2)3(org)">
+      <atomArray> C:48 H:102 O:9 P:3 Pr:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+          <t0 units="K">298.14999999999998</t0>
+
+          <h0 units="J/mol" updated="Updated at 12:9 7-31-2020">-3985038.4367615725</h0>
+          <s0 units="J/mol/K"> 1117.965 </s0>
+          <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+      <standardState model="constant_incompressible">
+        <molarVolume updated="Updated at 21:02:32">0.979936421078</molarVolume>
+      </standardState>
+    </species>
+
+
+
+    <species name="Ce(H(A)2)3(org)">
+      <atomArray> C:48 H:102 O:9 P:3 Ce:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+          <t0 units="K">298.14999999999998</t0>
+
+          <h0 units="J/mol" updated="Updated at 12:9 7-31-2020">-3978119.7710579354</h0>
+          <s0 units="J/mol/K"> 1117.965 </s0>
+          <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+      <standardState model="constant_incompressible">
+        <molarVolume updated="Updated at 21:02:32">1.0060487218</molarVolume>
+      </standardState>
+    </species>
+
+
+
+    <species name="La(H(A)2)3(org)">
+      <atomArray> C:48 H:102 O:9 P:3 La:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+          <t0 units="K">298.14999999999998</t0>
+
+          <h0 units="J/mol" updated="Updated at 12:9 7-31-2020">-3977135.4681356866</h0>
+          <s0 units="J/mol/K"> 1117.965 </s0>
+          <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+      <standardState model="constant_incompressible">
+        <molarVolume updated="Updated at 21:02:32">0.981485801577</molarVolume>
+      </standardState>
+    </species>
+
+
+    <species name="Dy(H(A)2)3(org)">
+      <atomArray> C:48 H:102 O:9 P:3 Dy:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+          <t0 units="K">298.14999999999998</t0>
+
+          <h0 units="J/mol" updated="Updated at 12:9 7-31-2020">-3985847.427512692</h0>
+          <s0 units="J/mol/K"> 1117.965 </s0>
+          <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+      <standardState model="constant_incompressible">
+        <molarVolume updated="Updated at 21:02:32">0.961666360676</molarVolume>
+      </standardState>
+    </species>
+
+
+
+    <species name="Y(H(A)2)3(org)">
+      <atomArray> C:48 H:102 O:9 P:3 Y:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+          <t0 units="K">298.14999999999998</t0>
+
+          <h0 units="J/mol" updated="Updated at 12:9 7-31-2020">-4001903.249479101</h0>
+          <s0 units="J/mol/K"> 1117.965 </s0>
+          <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+      <standardState model="constant_incompressible">
+        <molarVolume updated="Updated at 21:02:32">0.958888814485</molarVolume>
+      </standardState>
+    </species>
+
+
+
+
+    <species name="Sm(H(A)2)3(org)">
+      <atomArray> C:48 H:102 O:9 P:3 Sm:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+          <t0 units="K">298.14999999999998</t0>
+
+          <h0 units="J/mol" updated="Updated at 12:9 7-31-2020">-3976184.3569597593</h0>
+          <s0 units="J/mol/K"> 1117.965 </s0>
+          <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+      <standardState model="constant_incompressible">
+        <molarVolume> 0.9642 </molarVolume>
+      </standardState>
+    </species>
+
+
+
+    <species name="Gd(H(A)2)3(org)">
+      <atomArray> C:48 H:102 O:9 P:3 Gd:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+          <t0 units="K">298.14999999999998</t0>
+
+          <h0 units="J/mol" updated="yep">-4829446.858</h0>
+          <s0 units="J/mol/K"> 1117.965 </s0>
+          <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+      <standardState model="constant_incompressible">
+        <molarVolume>0.9642</molarVolume>
+      </standardState>
+    </species>
+    
+    
+    
+    <species name="dodecane">
+      <atomArray> dummy:1  </atomArray>  
+      
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+	   <t0 units="K">298.14999999999998</t0> 
+	   
+	   <h0 units="J/mol"> 0.0 </h0> 
+	   <s0 units="J/mol/K"> 0.0 </s0>  
+           <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+     <standardState model="constant_incompressible">
+	     <molarVolume> 0.227113 </molarVolume>  
+      </standardState>
+    </species>
+
+  </speciesData>
+
+
+
+  <phase dim="3" id="HCl_electrolyte">
+    <speciesArray datasrc="#species_waterSolution">
+      H2O(L) H+ OH- Cl- Nd+++ Pr+++ Ce+++ La+++ Dy+++ Sm+++ Y+++
+    </speciesArray>
+
+    <state>
+      <temperature units="K"> 298.15 </temperature>
+      <pressure units="Pa"> 100000.0 </pressure>
+      <soluteMolalities>
+        Cl-: 1.0E-7
+        H+: 1.0E-7
+      </soluteMolalities>
+    </state>
+
+    <thermo model="HMW">
+      <standardConc model="solvent_volume" />
+      <activityCoefficients TempModel="complex1" model="Pitzer">
+        
+        <A_Debye model="water" />
+        <ionicRadius default="3.042843" units="Angstroms">
+        </ionicRadius>
+        <binarySaltParameters anion="Cl-" cation="H+">
+          <beta0> 0.177000779, 0.000125778, 0.0, -33.4777082, -0.262214535 </beta0>
+          <beta1> 0.292922504, -0.027938838, 0.0, 3402.47027, 19.7936248 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi>  0.000362, -0.00003036, 0.0, -2.91038E-11, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 12 </Alpha2>
+          <source>
+            refit of Holmes, H.F., Busey, J.M., Simonson, J.M., Mesmer, R.E.,
+            Archer, D.G., and Wood, R.H., 1987, The enthalpy of dilution of HCl(aq)
+            to 648 K and 4p MPa.  Thermodynamic properties, Journal of Chemical
+            Thermodynamics, v. 19, p. 863-890.
+          </source>
+        </binarySaltParameters>
+
+        
+
+        
+
+      </activityCoefficients>
+      <solvent> H2O(L) </solvent>
+    </thermo>
+    
+    <elementArray datasrc="elementz.xml"> O H Nd Pr Ce La Dy Y Sm C N Cl P E </elementArray>
+    <kinetics model="none">
+    </kinetics>
+  </phase>
+
+  <speciesData id="species_waterSolution">
+
+    <species name="H2O(L)">
+      
+      <atomArray>H:2 O:1 </atomArray>
+      <thermo>
+        <NASA P0="100000.0" Tmax="600.0" Tmin="273.14999999999998">
+          <floatArray name="coeffs" size="7">
+            7.255750050E+01,  -6.624454020E-01,   2.561987460E-03,  -4.365919230E-06,
+            2.781789810E-09,  -4.188654990E+04,  -2.882801370E+02
+          </floatArray>
+        </NASA>
+      </thermo>
+      <standardState model="waterPDSS">
+        
+        <molarVolume> 0.018068 </molarVolume>
+      </standardState>
+    </species>
+
+
+    <species name="OH-">
+      <speciesChemFormula> OH- </speciesChemFormula>
+      <atomArray> O:1 H:1 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -37595 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -54977 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -2.56 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> 0.12527 </a1>
+        <a2 units="cal/gmol"> 7.38 </a2>
+        <a3 units="cal-K/gmol/bar"> 1.8423 </a3>
+        <a4 units="cal-K/gmol"> -27821 </a4>
+        <c1 units="cal/gmol/K"> 4.15 </c1>
+        <c2 units="cal-K/gmol"> -103460 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 172460 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -4.18 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+    <species name="NO3-">
+      <speciesChemFormula> NO3- </speciesChemFormula>
+      <atomArray> N:1 O:3 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -26507 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -49429 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> 35.12 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> 0.73161 </a1>
+        <a2 units="cal/gmol"> 678.24 </a2>
+        <a3 units="cal-K/gmol/bar"> -4.6838 </a3>
+        <a4 units="cal-K/gmol"> -30594 </a4>
+        <c1 units="cal/gmol/K"> 7.7 </c1>
+        <c2 units="cal-K/gmol"> -67250 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 109770 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> 29.0 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    
+
+    
+    
+    <species name="Cl-">
+      <speciesChemFormula> Cl- </speciesChemFormula>
+      <atomArray> Cl:1 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="273.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -31379 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_TR units="cal/gmol"> -39933 </DH0_f_Pr_TR>
+          <S0_Pr_Tr units="cal/gmol/K"> 13.56 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/mol/bar"> 0.4032 </a1>
+        <a2 units="cal/mol"> 480.1 </a2>
+        <a3 units="cal-K/mol/bar"> 5.563 </a3>
+        <a4 units="cal-K/mol"> -28470 </a4>
+        <c1 units="cal/mol/K"> -4.4 </c1>
+        <c2 units="cal-K/mol"> -57140 </c2>
+        <omega_Pr_Tr units="cal/mol"> 145600 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> 17.79 </molarVolume>
+      </standardState>
+      <source> ref:G9 </source>
+    </species>
+
+    <species name="H+">
+      <speciesChemFormula> H+ </speciesChemFormula>
+      <atomArray> H:1 </atomArray>
+      <charge> +1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="273.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> 0 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_TR units="cal/gmol"> 0 </DH0_f_Pr_TR>
+          <S0_Pr_Tr units="cal/gmol/K"> 0 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/mol/bar"> 0 </a1>
+        <a2 units="cal/mol"> 0 </a2>
+        <a3 units="cal-K/mol/bar"> 0 </a3>
+        <a4 units="cal-K/mol"> 0 </a4>
+        <c1 units="cal/mol/K"> 0 </c1>
+        <c2 units="cal-K/mol"> 0 </c2>
+        <omega_Pr_Tr units="cal/mol"> 0 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> 0 </molarVolume>
+      </standardState>
+      <source> ref:G9 </source>
+    </species>
+
+
+    <species name="Nd+++">
+      <speciesChemFormula> Nd+++ </speciesChemFormula>
+      <atomArray> Nd:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -160600 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -166500 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -49.5 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.33707 </a1>
+        <a2 units="cal/gmol"> -1454.52 </a2>
+        <a3 units="cal-K/gmol/bar"> 8.3211 </a3>
+        <a4 units="cal-K/gmol"> -21777 </a4>
+        <c1 units="cal/gmol/K"> 1.6236 </c1>
+        <c2 units="cal-K/gmol"> -118344 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 225500 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -43.1 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+    <species name="Pr+++">
+      <speciesChemFormula> Pr+++ </speciesChemFormula>
+      <atomArray> Pr:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -162600 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -168800 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -50 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.32406 </a1>
+        <a2 units="cal/gmol"> -1419.98 </a2>
+        <a3 units="cal-K/gmol/bar"> 8.1257 </a3>
+        <a4 units="cal-K/gmol"> -21920 </a4>
+        <c1 units="cal/gmol/K"> -1.1975 </c1>
+        <c2 units="cal-K/gmol"> -127511 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 223500 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -42.1 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    <species name="Ce+++">
+      <speciesChemFormula> Ce+++ </speciesChemFormula>
+      <atomArray> Ce:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -161600 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -167400 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -49 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.29292 </a1>
+        <a2 units="cal/gmol"> -1493.38 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.6196 </a3>
+        <a4 units="cal-K/gmol"> -21616 </a4>
+        <c1 units="cal/gmol/K"> 4.0445 </c1>
+        <c2 units="cal-K/gmol"> -108974 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 222510 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -39.8 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    <species name="La+++">
+      <speciesChemFormula> La+++ </speciesChemFormula>
+      <atomArray> La:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -164000 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -169600 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -52 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.2788 </a1>
+        <a2 units="cal/gmol"> -1438.24 </a2>
+        <a3 units="cal-K/gmol/bar"> 10.9602 </a3>
+        <a4 units="cal-K/gmol"> -21844 </a4>
+        <c1 units="cal/gmol/K"> 4.2394 </c1>
+        <c2 units="cal-K/gmol"> -106122 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 215720 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -38.6 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+
+    <species name="Dy+++">
+      <speciesChemFormula> Dy+++ </speciesChemFormula>
+      <atomArray> Dy:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -158700 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -166500 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -55.2 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.30003 </a1>
+        <a2 units="cal/gmol"> -1510.74 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.6879 </a3>
+        <a4 units="cal-K/gmol"> -21545 </a4>
+        <c1 units="cal/gmol/K"> 9.5076 </c1>
+        <c2 units="cal-K/gmol"> -94919 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 237920 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -40.7 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    <species name="Y+++">
+      <speciesChemFormula> Y+++ </speciesChemFormula>
+      <atomArray> Y:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -163800 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -170900 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -60 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.30140 </a1>
+        <a2 units="cal/gmol"> -1514.08 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.7010 </a3>
+        <a4 units="cal-K/gmol"> -21531 </a4>
+        <c1 units="cal/gmol/K"> 7.1634 </c1>
+        <c2 units="cal-K/gmol"> -103067 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 237920 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -40.8 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+    
+    
+
+    <species name="Sm+++">
+      <speciesChemFormula> Sm+++ </speciesChemFormula>
+      <atomArray> Sm:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -159100 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -165200 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -50.7 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.32065 </a1>
+        <a2 units="cal/gmol"> -1561.08 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.8857 </a3>
+        <a4 units="cal-K/gmol"> -21337 </a4>
+        <c1 units="cal/gmol/K"> 1.9385 </c1>
+        <c2 units="cal-K/gmol"> -118548 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 229550 </omega_Pr_Tr>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+
+    <species name="Gd+++">
+      <speciesChemFormula> Gd+++ </speciesChemFormula>
+      <atomArray> Gd:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -158600 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -164200 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -49.2 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.29771 </a1>
+        <a2 units="cal/gmol"> -1505.06 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.6656 </a3>
+        <a4 units="cal-K/gmol"> -21568 </a4>
+        <c1 units="cal/gmol/K"> 6.5606 </c1>
+        <c2 units="cal-K/gmol"> -103474 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 232650 </omega_Pr_Tr>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+    
+    
+    
+    
+  </speciesData>
+
+</ctml>
\ No newline at end of file
diff --git a/data/xmls/PC88A_HCL_NdPrCeLaDySmY_w_pitzer.xml b/data/xmls/PC88A_HCL_NdPrCeLaDySmY_w_pitzer.xml
index 79377c5..5342270 100644
--- a/data/xmls/PC88A_HCL_NdPrCeLaDySmY_w_pitzer.xml
+++ b/data/xmls/PC88A_HCL_NdPrCeLaDySmY_w_pitzer.xml
@@ -33,7 +33,7 @@
       <thermo>
         <const_cp Tmax="300.0" Tmin="298.0">
           <t0 units="K">298.14999999999998</t0>
-          <h0 units="J/mol" updated="Updated at 19:20 7-29-2020">-1377917.5269485628</h0>
+          <h0 units="J/mol" updated="Updated at 16:32 7-31-2020">-1376877.1544988335</h0>
           <s0 units="J/mol/K"> 558.9824 </s0>
           <cp0 units="J/mol/K"> 0.0</cp0>
         </const_cp>
@@ -50,7 +50,7 @@
         <const_cp Tmax="300.0" Tmin="298.0">
           <t0 units="K">298.14999999999998</t0>
 
-          <h0 units="J/mol" updated="Updated at 19:20 7-29-2020">-4929625.060282971</h0>
+          <h0 units="J/mol" updated="Updated at 16:32 7-31-2020">-4926549.797810851</h0>
           <s0 units="J/mol/K"> 1117.965 </s0>
           <cp0 units="J/mol/K">0.0</cp0>
         </const_cp>
@@ -67,7 +67,7 @@
         <const_cp Tmax="300.0" Tmin="298.0">
           <t0 units="K">298.14999999999998</t0>
 
-          <h0 units="J/mol" updated="Updated at 19:20 7-29-2020">-4941397.084772181</h0>
+          <h0 units="J/mol" updated="Updated at 16:32 7-31-2020">-4935519.640701385</h0>
           <s0 units="J/mol/K"> 1117.965 </s0>
           <cp0 units="J/mol/K">0.0</cp0>
         </const_cp>
@@ -85,7 +85,7 @@
         <const_cp Tmax="300.0" Tmin="298.0">
           <t0 units="K">298.14999999999998</t0>
 
-          <h0 units="J/mol" updated="Updated at 19:20 7-29-2020">-4932551.489263242</h0>
+          <h0 units="J/mol" updated="Updated at 16:32 7-31-2020">-4928317.781440989</h0>
           <s0 units="J/mol/K"> 1117.965 </s0>
           <cp0 units="J/mol/K">0.0</cp0>
         </const_cp>
@@ -103,7 +103,7 @@
         <const_cp Tmax="300.0" Tmin="298.0">
           <t0 units="K">298.14999999999998</t0>
 
-          <h0 units="J/mol" updated="Updated at 19:20 7-29-2020">-4935194.142426379</h0>
+          <h0 units="J/mol" updated="Updated at 16:32 7-31-2020">-4927428.65973482</h0>
           <s0 units="J/mol/K"> 1117.965 </s0>
           <cp0 units="J/mol/K">0.0</cp0>
         </const_cp>
@@ -120,7 +120,7 @@
         <const_cp Tmax="300.0" Tmin="298.0">
           <t0 units="K">298.14999999999998</t0>
 
-          <h0 units="J/mol" updated="Updated at 19:20 7-29-2020">-4935179.480883835</h0>
+          <h0 units="J/mol" updated="Updated at 16:32 7-31-2020">-4935155.356789877</h0>
           <s0 units="J/mol/K"> 1117.965 </s0>
           <cp0 units="J/mol/K">0.0</cp0>
         </const_cp>
@@ -138,7 +138,7 @@
         <const_cp Tmax="300.0" Tmin="298.0">
           <t0 units="K">298.14999999999998</t0>
 
-          <h0 units="J/mol" updated="Updated at 19:20 7-29-2020">-4947210.070800013</h0>
+          <h0 units="J/mol" updated="Updated at 16:32 7-31-2020">-4944228.17930387</h0>
           <s0 units="J/mol/K"> 1117.965 </s0>
           <cp0 units="J/mol/K">0.0</cp0>
         </const_cp>
@@ -157,7 +157,7 @@
         <const_cp Tmax="300.0" Tmin="298.0">
           <t0 units="K">298.14999999999998</t0>
 
-          <h0 units="J/mol" updated="Updated at 19:20 7-29-2020">-4929116.779371853</h0>
+          <h0 units="J/mol" updated="Updated at 16:32 7-31-2020">-4925606.187988869</h0>
           <s0 units="J/mol/K"> 1117.965 </s0>
           <cp0 units="J/mol/K">0.0</cp0>
         </const_cp>
@@ -245,10 +245,10 @@
         </binarySaltParameters>
 
 		<binarySaltParameters anion="Cl-" cation="Pr+++">
-          <beta0 updated="Updated at 19:20 7-29-2020"> 0.058791077174068576, 0.0, 0.0, 0.0, 0.0 </beta0>
-          <beta1 updated="Updated at 19:20 7-29-2020"> 0.5448320238819658, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta0 updated="Updated at 16:32 7-31-2020"> 0.5879108393945309, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1 updated="Updated at 16:32 7-31-2020"> 5.4483234694357385, 0.0, 0.0, 0.0, 0.0 </beta1>
           <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
-          <Cphi updated="Updated at 19:20 7-29-2020"> -0.02066999867229882, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Cphi updated="Updated at 16:32 7-31-2020"> -0.02066999867229882, 0.0, 0.0, 0.0, 0.0 </Cphi>
           <Alpha1> 2 </Alpha1>
           <Alpha2> 0 </Alpha2>
           <source>
@@ -261,10 +261,10 @@
         </binarySaltParameters>
 		
         <binarySaltParameters anion="Cl-" cation="Nd+++">
-          <beta0 updated="Updated at 19:20 7-29-2020"> 1.0825315776813356, 0.0, 0.0, 0.0, 0.0 </beta0>
-          <beta1 updated="Updated at 19:20 7-29-2020"> 6.211262701179254, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta0 updated="Updated at 16:32 7-31-2020"> 0.7459775851223264, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1 updated="Updated at 16:32 7-31-2020"> 7.68392131299453, 0.0, 0.0, 0.0, 0.0 </beta1>
           <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
-          <Cphi updated="Updated at 19:20 7-29-2020"> -0.01963615126026457, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Cphi updated="Updated at 16:32 7-31-2020"> -0.01963615126026457, 0.0, 0.0, 0.0, 0.0 </Cphi>
           <Alpha1> 2 </Alpha1>
           <Alpha2> 0 </Alpha2>
           <source>
@@ -277,10 +277,10 @@
         </binarySaltParameters>
 		
 		<binarySaltParameters anion="Cl-" cation="La+++">
-          <beta0 updated="Updated at 19:20 7-29-2020"> 0.5930048961782302, 0.0, 0.0, 0.0, 0.0 </beta0>
-          <beta1 updated="Updated at 19:20 7-29-2020"> 5.2771447352927785, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta0 updated="Updated at 16:32 7-31-2020"> 0.5929999713109059, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1 updated="Updated at 16:32 7-31-2020"> 5.2769992484445485, 0.0, 0.0, 0.0, 0.0 </beta1>
           <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
-          <Cphi updated="Updated at 19:20 7-29-2020"> -0.024339999997603376, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Cphi updated="Updated at 16:32 7-31-2020"> -0.024339999997603376, 0.0, 0.0, 0.0, 0.0 </Cphi>
           <Alpha1> 2 </Alpha1>
           <Alpha2> 0 </Alpha2>
           <source>
@@ -293,10 +293,10 @@
         </binarySaltParameters>
 		
         <binarySaltParameters anion="Cl-" cation="Dy+++">
-          <beta0 updated="Updated at 19:20 7-29-2020"> 0.5532852863195881, 0.0, 0.0, 0.0, 0.0 </beta0>
-          <beta1 updated="Updated at 19:20 7-29-2020"> 9.206787175138805, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta0 updated="Updated at 16:32 7-31-2020"> 0.612950248542597, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1 updated="Updated at 16:32 7-31-2020"> 5.3599329374557385, 0.0, 0.0, 0.0, 0.0 </beta1>
           <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
-          <Cphi updated="Updated at 19:20 7-29-2020"> -0.019699989216349984, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Cphi updated="Updated at 16:32 7-31-2020"> -0.019699989216349984, 0.0, 0.0, 0.0, 0.0 </Cphi>
           <Alpha1> 2 </Alpha1>
           <Alpha2> 0 </Alpha2>
           <source>
@@ -309,10 +309,10 @@
         </binarySaltParameters>
 		
 		<binarySaltParameters anion="Cl-" cation="Ce+++">
-          <beta0 updated="Updated at 19:20 7-29-2020"> 0.6005020116531858, 0.0, 0.0, 0.0, 0.0 </beta0>
-          <beta1 updated="Updated at 19:20 7-29-2020"> 4.91004437191519, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta0 updated="Updated at 16:32 7-31-2020"> 0.6005000182766684, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1 updated="Updated at 16:32 7-31-2020"> 4.910000193972841, 0.0, 0.0, 0.0, 0.0 </beta1>
           <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
-          <Cphi updated="Updated at 19:20 7-29-2020"> -0.02618999999473301, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Cphi updated="Updated at 16:32 7-31-2020"> -0.02618999999473301, 0.0, 0.0, 0.0, 0.0 </Cphi>
           <Alpha1> 2 </Alpha1>
           <Alpha2> 0 </Alpha2>
           <source>
@@ -325,10 +325,10 @@
         </binarySaltParameters>
 		
 		<binarySaltParameters anion="Cl-" cation="Y+++">
-          <beta0 updated="Updated at 19:20 7-29-2020"> 0.8866269789489127, 0.0, 0.0, 0.0, 0.0 </beta0>
-          <beta1 updated="Updated at 19:20 7-29-2020"> 9.867663759220948, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta0 updated="Updated at 16:32 7-31-2020"> 0.9656352678202726, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1 updated="Updated at 16:32 7-31-2020"> 8.514180603037651, 0.0, 0.0, 0.0, 0.0 </beta1>
           <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
-          <Cphi updated="Updated at 19:20 7-29-2020"> -0.015467323909969704, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Cphi updated="Updated at 16:32 7-31-2020"> -0.015467323909969704, 0.0, 0.0, 0.0, 0.0 </Cphi>
           <Alpha1> 2 </Alpha1>
           <Alpha2> 0 </Alpha2>
           <source>
@@ -341,10 +341,10 @@
         </binarySaltParameters>
 		
 		<binarySaltParameters anion="Cl-" cation="Sm+++">
-          <beta0 updated="Updated at 19:20 7-29-2020"> 0.5978087860273482, 0.0, 0.0, 0.0, 0.0 </beta0>
-          <beta1 updated="Updated at 19:20 7-29-2020"> 5.279188095059934, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta0 updated="Updated at 16:32 7-31-2020"> 0.5977991228263208, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1 updated="Updated at 16:32 7-31-2020"> 5.278978156246855, 0.0, 0.0, 0.0, 0.0 </beta1>
           <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
-          <Cphi updated="Updated at 19:20 7-29-2020"> -0.019920000110321332, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Cphi updated="Updated at 16:32 7-31-2020"> -0.019920000110321332, 0.0, 0.0, 0.0, 0.0 </Cphi>
           <Alpha1> 2 </Alpha1>
           <Alpha2> 0 </Alpha2>
           <source>
diff --git a/data/xmls/PC88A_HCL_Nd_w_pitzer.xml b/data/xmls/PC88A_HCL_Nd_w_pitzer.xml
new file mode 100644
index 0000000..a7a09e1
--- /dev/null
+++ b/data/xmls/PC88A_HCL_Nd_w_pitzer.xml
@@ -0,0 +1,341 @@
+<ctml>
+  <validate reactions="yes" species="yes" />
+                   
+ 
+  <phase dim="3" id="PC88A_liquid">
+    <elementArray datasrc="elementz.xml">
+        Cl O H C P Nd dummy 
+    </elementArray>
+    <speciesArray datasrc="#species_PC88A_liquid">
+      (HA)2(org) dodecane Nd(H(A)2)3(org)
+    </speciesArray>
+    <state>
+      <temperature units="K"> 298.15 </temperature>
+      <pressure units="Pa"> 100000.0 </pressure>
+      <soluteMolalities>
+        (HA)2(org): 0.25
+      </soluteMolalities>
+    </state>
+    <thermo model="IdealSolidSolution">
+       <standardConc model="molar_volume" />
+    </thermo>
+    <standardConc model="molar_volume" />
+    <transport model="None" />
+    <kinetics model="none" />
+  </phase>
+
+ 
+  <speciesData id="species_PC88A_liquid">
+
+    
+    <species name="(HA)2(org)">
+      <atomArray> C:16 H:35 O:3 P:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+           <t0 units="K">298.14999999999998</t0>
+	   <h0 units="J/mol" updated="Updated at 16:13 7-31-2020">-1460300.0</h0>  
+	   <s0 units="J/mol/K"> 558.9824 </s0>  
+           <cp0 units="J/mol/K"> 0.0</cp0>
+        </const_cp>
+      </thermo>
+     <standardState model="constant_incompressible">
+         <molarVolume updated="yep">0.32</molarVolume>
+      </standardState>
+    </species>
+
+    
+    <species name="Nd(H(A)2)3(org)">
+      <atomArray> C:48 H:102 O:9 P:3 Nd:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+           <t0 units="K">298.14999999999998</t0>
+	    
+	   <h0 units="J/mol" updated="Updated at 16:13 7-31-2020">-5177400.0</h0> 
+	   <s0 units="J/mol/K"> 1117.965 </s0>  
+           <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+     <standardState model="constant_incompressible">
+	     <molarVolume updated="yep">0.94</molarVolume>   
+      </standardState>
+    </species>
+
+     
+    
+    <species name="dodecane">
+      <atomArray> dummy:1  </atomArray>  
+      
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+	   <t0 units="K">298.14999999999998</t0> 
+	   <h0 units="J/mol"> 0.0 </h0> 
+	   <s0 units="J/mol/K"> 0.0 </s0>  
+           <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+     <standardState model="constant_incompressible">
+	     <molarVolume> 0.227113 </molarVolume>  
+      </standardState>
+    </species>
+
+  </speciesData>
+
+
+
+  <phase dim="3" id="HCl_electrolyte">
+    <speciesArray datasrc="#species_waterSolution">
+      H2O(L) H+ OH- Cl- Nd+++
+    </speciesArray>
+
+    <state>
+      <temperature units="K"> 298.15 </temperature>
+      <pressure units="Pa"> 100000.0 </pressure>
+      <soluteMolalities>
+        Cl-: 1.0E-7
+        H+: 1.0E-7
+      </soluteMolalities>
+    </state>
+
+    <thermo model="HMW">
+      <standardConc model="solvent_volume" />
+      <activityCoefficients TempModel="complex1" model="Pitzer">
+        
+        <A_Debye model="water" />
+        <ionicRadius default="3.042843" units="Angstroms">
+        </ionicRadius>
+        <binarySaltParameters anion="Cl-" cation="H+">
+          <beta0> 0.177000779, 0.000125778, 0.0, -33.4777082, -0.262214535 </beta0>
+          <beta1> 0.292922504, -0.027938838, 0.0, 3402.47027, 19.7936248 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi>  0.000362, -0.00003036, 0.0, -2.91038E-11, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 0 </Alpha2>
+          <source>
+            refit of Holmes, H.F., Busey, J.M., Simonson, J.M., Mesmer, R.E.,
+            Archer, D.G., and Wood, R.H., 1987, The enthalpy of dilution of HCl(aq)
+            to 648 K and 4p MPa.  Thermodynamic properties, Journal of Chemical
+            Thermodynamics, v. 19, p. 863-890.
+          </source>
+        </binarySaltParameters>
+
+        
+
+        <binarySaltParameters anion="NO3-" cation="H+">
+          <beta0> 0.123757018, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1> 0.31055417, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi>  -0.005539018, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 12 </Alpha2>
+          <source>
+            02mar (Table 3)
+            Marion, G. M.  (2002) "A molal-based model for strong acid chemistry at low
+            temperatures (less than 200 to 298 K)", Geochimica et Cosmochimica Acta, Vol. 66,
+            No. 14, pp. 2499-2516. These parameters are derived from the mole fraction model of
+            Clegg and Brimblecombe (1990) but were parameterized by Marion (2002) for the Pitzer
+            molality-based model.
+          </source>
+		</binarySaltParameters>
+		  
+		  
+		  
+        <binarySaltParameters anion="Cl-" cation="Nd+++">
+          <beta0 updated="Updated at 16:13 7-31-2020"> 0.5877, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1 updated="Updated at 16:13 7-31-2020"> 5.206, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi updated="Updated at 16:13 7-31-2020"> -0.01969, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 0 </Alpha2>
+          <source>
+            Table S1: Pitzer parameters for osmotic and activity coeff Supp Info.
+            May, P. M.;  Rowland, D.;  Hefter, G.;  Konigsberger, E. 
+			A generic and updatable Pitzer characterization of aqueous binary electrolyte 
+			solutions at 1 bar and 25 C.
+			Journal of Chemical and Engineering Data 2011,56, 5066&#8211;5077.
+          </source>
+        </binarySaltParameters>
+      </activityCoefficients>
+      <solvent> H2O(L) </solvent>
+    </thermo>
+    <elementArray datasrc="elements.xml"> O H Nd C Fe Si N Na Cl Ca P E </elementArray>
+    <kinetics model="none">
+    </kinetics>
+  </phase>
+
+  <speciesData id="species_waterSolution">
+
+    <species name="H2O(L)">
+      
+      <atomArray>H:2 O:1 </atomArray>
+      <thermo>
+        <NASA P0="100000.0" Tmax="600.0" Tmin="273.14999999999998">
+          <floatArray name="coeffs" size="7">
+            7.255750050E+01,  -6.624454020E-01,   2.561987460E-03,  -4.365919230E-06,
+            2.781789810E-09,  -4.188654990E+04,  -2.882801370E+02
+          </floatArray>
+        </NASA>
+      </thermo>
+      <standardState model="waterPDSS">
+        
+        <molarVolume> 0.018068 </molarVolume>
+      </standardState>
+    </species>
+
+
+    <species name="OH-">
+      <speciesChemFormula> OH- </speciesChemFormula>
+      <atomArray> O:1 H:1 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -37595 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -54977 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -2.56 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> 0.12527 </a1>
+        <a2 units="cal/gmol"> 7.38 </a2>
+        <a3 units="cal-K/gmol/bar"> 1.8423 </a3>
+        <a4 units="cal-K/gmol"> -27821 </a4>
+        <c1 units="cal/gmol/K"> 4.15 </c1>
+        <c2 units="cal-K/gmol"> -103460 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 172460 </omega_Pr_Tr>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+    
+    <species name="NO3-">
+      <speciesChemFormula> NO3- </speciesChemFormula>
+      <atomArray> N:1 O:3 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -26507 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -49429 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> 35.12 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> 0.73161 </a1>
+        <a2 units="cal/gmol"> 678.24 </a2>
+        <a3 units="cal-K/gmol/bar"> -4.6838 </a3>
+        <a4 units="cal-K/gmol"> -30594 </a4>
+        <c1 units="cal/gmol/K"> 7.7 </c1>
+        <c2 units="cal-K/gmol"> -67250 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 109770 </omega_Pr_Tr>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    <species name="Na+">
+      <speciesChemFormula> Na+ </speciesChemFormula>
+      <atomArray> Na:1 </atomArray>
+      <charge> 1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -62591 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -57433 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> 13.96 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> 0.1839 </a1>
+        <a2 units="cal/gmol"> -228.5 </a2>
+        <a3 units="cal-K/gmol/bar"> 3.256 </a3>
+        <a4 units="cal-K/gmol"> -27260 </a4>
+        <c1 units="cal/gmol/K"> 18.18 </c1>
+        <c2 units="cal-K/gmol"> -29810 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 33060 </omega_Pr_Tr>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+    
+    
+    <species name="Cl-">
+      <speciesChemFormula> Cl- </speciesChemFormula>
+      <atomArray> Cl:1 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="273.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -31379 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_TR units="cal/gmol"> -39933 </DH0_f_Pr_TR>
+          <S0_Pr_Tr units="cal/gmol/K"> 13.56 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/mol/bar"> 0.4032 </a1>
+        <a2 units="cal/mol"> 480.1 </a2>
+        <a3 units="cal-K/mol/bar"> 5.563 </a3>
+        <a4 units="cal-K/mol"> -28470 </a4>
+        <c1 units="cal/mol/K"> -4.4 </c1>
+        <c2 units="cal-K/mol"> -57140 </c2>
+        <omega_Pr_Tr units="cal/mol"> 145600 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> 17.79 </molarVolume>
+      </standardState>
+      <source> ref:G9 </source>
+    </species>
+
+    <species name="H+">
+      <speciesChemFormula> H+ </speciesChemFormula>
+      <atomArray> H:1 </atomArray>
+      <charge> +1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="273.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> 0 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_TR units="cal/gmol"> 0 </DH0_f_Pr_TR>
+          <S0_Pr_Tr units="cal/gmol/K"> 0 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/mol/bar"> 0 </a1>
+        <a2 units="cal/mol"> 0 </a2>
+        <a3 units="cal-K/mol/bar"> 0 </a3>
+        <a4 units="cal-K/mol"> 0 </a4>
+        <c1 units="cal/mol/K"> 0 </c1>
+        <c2 units="cal-K/mol"> 0 </c2>
+        <omega_Pr_Tr units="cal/mol"> 0 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> 0 </molarVolume>
+      </standardState>
+      <source> ref:G9 </source>
+    </species>
+
+
+    <species name="Nd+++">
+      <speciesChemFormula> Nd+++ </speciesChemFormula>
+      <atomArray> Nd:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -160600 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -166500 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -49.5 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.33707 </a1>
+        <a2 units="cal/gmol"> -1454.52 </a2>
+        <a3 units="cal-K/gmol/bar"> 8.3211 </a3>
+        <a4 units="cal-K/gmol"> -21777 </a4>
+        <c1 units="cal/gmol/K"> 1.6236 </c1>
+        <c2 units="cal-K/gmol"> -118344 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 225500 </omega_Pr_Tr>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+
+  </speciesData>
+
+</ctml>
\ No newline at end of file
diff --git a/data/xmls/PC88A_HCL_Pr.xml b/data/xmls/PC88A_HCL_Pr.xml
new file mode 100644
index 0000000..b96d35d
--- /dev/null
+++ b/data/xmls/PC88A_HCL_Pr.xml
@@ -0,0 +1,325 @@
+<ctml>
+  <validate reactions="yes" species="yes" />
+                   
+ 
+  <phase dim="3" id="PC88A_liquid">
+    <elementArray datasrc="elementz.xml">
+        Cl O H C P Pr dummy 
+    </elementArray>
+    <speciesArray datasrc="#species_PC88A_liquid">
+      (HA)2(org) dodecane Pr(H(A)2)3(org)
+    </speciesArray>
+    <state>
+      <temperature units="K"> 298.15 </temperature>
+      <pressure units="Pa"> 100000.0 </pressure>
+      <soluteMolalities>
+        (HA)2(org): 0.25
+      </soluteMolalities>
+    </state>
+    <thermo model="IdealSolidSolution">
+       <standardConc model="molar_volume" />
+    </thermo>
+    <standardConc model="molar_volume" />
+    <transport model="None" />
+    <kinetics model="none" />
+  </phase>
+
+ 
+  <speciesData id="species_PC88A_liquid">
+
+    
+    <species name="(HA)2(org)">
+      <atomArray> C:16 H:35 O:3 P:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+           <t0 units="K">298.14999999999998</t0>
+	   <h0 units="J/mol" updated="Updated at 15:9 7-31-2020">-1460300.0</h0>  
+	   <s0 units="J/mol/K"> 558.9824 </s0>  
+           <cp0 units="J/mol/K"> 0.0</cp0>
+        </const_cp>
+      </thermo>
+     <standardState model="constant_incompressible">
+         <molarVolume updated="yep">0.32</molarVolume>
+      </standardState>
+    </species>
+
+    
+    <species name="Pr(H(A)2)3(org)">
+      <atomArray> C:48 H:102 O:9 P:3 Pr:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+           <t0 units="K">298.14999999999998</t0>
+	    
+	   <h0 units="J/mol" updated="Updated at 15:9 7-31-2020">-5177400.0</h0> 
+	   <s0 units="J/mol/K"> 1117.965 </s0>  
+           <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+     <standardState model="constant_incompressible">
+	     <molarVolume updated="yep">0.94</molarVolume>   
+      </standardState>
+    </species>
+
+     
+    
+    <species name="dodecane">
+      <atomArray> dummy:1  </atomArray>  
+      
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+	   <t0 units="K">298.14999999999998</t0> 
+	   <h0 units="J/mol"> 0.0 </h0> 
+	   <s0 units="J/mol/K"> 0.0 </s0>  
+           <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+     <standardState model="constant_incompressible">
+	     <molarVolume> 0.227113 </molarVolume>  
+      </standardState>
+    </species>
+
+  </speciesData>
+
+
+
+  <phase dim="3" id="HCl_electrolyte">
+    <speciesArray datasrc="#species_waterSolution">
+      H2O(L) H+ OH- Cl- Pr+++
+    </speciesArray>
+
+    <state>
+      <temperature units="K"> 298.15 </temperature>
+      <pressure units="Pa"> 100000.0 </pressure>
+      <soluteMolalities>
+        Cl-: 1.0E-7
+        H+: 1.0E-7
+      </soluteMolalities>
+    </state>
+
+    <thermo model="HMW">
+      <standardConc model="solvent_volume" />
+      <activityCoefficients TempModel="complex1" model="Pitzer">
+        
+        <A_Debye model="water" />
+        <ionicRadius default="3.042843" units="Angstroms">
+        </ionicRadius>
+        <binarySaltParameters anion="Cl-" cation="H+">
+          <beta0> 0.177000779, 0.000125778, 0.0, -33.4777082, -0.262214535 </beta0>
+          <beta1> 0.292922504, -0.027938838, 0.0, 3402.47027, 19.7936248 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi>  0.000362, -0.00003036, 0.0, -2.91038E-11, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 12 </Alpha2>
+          <source>
+            refit of Holmes, H.F., Busey, J.M., Simonson, J.M., Mesmer, R.E.,
+            Archer, D.G., and Wood, R.H., 1987, The enthalpy of dilution of HCl(aq)
+            to 648 K and 4p MPa.  Thermodynamic properties, Journal of Chemical
+            Thermodynamics, v. 19, p. 863-890.
+          </source>
+        </binarySaltParameters>
+
+        
+
+        <binarySaltParameters anion="NO3-" cation="H+">
+          <beta0> 0.123757018, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1> 0.31055417, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi>  -0.005539018, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 12 </Alpha2>
+          <source>
+            02mar (Table 3)
+            Marion, G. M.  (2002) "A molal-based model for strong acid chemistry at low
+            temperatures (less than 200 to 298 K)", Geochimica et Cosmochimica Acta, Vol. 66,
+            No. 14, pp. 2499-2516. These parameters are derived from the mole fraction model of
+            Clegg and Brimblecombe (1990) but were parameterized by Marion (2002) for the Pitzer
+            molality-based model.
+          </source>
+        </binarySaltParameters>
+
+      </activityCoefficients>
+      <solvent> H2O(L) </solvent>
+    </thermo>
+    <elementArray datasrc="elements.xml"> O H Pr C Fe Si N Na Cl Ca P E </elementArray>
+    <kinetics model="none">
+    </kinetics>
+  </phase>
+
+  <speciesData id="species_waterSolution">
+
+    <species name="H2O(L)">
+      
+      <atomArray>H:2 O:1 </atomArray>
+      <thermo>
+        <NASA P0="100000.0" Tmax="600.0" Tmin="273.14999999999998">
+          <floatArray name="coeffs" size="7">
+            7.255750050E+01,  -6.624454020E-01,   2.561987460E-03,  -4.365919230E-06,
+            2.781789810E-09,  -4.188654990E+04,  -2.882801370E+02
+          </floatArray>
+        </NASA>
+      </thermo>
+      <standardState model="waterPDSS">
+        
+        <molarVolume> 0.018068 </molarVolume>
+      </standardState>
+    </species>
+
+
+    <species name="OH-">
+      <speciesChemFormula> OH- </speciesChemFormula>
+      <atomArray> O:1 H:1 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -37595 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -54977 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -2.56 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> 0.12527 </a1>
+        <a2 units="cal/gmol"> 7.38 </a2>
+        <a3 units="cal-K/gmol/bar"> 1.8423 </a3>
+        <a4 units="cal-K/gmol"> -27821 </a4>
+        <c1 units="cal/gmol/K"> 4.15 </c1>
+        <c2 units="cal-K/gmol"> -103460 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 172460 </omega_Pr_Tr>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+    
+    <species name="NO3-">
+      <speciesChemFormula> NO3- </speciesChemFormula>
+      <atomArray> N:1 O:3 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -26507 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -49429 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> 35.12 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> 0.73161 </a1>
+        <a2 units="cal/gmol"> 678.24 </a2>
+        <a3 units="cal-K/gmol/bar"> -4.6838 </a3>
+        <a4 units="cal-K/gmol"> -30594 </a4>
+        <c1 units="cal/gmol/K"> 7.7 </c1>
+        <c2 units="cal-K/gmol"> -67250 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 109770 </omega_Pr_Tr>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    <species name="Na+">
+      <speciesChemFormula> Na+ </speciesChemFormula>
+      <atomArray> Na:1 </atomArray>
+      <charge> 1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -62591 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -57433 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> 13.96 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> 0.1839 </a1>
+        <a2 units="cal/gmol"> -228.5 </a2>
+        <a3 units="cal-K/gmol/bar"> 3.256 </a3>
+        <a4 units="cal-K/gmol"> -27260 </a4>
+        <c1 units="cal/gmol/K"> 18.18 </c1>
+        <c2 units="cal-K/gmol"> -29810 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 33060 </omega_Pr_Tr>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+    
+    
+    <species name="Cl-">
+      <speciesChemFormula> Cl- </speciesChemFormula>
+      <atomArray> Cl:1 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="273.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -31379 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_TR units="cal/gmol"> -39933 </DH0_f_Pr_TR>
+          <S0_Pr_Tr units="cal/gmol/K"> 13.56 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/mol/bar"> 0.4032 </a1>
+        <a2 units="cal/mol"> 480.1 </a2>
+        <a3 units="cal-K/mol/bar"> 5.563 </a3>
+        <a4 units="cal-K/mol"> -28470 </a4>
+        <c1 units="cal/mol/K"> -4.4 </c1>
+        <c2 units="cal-K/mol"> -57140 </c2>
+        <omega_Pr_Tr units="cal/mol"> 145600 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> 17.79 </molarVolume>
+      </standardState>
+      <source> ref:G9 </source>
+    </species>
+
+    <species name="H+">
+      <speciesChemFormula> H+ </speciesChemFormula>
+      <atomArray> H:1 </atomArray>
+      <charge> +1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="273.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> 0 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_TR units="cal/gmol"> 0 </DH0_f_Pr_TR>
+          <S0_Pr_Tr units="cal/gmol/K"> 0 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/mol/bar"> 0 </a1>
+        <a2 units="cal/mol"> 0 </a2>
+        <a3 units="cal-K/mol/bar"> 0 </a3>
+        <a4 units="cal-K/mol"> 0 </a4>
+        <c1 units="cal/mol/K"> 0 </c1>
+        <c2 units="cal-K/mol"> 0 </c2>
+        <omega_Pr_Tr units="cal/mol"> 0 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> 0 </molarVolume>
+      </standardState>
+      <source> ref:G9 </source>
+    </species>
+
+
+    <species name="Pr+++">
+      <speciesChemFormula> Pr+++ </speciesChemFormula>
+      <atomArray> Pr:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -162600 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -168800 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -50 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.32406 </a1>
+        <a2 units="cal/gmol"> -1419.98 </a2>
+        <a3 units="cal-K/gmol/bar"> 8.1257 </a3>
+        <a4 units="cal-K/gmol"> -21920 </a4>
+        <c1 units="cal/gmol/K"> -1.1975 </c1>
+        <c2 units="cal-K/gmol"> -127511 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 223500 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -42.1 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+
+  </speciesData>
+
+</ctml>
\ No newline at end of file
diff --git a/data/xmls/PC88A_HCL_Pr_w_pitzer.xml b/data/xmls/PC88A_HCL_Pr_w_pitzer.xml
new file mode 100644
index 0000000..c333bd5
--- /dev/null
+++ b/data/xmls/PC88A_HCL_Pr_w_pitzer.xml
@@ -0,0 +1,341 @@
+<ctml>
+  <validate reactions="yes" species="yes" />
+                   
+ 
+  <phase dim="3" id="PC88A_liquid">
+    <elementArray datasrc="elementz.xml">
+        Cl O H C P Pr dummy 
+    </elementArray>
+    <speciesArray datasrc="#species_PC88A_liquid">
+      (HA)2(org) dodecane Pr(H(A)2)3(org)
+    </speciesArray>
+    <state>
+      <temperature units="K"> 298.15 </temperature>
+      <pressure units="Pa"> 100000.0 </pressure>
+      <soluteMolalities>
+        (HA)2(org): 0.25
+      </soluteMolalities>
+    </state>
+    <thermo model="IdealSolidSolution">
+       <standardConc model="molar_volume" />
+    </thermo>
+    <standardConc model="molar_volume" />
+    <transport model="None" />
+    <kinetics model="none" />
+  </phase>
+
+ 
+  <speciesData id="species_PC88A_liquid">
+
+    
+    <species name="(HA)2(org)">
+      <atomArray> C:16 H:35 O:3 P:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+           <t0 units="K">298.14999999999998</t0>
+	   <h0 units="J/mol" updated="Updated at 16:15 7-31-2020">-1460300.0</h0>  
+	   <s0 units="J/mol/K"> 558.9824 </s0>  
+           <cp0 units="J/mol/K"> 0.0</cp0>
+        </const_cp>
+      </thermo>
+     <standardState model="constant_incompressible">
+         <molarVolume updated="yep">0.32</molarVolume>
+      </standardState>
+    </species>
+
+    
+    <species name="Pr(H(A)2)3(org)">
+      <atomArray> C:48 H:102 O:9 P:3 Pr:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+           <t0 units="K">298.14999999999998</t0>
+	    
+	   <h0 units="J/mol" updated="Updated at 16:15 7-31-2020">-5177400.0</h0> 
+	   <s0 units="J/mol/K"> 1117.965 </s0>  
+           <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+     <standardState model="constant_incompressible">
+	     <molarVolume updated="yep">0.94</molarVolume>   
+      </standardState>
+    </species>
+
+     
+    
+    <species name="dodecane">
+      <atomArray> dummy:1  </atomArray>  
+      
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+	   <t0 units="K">298.14999999999998</t0> 
+	   <h0 units="J/mol"> 0.0 </h0> 
+	   <s0 units="J/mol/K"> 0.0 </s0>  
+           <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+     <standardState model="constant_incompressible">
+	     <molarVolume> 0.227113 </molarVolume>  
+      </standardState>
+    </species>
+
+  </speciesData>
+
+
+
+  <phase dim="3" id="HCl_electrolyte">
+    <speciesArray datasrc="#species_waterSolution">
+      H2O(L) H+ OH- Cl- Pr+++
+    </speciesArray>
+
+    <state>
+      <temperature units="K"> 298.15 </temperature>
+      <pressure units="Pa"> 100000.0 </pressure>
+      <soluteMolalities>
+        Cl-: 1.0E-7
+        H+: 1.0E-7
+      </soluteMolalities>
+    </state>
+
+    <thermo model="HMW">
+      <standardConc model="solvent_volume" />
+      <activityCoefficients TempModel="complex1" model="Pitzer">
+        
+        <A_Debye model="water" />
+        <ionicRadius default="3.042843" units="Angstroms">
+        </ionicRadius>
+        <binarySaltParameters anion="Cl-" cation="H+">
+          <beta0> 0.177000779, 0.000125778, 0.0, -33.4777082, -0.262214535 </beta0>
+          <beta1> 0.292922504, -0.027938838, 0.0, 3402.47027, 19.7936248 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi>  0.000362, -0.00003036, 0.0, -2.91038E-11, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 12 </Alpha2>
+          <source>
+            refit of Holmes, H.F., Busey, J.M., Simonson, J.M., Mesmer, R.E.,
+            Archer, D.G., and Wood, R.H., 1987, The enthalpy of dilution of HCl(aq)
+            to 648 K and 4p MPa.  Thermodynamic properties, Journal of Chemical
+            Thermodynamics, v. 19, p. 863-890.
+          </source>
+        </binarySaltParameters>
+
+        
+
+        <binarySaltParameters anion="NO3-" cation="H+">
+          <beta0> 0.123757018, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1> 0.31055417, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi>  -0.005539018, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 12 </Alpha2>
+          <source>
+            02mar (Table 3)
+            Marion, G. M.  (2002) "A molal-based model for strong acid chemistry at low
+            temperatures (less than 200 to 298 K)", Geochimica et Cosmochimica Acta, Vol. 66,
+            No. 14, pp. 2499-2516. These parameters are derived from the mole fraction model of
+            Clegg and Brimblecombe (1990) but were parameterized by Marion (2002) for the Pitzer
+            molality-based model.
+          </source>
+        </binarySaltParameters>
+		
+		<binarySaltParameters anion="Cl-" cation="Pr+++">
+          <beta0 updated="Updated at 16:15 7-31-2020"> 0.5879, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1 updated="Updated at 16:15 7-31-2020"> 5.4479999999999995, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi updated="Updated at 16:15 7-31-2020"> -0.020669999999999997, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 0 </Alpha2>
+          <source>
+            Table S1: Pitzer parameters for osmotic and activity coeff Supp Info.
+            May, P. M.;  Rowland, D.;  Hefter, G.;  Konigsberger, E. 
+			A generic and updatable Pitzer characterization of aqueous binary electrolyte 
+			solutions at 1 bar and 25 C.
+			Journal of Chemical and Engineering Data 2011,56, 5066&#8211;5077.
+          </source>
+        </binarySaltParameters>
+		
+      </activityCoefficients>
+      <solvent> H2O(L) </solvent>
+    </thermo>
+    <elementArray datasrc="elements.xml"> O H Pr C Fe Si N Na Cl Ca P E </elementArray>
+    <kinetics model="none">
+    </kinetics>
+  </phase>
+
+  <speciesData id="species_waterSolution">
+
+    <species name="H2O(L)">
+      
+      <atomArray>H:2 O:1 </atomArray>
+      <thermo>
+        <NASA P0="100000.0" Tmax="600.0" Tmin="273.14999999999998">
+          <floatArray name="coeffs" size="7">
+            7.255750050E+01,  -6.624454020E-01,   2.561987460E-03,  -4.365919230E-06,
+            2.781789810E-09,  -4.188654990E+04,  -2.882801370E+02
+          </floatArray>
+        </NASA>
+      </thermo>
+      <standardState model="waterPDSS">
+        
+        <molarVolume> 0.018068 </molarVolume>
+      </standardState>
+    </species>
+
+
+    <species name="OH-">
+      <speciesChemFormula> OH- </speciesChemFormula>
+      <atomArray> O:1 H:1 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -37595 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -54977 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -2.56 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> 0.12527 </a1>
+        <a2 units="cal/gmol"> 7.38 </a2>
+        <a3 units="cal-K/gmol/bar"> 1.8423 </a3>
+        <a4 units="cal-K/gmol"> -27821 </a4>
+        <c1 units="cal/gmol/K"> 4.15 </c1>
+        <c2 units="cal-K/gmol"> -103460 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 172460 </omega_Pr_Tr>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+    
+    <species name="NO3-">
+      <speciesChemFormula> NO3- </speciesChemFormula>
+      <atomArray> N:1 O:3 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -26507 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -49429 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> 35.12 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> 0.73161 </a1>
+        <a2 units="cal/gmol"> 678.24 </a2>
+        <a3 units="cal-K/gmol/bar"> -4.6838 </a3>
+        <a4 units="cal-K/gmol"> -30594 </a4>
+        <c1 units="cal/gmol/K"> 7.7 </c1>
+        <c2 units="cal-K/gmol"> -67250 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 109770 </omega_Pr_Tr>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    <species name="Na+">
+      <speciesChemFormula> Na+ </speciesChemFormula>
+      <atomArray> Na:1 </atomArray>
+      <charge> 1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -62591 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -57433 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> 13.96 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> 0.1839 </a1>
+        <a2 units="cal/gmol"> -228.5 </a2>
+        <a3 units="cal-K/gmol/bar"> 3.256 </a3>
+        <a4 units="cal-K/gmol"> -27260 </a4>
+        <c1 units="cal/gmol/K"> 18.18 </c1>
+        <c2 units="cal-K/gmol"> -29810 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 33060 </omega_Pr_Tr>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+    
+    
+    <species name="Cl-">
+      <speciesChemFormula> Cl- </speciesChemFormula>
+      <atomArray> Cl:1 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="273.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -31379 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_TR units="cal/gmol"> -39933 </DH0_f_Pr_TR>
+          <S0_Pr_Tr units="cal/gmol/K"> 13.56 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/mol/bar"> 0.4032 </a1>
+        <a2 units="cal/mol"> 480.1 </a2>
+        <a3 units="cal-K/mol/bar"> 5.563 </a3>
+        <a4 units="cal-K/mol"> -28470 </a4>
+        <c1 units="cal/mol/K"> -4.4 </c1>
+        <c2 units="cal-K/mol"> -57140 </c2>
+        <omega_Pr_Tr units="cal/mol"> 145600 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> 17.79 </molarVolume>
+      </standardState>
+      <source> ref:G9 </source>
+    </species>
+
+    <species name="H+">
+      <speciesChemFormula> H+ </speciesChemFormula>
+      <atomArray> H:1 </atomArray>
+      <charge> +1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="273.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> 0 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_TR units="cal/gmol"> 0 </DH0_f_Pr_TR>
+          <S0_Pr_Tr units="cal/gmol/K"> 0 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/mol/bar"> 0 </a1>
+        <a2 units="cal/mol"> 0 </a2>
+        <a3 units="cal-K/mol/bar"> 0 </a3>
+        <a4 units="cal-K/mol"> 0 </a4>
+        <c1 units="cal/mol/K"> 0 </c1>
+        <c2 units="cal-K/mol"> 0 </c2>
+        <omega_Pr_Tr units="cal/mol"> 0 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> 0 </molarVolume>
+      </standardState>
+      <source> ref:G9 </source>
+    </species>
+
+
+    <species name="Pr+++">
+      <speciesChemFormula> Pr+++ </speciesChemFormula>
+      <atomArray> Pr:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -162600 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -168800 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -50 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.32406 </a1>
+        <a2 units="cal/gmol"> -1419.98 </a2>
+        <a3 units="cal-K/gmol/bar"> 8.1257 </a3>
+        <a4 units="cal-K/gmol"> -21920 </a4>
+        <c1 units="cal/gmol/K"> -1.1975 </c1>
+        <c2 units="cal-K/gmol"> -127511 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 223500 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -42.1 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+
+  </speciesData>
+
+</ctml>
\ No newline at end of file
diff --git a/data/xmls/PC88A_HCL_Sm.xml b/data/xmls/PC88A_HCL_Sm.xml
new file mode 100644
index 0000000..86907d0
--- /dev/null
+++ b/data/xmls/PC88A_HCL_Sm.xml
@@ -0,0 +1,483 @@
+<ctml>
+  <validate reactions="yes" species="yes" />
+                   
+ 
+  <phase dim="3" id="PC88A_liquid">
+    <elementArray datasrc="elementz.xml">
+        Cl O H C P Nd Pr Ce La Dy Y Sm dummy 
+    </elementArray>
+    <speciesArray datasrc="#species_PC88A_liquid">
+      (HA)2(org) dodecane Sm(H(A)2)3(org)
+    </speciesArray>
+    <state>
+      <temperature units="K"> 298.15 </temperature>
+      <pressure units="Pa"> 100000.0 </pressure>
+      <soluteMolalities>
+        (HA)2(org): 0.25
+      </soluteMolalities>
+    </state>
+    <thermo model="IdealSolidSolution">
+       <standardConc model="molar_volume" />
+    </thermo>
+    <standardConc model="molar_volume" />
+    <transport model="None" />
+    <kinetics model="none" />
+  </phase>
+
+ 
+  <speciesData id="species_PC88A_liquid">
+
+
+    <species name="(HA)2(org)">
+      <atomArray> C:16 H:35 O:3 P:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+          <t0 units="K">298.14999999999998</t0>
+          <h0 units="J/mol" updated="Updated at 15:11 7-31-2020">-1460300.0</h0>
+          <s0 units="J/mol/K"> 558.9824 </s0>
+          <cp0 units="J/mol/K"> 0.0</cp0>
+        </const_cp>
+      </thermo>
+      <standardState model="constant_incompressible">
+        <molarVolume updated="yep">0.32</molarVolume>
+      </standardState>
+    </species>
+
+
+
+
+    <species name="Sm(H(A)2)3(org)">
+      <atomArray> C:48 H:102 O:9 P:3 Sm:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+          <t0 units="K">298.14999999999998</t0>
+
+          <h0 units="J/mol" updated="Updated at 15:11 7-31-2020">-5177400.0</h0>
+          <s0 units="J/mol/K"> 1117.965 </s0>
+          <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+      <standardState model="constant_incompressible">
+        <molarVolume> 0.9642 </molarVolume>
+      </standardState>
+    </species>
+
+
+    
+    
+    <species name="dodecane">
+      <atomArray> dummy:1  </atomArray>  
+      
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+	   <t0 units="K">298.14999999999998</t0> 
+	   <h0 units="J/mol"> 0.0 </h0> 
+	   <s0 units="J/mol/K"> 0.0 </s0>  
+           <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+     <standardState model="constant_incompressible">
+	     <molarVolume> 0.227113 </molarVolume>  
+      </standardState>
+    </species>
+
+  </speciesData>
+
+
+
+  <phase dim="3" id="HCl_electrolyte">
+    <speciesArray datasrc="#species_waterSolution">
+      H2O(L) H+ OH- Cl- Sm+++
+    </speciesArray>
+
+    <state>
+      <temperature units="K"> 298.15 </temperature>
+      <pressure units="Pa"> 100000.0 </pressure>
+      <soluteMolalities>
+        Cl-: 1.0E-7
+        H+: 1.0E-7
+      </soluteMolalities>
+    </state>
+
+    <thermo model="HMW">
+      <standardConc model="solvent_volume" />
+      <activityCoefficients TempModel="complex1" model="Pitzer">
+        
+        <A_Debye model="water" />
+        <ionicRadius default="3.042843" units="Angstroms">
+        </ionicRadius>
+        <binarySaltParameters anion="Cl-" cation="H+">
+          <beta0> 0.177000779, 0.000125778, 0.0, -33.4777082, -0.262214535 </beta0>
+          <beta1> 0.292922504, -0.027938838, 0.0, 3402.47027, 19.7936248 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi>  0.000362, -0.00003036, 0.0, -2.91038E-11, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 12 </Alpha2>
+          <source>
+            refit of Holmes, H.F., Busey, J.M., Simonson, J.M., Mesmer, R.E.,
+            Archer, D.G., and Wood, R.H., 1987, The enthalpy of dilution of HCl(aq)
+            to 648 K and 4p MPa.  Thermodynamic properties, Journal of Chemical
+            Thermodynamics, v. 19, p. 863-890.
+          </source>
+        </binarySaltParameters>
+
+        
+
+        
+
+      </activityCoefficients>
+      <solvent> H2O(L) </solvent>
+    </thermo>
+    
+    <elementArray datasrc="elementz.xml"> O H Nd Pr Ce La Dy Y Sm C N Cl P E </elementArray>
+    <kinetics model="none">
+    </kinetics>
+  </phase>
+
+  <speciesData id="species_waterSolution">
+
+    <species name="H2O(L)">
+      
+      <atomArray>H:2 O:1 </atomArray>
+      <thermo>
+        <NASA P0="100000.0" Tmax="600.0" Tmin="273.14999999999998">
+          <floatArray name="coeffs" size="7">
+            7.255750050E+01,  -6.624454020E-01,   2.561987460E-03,  -4.365919230E-06,
+            2.781789810E-09,  -4.188654990E+04,  -2.882801370E+02
+          </floatArray>
+        </NASA>
+      </thermo>
+      <standardState model="waterPDSS">
+        
+        <molarVolume> 0.018068 </molarVolume>
+      </standardState>
+    </species>
+
+
+    <species name="OH-">
+      <speciesChemFormula> OH- </speciesChemFormula>
+      <atomArray> O:1 H:1 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -37595 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -54977 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -2.56 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> 0.12527 </a1>
+        <a2 units="cal/gmol"> 7.38 </a2>
+        <a3 units="cal-K/gmol/bar"> 1.8423 </a3>
+        <a4 units="cal-K/gmol"> -27821 </a4>
+        <c1 units="cal/gmol/K"> 4.15 </c1>
+        <c2 units="cal-K/gmol"> -103460 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 172460 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -4.18 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+    <species name="NO3-">
+      <speciesChemFormula> NO3- </speciesChemFormula>
+      <atomArray> N:1 O:3 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -26507 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -49429 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> 35.12 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> 0.73161 </a1>
+        <a2 units="cal/gmol"> 678.24 </a2>
+        <a3 units="cal-K/gmol/bar"> -4.6838 </a3>
+        <a4 units="cal-K/gmol"> -30594 </a4>
+        <c1 units="cal/gmol/K"> 7.7 </c1>
+        <c2 units="cal-K/gmol"> -67250 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 109770 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> 29.0 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    
+
+    
+    
+    <species name="Cl-">
+      <speciesChemFormula> Cl- </speciesChemFormula>
+      <atomArray> Cl:1 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="273.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -31379 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_TR units="cal/gmol"> -39933 </DH0_f_Pr_TR>
+          <S0_Pr_Tr units="cal/gmol/K"> 13.56 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/mol/bar"> 0.4032 </a1>
+        <a2 units="cal/mol"> 480.1 </a2>
+        <a3 units="cal-K/mol/bar"> 5.563 </a3>
+        <a4 units="cal-K/mol"> -28470 </a4>
+        <c1 units="cal/mol/K"> -4.4 </c1>
+        <c2 units="cal-K/mol"> -57140 </c2>
+        <omega_Pr_Tr units="cal/mol"> 145600 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> 17.79 </molarVolume>
+      </standardState>
+      <source> ref:G9 </source>
+    </species>
+
+    <species name="H+">
+      <speciesChemFormula> H+ </speciesChemFormula>
+      <atomArray> H:1 </atomArray>
+      <charge> +1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="273.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> 0 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_TR units="cal/gmol"> 0 </DH0_f_Pr_TR>
+          <S0_Pr_Tr units="cal/gmol/K"> 0 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/mol/bar"> 0 </a1>
+        <a2 units="cal/mol"> 0 </a2>
+        <a3 units="cal-K/mol/bar"> 0 </a3>
+        <a4 units="cal-K/mol"> 0 </a4>
+        <c1 units="cal/mol/K"> 0 </c1>
+        <c2 units="cal-K/mol"> 0 </c2>
+        <omega_Pr_Tr units="cal/mol"> 0 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> 0 </molarVolume>
+      </standardState>
+      <source> ref:G9 </source>
+    </species>
+
+
+    <species name="Nd+++">
+      <speciesChemFormula> Nd+++ </speciesChemFormula>
+      <atomArray> Nd:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -160600 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -166500 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -49.5 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.33707 </a1>
+        <a2 units="cal/gmol"> -1454.52 </a2>
+        <a3 units="cal-K/gmol/bar"> 8.3211 </a3>
+        <a4 units="cal-K/gmol"> -21777 </a4>
+        <c1 units="cal/gmol/K"> 1.6236 </c1>
+        <c2 units="cal-K/gmol"> -118344 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 225500 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -43.1 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+    <species name="Pr+++">
+      <speciesChemFormula> Pr+++ </speciesChemFormula>
+      <atomArray> Pr:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -162600 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -168800 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -50 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.32406 </a1>
+        <a2 units="cal/gmol"> -1419.98 </a2>
+        <a3 units="cal-K/gmol/bar"> 8.1257 </a3>
+        <a4 units="cal-K/gmol"> -21920 </a4>
+        <c1 units="cal/gmol/K"> -1.1975 </c1>
+        <c2 units="cal-K/gmol"> -127511 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 223500 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -42.1 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    <species name="Ce+++">
+      <speciesChemFormula> Ce+++ </speciesChemFormula>
+      <atomArray> Ce:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -161600 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -167400 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -49 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.29292 </a1>
+        <a2 units="cal/gmol"> -1493.38 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.6196 </a3>
+        <a4 units="cal-K/gmol"> -21616 </a4>
+        <c1 units="cal/gmol/K"> 4.0445 </c1>
+        <c2 units="cal-K/gmol"> -108974 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 222510 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -39.8 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    <species name="La+++">
+      <speciesChemFormula> La+++ </speciesChemFormula>
+      <atomArray> La:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -164000 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -169600 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -52 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.2788 </a1>
+        <a2 units="cal/gmol"> -1438.24 </a2>
+        <a3 units="cal-K/gmol/bar"> 10.9602 </a3>
+        <a4 units="cal-K/gmol"> -21844 </a4>
+        <c1 units="cal/gmol/K"> 4.2394 </c1>
+        <c2 units="cal-K/gmol"> -106122 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 215720 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -38.6 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+
+    <species name="Dy+++">
+      <speciesChemFormula> Dy+++ </speciesChemFormula>
+      <atomArray> Dy:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -158700 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -166500 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -55.2 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.30003 </a1>
+        <a2 units="cal/gmol"> -1510.74 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.6879 </a3>
+        <a4 units="cal-K/gmol"> -21545 </a4>
+        <c1 units="cal/gmol/K"> 9.5076 </c1>
+        <c2 units="cal-K/gmol"> -94919 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 237920 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -40.7 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    <species name="Y+++">
+      <speciesChemFormula> Y+++ </speciesChemFormula>
+      <atomArray> Y:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -163800 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -170900 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -60 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.30140 </a1>
+        <a2 units="cal/gmol"> -1514.08 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.7010 </a3>
+        <a4 units="cal-K/gmol"> -21531 </a4>
+        <c1 units="cal/gmol/K"> 7.1634 </c1>
+        <c2 units="cal-K/gmol"> -103067 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 237920 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -40.8 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+    
+    
+
+    <species name="Sm+++">
+      <speciesChemFormula> Sm+++ </speciesChemFormula>
+      <atomArray> Sm:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -159100 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -165200 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -50.7 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.32065 </a1>
+        <a2 units="cal/gmol"> -1561.08 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.8857 </a3>
+        <a4 units="cal-K/gmol"> -21337 </a4>
+        <c1 units="cal/gmol/K"> 1.9385 </c1>
+        <c2 units="cal-K/gmol"> -118548 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 229550 </omega_Pr_Tr>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+
+    <species name="Gd+++">
+      <speciesChemFormula> Gd+++ </speciesChemFormula>
+      <atomArray> Gd:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -158600 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -164200 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -49.2 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.29771 </a1>
+        <a2 units="cal/gmol"> -1505.06 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.6656 </a3>
+        <a4 units="cal-K/gmol"> -21568 </a4>
+        <c1 units="cal/gmol/K"> 6.5606 </c1>
+        <c2 units="cal-K/gmol"> -103474 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 232650 </omega_Pr_Tr>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+    
+    
+    
+    
+  </speciesData>
+
+</ctml>
\ No newline at end of file
diff --git a/data/xmls/PC88A_HCL_Sm_w_pitzer.xml b/data/xmls/PC88A_HCL_Sm_w_pitzer.xml
new file mode 100644
index 0000000..4104d34
--- /dev/null
+++ b/data/xmls/PC88A_HCL_Sm_w_pitzer.xml
@@ -0,0 +1,498 @@
+<ctml>
+  <validate reactions="yes" species="yes" />
+                   
+ 
+  <phase dim="3" id="PC88A_liquid">
+    <elementArray datasrc="elementz.xml">
+        Cl O H C P Nd Pr Ce La Dy Y Sm dummy 
+    </elementArray>
+    <speciesArray datasrc="#species_PC88A_liquid">
+      (HA)2(org) dodecane Sm(H(A)2)3(org)
+    </speciesArray>
+    <state>
+      <temperature units="K"> 298.15 </temperature>
+      <pressure units="Pa"> 100000.0 </pressure>
+      <soluteMolalities>
+        (HA)2(org): 0.25
+      </soluteMolalities>
+    </state>
+    <thermo model="IdealSolidSolution">
+       <standardConc model="molar_volume" />
+    </thermo>
+    <standardConc model="molar_volume" />
+    <transport model="None" />
+    <kinetics model="none" />
+  </phase>
+
+ 
+  <speciesData id="species_PC88A_liquid">
+
+
+    <species name="(HA)2(org)">
+      <atomArray> C:16 H:35 O:3 P:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+          <t0 units="K">298.14999999999998</t0>
+          <h0 units="J/mol" updated="Updated at 16:19 7-31-2020">-1460300.0</h0>
+          <s0 units="J/mol/K"> 558.9824 </s0>
+          <cp0 units="J/mol/K"> 0.0</cp0>
+        </const_cp>
+      </thermo>
+      <standardState model="constant_incompressible">
+        <molarVolume updated="yep">0.32</molarVolume>
+      </standardState>
+    </species>
+
+
+
+
+    <species name="Sm(H(A)2)3(org)">
+      <atomArray> C:48 H:102 O:9 P:3 Sm:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+          <t0 units="K">298.14999999999998</t0>
+
+          <h0 units="J/mol" updated="Updated at 16:19 7-31-2020">-5177400.0</h0>
+          <s0 units="J/mol/K"> 1117.965 </s0>
+          <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+      <standardState model="constant_incompressible">
+        <molarVolume> 0.9642 </molarVolume>
+      </standardState>
+    </species>
+
+
+    
+    
+    <species name="dodecane">
+      <atomArray> dummy:1  </atomArray>  
+      
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+	   <t0 units="K">298.14999999999998</t0> 
+	   <h0 units="J/mol"> 0.0 </h0> 
+	   <s0 units="J/mol/K"> 0.0 </s0>  
+           <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+     <standardState model="constant_incompressible">
+	     <molarVolume> 0.227113 </molarVolume>  
+      </standardState>
+    </species>
+
+  </speciesData>
+
+
+
+  <phase dim="3" id="HCl_electrolyte">
+    <speciesArray datasrc="#species_waterSolution">
+      H2O(L) H+ OH- Cl- Sm+++
+    </speciesArray>
+
+    <state>
+      <temperature units="K"> 298.15 </temperature>
+      <pressure units="Pa"> 100000.0 </pressure>
+      <soluteMolalities>
+        Cl-: 1.0E-7
+        H+: 1.0E-7
+      </soluteMolalities>
+    </state>
+
+    <thermo model="HMW">
+      <standardConc model="solvent_volume" />
+      <activityCoefficients TempModel="complex1" model="Pitzer">
+        
+        <A_Debye model="water" />
+        <ionicRadius default="3.042843" units="Angstroms">
+        </ionicRadius>
+        <binarySaltParameters anion="Cl-" cation="H+">
+          <beta0> 0.177000779, 0.000125778, 0.0, -33.4777082, -0.262214535 </beta0>
+          <beta1> 0.292922504, -0.027938838, 0.0, 3402.47027, 19.7936248 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi>  0.000362, -0.00003036, 0.0, -2.91038E-11, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 12 </Alpha2>
+          <source>
+            refit of Holmes, H.F., Busey, J.M., Simonson, J.M., Mesmer, R.E.,
+            Archer, D.G., and Wood, R.H., 1987, The enthalpy of dilution of HCl(aq)
+            to 648 K and 4p MPa.  Thermodynamic properties, Journal of Chemical
+            Thermodynamics, v. 19, p. 863-890.
+          </source>
+        </binarySaltParameters>
+	
+		<binarySaltParameters anion="Cl-" cation="Sm+++">
+          <beta0 updated="Updated at 16:19 7-31-2020"> 0.5978, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1 updated="Updated at 16:19 7-31-2020"> 5.279, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi updated="Updated at 16:19 7-31-2020"> -0.01992, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 0 </Alpha2>
+          <source>
+            Table S1: Pitzer parameters for osmotic and activity coeff Supp Info.
+            May, P. M.;  Rowland, D.;  Hefter, G.;  Konigsberger, E. 
+			A generic and updatable Pitzer characterization of aqueous binary electrolyte 
+			solutions at 1 bar and 25 C.
+			Journal of Chemical and Engineering Data 2011,56, 5066&#8211;5077.
+          </source>
+        </binarySaltParameters>
+        
+
+        
+
+      </activityCoefficients>
+      <solvent> H2O(L) </solvent>
+    </thermo>
+    
+    <elementArray datasrc="elementz.xml"> O H Nd Pr Ce La Dy Y Sm C N Cl P E </elementArray>
+    <kinetics model="none">
+    </kinetics>
+  </phase>
+
+  <speciesData id="species_waterSolution">
+
+    <species name="H2O(L)">
+      
+      <atomArray>H:2 O:1 </atomArray>
+      <thermo>
+        <NASA P0="100000.0" Tmax="600.0" Tmin="273.14999999999998">
+          <floatArray name="coeffs" size="7">
+            7.255750050E+01,  -6.624454020E-01,   2.561987460E-03,  -4.365919230E-06,
+            2.781789810E-09,  -4.188654990E+04,  -2.882801370E+02
+          </floatArray>
+        </NASA>
+      </thermo>
+      <standardState model="waterPDSS">
+        
+        <molarVolume> 0.018068 </molarVolume>
+      </standardState>
+    </species>
+
+
+    <species name="OH-">
+      <speciesChemFormula> OH- </speciesChemFormula>
+      <atomArray> O:1 H:1 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -37595 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -54977 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -2.56 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> 0.12527 </a1>
+        <a2 units="cal/gmol"> 7.38 </a2>
+        <a3 units="cal-K/gmol/bar"> 1.8423 </a3>
+        <a4 units="cal-K/gmol"> -27821 </a4>
+        <c1 units="cal/gmol/K"> 4.15 </c1>
+        <c2 units="cal-K/gmol"> -103460 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 172460 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -4.18 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+    <species name="NO3-">
+      <speciesChemFormula> NO3- </speciesChemFormula>
+      <atomArray> N:1 O:3 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -26507 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -49429 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> 35.12 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> 0.73161 </a1>
+        <a2 units="cal/gmol"> 678.24 </a2>
+        <a3 units="cal-K/gmol/bar"> -4.6838 </a3>
+        <a4 units="cal-K/gmol"> -30594 </a4>
+        <c1 units="cal/gmol/K"> 7.7 </c1>
+        <c2 units="cal-K/gmol"> -67250 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 109770 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> 29.0 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    
+
+    
+    
+    <species name="Cl-">
+      <speciesChemFormula> Cl- </speciesChemFormula>
+      <atomArray> Cl:1 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="273.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -31379 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_TR units="cal/gmol"> -39933 </DH0_f_Pr_TR>
+          <S0_Pr_Tr units="cal/gmol/K"> 13.56 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/mol/bar"> 0.4032 </a1>
+        <a2 units="cal/mol"> 480.1 </a2>
+        <a3 units="cal-K/mol/bar"> 5.563 </a3>
+        <a4 units="cal-K/mol"> -28470 </a4>
+        <c1 units="cal/mol/K"> -4.4 </c1>
+        <c2 units="cal-K/mol"> -57140 </c2>
+        <omega_Pr_Tr units="cal/mol"> 145600 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> 17.79 </molarVolume>
+      </standardState>
+      <source> ref:G9 </source>
+    </species>
+
+    <species name="H+">
+      <speciesChemFormula> H+ </speciesChemFormula>
+      <atomArray> H:1 </atomArray>
+      <charge> +1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="273.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> 0 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_TR units="cal/gmol"> 0 </DH0_f_Pr_TR>
+          <S0_Pr_Tr units="cal/gmol/K"> 0 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/mol/bar"> 0 </a1>
+        <a2 units="cal/mol"> 0 </a2>
+        <a3 units="cal-K/mol/bar"> 0 </a3>
+        <a4 units="cal-K/mol"> 0 </a4>
+        <c1 units="cal/mol/K"> 0 </c1>
+        <c2 units="cal-K/mol"> 0 </c2>
+        <omega_Pr_Tr units="cal/mol"> 0 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> 0 </molarVolume>
+      </standardState>
+      <source> ref:G9 </source>
+    </species>
+
+
+    <species name="Nd+++">
+      <speciesChemFormula> Nd+++ </speciesChemFormula>
+      <atomArray> Nd:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -160600 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -166500 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -49.5 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.33707 </a1>
+        <a2 units="cal/gmol"> -1454.52 </a2>
+        <a3 units="cal-K/gmol/bar"> 8.3211 </a3>
+        <a4 units="cal-K/gmol"> -21777 </a4>
+        <c1 units="cal/gmol/K"> 1.6236 </c1>
+        <c2 units="cal-K/gmol"> -118344 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 225500 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -43.1 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+    <species name="Pr+++">
+      <speciesChemFormula> Pr+++ </speciesChemFormula>
+      <atomArray> Pr:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -162600 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -168800 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -50 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.32406 </a1>
+        <a2 units="cal/gmol"> -1419.98 </a2>
+        <a3 units="cal-K/gmol/bar"> 8.1257 </a3>
+        <a4 units="cal-K/gmol"> -21920 </a4>
+        <c1 units="cal/gmol/K"> -1.1975 </c1>
+        <c2 units="cal-K/gmol"> -127511 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 223500 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -42.1 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    <species name="Ce+++">
+      <speciesChemFormula> Ce+++ </speciesChemFormula>
+      <atomArray> Ce:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -161600 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -167400 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -49 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.29292 </a1>
+        <a2 units="cal/gmol"> -1493.38 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.6196 </a3>
+        <a4 units="cal-K/gmol"> -21616 </a4>
+        <c1 units="cal/gmol/K"> 4.0445 </c1>
+        <c2 units="cal-K/gmol"> -108974 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 222510 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -39.8 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    <species name="La+++">
+      <speciesChemFormula> La+++ </speciesChemFormula>
+      <atomArray> La:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -164000 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -169600 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -52 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.2788 </a1>
+        <a2 units="cal/gmol"> -1438.24 </a2>
+        <a3 units="cal-K/gmol/bar"> 10.9602 </a3>
+        <a4 units="cal-K/gmol"> -21844 </a4>
+        <c1 units="cal/gmol/K"> 4.2394 </c1>
+        <c2 units="cal-K/gmol"> -106122 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 215720 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -38.6 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+
+    <species name="Dy+++">
+      <speciesChemFormula> Dy+++ </speciesChemFormula>
+      <atomArray> Dy:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -158700 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -166500 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -55.2 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.30003 </a1>
+        <a2 units="cal/gmol"> -1510.74 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.6879 </a3>
+        <a4 units="cal-K/gmol"> -21545 </a4>
+        <c1 units="cal/gmol/K"> 9.5076 </c1>
+        <c2 units="cal-K/gmol"> -94919 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 237920 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -40.7 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    <species name="Y+++">
+      <speciesChemFormula> Y+++ </speciesChemFormula>
+      <atomArray> Y:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -163800 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -170900 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -60 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.30140 </a1>
+        <a2 units="cal/gmol"> -1514.08 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.7010 </a3>
+        <a4 units="cal-K/gmol"> -21531 </a4>
+        <c1 units="cal/gmol/K"> 7.1634 </c1>
+        <c2 units="cal-K/gmol"> -103067 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 237920 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -40.8 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+    
+    
+
+    <species name="Sm+++">
+      <speciesChemFormula> Sm+++ </speciesChemFormula>
+      <atomArray> Sm:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -159100 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -165200 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -50.7 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.32065 </a1>
+        <a2 units="cal/gmol"> -1561.08 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.8857 </a3>
+        <a4 units="cal-K/gmol"> -21337 </a4>
+        <c1 units="cal/gmol/K"> 1.9385 </c1>
+        <c2 units="cal-K/gmol"> -118548 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 229550 </omega_Pr_Tr>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+
+    <species name="Gd+++">
+      <speciesChemFormula> Gd+++ </speciesChemFormula>
+      <atomArray> Gd:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -158600 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -164200 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -49.2 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.29771 </a1>
+        <a2 units="cal/gmol"> -1505.06 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.6656 </a3>
+        <a4 units="cal-K/gmol"> -21568 </a4>
+        <c1 units="cal/gmol/K"> 6.5606 </c1>
+        <c2 units="cal-K/gmol"> -103474 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 232650 </omega_Pr_Tr>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+    
+    
+    
+    
+  </speciesData>
+
+</ctml>
\ No newline at end of file
diff --git a/data/xmls/PC88A_HCL_Y.xml b/data/xmls/PC88A_HCL_Y.xml
new file mode 100644
index 0000000..4220ca3
--- /dev/null
+++ b/data/xmls/PC88A_HCL_Y.xml
@@ -0,0 +1,481 @@
+<ctml>
+  <validate reactions="yes" species="yes" />
+                   
+ 
+  <phase dim="3" id="PC88A_liquid">
+    <elementArray datasrc="elementz.xml">
+        Cl O H C P Nd Pr Ce La Dy Y Sm dummy 
+    </elementArray>
+    <speciesArray datasrc="#species_PC88A_liquid">
+      (HA)2(org) dodecane Y(H(A)2)3(org)
+    </speciesArray>
+    <state>
+      <temperature units="K"> 298.15 </temperature>
+      <pressure units="Pa"> 100000.0 </pressure>
+      <soluteMolalities>
+        (HA)2(org): 0.25
+      </soluteMolalities>
+    </state>
+    <thermo model="IdealSolidSolution">
+       <standardConc model="molar_volume" />
+    </thermo>
+    <standardConc model="molar_volume" />
+    <transport model="None" />
+    <kinetics model="none" />
+  </phase>
+
+ 
+  <speciesData id="species_PC88A_liquid">
+
+
+    <species name="(HA)2(org)">
+      <atomArray> C:16 H:35 O:3 P:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+          <t0 units="K">298.14999999999998</t0>
+          <h0 units="J/mol" updated="Updated at 15:12 7-31-2020">-1460300.0</h0>
+          <s0 units="J/mol/K"> 558.9824 </s0>
+          <cp0 units="J/mol/K"> 0.0</cp0>
+        </const_cp>
+      </thermo>
+      <standardState model="constant_incompressible">
+        <molarVolume updated="yep">0.32</molarVolume>
+      </standardState>
+    </species>
+
+
+
+    <species name="Y(H(A)2)3(org)">
+      <atomArray> C:48 H:102 O:9 P:3 Y:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+          <t0 units="K">298.14999999999998</t0>
+
+          <h0 units="J/mol" updated="Updated at 15:12 7-31-2020">-5177400.0</h0>
+          <s0 units="J/mol/K"> 1117.965 </s0>
+          <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+      <standardState model="constant_incompressible">
+        <molarVolume updated="yep">0.94</molarVolume>
+      </standardState>
+    </species>
+
+
+
+    <species name="dodecane">
+      <atomArray> dummy:1  </atomArray>  
+      
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+	   <t0 units="K">298.14999999999998</t0> 
+	   <h0 units="J/mol"> 0.0 </h0> 
+	   <s0 units="J/mol/K"> 0.0 </s0>  
+           <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+     <standardState model="constant_incompressible">
+	     <molarVolume> 0.227113 </molarVolume>  
+      </standardState>
+    </species>
+
+  </speciesData>
+
+
+
+  <phase dim="3" id="HCl_electrolyte">
+    <speciesArray datasrc="#species_waterSolution">
+      H2O(L) H+ OH- Cl- Y+++
+    </speciesArray>
+
+    <state>
+      <temperature units="K"> 298.15 </temperature>
+      <pressure units="Pa"> 100000.0 </pressure>
+      <soluteMolalities>
+        Cl-: 1.0E-7
+        H+: 1.0E-7
+      </soluteMolalities>
+    </state>
+
+    <thermo model="HMW">
+      <standardConc model="solvent_volume" />
+      <activityCoefficients TempModel="complex1" model="Pitzer">
+        
+        <A_Debye model="water" />
+        <ionicRadius default="3.042843" units="Angstroms">
+        </ionicRadius>
+        <binarySaltParameters anion="Cl-" cation="H+">
+          <beta0> 0.177000779, 0.000125778, 0.0, -33.4777082, -0.262214535 </beta0>
+          <beta1> 0.292922504, -0.027938838, 0.0, 3402.47027, 19.7936248 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi>  0.000362, -0.00003036, 0.0, -2.91038E-11, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 12 </Alpha2>
+          <source>
+            refit of Holmes, H.F., Busey, J.M., Simonson, J.M., Mesmer, R.E.,
+            Archer, D.G., and Wood, R.H., 1987, The enthalpy of dilution of HCl(aq)
+            to 648 K and 4p MPa.  Thermodynamic properties, Journal of Chemical
+            Thermodynamics, v. 19, p. 863-890.
+          </source>
+        </binarySaltParameters>
+
+        
+
+        
+
+      </activityCoefficients>
+      <solvent> H2O(L) </solvent>
+    </thermo>
+    
+    <elementArray datasrc="elementz.xml"> O H Nd Pr Ce La Dy Y Sm C N Cl P E </elementArray>
+    <kinetics model="none">
+    </kinetics>
+  </phase>
+
+  <speciesData id="species_waterSolution">
+
+    <species name="H2O(L)">
+      
+      <atomArray>H:2 O:1 </atomArray>
+      <thermo>
+        <NASA P0="100000.0" Tmax="600.0" Tmin="273.14999999999998">
+          <floatArray name="coeffs" size="7">
+            7.255750050E+01,  -6.624454020E-01,   2.561987460E-03,  -4.365919230E-06,
+            2.781789810E-09,  -4.188654990E+04,  -2.882801370E+02
+          </floatArray>
+        </NASA>
+      </thermo>
+      <standardState model="waterPDSS">
+        
+        <molarVolume> 0.018068 </molarVolume>
+      </standardState>
+    </species>
+
+
+    <species name="OH-">
+      <speciesChemFormula> OH- </speciesChemFormula>
+      <atomArray> O:1 H:1 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -37595 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -54977 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -2.56 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> 0.12527 </a1>
+        <a2 units="cal/gmol"> 7.38 </a2>
+        <a3 units="cal-K/gmol/bar"> 1.8423 </a3>
+        <a4 units="cal-K/gmol"> -27821 </a4>
+        <c1 units="cal/gmol/K"> 4.15 </c1>
+        <c2 units="cal-K/gmol"> -103460 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 172460 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -4.18 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+    <species name="NO3-">
+      <speciesChemFormula> NO3- </speciesChemFormula>
+      <atomArray> N:1 O:3 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -26507 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -49429 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> 35.12 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> 0.73161 </a1>
+        <a2 units="cal/gmol"> 678.24 </a2>
+        <a3 units="cal-K/gmol/bar"> -4.6838 </a3>
+        <a4 units="cal-K/gmol"> -30594 </a4>
+        <c1 units="cal/gmol/K"> 7.7 </c1>
+        <c2 units="cal-K/gmol"> -67250 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 109770 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> 29.0 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    
+
+    
+    
+    <species name="Cl-">
+      <speciesChemFormula> Cl- </speciesChemFormula>
+      <atomArray> Cl:1 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="273.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -31379 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_TR units="cal/gmol"> -39933 </DH0_f_Pr_TR>
+          <S0_Pr_Tr units="cal/gmol/K"> 13.56 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/mol/bar"> 0.4032 </a1>
+        <a2 units="cal/mol"> 480.1 </a2>
+        <a3 units="cal-K/mol/bar"> 5.563 </a3>
+        <a4 units="cal-K/mol"> -28470 </a4>
+        <c1 units="cal/mol/K"> -4.4 </c1>
+        <c2 units="cal-K/mol"> -57140 </c2>
+        <omega_Pr_Tr units="cal/mol"> 145600 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> 17.79 </molarVolume>
+      </standardState>
+      <source> ref:G9 </source>
+    </species>
+
+    <species name="H+">
+      <speciesChemFormula> H+ </speciesChemFormula>
+      <atomArray> H:1 </atomArray>
+      <charge> +1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="273.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> 0 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_TR units="cal/gmol"> 0 </DH0_f_Pr_TR>
+          <S0_Pr_Tr units="cal/gmol/K"> 0 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/mol/bar"> 0 </a1>
+        <a2 units="cal/mol"> 0 </a2>
+        <a3 units="cal-K/mol/bar"> 0 </a3>
+        <a4 units="cal-K/mol"> 0 </a4>
+        <c1 units="cal/mol/K"> 0 </c1>
+        <c2 units="cal-K/mol"> 0 </c2>
+        <omega_Pr_Tr units="cal/mol"> 0 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> 0 </molarVolume>
+      </standardState>
+      <source> ref:G9 </source>
+    </species>
+
+
+    <species name="Nd+++">
+      <speciesChemFormula> Nd+++ </speciesChemFormula>
+      <atomArray> Nd:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -160600 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -166500 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -49.5 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.33707 </a1>
+        <a2 units="cal/gmol"> -1454.52 </a2>
+        <a3 units="cal-K/gmol/bar"> 8.3211 </a3>
+        <a4 units="cal-K/gmol"> -21777 </a4>
+        <c1 units="cal/gmol/K"> 1.6236 </c1>
+        <c2 units="cal-K/gmol"> -118344 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 225500 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -43.1 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+    <species name="Pr+++">
+      <speciesChemFormula> Pr+++ </speciesChemFormula>
+      <atomArray> Pr:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -162600 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -168800 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -50 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.32406 </a1>
+        <a2 units="cal/gmol"> -1419.98 </a2>
+        <a3 units="cal-K/gmol/bar"> 8.1257 </a3>
+        <a4 units="cal-K/gmol"> -21920 </a4>
+        <c1 units="cal/gmol/K"> -1.1975 </c1>
+        <c2 units="cal-K/gmol"> -127511 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 223500 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -42.1 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    <species name="Ce+++">
+      <speciesChemFormula> Ce+++ </speciesChemFormula>
+      <atomArray> Ce:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -161600 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -167400 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -49 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.29292 </a1>
+        <a2 units="cal/gmol"> -1493.38 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.6196 </a3>
+        <a4 units="cal-K/gmol"> -21616 </a4>
+        <c1 units="cal/gmol/K"> 4.0445 </c1>
+        <c2 units="cal-K/gmol"> -108974 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 222510 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -39.8 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    <species name="La+++">
+      <speciesChemFormula> La+++ </speciesChemFormula>
+      <atomArray> La:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -164000 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -169600 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -52 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.2788 </a1>
+        <a2 units="cal/gmol"> -1438.24 </a2>
+        <a3 units="cal-K/gmol/bar"> 10.9602 </a3>
+        <a4 units="cal-K/gmol"> -21844 </a4>
+        <c1 units="cal/gmol/K"> 4.2394 </c1>
+        <c2 units="cal-K/gmol"> -106122 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 215720 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -38.6 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+
+    <species name="Dy+++">
+      <speciesChemFormula> Dy+++ </speciesChemFormula>
+      <atomArray> Dy:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -158700 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -166500 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -55.2 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.30003 </a1>
+        <a2 units="cal/gmol"> -1510.74 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.6879 </a3>
+        <a4 units="cal-K/gmol"> -21545 </a4>
+        <c1 units="cal/gmol/K"> 9.5076 </c1>
+        <c2 units="cal-K/gmol"> -94919 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 237920 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -40.7 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    <species name="Y+++">
+      <speciesChemFormula> Y+++ </speciesChemFormula>
+      <atomArray> Y:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -163800 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -170900 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -60 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.30140 </a1>
+        <a2 units="cal/gmol"> -1514.08 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.7010 </a3>
+        <a4 units="cal-K/gmol"> -21531 </a4>
+        <c1 units="cal/gmol/K"> 7.1634 </c1>
+        <c2 units="cal-K/gmol"> -103067 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 237920 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -40.8 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+    
+    
+
+    <species name="Sm+++">
+      <speciesChemFormula> Sm+++ </speciesChemFormula>
+      <atomArray> Sm:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -159100 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -165200 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -50.7 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.32065 </a1>
+        <a2 units="cal/gmol"> -1561.08 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.8857 </a3>
+        <a4 units="cal-K/gmol"> -21337 </a4>
+        <c1 units="cal/gmol/K"> 1.9385 </c1>
+        <c2 units="cal-K/gmol"> -118548 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 229550 </omega_Pr_Tr>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+
+    <species name="Gd+++">
+      <speciesChemFormula> Gd+++ </speciesChemFormula>
+      <atomArray> Gd:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -158600 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -164200 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -49.2 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.29771 </a1>
+        <a2 units="cal/gmol"> -1505.06 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.6656 </a3>
+        <a4 units="cal-K/gmol"> -21568 </a4>
+        <c1 units="cal/gmol/K"> 6.5606 </c1>
+        <c2 units="cal-K/gmol"> -103474 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 232650 </omega_Pr_Tr>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+    
+    
+    
+    
+  </speciesData>
+
+</ctml>
\ No newline at end of file
diff --git a/data/xmls/PC88A_HCL_Y_w_pitzer.xml b/data/xmls/PC88A_HCL_Y_w_pitzer.xml
new file mode 100644
index 0000000..18a1440
--- /dev/null
+++ b/data/xmls/PC88A_HCL_Y_w_pitzer.xml
@@ -0,0 +1,496 @@
+<ctml>
+  <validate reactions="yes" species="yes" />
+                   
+ 
+  <phase dim="3" id="PC88A_liquid">
+    <elementArray datasrc="elementz.xml">
+        Cl O H C P Nd Pr Ce La Dy Y Sm dummy 
+    </elementArray>
+    <speciesArray datasrc="#species_PC88A_liquid">
+      (HA)2(org) dodecane Y(H(A)2)3(org)
+    </speciesArray>
+    <state>
+      <temperature units="K"> 298.15 </temperature>
+      <pressure units="Pa"> 100000.0 </pressure>
+      <soluteMolalities>
+        (HA)2(org): 0.25
+      </soluteMolalities>
+    </state>
+    <thermo model="IdealSolidSolution">
+       <standardConc model="molar_volume" />
+    </thermo>
+    <standardConc model="molar_volume" />
+    <transport model="None" />
+    <kinetics model="none" />
+  </phase>
+
+ 
+  <speciesData id="species_PC88A_liquid">
+
+
+    <species name="(HA)2(org)">
+      <atomArray> C:16 H:35 O:3 P:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+          <t0 units="K">298.14999999999998</t0>
+          <h0 units="J/mol" updated="Updated at 16:22 7-31-2020">-1460300.0</h0>
+          <s0 units="J/mol/K"> 558.9824 </s0>
+          <cp0 units="J/mol/K"> 0.0</cp0>
+        </const_cp>
+      </thermo>
+      <standardState model="constant_incompressible">
+        <molarVolume updated="yep">0.32</molarVolume>
+      </standardState>
+    </species>
+
+
+
+    <species name="Y(H(A)2)3(org)">
+      <atomArray> C:48 H:102 O:9 P:3 Y:1 </atomArray>
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+          <t0 units="K">298.14999999999998</t0>
+
+          <h0 units="J/mol" updated="Updated at 16:22 7-31-2020">-5177400.0</h0>
+          <s0 units="J/mol/K"> 1117.965 </s0>
+          <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+      <standardState model="constant_incompressible">
+        <molarVolume updated="yep">0.94</molarVolume>
+      </standardState>
+    </species>
+
+
+
+    <species name="dodecane">
+      <atomArray> dummy:1  </atomArray>  
+      
+      <thermo>
+        <const_cp Tmax="300.0" Tmin="298.0">
+	   <t0 units="K">298.14999999999998</t0> 
+	   <h0 units="J/mol"> 0.0 </h0> 
+	   <s0 units="J/mol/K"> 0.0 </s0>  
+           <cp0 units="J/mol/K">0.0</cp0>
+        </const_cp>
+      </thermo>
+     <standardState model="constant_incompressible">
+	     <molarVolume> 0.227113 </molarVolume>  
+      </standardState>
+    </species>
+
+  </speciesData>
+
+
+
+  <phase dim="3" id="HCl_electrolyte">
+    <speciesArray datasrc="#species_waterSolution">
+      H2O(L) H+ OH- Cl- Y+++
+    </speciesArray>
+
+    <state>
+      <temperature units="K"> 298.15 </temperature>
+      <pressure units="Pa"> 100000.0 </pressure>
+      <soluteMolalities>
+        Cl-: 1.0E-7
+        H+: 1.0E-7
+      </soluteMolalities>
+    </state>
+
+    <thermo model="HMW">
+      <standardConc model="solvent_volume" />
+      <activityCoefficients TempModel="complex1" model="Pitzer">
+        
+        <A_Debye model="water" />
+        <ionicRadius default="3.042843" units="Angstroms">
+        </ionicRadius>
+        <binarySaltParameters anion="Cl-" cation="H+">
+          <beta0> 0.177000779, 0.000125778, 0.0, -33.4777082, -0.262214535 </beta0>
+          <beta1> 0.292922504, -0.027938838, 0.0, 3402.47027, 19.7936248 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi>  0.000362, -0.00003036, 0.0, -2.91038E-11, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 12 </Alpha2>
+          <source>
+            refit of Holmes, H.F., Busey, J.M., Simonson, J.M., Mesmer, R.E.,
+            Archer, D.G., and Wood, R.H., 1987, The enthalpy of dilution of HCl(aq)
+            to 648 K and 4p MPa.  Thermodynamic properties, Journal of Chemical
+            Thermodynamics, v. 19, p. 863-890.
+          </source>
+        </binarySaltParameters>
+		
+		<binarySaltParameters anion="Cl-" cation="Y+++">
+          <beta0 updated="Updated at 16:22 7-31-2020"> 0.6248, 0.0, 0.0, 0.0, 0.0 </beta0>
+          <beta1 updated="Updated at 16:22 7-31-2020"> 5.66, 0.0, 0.0, 0.0, 0.0 </beta1>
+          <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
+          <Cphi updated="Updated at 16:22 7-31-2020"> -0.01563, 0.0, 0.0, 0.0, 0.0 </Cphi>
+          <Alpha1> 2 </Alpha1>
+          <Alpha2> 0 </Alpha2>
+          <source>
+            Table S1: Pitzer parameters for osmotic and activity coeff Supp Info.
+            May, P. M.;  Rowland, D.;  Hefter, G.;  Konigsberger, E. 
+			A generic and updatable Pitzer characterization of aqueous binary electrolyte 
+			solutions at 1 bar and 25 C.
+			Journal of Chemical and Engineering Data 2011,56, 5066&#8211;5077.
+          </source>
+        </binarySaltParameters>
+        
+
+        
+
+      </activityCoefficients>
+      <solvent> H2O(L) </solvent>
+    </thermo>
+    
+    <elementArray datasrc="elementz.xml"> O H Nd Pr Ce La Dy Y Sm C N Cl P E </elementArray>
+    <kinetics model="none">
+    </kinetics>
+  </phase>
+
+  <speciesData id="species_waterSolution">
+
+    <species name="H2O(L)">
+      
+      <atomArray>H:2 O:1 </atomArray>
+      <thermo>
+        <NASA P0="100000.0" Tmax="600.0" Tmin="273.14999999999998">
+          <floatArray name="coeffs" size="7">
+            7.255750050E+01,  -6.624454020E-01,   2.561987460E-03,  -4.365919230E-06,
+            2.781789810E-09,  -4.188654990E+04,  -2.882801370E+02
+          </floatArray>
+        </NASA>
+      </thermo>
+      <standardState model="waterPDSS">
+        
+        <molarVolume> 0.018068 </molarVolume>
+      </standardState>
+    </species>
+
+
+    <species name="OH-">
+      <speciesChemFormula> OH- </speciesChemFormula>
+      <atomArray> O:1 H:1 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -37595 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -54977 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -2.56 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> 0.12527 </a1>
+        <a2 units="cal/gmol"> 7.38 </a2>
+        <a3 units="cal-K/gmol/bar"> 1.8423 </a3>
+        <a4 units="cal-K/gmol"> -27821 </a4>
+        <c1 units="cal/gmol/K"> 4.15 </c1>
+        <c2 units="cal-K/gmol"> -103460 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 172460 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -4.18 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+    <species name="NO3-">
+      <speciesChemFormula> NO3- </speciesChemFormula>
+      <atomArray> N:1 O:3 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -26507 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -49429 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> 35.12 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> 0.73161 </a1>
+        <a2 units="cal/gmol"> 678.24 </a2>
+        <a3 units="cal-K/gmol/bar"> -4.6838 </a3>
+        <a4 units="cal-K/gmol"> -30594 </a4>
+        <c1 units="cal/gmol/K"> 7.7 </c1>
+        <c2 units="cal-K/gmol"> -67250 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 109770 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> 29.0 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    
+
+    
+    
+    <species name="Cl-">
+      <speciesChemFormula> Cl- </speciesChemFormula>
+      <atomArray> Cl:1 </atomArray>
+      <charge> -1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="273.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -31379 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_TR units="cal/gmol"> -39933 </DH0_f_Pr_TR>
+          <S0_Pr_Tr units="cal/gmol/K"> 13.56 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/mol/bar"> 0.4032 </a1>
+        <a2 units="cal/mol"> 480.1 </a2>
+        <a3 units="cal-K/mol/bar"> 5.563 </a3>
+        <a4 units="cal-K/mol"> -28470 </a4>
+        <c1 units="cal/mol/K"> -4.4 </c1>
+        <c2 units="cal-K/mol"> -57140 </c2>
+        <omega_Pr_Tr units="cal/mol"> 145600 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> 17.79 </molarVolume>
+      </standardState>
+      <source> ref:G9 </source>
+    </species>
+
+    <species name="H+">
+      <speciesChemFormula> H+ </speciesChemFormula>
+      <atomArray> H:1 </atomArray>
+      <charge> +1 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="273.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> 0 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_TR units="cal/gmol"> 0 </DH0_f_Pr_TR>
+          <S0_Pr_Tr units="cal/gmol/K"> 0 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/mol/bar"> 0 </a1>
+        <a2 units="cal/mol"> 0 </a2>
+        <a3 units="cal-K/mol/bar"> 0 </a3>
+        <a4 units="cal-K/mol"> 0 </a4>
+        <c1 units="cal/mol/K"> 0 </c1>
+        <c2 units="cal-K/mol"> 0 </c2>
+        <omega_Pr_Tr units="cal/mol"> 0 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> 0 </molarVolume>
+      </standardState>
+      <source> ref:G9 </source>
+    </species>
+
+
+    <species name="Nd+++">
+      <speciesChemFormula> Nd+++ </speciesChemFormula>
+      <atomArray> Nd:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -160600 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -166500 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -49.5 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.33707 </a1>
+        <a2 units="cal/gmol"> -1454.52 </a2>
+        <a3 units="cal-K/gmol/bar"> 8.3211 </a3>
+        <a4 units="cal-K/gmol"> -21777 </a4>
+        <c1 units="cal/gmol/K"> 1.6236 </c1>
+        <c2 units="cal-K/gmol"> -118344 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 225500 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -43.1 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+    <species name="Pr+++">
+      <speciesChemFormula> Pr+++ </speciesChemFormula>
+      <atomArray> Pr:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -162600 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -168800 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -50 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.32406 </a1>
+        <a2 units="cal/gmol"> -1419.98 </a2>
+        <a3 units="cal-K/gmol/bar"> 8.1257 </a3>
+        <a4 units="cal-K/gmol"> -21920 </a4>
+        <c1 units="cal/gmol/K"> -1.1975 </c1>
+        <c2 units="cal-K/gmol"> -127511 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 223500 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -42.1 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    <species name="Ce+++">
+      <speciesChemFormula> Ce+++ </speciesChemFormula>
+      <atomArray> Ce:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -161600 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -167400 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -49 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.29292 </a1>
+        <a2 units="cal/gmol"> -1493.38 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.6196 </a3>
+        <a4 units="cal-K/gmol"> -21616 </a4>
+        <c1 units="cal/gmol/K"> 4.0445 </c1>
+        <c2 units="cal-K/gmol"> -108974 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 222510 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -39.8 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    <species name="La+++">
+      <speciesChemFormula> La+++ </speciesChemFormula>
+      <atomArray> La:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -164000 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -169600 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -52 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.2788 </a1>
+        <a2 units="cal/gmol"> -1438.24 </a2>
+        <a3 units="cal-K/gmol/bar"> 10.9602 </a3>
+        <a4 units="cal-K/gmol"> -21844 </a4>
+        <c1 units="cal/gmol/K"> 4.2394 </c1>
+        <c2 units="cal-K/gmol"> -106122 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 215720 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -38.6 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+
+    <species name="Dy+++">
+      <speciesChemFormula> Dy+++ </speciesChemFormula>
+      <atomArray> Dy:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -158700 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -166500 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -55.2 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.30003 </a1>
+        <a2 units="cal/gmol"> -1510.74 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.6879 </a3>
+        <a4 units="cal-K/gmol"> -21545 </a4>
+        <c1 units="cal/gmol/K"> 9.5076 </c1>
+        <c2 units="cal-K/gmol"> -94919 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 237920 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -40.7 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+    <species name="Y+++">
+      <speciesChemFormula> Y+++ </speciesChemFormula>
+      <atomArray> Y:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -163800 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -170900 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -60 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.30140 </a1>
+        <a2 units="cal/gmol"> -1514.08 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.7010 </a3>
+        <a4 units="cal-K/gmol"> -21531 </a4>
+        <c1 units="cal/gmol/K"> 7.1634 </c1>
+        <c2 units="cal-K/gmol"> -103067 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 237920 </omega_Pr_Tr>
+        <molarVolume units="cm3/mol"> -40.8 </molarVolume>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+    
+    
+
+    <species name="Sm+++">
+      <speciesChemFormula> Sm+++ </speciesChemFormula>
+      <atomArray> Sm:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -159100 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -165200 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -50.7 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.32065 </a1>
+        <a2 units="cal/gmol"> -1561.08 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.8857 </a3>
+        <a4 units="cal-K/gmol"> -21337 </a4>
+        <c1 units="cal/gmol/K"> 1.9385 </c1>
+        <c2 units="cal-K/gmol"> -118548 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 229550 </omega_Pr_Tr>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+
+
+
+    <species name="Gd+++">
+      <speciesChemFormula> Gd+++ </speciesChemFormula>
+      <atomArray> Gd:1 </atomArray>
+      <charge> +3 </charge>
+      <thermo model="HKFT">
+        <HKFT Pref="1 atm" Tmax="625.15" Tmin="298.15">
+          <DG0_f_Pr_Tr units="cal/gmol"> -158600 </DG0_f_Pr_Tr>
+          <DH0_f_Pr_Tr units="cal/gmol"> -164200 </DH0_f_Pr_Tr>
+          <S0_Pr_Tr units="cal/gmol/K"> -49.2 </S0_Pr_Tr>
+        </HKFT>
+      </thermo>
+      <standardState model="HKFT">
+        <a1 units="cal/gmol/bar"> -0.29771 </a1>
+        <a2 units="cal/gmol"> -1505.06 </a2>
+        <a3 units="cal-K/gmol/bar"> 11.6656 </a3>
+        <a4 units="cal-K/gmol"> -21568 </a4>
+        <c1 units="cal/gmol/K"> 6.5606 </c1>
+        <c2 units="cal-K/gmol"> -103474 </c2>
+        <omega_Pr_Tr units="cal/gmol"> 232650 </omega_Pr_Tr>
+      </standardState>
+      <source>
+        ref:G9
+      </source>
+    </species>
+    
+    
+    
+    
+  </speciesData>
+
+</ctml>
\ No newline at end of file
diff --git a/docs/Examples/2_basic_fit_method.ipynb b/docs/Examples/2_basic_fit_method.ipynb
new file mode 100644
index 0000000..2f6f053
--- /dev/null
+++ b/docs/Examples/2_basic_fit_method.ipynb
@@ -0,0 +1,477 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This script performs the basic fit method and shows the parity plots for each of the species and requires that the elementz.xml file in the data folder has been copied into Cantera's data folder. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fitting linear parameters to single component data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from llepe import LLEPE, lmse_perturbed_obj\n",
+    "import numpy as np\n",
+    "import itertools\n",
+    "import pandas as pd\n",
+    "import matplotlib\n",
+    "from scipy.optimize import curve_fit\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "font = {'family': 'sans serif',\n",
+    "        'size': 24}\n",
+    "matplotlib.rc('font', **font)\n",
+    "plt.rc('xtick', labelsize=18)\n",
+    "plt.rc('ytick', labelsize=18)\n",
+    "plt.rcParams['lines.linewidth'] = 4\n",
+    "matplotlib.rcParams['lines.markersize'] = 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Nd\n",
+      "slope = 3.00\n",
+      "interecept = -800247.45\n",
+      "Pr\n",
+      "slope = 3.00\n",
+      "interecept = -806939.71\n",
+      "Ce\n",
+      "slope = 3.00\n",
+      "interecept = -799495.56\n",
+      "La\n",
+      "slope = 3.00\n",
+      "interecept = -798511.25\n",
+      "Dy\n",
+      "slope = 3.00\n",
+      "interecept = -807223.22\n",
+      "Sm\n",
+      "slope = 3.00\n",
+      "interecept = -797560.15\n",
+      "Y\n",
+      "slope = 3.00\n",
+      "interecept = -823279.05\n",
+      "  species  slope      intercept\n",
+      "0      Nd    3.0 -800247.445370\n",
+      "1      Pr    3.0 -806939.706173\n",
+      "2      Ce    3.0 -799495.559690\n",
+      "3      La    3.0 -798511.252566\n",
+      "4      Dy    3.0 -807223.215960\n",
+      "5      Sm    3.0 -797560.154414\n",
+      "6       Y    3.0 -823279.048921\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "def linear(x, a, b):\n",
+    "    return a * x + b\n",
+    "\n",
+    "\n",
+    "lp_dict = {'species': [],\n",
+    "           'slope': [],\n",
+    "           'intercept': []}\n",
+    "species_list = 'Nd,Pr,Ce,La,Dy,Sm,Y'.split(',')\n",
+    "\n",
+    "n_guesses = 11\n",
+    "complex_guesses = -4e6 * np.logspace(-1, 1, n_guesses)\n",
+    "extractant_guesses = -1e6 * np.logspace(-1, 1, n_guesses)\n",
+    "x_guesses = list(itertools.product(*[complex_guesses, extractant_guesses]))\n",
+    "guess_ext_h0 = 0\n",
+    "for species in species_list:\n",
+    "    print(species)\n",
+    "    lower_species = species.lower()\n",
+    "\n",
+    "    labeled_data = pd.read_csv(\"../../data/csvs/labeled_zeroes_removed_{0}.csv\".format(lower_species))\n",
+    "    exp_data = labeled_data.drop(labeled_data.columns[0], axis=1)\n",
+    "    xml_file = '../../data/xmls/PC88A_HCL_{0}.xml'.format(species)\n",
+    "    opt_values = {\n",
+    "        'optimizer': [],\n",
+    "        'species': [],\n",
+    "        'parameter': [],\n",
+    "        'value': [],\n",
+    "        'obj_value': [],\n",
+    "        'guess': []}\n",
+    "    new_x_guesses = []\n",
+    "\n",
+    "    for x_guess in x_guesses:\n",
+    "        info_dict = {'{0}(H(A)2)3(org)_h0'.format(species): {'upper_element_name': 'species',\n",
+    "                                       'upper_attrib_name': 'name',\n",
+    "                                       'upper_attrib_value': '{0}(H(A)2)3(org)'.format(species),\n",
+    "                                       'lower_element_name': 'h0',\n",
+    "                                       'lower_attrib_name': None,\n",
+    "                                       'lower_attrib_value': None,\n",
+    "                                       'input_format': '{0}',\n",
+    "                                       'input_value': x_guess[0]},\n",
+    "                     '(HA)2(org)_h0': {'upper_element_name': 'species',\n",
+    "                                       'upper_attrib_name': 'name',\n",
+    "                                       'upper_attrib_value': '(HA)2(org)',\n",
+    "                                       'lower_element_name': 'h0',\n",
+    "                                       'lower_attrib_name': None,\n",
+    "                                       'lower_attrib_value': None,\n",
+    "                                       'input_format': '{0}',\n",
+    "                                       'input_value': x_guess[1]}}\n",
+    "        reeps_params = {\n",
+    "            'exp_data': exp_data,\n",
+    "            'phases_xml_filename': xml_file,\n",
+    "            'opt_dict': info_dict,\n",
+    "            'phase_names': ['HCl_electrolyte', 'PC88A_liquid'],\n",
+    "            'aq_solvent_name': 'H2O(L)',\n",
+    "            'extractant_name': '(HA)2(org)',\n",
+    "            'diluant_name': 'dodecane',\n",
+    "            'complex_names': ['{0}(H(A)2)3(org)'.format(species)],\n",
+    "            'extracted_species_ion_names': ['{0}+++'.format(species)],\n",
+    "            'aq_solvent_rho': 1000.0,\n",
+    "            'extractant_rho': 960.0,\n",
+    "            'diluant_rho': 750.0,\n",
+    "            'objective_function': lmse_perturbed_obj\n",
+    "        }\n",
+    "        estimator = LLEPE(**reeps_params)\n",
+    "        estimator.update_xml(reeps_params['opt_dict'])\n",
+    "        exp_df = estimator.get_exp_df()\n",
+    "        pred_dict = estimator.get_predicted_dict()\n",
+    "        init_obj_value = lmse_perturbed_obj(pred_dict, exp_df, [species], 1e-100)\n",
+    "        obj_kwargs = {'species_list': [species], 'epsilon': 1e-100}\n",
+    "        optimizer_kwargs = {\"method\": 'l-bfgs-b',\n",
+    "                            \"bounds\": [(1e-1, 1e1), (1e-1, 1e1)]}\n",
+    "        opt_dict, obj_value = estimator.fit(objective_kwargs=obj_kwargs,\n",
+    "                                            optimizer_kwargs=optimizer_kwargs)\n",
+    "        estimator.update_xml(opt_dict)\n",
+    "        \n",
+    "        keys = list(opt_dict.keys())\n",
+    "        info1 = ['lbfgsb',\n",
+    "                 keys[0],\n",
+    "                 'h0',\n",
+    "                 opt_dict[keys[0]]['input_value'],\n",
+    "                 obj_value,\n",
+    "                 x_guess[0]\n",
+    "                 ]\n",
+    "        info2 = ['lbfgsb',\n",
+    "                 keys[1],\n",
+    "                 'h0',\n",
+    "                 opt_dict[keys[1]]['input_value'],\n",
+    "                 obj_value,\n",
+    "                 x_guess[1]\n",
+    "                 ]\n",
+    "        if obj_value > init_obj_value*0.95:\n",
+    "            continue\n",
+    "        opt_values_keys = opt_values.keys()\n",
+    "        for ind, key in enumerate(opt_values_keys):\n",
+    "            opt_values[key].append(info1[ind])\n",
+    "        for ind, key in enumerate(opt_values_keys):\n",
+    "            opt_values[key].append(info2[ind])\n",
+    "        new_x_guesses.append(x_guess)\n",
+    "    x_guesses = new_x_guesses\n",
+    "    opt_df = pd.DataFrame(opt_values)\n",
+    "    min_ext_h0 = opt_df[opt_df['species'] == '(HA)2(org)_h0']['value'].min()\n",
+    "    if min_ext_h0 < guess_ext_h0:\n",
+    "        guess_ext_h0 = min_ext_h0\n",
+    "    complex_df = opt_df[opt_df['species'] != '(HA)2(org)_h0']\n",
+    "    ext_df = opt_df[opt_df['species'] == '(HA)2(org)_h0']\n",
+    "    p_opt, p_cov = curve_fit(linear, ext_df['value'].values,\n",
+    "                             complex_df['value'].values)\n",
+    "    slope, intercept = p_opt\n",
+    "    lp_dict['species'].append(species)\n",
+    "    lp_dict['slope'].append(slope)\n",
+    "    lp_dict['intercept'].append(intercept)\n",
+    "    print(\"slope = {0:.2f}\".format(slope))\n",
+    "    print(\"interecept = {0:.2f}\".format(intercept))\n",
+    "\n",
+    "lp_df = pd.DataFrame(lp_dict)\n",
+    "print(lp_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fitting extractant enthalpy to combined single and multicomponent dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\users\\tq220\\documents\\tits things\\anl research\\gitlab\\parameter-estimation\\llepe\\llepe.py:988: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  d_array = extracted_species_org_array / extracted_species_aq_array\n"
+     ]
+    }
+   ],
+   "source": [
+    "def ext_to_complex(h0, dummy_holder_for_custom_dict, species, linear_params):\n",
+    "    row = linear_params[linear_params['species'] == species]\n",
+    "\n",
+    "    return row['slope'].values[0] * h0[0] + row['intercept'].values[0]\n",
+    "\n",
+    "labeled_data = pd.read_csv(\"../../data/csvs/zeroes_removed_PC88A_HCL_NdPrCeLaDySmY.csv\")\n",
+    "exp_data = labeled_data.drop(labeled_data.columns[0], axis=1)\n",
+    "xml_file = '../../data/xmls/PC88A_HCL_NdPrCeLaDySmY.xml'\n",
+    "complex_names = ['{0}(H(A)2)3(org)'.format(species)\n",
+    "                 for species in species_list]\n",
+    "dependant_params_dict = {}\n",
+    "for species, complex_name in zip(species_list, complex_names):\n",
+    "    inner_dict = {'upper_element_name': 'species',\n",
+    "                  'upper_attrib_name': 'name',\n",
+    "                  'upper_attrib_value': complex_name,\n",
+    "                  'lower_element_name': 'h0',\n",
+    "                  'lower_attrib_name': None,\n",
+    "                  'lower_attrib_value': None,\n",
+    "                  'input_format': '{0}',\n",
+    "                  'function': ext_to_complex,\n",
+    "                  'kwargs': {\"species\": species,\n",
+    "                             \"linear_params\": lp_df},\n",
+    "                  'independent_params': '(HA)2(org)_h0'}\n",
+    "    dependant_params_dict['{0}_h0'.format(complex_name)] = inner_dict\n",
+    "info_dict = {'(HA)2(org)_h0': {'upper_element_name': 'species',\n",
+    "                                  'upper_attrib_name': 'name',\n",
+    "                                  'upper_attrib_value': '(HA)2(org)',\n",
+    "                                  'lower_element_name': 'h0',\n",
+    "                                  'lower_attrib_name': None,\n",
+    "                                  'lower_attrib_value': None,\n",
+    "                                  'input_format': '{0}',\n",
+    "                                  'input_value': guess_ext_h0},\n",
+    "                }\n",
+    "reeps_params = {\n",
+    "            'exp_data': exp_data,\n",
+    "            'phases_xml_filename': xml_file,\n",
+    "            'opt_dict': info_dict,\n",
+    "            'phase_names': ['HCl_electrolyte', 'PC88A_liquid'],\n",
+    "            'aq_solvent_name': 'H2O(L)',\n",
+    "            'extractant_name': '(HA)2(org)',\n",
+    "            'diluant_name': 'dodecane',\n",
+    "            'complex_names': complex_names,\n",
+    "            'extracted_species_ion_names': ['{0}+++'.format(species)\n",
+    "                                            for species in species_list],\n",
+    "            'aq_solvent_rho': 1000.0,\n",
+    "            'extractant_rho': 960.0,\n",
+    "            'diluant_rho': 750.0,\n",
+    "            'objective_function': lmse_perturbed_obj\n",
+    "        }\n",
+    "estimator = LLEPE(**reeps_params)\n",
+    "estimator.set_dependant_params_dict(dependant_params_dict)\n",
+    "estimator.update_xml(reeps_params['opt_dict'],\n",
+    "                     dependant_params_dict=dependant_params_dict)\n",
+    "obj_kwargs = {'species_list': species_list,\n",
+    "              'epsilon': 1e-100}\n",
+    "optimizer_kwargs = {\"method\": 'l-bfgs-b',\n",
+    "                    \"bounds\": [(1e-1, 1e1)]}\n",
+    "opt_dict, obj_value = estimator.fit(\n",
+    "    objective_kwargs=obj_kwargs,\n",
+    "    optimizer_kwargs=optimizer_kwargs)\n",
+    "estimator.update_xml(opt_dict,\n",
+    "                     dependant_params_dict=dependant_params_dict)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Estimated standard enthalpy values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PC88A:\t\t\t-1059540.78 J/mol\n",
+      "Nd-PC88A complex:\t-3978869.78 J/mol\n",
+      "Pr-PC88A complex:\t-3985562.05 J/mol\n",
+      "Ce-PC88A complex:\t-3978117.92 J/mol\n",
+      "La-PC88A complex:\t-3977133.61 J/mol\n",
+      "Dy-PC88A complex:\t-3985845.57 J/mol\n",
+      "Sm-PC88A complex:\t-3976182.50 J/mol\n",
+      "Y-PC88A complex:\t-4001901.39 J/mol\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('PC88A:\\t\\t\\t{0:.2f} J/mol'.format(opt_dict['(HA)2(org)_h0']['input_value']))\n",
+    "for species in species_list:\n",
+    "    est_ext_h0 = opt_dict['(HA)2(org)_h0']['input_value']\n",
+    "    est_comp_h0 = ext_to_complex([est_ext_h0], 'bla', species, lp_df)\n",
+    "    print('{0}-PC88A complex:\\t{1:.2f} J/mol'.format(species, est_comp_h0))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Plotting organic phase parity plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\users\\tq220\\documents\\tits things\\anl research\\gitlab\\parameter-estimation\\llepe\\llepe.py:988: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  d_array = extracted_species_org_array / extracted_species_aq_array\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 743.226x572.185 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 743.226x572.185 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 743.226x572.185 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 743.226x572.185 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 743.226x572.185 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 743.226x572.185 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 743.226x572.185 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "feed_cols = []\n",
+    "labeled_complete_data = pd.read_csv(\"../../data/csvs/PC88A_HCL_NdPrCeLaDySmY.csv\")\n",
+    "complete_exp_df = labeled_data.drop(labeled_data.columns[0], axis=1)\n",
+    "estimator.set_exp_df(complete_exp_df)\n",
+    "exp_data = estimator.get_exp_df()\n",
+    "for col in exp_data.columns:\n",
+    "    if 'aq_i' in col:\n",
+    "        feed_cols.append(col)\n",
+    "exp_data['total_re'] = exp_data[feed_cols].sum(axis=1)\n",
+    "for species in species_list:\n",
+    "    fig, ax = estimator.parity_plot('{0}_org_eq'.format(species),\n",
+    "                                    c_data=\n",
+    "                                    exp_data['total_re'].values,\n",
+    "                                    c_label='Feed total RE '\n",
+    "                                            'molarity (mol/L)',\n",
+    "                                    print_r_squared=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/Examples/3_filtered_fit_method.ipynb b/docs/Examples/3_filtered_fit_method.ipynb
new file mode 100644
index 0000000..2a1b615
--- /dev/null
+++ b/docs/Examples/3_filtered_fit_method.ipynb
@@ -0,0 +1,594 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This script performs the filtered fit method and shows the parity plots for each of the species. This code requires that the elementz.xml file in the data folder has been copied into Cantera's data folder. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Filtering single component data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from llepe import LLEPE, lmse_perturbed_obj, ind_lmse_perturbed_obj\n",
+    "import numpy as np\n",
+    "import itertools\n",
+    "import pandas as pd\n",
+    "import matplotlib\n",
+    "from scipy.optimize import curve_fit\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "font = {'family': 'sans serif',\n",
+    "        'size': 24}\n",
+    "matplotlib.rc('font', **font)\n",
+    "plt.rc('xtick', labelsize=18)\n",
+    "plt.rc('ytick', labelsize=18)\n",
+    "plt.rcParams['lines.linewidth'] = 4\n",
+    "matplotlib.rcParams['lines.markersize'] = 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Nd\n",
+      "# of removed data points = 1\n",
+      "Pr\n",
+      "# of removed data points = 3\n",
+      "Ce\n",
+      "# of removed data points = 0\n",
+      "La\n",
+      "# of removed data points = 0\n",
+      "Dy\n",
+      "# of removed data points = 0\n",
+      "Sm\n",
+      "# of removed data points = 0\n",
+      "Y\n",
+      "# of removed data points = 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "species_list = 'Nd,Pr,Ce,La,Dy,Sm,Y'.split(',')\n",
+    "outlier_dfs = {}\n",
+    "exp_dfs = {}\n",
+    "for species in species_list:\n",
+    "    print(species)\n",
+    "    lower_species = species.lower()\n",
+    "    labeled_data = pd.read_csv(\"../../data/csvs/labeled_zeroes_removed_{0}.csv\".format(lower_species))\n",
+    "    exp_data = labeled_data.drop(labeled_data.columns[0], axis=1)\n",
+    "\n",
+    "    xml_file = '../../data/xmls/PC88A_HCL_{0}.xml'.format(species)\n",
+    "    x_guess = [-4e6, -1e6]\n",
+    "\n",
+    "    len_outliers = 100\n",
+    "    outlier_df = None\n",
+    "    count = 0\n",
+    "    while len_outliers > 0:\n",
+    "        info_dict = {'{0}(H(A)2)3(org)_h0'.format(species): {'upper_element_name': 'species',\n",
+    "                                       'upper_attrib_name': 'name',\n",
+    "                                       'upper_attrib_value': '{0}(H(A)2)3(org)'.format(species),\n",
+    "                                       'lower_element_name': 'h0',\n",
+    "                                       'lower_attrib_name': None,\n",
+    "                                       'lower_attrib_value': None,\n",
+    "                                       'input_format': '{0}',\n",
+    "                                       'input_value': x_guess[0]},\n",
+    "                     '(HA)2(org)_h0': {'upper_element_name': 'species',\n",
+    "                                       'upper_attrib_name': 'name',\n",
+    "                                       'upper_attrib_value': '(HA)2(org)',\n",
+    "                                       'lower_element_name': 'h0',\n",
+    "                                       'lower_attrib_name': None,\n",
+    "                                       'lower_attrib_value': None,\n",
+    "                                       'input_format': '{0}',\n",
+    "                                       'input_value': x_guess[1]}}\n",
+    "        reeps_params = {\n",
+    "            'exp_data': exp_data,\n",
+    "            'phases_xml_filename': xml_file,\n",
+    "            'opt_dict': info_dict,\n",
+    "            'phase_names': ['HCl_electrolyte', 'PC88A_liquid'],\n",
+    "            'aq_solvent_name': 'H2O(L)',\n",
+    "            'extractant_name': '(HA)2(org)',\n",
+    "            'diluant_name': 'dodecane',\n",
+    "            'complex_names': ['{0}(H(A)2)3(org)'.format(species)],\n",
+    "            'extracted_species_ion_names': ['{0}+++'.format(species)],\n",
+    "            'aq_solvent_rho': 1000.0,\n",
+    "            'extractant_rho': 960.0,\n",
+    "            'diluant_rho': 750.0,\n",
+    "            'objective_function': lmse_perturbed_obj\n",
+    "        }\n",
+    "        estimator = LLEPE(**reeps_params)\n",
+    "        estimator.update_xml(reeps_params['opt_dict'])\n",
+    "        obj_kwargs = {'species_list': [species], 'epsilon': 1e-100}\n",
+    "        optimizer_kwargs = {\"method\": 'l-bfgs-b',\n",
+    "                            \"bounds\": [(1e-1, 1e1), (1e-1, 1e1)]}\n",
+    "        opt_dict, obj_value = estimator.fit(\n",
+    "            objective_kwargs=obj_kwargs,\n",
+    "            optimizer_kwargs=optimizer_kwargs)\n",
+    "        estimator.update_xml(opt_dict)\n",
+    "        \n",
+    "        pred_dict = estimator.get_predicted_dict()\n",
+    "        exp_df = estimator.get_exp_df()\n",
+    "        objectives = ind_lmse_perturbed_obj(pred_dict, exp_df, [species])\n",
+    "        lower_quartile = np.percentile(objectives, 25)\n",
+    "        upper_quartile = np.percentile(objectives, 75)\n",
+    "        iqr = upper_quartile - lower_quartile\n",
+    "        hi_bound = upper_quartile + 3 * iqr\n",
+    "        cols = []\n",
+    "        for col in exp_df.columns:\n",
+    "            if \"org\" not in col:\n",
+    "                cols.append(col)\n",
+    "        exp_df['objective'] = objectives\n",
+    "        if np.any(objectives > hi_bound) or np.median(objectives) > 10:\n",
+    "            outlier_max = exp_df['objective'].max()\n",
+    "            if outlier_df is None:\n",
+    "                outlier_df = exp_df[exp_df['objective'] == outlier_max]\n",
+    "            else:\n",
+    "                outlier_df = pd.concat(\n",
+    "                    [outlier_df,\n",
+    "                     exp_df[exp_df['objective'] == outlier_max]])\n",
+    "            exp_data = exp_df[exp_df['objective'] < outlier_max][cols]\n",
+    "            len_outliers = 1\n",
+    "            count += 1\n",
+    "        else:\n",
+    "            len_outliers = 0\n",
+    "    \n",
+    "    if outlier_df is not None:\n",
+    "        outlier_dfs[species] = outlier_df\n",
+    "        print(\"# of removed data points = {0}\".format(len(outlier_df)))\n",
+    "    else:\n",
+    "        print(\"# of removed data points = 0\")\n",
+    "        \n",
+    "    exp_dfs[species] = exp_df.iloc[:, 0:7]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fitting linear parameters to filtered single component data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Nd\n",
+      "slope = 3.00\n",
+      "interecept = -800025.40\n",
+      "Pr\n",
+      "slope = 3.00\n",
+      "interecept = -806414.23\n",
+      "Ce\n",
+      "slope = 3.00\n",
+      "interecept = -799495.56\n",
+      "La\n",
+      "slope = 3.00\n",
+      "interecept = -798511.25\n",
+      "Dy\n",
+      "slope = 3.00\n",
+      "interecept = -807223.22\n",
+      "Sm\n",
+      "slope = 3.00\n",
+      "interecept = -797560.15\n",
+      "Y\n",
+      "slope = 3.00\n",
+      "interecept = -823279.05\n",
+      "  species  slope      intercept\n",
+      "0      Nd    3.0 -800025.401609\n",
+      "1      Pr    3.0 -806414.234550\n",
+      "2      Ce    3.0 -799495.559690\n",
+      "3      La    3.0 -798511.252566\n",
+      "4      Dy    3.0 -807223.215960\n",
+      "5      Sm    3.0 -797560.154414\n",
+      "6       Y    3.0 -823279.048921\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "def linear(x, a, b):\n",
+    "    return a * x + b\n",
+    "\n",
+    "\n",
+    "lp_dict = {'species': [],\n",
+    "           'slope': [],\n",
+    "           'intercept': []}\n",
+    "species_list = 'Nd,Pr,Ce,La,Dy,Sm,Y'.split(',')\n",
+    "\n",
+    "n_guesses = 11\n",
+    "complex_guesses = -4e6 * np.logspace(-1, 1, n_guesses)\n",
+    "extractant_guesses = -1e6 * np.logspace(-1, 1, n_guesses)\n",
+    "x_guesses = list(itertools.product(*[complex_guesses, extractant_guesses]))\n",
+    "guess_ext_h0 = 0\n",
+    "for species in species_list:\n",
+    "    print(species)\n",
+    "    lower_species = species.lower()\n",
+    "    \n",
+    "    exp_data = exp_dfs[species]\n",
+    "    xml_file = '../../data/xmls/PC88A_HCL_{0}.xml'.format(species)\n",
+    "    opt_values = {\n",
+    "        'optimizer': [],\n",
+    "        'species': [],\n",
+    "        'parameter': [],\n",
+    "        'value': [],\n",
+    "        'obj_value': [],\n",
+    "        'guess': []}\n",
+    "    new_x_guesses = []\n",
+    "\n",
+    "    for x_guess in x_guesses:\n",
+    "        info_dict = {'{0}(H(A)2)3(org)_h0'.format(species): {'upper_element_name': 'species',\n",
+    "                                       'upper_attrib_name': 'name',\n",
+    "                                       'upper_attrib_value': '{0}(H(A)2)3(org)'.format(species),\n",
+    "                                       'lower_element_name': 'h0',\n",
+    "                                       'lower_attrib_name': None,\n",
+    "                                       'lower_attrib_value': None,\n",
+    "                                       'input_format': '{0}',\n",
+    "                                       'input_value': x_guess[0]},\n",
+    "                     '(HA)2(org)_h0': {'upper_element_name': 'species',\n",
+    "                                       'upper_attrib_name': 'name',\n",
+    "                                       'upper_attrib_value': '(HA)2(org)',\n",
+    "                                       'lower_element_name': 'h0',\n",
+    "                                       'lower_attrib_name': None,\n",
+    "                                       'lower_attrib_value': None,\n",
+    "                                       'input_format': '{0}',\n",
+    "                                       'input_value': x_guess[1]}}\n",
+    "        reeps_params = {\n",
+    "            'exp_data': exp_data,\n",
+    "            'phases_xml_filename': xml_file,\n",
+    "            'opt_dict': info_dict,\n",
+    "            'phase_names': ['HCl_electrolyte', 'PC88A_liquid'],\n",
+    "            'aq_solvent_name': 'H2O(L)',\n",
+    "            'extractant_name': '(HA)2(org)',\n",
+    "            'diluant_name': 'dodecane',\n",
+    "            'complex_names': ['{0}(H(A)2)3(org)'.format(species)],\n",
+    "            'extracted_species_ion_names': ['{0}+++'.format(species)],\n",
+    "            'aq_solvent_rho': 1000.0,\n",
+    "            'extractant_rho': 960.0,\n",
+    "            'diluant_rho': 750.0,\n",
+    "            'objective_function': lmse_perturbed_obj\n",
+    "        }\n",
+    "        estimator = LLEPE(**reeps_params)\n",
+    "        estimator.update_xml(reeps_params['opt_dict'])\n",
+    "        exp_df = estimator.get_exp_df()\n",
+    "        pred_dict = estimator.get_predicted_dict()\n",
+    "        init_obj_value = lmse_perturbed_obj(pred_dict, exp_df, [species], 1e-100)\n",
+    "        obj_kwargs = {'species_list': [species], 'epsilon': 1e-100}\n",
+    "        optimizer_kwargs = {\"method\": 'l-bfgs-b',\n",
+    "                            \"bounds\": [(1e-1, 1e1), (1e-1, 1e1)]}\n",
+    "        opt_dict, obj_value = estimator.fit(objective_kwargs=obj_kwargs,\n",
+    "                                            optimizer_kwargs=optimizer_kwargs)\n",
+    "        estimator.update_xml(opt_dict)\n",
+    "        \n",
+    "        keys = list(opt_dict.keys())\n",
+    "        info1 = ['lbfgsb',\n",
+    "                 keys[0],\n",
+    "                 'h0',\n",
+    "                 opt_dict[keys[0]]['input_value'],\n",
+    "                 obj_value,\n",
+    "                 x_guess[0]\n",
+    "                 ]\n",
+    "        info2 = ['lbfgsb',\n",
+    "                 keys[1],\n",
+    "                 'h0',\n",
+    "                 opt_dict[keys[1]]['input_value'],\n",
+    "                 obj_value,\n",
+    "                 x_guess[1]\n",
+    "                 ]\n",
+    "        if obj_value > init_obj_value*0.95:\n",
+    "            continue\n",
+    "        opt_values_keys = opt_values.keys()\n",
+    "        for ind, key in enumerate(opt_values_keys):\n",
+    "            opt_values[key].append(info1[ind])\n",
+    "        for ind, key in enumerate(opt_values_keys):\n",
+    "            opt_values[key].append(info2[ind])\n",
+    "        new_x_guesses.append(x_guess)\n",
+    "    x_guesses = new_x_guesses\n",
+    "    opt_df = pd.DataFrame(opt_values)\n",
+    "    min_ext_h0 = opt_df[opt_df['species'] == '(HA)2(org)_h0']['value'].min()\n",
+    "    if min_ext_h0 < guess_ext_h0:\n",
+    "        guess_ext_h0 = min_ext_h0\n",
+    "    complex_df = opt_df[opt_df['species'] != '(HA)2(org)_h0']\n",
+    "    ext_df = opt_df[opt_df['species'] == '(HA)2(org)_h0']\n",
+    "    p_opt, p_cov = curve_fit(linear, ext_df['value'].values,\n",
+    "                             complex_df['value'].values)\n",
+    "    slope, intercept = p_opt\n",
+    "    lp_dict['species'].append(species)\n",
+    "    lp_dict['slope'].append(slope)\n",
+    "    lp_dict['intercept'].append(intercept)\n",
+    "    print(\"slope = {0:.2f}\".format(slope))\n",
+    "    print(\"interecept = {0:.2f}\".format(intercept))\n",
+    "\n",
+    "lp_df = pd.DataFrame(lp_dict)\n",
+    "print(lp_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fitting extractant enthalpy to unfiltered combined single and multicomponent dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\users\\tq220\\documents\\tits things\\anl research\\gitlab\\parameter-estimation\\llepe\\llepe.py:988: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  d_array = extracted_species_org_array / extracted_species_aq_array\n"
+     ]
+    }
+   ],
+   "source": [
+    "def ext_to_complex(h0, dummy_holder_for_custom_dict, species, linear_params):\n",
+    "    row = linear_params[linear_params['species'] == species]\n",
+    "\n",
+    "    return row['slope'].values[0] * h0[0] + row['intercept'].values[0]\n",
+    "\n",
+    "labeled_data = pd.read_csv(\"../../data/csvs/zeroes_removed_PC88A_HCL_NdPrCeLaDySmY.csv\")\n",
+    "exp_data = labeled_data.drop(labeled_data.columns[0], axis=1)\n",
+    "xml_file = '../../data/xmls/PC88A_HCL_NdPrCeLaDySmY.xml'\n",
+    "complex_names = ['{0}(H(A)2)3(org)'.format(species)\n",
+    "                 for species in species_list]\n",
+    "dependant_params_dict = {}\n",
+    "for species, complex_name in zip(species_list, complex_names):\n",
+    "    inner_dict = {'upper_element_name': 'species',\n",
+    "                  'upper_attrib_name': 'name',\n",
+    "                  'upper_attrib_value': complex_name,\n",
+    "                  'lower_element_name': 'h0',\n",
+    "                  'lower_attrib_name': None,\n",
+    "                  'lower_attrib_value': None,\n",
+    "                  'input_format': '{0}',\n",
+    "                  'function': ext_to_complex,\n",
+    "                  'kwargs': {\"species\": species,\n",
+    "                             \"linear_params\": lp_df},\n",
+    "                  'independent_params': '(HA)2(org)_h0'}\n",
+    "    dependant_params_dict['{0}_h0'.format(complex_name)] = inner_dict\n",
+    "info_dict = {'(HA)2(org)_h0': {'upper_element_name': 'species',\n",
+    "                                  'upper_attrib_name': 'name',\n",
+    "                                  'upper_attrib_value': '(HA)2(org)',\n",
+    "                                  'lower_element_name': 'h0',\n",
+    "                                  'lower_attrib_name': None,\n",
+    "                                  'lower_attrib_value': None,\n",
+    "                                  'input_format': '{0}',\n",
+    "                                  'input_value': guess_ext_h0},\n",
+    "                }\n",
+    "reeps_params = {\n",
+    "            'exp_data': exp_data,\n",
+    "            'phases_xml_filename': xml_file,\n",
+    "            'opt_dict': info_dict,\n",
+    "            'phase_names': ['HCl_electrolyte', 'PC88A_liquid'],\n",
+    "            'aq_solvent_name': 'H2O(L)',\n",
+    "            'extractant_name': '(HA)2(org)',\n",
+    "            'diluant_name': 'dodecane',\n",
+    "            'complex_names': complex_names,\n",
+    "            'extracted_species_ion_names': ['{0}+++'.format(species)\n",
+    "                                            for species in species_list],\n",
+    "            'aq_solvent_rho': 1000.0,\n",
+    "            'extractant_rho': 960.0,\n",
+    "            'diluant_rho': 750.0,\n",
+    "            'objective_function': lmse_perturbed_obj\n",
+    "        }\n",
+    "estimator = LLEPE(**reeps_params)\n",
+    "estimator.set_dependant_params_dict(dependant_params_dict)\n",
+    "estimator.update_xml(reeps_params['opt_dict'],\n",
+    "                     dependant_params_dict=dependant_params_dict)\n",
+    "obj_kwargs = {'species_list': species_list,\n",
+    "              'epsilon': 1e-100}\n",
+    "optimizer_kwargs = {\"method\": 'l-bfgs-b',\n",
+    "                    \"bounds\": [(1e-1, 1e1)]}\n",
+    "opt_dict, obj_value = estimator.fit(\n",
+    "    objective_kwargs=obj_kwargs,\n",
+    "    optimizer_kwargs=optimizer_kwargs)\n",
+    "estimator.update_xml(opt_dict,\n",
+    "                     dependant_params_dict=dependant_params_dict)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Estimated standard enthalpy values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PC88A:\t\t\t-1059541.40 J/mol\n",
+      "Nd-PC88A complex:\t-3978649.62 J/mol\n",
+      "Pr-PC88A complex:\t-3985038.44 J/mol\n",
+      "Ce-PC88A complex:\t-3978119.77 J/mol\n",
+      "La-PC88A complex:\t-3977135.47 J/mol\n",
+      "Dy-PC88A complex:\t-3985847.43 J/mol\n",
+      "Sm-PC88A complex:\t-3976184.36 J/mol\n",
+      "Y-PC88A complex:\t-4001903.25 J/mol\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('PC88A:\\t\\t\\t{0:.2f} J/mol'.format(opt_dict['(HA)2(org)_h0']['input_value']))\n",
+    "for species in species_list:\n",
+    "    est_ext_h0 = opt_dict['(HA)2(org)_h0']['input_value']\n",
+    "    est_comp_h0 = ext_to_complex([est_ext_h0], 'bla', species, lp_df)\n",
+    "    print('{0}-PC88A complex:\\t{1:.2f} J/mol'.format(species, est_comp_h0))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Plotting organic phase parity plots for complete unfiltered dataset "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 743.226x572.185 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 743.226x572.185 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 743.226x572.185 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 743.226x572.185 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 743.226x572.185 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 743.226x572.185 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 743.226x572.185 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "feed_cols = []\n",
+    "labeled_complete_data = pd.read_csv(\"../../data/csvs/PC88A_HCL_NdPrCeLaDySmY.csv\")\n",
+    "complete_exp_df = labeled_data.drop(labeled_data.columns[0], axis=1)\n",
+    "estimator.set_exp_df(complete_exp_df)\n",
+    "exp_data = estimator.get_exp_df()\n",
+    "for col in exp_data.columns:\n",
+    "    if 'aq_i' in col:\n",
+    "        feed_cols.append(col)\n",
+    "exp_data['total_re'] = exp_data[feed_cols].sum(axis=1)\n",
+    "for species in species_list:\n",
+    "    fig, ax = estimator.parity_plot('{0}_org_eq'.format(species),\n",
+    "                                    c_data=\n",
+    "                                    exp_data['total_re'].values,\n",
+    "                                    c_label='Feed total RE '\n",
+    "                                            'molarity (mol/L)',\n",
+    "                                    print_r_squared=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/Examples/4_Pitzer_fit_method.ipynb b/docs/Examples/4_Pitzer_fit_method.ipynb
new file mode 100644
index 0000000..c2138f4
--- /dev/null
+++ b/docs/Examples/4_Pitzer_fit_method.ipynb
@@ -0,0 +1,441 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This script performs the Pitzer fit method and shows the parity plots for each of the species. This code requires that the elementz.xml file in the data folder has been copied into Cantera's data folder. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fitting Pitzer and linear parameters to single component data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from llepe import LLEPE, lmse_perturbed_obj, ind_lmse_perturbed_obj\n",
+    "import numpy as np\n",
+    "import itertools\n",
+    "import pandas as pd\n",
+    "import matplotlib\n",
+    "from scipy.optimize import curve_fit\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "import json\n",
+    "\n",
+    "font = {'family': 'sans serif',\n",
+    "        'size': 24}\n",
+    "matplotlib.rc('font', **font)\n",
+    "plt.rc('xtick', labelsize=18)\n",
+    "plt.rc('ytick', labelsize=18)\n",
+    "plt.rcParams['lines.linewidth'] = 4\n",
+    "matplotlib.rcParams['lines.markersize'] = 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Nd\n",
+      "Pr\n",
+      "Ce\n",
+      "La\n",
+      "Dy\n",
+      "Sm\n",
+      "Y\n"
+     ]
+    }
+   ],
+   "source": [
+    "def linear(x, a, b):\n",
+    "    return a * x + b\n",
+    "lp_dict = {'species': [],\n",
+    "           'slope': [],\n",
+    "           'intercept': []}\n",
+    "species_list = 'Nd,Pr,Ce,La,Dy,Sm,Y'.split(',')\n",
+    "pitzer_params = pd.read_csv(\"../../data/csvs/may_pitzer_params.csv\")\n",
+    "x_guesses = [[-5178500.0, -1459500.0],\n",
+    "             [-5178342.857142857, -1460300.0],\n",
+    "             [-5178342.857142857, -1459500.0],\n",
+    "             [-5178342.857142857, -1458300.0],\n",
+    "             [-5178185.714285715, -1459900.0],\n",
+    "             [-5178185.714285715, -1459500.0],\n",
+    "             [-5178185.714285715, -1459100.0],\n",
+    "             [-5178185.714285715, -1458300.0],\n",
+    "             [-5178028.571428572, -1459900.0],\n",
+    "             [-5178028.571428572, -1459100.0],\n",
+    "             [-5178028.571428572, -1458300.0],\n",
+    "             [-5177557.142857143, -1459900.0],\n",
+    "             [-5177400.0, -1460300.0]]\n",
+    "optimizer = 'scipy_minimize'\n",
+    "opt_dfs = {}\n",
+    "pitzer_params_dict = {}\n",
+    "for species in species_list:\n",
+    "    print(species)\n",
+    "    lower_species = species.lower()\n",
+    "\n",
+    "    labeled_data = pd.read_csv(\"../../data/csvs/labeled_zeroes_removed_{0}.csv\".format(lower_species))\n",
+    "    exp_data = labeled_data.drop(labeled_data.columns[0], axis=1)\n",
+    "    xml_file = '../../data/xmls/PC88A_HCL_{0}_w_pitzer.xml'.format(species)\n",
+    "    opt_values = {\n",
+    "        '(HA)2(org)_h0': [],\n",
+    "        '{0}(H(A)2)3(org)_h0'.format(species): [],\n",
+    "        'beta0': [],\n",
+    "        'beta1': [],\n",
+    "        'Cphi': [],\n",
+    "        'obj_value': [],\n",
+    "        'guess': []}\n",
+    "    new_x_guesses = []\n",
+    "    for x_guess in x_guesses:\n",
+    "        opt_dict = {'(HA)2(org)_h0': {'upper_element_name': 'species',\n",
+    "                                      'upper_attrib_name': 'name',\n",
+    "                                      'upper_attrib_value': '(HA)2(org)',\n",
+    "                                      'lower_element_name': 'h0',\n",
+    "                                      'lower_attrib_name': None,\n",
+    "                                      'lower_attrib_value': None,\n",
+    "                                      'input_format': '{0}',\n",
+    "                                      'input_value': x_guess[1]},\n",
+    "                    '{0}(H(A)2)3(org)_h0'.format(species): {\n",
+    "                        'upper_element_name': 'species',\n",
+    "                        'upper_attrib_name': 'name',\n",
+    "                        'upper_attrib_value': '{0}(H(A)2)3(org)'.format(\n",
+    "                            species),\n",
+    "                        'lower_element_name': 'h0',\n",
+    "                        'lower_attrib_name': None,\n",
+    "                        'lower_attrib_value': None,\n",
+    "                        'input_format': '{0}',\n",
+    "                        'input_value': x_guess[0]},\n",
+    "                    }\n",
+    "        pitzer_param_list = ['beta0', 'beta1', 'Cphi']\n",
+    "        for pitzer_param in pitzer_param_list:\n",
+    "            inner_dict = {'upper_element_name': 'binarySaltParameters',\n",
+    "                          'upper_attrib_name': 'cation',\n",
+    "                          'upper_attrib_value': '{0}+++'.format(species),\n",
+    "                          'lower_element_name': pitzer_param,\n",
+    "                          'lower_attrib_name': None,\n",
+    "                          'lower_attrib_value': None}\n",
+    "            pitzer_row = pitzer_params[\n",
+    "                pitzer_params['species'] == species]\n",
+    "\n",
+    "            inner_dict2 = {'input_format': ' {0}, 0.0, 0.0, 0.0, 0.0 ',\n",
+    "                           'input_value': pitzer_row[pitzer_param].values[\n",
+    "                               0]}\n",
+    "            inner_dict.update(inner_dict2)\n",
+    "            opt_dict['{0}_{1}'.format(species, pitzer_param)] = inner_dict\n",
+    "\n",
+    "        reeps_params = {\n",
+    "            'exp_data': exp_data,\n",
+    "            'phases_xml_filename': xml_file,\n",
+    "            'opt_dict': opt_dict,\n",
+    "            'phase_names': ['HCl_electrolyte', 'PC88A_liquid'],\n",
+    "            'aq_solvent_name': 'H2O(L)',\n",
+    "            'extractant_name': '(HA)2(org)',\n",
+    "            'diluant_name': 'dodecane',\n",
+    "            'complex_names': ['{0}(H(A)2)3(org)'.format(species)],\n",
+    "            'extracted_species_ion_names': ['{0}+++'.format(species)],\n",
+    "            'aq_solvent_rho': 1000.0,\n",
+    "            'extractant_rho': 960.0,\n",
+    "            'diluant_rho': 750.0,\n",
+    "            'objective_function': lmse_perturbed_obj,\n",
+    "            'optimizer': optimizer\n",
+    "        }\n",
+    "        estimator = LLEPE(**reeps_params)\n",
+    "        estimator.update_xml(reeps_params['opt_dict'])\n",
+    "        exp_df = estimator.get_exp_df()\n",
+    "        pred_dict = estimator.get_predicted_dict()\n",
+    "        init_obj_value = lmse_perturbed_obj(pred_dict, exp_df, [species], 1e-100)\n",
+    "        \n",
+    "        obj_kwargs = {'species_list': [species], 'epsilon': 1e-100}\n",
+    "        bounds = [(1e-1, 1e1)] * len(opt_dict)\n",
+    "        optimizer_kwargs = {\"method\": 'l-bfgs-b',\n",
+    "                            \"bounds\": bounds}\n",
+    "        opt_dict, obj_value = estimator.fit(\n",
+    "            objective_kwargs=obj_kwargs,\n",
+    "            optimizer_kwargs=optimizer_kwargs)\n",
+    "        keys = list(opt_dict.keys())\n",
+    "        info1 = [opt_dict[key]['input_value'] for key in keys]\n",
+    "        info1.append(obj_value)\n",
+    "        info1.append(x_guess)\n",
+    "        if obj_value > init_obj_value*0.95:\n",
+    "            continue\n",
+    "        \n",
+    "        opt_values_keys = opt_values.keys()\n",
+    "        for ind, key in enumerate(opt_values_keys):\n",
+    "            opt_values[key].append(info1[ind])\n",
+    "        new_x_guesses.append(x_guess)\n",
+    "    x_guesses = new_x_guesses\n",
+    "    opt_df = pd.DataFrame(opt_values)\n",
+    "    opt_dfs[species] = opt_df\n",
+    "                                               \n",
+    "    p_opt, p_cov = curve_fit(linear,\n",
+    "                             opt_df['(HA)2(org)_h0'].values,\n",
+    "                             opt_df['{0}(H(A)2)3(org)_h0'.format(\n",
+    "                                 species)].values)\n",
+    "    slope, intercept = p_opt\n",
+    "    lp_dict['species'].append(species)\n",
+    "    lp_dict['slope'].append(slope)\n",
+    "    lp_dict['intercept'].append(intercept)\n",
+    "    min_h0_row = opt_df[opt_df['(HA)2(org)_h0'] == opt_df['(HA)2(org)_h0'].min()]\n",
+    "    for pitzer_param in pitzer_param_list:\n",
+    "        value = min_h0_row[pitzer_param].values[0]\n",
+    "        inner_dict = {'upper_element_name': 'binarySaltParameters',\n",
+    "                      'upper_attrib_name': 'cation',\n",
+    "                      'upper_attrib_value': '{0}+++'.format(species),\n",
+    "                      'lower_element_name': pitzer_param,\n",
+    "                      'lower_attrib_name': None,\n",
+    "                      'lower_attrib_value': None,\n",
+    "                     'input_format': ' {0}, 0.0, 0.0, 0.0, 0.0 ',\n",
+    "                       'input_value': value}\n",
+    "        pitzer_params_dict['{0}_{1}'.format(species, pitzer_param)] = inner_dict\n",
+    "lp_df = pd.DataFrame(lp_dict)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fitting extractant enthalpy to combined single and multicomponent dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\users\\tq220\\documents\\tits things\\anl research\\gitlab\\parameter-estimation\\llepe\\llepe.py:988: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  d_array = extracted_species_org_array / extracted_species_aq_array\n"
+     ]
+    }
+   ],
+   "source": [
+    "def ext_to_complex(h0, custom_objects_dict, species):\n",
+    "    linear_params = custom_objects_dict['lp_df']\n",
+    "    row = linear_params[linear_params['species'] == species]\n",
+    "    return row['slope'].values[0] * h0[0] + row['intercept'].values[0]\n",
+    "\n",
+    "labeled_data = pd.read_csv(\"../../data/csvs/zeroes_removed_PC88A_HCL_NdPrCeLaDySmY.csv\")\n",
+    "exp_data = labeled_data.drop(labeled_data.columns[0], axis=1)\n",
+    "xml_file = '../../data/xmls/PC88A_HCL_NdPrCeLaDySmY_w_pitzer.xml'\n",
+    "complex_names = ['{0}(H(A)2)3(org)'.format(species)\n",
+    "                 for species in species_list]\n",
+    "dependant_params_dict = {}\n",
+    "for species, complex_name in zip(species_list, complex_names):\n",
+    "    inner_dict = {'upper_element_name': 'species',\n",
+    "                  'upper_attrib_name': 'name',\n",
+    "                  'upper_attrib_value': complex_name,\n",
+    "                  'lower_element_name': 'h0',\n",
+    "                  'lower_attrib_name': None,\n",
+    "                  'lower_attrib_value': None,\n",
+    "                  'input_format': '{0}',\n",
+    "                  'function': ext_to_complex,\n",
+    "                  'kwargs': {\"species\": species},\n",
+    "                  'independent_params': '(HA)2(org)_h0'}\n",
+    "    dependant_params_dict['{0}_h0'.format(complex_name)] = inner_dict\n",
+    "extractant_guess = -1.37e6\n",
+    "info_dict = {'(HA)2(org)_h0': {'upper_element_name': 'species',\n",
+    "                              'upper_attrib_name': 'name',\n",
+    "                              'upper_attrib_value': '(HA)2(org)',\n",
+    "                              'lower_element_name': 'h0',\n",
+    "                              'lower_attrib_name': None,\n",
+    "                              'lower_attrib_value': None,\n",
+    "                              'input_format': '{0}',\n",
+    "                              'input_value': extractant_guess},\n",
+    "            }\n",
+    "reeps_params = {\n",
+    "    'exp_data': exp_data,\n",
+    "    'phases_xml_filename': xml_file,\n",
+    "    'opt_dict': info_dict,\n",
+    "    'phase_names': ['HCl_electrolyte', 'PC88A_liquid'],\n",
+    "    'aq_solvent_name': 'H2O(L)',\n",
+    "    'extractant_name': '(HA)2(org)',\n",
+    "    'diluant_name': 'dodecane',\n",
+    "    'complex_names': ['{0}(H(A)2)3(org)'.format(species)\n",
+    "                      for species in species_list],\n",
+    "    'extracted_species_ion_names': ['{0}+++'.format(species)\n",
+    "                                    for species in species_list],\n",
+    "    'aq_solvent_rho': 1000.0,\n",
+    "    'extractant_rho': 960.0,\n",
+    "    'diluant_rho': 750.0,\n",
+    "    'objective_function': lmse_perturbed_obj,\n",
+    "    'custom_objects_dict': {'lp_df': lp_df}\n",
+    "}\n",
+    "estimator = LLEPE(**reeps_params)\n",
+    "estimator.set_dependant_params_dict(dependant_params_dict)\n",
+    "estimator.update_xml(pitzer_params_dict)\n",
+    "estimator.update_xml(reeps_params['opt_dict'],\n",
+    "                     dependant_params_dict=dependant_params_dict)\n",
+    "obj_kwargs = {'species_list': species_list,\n",
+    "              'epsilon': 1e-100}\n",
+    "optimizer_kwargs = {\"method\": 'l-bfgs-b',\n",
+    "                    \"bounds\": [(1e-1, 1e1)] * len(info_dict)}\n",
+    "opt_dict, obj_value = estimator.fit(\n",
+    "    objective_kwargs=obj_kwargs,\n",
+    "    optimizer_kwargs=optimizer_kwargs)\n",
+    "estimator.update_xml(opt_dict,\n",
+    "                     dependant_params_dict=dependant_params_dict)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Plotting organic phase parity plots for complete unfiltered dataset "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\users\\tq220\\documents\\tits things\\anl research\\gitlab\\parameter-estimation\\llepe\\llepe.py:988: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  d_array = extracted_species_org_array / extracted_species_aq_array\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 743.226x572.185 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 743.226x572.185 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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JcubuNYRJPQB6mlnyKBZLRe/RBtHLfxXjxrOozMRNresShrUrpzejx07UfVeWBfG/MydnSRvf/mLaVKklujn8SJiUpBT6xp6/mTaVSJEo8G3moqGdDiMMtA5wq5ldaGZt0uWx4DfAOzRsbb0qVtaNZpZp+C/MrJuZpQ0wsrgi9vwxM/tFhv2sbmatE6+j4070QV4FuCtVi4+Z7UCYfAFCS9WDjaxrsVxoZpsnrzSzFQmzYSXcmk+hUYAyKHrZEhhuZhmHVzOzXma2b4r1qxKGv2pBaEk8KtaX/AzqRgi41sy2yqeesfrOIYwLDWEEixHRfjPVd7cUgeAz1LV0/p+ZNbiaEH037ieMB5xR0kgSjXUbdX0hrzCzvdIlNLP2Ka4C3Bx7fk+qqwTRD4X4yBqDG1vZHFxL3U1NfzezrdMlNLPOZtYpxfopsXPbowl1iQeYfdOmKjF3f5e6IQJ3N7MBqdKZ2TmEGRsBXnT3D/PcVaLh4pkm3t+Rj8R5duBfJdqnyFLq4yu4+wQzO5TQ/7IKuJzQavYYYXD076OkXQmX3fpTN8Zrclmfm9kJwFDC5eLnzWwEYbzOzwk303UhXEbekzDG6FtkGI82Q71fNrPrCVPergu8Z2Z3EcZX/Y5wx/7GhEvfBxL6ysVvprg6OpYtCMF/DzO7hTC7VSfCeKMDCGN9OnCiuxdqvONC+Ixwl/wbZnYDoWVvIeFmsfMIQ0dBmKTh1dRFpOfuD5rZjoSW+w0I5/d+wn9W3xD+fqwe7e9AwmxUVwLPJcqILmU/QN0wdyfGxwl19/lmdgRh7Ne2wD/MrFcjz/NVhHF59yL85/qRmd1NCCC+j8pfizBb3EHRMZ0ILB3b1t2XmNlJhICoNfCCmd1KCIjnAD0JXYI2j+ocH0atKNx9ppn9DhhOGH3gOTN7nPCdmkJo+e8B7Ez4HJ9IbFgqdx9jZn8ntAxuDEwys+uoG45tO8LnJfEe3ezuuQx91tjj+djMziYE9KsA/zGzBwjn+GvCeV+fcM/BIYQxmot109VYwqgIaxBa8O8p0n4a40TClakqQoPEDoQfkNMI/aOPBX4TpZ1F/StTWUU/gBLdJPLp5lBlZsflmPYRd1+UtC5xpWSsu3+bnEGk6Mo9g4aWZWch/Kcen2402zIC2CBNWQdSNzNatuXZJtb7IurPopVuWTFF3lUJgU+mfPOBYzLsf0qUbkyWeo4hh5mPiM2QlW1/hCDv5yzvUds05QyKpeuRJo1F53dRhn3El3OT8v8ptu3ODMc8IJZuSBM+C22A26mbDjbTUgscnqac32c55juB42Kvd05TztI8Bfh+7ke46pDtuPqnyNsqqnO2vDcDLRrzuUxz3GnfS0If73k51CnVlNxTYttTfnbzOK9/jcpZQOYZHMdQgO9vnt+/PtTNMphu+SrVOcr2XhC68iSOu2Me72k+y4pJZfSMbTuhqd8JLVoas6irgyzl7pPdfVdCK8C1hP5X0witiAsIrTEvEgKh9d29v7t/lqaspwmtUKcTWgC/icpZRGh9+zdwA7AbYX77ptT7ckJL1vWElqFZ0X6+jo7hGmBrD/2Ck/NOJ8wAdyTwdHS8iwg3g02KzsMG7p5ppqqycfcXCS2udxL65FYTBuN/hdCloL+7L8xQRLbyPTq/6xKuBLxB+EGzhPCZmEr4TFwIbObu1yXyRv2ur45eTgbOzrCf2wjnH+B3ZnZUI+u7yN1PI/wHez0wgRAs1hCCrP8S+pj+ifAZTjmDnrvfT5gk5QHC5yjxuX0eOMjd82pdKwR3f5bwPpxHaMWeTvjB9wPhs3oP4YfQMynyLonq3I8QmH1BeP/mE67E3Ads4+5nevbxiwvC3e8lHM9lhNbnHwnH8y3hfbuFcBm/2DeUJn4otaOuBXWZ4GFGyg0IV7VeJ4y9viR6fJXwndrYGzcMWaKbwyh3n1eA6ubit9HjTODhEu1TpB5z93LXQUTyYGZTCLNqveruO5e3Ns1XdLn3/ujlLu4+pny1kaYws6GEoGycuxd9tI5yi0YHmUHojnZC9COk2PtsS7iReg3g4ugHtZRBdD/LmYTuTz0IP6IfJ7wvGX8EmdlGwMXA1oSh9FoTrjo8B1znKbqvRHmuBXYiXJWbSLjBeXSBDikvavEVEZHmbhChtXmbVDdoVqC9CEFvLfWnji+mUwhB7wyKe/OkZHcTcCPwIeGq7BOEG41H5jCs31qE93E4cD5wFuG+mpOAt82s3vjm0ZB5Ywn3EvyVcLN4FfCime1eqAPKh25uExGRZs3dvzCzmwldCi4ldoNmhZpHOM7p7p7L1OdNYmbtgYHRy794GIVFyiAamvJ0YJi7HxJb/yWhe9ERhBvdU3L3l0kx06aZvUZoNT6OEOAmXE2YtKhXoktOdIVlMnC7mW3sJe56oMBXREQkBIJzCYORdHX377NlWF65+78o7VBiPQjD5S1g2Ro5ozk6knDTcnKr+92E+2GOIUPgm0FitJ7ErHyYWUfCje5j4v3QPcwaew+hf38fwgg5JaPAV0REmj0PQ+hdWu56VCJ3/4i6ccGlvPoQurjUCzbdvdrMJkXbszKzdoQuC+0Ik79cG22KXy3ZnDCMZKrhEROTlyjwbY66dOniPXr0KHc1ZDnRpk0bFi1aRFVV1U69e/fW3all0r17d6ZODY0cG2644Su9e+c7eaCIlNvbb789w90zTnZTaHvt0tFn/lhT8HLffm/hZMLIPgl3uftdScnWBGakGe3nG6CfmbXxhuMvJzuB+hMjTSEM+xmfAXDNWLmp9gVhTOqSUuC7DOjRowcTJkwodzVERESaFTObmj1VYc38sYZxL65T8HJbrvFZtbtn+wXegbrZVZNVx9JkC3xHECZ7qiIM/XggYVz85H2RZn/VSWlKRoGviIiISPMwH1gtzbZ2sTQZufvXhDHOIUwR/09gvJm1d/fE+O2Jcto2ZV+FpuHMREREREokTBtZ+H85mgZ0icZVTtaN0A0iW2tvw2Nyfw94Bzg1aV+JclPtC1J3gygqBb4iIiIiJePUeG3BlxyNJ8R+28RXRjerbUmYNbGx2gMrx16/T+jmsF2KtH2jx5L381TgKyIiItI8PEZodD4raf2JhP62S6eSNrP1zGzjeCIzWz1VoWa2C7ApdaM1JEZKGQnsbGZbxNJWEW6O+4wSj+gA6uMrIiIiUjKhq0N5BuRx9/fN7HZggJkNIww/9kvCzG2vUn8M35eB7oRxfxPuMLM1gNGEsXvbAb0IE1/MAf6UtMvzgd2Af5nZTcDPhCC7G7BfqSevAAW+IiIiIs3JWYThx04C9iNMI30rcLF71j4TjwK/A35LGMXBCQHw34Hr3P2reGJ3/9zMtidMjjEQaANMBPZ295cKdUD5UOArIiIiUkJ53IxWcO5eA9wQLZnS9Uix7nHC1MT57O8j4Nf55Ckm9fEVERERkWZBLb4iIiIiJeI4NaXv2ioRBb4iIiIiJVSum9tEXR1EREREpJlQi6+IiIhIiThQoxbfslGLr4iIiIg0C2rxFRERESkh9fEtHwW+IiIiIiXioFEdykhdHURERESkWVCLr4iIiEgJlW/eNlGLr4iIiIg0C2rxFRERESkRxzWcWRmpxVdEREREmgW1+IqIiIiUikONGnzLRoGviIiISIk4urmtnNTVQURERESaBbX4ioiIiJSMUYOVuxLNllp8RURERKRZUIuviIiISIk4UKub28pGga+IiIhICamrQ/moq4OIiIiINAtq8RUREREpEUctvuWkFl8RERERaRbU4isiIiJSQrWuFt9yUYuviIiIiDQLavEVERERKRH18S0vBb4iIiIiJeIYNbrgXjY68yIiIiLSLKjFV0RERKSEdHNb+ajFV0RERESaBbX4ioiIiJSIbm4rLwW+IiIiIiVj1LguuJeLzryIiIiINAtq8RUREREpEQdq1e5YNjrzIiIiItIsVFyLr5m1AM4ETgZ6ANOBx4GL3X1eIfOb2RhgpzRF9XH3CY06CBEREalYurktNTO7vxDluPvv022ruMAXuAk4AxgO3AD8Mnq9lZnt7u61Bc4/Azg7RTlfNP4QRERERJqdY6EgvwqaR+BrZj2B04Fh7n5IbP2XwC3AEcAjBc4/z90fKthBiIiISMVy16gOGYwhe+C7E7AEeCPFtn5Am0yZKyrwBY4knLDBSevvBq4BjiFD4NvY/FH3iCpgjrt7o2ouIiIizUKtujqk5O67ZUtjZjXAj+6+a4pt04DVM+WvtJ8cfYBaYFx8pbtXA5Oi7YXO3w2YC8wG5prZMDPbuFG1FxEREZGiqbQW3zWBGe6+MMW2b4B+ZtbG3RcVKP+XhKb294AaYFtgALCbme3g7u835WBERESksoSZ2yqt3XH5UWmBbwcgVdAKUB1Lky7wzSt/irsGnzSzpwl9VG4E9khXUTM7CTgJYJ111kmXTEREREQKpNJ+cswH2qbZ1i6Wplj5cffXgdeAXcysfYZ0d7l7b3fvveqqq2YqUkRERCpGuLmt0IvkptLO1DSgi5mlCl67EboxpGvtLUT+hClAS2ClHNKKiIhIM5GYua3Qi+Sm0s7UeMIxbRNfaWbtgC2BbBNKNDV/wgaEoTZ+zDG9iIiIiGQ3AnguzbasI2tVWuD7GOGgz0pafyKhb+7DiRVmtl6K0Rfyyd/ZzFomV8DM9gO2B0ZFo0GIiIiILFXjVvCluXD3Q9z9D2k2X0zDGK6eirq5zd3fN7PbgQFmNozwiyAx89qr1B+D92WgO7GBkvPMvwtwo5mNJMzStoTQUnwMYTa3jCdeRERERArH3e/NlqaiAt/IWYQ+ticB+xGC0FuBi3OYrjif/J8AbwP7A12B1sDXwJ3AVe7+TQGORURERCqIYxrOrIwqLvB19xrghmjJlK5HE/N/BBzauFqKiIiISDpm1gU4HNiO0MBYC3wLjAWecPefGlNuxQW+IiIiIsuyWg0/lpGZnQVcQbi/CsL9V4muqccSuppe4u4ZGylT0ZkXERERKZHEzG2FXiqFmV1EmASsPTAPeAa4jnDq5gOjCEPGXmdm1+dbfuWcKRERERFZbpnZZsAl0cu7gbXc/UB3Hxitm+PuexG6P3wP/NHMdsxnHwp8RURERErEKfxQZhU0nNkAQpeGf7j7Ke4+O1Uid58EXERoBT4tnx0o8BURERGRZcHOhMD3ihzSPh499stnB7q5TURERKSENMVwWt2A+e7+YbaE7v6zmc0BVs1nBwp8RURERErEHWo0qkM6LQk3sGVlZm2BKmBWPjvQmRcRERGRZcF0oHMU1GZzIKFbxMR8dqDAV0RERKRkjNoiLBXiPUJs2idTIjPbDhhMCHzvyWcHCnxFREREZFkwgjBSw9Fptq9sZl8CbwCrAw+5++Np0qakwFdERESkRJzQx7fQS4UYDnwGbG9mK6bY3gpYB5gBnOfux+a7A93cJiIiIiJl5+4zgY3TbP4t4ca3/wHvuHttY/ahwFdERESkhCppiuFScfdHClGOAl8RERGREnGM2sqZaW25o58cIiIiIrJMMbP9zOwJM/vazBaZWbWZTTGzR81s78aWqxZfERERkRJSV4f0zKwj8BDw62iVR48tgbWj5XAzexY42t1/zqd8nXkRERERWVY8TAh6HRgLnAvsF22bDVxIGPlhP2CEmeXVb0QtviIiIiIl4kBt5Qw/VlBmth9wALAEONbd/xHbBrDQ3a82s9uAV4CdCaM9DM11HzrzIiIiIiVj1BRhqRC/J8zGdkU86E3m7nOA8wm/I/Iay1eBr4iIiIgsC7aNHv+eQ9rXCIHvFvnsQF0dREREREpEXR0y6gLMcvfvsyV094VmNgdYIZ8d6MyLiIiIyLKghhxjUzPrTAh6p+ezA7X4ioiIiJRQBfXJLbSvgQ3NbCV3/ylL2lOjx9fz2YFafEVERERkWTAuetwpXQIz625mA4FBhJ4jN+SzA7X4ioiIiJSIu6mPb3rDgKOB44ERKbZ3Bb6Mni8BBrj7hHx2oDMvIiIiUkI13qLgS67MrIWZnW1mH0fTAP/PzG6IZkzLlndDM7vMzN40s+lmNsfMJpnZBanym9kgM/M0yzkpdvE8cDfwrZmtmLTNo2UOISjewd3vzPnAI2rxFREREWk+bgLOAKjbn2wAACAASURBVIYTugn8Mnq9lZnt7u61GfIeD5wGPE2YYW0xsAtwBXCYmfV19wUp8p0NzEha93ZyIndfCJySZt/rAPPdfVaG+mWlwFdERESkRByoLdPNbWbWEzgdGObuh8TWfwncAhwBPJKhiCeBq919dmzdnWb2GXAB8AfgthT5Rrj7lKbU3d2nNSV/gro6iIiIiDQPRxJmRhuctP5uYD5wTKbM7j4hKehNeCx63DRdXjNbwczyanC1YEMz2z5aNrRo7uLGUuArIiIiUjJWzj6+fYBa6kZPAMDdq4FJ0fbGWCt6TDfxxHvAbKDazMaa2T6ZCjOzDczsfmAW8DFhyLLXo+ezzOwBM9ugMRVV4CsiIiJSImHmNiv4AnQxswmx5aQUu18TmBH1pU32TVRGm3yOx8xaAhcTRllI7iYxC7iL0L3i18D5QHfgWTM7Lk15RxEC5d8BVdTd1JZYqoDfAu+Z2dH51BXUx1dERESkEsxw995Z0nQAUgW9ANWxNIvy2O9goC/wF3f/JL7B3ZO7VGBm9wEfADeZ2ZPuPje2bVvgAULD7PuEG/FeAxL9e9cEdgT+CPQEHjCzL9z9P7lWVi2+IiIiIiVUQ4uCLzmaD7RNs61dLE1OzOxyYABwl7tfnUsed58J3AmsCPRL2nwhITZ9Hujl7kPc/Qt3r46WL9x9CNArSmOEm+pypsBXREREpHmYRujOkCr47UZoNc6ptdfMBhEC1ftJPwRZOlOixy5J6/sRgtnT3H1Juszuvpi6KYu3y2fHCnxFRERESsQpfP/eqI9vLsYTYr9t4ivNrB2wJZDTLGhmdglwCTAUOMHdPecTECRuTEu+Ga4NMMvdp2YrwN2/IvQhzqtPsgJfERERkebhMcINYmclrT+R0Lf34cQKM1vPzDZOLsDMLgYGAQ8Cv0834YWZtTKzzinWrw38HzATGJu0+TOgyszaZzuQKE1VlCdnurlNREREpIRqy9Tu6O7vm9ntwAAzGwY8R93Mba9Sf1SGlwkjMCxtTjaz04BLga+Al4CjkobV/d7dR0XPq4AvzWwE8BHwE7ARcEK07cgUs7zdA9xKCIxvzHI4pxLi2PuyH3mdigt8zawFcCZwMtADmA48Dlzs7vOKmd/MHgcOBSa7e9pBnEVERKR5coea3LsmFMNZhD62JwH7EaYSvpUQ52SarhjqxvldhzD6QrJXgUTguwD4J7At0J8Q7M4gBMx/dfdxyZnd/W9m9ivgWjPrCNwUH/UBwMw6EaZAvhh4wt1TzRSXVsUFvjRtDupG5zez/YFDCG+0iIiIyDLH3WsI8c0NWdL1SLHuOOC4HPezkNC6mzMze5AwHvAcQsvy+WY2EfiW0EWjG7AVYQSK2cDiKE+Dotw95Sx0FRX4NnUO6sbmN7Mq4G/A7cCBBTkYERERqUh53IzW3BxF6FqRmKyiLalHbXBghSh9Kkaa6ZcrKvAl8xzU1xBOQtrAtwn5ryScywtR4CsiIiLSGBcVeweVFvimnYPazHKZgzrv/Ga2DWHw5iPd/eekTt4iIiIiS4XhzDSoViruflWx91FpZ76pc1Dnld/MWhFag//l7o83od4iIiLSTNRgBV8kN5UW+OY6B3Wh8p9LGIT5tFwrmGBmJ5nZBDObMH369Hyzi4iIiEieKi3wbeoc1DnnN7P1CUNpXOnuX+RZT9z9Lnfv7e69V1111Xyzi4iIyHLIoZwztzV7ldbHdxqwiZm1TdFdIZc5qPPJfwPwIzA8CoITWgFtonXz3P3bRh+NiIiIiBRMpbX4NnUO6nzydyf0CZ5MmC4vsXQjdH/4jND/V6RJxo8fzxlnnMFmm21GVVUV3bp1Y//992fChJymVBcRkWVKuLmt0IvkptLOVFPnoM45P3AOYZa25GU68L/o+dVNOBYRAK699loef/xxdt55Z2666SbOOussPvnkE7bddluee+65cldPpKItWgRnnQWTJpW7JiJSCObu5a5DQZnZrYThxYZTfw7qN4BdEzOvmdkUoLt7/Y4xuebPsP8pwNx8pizu3bu3q/VO0hk7diy9e/emTZu6AUl++uknevbsyeqrr87EiRPLWDuRyjVtGhx6KIwdCz16wIQJsMoq5a6VFJKZve3uvUu5z1U36eK/Hrp/wcu9t88DJT+W5VGltfhCaK09B+hJmEntCMIc1PvnMF1xIfKLFFS/fv3qBb0AK620ErvssgsffvhhmWolUtneeAN69QpBL8CUKXDUUVBTU9ZqSQVwhxq3gi+Sm0q7ua1Jc1Dnkz/fckUKbdq0aXTp0qXc1RCpKO5wxx1w5pmwZEn9bZ99FlqB1167PHUTkfrMrDvQyt3/m2ueSmzxFVmmzJs3j5YtW2Jm9ZZWrVqx2mqrccABBzBq1Ki8ynzjjTd49dVXOfzww4tU64a+++47zjzzTNZbbz3atWtH165dOeCAA3j55ZcbXeaQIUManJfkpaqqKq8y586dy9prr700/5AhQxpdP2leFiyA44+H005rGPTutVfo6qCgVwpBN7cVzH8IgwnkrOJafEWWNe+99x61taGXTNeuXZeunz17NtOnT+eZZ57hmWeeYfDgwZx55plZy/vhhx846qij6N69O5dccknR6h333nvvseuuuzJz5kwAVlhhBWbMmMEzzzzDs88+y1VXXcXAgQMbXX7r1q1ZeeWVU27r2LFjXmVdeOGFfP31142uizRPU6fCIYfA22833PaXv8Bll0HLlqWvl0hzY2atgd0IXU5XIHMjbVWU5/LYutHu/kq6DAp8RYrsnXfeAUK/3O+++27pendn7NixHHPMMUyZMoWBAwdy9NFHZ+y+MGfOHPbdd1/mzJnDa6+9xgorrFD0+i9YsIADDzyQmTNnstVWW/Hggw/Ss2dPfv75Zy677DJuuOEGzj//fLbeemv23HPPRu2jX79+jBkzpsl1nThxIrfddhvbbrstb731VpPLk+Zh9Gg4/HCYMaP++qoqGDoUDjqoPPWSyuRowol0oq4LrwA98sx6Qez5kqiMlJpt27hIqSQC36222qreejNj++23Z/DgwQBUV1fz2muvpS1nwYIFHHDAAXz00Uc888wzbLppzgOHNMnf//53pk6dSlVVFSNHjqRnz55AaPW9/vrr6d+/PwDnn39+SeqTTm1tLSeffDIAd9xxR1nrIssHd7j+ethjj4ZB70YbwbhxCnpFSuwywjwJ04F/AvcC92RYErPxxtdlHOpILb4iRTYpGgA0OfBN2GKLLZY+nzVrVso0ixcv5je/+Q1jx47l6aefpl+/foWvaBoPPxyGrz7qqKPo1q1bg+3nnnsuI0aMYOLEiXz88cdsvHHy8NilceuttzJhwgQGDBiQ9lyLJMybB3/4Azz2WMNtv/51aOktwQUVaaZqUYtvGrtEj9u5+xfZEpvZ/kAHdz8p1x2oxVekiJYsWcIHH3wAwNZbb50yTbw/6lprrdVge21tLUcffTQvvPACQ4cOZe+99y5OZVOYM2cOb0edHvfaa6+Uafr27Uvnzp0BGD16dMnqFvfNN99w0UUX0bVrV6644oqy1EGWH59/Dn37Ngx6zeDyy2HYMAW9UjwO1LoVfKkQXYHpuQS9jaUWX5Ei+uijj6iurgZSt/i6O3/9618BWHnllfnVr37VIM0555zDE088wR577MGSJUt46KGH6m0/5phjilDz4KOPPiIxyU2ii0OyFi1asNFGGzFu3LhGjys8efJkevbsyRdffEGrVq3o3r07e+yxB2eccQa/+MUvsuY//fTTmTNnDn/729+WBuEiqTz7LBx9NMyeXX/9iivCI4/APvuUp14iAoS4dEExd6AWX5EiSnRz6NChAxtuuOHS9UuWLGHixInsvffejBw5EjNj8ODBtG/fvkEZiZnZRo0axW9/+9sGS7JBgwZlHSIs3TJo0KB6ZX377bdLn6+55pppjzOxLZ4+HzNmzOCjjz6iQ4cOVFdXM3nyZAYPHkzPnj155JFHMuYdOXIkw4cPZ+eddy7qjwBZvtXWhpEZDjigYdC72WZhqDIFvVIqGs4srXynE857+mG1+IoUUeLGtoULF9brH/vjjz+yePFiAFZZZRVuv/32tGPy5jvaQVVVVb1h0/LNGzdv3rylz1MF5QkdOnQAwhi6+VhzzTW59NJLOeSQQ9hggw1o06YNCxcu5OWXX+bcc8/lww8/5Nhjj2WttdZixx13bJB/3rx5DBgwgNatW3P77bfntW9pPmbPhmOPhaefbrjtiCPgnnsgz1HzRKQ41iGMypCrrYG8BhpU4CtSRInAt6amhu+//77B9o022ojRo0dnbE3N1znnnMM555xTkLIS3RyKZc8992wwBFrbtm3Zd9992X777enduzeff/45AwcOZGxi7tiYiy++mK+++orzzjuPTTbZpKh1leXThx+GkRk+/bT++pYt4brr4KyzQt9ekZKprD65BeXu0/JM3/A/1iwqpm1cZFn07rvvAnD//ffj7rg7s2fP5oUXXmC99dbjk08+Kensa/mKtwAvWJC+29X8+fMbpG+qzp0785e//AWAN998k+nTp9fbPmnSJG6++WbWXnttLr744oLtVyrHk0/CNts0DHpXXRVGjYKzz1bQK6XnhFEdCr1UGjPb2cxuM7OxZvZZtIyN1u3c2HIV+IoUyZQpU/jpp58A2HzzzZeuX2GFFdhrr7149NFHAfj3v/+dcfzecoq3RE+blv6HeGLbGmusUdD9b7vttkBoeZ4yZUq9bWeeeSY1NTVceeWVuDtz586ttyQsXLiQuXPnLg3OpfLV1MDAgXDooWHYsrjevcPsbLvskjqviJSXma1mZv8CRgOnAtsC60bLttG60WY2ysxWzbd8dXUQKZJEN4eWLVumvAzfp08f+vTpw/jx43nooYdS9mFtjOuvv57rr7++UXmTu0lsvPHGmBnuzuTJk9loo40a5KmtreWTTz4BKHh3g3hXC0tqmps6dSoAxx57bMYyTjnlFE455RS6d+/eIHiWyjNzJhx5ZGjRTXb88XD77dCuXenrJRKnrg6pmVkb4EVgC0Lj+HvAGGAqsBDoRhjrty9hWuNRZratuy/MdR8KfEWKJDGiw4Ybbki7NP/T9u/fn/HjxzNixAjuvPNOWrRo+kWYuXPnpuxPnGveuE6dOtG7d2/Gjx/PqFGjOPjggxvkeeutt5gd3Sa/2267NWq/6YwbN27p8+7duxe0bKk877wDBx8Myb9vWreGW26Bk09W1waRZdzpwObAj8Cx7v5cqkRmtgvweJT2dCDn1h51dRApkkSLb7ybQ7LEZBTTp0/nrbfeKsh+Bw0atLQ/cb5L8nBmEGZsgzCDW6rhyhKty7169UrZIpxOthvnfv75Z6655hoAttlmG1Zdtf4VrSlTpmQ8loRE/2q19la2Bx+Efv0aBr1rrAGvvgqnnKKgV5YNmsAio8MBA05MF/QCuPsrwImE03loPjtQ4CtSJLkEvltttdXSocdGjhxZknrl6+STT6Z79+7MmTOH/ffff+kkFXPmzOG8885j2LBhAFx11VUp8w8ZMmTpOMHx4HPq1Kn07duXe++9l6+++mrp+kWLFvHCCy+w/fbb8+mnn9KiRQuuvvrq4h2gLNcWL4YzzgjDlUVzxSy1/fahP+9225WnbiKSt42AauCpHNI+Rej+kHuLC+rqIFIUM2fOXDoVcabA18zYa6+9GDp0KCNHjkwbPJZT+/bteeqpp9htt92YOHEiPXv2ZIUVVmDu3LnU1tZiZlx11VUNhiXLxVtvvbW0pbtdu3Z07NiRn3/+eekYxx06dODOO+9k1113LegxSWX47js47DB4/fWG2wYMgBtugDZtSl8vkWwqqIW20FoBi929NltCd3czWwTk9S1Xi69IESRaeyFz4AuwTzRd1AcffMCXX35Z1Ho11hZbbMEHH3zAGWecwbrrrsvChQtZZZVV2G+//Rg1ahQDBw7Mu8yuXbtyyy23cNhhh7HRRhvRoUMHZs+eTYcOHejduzd//vOf+fDDD1POTify5pvQq1fDoLddO3jgAbj1VgW9smxyCt/NoYIC6a+ATmbWJ1tCM+sFdCLc+JYzK/YA9ZJd7969fcKECeWuhojIcuGuu0KLbnRhYKnu3WHYMNh66/LUS5Y/Zva2u/cu5T47b9zVd7ir8OO3P7fTrSU/lkIzs78CfwI+AvZw94Y3loR0qwP/AjYFbnT3nGdtUlcHERFZLlRXw+mnhymGk+2+Ozz6KHTpUvp6ieSrEiecKJDrgOOATYAPzexu4GXg62j7WsCuwElAZ2Am8Nd8dqDAV0RElnn/+x/85jcQG+FuqfPOgyuvhFb6H01kuebu081sH2AEsCZwTrQ0SAp8Axzk7j/ksw/9mRARkWXamDHhJrakWavp2BHuvz/M0Cay3HDd3JaJu79tZpsBpxGGN/sldfek1RK6QTwO3Obus/ItX4GviIgsk9xh8GA499wwDXHc+uvD8OGw6ablqZtIYyXG8ZX0ooD2SuDKaDa3VaJNM919UVPKVuArIiJl5e6waCw+/zGo/Q5arMICP5STTt+ZRx5pOPjQ/vuHCStWXLEMlRWRkooC3ZQ3uTWGAl8RESkbr/kB//E4qJ0GPh+AL6auzSHHr8V7HzYMegcNgosuggLM7i1SNmrxLQwzmwCs7u5r5ZpHga+IiJSF+wL8xyOg5jtgCQAvjP4Vx5x2Az/Nqt+c27kzPPRQaO0VkcpmZp2ADYEVyDznRA9gZTPbndCLBOALd087KL4CXxERKQuf/zTUzASW4A7X3HIKF117Fu71/5/r+csZDH+qCxtsUJ56ihRSYgILacjM2gH3AIeRe4zqhDF9Ey6NlpQU+IqISHnMvx9YwM9zOvL7M69lxPMNp73+zQHPce/ga1hh3ddKXz8RKbWLgSOj5zOBWdS15KbSgxDLfh5b92OmHSjwFRGR8qiZxsefrcshx9/Ox5+vV29TixY1XH3B9fzp/+7FrAXuCzFrW6aKihSWq8U3nf6AAf3d/elsic1sGqGP74a57kCBr4iIlMWIF3bnuNMvY87cqnrrV1npJx6582x233FsbK3+u5LKoZnb0uoBzM4l6G0s3RcrIiIlVVMDF1wAh/z+xgZB71abTmbciwfXD3pbb4lZyxLXUkTKoC2he0PR6Ce0iIiUzI8/wtFHwwsvNNz220OHc8e1F9O+/cLY2vZYx5NLVj+RYnPN3JZJpv68hUivwFdERErj3Xfh4IPhiy/qr2/VajE3Xno1p/7+IaxePNAe2h8IbXcuYS1FpIyOBebmkX4A0D6fHSjwFRGRonv0UfjDH2DBgvrrV18dHntoPDts/hrUtAdrBdSAVUHH07AOR2Cm1jGpLLq5LTV3fyTP9MPz3YcCXxERKZolS+C88+Cmmxpu2247ePJJWHPNfriPgiWfQ+0MaNEZWv1SAa9UKI3jm42Z/RLYA+gMfA086+4/FKJsBb4iIlIUP/wAhx8OY8Y03HbKKXDzzdCmTXhtZtB6A0CzVIg0Z2Y2GDgd6g19Md/MTnb3h2PpzgbWAS5394xj98ZpVAcRESm48eOhV6+GQW/btnDvvXDHHXVBr0hz424FXyqBmZ1ICHoBJgEPAu8R+vHeH7UEJ6wMnAn8Op99VFzga2YtzOxsM/vYzKrN7H9mdoOZdSxkfjNrbWZ3mtnbZjbDzBaa2Zdm9piZbVWcoxMRWfbdey/ssAN8/XX99WuvDa+/DscfX556icgy7xRCS+8t7r61u//O3bcEHgZaEm5mS3iCMKrDAfnsoOICX+Am4EbgQ8KvhieAM4CRZpbL8eaavw3QG3gDuAI4FRgKbAe8ZWa7FuRoRESWEwsXhi4MJ5wAixbV37bzzjBhAvTpU5aqiSwznDCcWaGXCpFo0b0kaf2N0eOvYus+S8qTk4rq42tmPQnB6jB3PyS2/kvgFuAIIO0dg/nkd/d5hMA3uYw7ga+Ac4DRTT8qEZFl3zffwG9+A2++2XDbH/8I114LrSrqfxwRKYKFwAJ3/zlp/ftALWFmNwDcfYGZzQa65bODSmvxPZLQRD44af3dwHzgmCLnB/gBqAZWyiGtiMhy7/XXQ3/e5KC3fXt45BG44QYFvSJLeZjEotBLhfgU6GxmK8RXunsNMBtY2u3UwrAvVZDf/M+VFvj2IfwiGBdf6e7VhE7S2S6y5Z3fzFqaWRczW93M+hBahKuA5xp7ECIiywN3uPVW2HVX+P77+tvWXTcEwkceWZ66iSzLarGCLxViCCGQPTXFtkXUD3K3IvT7nZrPDirtN/iawAx3X5hi2zdAPzNr4+6LUmxvbP5fEprgE2YDV0eLiEhFWrAATj4ZHnyw4bZ99oGHH4aVdN1LRPLg7neY2T7A5Wb2M3CHe8P2bDNrA1xFCISfz2cflRb4diD0D0mlOpYmXeDbmPxfEgZZbgOsT+gO0RloCyxJV1EzOwk4CWCdddZJl0xEZJkzZUqYeviddxpuu/BCGDQIWrYsda1Elg+OZm5Lx8wuBz4A+gK3AeeY2SvA94Sr6ZjZrcDuwEbR+uvz2UelBb7zgdXSbGsXS1Ow/NFNbi8lXpvZfcBEYBiwV7oduftdwF0AvXv3rpzeOSJS0V56CY44AmbOrL++UycYOhT69y9PvUSkIvyF0Irr0dId+H20LbEu0Q1iEnCMu3+fXEgmlRb4TgM2MbO2KbordCN0Y0jX2luI/Lj7XDMbBvzZzNZz9//mexAiIsXkiz/F590LC18ErwbrDB0Oxzocg7VM/dvfHa67Ds4/H2pr62/beGMYPjw8ikg2FTX8WKHdQ/qb1RyYR4jV3nD3/zRmB5UW+I4H9gS2AV5PrDSzdsCWwGtFzp/QPnpcGVDgKyLLjNr5/4SfLwUWAzVhpf8E8+7D5z8EKz+Atd6sXp45c+DwYxfx/IiGU60d2L+Wh4a2oFOn4tddpFJU0CgMBeXuJxd7H5U2qsNjhF8EZyWtP5HQNzc+x/N6ZpbcPpFP/lVTTYhhZqsDhwJzgcmNOwwRkcLzRe9EQW81S4PepRaBz8V/PA6vnb107aefQs9e1Q2DXnNWO+pVvv/drcyyWcWuuohIQVRUi6+7v29mtwMDou4GzxFGXTgDeJX6k1e8TOg7Yo3MfzRwlpkNJ9zgtgjYEPgdYQzfE9w9U39iEZGS8rm3k/7+3USixfj8J7GqPzByJBx5dC3z5rSrl6RFxwWsdsYIOmzxBTMXGkeOfpCX9/s/2rasqP9SRIpGN7eVTyX+lToLmEIYMWE/YAZwK3Cxu9dmyJdv/tcJ4/oeAKxOGNXhe8KNbje7+9gCHIuISEG4V8OisYSLWplUUzvvcS697g9cdhkkXxhs0/17uv7xSVp3Da28tTg/L67mxa8/4cDuPYtRdRFpJsysYOPBRJNeNFBxgW90oDdES6Z0PZqY/21Cq6+IyLKv9mfCn/y0oywCMGt2J3474CKee6nhtqrtP6DLSc/Som39MuYtWcTQz8Yr8BXJQZhpTS2+aSwuYFkpu/NWXOArIiIptOhEtqD3g4834JDjb+fzL3sk5a1llWNeYoV9xmNp/r+eXj23INUUkWatljynIM6XAl8RkWbArD3z6EU7f4sWKf5befzpfTjh7KuYN79jvfUtO89jtTOH0X6TrzKWv0Lrdhm3i0gdDWeWVtFn9FLgKyLSDDw76WOeHLs2tx3wNu1b17X8LlnSkguu/iPX/+3EBnm22QY6DHiKr9pkDno7tGzNYetuWfA6i1QqDWeWmrtPK/Y+Km04MxERSfLRtB+4eNgoxv1vVa4Zsz0LFreiptaYMXMl9j3q3pRB7wknwGuvwcBdtqZ9y9YZy2/RogX9e2yWMY2IyLJALb4iIhXu7jHjWLQk3OA87MNN+OCH1fhV1VwGX/5n/vfNmvXStmkDt90GJ0ax8F5rbcy7G0xj6GcTWFBT/76TVtaCti1bcf+OR9CpdduSHItIJdDNbeWjwFdEpILV1Nby8of/pTZ2bXXcKzvy1Au74TX1/wtos8JcXn2xir5965dx3ha7su1q3fnbh2/wzsyvaWEtaGnGQT024+SN+7F21YqlOBQRkSZT4CsiUsEWLKprpfWaFnz38k78NLFhf9wOa33N+oe9SN++f0hZzk5rrMdOa6zHwpolVNcspqpVW1q2UG85kXw5phbfMlLgKyJSwdq3aY0Bi+d25OsR+7Hg624N0qzceyJdd3mdVVbqkLW8ti1baYY2kSbSvW3lo79eIiIVrGWLFvyyVR+eHLI5S+ZW1dtmrRazxt4vseKmH9O2VUsO6bNpmWopIlIaCnxFRCqUO9x5J/zzqu1Ysrj+pdXWnWez9sEjadd1OgCtWrbgsG03L0c1RZoXzdxWVjkFvmZ2X4H25+6eugOZiIjUU1NTy1vj/svUqTNp1aoFW23ZnfXX75pT3upqOPVUuP9+SJ4IqeMvptDtwOdp1b6aNi1b0qplC+447iBW7dQxZVkiIpUi1xbf4whdUlL9RIl3VUnenrzNAQW+IiJZvDx6MrfcNoolS2pZtGgJLVqE8XK7rbkSF13wa7p375I271dfwcEHw9tvN9y2/UFfsnDT56hesphO7dtxSO9NObrflqzeuVMRj0ZE6iljJ18zawGcCZwM9ACmA48DF7v7vCx5NwSOAfYE1gPaAf8FngAGp8pvZhsB1wI7AW2AicAl7j66QIeUl1wD30vTrG8DnAp0BqYCrwHfEILcNYAdCSd1FnAnsLAJdRURaRaef+E9brntXyxcWDfDWk0NQC1fTpnOaWcM5c7bj2OttVZukHf0aDj8cJgxo/76qioYMgQOOeQXwGnFrL6ILNtuAs4AhgM3AL+MXm9lZru7e22GvMcT/oA8DTwMLAZ2Aa4ADjOzvu6+IJHYzNYDxgJLgL8Cs4ETgRfNbB93f6nQB5dNToGvuzcIfM2sDfBKVMZv3f3hVHnN7EjgLuBXwG6Nr6qISOWbP39hJmCFkQAAIABJREFUg6A3zh0WLFjETTe/yA3XHVlv/Y03wnnnQW3Sf1sbbgjDh8MmmxSz5iKSq3L18TWznsDpwDB3PyS2/kvgFuAI4JEMRTwJXO3us2Pr7jSzz4ALCFf1b4ttuxpYEejl7pOifQ0FJgO3m9nG7nWDjJvZg005vkQx7n5Muo1NubltINAX+F26oBfA3R81s5bAUOA8wq8CERFJYdRLkzHL/J+iO0ye/DXf/zCbrqt1Zt68MMXwP/7RMO2BB8LQodC5c5EqLCJ58/J1dTiScFV+cNL6u4FrCN0Y0ga+7j4hzabHCIHv0qFhzKwjcCAwJhH0RmXMNbN7gMuAPsC4WDlHkbpbbb6KEvgeCSwCHs0h7T8IJ/UoFPiKiKT1zqSpVFcvzpquVeuWfPrpd8yd05mDDoL336+/3QwuvRQuuAA0z4SIRPoAtdQPNnH3ajObFG1vjLWix+9j6zYH2gL/SZH+zVh94nV5icyB726E7hWvpdn+q2ifaTUl8O0OVLt7TbaE7r7EzKqjPCIiUgBvvtmRq66EWbPqr19xRXj4Ydh33/LUS0TSc4rW1aGLmcVbZO9y97uS0qwJzHD3VPdcfQP0M7M27r4o151GV/UvJvTjjbcWrxkrN9W+AOrNqOPue2XZVw3wk7vvmWb7NGD1TGU0JfCdQzjJm7r7B5kSmtlmhBvgfmjC/kREKt4Wm6/DuPFfZGz1dYePP+rDcyO7NbhkutlmMGwYrL9+kSsqIsuaGe7eO0uaDqQfaKA6libnwJfQbaIv8Bd3/yRpX6TZX3VSmpJpygWw0YTm6PvMbKV0icxsReBewo+csgxdISKyvNhjj554hg6AS5a05d1PD+a/n/2qQavRyjt8wo0jpijoFVmWOeBW+CU380nfFaBdLE1OzOxyYAChdfnqFPsizf4as682iaeZkmUrpymB7yWECvcCPjGzK8xsTzPbNFr2NLMrgE+A3sCCKI+IiKRR1bEdp56yG23bNrwgN2/uKoyfeCw//W/D+huslpWPeYnOpz3JmRMf443vvyxRbUWkMdwLv+RoGuFqfapgtBuh1Tin1l4zGwRcCNwPnJJmX4lyU+0LUneDSGfj6LGLmbVOUZ9WQBdgbqZCGh34uvunwL7AjGhH5wPPA+9Gy/PRulUJXRz2dffPGrs/EZHm4oD9t+KMAXvSsWNbOrQPjRwzpm/E2+N/R/XPq9RL26LTPNa44BFW3P8tzKC6ZgnnvPk0tWW8bVxEllnjCbHfNvGVZtYO2BJIN2pDPWZ2CaExcyhwgqe+TPU+oZvDdim29Y0ec92f8f/s3Xd4lFX2wPHvmZl0em9SBAUEFqTapYm9ICL2LuouKuxa1r6ru6y7K/b96aqroqILCFhQ14JS7IB0KdKRDgECSSbJZM7vj3cCSWYmmclMMiGcz/PME/K+9773Dup4cnPuPU4eMTjzPyNEs8GBexvLelZMe31VdTbQEefNL8HZKSiBlz9w7SGgU6CtMcaYCJx91m+YOvkOfj/mHNJTr2TJoqEUFiaXaJN89FZa/e1V0rpuKHE925fPd9vXV+FsjTFR0Up4RWZioPXoUtdvxsm3PXg8rYi0F5FOpdohIg8DfwLeBK4PV/BCVQ8AHwL9RaR7sf61gJuAXyh1ukQogTl8BlwM7A5c/j8ROV0OORV4ASf+nFnW82LZ3AaAqu4FHgMeCyw9F5USylTV8s/kMcaYBMje7+XTST8w7fU57N6ehdvt4jf92jP8lgH0OLF6JMlmZbn5++Od+Oyz4Hu1Tl9Eoxv/hys5uNCFt7CApXu2cnKzdlUwS2PM4UJVl4jIv4BRIjIV+JhDldtmUfJUhhk4p3EdzJsVkd/hVPPdiHP02BWlzh3frqqfF/v+PpwjyD4TkaeALJwguyVwbpiV4qKxegF3A5fgLNQuwCmQMRdojVNEzYsTyKcFuuXhFOIIK+bAt7hAoLu93IbGGJNAO7fu5ffDn2f/3hzyAqcn+PyF/PT1KpbNX8fZI05g5APnl1tIoiLW79zDW98uYNGmrbhFOPmYNow4oTtN6tQq0W7hQhg6FNavL/UAdyGNrv2M2mf8RLjpSVzOfzfGVA5JWOW2gNHAemAkcC5OyupzwMPllCuGQ+f8tgbGh7g/CzgY+KrqahE5Gac4xh+BZOAn4Kxw5YpF5Cycgmf9ix6DE5D/VlWzROQ64JXAs1KKtdkPXBdIxQ0rboGviDQFjgLSLa3BGFNd+f1+7rvm32Tu3I+/MPgzPi+3gE8mfk+bY5ty1qX94jiuMvbDr5g6bxmFfj++QF3hVdt28dqc+Yw+8xSuOaUn4JzBe/PNkJtb8hkNm/qoe/tE6LC+zLGS3W66Nwy1n8QYc6QL1F8YF3iV1a5tiGvXAddFOd5y4MJI2orIIqAbhxI4ZgEPquq3xZ43QUS+AC4FjgYKgZ+Baaq6p7wxYg58RWQETpm6LkVzKv7cwHFmk3GWyoeq6v5YxzTGmIpa+O1qMndkhQx6i+TlFjDh2c85c3jfuK36Pv3p17w3fxl5vpKpCXk+pwbQM599Q3pSCl+/04Vnngnuf/LJMGmSm+HzdrMtN/h+cXWT0+jXuHVc5m2MqQS29zScrjh/O58Cfyke8BanqttxVqmjFtPmNhF5HGf5uSvOYcdKqTPUAjnA24ABODWbjTEmYf436Qdys8s/redAVi5rlkVz0k54e3O8vPXtAnILgvNxD463N5kbL6sfMuj93e/gyy+hRQvhyRMuJNUdfs0i1e3hqRMurJQ0DWNMHKhTuS3erxriA6Cvqp4TLuiNVYUDXxEZgpODkYWz3FwL2Bmm+XgCK74VHc8YY+Jhz47IfunkdrvYl5kdlzE/Wri8zEA0Z3Mz1r52JVnrW5S4npICr78Ozz8PyYEDHfo1acOrp11Gi/Q6pHuSSBIXSS436Z4kWqbX5fXTL6ePrfYaYw5DqjpUVedX5hixpDqMwlnhvVtV3wXK+mD/LtC2ZwzjGWNMzOo3qR1Ru8JCP3UbZMRlzLU7MvGGWe3ds7Ab2z7vjxaW/Dhu3dopPdyrV3Cffk3aMPu8Ufy4cyNLMrcC8JsGLejT+Chb6TXmcGCpDgkTS+BbtOvj7TJbAaqaLSL7gGYxjGeMMTE769J+zJ25Am9O2ekOGbXTaN8l8g1iG1dsZvVPaxGXi059O9D86KYH76WnJCOU/H+d3+dm2+cD2LuoW9CzBg6E//4XGjcOP56I0K9JG/o1aRPxHI0xpjoTEXc8nhPYwBdSLIFvPSBLVSOtsxyXN2OMMbHocVIHGjatw9aNmWE3uKWkJXHlHYMjWj1dvWAdT458kY0//4rL42SPFRYUcmyf9vz+pVs5qmNLBnQ+mne+X0RuvnN0WkFWLTZNOw/v1uZBz/vt7fk882QynrgeNmmMqV7sNzNh5BOfv5ywqbyxbG7LBOqISHp5DUWkHVAbZ5ObMcYkjMvl4m/jb6FB49qkpAaVeyc1LYmzRvSL6CizlXNXM+a0h/hl/lrycvPJ3e8ld7+XfG8By75eyah+97FxxWaOb9OCJnUyEIHsjS1Z+/oVQUGvK6mAM26by7+etaDXmBovcZXbDheV9jcRy8frj8B5gdekctr+IfB1TgzjGWNMXDRuUY8XP7kruHLbCe0ZPjKyym2qyqPDx+HNzgt7P3d/LmOveJqHpt/PaanNee7Hdmz58jTQkmsOyfX30vPaGUz8yzlxeX/GGHMY2wf8Lsw9Bd7COVghVJvncTISwool8H0FOB8YKyI/qOqG0g0CuRr3Ab8NTPbFGMYzxpi4yaidysU3ns7FN55eZjv1H0Bzp4NvGZCEpJwMKf1ZPGsF+zMPlNnXj7DG7+ba615n2dIhbNvaJahN3WPW8dtHNnLP0HOpl54a0dxVFfJ/RPO+AM0Gdwck/SLE1aD8zsaYxKt5K7Tx5FXVsPvHROStcG1E5InyHl7hwFdVPxSRt4ErgJ9E5D0gIzDwKOA4nMC46HyeF1T1u4qOZ4wxVc2f/Rrsfwon5cypGqHeaUAyW1YOJTfbG7avAv5uR5Ob1pql3w3jwIGmQW2O7vIdn00/hvZtyw6+SzzXtxrNHAmaCZobGCkFPfAUmn4ZUvs+RGI6ot0YY2qsWD8drwOeAeoC1+Oc5Uvg2i1AS5xP5XHAHTGOZYwxVcaf/R/Y/zTgpSjoBZwVVt3DwHPepNPxZZzzWyeD3dqdefOuDwp63W4v3bq/S7uWs5n4TuRntKtvI7p7BPg3g+ZwaNkoz3nlTEKzHon4ecaYBFBAJf4vE5GYAl9V9anqGKAT8FfgS2AFsAonn/fvQFdVvVtVw9cHNcaYakT9WYGgN3xt4KSkAu78x5bQ/RXWpY5g8eLL8PnSStxLz9hJ777jadR4NX6/MvvrVeTkhM4TDnru/iecwDvs70lzIfd91Lc2oucZY8yRJi6/D1PV1ar6kKoOVtUuqtpZVfur6n2quiIeY0RKRFwiMkZEVoiIV0Q2icg4EYnoJPpI+4tIfRG5U0Q+C7TJFZGVIvKSiBxVOe/OGFMVNPc9Ivl4bNnOS5tjS6Y7+DSVxdzF2u1nU/pUnsZNVtCrz5ukZ+w5eM3jcbF9R1b5c/Lvg7yvgPLWEHxo9pvlPs8Ykziq8X+ZyFQ4x1dEWgOFqhpRMXsRaQF4VHVjRceM0FM4aRXTcFIsOge+P15EBkew8hxp/36B+zNwdhHuArripHhcKiInqerPcX1nxpiqUbCIslZ7i7g9SRzTvYANq5xNadnagkXcTQ6tSrX0c3SHWbRu8wO+OkrmcX7ymoAo1N6azwEtu5gGAL71IEmg5a0O+wLzN8ZUWxaoJkwspzqsB7bi5PFG4hvgqBjHLJOIdAFuB6aq6rBi19cBzwKXUUaluSj7rwA6quqaUs/4CPgceBS4JA5vyxhT5SL7mPIkexh89QDmTF/Mdn8vFnhvpZCSR5t7knLp0vV96jdcx54T/BzoFEjHC5T0yW9UyIh5b/FQ4Rlc3qGMqu5RbVizekHGGBNKrKkO0WZTV3b29eWBMZ4udf1lIAe4Kl79VXV96aA3cP0LnOIeXaOauTGm2pCUkyGS7CgtoPuQa+g66nXmeX8fFPTWrrOd3n1fp0HD9ezt4+dAR1APJeJS9UCe38dfFn7O9I3Lwo/lOQYIW4WzmBRIOS2CdsaYhLHNbeFEshb+LvBBRftX5Zk36YCvksfog5MA92Pxi6rqBRYG7ldmf0SkLk6Vuu0Rz9oYU72knkX5P6e72JNzOhdc1Jy//yO4AtzVV8PLr2ymXr1sClOUrC6gwc0O8hb6+OuCL/CHSdYTSYXUYUAZDylqm35ZuW2MMaYaOg3nKNywVHWEqt4S5nZ7Dp0wFlKVBL4i0gFoROWXLG4B7FINmQS3GWgkIsmV2B/gQZz/M42PZMLGmOpHJBmpOw4IV1DCxeLlx9P3jGf45JOSdzweePZZGD8eRgzvyR/vOQ/pkRLRr7uyffn8sCOoFtChedUeDe6mhA9+U6H2vYi7cQSjGWMSRTT+r5pAVb9V1Xkx9Peqak5ZbSLOtxWRC4ELS12uKyKvltUNp3TcKYHvv4p0vApKxznQMhRvsTbhdpLE1F9ELsEpz/wp8FpZExWRkcBIgNatW5fV1BiTAJI6AOq/gGY9DP7dzrZpEdBC3v5gJCNH/47c3JJrB02bwuTJcOqph66dflonTkpbw8S1C8sd06/KxgN7OLFp29BzctWBhlPRfQ9C3kycj3B18n8lDWrfhyutzMUSY0yiKba5LYGi2WjWA6dgRXFpIa6FswZ4KIrxKiIHaBLmXmqxNnHvLyLnABOA+cClqmUfLqKqLwEvAfTu3dv+EzCmGpKUk6HRF1CwGHy/sGWPl0tu7cH304NT+Pv1U6ZMEVqG2O6b7invF0UOlwgp7rI/lsVVD6n/PFq4C/K/dU55cLeC5H5Wsc0YY8oRTeA7s9T3jwAHcI70CscPZAHLgJmqWtk5vluA40QkJUS6QkucNIayzg2qUH8ROQuYivM+h6hq+YdyGmMOCyICyd35cmlTLhrmY//60keVQaOeSzjm6lU0aXYRoU5UGNTiGCauXUCOr6DMsQr8hZwUZrU3aF7uRpB2QURtjTHVSY3ajHbYiTjwVdVZwKyi70XkEeCAqv65MiZWQXOBIUBfnMpxAIhIKs6K9ex49xeRM3HO/F0BDFbVPaXbGGMOb9985+PsszPI31e7xHVx+2g25Evqd1/Gz9s8vDjjB24fclJQ/xOatKF+cjo5vn1hx0gSF6c2O5omabXDtjHGGBObWH4v1g4nQKxOJuJkzowudf1mnNzcCUUXRKS9iHSqaP/AM4YA7+GUaB6kqpmxvgFjTPXy6qswoL8rKOj11N5P2ysnUb+7cwSZ1+djwncLKSgMPnJMRHj51Eup5UnGFWKbm8flolFqLf7W59zKeRPGmOpFK+FlIlLhYhKqGn7rcYKo6hIR+RcwSkSmAh9zqPLaLEoWr5gBtKHYmUXR9BeR3sD7gf6vAWeLlPwfmqq+Fe/3aIypGvn5cOed8OKLUHqNIP2oTbS66CM8GSWru/lVWfbrdnq0aRH0vI71mvDekBt5fOEXzNm2lmS3BwV8/kIuaNOFe34zkPop6UH9jDHGxE8sJYt7Ak8A81X17nLaPgN0A8aoamXX0hyNU1VuJHAuTinh54CHIyhXHE3/rhza8PZUmGdZ4GvMYWjLFrjkEvjuu+B7DfrMp2n/rxF38MeJSyC3IHweb7vaDfj3qZey25vNuv2ZiAid6jYhIymyzW/GmBrCVmgTJpbywdcCp+NUNSvPUpxSwNfgHPdVaVS1EGfDXVmb7lDVtjH2fx14vSJzNMbEhzcnjzlTvmfTys0kpyTTa0h3OvXtQOnfvkTj669h+HDYVurUcfEU0OLsz6nbZWXYvgWFflrUq1vuGA1TM2iYGkFlOGNMzWSBb8LEEvgOCHz9MoK2HwL/BgbGMJ4xxgCgqkx95iNef+i/iAi5B7yIS5j4j/do1Kohf5p6N206B5++UPYz4V//gjFjwFfq/JlWrX3UOvNdXA3LrsHTrnED2jSqF+3bMcYYU0ViCXyPAnJVtdzSvKq6TURyA32MMSYm/318GhP+OpW8nEOnDqpf8WbnsXnVFu486QH+NfdxWnZoXuZzVq7cyjff/cKePQVM/6AHM2c2DGpz5pnw9tse7p2WzLz1bvJ9wZvXAFKTPNx1zqkh7xljzEGKHWcWhog8VpF+qhpxnYhYAt8knHN6I1WIczKCMcZU2J7te3nrsXfJ94bOpVWFnP25/N/o1/nr9PtCttm6dS8PPjKFrVv3sndvGksWXcyB/cFB7wMPwJ//DG43PHP1Bfxu/Hss+XU73vyCg7+pTPG4AeGxYWdwQnurwmiMMTG4HyKq8F5EA+2rJPDdDHQQkY6qGj7pDRCRjkAtYF0M4xljDB+9/EW5bdSvLPxyCZnb9tCgWf0S93bvPsBvbx/P/v1edu1szc9LL6SgoOTP5CkpBbzzThJDhx66lp6cxKs3XcJP6zfzxjcLWLNjNykeD0O6HsMlfbvRsJb9XG+MiYxYjm84r1B24Nsc6IJzKlcW8C5RZkzHEvh+BRwD/Bm4rJy2j+JM7KsYxjPGGJbMWR52tbe4pJQk1i7eGBT4vjZ+Dvv357F+bR/WrO5P0FFl6bvp2ed9uvc4j9IVzEWEXu1a0atddPnDxhhTggW+IanqLZG0E5HLgBeAusCIaMaIpYDF0zjpC8NF5E0RCUqmE5HmIvIWMBwnLeLpGMYzxpioTmwo3TQ3N59PP1vF4oXns2b1QEp/BDZqvIpefceTkrKTd6fMjcNsjTHGxJuq/he4BbgEGBVN3woHvqq6Avg9zpL0FcAGEZkrIlMCr3nABuDyQJe7VXVpRcczxhiAnoO6kZJW/rm3BXkFdDi+HeCcAvHDjg1c+s5HzF56OTt3dC7VWmnXfhZdfzMVjycfv19ZvnxLJczeGGNMnEwB8oAbo+kUS6oDqvqciGwDngRaAr0Cr+I2A39Q1UmxjGWMMQBn3TCQ8Y9MLLONy+2i7zk9qduoDn5V7v3xAya9V8Cvz56HPye1RFt3Wi7Hdf6ARg1KbkEQl+26NsaY6kpVC0XEB3SIpl9MgW9g4MkiMg0YBJwANMVZBd4GfA/MUFVfGY8wxpiI1WlYm1vGXctLd79BXk5+0H2X20Wtehn89unrARi3eCbjxzVk5+RTgtomt95Os9GTIXsfzHIfvO7xuOjVs22lvQdjzJHNNrfFTkROxTktrOwD1kuJOfAFCAS2nwZexhhTqS647UzSaqXywpjX8RUUUpCXj8vlZG6179GWB94ZQ+NWDdmyK5/HbjyK/fODFwRqnbyUxiM/wpVaQI4PCn9U3LnOKq/L5WLoRb2r9D0ZY8yRTkTc5TRx4+w6Hgw8jrPQ+lk0Y8Ql8DXGmKp2xtWnM/DyU5j7v4X8umoLSSlJ9BzcjaM6tgRg2TIYcr6yf12poNflp+FVX1D3nB9LbH7Lbq/UWSqkpiQxYkQ/WjS3CmzGmEpiBSzCKf/InkMUWA08EM0AFvgaYw5LvkI/X61Yy2zvbrwt3HRq3pCMls7RZZMnw/XXQ3Z2Sok+rjrZNBs9lbQuG0o+zANS10WtWinccP1pXHRB6a0KxhgTJ4odZxaen8gKWAjwIvCgqu6JZoCIAl8R+TLwxw2qen2pa9FQVR1UgX7GGHPQ/PWbuePNDygoLCQ7z1kgmLFsNc999j0NVl3EV+8GV0dPab+ZZr+fgqdRVtA9N8LgEzvz2IPn4fGU95s2Y4wxlaS88pfNga7A3cB5wNvAN9EMEOmKb//A1xUhrkXDfsYxxsRk2ebtjHx1Kt6Ckntms7OS2PzBOWSvDw56aw9YQKMb/ocruTDkMz1uNyNPOsWCXmNM1bBoKCRVLe8cyS3AfBF5A6dq23QR6aeqqyIdI9LA9/rA130hrhljTJUZ+8FXQUFv7rYm/Dr1PAqy6pa4npQEp/9+IZt7fUq+hg56k1xujm/Qkna1G1banI0xxsSPqqqIjAHWAQ8BV0faN6LAV1XHR3LNGGMq06bMvSzfsqPEtb1LOrP108Gor+THWd2Gefxvegrde3fhqpk/sXLfDryFJQPmFJeHZum1ee6kYZU+d2OMKWLHmcVOVTeKyD6izECwzW3GmMPGmu2ZJHnc5PkK0UIX22aczp6fegS1S2/1Kxf8YTEnnHAOkMTbA65h8toFvLzye7bn7gegXnIa1x/bjys79KJWUkrQM4wxptJY4BszEakF1AHSoulnga8x5rDhcTtn9foOpLPpvfPI/bVlUJsGvRbQdOBs6jRoe/BaitvDVcf04coOvcn25aOq1EpKQcSOFDLGmMONiLiAJ3BOd/glmr6RnupwWgXmFZKqzo7Xs4wxR5aurZqRubYR66eei+9ArRL3xOOj+VlfUK/rctKTkzi9U7ug/iJiq7vGmMSzFd+QROSxcpq4cSoE9weKPuSfjmaMSFd8ZxKff0waxZjGmCNcfr6PmbOWM/+n9RQWKrM/qsea74ahWvJjJKnOPlpdPJ20Zk7+r6pybo/OiZiyMcaYiruf8s/xLYpHs3HO8X01mgEiDUI3Ej7wbYxTKxnAB+zCmXTDYs/PDlw3xpiIzPl6JY//YzoABw74+WXlELZu6R7ULqPtBlpe+DGeNC8AqUke/j7ibNKTk6p0vsYYEwlR29xWhlcoO/AtAPYAS4FPVHVfGW1DivRUh7ahrovI7Tg5Fl8AY4FvVTU/cC8JOAm4D2dJepyqPh/tBI0x1Vtudh779+WQUTuVjNpR7TEI64cf1zD28Q/Jy/Ph9dZm6eKh7M9qEdSuYd8faXribMSlkC94cgq47aTeDOrSIcRTjTHGVGeqektlj1HhtAMROQcnr+KNompuxalqATALmCUirwHPiMhqVf1fhWdrjKk2Vi7ayIRnP2fBt7/g9rgo9Pnp1KM1V4wazPEnH1vh56oqTz79P/LyfOzJbM2yJRdSUJBRoo3bnU+njh/SePlUCrJqoy7Bk5lL0vZsNmwWuPqMWN+eMcZUHrWNtYniiqHvH3DSH+6JoO29ga93xTCeMaaamPnhAu698kXmzV6Br6CQvNwCfAWFLJ27jj/f+jrvvjyzws9evGQT+/d72bihD4sWXBYU9KalZ9Krzxs0abICV61apK3cTfryXSRvz0aAzK1RlW03xpiqp5XwMhGJJfDtAexT1Z3lNVTVHcBe4PgYxjPGVAOb1+/k6fsmk+ctQEN82OblFvDWs5+xdN66Cj1/xYpMFsw/hzW/DEK15EdUw0a/0LvveDJq7QKPG62dHtS/frN6FRrXGGNM9SAiySJynYhMF5EtIpIfeG0JXLtORJIr8uxYAt9koI6I1CmvoYjUxTlkuEKTNMZUH++9NgefL3T53yL53gImvvBl1M9eswb+eG9Htm0tfSKD0vboOXTrPgWPJy9wScHvL9EqrXYq59w8OOpxjTGmKhVtcIvnq6YQkWOA+cCrwDk4x5e5OXSU2dmBez+JSNR5dbEcLbYU6Itz9MQfy2l7H86El8QwnjGmGpj98SIKff4y26jCT1+vxO/343JF9vP1J5/AFVfA3r0lN8h5PF46d/2QRo3WlOxQ6Me1Z//Bb10uoXaDWvQ7t2dkb8QYY0y1ElhM/RxoDfiB6cBXwAYgD2gJDAAuBo4DPheRbqqaFekYsQS+zwNvAneLSGPgcVUtUT1DRDrg5PfegJOB8lwM4xljqoG83IIIWwr5eT5S08r+RY/fD2PHwsMPE5Q6kZGxk67dp5KeHiJvt7AQMp3ANzktmYw6aTwx40+43e4I52eMMQlSg1Zo4+wPwFHAeuB8VV0Wos0rgZXeT3CKWNwFPBzpABUOfFV1goicCPwWuA64TkR2AJsDTVrgLEmDcybb86r6TkXHM8ZUD3UbZLBjy95y2yUluUlJLfss3awsuOYaeP/MpzsVAAAgAElEQVT94HstWq7imI7Tcbnyg+65UNzLN6AuoUGzegy94xzOuXkwtevXCn6QMcaYw8VFODHjDWGCXgBUdZWI3AB8CVxIVQS+gYFHich3wJ+A9jiBbtNSzVYDf1LVt2MZyxhTPZx39cm89cxn5HvDr/y6PS4GD+uNSPgje5Yvh6FDYeXKktddLvj73+HyK5rw5FPNWbbsVzxJzipuoc9P27aNGHPnWRx7bDNUtcwxjDGm2qlhOblxdjSQraozy2uoqrNEJCfQJ2Ixlw9W1QnABBHpAfTEqeQGsBP4SVUXxjqGMab6OOvSvkz+91dlBr5JyR6G3Xh62PvTpjkrvQcOlLzesCFMnAiDBgHUY9w/L2f7jn0sX74VVeXodo1p06bRwfYW9BpjDksW+JalUj/YYw58iwQCXAtyjanhatdN559v38Y9V75Ifl4B3pxDqQjJqUm43S4e/c+NNG/dMKhvYaGTyzt2bPBze/aEqVOhTZuS15s2qUvTJnXj/TaMMcZUP2uBbiJyhqp+XlZDERkEpBPlwQlxC3yNMUeONsc24/WZ9/Hl+z8x/a1v2Zt5gIzaaZw5vA9nDu9LnfoZQX0yM51TGz79NPh5114LL7wAafGpeGyMMdWbrfiG8y7QFfiPiJynqotDNRKRbjhHmgkwLZoBYg58A0dP3AScgbMTL01V25e6fxGgqvpmrOMZY6qHtIwUzr3iRM694sRy2y5a5OTzritV08LjgWeegdtuA8taMMaYI95TwLU4ebvzRGQazga2XwP3WwEDgaE4Mex64MloBogp8A2c6jAFZ0Nb0f+2Svwco6pZInIn0ENE1qnq17GMaYypPnZv3cOXb89h+4ad1G5Qi9MuOZF2XVuXaDNhAtx8M+TmluzbrBm8+y6cfHIVTtgYY6oB29wWmqoeEJEhOCu/3YHhgVdQU2ARcLGq7g9xP6wKB74i0grnYOH6wMfAO8CzQKh6oS8C/waGARb4GnOYK8gv4MmR/2bWxG+d7/MKcLldTPrnB7Tv3oY/T7uHWg3qcffdzopuaSedBJMnQ4sWVTxxY4wx1ZqqrhWRvsCIwCvo4ARgEvBfVfVF+/xYVnzvxgl631DV6wBE5IkwbT8JfO0fw3jGmGpAVXlk6D9ZPHMZBXmHTnbwF/rJz81n1by13NhnLDtb/42vvwkuJnHbbfD005BsBcyNMcaEEAhoJwRecRVL4Hs2zlJzuYcGq+qvIpKLU2Gj0omIC7gTuAVoi/MTwiTgYVXNjmd/EbkU5++iJ075PA/QTlXXx+fdGFO9/PTFYpbMWU5ebnBhCYDMgnbM3HQXeZtKBr0pKc4Gtuuvr4pZGhO7Xd4DfLxpObu92dRPSefMVp1onl4n0dMyNYGlOiRMLIHvUTiHDG+MsH0uUDuG8aLxFHAHzk6/cUDnwPfHi8hgVfXHsf9vgX44uSZrgI7xfCPGVDeTx32I94A35L3NOpAV3IRSsmLbUUc5R5X17l0VMzQmNnmFPh6YN52PNy3HheD1+0hxufnH4hn0b96Bf/a9kIwk+5WFMYejWALfPCBNRFzlBZIikoGT+7s7hvEiIiJdgNuBqao6rNj1dTg5yJcBYavIVaD/NcAWVfWJyPNY4GtquNUL1gVd86uHldzAZs4IujdggFOUonHjoFvGVDuFfj83zn6HBbs3k+8vPHg9L/DnmVtXc+XMN5k48FpS3HYiqKkAq9wWlojcGK9nqep/Ql2P5b/aVUAvoBvOamdZhgEuojxkuIIuxzlh4ulS118GHgeuoozAN9r+Uax4G3NY2LRmB6uXbUYEju12FC3aNipxv3S1NK82YDF/IItjg551++/yefLpZDwWH5jDxGebV7I4cwt5/tB7ZvL9hazJ2sV7G5Yw4ujjq3h2xtR4LxG/ym1xD3zfA3oDDwGXhGskIh2Bf+JktEyOYbxI9QH8wI/FL6qqV0QWBu5XZn9jDktrft7MM/e/y4bV23G7XQAU+gpp36Ulo8cOp3WHpgB0O60zX0/9AfUre7QzS/g9+aUOc3Hh5YQGb/LMczfZ+bzmsPLSym/JKQxfjhsgt7CAl1Z8a4GvqThb8Q3nC6pxyeJngJHAUBGZgrNC6oKDqQ1dgItxcmBrAT/jVNmobC2AXaqaF+LeZuAkEUlW1dA7c2Lvb8xhZ9WSTdx75Yslyg8XWbFgA6OHPcdTk0fR5thmDP/DBfzw0QJW5wzkF65BS32MpLGN3qlPM/rhU4NWh42p7lbt3RlRu40H9lDo9+N2uSp5RqZGssA3JFU9s7LHqPB/sYHTDc4GNuJU0JgJFP1ONAv4DufIs1o4tZcvUNWyf4yOj3Sc/ONQvMXaVFb/iIjISBGZJyLzdu6M7IPWmMqgqoy9/c2QQa9zH7w5eTw+xsnwadPtGHa0fIRV3BAU9DbkJ05JfYiuXZXzbgnO9zWmurOf1Yyp2WL6UVVVl+NU1hiLsxoqpV47gL8DvVR1bWxTjVgOkBLmXmqxNpXVPyKq+pKq9lbV3o1t149JoKVz17Evs+xT/lRh64ZdzPxsGyefDPN+OSaozTHJ0+iTMo5Bw3/DuJl/JjnVdr2bw0/nek0jateudkNb7TUVIjib2+L9MpGJecuJqmYBDwIPBqq5NccJqLcn6CzbLcBxIpISIl2hJU4aQ1lpCrH2N+awsuTHNeR5y/9lzLasDpx/cUMOlIqR09MKufPqRQw4pQH9znmROg2r6tRCY+JvZKeT+MMP75HjC//fRJo7iVs6nVSFszLmyCIinYEzgLrAr8BHqrojHs+OpWTxBYE/fququ8ApVIEzwUSaCwwB+gJzii6KSCrQA5hdyf2NOaz4CgpRf/jlAlVYt3cAqzLPovQviTp3hmnT3HTs2LOSZ2lM1RjU4lj6NGrNDzs34C0MPtkhxe2hc72mXNCmawJmZ2oMW6ENS0SexjlWtnjiUY6I3KKqE4q1GwO0Bh5T1cxInx/L72neA97lUN5rdTER51+p0aWu34yTm1v8L629iHSqaH9jaoJ2HZuTlhE6u8fnT2Hh9qtZlXkOpT8uLr4YfvgBOtrJ1aYGcYnw4imXcnHb7qS4PKS7k3CLkOZOIsXl5uxWnXjj9CtJcgWX4zYmIpWQ5lBTUh1E5GacoBdgIfAmsBhIA14LrAQXaYBTZffCaMaIJdUhE0BVD8TwjLhT1SUi8i9glIhMBT7mUOW1WZQ8w3cG0IZiP1VE2R8ROQ04LfBtUV2qUSKyN/C8v8T3HRoTXycM7oI88G7Q9QP5jVmw7VqyC0rmPIrA2LFw7722EcjUTEkuN4/2Ops/dOvP55tXssubTb3kNM5o2ZGGqRmJnp4xMRERF07AeAvQFtgJTAIeDhxcUF7/+4CeOLUc2gEbVLVtmLavA9eGedRwVS39P59bcWKyZ1R1TLHnvAFcCYwCfhe4PBm4HzgfeK28eReJJfBdhnO0V51Anm91MhpYj3Pc2rnALuA5nH+o5ZUrjrb/QOCRUtf+UOzPFviaai0p2cNtD1/I8w9NPZjruyP7OBbvuAyfP61E2wYN4J13YMiQRMzUmKpVNzmNS9r1SPQ0TE2U2BXap3AW86YB4zi0uHe8iAyOIE4ai7P4+ROUOsQ9vKtDXPsxxLWiFd3ScdWTOIHvqcWu/VKqT0RiCXxfCkzgduCvMTwn7lS1EOcf5rhy2rWNpX+g7Z+AP0U7R2Oqk8FDe1Po8/N/f36flTsHsXLHwKA2PXrA1KnQrl0CJmiMMSZmItIFJ26bqqrDil1fBzwLXEbZ1W0B2hed1CUiS3GOrS2Tqr4V4RTzgNwQC6pLcIqLtS32zFwR2Ydz8EDEYjnHdwLOKuifReQxEWlQ0WcZYxKv7+C+5Ld6LGTQe9VV8M03FvQaY0xcaCW8InM5TirB06Wuv4xzVOtV5U69AsfTiqNOIM2iLKuAuiJSp9SYhcA+4GCukTgVkmoRZaW3WE51+DLwxxycHIt7RWQ1Tq5IYZhuqqqDKjqmMaZyLFkCQ4fCmjUlP5PcbnjySbj9dsvnNcaYGqAPzsppiTQDVfWKyMLA/cqwD6gN5IvIbOBBVf0hRLvXcfZL/RZ4vNS9fEoGuccDbmBDNBOJJdWhf4hndQq8wqkh+w6NOTyoKjkH8vAkuUlJTQrZZuJEuOEGyClVlqVJE5g8GU47LWQ3Y4wxFVRJpzA0EpF5xb5/SVVfKtWmBU49glAVajfj7N1KjmO9gm04OcXzgWycomejgTkico6qflG8saq+ICJnA4+JSBbwgqoG/W2JSDJOrrEAn0QzoVgC3+tj6GuMqUT7MrN5f/wcPnzzW7w5+fj9fo7q0IRLbxlI//N74HK58Pngj3+EcSEy2fv1g3ffhVatqn7uxhhT41VO4LtLVXuX0yYdJ482FG+xNnEJfFX1j6UuvScib+McVfYCUKIMqIg8BiwFTgCeB+4Ska+A7QRyiUXkOWAw0DFw/Ylo5lThwFdVx1e0rzGm8mz/NZPRlzxHdpaXgvxDB/BvWLWd5x6cwtefLGbkn67hyitdfPllcP+bb4bnnoOUcIW7jTHGHK5ygCZh7qUWa1NpVPUXEZkEXCcix6rqqmK378dZxS3KXG7DoYXWomu/DXy/ELhKVbdHM37Uga+IpAAX4ZzfVgfYC/wAfKiqwWVujDFVRlW5/7qXycrMxh+iGps3N58Zn2fz7KQ8du8peVRZcjI8/7wT+BpjjKkk0W1Gi7ctwHEikhIi3aElzqpxvNIcyrI+8LURzoa2Iq8QfrOa4qRLbAG+UdXvKjJwVIGviJyEc2BwsxC314vIRaq6pCITMcbEbtH3a9izIytk0Avwa1Zvft51MX4tme/bsiVMmeKkOBhjjKmx5gJDgL7AnKKLIpIK9ABmV9E8ilIcSqzWquotlT1wxMeZiUhLYDpO0Fu0DL2z6DZO9Y6PRaRuvCdpjInMjGnzyc0J/mHdr26W7RzK0p0jgoLe006D+fMt6DXGmKqSwJLFE3Hit9Glrt+Mk9s74eAcRdqLSFkHFpT9HkUyAgF16evHA8OB5aq6pqLPr6hoVnzvxKnQsSfw50mqmh94UzcBf8fZLXgjToUNY0wV25cZXEHc66vDwu1Xs9fbNuje6NHwj39AUugDH4wxxlSGBKU6qOoSEfkXMEpEpgIfc6hy2yxKFq+YgZNjWyL1QESuDlwHaAwki8iDge83qOqbgT8fA3wiIu/hVFkrOtXhBpxjb0fG+e1FJJrA9wycf1R3BIpXAM7Zb8DzgQD4HzhL6Bb4GpMATVrWx+WSg6kOe3LbsnD71eQVljgLHLfkM/bRfdzzYONETNMYY0zijMbJsR0JnAvswilI9nAE5YrBWeA8vdS1xwJfZwFFge824AtgAE654TRgK86q899UdUXpB4tI+2jeSJFoVo6jCXyPxgl8p4S5Pxkn8D06imcaY+LorOF9+WLqPLw5BWzKOpHluy5EcZdok+bZTf/jpnDX/TclaJbGGHNkq6RzfCMSqII2LvAqq13bMNf7RzjONuDqKKf3S5Tti0ScuhtN4Fsb2B5Y4Q2iqhuc6nGHyskZY6pWh66taNOxDdO+6MWmfcHHOTZMW0nfNpO444EhuFwVrlhujDHGVIYCwp/q4Cp1T3Gq0EUl2uPMIvkZxQqbGpMgGzbA7NU3s2lfcFDbvv6XdGk+g6HXn8JZl9pONmOMSRirYxuSqpZ5gryINABOBH6PczLFSFV9J5oxYqncZoypRmbMgBEjYPfukkGvx5VHn7bTOP88H8NuupVjux2VoBkaY4wxFaeqmcBHwEeBCnBviMgGVf020mdEG/g2EJEQtZ4ibqOqOijKMY0xZVCFJ55wyg/7S/3Sp2NHmDYthc6dL0vM5IwxxpSU2AIWNck9wAjgLuDiSDtFG/gmA/1jaGP/qI2JowMH4MYbYdKk4HsXXghvvAF16gTfO1Jty8lilzeb2skptM6oT2BfgjHGVBnBckLjQVV/FZF9wAnR9Ism8B0f3ZSMMRWxf28Ou3dkkZqWTNNW4YOz1ath6FBYurTkdRF49FG4/36w/WuOb7avZdySmazcu4MklxufFtI0rTa3dzmNC1t3tQDYGGMOMyLiwim6EdXyTsSBr6peH+2kjDGRW730V8Y/9SmLvltNUrIbn89PvQYZjLhtIGeN6FfiFIaPPoIrr4R9+0o+o149ePttOPvsKp58NTZxzQIeW/gp3kIfAHl+5+uGA3t4aN7HLNq9mUd6npXIKRpjjjT2++94aAU8G20nWw8yphqYN2sFd132f8yfvYKCfB85B/LI9xawY8teXhr7IY+PnoDf78fvd1Zzzz8/OOjt1g3mzbOgt7h1+3eXCHpLyy0s4N11i5ixZVUVz8wYY0wsVHWjqt6jqvdE089OdTAmwfbvy+Gvo94kz1sQ8n5ebgE/frWcd1+dx1sf9OXDD4PbXHYZvPIKZNgp2iW8vupHfKV3/JWSW1jACz9/w6AWx1bRrIwxR7pEFrCozmKt3CYiR+HsNQtbzc0CX2MS7LN356Ja9qfgrn0NuOmO9uzPLXnd7YZ//APGjHFye01Jn/66Al8EFTiX7NmC11dAqiepCmZljDniWeAbTqyV2z4FOpW6VoIFvsYk2Ffv/xR2tRdg24FuLNkxgsJS53o3auSc5jBgQGXP8PBVlM9bHpe48Bb6LPA1xpjEKqtyW6T9y/zgt8DXmATz5uaHvK4qrMo8i3V7Bwbd690bpkyB1q0re3aHt+ZpddhfsLPcdh5xUTupzIJBxhgTP7biG1J5ldsi6N+9vDa2uc2YBGt2VMOga/mF6czbelPIoPf662HOHAt6I3H9sf1Id5e9iusRF8PadcdtZ78ZY0yNZ5/0xiTYhdeeQlpG8sHvs/Ja8O2vd7I7t+RmK5erkBdegP/8B1JTq3qWh6fzWnehXko67jISoFPdHkZ2PLEKZ2WMOaKps7kt3q8jkYi0iXZDnAW+xiRYr1OPpdlRDfF43Gze35PvN4/C62tQok2KJ4t3xu/m1lttE1s00jxJTBx4LS3T65HhSS5xL92TRN3kVN7qfzUtMuomaIbGGGNi8B1RboizHF9jEszlcvHYq7cwoO8qluw4Puh+g/T1vPVmAWdffEwCZnf4a55eh8/Ovo2vtv7ChNXz2Ja7n7pJqQw/ugfnHdXFNrQZY6reEbpCGwkRSQIGAV1wqrKVtUhbK9DnsWLXvlTVr8J1sMDXmATbtg0uvTSDJRuCg96zBvzKW/9tQsMm6QmYWc3hcbk4o2VHzmjZMdFTMcaYIzY1oTwi0gb4CmgbZdcHiv3ZF3hGSBb4GpNA330Hl1wCW7aUvJ6aCi++CNde2yoxEzPGGGOq3qNAG2AnMBvYS9nr41cA6cArxa79VNYAFvgakwCq8NJLcPvtUFDqCN/WrWHaNOjZMzFzM8YYU8lsxTecopPpT1TVteU1FpHzgHRVHRnpALa5zZgq5vXCzTfDrbcGB72DBsH8+Rb0GmOMOSI1BXZGEvRWlK34GhNH3tx8Zk1fyE9fr0L9ynG92jL44t7UqpMGwKZNMGwYzJ0b3Peee+CvfwWP/VdpjDE1muX4huUBcit7AGNMHMz5eBFP/nESAN4cpxrbjzOX89o/P+ba359Fg/anc+mlsLNUIbGMDHjtNRg+vKpnbIwxpsopluoQXrR/M1H/TVrga0wc/PDlz4y7ZyJ53pK5C3m5BajCIw8e4Ocdit9f8hDeDh2cfN6uXatytsYYY0y11BrnVIZI9QTc0QxgOb7GxEhVee6hKUFBL0ChP4nFOy5n6bZzg4Le885zUh4s6DXGmCOMVsKrBlDVLaq6I4ou+4EzoxnDAl9jYrTkh7Xk7PcGXc8paMD3m0ex9UDwTrX6l8xm2w1PMv7XWez2ZlfFNI0xxphqQ0RuFJFLK9i3t4j8G9gK/CeavpbqYEyMNq7eTmGhv8S1nTkdWbT9Cnz+koUn3EleGo95j4xeq9lTAC+t+Ja3Vs/jvwOvpX2dRlU5bWOMMQkg2Oa2gJeATcCkSBqLSD3gKuAm4DeBywr8Gs2gNXLFV0RcIjJGRFaIiFdENonIOBHJqIz+InKOiHwrItkikikik0WkXXzflamu3B43Ik4ag6qwZs9A5m+9ISjozUjaRpfz/4+MXqsPXsvzF7I3P5erZr5JfmFhlc7bGGOMSSAv0ChQojgsETldRN4EtgDPAt2APJyA+RycghcRq5GBL/AU8CTwM3A7MBm4A/hQRCJ5zxH3F5GLgelAGnA38E/gNOAbEWkRl3djqrVu/Y5GVfH5U1iw/Rp+yTyb0v9pNc1YRN+jn0N77gvqr0COr4DPN6+omgkbY4xJLMvxBSfGSgdeCazmHiQiTUTkHhFZiVN++AogBacq2x1Ac1W9TFU/VdWo3n2NS3UQkS44wepUVR1W7Po6nJ8ULgPejkf/wE8pz+Es1Z+qqgcC1z8B5gN/AiKuJmIOT63aNaZ+qy7M+OoMsgualrrr59gGn9Cu3kx86UJO+9SQz8j25fPftQs4t3WXyp+wMcaYhJLoYrWa6hHgfeBqYJiITAZmABcD5+HEqIpTvngC8JqqLol10Jq44ns5TgrN06Wuvwzk4OSHxKv/6UAL4JWioBdAVRcCM4ER5S3hm5qhzjEXBwW9Sa5sejd/hXb1Z6IpsPXKdHBJmCdgm9yMMcYcMVT1Y2AgMA/nt+bXAm8AF+EcUfYDMBxoqaq/j0fQCzUz8O0D+IEfi19UVS+wMHA/Xv2L/vxdiOd8D9QBjo104ubw9eTT6Zx04qHjzOqmbWFgxxdoXn8d+a09bBpZi7yjyv4FS+O0WpU9TWOMMYlWGWkOh+kCsqrOUdW+OAuJb+IsMBa9m17AdcAFIhK3DIUal+qAswK7S1XzQtzbDJwkIsmqmh+H/i2KXQ/VFqAlsCzy6ZvDkccD095LolcvOOnEfG65KpOkpCEc3bk5N6+YSn7WzjL7Z3iSubx98LFnxhhjTE2nql8DX4vIKJyU0htxFhfPxUl72Ckib+P8hj2mmKomBr7pOLv9QvEWaxMu8I2mf9G2/VDti7cNIiIjCeT/tm7dOsxw5nDSpAnMmwdNmiQjcqgqxZik/oz5/j28hcEFLgBcCLWTUhjUwn45YIwxRwI7ziw0Vd2Pk1r6cmDP1fU4OcCNgDuBO0VkPvAaMEFVg3eMl6Mmpjrk4Oz8CyW1WJt49C/6Gqp9mWOp6kuq2ltVezdu3LiM6ZjDSdOmIKXSeM9o2ZFRx51CqtuDi5I3U90eGqSkM2HA1SS5oqq6aIwx5nBlqQ7lUtVlqnoXzm/Xh+GcoOXDKVP8PLBVRN4RkcHRPLcmBr5bcM6FCxWMtsRJYwi32htt/y3FrodqC6HTIMwR5tbOJzOh/9UMadWRNHcSHnHRLK0Oo7uczmdn30abWg0SPUVjjDGm2lHVQlV9T1UvwDmz937gF5xFx0uBz6J5Xk1MdZgLDAH6AnOKLopIKtADmB3H/nMDX08Evij1nBOALGBV1O/A1EjdG7bk+ZMuSfQ0jDHGJJilOlSMqm4F/g78XUROwUmFGB7NM2riiu9EnEX/0aWu34yTbzuh6IKItBeRThXtD8zCqRN9k4gc3JIvIt2B/sBkVQ2d2GmMMcYYYypEVb9W1RuB5tH0q3Ervqq6RET+BYwSkanAx0BnnEofsyhZvGIGzrK5VKS/qhaIyJ04wfIcEXkZ5wizMTgHLj9SaW/UGGOMMYcnW/GNG1WN6hD8Ghf4BowG1uOcmnAusAunwtrDquqPZ39VnSwiucCDwBM4JzzMAO5VVcvvNcYYY4ypJmpk4KuqhcC4wKusdm1j6V+s/XSc3YbGGGOMMeGp5fgmUo0MfI0xxhhjqi0LfBOmJm5uM8YYY4wxJoit+BpjjDHGVBHBUh0SyVZ8jTHGGGPMEcFWfI0xxhhjqpLakm+iWOBrjDHGGFOFLNUhcSzwNcYYY4wx1Y6I1AOOBRoBDXBqJezCqZq7UjX6pXMLfI0xxhhjqopix5mVQUQGACOAU3Aq50qYpvtE5Hvgc+BNVd0ZyfNtc5sxxhhjjEkYEaklIveKyGqc6rcjcYJecH5MyAO2A3sAf+BaHWAITtXcX0VkioicXN5YtuJrjDHGGFOFxJ/oGVQPIpIE3AI8CDTBCWjXAh8Dc4F5wDpV9Zbq1wD4DdALOBk4E7gIGCoi04H7VHVZqDEt8DXGGGOMMYlwD/AosB94GnhHVeeW10lVM4GZgdc4EakFXIizUnxu4OUO1dcCX2OMMcaYqmQ5vkX2AX8GnlHVfRV9iKoeACYAE0TkVODhcG0t8DXGGGOMqUJ2nJlDVZ+vhGfOAc4Id982txljjDHGmCOCrfgaY4wxxlQVxSq3JZAFvsYYY4wxJuFEJOSGtPKoamGkbS3wNcYYY4ypQpbjG1ZBBftFnLprga8xxhhjTFWywDccP+ErtcWFBb7GGGOMMaY6aF3O/eZAN+AuoCVwM/BtNANY4GuMMcYYU0UES3UIR1W3lNNkCzBfRMYD7wKvAv0C1yNix5kZY4wxxpjDhqoqMBrIAB6Kpq+t+BpjjDHGVBVVO84sDlR1k4jsA/pH088CX2OMMcYYc1gRkdpAHSA5mn4W+BpjjDHGVCHL8Y2NiAjwBE7K9Mpo+lrga4wxxhhTlSzwDUlEHiuniRtoAgwA2gWujYtmDAt8jTHGGGNMdXA/kZ/jux94UFXfjmYAC3yNMcYYY6qQpTqE9QplB77NgS5AG+BZ4PloB7DA1xhjjDHGJJyq3hJJOxEZAbwIdAQujWYMC3yNMcYYY6qKAn5b8o2Fqk509rfxjojcqqovRtrXClgYY4wxxlQlrYTXkWcKkIdTtjhiFvgaY4wxxpjDiqr6gALg2Gj6WaqDMcYYY0wVss1tsRORU3BKFu+Jpp8FvsYYY4wxJuFExF1OEzfQFBgE/A3nBIhPoxnDAl9jjDHGmKqktuQbRkEUbRVYB9wTzQCW42uMMT5bPjIAAB5CSURBVMYYc4QQEZeIjBGRFSLiFZFNIjJORDIi7H+fiEwWkbUioiKyvpz2/UTkCxHZLyJZIvI/EekRprmf8rfx+YFVOCu+PVV1cyTzLmIrvsYYY4wxVSjBOb5PAXcA03DK/XYOfH+8iAxWVX85/ccCmcBPQL2yGorICcBMYDPwcODyKGCOiJykqktKdWldztgFwD5VzS+nXVgW+BpjjDHGVJUEHj8mIl2A24Gpqjqs2PV1OJXQLgPKKwHcXlXXBvotBWqV0fZZIB84rWhlVkQmActxgu4hxRur6pao3lAF1MhUBxG5RkQWiEiuiGwXkVdEpHFlPENEzhCRF0VkbuBXBioi/eP2Zowxxhhj4uNynA1hT5e6/jKQA1xV3gOKgt7yiEgHoA8wuXg6QuDPk4HBItIswnnHTY0LfEVkDDAe2AfcCfwb5yeYmVHkr0TzjCuBG3B2Gi6Px3swxhhjTM0kgKjG/RWhPjg5sj8Wv6iqXmBh4H68FD3ruxD3vsf5q+gVrrM4eovISBF5IPAaGbgmFZ1UjUp1EJFGwF+AucAgVS0MXJ8LfIATxI6N8zMeAG5R1TwRuQsIl7BtjDHGGFNZGonIvGLfv6SqL5Vq0wLYpap5IfpvBk4SkeRYcmhLjVX03FBjAbQM1VFErgAeA9qFefY6EXlEVd+KdlI1KvAFLgLSgeeKAlYAVf1QRNbiLOGXGfhG+4xodxMaY4wx5ghX3vaxitmlqr3LaZOOU+Y3FG+xNvEIfNMDX0ON5y3V5iARGQv8kUPZ0FnAhsBzWgLNgbbAGyLSVVX/GM2kalqqQ3nL6p1EpKwk7Hg9wxhjjDEmpASmOuQAKWHupRZrEw9Fzwk1XsixRGQgcC9OwDsF6KWq9VW1h6r2U9VWQCfgzUCbe0RkUDSTqmmBb3nL6lKsTWU+wxhjjDGmutmCkxIRKhhtibNqHI/V3qKxip4baiwIjrVux4mznlXVS1V1YemOqvqLql6Hs0FPcY5Hi1i1THUQkXrA6Ci6PKuqmVRwWb2UeDyjXCIyEhgJ0Lp1ecfWGWOMMaZGSOBxZjj7l4YAfYE5RRdFJBVnj9LsOI8FcCLwSql7J+D8Lcwvdf1EnESQRyN4/l9w9l2dEM2kqmXgi3Mg8iNRtH8L5zDl4svquaXaRLqEH49nlCuQcP4SQO/eva12oTHGGGMq20TgfpzFxTnFrt+Ms6g3oeiCiLQHklR1RUUG+v/27j1MrqrM9/j3RwgEiASQBElUwoBIZLzggMwAAwEEFGGGo6DoAEdlYA4IAoPOoAdHmRE9j8cAgvgcg6MQQQ4Xg8AZbjIa5NEwglwGGYLDpRETgYRAmBDCLe/5Y60ylaaquqp6197dVb/P8+ynuvdea+29dld3v7363WtFxEP5YbvDJX2hNkevpOnA4cBPIuKJYdU2Jy1Q8Uwb7T8jaQWwRSfXNSYD34gYIg11d6p+WP2hYcdmkP66GGly5CLaMDMzM2sgoP2c3GLPHHGfpAuAEyXNB65n7cptt7Lu4hX/CmzDsHhM0lF5P8BUYANJZ+TPH4uI79cVPxn4KWmltvPzvpNIqbanNbjE5cBUSa+PiKdb9UXSFsAUYGmrcsP1W45v/bD6cLsBD0bEyhLaMDMzMxuLTgE+A+wEXEBap+B84OA2lisGOIY01dg/AdNI/6WvfX5MfcGI+AUwGxgipSb8E2lQca+IuLdB278gxabtpDqcmcv+oo2yf9Bvge81pPSEEyVNqO2UdAiwHXVD+Hn/myXtKGlit22YmZmZdUJR/NauiHg1IuZExFsjYsOImBERfzt8UC8iZkbEa/77HhGzI0JNttkNyi+MiP0iYnJEvC4iDoyIu5pc3jdI/1k/XtIVkma95t5JsyRdDnwq7zqv/d6P0VSHbkXEUklfAL4O3CLpMlJ6wmnAIl67RN88YG/SBMlD3bQh6R3AX+RP98ivR0naM398fkSsKKyTZmZmNr5VlOow1kXEzyR9GTgDOAw4TNKTwO9ykTcCW9WKA1+OiFs7OUdfBb4AETFH0tPAqaS/Ap4DrgBObzdFocM23k0auq/3ybqPLyEtfWxmZmZmLUTEFyXdB5wFbE9Kp5hWXwR4GDgjIq7otP2+C3wBIuIi4KI2ys0uoI22ypmZmZkRoN6s3NY3IuIq4CpJ7yYNME7Nh5YCd7VIlRhRXwa+ZmZmZja+5QC36yC3EQe+ZmZmZmVyjm9l+m1WBzMzMzOzhjzia2ZmZlYmD/g2JOnxopqKiDc2OuDA18zMzKxEcqpDM1vT3cq9wzVtw4GvmZmZmY0Fe/X6BA58zczMzMrkEd+G8hLHPeXA18zMzMwqIWm3/OEvI3r/F4FndTAzMzMrSwBrerCNXwcCC4EnJF0k6UOSJvfqZA58zczMzEoiAkXx2zi2MG9bAEcBVwLLJN0i6WRJf1TkyRz4mpmZmVklIuLHEbEHaUaHT5AC39XAPsA5wEOSHpD0dUl7SZowmvM5x9fMzMysTON7hLYnImIZMA+YJ2l90gwPHwAOAXbI298Cz0i6GbgOuCEinunkPB7xNTMzM7MxIyJeiYifRMRpEbEDMAv4LPBTYDLwYeAS4ClJt0n6O0lva6dtB75mZmZmZYoofutjEfGbiDg7IvYDpgEfIwW+zwK7A/8L+LWkhyWd36otB75mZmZmNi5ExIqIuDwijiYFwXuRAt/7gZnAp1rVd46vmZmZWVlq05nZqOV5f3+et89L2gY4uFUdB75mZmZmJRrn04+NWRHxGHBBqzIOfM3MzMyscqOdqqwmIl5tdsyBr5mZmVmZPOLbzEuACmin6TNsDnzNzMzMbCwZzV8GLQNnB75mZmZmpen/6ccKcDywshcNO/A1MzMzK0vgwHdkP4yIp3vRsOfxNTMzM7OB4BFfMzMzszJ5Ht/KeMTXzMzMzAaCR3zNzMzMSuQFLKrjEV8zMzMzGwge8TUzMzMrk0d8K+PA18zMzKwsAaxx4NvEQ6QFKJouOTxaDnzNzMzMrHIR8dZen8OBr5mZmVlpvHJbjaT1IqLwyd1ateuH28zMzMysCn8v6SJJhYz0Slpf0ieBRc3KOPA1MzMzK1NE8dv49AJwFHC/pGskHSFpo04bkTRL0j+ScoS/A2zVrKxTHczMzMzKNH4D1UJFxLmSHgS+AhwMHAI8L+lW4I68DQHP5G0SsDmwJfBOYBdgD2Cn3ORq4FzgrGbndOBrZmZmZpWIiBuAGyR9EDgW2B94P3BQO9Xz6xPAxcC3IuLxVhUc+JqZmZmVxdOZNRQR84H5kt4EHA78ObAnsEWD4muA+4DbgJuBG9p9SM6Br5mZmZmNCXnE9uy8IWlLUmrD60k5wcuApyJidTftO/A1MzMzK01A8TN49a2IWEYKdgvRl7M6SDpa0t2SXpD0pKTvSJpadBuSJkk6Nj+JOJTLPiLpMkmziu2VmZmZmY1G3wW+kk4lJTivAE4Gvg0cASyQtEnBbcwE5pLyT/4ZOBG4DDgQuEfSPgV0yczMzPqJpzOrTF+lOuQ8kC+Tpr/YLyJezfvvAK4lBbFfKbCNpcDOEXHPsDYuBe4G/jdpqg0zMzMzP9xWsX4b8T0U2Bg4vxawAkTEdcAjwJFFthERTw8PevP+/wB+Dfxxl/0wMzMzs4L1W+C7a35d2ODY7cCOkib3ug1J6wFbA0+OcC4zMzMbNE51qEy/Bb7T8+viBscWA6or08s2jicFvhc3KyDpOEl3Srpz6dKlIzRnZmZmZqM1JnN8JW0GnNJBlfMiYjkpRQHgxQZlavO9bdzgWL1RtSFpd2AO8O+0yCeOiLmkB+PYZZdd/KeamZnZoPAIbWXGZOALbAZ8sYPylwDLgVX58w1JkxzXm5RfV9Fa121I+hPgX4AlwEHdTq5sZmZm/cqpCVUak4FvRAyRUgo6tSS/zgAeGnZsBulZyiW01lUbkt4N/Jg0Bdo+EdEoVcLMzMzMKtJvOb535Nc/a3BsN+DBiFhZdBuSdiYFvf9FCnofa/+SzczMbGAEsGZN8Zu1pd8C32tI6QknSppQ2ynpEGA74NL6wpLeLGlHSRNH0cbOwC3A86Sg99Fiu2RmZmZmRRiTqQ7dioilkr4AfB24RdJlpPSE04BFwLnDqswD9ga2BYY6bUPSNqSR3s2B84Dd88Nt9a6OiOeL7KeZmZmNY87xrUxfBb4AETFH0tPAqaRg9DngCuD0NtIcOm1jW+D1+eMvNWluW9JosJmZmZlVqO8CX4CIuAi4qI1ys0fTRkQsoLuH8MzMzGxQecS3Mn0Z+JqZmZmNTQFrHPhWpd8ebjMzMzMza8gjvmZmZmZlCYjw9GNV8YivmZmZmQ0Ej/iamZmZlck5vpVx4GtmZmZWJs/qUBmnOpiZmZnZQPCIr5mZmVlZImCNH26rikd8zczMzGwgeMTXzMzMrEzO8a2MR3zNzMzMbCB4xNfMzMysROEc38o48DUzMzMrTTjVoUJOdTAzMzOzgeARXzMzM7OyBF65rUIe8TUzMzOzgeARXzMzM7MyhR9uq4oDXzMzM7OSBBBOdaiMUx3MzMzMbCA48DUzMzMrS0RKdSh6a5Ok9SSdKmmRpNWSHpc0R9ImRdeXtEBSNNl26eCuFcapDmZmZmaD4xzg08DVwBxgVv58Z0nvjRgxiu60/jLg1AbtPNJ9F7rnwNfMzMysRFXl+EraCTgJmB8RH6rb/yhwHnAE8IOC6z8fEZcU1olRcqqDmZmZ2WD4KCDg3GH7LwRWAUf2on5Oj9hUkjq+4oI58DUzMzMrU3U5vrsCa4BfrnM5EauBe/LxouvPAFYCK4CVkuZL2rHdCy6awutFV07SUuCxqq9jlLYk5fEMqkHvP/geuP+D3X/wPRiP/d8mIqaWeUJJN5LuVdEmAavrPp8bEXOHnfs+YFpEbNXguq4ADgc2jIiXGp2g0/qSvgcsAf4deBXYDTgReAnYMyLu67iXo+Qc3zGg7G+6XpB0Z0RU8oTmWDDo/QffA/d/sPsPvgeD3v92RcT7Kjz9xsCLTY6trivTMPDttH5EfGJYmaskXQssAM4G9h/5kovlVAczMzOzwbAK2LDJsUl1ZXpVn4i4DfgZsI+kjVqV7QUHvmZmZmaDYQmwpaRGwesMYFmzNIeC6tcMAROAzdsoWygHvlaUuSMX6WuD3n/wPXD/bdDvwaD3fzy4gxT7vad+p6RJwLuAO3tcv+YtwCvA8jbLF8YPt5mZmZkNAElvB+4Frh42D+9JpHl4j6rNuStpO2BiRCzqsv4UYGVEvDrsGj4A/D/ghog4qDc9bc6Br5mZmdmAkHQ+aWaFq4HrWbvy2s+BfWsrr0kaIs16oS7rH0p6gO060iptr5BGio8kjfTuERG/6WVfG3Hga2ZmZjYgJE0ATgGOA2aSpqC7HPiHiFhZV26IxoFvu/VnAf8IvBvYCpgI/A64EfhKRCzuSQdH4MDXzMzMzAaCH26zliQdLeluSS9IelLSdyR1NO9wu21I2l/S/5F0h6TVkkLS7MI60/z61pN0qqRF+byPS5ojaZNe1Jd0kKRfSHpe0nJJV0rattheta/M/kv6sKTvSbpX0sv5azyz6D51oqz+S9pc0smSbs5lXpD0oKS5kt7Um961p8R7MDF/j/9K0jJJL0p6VNLlknbuTe+Ku/5e1Jd0Rf4++PXoe9K9kn8OLMh9brR5HmDrrYjw5q3hBpwKBGmi6eNI/7JYCdwPbFJ0G8BFpEmv7wLuzvVml9DPb+RzzQeOJeUkvQz8BFivyPrAB0nLPd4NnAB8DniSNEXM9Iq+zmX2fwHwAnA7sCjXm1nx+7yU/gPvI+W43QT8PXAMcA5pzstngbcNwD3YhPTU93mkf5UeA5wJ/DZ/7+/bz/1vUO9g0mpWq4BfD8L3QS67AFhKyvUcvm1R5X3w1v9b5RfgbWxupOUUnyetxz2hbv8h+Yfb54tugzQH4Ib5489QQuAL7EQKRH84bP9J+fwfK6o+Kb9pMWl56sl1+9+Vf/nNreDrXFr/8/43A+vnj79JxYFvyV//mcB2Ddp4by57Vb/fgxZtbJ2DpOsHpf/AZFLAfx5pTtPKAt8Kfg4sAIaq6q+3wd6c6mDNHEpadvD8qJuKJCJqT2ceWXQbEbE4IpothdgrHwUEnDts/4WkUZiR+tlJ/b2B6cB3ou4BgIi4h/SL4COSJnZ4/aNVZv+JiN9GxCtdX23xSut/RAxFxMPDG4iIW0hPOP9xR1denFLfA008RVrutPTJ7Kmu/2cB6wNndHKxPVLJPcjpEZtKUqPjZr3gwNea2TW/Lmxw7HZgR0mTS2ij13YljVT8sn5nRKwG7mFtH4qoP9L92BTYod0LL0iZ/R+LKu+/0lyXryOlvFSh9HsgaYKkLSW9QdKuwA9II6DXd9uJUaii/+8hTQd1SkQ81/WVF6eK74MZpLS3FcBKSfMl7djV1Zt1wIGvNTM9vzaabmQx6a/76Q2OFd1Gr00nLbHYaKR5MWlpxg0Kqj/S/YD0y6BMZfZ/LBoL/T+DlAZzcTsX3ANV3INZpBzP35OCpQOBr+atbKX2X9L6pJHQmyPiilFcd5HKfg88CnwN+ARwOPAt4P3AvyktkGDWM+tXfQHWW5I2Iz1E0q7zImI5KUUBoNEPstX5deMGx+oV0UavbUzj64N1r7HZ2uOd1B+L96PM/o9FlfZf0mHAaaQH3r430sX2SBX34FFgf2ADYHvSv8KnABuSHgAsU9n9/yxpudb/1tll9lSp9yAiPjGszFWSriWlfJ1Nem+Y9YQD3/63GfDFDspfQso3XJU/35D0FH69Sfl1Fa0V0UavrQKmNTnWzjV2Ur/+fnRzrl4os/9jUWX9l3QQcCnwK+DDEVHVpOql34OIeB64pfa5pO+SZnOZTxr9LVNp/Ze0PfAPwJcj4pEOr7OXKv85EBG3SfoZsI+kjSJi+O8Ms0I41aHP5Qdq1MH2UK66JL82+tf7DNKTuksaHKtXRBu9toT0b7hGwegM0r/vWo1WdlJ/pPsBjdMgeqnM/o9FlfRf0vtIQd79wAEV53lW/h7ID3vOBw6QtF2b112UMvs/hzSwcLWk7WsbaRBqg/z51t13pWuVvweyIWAC1TzkaAPCga81c0d+/bMGx3YDHqyfmaCHbfTaHaTvg/fU75Q0iTTN2J0F1m91P/4UeA4oe93yMvs/FpXef0kHkta4XwS8NyKe6fbiCzJW3gMb5dct2ixflDL7vw0pH/Z+4D/rthmk9If/JOX/lm2svAfeQkp1Wd5mebOOOfC1Zq4hpSecqLQuNwCSDgG2I/2Llrr9b5a047DpuDpqoyKXk0aeh+dBH0vKSfvDNUrarsFTx23XB24lPczz1/WzWUh6JzAbuDIiXu66J90ps/9jUan9l3QA8CPSHzj75Xz6qpV2DyRNlfSa3zuS3kB6yKm2uE2ZynwPfIbUz+HbUuDx/HEVD/iV+R6YUv/7oG7/B4A9gB/n2SDMeqPqiYS9jd2N9NBNAD8lrbp2JukX0wPULcCQyy6gwWIEHbbxDtIT7mcAN+R6/1y3b0qP+nk+a1cc+mvSvyNfzn2qX3VqKH3LdFc/lz2cdVduO500jdUTwIyKvs5l9n+vuq/n7bne12v7+rn/wC6kPwRXkwKE16xaVUX/S74Hp+Q2zgE+DfwP0sNMT+fvi0/2c/9bnH+I6lduK+s9cChpHvdvACcDnyLNaPIq6Q+AHaq8D976f6v8AryN7Q34OHBv/mX9FPBdYFqDcgtosgpXB218PLfRbHtN2wX1cQIpQH+Q9GTy4vzLeHhg3uwHflv168ofTAr6VgHPAFfRYEWvEr/GpfUf+FKrr3E/97+N93cl/S/5HvwJafTvIdIfwC+RRjovB3bv9/63OP8Q1Qe+Zb0HZgFXAg/n98CL+eMLqOiPf2+DtSmiqgeJzczMzMzK4xxfMzMzMxsIDnzNzMzMbCA48DUzMzOzgeDA18zMzMwGggNfMzMzMxsIDnzNzMzMbCA48DUzMzOzgeDA18zM1iHpS5JC0kVVX4uZWZEc+JpZz0i6KAdQIellSdNGKH9oXfmQ9PGSLtXMzAaAA18zK8v6wMdGKHN0GRdiZmaDyYGvmZXht/m1aWAraQvgA8BKYHkZF2VmZoPFga+ZlWEh8DCws6SdmpQ5AtgA+CHwQlkXZmZmg8OBr5mV5fv5tdmob23/vJEaknSIpGskPSHpJUlPSbpO0oEt6uwm6auSbpe0uK7ejZIOG+F8fynpeklP5lzl5ZIelHSZpI8MKzs75ycPtWjv47nMggbHavnNMyXNknSxpMfzeX9UxL3I9d6ar/8pSS9IWiTpi5I2bFXPzGw8c+BrZmWpBb5/JWmdnz2SdgB2Ax4HFjRrQNJESZcA1wJ/AWxFGh2eChwM3Cjpaw3qTQZuB07P55kKrM6vBwJXSvp2k3OeBfwIeD8wLZ9vI2AH0ij1N0buelf+HLiT9AfBFOCVYdfV1b3IdfcC7srXPxV4CdgW+BLwU9LIu5lZ33Hga2aliIhHgJ8DM4B9hx2ujfZeGhFrWjTzNeCvgCHSg3Kvi4gpwOuAvwGeAz4r6aPD6q0Brgc+ms8/KSI2BTYHTiLlFR8n6fD6SpJmkoJlgK8CUyNi04jYiBRoHgb8y0h979K3gDuAt+dr3Rg4re54V/dC0ubAlbm9u4B35XqTgf8OvBM4oUd9MjOr1PpVX4CZDZR5wB6kQPcWAEkCjqw73pCktwCfBp4F9suBNAARsRKYK+lZ4HLgfwKX1R1fRXpwbh0R8SzwTUkr8rlPIAWFNe8hDRAsiojPD6v7FCkf+YftdLwLTwHvj4gX8vmClCc9qnsBnEgauX4aODAiluV6LwPzJK1h7ei8mVlf8YivmZXpClKKwQclbZL37Q1sA9wZEQ+0qHs06WfWj+oDvWHmAy8CO0nauoPrui6//qmkCXX7n8uvUyRt3EF7RfhmLehtYDT3opbPfGEt6B3mUuCxbi7YzGysc+BrZqXJI6zXAZsAH8q7232obff8elh+kOs1G/A7YGIu96b6ypLWl3RMfpjt95JerD1IBjyTi00ipT/U/BtparWtgYWSjpO0bYfd7tbCFse6uheSNgBqs2rc2qjhPLL8s9FfvpnZ2ONUBzMr2zzgcOAoSVeSAuCXWfff8Y3URi0n520kfxihzQ+33cTagBHSg2BLSfm/kHJ2IQXltX//PyPpKNIo6DuAb+f2ngBuBr4bEQ0DyAIsbXGs23uxBVAb0V7SovziNto0Mxt3POJrZmW7kZS/ui8p33RT4IYm/3avV/t5dXJEqI1tQV3dL5CC3mWkB7i2ioiNI2JaRLyB9MBbjepPGhHXAzOB40ipGkuAN5BGqhdImtvxHWjPqy2OjeZetEMjFzEzG38c+JpZqSLiFeD/kn7+nJV3t/Mw1ZP59W1dnLY2W8NJETEvP5hWb6vhFepFxIqIuDAiPhIRM0jpAhfmw8dKqn9wrjbt2KQWTU5p98Kb6PZeLGdtQD29RblO8qPNzMYNB75mVoVaPu9EUn7tdS3K1tRyXg+RNLFlydd6Y369u8nx93bSWET8R0QcR5obGNIDejXP5tdpOae2kV07OV8DXd2LiHgJuD9/ulejMnmWjYbHzMzGOwe+Zla6iPgVabGEOcApEfFiG9UuJuXjTgc+16pgnqu23or8+vYGZSeTpvxq1M5ICznUZl2oX+3sN6TZFAQc0qDN7Vn7YF+3RnMvatO1HStpiwZVjiCldpiZ9R0HvmZWiYg4MyI+ExEjLlGcyz8AnJs/PVPSBZL+qHZc0mRJ+0v6PuvOxQvw4/x6tqS986gmknYF/hXYsslpj5d0k6SP1U8JJmkzSZ8HZuddN9Vd50vANfnTcyTtKWm9vB2Qr6XZNGVtGeW9uICUY70lcJOkd+Q6EyUdSUrhWIGZWR/yrA5mNp78HWm54ONJi02cIOm/SHmrU1j7UNaCYfXOAPYnTeu1AFgt6VXSDA4vAIdSF7zWEXBA3pD0PGkGis3qyszND8DV+xywXz7fbcAq0kDDJOAe4BxGv9RxV/ciz1TxYeAGYBfg3ryAxyTSyPVC0lRnp2Nm1mc84mtm40ZEvBoRJwB7ApeQFlrYgBQA/ha4mjRrw6HD6j1CWoXtEtJo5wRSLu6lwK4RcXOTU/4AOJa0AtoDpKB3MvB74FrgLyPibxpc5yPAbqQp2pbm8/2O9DDfHqxdGKNr3d6LXPdWYOfcr6WkgHeIlH6yLylVw8ys7yjNVW5mZmZm1t884mtmZmZmA8GBr5mZmZkNBAe+ZmZmZjYQHPiamZmZ2UBw4GtmZmZmA8GBr5mZmZkNBAe+ZmZmZjYQHPiamZmZ2UBw4GtmZmZmA8GBr5mZmZkNhP8PueNtRocKrsUAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 743.226x572.185 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 743.226x572.185 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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p6RKZWQW1g0kXkf/rJVcNfd8WUnzAbGN9gckqaixI/HK2j5n1ypD8aGoHET+fIV0dZtaV2i8djdKdwcyMsFAJhP9PGyvIFslKAa9gZhVmNpDwYZV4TfzB3b/Ocupoan9y/ye1XSEKMaXTf4Bvottnm9mJ6RKaWQszS/5pcBi1g1ZuSNXCaGbtCCtrJa55SMOqXHBbEpZGTuUqwsT0AA+6ez5dGiCsBpc452IzOy5TYjNb3czOSXP4ZmCz6PYf3P19AHcfQ1joAcKHYLYvUJlcQwimAW40s4xTNJnZumZ2Unxf1LKdmGFkLcJczanOPYWwullGZjY8NjvECdnSp+Lu31Dbx3kbQmt42sGMZrZe0q6nCfPrApxkZgeT2o2EOX8B7i3WFzt3f53an84PMrN0r18s6J5i/+DY4zq4AXX5itr/Q/rVN58iSbz2WgL3mVmb5ARmtilhTlsIs17k03cdQveyMsIXnBfqWc98bUrtwLrGKlMkJ+rDu2pYzcx6x+63JPyntB6h5eNQaqd9qgH+6u43ZsvU3d3Mbif8lJ0IOBcTgsgGcfelZvZ7wodna+BuMzucEAh/SWgVW48wL+zvCR/oQ2Pnf21mfyasSNYNmGBmNxDmaF1M+JnwYuAX0Smjorlkm5JxwJVmtjVhDtXvCX2aT6L2p8oZwB/zzdjd50SP5wuE18O9ZnYsIfj6jPAh2ZkQVO8J7E8IkG+J52NmR0T1gfCz6U1JRf0lOn9X4EIze9nd8/4gjJ7PUwivrTbAc2b2JGHVta8JX27WIHxJ2Ccq8x1q555NuIbQ4r8RcEr0RehWQmDUFTiSsJzuOLJ3eyiUcwkB2S8Irdc7mdlthBbp+VG9+hB+RfmO2uced6+Ogu3RhP/PR5rZnYTHZSZhDtgzqf0VYzJwSZGv5zjCCoZrAFdFvxzdTehisJjwf0U/wvPwPDCwiHV5jPALRh8z69TAcQWFdC/h/639CEsJvx/9//QBYUaJPQnzWCemNPtjvtMzUvs6eSmaQSVX+2VpdU4Y5+6fJu37Vez243mUKVJ8pV75QltxNlZcaSvX7Q2yLMGZopzVqV3y1YERBb6OHahdjjfTNjDN+VdQuzJcuu0R0q8sNTiWrleGep4QS9c/Q7r+sXQnZClvG2qX5k21TQe2zuH5H5yhPjsRBvfk8vr4JOnc9aldUvg7klbviqXrQe1AwulAtwa8Hg6mdrW0bNszGR6brzKc90VU58T94WnyGZ7puczzuroBY3K4pifTnH8AYUR8pnM/BnrW53WZ7rozpNmIEOBmu56hWd4DaV+7OT6um8fyOq3Y79983n+EOWqfzPL4LCXFMsfZngvCdHyJJbZPyuFxGp6lHqm2Ov/nUrtU+5sNed60aSvGpi4Nq6YFhMDjE0Lfy4uBzdx9N8+z762Hn0afju1q6GC15PzHARsDZxFaI6cTPgRmET5Q/0Noof53mvP/Qphk/1bgc8KqRYsILV0PAvu4++88zcpSJTaH0PpzAeGDZA7hy8VnhJbrzbyBE8m7+9uE4OQEQr/eyYTXx1JCf9dxhO4hBxMCcACi/rgPEVqgaoBjPE23Cg/z8Cb63XYjtCbXa0o1d/8vIZg4F3iW0Oq9mPCT7w/Am8ANwF7UzhiSnMckwmviT4TBW/OBudHtK4A+3vC5g/Pi7j+4++6Ex/khwvOwkPB6/RJ4DjiDNP2X3f1ZQpeFqwivldmE5/AHwvvmFGBbz7+VsF48dCfYGjiGENR9R3ieqgiv3yeAY4E/F7kenwIvR3ePLWZZ+XL3+e5+COHLysOEL/aLCc/5Z4RfU3q7+3Xpc0lrX8IvITWsuBBL0URdMBIrGyb/0iNScubupa6DrOTM7AtCUPqFu2+aLb2kF/VZvDK6u75nXnZUiihatQ1Cn9cTSlkXqT8z2xN4hdDy2Nvr/gzf7JjZvYSuJW+6+26NVOaNhHm4vyJ8GdeAtSYkGgB9PmFway/CL2WPAFd45mXWMbNNCI0B2xGmT2xJ+IL2LPBPd5+W5pzrCMveVxC6aF3p7q8W6JLyphZeaRAz608IdqHArbsiIg0VfcC+SphucXBpa1N80aDHA6O7jTU7w1qEXyAA/qxgt0kaQhjr8inhF7JHgfOAp9LNBhTTndD3/gngUkK/+5eA04B3zWyF1TCjsRFjCV3m/kH4Fbkd8IKZ7V2oC8qXBq1JQ10a/V0A3FPKioiIpDGI0ML0WzPbyt2b0nzbhdaF2sGljTUQ91JCF4q3CK2G0oSY2RaEIHekux8W2z+R0P3kCEL3wJTc/RXCryTJ+Y4hPN8nEALbhGsJA+O3d/cPorT3EbpRDjOzTb0E3QvUwit5iaan+oWZ9TGzWwgj4gFucffZpaybiEgqUYB7PGHWkFxWN1tpufuP7j442r4vdnlRf/xphP7jp5YikJGsjiT8wjE0af8dhMaqY+qZb2JMwPLV9cxsNcJYhNcSwS6Au88D7iT8ItyXElALr+TrPGr7mCZ8CVxdgrqIiOTEG7a8taQRBbh/L3U9JKO+hAGM4+I73X2RmX1AjgFotPJfO8JUoZtTO0/0s7FkWxGm1ns7RRaJxWD6JtelMSjgLZA11ljDe/XqVepqFN3aa6/NtGmhf3pFRQUdO3Zk7bXX3rhly5Zz+/Tpk+VsySb++Pbu3XuiHtPS69Kly/F9+vQ5vtT1EJHCePfdd39y966NXe6+e6zmM2cVvnvzux8t/oQw+1DC7e4eH1OzDvCTuy+mru+Bnc2swt2XZCnqFMJCQwmTCDP0vBHbt04s31RlQZhPvtEp4C2QXr16MWHChOwJRUREpGTMrFGm50s2c1Y1417oUfB8y9f+apG7Z2odaUvtSpXJFsXSZAt4nyRM79mOsHjTwYSFcZLLIk15i5LSNCoFvCIiIiLN1wJgzTTHWsfSZOTu3xHm1AZ40sweB8abWRt3vzYpn1YNKasYNGhNREREpMgcqCnCvxxMBdYws1RB6LqE7g7ZWnfrXk8YDPo+YWGoeFmJfFOVBam7OxSdAl4RERGRonOqvabgWw7GE+K9HeI7o0Fo2wAN6Y/ZBlg9dv9jQneGnVKk7Rf9LUn/TwW8IiIiIs3Xw4QG5oFJ+08l9KddPoOJmW0YLRNNbN9aqTI1sz2A3tTOvpCYfuwpoL+ZbR1L244w6O0rSjBDA6gPr4iIiEjRhS4NjT9Nsbt/bGbDgHPMbCRhGrHNCNOMvs6Ki068AvQkzNub8C8zW5uwYuFkQl/c7QkLVswFLkwq8lJgL+BFMxsC/EwIrtcFDizVXM0KeEVERESat4GEacROIyw9/RNhirEr3LP2i3iQsHDLsYRZGZwQ+N4G/NPdv40ndvevzWwXwvzMlwAVhJUO93P3lwt1QflSwCsiIiLSCHIcZFZw7l4N3BBtmdL1SrHvEfJcMtrdPwN+nc85xaY+vCIiIiLSrKmFV0RERKTIHKe6NN1XBQW8IiIiIo2iFIPWJFCXBhERERFp1tTCKyIiIlJkDlSrhbdk1MIrIiIiIs1akwx4zazMzAaZ2edmtsjMppjZDWa2Wo7nX2pmj5rZN2bmZjYpj7L/EZ0zr94XICIiIpKkBi/4Jrlpql0ahhBWAHmCMGdcYkWQbc1s7xwmSb4GmEWY6LhTroWa2TbAIGAeK64yIiIiIlJvDpqloYSaXMBrZlsA5wIj3f2w2P6JwE2Epez+k+b0hA3d/ZvovEqgXQ7llgN3AM8BHYA+9boAEREREWlSmmKXhiMJratDk/bfASwAjsmWQSLYzdN5wOaEYFtERESkoGqKsElummLA25fwHI6L73T3RcAH0fGCMrOewNXAVe4+udD5i4iIiEjpNLkuDcA6wE/uvjjFse+Bnc2swt2XFLDMfwETgRvzOcnMTgNOA+jRo0cBqyMiIiLNieOalqyEmmILb1sgVbALsCiWpiDM7EhgP+B0d1+Wz7nufru793H3Pl27di1UlURERESkgJpiC+8CYM00x1rH0jSYma1O6Ct8l7uPLUSeIiIiInU4VKuBt2SaYsA7FdjczFql6NawLqG7Q6G6M1wJrAbcYWa/iO1vA1i0b7G7TylQeSIiIrIKcjTIrJSaYsA7HtgH2AF4I7HTzFoD2wBjClhWT0LA+06a418BnwC9C1imiIiIiDSiphjwPgxcBgwkFvACpxL67o5I7DCzDYGW7v55Pcu6Dnggxf6rgA2AY4GqeuYtIiIiEjGqtaZVyTS5gNfdPzazYcA5ZjYSeJbaldZeZ8VFJ14htNKu8Aoys2Oj/QBdgQozuzy6P9nd74/KejtVHczsHKCnuz9WmKsSERERkVJpcgFvZCAwiTDl14HAT8DNwBU5LCsMcDLwy6R9V0d/XwfuL0w1RURERLJzoEaD1kqmSQa87l4N3BBtmdL1SrO/fwPLb9D5IiIiIsnUpaF0muI8vCIiIiIiBdMkW3hFREREmhNHLbylpBZeEREREWnW1MIrIiIi0ghqXC28paIWXhERERFp1tTCKyIiIlJk6sNbWgp4RURERIrMMar1w3rJ6JEXERERkWZNLbwiIiIijUCD1kpHLbwiIiIi0qyphVdERESkyDRorbQU8IqIiIgUnVHt+mG9VPTIi4iIiEizphZeERERkSJzoEbtjCWjR15EREREmjW18IqIiIg0Ag1aS83M7ilEPu5+YrpjCnhFREREpJSOg4J8G1DAKyIiIlIq7pqlIYPXyB7w/hJYBryV4tjOQEWmkxXwioiIiDSCGnVpSMnd98qWxsyqgVnuvmeKY1OBtTKdr68aIiIiItKsqYVXREREpMjCSmtqZywVPfIiIiIi0qyphVdERESk6DRorZQU8IqIiIgUmVZaKy0FvCIiIiLS1D0JzElzzLOdrIBXREREpBFUu6Ylqy93PyzD4SuA1TKdr4BXRERERFZa7n5XtjQKeEVERESKzDFNS1ZCCnhFREREpEkwszWA3wM7Ad2AGmAaMBZ41N1n1ydfBbwiIiIijaBG05JlZGYDgb8CbaNdDsvXYz4OuNHMrnT3G/LNW4+8iIiISJElVlor9NZcmNmfgRuBNsB84Gngn4SHbgHwElAO/NPMrs83/+bzSImIiIjISsfMtgSujO7eAXR394Pd/ZJo31x335fQzeEH4AIz2z2fMhTwioiIiBSZY1R74bdm4hxC14WH3P0Md69KlcjdPwD+TGj1PTufAhTwioiIiEgp9ScEvH/NIe0j0d+d8ylAg9ZEREREGoGWFk5rXWCBu3+aLaG7/2xmc4Gu+RSggFdERESkyNyhWrM0pFNOGJiWlZm1AtqRfpnhlPTIi4iIiEgpzQA6RsFsNgcTuj+8l08BCnhFREREis6oKcLWTHxEiEn7ZkpkZjsBQwkB7535FKCAV0RERERK6UnCzAtHpzm+uplNBN4C1gIecPdH0qRNqUkGvGZWZmaDzOxzM1tkZlPM7AYzWy3H8y81s0fN7BszczOblCZdazM71cxGmdkkM1sYnfOgmW1W0IsSERGRVZYT+vAWemsmngC+AnYxs04pjrcAegA/AX9w9+PyLaCpDlobApxHeABuADaL7m9rZnu7e02W868BZhH6d6R64BJ6AbcDbwJ3AVOBDYAzgUPNbD93H92A6xARERGRDNx9JrBpmsPHEga0TQHezyEGTKnJBbxmtgVwLjDS3Q+L7Z8I3AQcAfwnSzYbuvs30XmVhNF8qcwAto0mMo7XYQTwPmFJuz71uQ4RERGRuOa0FHBjcfdsMV9OmuIjfyShM/LQpP13ECL8Y7JlkAh2c0g3MznYjfZ/ClQCvXPJR0RERCQTx6jxwm+Sm6YY8PYFaoBx8Z3uvgj4gCwj+ArBzMqAtQnrNYuIiIhIIzCzA6NxWN+Z2ZJoLNekaHzVfvXNt8l1aQDWAX5y98Upjn0P7GxmFe6+pIh1OJMQ8F6dKZGZnQacBtCjR48iVkdERERWdurSkF40McEDwK+jXR79LQfWi7bfm9kzwNHu/nM++TfFR74tkCrYBVgUS1MUZrYzYaDcR4TBb2m5++3u3sfd+3TtmtcKdyIiIiJSawQh2HVgLHAxcGB0rAq4nDCTw4HAk2aWV3+OptjCuwBYM82x1rE0BWdm2wPPEGZrOCDqRiEiIiLSIA7UNJ9pxDOT2kAAACAASURBVArKzA4EBgDLgOPc/aHYMYDF7n6tmd0CjAb6E2ZvuC/XMpriIz8VWCPN8nLrEro7FLw7g5ltB7xE+Baxh7t/X+gyRERE0vGaudTMv5eanw6n5qeDqJl9Nr74Hdw9+8myEjCqi7A1EycSJiz4azzYTebuc4FLCd8f8pqLtym28I4H9gF2AN5I7DSz1sA2wJhCF2hm2xKC3bmEYHdyocsQERFJxxe/gc85F3DwhWHnsq/wJW9Bi42h811YWfuS1lGkiHaM/t6WQ9oxhIB363wKaIotvA8TLmRg0v5TCX13RyR2mNmGZpZuouKcRMHuy8B8QrA7sSH5iYiI5MOXVuKzzwZfUBvshiNh39JP8dknr1Itvc3xUhNdGgq9NRNrAHPcPevsWNGkBnOBDvkU0ORaeN39YzMbBpxjZiOBZ6ldae11Vlx04hWgJ6zYpm9mx0b7AboCFWZ2eXR/srvfH6XrSWjZ7UxY1GLnaNBa3BPuPr9Q1yciIhLnc2+gdkx2Kktg2ZewZBy02jFDuubh9dfhnHPgySdhww1LXRtpJNXk2AhrZh0Jwe60fApocgFvZCAwiTDl14GEtZNvBq7IcUm5k4FfJu1LTDH2OnB/dHt9oEt0e3CavNYntP6KiIgUlNfMgiXjc0i4EF9wL9aMA95Fi+BPf4IhQ0IL7wknwGuvQXl5qWtWOM2oz22hfQdsbGad3X12lrRnRX/fyJgqSZNsC3f3ane/wd03cfdW7r6uu1/g7vOS0vVyr7vMiLv3d3dLs/WPpXstQ7rENqn4VywiIquk6mlgFTkkdFjWfHvcvfsubLcd3HhjbXeGN98M92WVkFhsLLmxcjkz62lmlxAaKJ0whWzOmmTAKyIiskqw1oRfc3NJW7Qp6Etm6VL4y1+gXz/47LO6x2fObPw6FYu7lawPr5mVmdkgM/s8WrlsipndEC32kO3cjc3sL2b2PzObYWZzzewDM/tTqvPNbLCZeZrtojTFjCQEsSelOd4NmEhYH8GAc9x9Qk4XH2mqXRpERESav/L1wdolDVZLpQ20GdAoVWosn38Oxx4LE1KELT17wvDh0L9/Y9equKpLN8hsCGEs1BOEltHE2KhtzWzvLN1FTwLOBv5LmDhgKbAH8Ffgd2bWzz3lC3gQoUtq3LtpyngOuAMwM+vk7nNixxJDGOcRxm793d3HJWeQjQJeERGREjErw1c7BeYOIePANQNrc2ij1auYamrg5pvhkktCv91kJ50U+vF2yGsMvqRjZlsA5wIj3f2w2P6JhAH7R7DihADJHgOudfeq2L5/m9lXwJ8I46ZuSXHek7l2C41mXjgjzeEewIKkIDhv6tIgIiJSQtb2OKjYgdrFRJO1xjrdhJWt/BHg5Mmw994wcGDdYHfNNeG//4W77mqewa4DNVjBtxwcSegGMDRp/x2ElWuPyVhv9wlJwW7Cw9Hf3unONbMOZtagxlV3n9rQYBcU8IqIiJSUWTnW+TZoNxDK1gBrE7o5UAEVO2Gr34+1SjuWZ6XgHroobLkljB5d9/ihh0JlJQxoXr02moq+QA21A8MAcPdFwAfR8froHv1NN3fuR4TVaxeZ2Vgz2z/XjC3Y2Mx2ibaNLVpjuL7UpUFERKTEzMqxdifhq50A1d+EachsLcpadC111Rrshx/gtNNC622yjh3hllvg6KOhYeHMysBK1Yd3HeCnqNtAsu8JaxBUuPuSXDM0s3LgCmAZdbtDzAFuB8YCs4FNCNPNPmNmJ7n78Az5bgRcBhwKJC8tONfMniQsP/xVrnVNUMArIiLSRMxY/D1vzniWyqqxLPXFtCpry3ad92TnNQbQqWLNUlcvbyNHwumnw0/JQ5eAX/0qdF9Yb73Gr1cphJXWihLVr2Fm8aF/t7v77bH7bYFUwS7UdhxvC+Qc8BK6R/QDLnP3L+IH3D256wRmdjdQCQwxs8eSp5mN0hwF3AW0IjxcyevttQOOJQyUO8XdR5AHdWmQJm38+PGcd955bLnllrRr1451112Xgw46iAmphvWKiKzEPprzBv/++mI+nDOGpVFj3OKaBYyb+Tw3f3U+E+dVlriGuZszB447Dg47rG6w26YNDBsGL7yw6gS7RfaTu/eJbbcnHV9ACCJTaR1LkxMzuxo4hxBYX5vLOe4+E/g30AlIXtEWM9sRuBeoAD4mDITbCFgt2jaK9lVGae41s51yrTMo4JUm7rrrruORRx6hf//+DBkyhIEDB/LFF1+w44478uyzz5a6eiIiBTFt4USe/G4YS30xNUnz8lazjCU1i3hg8t/4eemsEtUwdy+/HPrq3n9/3WP9+sGHH8JZZ60KXRjqqqas4FsOphJagVMFvesSAuacWnfNbDBwOXAP6WdVSGdS9HeNFMcuJ8SkzwHbu/twd//G3RdF2zdRV4jtozRGmCEiZwp4pUm74IIL+Pbbb7n55ps59dRTufjiixk3bhzdunXj8ssvL3X1REQK4o0Zj7PMl2ZMU+PVjJv5XCPVKH8LFsC554auCt99t+Kxli3hmmvgjTdgo41KU79V2HhCvLdDfKeZtQa2AXL6ydTMrgSuBO4DTnH35C4H2SSe+VSD3HYmBLFnu/uydBm4+1JqlxZWC680HzvvvDMVFSsuu9m5c2f22GMPPv300xLVSkSkcGq8hk9/fgcn09z/sMyX8u7sVxqpVvl55x3YdtswAC1Z794wbhxceim0WIVHDjlGjRd+y8HDhP6wA5P2n0rou7u8L6yZbWhmmyZnYGZXEJb0vR84Md1CFWbWwsw6pti/HnAmMJMwmC1ZBTDH3Sdnuxh3/5YwMC6XNbmXW4VferIymzp1KmuskepXERGRlcvSmsXk2li2uDrnrpaNYsmSsDTwtdeGBSXizOAPf4CrroJW6XqQStG5+8dmNgw4x8xGAs9Su9La66w4y8IrQE+oneDXzM4GrgK+BV4GjkqaIewHd38put0OmBjNpvAZtbM0nBIdOzLNqmxfAb3NrE2a48uZWZsor09yuPzl1MIrRTV//nzKy8sxsxW2Fi1asOaaazJgwABeeuml7BnFvPXWW7z++uv8/ve/L1Kt65o+fTrnn38+G264Ia1bt6Zbt24MGDCAV15pWGuLuzNixAj22msvunTpQuvWrVl//fU544wzmDhxYsZzv/32W4YOHcqAAQPo0aMHrVq1on379my99dZccsklTJs2rUF1E5HG0bKsFblOMdq6vG2Ra5O7ykrYcUf429/qBrsbbABjxsDf/65gN66GsoJvORoIXARsAQwjrK52M3BQlmWFoXae3h6EgWX3J23xvrQLgceBPsClwK3A0YRAeWd3fzRNGXcC5YRW4GzOIjTY3p1D2uUs/y4YkkqfPn1cMwfU9fbbb7PzzmFAZrdu3Zbvr6qqYlFsmZ2hQ4dy/vnnZ83vxx9/pG/fvpSVlfHhhx/SoRGW4/noo4/Yc889mTlzJgAdOnRg3rx51NTUYGZcc801XHLJJXnnu3TpUg4//HBGjRoFQIsWLWjfvj2zZ88GoF27dowaNYo999yzzrlTpkyhZ8+eK7QKdejQgfnz51NdHQa8dO7cmccff5w99tgj77qJSON65NsbqKwam7FbQwtrya5r/Ia91jqyEWtWV3U13HgjXH55aOFNdsYZ8M9/Qrt2jV+3XJjZu+7ep7HL7bb56n7Uf/YpeL5Dt324JNdTaGb2IPBb4C/AkOSpy8ysPTCIMP/vY+5+RD75q4VXiur9998HQvA1ffr05duCBQt488036dWrFwCXXHIJP6WaqDFm7ty5HHDAAcydO5ennnqqUYLdhQsXcvDBBzNz5ky23XZbKisrqaqqYvbs2Vx44YW4O5deeikvvvhi3nn/8Y9/ZNSoUbRo0YIhQ4ZQVVXFrFmzmDJlCocffjjz5s3j0EMPTdlSmwhqDzzwQB599FFmzZpFVVUVCxYs4Nlnn2X99ddn9uzZHHLIIUyfPr3Bj4OIFNfuXQ+jRZYVWMusBTt02beRapTa//0f9O8fuiokB7vrrAPPPQf/+lfTDXalaTKz+wmLWMwldJ/40czeNLNHzewRM3uLMNhtcJRmqZndn2J7IF0ZCnilqBIB77bbbrvCfjNjl112YejQMD/1okWLGDNmTNp8Fi5cyIABA/jss894+umn6d077dLdBXXbbbcxefJk2rVrx1NPPcUWW2wBhNbU66+/nkMOOQSASy+9NK98f/zxR4YNGwaEmSgGDhxI27bhp8ru3bvz4IMPstlmm1FVVcVf//rXOud37tyZ999/n6effprf/va3dO7cGYCKigr2339/nn32WVq3bs3PP//MbbfdVu/rF5HGsVabXhza/TxaWivKkj6ay2lBRVlrju31J9q3XL0k9XOH22+HrbeGN9+se/zII+Hjj2G//Rq/biuTEg1aWxkcRej60JEwwK4VYRaGQ4HDCItcJBak6BClT7elpEFrUlQffPABUDfgTdh6662X354zZ07KNEuXLuW3v/0tY8eO5b///e/yLhKNYcSIMHj1qKOOYt11161z/OKLL+bJJ5/kvffe4/PPP2fTTesMbk3p1VdfZUnUPDJo0KA6x8vLyznvvPM488wzefDBBxk6dCgtW7Zcfrxjx44rPHbJNt10U/r168drr73Gu+++m1OdRKS0enfahW6te/LWT6P4uOpNltYsplV5W7bvvDf9uhxIp4rSLDM8dSqcckpovU22+uqhRfd3v2v8ekmz8udiF6CAV4pm2bJlVFaGlYG22267lGm+i03W2L179zrHa2pqOProo3n++ecZMWIE+zVi88HcuXOXB4v77pv6Z8R+/frRsWNHqqqqePXVV3MOeCdPDjOvdOzYkbXWWitlmkRes2fP5r333mPHHXfMq/5dunQBars/iEjT17V1dw7pfjaHdD+71FUB4OGH4cwzIRpasIIDDoA774S11278eq2MwrRk+mE9FXe/pthlKOCVovnss8+WD0xL1cLr7vzjH/8AYPXVV2e33Xark+aiiy7i0Ucf5Ve/+hXLli3jgQdW7J5zzDHHFKHmwWeffbZ8UFiiK0OysrIyNtlkE8aNG5fXvMCJEdk1yUObY5Ytq517+5NPPskr4F22bBlvvfUWQKN1/xCR5mPWLDj7bHjoobrH2rULg9ZOOWXVXC2tIarRA1YqCnilaBLdGdq2bcvGG2+8fP+yZcv46KOPlg/2MjOGDh1KmzZt6uTx3nvvAfDSSy+lnL4sOeAdPHgwV111Vb3qe+WVVzJ48ODl9+ODxdZZZ5205yWO5TMNWM+ePYHQijxlyhTWS7GgfDyAnjp1as55AwwbNozp06dTVlbGcccdl9e5IrJqe+45OPlkSPVf2m67wfDhYdoxkZWJAl4pmsSAtcWLF6/Q/3XWrFksXRqW0OzSpQvDhg1LO6fua6+9lleZ7dq1W2H6s3zPjZs/f/7y26mC8YTEYLN58+alTZNsjz32oKKigiVLlnDddddxS9LyREuWLFk+oA9CYJyrjz76iMsuuwyAc845J23rtIhI3Lx5cNFFkGqca0VFmG930CAoL2/8ujUHDs1pkNlKRwGvFE0i4K2uruaHH+ounb3JJpvw6quvZmw9zddFF13ERRddVJC8ijlH9ZprrskZZ5zBTTfdxK233krHjh0588wz6datG5WVlVx88cVMnDiRli1bsnTpUsrKcuv3NW3aNA455BAWLFjA9ttvz3XXXVe0axCR5uPNN+H44+Gbb+oe23ZbuO++sESwyMpKvaelaD788EMA7rnnHtwdd6eqqornn3+eDTfckC+++KJRV0vLV7zFd+HC9CsdLliwoE76XPzjH/9gwIABuDvXXHMN6623HhUVFWy33Xa88sornH322WwQ/W7YqVOnrPnNmjWLffbZh4kTJ7LRRhvxzDPP0Lp167zqJCIN9/OSRXxVNYPv588p6hfnQli0KMypu/vudYPd8vKwuMT//qdgtzDCoLVCb5IbtfBKUUyaNGn5imFbbbXV8v0dOnRg33335cEHH2SHHXbgzTffZMyYMey+++6lqmpa8ZbnqVOnsskmm6RMl+hfu3aeQ5VbtWrFqFGjeOyxxxgxYgSffPIJ1dXVbLrpppx66qkMGDBg+eIaG220Uca8qqqq2HfffamsrKRHjx68/PLL9e7aISL18+ns6QypfJ23fviGlmXlVHsNa7Rux1mb7cLh62+T8/LBjeWDD+DYY8MSwck23hjuvx922KHx6yVSDAp4pSgS3RnKy8vZfPPN6xzv27cvffv2Zfz48TzwwAMFC3ivv/56rr/++nqdm9wdYtNNN8XMcHc++eSTlAFvTU0NX3zxBUDK68zGzDj88MM5/PDD6xwbN27c8pblfv36pc1j/vz5HHDAAUyYMIG11lqLl19+mR49euRdFxGpvzenf8MZbz3C4uplOLCkJkwH+N38OVz9/ouM/WEiQ/r9pkkEvcuWwXXXweDB4Xay886Da6+FaHiCFFCNZmkoGQW8UhSJGRo23njjtD+rH3LIIYwfP54nn3ySf//73zn3U81k3rx5KfsL53puXPv27enTpw/jx4/npZde4tBDD61zzjvvvENVVRUAe+21V73KTeeee+4BoH///mn7OSdWoBs7dixdunTh5ZdfztoaLCKFVbVkIWeNfZRF1SmiR2Bh9VJemfoVD3/zPkdsmHpO8sby5Zdw3HHwzjt1j623HtxzDxT4vzKJuEO1Bq2VjDp/SFEkWnjj3RmSJRaRmDFjBu+k+t+3HgYPHry8v3C+W3xKsoSjjgqrFI4YMSLltGOJ1uTtt98+bZeH+nj77be58847gfTLFi9ZsoRDDz2U0aNH06lTJ1588UXNyCBSAo9N/DBrX92F1Uv512dvlaxPb00N3HILbLNN6mD3+OPD0sAKdmVlYGY9zWzDfM5RwCtFkUvAu+222y7vZ/rUU081Sr3ydfrpp9OzZ0/mzp3LQQcdtHxu3Llz5/KHP/yBkSNHAnDNNakXiRk+fDhmhpkxadKkFY6NHj2aIUOG8M033yxfDW327NncfPPN7LvvvixbtozTTjuNffbZp06+1dXVHHXUUTz//PO0b9+e5557Lu1qdiJSXKMmV7IwTetu3MzF85kyP/US6sU0ZQrsuy+cey4kj7/t2hVGjgxz63bs2OhVW+Vo0FrBvA18lc8J6tIgBTdz5szlSwZnCnjNjH333Zf77ruPp556Km3QWEpt2rRh1KhR7LXXXrz33ntsscUWdOjQgXnz5lFTU4OZcc0116QMSrOZPHkyF1xwARdccAEtWrSgXbt2VFVVLW8BOuWUU7j11ltTnvvWW2/x+OOPA7B06VIOOeSQtOWst956jB8/Pu/6iUhuFi5bklO6FlbGghzTFoI7PPBACHSjnlcr+PWv4fbbYc01G61KImmZWUtgL2ALoAOZG2XbRedcHdv3qruPTneCAl4puETrLmQOeAH2339/7rvvPiorK5k4cSLrr79+sauXt6233prKykquvfZann76ab7//nu6dOnCDjvswKBBg+rdd3fXXXfl/PPPZ8yYMXz77bfMnTuX7t27s8suu3Daaaexxx57pD03viTxokWLli/hnIqmJhMpru6rdWLivFlZ0y2pqWbNNu0boUYwYwacfjo88UTdYx06wE03hb68TWAM3SrDMS08kYaZ9QRGA73yPPVPsdvLojxSl9HU5whcWfTp08cnTJhQ6mqIiEgje23a15z/9kjmZ2m93a3bBtzzy6OKXp9Ro+C00+DHH+se23PPMDBtVZ7Ixczedfc+jV1ul826+gHDf13wfB/od1dJrqeQzOxe4BjgJ2AMMIewOF06RwFtgTtj+55y97T9I9XCKyIi0gC7dduA7qt15JufZ7LUa1KmaV3eggu27F/UelRVwcCBoT9unfJbwz/+AWefDQWYEEfqSdOSpZX4SXMnd0+x3t+KzOwgoK27n5ZrAXrZi4iINEB5WRn39z+W9Tt0YbUWFSsca13egjblLbl5p8PYcvXCLaOebPRo2Gqr1MFu377w/vuhL6+C3dJxoMat4Fsz0Q2YkUuwW19q4RUREWmg1Vu15alfncrr0/+P4V+OY8r82bRtUcHBPXpz+AbbsHqr4qzisHAhXHYZDB1a91iLFnDFFXDppeG2SBPWAliYNVUDCxAREZEGKi8rY891NmLPdRpn8Zfx48PAs88/r3ts883D0sCarbBpWYWnEcsm3wFleQ9A0yMvIiKyElm6NCwLvNNOdYNdM7jwQnj3XQW7slLpAeyYR/rtgO75FKAWXhERkZXEp5+GVt133617rFcvuPde2H33Rq+W5KJ59bktKHefmmf6H/ItQwGviIhIE1dTE/rpXnYZLF5c9/gpp8CNN0L7xpnmV+rB0SwNuTCz/sBvCa24XaPdM4D3gMfc/bX65KuAV0REpAmbNAlOOAFef73usbXWgjvvhAMPbOxaiRSWma0JPADsHe2K99PdAOgHnGVmrwBHufuMfPJvsn14zazMzAaZ2edmtsjMppjZDWa2Wo7nX2pmj5rZN2bmZjYpS/odzexlM5trZj+b2fNmtk1BLkZERCRP7nDXXbDllqmD3cMPh8pKBbsrE01LlpqZVQAvEIJdBz4EbgIuBM4BrgXejo7tBbxkZq3yKaMpt/AOAc4DngBuADaL7m9rZnu7p5ndu9Y1wCxCE3inTAnNrB/wGvA9cEW0+xzgDTPb2d0/ru9FiIiI5Gv6dDj1VHj66brHOnWCW2+FI47Q0sDSbJwLbEWI245z92dTJTKzPYBHorTnAtfnWkCTDHjNbAvChYx098Ni+ycSIv4jgP9kyWbDxATGZlYJtMuQ9iZgCbC7u38fnfMI8Bkh2N6nnpciIiKSl8cegzPOgJkz6x7bd9/Q6rvuuo1fL2mYxMITktLvAQNOTRfsArj7aDM7FXgcOJw8At6m2qXhSMKFJ0+lfQewgLDecka5rtZhZr8A+gKPJoLd6PzvgUeBvc1srRzrLSIiUi+zZ8Mxx4SuCsnBbtu28K9/wXPPKdiVZmkTYBEwKoe0o4DF0Tk5a6oBb1+gBhgX3+nui4APouOFLAtC35Bk/yME3tsXsDwREZEVvPhi6Ks7YkTdYzvvDB9+GFp91YVh5aY+vGm1AJbm0F0Vd3fCr/IV2dLGNdWAdx3gJ3dPMfkK3wNrRB2cC1VWIt9UZQGk/D5tZqeZ2QQzmzBjRl6DBUVERJg/H84+O3RV+D7pU6hlS/j732HMGPjFL0pTPykcp/DBbjMKeL8F2ptZ1gZNM9seaA9MzqeAphrwtiU0V6eyKJamUGWRpryMZbn77e7ex937dO3aNVUSERGRlMaOhW22CQPQkm21FUyYAH/8I5SXN37dRBrZU4RuzveY2drpEkVdTO8h/Pr+TD4FNMlBa4R+umumOdY6lqZQZQGkmt6i0GWJiMgqbvFiuOoquO66sKBEXFkZXHIJXHklVBTqd0xpMrTwRFr/BE4ANgc+NbM7gFeA76Lj3YE9gdOAjsBM4B/5FNBUA96pwOZm1ipFt4Z1Cd0dlhSwrES+yRL7UnV3EBERyctHH8Gxx4a/yX7xC7jvPthpp8avl0gpufsMM9sfeJLQ1fSiaKuTlBCT/cbdf8ynjKbapWE8oW47xHeaWWtgG2BCgcsCSPVfTD/Cg5ti1XIREZHcVFeHFt0+fVIHu2edBR98oGC3WXMNWsvE3d8FtiSsh1AJVBNiMI9uVwJXAlu6e95xYFNt4X0YuAwYCLwR238qoT/t8nGsZrYh0NLdP69PQe7+tZlNAA43sz+7+9Qo33UIc7y96u7T63cZIiKyMllWXcPLn37NXWMm8H8/zsSALbp34+Td+7L7xr2wekyT8PXXcPzxoc9usnXXhbvvhn0023uzp3l4s3P3OcDfgL9FkxN0iQ7NbOgv+00y4HX3j81sGHCOmY0EnqV2pbXXWXHRiVeAnrBixxgzOzbaD9AVqDCzy6P7k939/ljy84HRhJXVbo72nUtoZb6wYBcmIiJN1sIlSzn1npF8Nm0GC5csXb5/wsTv+eT7H9l1o57ceOSBlJfl9uOoO9x2G1x4ISxIMRLk6KPh5puhc+dCXYFI8xEFuNMKlV+TDHgjA4FJhA7KBwI/ATcDV+QyTxtwMvDLpH1XR39fB5YHvO4+1sz6A3+NNgfGAoe7+4f1vwQREVlZXPbYC3zy/Q8sXlZd59jCJUt548tJ3PD8G/zhgOSPlrq+/x5OPhleeKHusS5d4N//ht/+thC1lpWJWngLI/plfi13757rOU024HX3asKyvjdkSdcrzf7+eZb3NrBXPueIiEjzML1qLqM//4YlKYLdhEVLl/HQOx9x9l47sVqr1FMouMODD4a5defMqXv8oIPgjjtgLa3fKVKHmbUHNgY6kHmcWS9gdTPbm9BICfCNu09Md0KTDXhFREQay7MffVH7sZlBuRmvff4NB269aZ1jP/0UBp89+mjd89q1g//3/+DEE7Va2qoqsfCE1BVNSnAn8Dtyj00deDF2/6poS0kBr4iIrPJm/DyfJdXpW3cTllRXM2te3Q65zzwDp5wC01MMcf7lL2H4cOjVq+H1FGmmrgCOjG7PBOaQ+StoL0IM+3Vs36xMBSjgFRGRVd7q7drQoqyMZckrQSRpWV5Ox7atl9+fOxcuuADuvLNu2lat4Npr4fzzw4ISIq4W3nQOIUw+cIi7/zdbYjObSujDu3GuBegtKCIiq7x9e29MeVn2YKS6pob+m24AwJgxYQngVMHu9tvDe+/BoEEKdqVWDVbwrZnoBVTlEuzWl96GIiKyyuvRpRN91u9Oy/L0H4utWrRgwDabUWGtuegi6N8fJk1aMU15eVgW+O23YfPNi1plkeakFaEbQ9GoS4OIiAhw45EHcsxtDzNlVhWLli5b4Vibli3o3X0tDuy+B9tvD59+Wvf8TTcNSwP37dtIFZaVirumJcsghyGjDUqvURs/ywAAIABJREFUgFdERASgfetWPHzWUTzxbiV3jXmXaVU/g8P6XVfnxF36UPncZux6VhnLltU9d+BAuOYaaNOm8est0gwcB8zLI/05QF7vNgW8IiIikdYtW3Bkv204st82LKuuwQy++rKM446D8ePrpu/RI8zAsMcejV5VWQlp0Fpq7v6f7KlWSP9EvmUo4BUREUmhzMq45Rb44x9h0aK6x088EYYOhQ4dGr9usjLSPLzZmNlmwK+AjsB3wDPu/mMh8lbAKyIikuTbb+GEE2D06LrH1lwzrJZ28MGNXi2RZsvMhgLnwgpTTywws9PdfUQs3SCgB3C1u2ecezdOszSIiIhE3OHee2HLLVMHu4ceCpWVCnalftyt4FtzYGanEoJdgA+A+4GPCP1074lafhNWB84Hfp1PGQp4RUREgB9/hN/8JrTs/vzzisc6doT774fHHoOuXUtSPZHm7AxCy+5N7r6dux/v7tsAI4BywiC1hEcJszQMyKcAdWkQEZFV3hNPwOmnw4wZdY/tvTfcfTest17j10uaD0fTkmWQaMG9Mmn/jcDRwG6xfV8lnZMTtfCKiMgqq6oKjj8+dFVIDnbbtIFbboEXXlCwK1Jki4HZ7p702wofAzWEldgAcPeFQBWwbj4FqIVXRERWSa+8EmZamDKl7rEddwyLSGy8cePXS5opD33EJaUvge3NrEM86HX3ajOrAjon9pmZAe0IQXLO1MIrIiKrlAUL4LzzQleF5GC3ZUv429/gzTcV7Erh1WAF35qJ4YQ+vGelOLaEFWdu2JbQr3dyPgWohVdERFYZ77wDxx0HX35Z91jv3mFg2jbbNH69RFZl7v4vM9sfuNrMfgb+5V63PdzMKoBrCAHwc/mUoYBXREQazKt/xBc8BEveBhwq+mJtj8LK1y511QBYsgSuvjos/1tTs+IxM7j4YvjLX6BVq9LUT5o/RyutpWNmVwOVQD/gFuAiMxsN/EDovoCZ3QzsDWwS7b8+nzIU8IqISIPUzLsD5v2/6N6S8GdpJT5/OL7aKVi78wjd7kqjsjK06r7/ft1jG2wQ5t3dddfGr5eILHcZodXWo60ncGJ0LLEv0d3hA+AYd/8hnwIU8IqISL3VzH8Q5t3C8kB3uej+/Ltxa4O1O62xq0Z1NQwZAn/6U2jhTXb66XD99dCuXaNXTVZJWlo4gzshbYdkB+YDU4G33P3t+hSggFdEROrFfQnMux5YmCHVQpg/DF/tWMzaNFbV+OabsIDEG2/UPbb22nDXXbD//o1WHRFAszSk4+6nF7sMzdIgIiL1s/g1whSZWbjBoueLXZtQlMPtt8NWW6UOdo84InRxULArsmpRC6+IiNRP9bfguUyFuQBfNrnoEyhNmwYnnwzPpRi73bkz3HprCHhFSkWD1kpHAa+IiNRTBWE6zGVZ0pVjVtzpDx55BM48E2bNqnts//3hzjthnXWKWgURqSczKy9UXu5enWq/ujSIiEj9tNotexoAWkKr3YtShVmz4Mgj4fe/rxvsrrZa6N7wzDMKdqX03EMLb6G3ZmJpAbeU1MIrIiL1Yi3Wx1v2hqUfACkbVYAyaNETa7lFwct//nk46aTQlSHZrruG6cY22KDgxYpI4dWQfpaGglDAKyIi9WadhuAzfwM1c6gb9JaBtcc6DStomfPmwUUXwW231T1WURGWBh40CP4/e/cdH3WR/3H89UkndAUUUAQVUUEFQWynomD37J56FrBhA8X+07N3TxHsHvaCDdtZ72yInhVRBFRUFFCKFFF6+uf3x2w0ZHeT3eymkLyfj8c+NvnOzHdmoySfnf3MTGbaPiQVSQ9tSxZXl9ruQAGviIjUmGWuD+v+G1/+Tyj4L1hOKPAiyN0Da3URlpm+fIIPPgiHSPz4Y3RZ797haOBevdLWnUhaaVuy2Nx9Xm33oYBXRERSYpkdsDa34GVXQMm34WLWplhGm7T1UVgIl18ON98cHTRkZMAll8Bll4UZXhGRyhTwiohIWlhGS8jpl/b7Tp4cZnWnTo0u694dHn0Udtgh7d2KpF0jWmS21tEuDSIi0iCVlMD110P//rGD3eHDQzCsYFdEqqMZXhERaXC+/z7M6n78cXTZBhvAww/DwIF1PiyRGnMa1TZiax3N8IqISINRVgZ33QXbbBM72C1PbVCwK2sjr4WHJEYzvCIi0iDMmRP21X3zzeiydu3CIRKHHFL34xKRtZ8CXhERqVfuMHYsDBsGS5dGlx90UNhzd7316n5sImnjWrRWnxIKeM3swTT15+5+UpruJSIia7lFi+D00+G556LLWraE22+HwYPBFCeI1JiZZQBnA6cCXYFFwDPA5e6+spq2mwHHAnsBmwB5wA/AOGB0rPZm1gO4CdgNyAE+B65w93fS9JKSlugM7xBCqkisXzkVU0gql1cuc0ABr4iI8NJLcMopsHBhdNnuu8NDD8FGG9X9uERqTf0l3Y4CzgJeAEYCW0S+72Nmg9y9rIq2JwJnAi8BY4FiYHfgWuBvZraDu68ur2xmmwAfAiXAP4GlwCnAf81sX3d/K90vLhGJBrxXxbmeA5wBtAZmA+8BcwnBbUdgV8I7id+Be4HCFMYqIiKNwLJlMGJECGgry8uDG28MW45laFm1SMrMrCcwHHje3Q+rcH0mcDtwFPBEFbd4FrjB3SsmHN1rZt8D/yBMZN5ZoewGoA3Q190nR/p6FPgKuMvMNnev+zPnEgp43T0q4DWzHGB85B7HufvYWG3N7GhgDLALoHW1IiJN2LvvwpAhMHt2dNl224VDJDbfvK5HJVI36imH92jCROToStfvA24kpCvEDXjd/bM4RU8TAt4/DvM2s+bAgcC75cFu5B4rzOx+4GpgO+DTijcys8cSfTFVMHc/Nl5hKovW/g/YARgcL9gFcPcnzSwTeBS4kDAFXq1U8k2SbW9mRvgfYhiwGZAL/ET4jzna3ZclMmYREYlt9epw/O/oyn9ygayscGzwxReHr0Uaq7qf1wRCgFlGpSDT3QvMbHKkvCY2iDwvqHBta0IM9VGM+uUbDUYFvMDfiZ02m6xaCXiPBoqAJxOo+xThncTfSTDgJbV8k2TbXwtcArxDSN8oBgZEvt7PzHasj+l3EZHG4LPP4LjjYPr06LIttwyzun371v24RBqJdmZWcRZ2jLuPqfB9J2Cxu8dKK50L7GRmOe5elGiHkYnMywl5uhVnhztVuG+svgA6xyh7i6oD3oGE2Oy9OOW7EALtuFIJeDcCCty9tLqK7l5iZgWRNtVKNd8kmfZmlgWMIKwg3LNCIHyvmZUAxwDbAJMRERHmzf+dzybNoqi4hA06t2W7vt3IzIxOuC0uhuuug2uvhdJKfynM4NxzQ1leXh0NXKQeObWW0rDY3ftVUZ5P/DVUBRXqJBzwEtIjdgAucfdvK/VFnP4KKtX5g7vvXVVnZlYK/Obue8UpnwesX9U9Ugl4lxPeVfRy92lVVTSzrQgL22KsxY0ppXyTJNtnA82AX2LMGs+LPFebQiEi0tgtWryc6258mW++mYdlGGVlTnZWJlnZGZw+dA/22WurP+p+802Y1Z00Kfo+XbuGo4F3263Ohi7SlK0COsQpy6tQJyFmdg0hBXSMu98Qoy+IPduadF/plMoa2HcIQeWDZtY2XiUzawM8QHhzk+j+a3HzTQgzrdXlmyTcPrKVxnvAPmZ2kZltamZdzWwIYQeKx939+wTHLSLSKC1ZsoJTz3yYaV/Npai4lMLCEoqLS1m1uohlywoYfccbvPDvSZSVwahR0KdP7GD35JNhyhQFu9IEOeCW/kf15hEmKGMFoZ0JM8QJze6a2ZXApcBDwGlx+iq/b6y+IHa6Q1V95pR/WVW16u6TSsB7BSFK7wt8a2bXmtleZtYr8tjLzK4FvgX6AasjbRJRXb5Juwo/gHS0P4aw48SNwPfATOBBQh7w8QmOWUSk0bpnzHiWLSugtDT28onCwhJG3TaJ3XYr5dxzobDSb9/11oOXX4b77gsHSog0Re7pfyRgIiHe61/xopnlAb2BeLswrMHMriDEcY8CJ8dZ2zSVkM6wY4yyHSLPCfVXQfm+Le3MLDvGuLKAdsCKqm5S45QGd//OzPYjnLTRHrg48ogaCyGV4W9JzJSmmm+SbPtC4EdCMPwfwvuwwwjvYgqA62LdyMyGAkMBunTpEqc7EZG124oVBbz3v+/iBrvu8Mu8rZjx3UBKSzKjyg8/HO65B9q1q+2RikgMTxMW5o8A3q9w/RRCLPTHTluRQyOy3X2NJaZmdjlwJfAYcEK8jQMi24+9DBxqZtu4+5eR9i2AkwmTipV3aIgrsovW5ZFvM4A9gdcqVRsUKfupqnultAGMu78XOT5uOCFA7AmU/7YrJWwyPA64y91/T+LWqeabJNzezPIJJ4J87u5HVaj3lJk9BVxtZs9WSsoGILIKcgxAv379tIuDiDRKM35cSHZ2JkVFJVFlhYXN+e7rvfl1cfeosjZt4K674OijdTSwCFAvJ625+1QzuwsYZmbPEwLG8p2rJrDmmqi3CRsM/PEv1szOJOxa9RNhN4W/25r/oBe4+5sVvr+YsKvCG2Y2ClhGCK47A/snuuuVmW0O3BG512LCLO7dZjaYP3dr+AtwT2S871Z1v5R3PIwEstcA10SmmteJFC1x9+Ia3nYesKWZ5cZIS0gk3ySZ9ocD3Yk9Oz0OOJLwA40KeEVE1nbzVy1j7IzPeGfe9xSXlbJ5m/U4YbPt6bNuZ8r/qHlZ7L9PixZsxrff7E1JcdSia/baCx58EDrHyuQTkbo2AphF+FR6f0IAeQfhbILqtnktX/fUBXgkRvkE4I+A191nmNnOhDTR/yOcyvs5sE8ixwqbWV/gAkJ8lgF8QTjNbWJkDOMJn747YdMBCJ/U317VfdO6xXckwF1QbcXqTQT2IuSb/DH9XiHfJN4+bDVpX/7rOPpzuD9/PtoKXUQanbEzJnH9l2/i7hSVhX3DZq1YwrvzZ7BDh424c6fDyc3MolvX9hRXmN0tLs5lxreDWDC/V9Q9c3JKGT06k9NO06yuyJqsvk5aI7KF7MjIo6p6XWNcGwIMSbK/b4CDkmljZvsQDigbUH4bwuzzGe6+LLKZwP2EADq3Qp3lwBB3/66q+6ftpHIzW8/M+pnZrmm43dOEFzGi0vWY+SaRae8atQe+jjwPjjGO8msTEx65iMha4D8/f8MNX75JYWnJH8EuhF+cq0uL+XDhLM7/5N8AtGmTT79+3TAzlvzalYkfnRQz2G3dZi7vvbeK009XsCsiiTOzLwmpFrsRfg29C+zi7seWn3YbOdW3KyG2u52wscApQDd3f7G6PlKeuTSzIwlnKfeMXPKK941sSzaOkF9xiLsvr+6eqeabJNn+FUIC9X5m9h7wXORehxJO7hjn7p8n9tMQEWn43J0bv3yLgtLonNxyhaUlvDPve2avWMJGLdbhhOMHMvaxTZg9s3dUXbNSuvf4kPPPd7bfPh1zHiKNlFb7xNOL8NP5L3Ctu38Yq5K7LyCkYiQtpYDXzG4k5FkYIX8im0p7obn772b2C+FY4QNZc3a1KqnkmyTc3t1LzWwQIYf3UOCfhB/698BFwK0JjldEZK0w9bf5LCmsfu/3Mi/jqR++YNdVAzn++DYxg92WrRbRq/frnHHaphxzVKydiEQEAK+1k9Yag5cIgW6M3bvTo8YBr5ntRci1WEqYUn4BmEPs3REeIex1ewgJBryp5Jsk0z5Sdzlhy45LEhmbiMjabO7KpWQkkHNQVAxP3dKJS56AskrTDGbOXnv/xGmnL2XvPY+iWbOqtkYXEYnP3Q+p7T5SmeEdRpgJvcDdnwWw+L9AP4rU3TaF/kREJA3ys6I+jItSOLsDC+86kKLZ0cfTb7IJPPqosdNOG9XSCEUaKaU01JtUAt7tI89PVFkLcPeVZrYUiP7NKSIidWq79l0ojZMZ5mXG7y/vwJJndoOS6D8RZ5wB//wnNG9e26MUkabCzGLtlJW0yKf7MaUS8LYBlrl79YlgQVpejIiIpCY/K4fDum7NuFlfUlhh4VrxL21ZePeBFHy7YVSbTp3Cvrp7712XIxVpbJTDG0cR6fnhxN19LJWAdwnQwczyqwt6zawb0JKwiExEROrZ/20ziClL5vPdsoWsLilh2Vvb8utjg/DC6FzcY46BO+6Atm3rYaAijYlSGqqTyk+oyoA5lYD3U+CAyOOZauqeF3l+v8paIiJSJ/Kysnlyj+O5ecIkbj6/A8u+6BZVZ9114Z574Igj6mGAItLULAXOjFPmwOOEY4pj1bmTkHkQVyoB7/3AX4HrzewTd59duUIkJ+Ni4IzIYO9NoT8REUmjF8ZlcesZ27Pst+iyAw6A++6D9bXyQiR9NMNblQJ3j7suzMwej1fHzG6p7uY1Dnjd/WUze4Kwv+7nZvYi0DzS8TBgS0JA3CnS5B53/6im/YmISHr8+mtYfPZMjM/mWrSA0aPhxBN1WpqINB6pnrQ2BFgEDAdOiFxz4LbI1waUEQ5vuCjFvkREJEWvvQYnnQS//BJdtuuu8PDD0C06u0FEUuWADp6oNykFvO5eApwTOcZ3MLAj0JGwSm4BYf/dR9x9eqoDFRGRmlu+HM47L6QpVJabC9dfDyNGQEbcNc4iImuvVGd4AXD3GcBl6biXiIik13vvwZAhMHNmdNm228Jjj8GWW9b5sESaHFcOb72p8Xt5M+tiZp2TqN/JzLrUtD8REUlOQQFccAEMGBAd7GZmwuWXw8cfK9gVqTNeCw9JSCozvLOA+UCiQe8HwIYp9ikiIgn4/HM4/nj46qvosh49wqzudtvV/bhEROpDqtlayWZfK1tbRKQWlZTAtdfC9tvHDnbPPhu++ELBrki9cEv/o3FIZK76WeClmravy9nWfKCk2loiIlIj334bZnU//TS6rEsXeOgh2GOPuh+XiEg1diUcLxyXux9ZRfEmVDOJWycBr5ltCrQD5tRFfyIijZm788VP85izZCm5WVls121Dxj7UjIsugtWro+sPGRL21m3dus6HKiIVmHJuY3L3D1NsX1BdnYQDXjM7CDio0uXWZvZgVc0IR739JfL9+ET7ExGRaG9M+54bX32XZasLASha2oKZLw5kxawNo+q2bx+2ITuo8m9uEal7WmRWr5KZ4e1NOGiiomYxrsXzA9q6TESkxp6dOJXrX3mXguIS3GHp1C1Z8NYAygpzo+oecgjcey906FAPAxURaWCSCXjfrfT9FcAKYGQVbcqAZcBXwLuRgypERCRJv65YxXUvj6ewpJSSlc2Y/59BrPhu06h6ufkl3HdvFsceq6OBRRqWRrXIbK2TcMDr7hOACeXfm9kVwAp3v6o2BiYiIn8a9+kUAJZ/twnzXx9E6ar8qDr5XX9ik4Pf4YijjsVMO0CKiJRL5TdiN6A0XQMREZH43pw8l5kvDmTp1J5RZZZVTIfd36dt3y/JzcthxoLF9Npg/XoYpYhUSTm89abGAa+7z07nQEREJLZ33oHXr92H1b81jyrL6zSfTgf8h9x1fwdCGkNpmf6qiohUlMrRwtua2TtmdnMCdW+L1N2mpv2JiDQ1q1aFgyIGDiQ62M0opf2uH9D1uKf/CHYBikpK2ahdmzoeqYgkREcL15tUTlobDOwGfJ5A3WnAAOD4FPoTEWkyPv0Utt0Wbr89uiy33WK6DX6Sdjt/imX8+Rcvw4xdN+tKm/xmdThSEUmYAt56k0rAu3vk+Z0E6r4cedYZPyIiVSgqgssvh512CienrcGcDjtOousJT5C3/qKots1ysjh3n13qZqAiImuRVBatbQisdvcF1VV091/MbHWkjYiIxPDVV3DccfDFF9Fl3brBgw857y9dynOfAZ5JUWlYN5yfk03LvFzuGXwwXdu1rdtBi0hiHG1LFoeZXVOTdu6e8PkOqQS82YR9dhNVCkTvoyMi0sSVloajf//xDygsjC4fOhRuuQVatsxgAHtw2oD+vDDpa35Y9CvNcrLZs2d3dtykCxkZ+mMqImulSwin8ybKI/XrJOCdC2xqZj3cvfIHb2swsx5AC2BmCv2JiDQ6M2fC4MHw/vvRZR07wv33w377rXm9fasWDN29f90MUETSxpRzG8/9VB3wdgR6AhsRDjR7liQzmFMJeMcD3YGrgKOqqXs1YWDjU+hPRKTRcIcHHoBzzoEVK6LLjzwS7roL1l237scmIrVEAW9M7n5qIvXM7CjgHqA1cGQyfaSyaG00IU3hCDN7zMw6xhhYRzN7HDiCkP4wOoX+REQaLPdivOB1yn79G2ULd6Rs4a6ULb0GL/kpqu78+fDXv8Ipp0QHu23bwpNPwlNPKdgVEanI3Z8CTgUOB4Yl0zaVgyemm9m5wG3A34EjzexLoPy3+0bA1kBm5PsL3H1aTfsTEWmovGwJvuQ4KJ0LvurPgtVP4aufwVteQkbzowEYNw5OOw2WLIm+zz77hFnfTp3qaOAiImuf54BC4CTgjkQbpXTYurvfYWa/ALcCnYG+kUdFc4Hz3P2ZVPoSEWmI3B1fciKUzARKKpUWh6flN/Drsg0Zft5fePLJ6Hs0bw4jR4bFaaZ1ZyIicbl7qZmVAJsm0y6lgDfS8TgzewEYCOwArEdIPP4F+Bh4290r/xUQEWkcij6FkllEB7t/+u/4fpx87hbM+yW6bOed4ZFHYJNNam2EItJAaNFa6sxsF8KuXzF+o8aXcsALEAlo/xt5iIg0Gb7qCWB1zLIVK/O58OoL+dejf48qy8mBa6+Fc8+FzMwYjUVEmggzq+63YCbQARgE3EiYWH0jmT7SEvCKiDRZpXOJtfT6g0+35YSzb+KHWRtFlfXuDY8+ClttVQfjE5GGQwdPxFOcRF0HZgD/SKaDVHZpEBGRjJZrfFtYmM3F153PgEPGRgW7GRnOP/4Bn3yiYFekyfFaejQOZST+au8Ftnf3ecl0kNAMr5m9E/lytrufUOlaMtzdB9agnYhIg2TNDsOLJ4Ov5MuvNmfw8H8y9ZvNo+p13/gnHnl8A3bcUTM8IiKVdKmmvCPQC7gAOAB4AvggmQ4STWkYEHmeHuNaMhrPexEREYC8vShZcj233HUsV94ynOLinKgqZ574JDfcUEbLDsfUwwBFpMFQFBRTArO184BJZvYo4ZS1V8xse3f/LtE+Eg14T4g8L41xTUSkyZoxI4fBx7/FRx/nR5V17vgLD4y+gj33bIa1ubUeRici0ni4u5vZOcBM4DLguETbJhTwuvsjiVwTEWkq3OGee+CCC2DVquhg99jD/81tNzxG287HQt5BmDbYFWnytC1Z6tz9JzNbSpKZBtqlQUQkSXPmwIknwptvRpe1a+fce89SDj10NyzjoLofnIg0XAp4U2ZmLYBWQLNk2jXYXRrMLMPMzjGz6WZWYGY/m9lIM2teG+3NLMvMzjKzz81spZktjXx9anpfmYisrdxh7Fjo1St2sHvggTBtmnHY4W2wjDZ1P0ARkUbMzDKAWwj78H6fTNtEd2nYtQbjisnd30uw6ijgLOAFYCSwReT7PmY2yN3L0tXezHKAl4DdgbGELS+ygO5A9CaaItLkLF4Mp50Gzz0XXdayJdx+OwwerKOBRaQKmuGNycyuqaZKJuEk3wFAt8i10cn0kWhKw7uk5z+TJ9KnmfUEhgPPu/thFa7PBG4HjiJsSZGu9pcRTu/Y093HJ/OCRKTxe/llOOUUWLAgumzAAHj4YdhIb41FRGrqEsKsbVXK49CVwKXu/mAyHSQa8P5E/IC3PeFMYwiHyS8mDHrdCvdfGbmeqKMj96gcvd9HOFLuWKoIeJNpH0lxOBv4t7uPt7CypIW7L09ivCLSCC1bFo7+feCB6LK8PLjhBjjrLMhosMlhItJQmGvRWhXup+qAtxj4DZgGvO7uS6uoG1OiuzR0jXXdzIYTcineAq4HPnT3okhZNrATcDFhCnqku9+Z4Li2I5y68WmlcRSY2eRIebra7wK0JOzvdhtwItDCzBYTAuTL3b0kwXGLSCMxYUJIUZg9O7qsX79wNPAWW9T9uEREGht3r/X1UjWelzCz/QgzqE+4+17u/m55sAvg7sXuPsHd9wGeBG4zs30SvH0nYLG7F8Yomwu0i+TdpqN9j8jzCOAw4ELgSOBDQrAeY24nMLOhZvaZmX22aNGiKl+QiKwdVq8Os7q77x4d7GZmwpVXwocfKtgVkRpwS/9DEpLKB3HnEdIcLkyg7kWR5/MTvHc+ECtYBSioUCcd7VtGntcBBrn7Pe7+jLsfRMhdPt7Mtox1I3cf4+793L1f+/btqxiOiKwNJk50evUqZtSosCNDRVtsAR9/DFdcAdnZ9TM+EVnLeS08JCGpBLy9gaXuXu3UprsvBH4H+iR471VAbpyyvAp10tF+deT5Y3efXqnuo5Hn3aroS0TWcsXFMGLESrbf3vnxx8rRrHPQwQuZNCmkMoiISO0wsxwzG2Jmr5jZPDMrijzmRa4NqeYT/rhSCXhzgFZm1qq6imbWmrBJcKKDnEdIO4gVtHYmpCsUxSirSfs5kedfYtSdH3lum8CYRWQt9M030K9fMbfd1hz3NX8l5uYtpXffJykoeYxX/zOxnkYoIo1F+cK1dD4aCzPrDkwCHgT2I2xDlsmfW5LtGyn73Mw2S/b+qQS80yLtL0mg7sWEAU9N8N4TI/fuX/GimeURZpY/S2P78oVtG8S4T/m1hQmNWkTWGmVlMHo0bLstTJkSnaOwfqcv2W7HB2mzzs8UFpZw3wPvsXx5QYw7iYhIKiKTp28CPQmbDrwEnEtYW3UAcCrwFCFddUvgzUQmXCtKJeC9k7CFxAVm9kAkMq/8AjY1s/uACwiZJnckeO+nI/VHVLp+CiH3dmyFPjYxs81r2t7dZwIfAP3NbNsK982M1C8B3khw3CKyFpg9GwYOhHPOgYJKMWx2zgp69X6WzXv+h6ysPz9IMoPX/zuljkcqIo2KcnjjOQ/YEJhiQyMwAAAgAElEQVQFbOPuB7v7be7+oru/7u73u/sxwDbAzEjdRNeFASkEvO4+FribEPQOAaab2fzyXQvMbB7wLWGbLwPucvcnE7z3VOAu4FAze97MTjazkcCtwATW3IP3beCbFNpDOKRiFfCWmV0Z2W5tAmGG+Hp3/ynBH4uINGDu8NBDsNVW8O670eXtOnzLdjs+SLv2P0SVFRaWMHXanOhGIiKSqoMJseKJ7v5VvEru/h0hrnTgoGQ6SPTgiXgdDzOzj4ArgU0IORbrVao2A7jS3as6KCKWEYRIfyiwP+HgijsI++JWd6xwUu3d/Qsz2wm4NtIujxBEn+DuDyc5bhFpgBYsgKFD4aWXosuysgrovvmbdFj/6yqPBjadGywiNdXIcm7TbGNgpbu/W11Fd59gZqsibRKWUsAb6XgsMNbMegPbEk5eA1gEfO7uk2t431JgZORRVb2uqbSvUH8KcGByoxSRtcFzz8Fpp8HiGOc97jagBLeHyciq+uCevLxs+vTuUksjFJEmQQFvVWp1RiFtB2K6+2R3f9Ddb4o8HqxpsCsikg6//w7HHQeHHx4d7Obnw113wfh3sth++7ZkZFT9u9bd2WtQr1ocrYhIk/UjkG9me1ZX0cwGEtZjReeeVUEnwItIo/TmmyFX9/HHo8t23BEmT4YzzgiL0UactTf5+TlxUxZyc7M468w9ad483vbeIiIJ0KK1eJ4lvJoHzGzreJXMbCvC1mQGvJBMBykHvGbWyszONbPXzWyamf0Qo/x4Mzsu1b5ERKqzciUMGwZ77QVzKq0xy86GG26A99+H7hX2lenUsQ333jGY7pt2IDc3i+zsTLKyMshvlkOrVs244Nx92W+fuL+DRUQkNaMIuy9sAHxmZk+b2almtn/kcaqZPU3Yp7d8N4dbk+kgpRxeM9sReI6wUK18amSN9xvuvszMzgZ6m9lMd/9fKn2KiMTz8cdw/PHw/ffRZVttBY89BttsE7tt585t+dddQ5g5cxGfT55NSUkpG23Uju36diMzUx+GiUjqtGgtNndfYWZ7EWZ6twGOiDyiqgJfAoe6+/Jk+qhxwGtmGwCvEE4hew14ErgdaBOj+r3AvwgbCCvgFZG0KiqCq68Os7dllfZwyciACy6Aq66C3AQyErp1a0+3bu2rrygiImnj7j+aWX/gyMgjaiME4BngKXcvSfb+qczwXkAIdh919yEAZnZLnLqvR54HpNCfiEiUqVPDrO7kGEtkN9kEHnkEdt657sclIiLJiQSyY6lwQFi6pBLw7kuYWr68uoruPsfMVgPdUuhPROQPpaVw661w6aVhhrey006Dm2+GFi2qv1dBSTGv/vw1/5nzDatLi+nRugPHbtqPbi3XTf/ARaTpUkpDvUkl4N2QsElwoqeQrQZaptCfiAgAP/wAQ4bA/2IkSHXqBA88APvsk9i9Plwwk9M/GIfjrCopBuCzRT/z1A9fsNcGPfhn/wPJzshM3+BFRKTOpRLwFgLNzCyjupPPzKw5Ibf31xT6E5Emzh3GjIHzzgu7MVR29NFw552wzjqJ3W/Kknmc+r9nWF1avMb1Ei+jxMt4c+63XPjpS4za4ZA0jF5EmjSdtBaXmZ2Urnu5+wOxrqcS8H4H9AW2IqyYq8phhC3QpqbQn4g0YfPmwUknwX/+E122zjpw771wRKw1vVW4bvKbUcFuRQWlJbwx91tmLFvMpq3aJTliERFJ0BjSd9Ja2gPeF4F+wGXA4fEqmVkP4GZC5sq4FPoTkSbqqafCIRG//RZdtv/+cN990LFjcvecu/J3pi2ZX229krJSHv9+Ilf23Te5DkREKtMMbzxvUctHC6cS8N4GDAUOMbPngNFEDrKIpDD0BA4FzgBaAF8TTscQEUnIr7/CmWfC009Hl7VoAaNGhVnfOAekVemnFb+Rk5lJYVnVu9uUujN96cLkOxARqUwBb0zuvndt91HjgNfdV5rZvoQ9eA8BDq5QvKzC10Y4I/lAd4//2aGISAWvvx6C2fkxJmF32SVsN9YthX1fsjMy8QT/+ORmpHRGj4iI1LOUjg9y928IJ2JcD8wlBLcVHwuBm4C+7v5jakMVkaZgxQo49VTYb7/oYDcnB265BcaPTy3YBejVtiNlVLneFoD8zGz22XDz1DoTkSbPCIvW0v2QxKQ8beHuy4BLgUsjp691JATSC9x9Vqr3F5Gm4/33YfBgmDkzuqxPn3A0cM+e6ekrLyubw7v25qkfP6eorLTKugd22So9nYqISFxmtgWwJ9AamAO86u5pySmr8QyvmR0YefyxdNnd57j7RHf/RMGuiCSqoCAc/7vbbtHBbmYmXHYZfPxx+oLdcudvvTtdW65DTpx9dvMys7h9p8Nonp2T3o5FpGnyWngkwMwyzOwcM5tuZgVm9rOZjYysuUqk/cVmNs7MfjQzN7NZVdR9OFIn1qOqTQ5GA9MIa8KuIuy28KOZHVOp3jlmNsrMEtyAMkh1l4YSIKkORUQq+uILOO44+Oqr6LIePeDRR6F//9rpOz8rh3F7nMDNU9/huZlfkmkZmEFxWSndW7fn0t570bfdhrXTuYg0LfWbgjAKOAt4ARgJbBH5vo+ZDaruPAVC6uoS4HPCuQqJOC7GtU9jVTSzU4DhkW8nE7ax3Yaw9e1DZvZ5JI0WQtx5NjAFeCjBsaQU8C4BcPcVKdxDRJqokhK48Ua46qrwdWVnnw3XXw/5+bU7jubZOVy57T5ctPVAvlwyl6KyUro0b0vXlnovLyJrPzPrSQgmn3f3wypcnwncDhwFPFHNbTYpX4tlZtMIu29Vyd0fT2KYpxHSnG9z93MqjPFR4BhgGHBm5PI44BLgryQR8KayaO0roLWZtUrhHiLSBH37LfzlLyFVoXKwu+GG8PbbMHp07Qe7FTXLymaHDl3Zdf1NFOyKSO2on5SGownB5OhK1+8DVgHHVjvsGmw8YEErM0sk1twi8nxFpeu3Rp53qXDt+0ptEpJKwDsGyOTPKWgRkSqVlYWjf/v0gU8+iS4fPBimToU99qj7sYmINFLbAWVUSidw9wJC+sB2tdTv0shjtZm9aWbbV1G3EPgtshFCRVMJY+9afsHdV0fu2zmZwaSyD+9YM+sPXGVmecAod19S0/uJSOP2889wwglh9ray9u1hzBg4+ODoMhGRRqN2cnjbmdlnFb4f4+5jKnzfCVjs7oUx2s4FdjKzHHcvStN4fiHkDE8CVhJycUcA75vZfu7+Vow23wF9zaxVxaDX3UvNbCnQtvyamRkhpSLW64mrxgGvmb0T+XIVIZfiIjObASwC4u3x4+4+sKZ9isjaxx0efxyGD4elS6PLDz4Y/vUv6NCh7scmItIILHb3flWU5xM/OCyoUCctAa+7/1+lSy+a2ROE2eR7gO4xmj0M9COczntjpbIi1jx2uA8hw2B2MuNKZdHagBj32jzyiEdbJIs0IYsWhUMkXnghuqxVK7jjjrBDQ6yjgYtLSxn/zY98NnMOZe5s06Uje/XsTm62Tj0TkbVTPe3SsAqIN6WQV6FOrXH3783sGWCImW3m7t9VKr8ncnrvNWa2DLjHPfosTDPLIewYYcDryYwhlb8cJ6TQVkQauX//G045JQS9lQ0cCA8+CF26xG474duZ/N8zr1NcWsaqonAi+Yuff83VL77NFQcN5IA+Sa1VEBFpGOon4J0HbGlmuTHSGjoTZojTlc5QlVmR53aEFIY/mNk1hD14dwDuBM43s/HAAiI7QpjZHcAgoEfk+i3JdJ5KDu8jNW0rIo3X0qUwYgQ8/HB0WbNmcNNNcOaZkBFnyewH38/mnCdeoaB4ze0bygPfy198C8sw9t9Gx/2KiCRgIrAX0B94v/xiZP1Vb+C9OhpHeSrDghhllxBmbcv3ntiIPydWy6+dEfl+MnCsu8e6T1xJB7xmlgscDPQFWgG/A58AL7t7jN00RaSpGD8ehgyBn36KLuvfPxwi0aNH/PbuzmXPvxEV7FZUUFzCVf9+mz17dicnK/YJaSIiDU4SJ6Ol2dOEgHIEFQJe4BRC7u7Y8gtmtgmQ7e7Ta9JR5OS20sgOEBWv9wGOAL5x9x9iNL2fNfN0K3LC4rd5wAfu/lFNxpZUwGtmOxE2/F0/RvEsMzvY3afWZCAisvZavRouvhhuuy26LCsLrrgC/u//wtdV+WzmXJatrn7hrZc573zzA/tstVkNRywi0jS4+1QzuwsYZmbPA6/x50lrE1jz0Im3CbOrawSfZnZc5DpAeyDHzC6NfD/b3R+LfN0deN3MXiTsl1u+S8OJhA0NhsYZ46kpvcgEJBzwmlln4BWgNeEHUQYsJrxwA7oBr5lZL3ePsRZbRBqjiRPh+ONheoz5gJ49w6xunz6rYfVrlC37D3gBZG+G5R+DZW28Rv3p8xdSXBpvk5c/rSwq5pt5CxXwishapR6PFh5ByKEdCuxPiN/uAC5P4FhhgJOA3SpduybyPAEoD3h/Ad4CdieckNYMmE+YZb6hpjPH6ZDMDO/ZhPOTf4t8/Yy7F0VyQE4GbiLs9XYSf56MISKNVHExXHstXHcdVI5RzeC88+CaayDXPsAXDgMcPLIQuHgSvuoZPG8Q1vomwsJbyMjIICPWlg0xZCZ0eI+ISANSTwGvu5cCIyOPqup1jXN9QIL9/AIcl+Tw6kQyAe+ehP9UZ7n7H/kekTyNOyOB7z8JidEKeEUasa+/DtuJff55dFm3bmHB2q67ghdPwX89nT+3eixXEh4Fb+NchLUZBcC2G3XCEgh483Oy6dstqUN2RESkgYrkDictTj5wTMkEvBsTAt7n4pSPIwS8G8cpF5G1XGkpjB4N//gHFMZItT3lFBg5Elq2DN/7shuIDnYrKghBb8kMLGtTtujUgQ3Xac33C36tchwtcnPYcZM4e5qJiDRQ9ZjS0NB9X8N2CX/Ul8xngi2BRZVX3pVz9/ITL5oncU8RWUvMnAl77AHnnx8d7K6/PrzySjge+I9gt3QeFE9L4M7F+Mo/PjTin3/bl/yc7Li187KzuOWo/cjISCz1QUREGrxi/vjoL+pRxp97XHjk+/KyhCWbBJfIexP9FRJpRNzhgQdg663hvRi7Nf7tbzBtGuy/f6WCktkQyc2tWimU/LmOoUfH9jxx2lFs0ak9edlZNIs88rKz2Lj9Ojx40mH067ZBSq9JRKReeC08GgF3z3X3nDiPLMIGCQcC7wKrgcHunsgfmD/ojE4RieuXX+Dkk+HVV6PL2raFu++Go46K09iySfi3seWu8e1m67fjuWHH8v2CxUz5+RfK3NmyUwd6dl4vqfGLiMjaz92XAK8Cr5rZE8CjZjbb3T9M9B7JBrzrmNk7KdRxdx+YZJ8iUg+efRZOOw1+jZFOu/feYda3c1XrxrJ7ET55qk4+5O4Ts6T7eu3ovl67RIYrItKwNaIZ2Xp2IXAkcD5waKKNkg14c4ABKdTRf2qRBu6332DYMHjiieiy/Hy49VYYOjRsPVYVszy82eGw6mmgisMkDKzZX1Mas4hIQ2co5zMd3H2OmS0FdkimXTIB7yPJDUlE1jZvvAEnnghz50aX7bQTPPIIbLpp4vezFufiRZ9AySxiB715WJvRWIbWuoqISPXMLINwJHKrZNolHPC6+wnJDkpE1g4rV8IFF8A990SX5eSEAyTOOw8yM5O7r2XkwzpP48tHQsGzQCZg4EWQ3QNreTGW0zcdL0FEpOHT59zpsAFwe7KNtGhNpIn78MNwNPAPMbbv3npreOyx8FxTlpGPtb4Mb3V+2KbMCyGzC5alfXRFRCQ57v4TIY83KQ0y4I1MV58NnAp0BRYBzxDOfF5Zm+3N7BngCOArd+9V81ch0rAVFsKVV8I//wllldaWZWTA//0fXHFFmOFNB7NmkLNdem4mIrIW0sETsaV60pqZbUhYQxb39LUGGfACo4CzgBcI5z5vEfm+j5kNcvfqln7XqL2ZHQAcRtjjTaTRmjIlHA08ZUp0WffuIVd3xx3rflwiIo2aAt54Uj1p7b/A5pWuraHBBbxm1hMYDjzv7odVuD6TkLNxFBBj/Xhq7c2sBXA3cBdhc2ORRqe0FG6+GS6/HIqLo8vPPBNuugmaaw2ZiIjUnWJS28Si/KS2uJI9aa0uHE140aMrXb8PWAUcW0vtryO8Abg0mcGKrC1mzIBdd4WLL44Odjt3Djs03Hmngl0RkVqjk9ZiquaktbiPCu23qXytsoYY8G5H2K3+04oX3b0AmBwpT2t7M+sPDANGuPuyGo9cpAFyD7svbLNNWKBW2bHHwtSpsOeedT82ERGRutAQA95OwGJ3j7Vp51ygnZlVtYwmqfZmlkWY/X3D3Z9JZqBmNtTMPjOzzxYtWpRMU5E6MXcu7LsvnHEGrFq1Ztm664bT1B57LBwTLCIitcjDorV0P5oiM9so2YVuDS6Hl7CZcLxjmQoq1ClKU/sLgO7AIckNE9x9DDAGoF+/fk30fztpiNzDSWnDhsHvv0eX//WvMGYMrL9+dFlJSSlfTv2ZFcsLaNOmOb16diYzsyG+NxYRkSbqI2B9kpi4bYgB7yqgQ5yyvAp1Um5vZpsClwPXuvuPSY5TpEFavBhOPz3M3lbWsiWMHg0nnBB9NHBZmTP2qY94ZtynlPmf79+yszM5/pidOOSgvlh15wmLiEh8mhqLy8yygYFAT8IpalUFsy0iba6pcO0ddx8fr0FDDHjnAVuaWW6MtITOhHSFeLO7ybYfCSwBXogEv+WygJzItZXuPr/Gr0akDr3yCpx8MixYEF22YdvZXH/5Av72995Uzgpyd6654SU++ngGhYXRC13ve/A95sz9nbPOHFRbQxcRafSaagpCdcxsI2A84eyEZPyjwtclkXvE1BAD3onAXkB/4P3yi2aWB/QG3ktj+40IOb9fxbnX98CrwAFJvQKROrZsGZx7LjzwQHRZBkVsmvEkXZb9h3FX5fLCDVlc/8rF9Njuz/Sn9z/4jo8/+SFmsAtQUFDM6/+dwm679GCbrTesrZchIiJN09WEmGwRIU77narnw/9OSE+9v8K1z6vqoCEGvE8DlwAjqBCwAqcQXtzY8guRhOVsd59ek/bA+UCbGGO4m5Dvey6g2V1p0CZMgCFDYNas6LKW/ECvzLtpYXMBWL0ipLFfuPc13DPxRjptEpJ4n3jqYwoKYmzMW0FhYTFPPfOJAl4RkZrSDG88u0eed0wkxTRyUFi+uw9NtIMGF/C6+1QzuwsYZmbPA6/x50lpE1jz0Ii3Ce8IrCbt3f2tWGMws1uAFe4eIwtSpGEoKIB//ANGjQqL1CoySumW8Tzd7EUyrDSqbeGqIp64/gXOf+B0ysqc777/pdr+3OHLKT+la/giIiLl1gMW1eZ6qgYX8EaMAGYBQ4H9gcXAHcDlCRwrnI72Ig3apElw/PHw9dfRZevkL2LTwlG0tvi/N8pKy3h33EcMv/MkMrMzE+63tEz/fEREako5vHFlAatru4MGx91LCQvKRlZTr2sq7ZO9r0h9Ky6GG26Aa66BkhjptuecAz8/cw1LFyys9l4ZZvy+cCnrbdSetm2as+S3ldW26dgxVgaQiIhUqxGdjFYLkv3JJP2T1OaaImuJ6dNh553hiiuig90uXeCdd+DWW6FZfmL3Ky0tJadZ2K3hsEP7kZNT9fvfvLxsjjpi+5oMXUREpCpdgGT+wGwLbJBMBwp4RRq4sjK47Tbo0wcmTowuP/HEcDTw7pGU/78c0p+saoJXgA5d2tOmfSsADjygD61bN4t7wERWVgbrrdeKPQZsUePXISLS5HktPBoBd5/n7tV/NPmn5cDeyfShgFekAZs9GwYNghEjwiK1ijp0gJdeCluRtWr15/WDztibjIyqD4jIy8/lqAsP+uMgiRbNc7nrtuPosuE65OVl/3EoRUaGkZubRY/N1ue2kcdUOwssIiJSFTM7ycz+VsO2/czsX4QdtGJsxBmf/nqJNEDu8MgjcNZZsHx5dPmhh8K990L79tFl623UnmF3nMRdZz1I4eroM1py83PZbp/e7Hn8rmtcb9+uJQ/860SmfT2X/74xjd9+W0n79i3Zb5+t2ax7jDOIRUQkYYYWrUWMAX4Gnkmkspm1AY4FTga2jlx2YE4ynSrgFWlgFi6EoUPh3/+OLmvdGu68E445Jvpo4Ir2GTKA9huswwOXPMFP0+eRnZNFWWkZec1zOfKCAzl4+L5kZGTg7qwqKSYvM4vMjAzMjK16bsBWPZNKjRIREUlUAdDOzLLdPe4G8Ga2GyHIPQzIIwS5hcC/gYeAN5LpVAGvSAPywgsh2F28OLps0CB48EHYMMFzH/oO2pq+g7Zm/o8LWDxvCfktmtFt6y5kZGQwZ+Xv3Df9I56fNYWislIcZ6cOXTl9i7+wfYeN0vuiREQk0AwvwNdAX+B+Mzvb3X8vLzCzDsAQ4CSgO3/+xD4nBLljK9ZPhgJekQbg99/h7LPh0Uejy5o1g5tvhtNPh4waZN133Hg9Om683h/ff774Z05470kKS0soqbAt9f8WzGTS4jmcvsXOnLHlX2ryMkREpApW+ZSgpukKwiztccBhZjaOcJDYocABhNjUCccMjwUecvepqXaqgFeknr31FpxwAsyJkY20ww4hl3ezzdLT17KiAk587ylWlkTn9gKsLi3mnm8+oGfb9dmt46bp6VRERCTC3V8zsz0IZyX0BQZHHhAC3U+Am4GX3D3GjvM1o10aROrJqlUwfDjsuWd0sJudDdddB++/n75gF+DZmZMp9eijhitaXVrM7V+9l75ORUSkdrYkW0snjN39fXfvD+wGPAas4s9X05eQ1nCgmaVtYlYBr0g9+OSTsK/unXdGl/XqBZ9+CpdcAllp/gzmmZmTWV1a/Rvmr39fwG+Fq9LbuYiISAXu/j93Hwx0BE4DJgKZwP7As8BcMxtlZj1T7UsBr0gdKiqCSy+FnXaC775bs8wMLroIPvsMeveunf6XFxcmVC/bMhKuKyIiiTFP/6MxcPfl7n6fu+9A2HpsFCGHtx1wNjDVzCaa2Rlm1romfSjgFakj06bB9tuHVIWysjXLNt4Y3nsPbrwRcnNrbwztcpsnVK/Yy2ib06z2BiIi0hQppaFa7v6Vu58PdCJsSfYKUEI4TvhOYL6ZPWlmg5K5rwJekVpWWhp2WejbFyZPji4/7TT48kv4Sx1sjHDspv3Iz8yutt727bvQMiev9gckIiISg7uXuvuL7n4gsBFwCfA9kAv8jST34VXAK1KLfvwRBgyACy8M6QwVdewIr70G99wDLVrUzXgO6NKTZlk5VHXwcF5mFmf13K1uBiQi0oQopaFm3H2+u9/k7j0IC90eBlYkcw8FvCK1wB3GjIGtt4b//S+6/OijQ4rDvvvW7biaZWXz5O7H0zYnn2aZa66Iy7IM8jKzuLbv/mzbTietiYhIwxNZ6HYSYaFbwrQPr0iazZsHJ58Mr78eXbbOOnD33XDkkXU/rnIbt1qXt/Y7g2dnTuaxGZ+xuGAleZlZ7LfhlgzZrD/dWq5bf4MTEWnMmsiMbF1w95XJ1FfAK5JGTz8dTkT77bfosv32g/vvD6kM9a1VTh4n9tiBE3vsUN9DERERqXUKeEXSYMkSOPNMeOqp6LIWLeDWW8Osr1WVPJugsjLnox9+4qH3P+PreQvJMGPbrp05cZe+9O7SKfUOREQk/ZpQzm1DpIBXJEWvvw4nnQTz50eX7bILPPxw2HYsHYpKShn++EtMmjWXVUXFf1x/++sZfPDdLPbfZnOuPHgQGRlpiKxFRCS9FPDWGy1aE6mhFSvClmL77Rcd7ObkhK3Ixo9PX7ALcMULbzHxxzlrBLsQFsmtLi7h1S+nc887H6evQxERkUZAAa9IDfzvf7DNNvCvf0WX9ekDkybB+edDZmb6+ly0bAWvT/2WgpL4RwOvLi7hof9NoqC4+uODRUSk7hjalqw+KeAVSUJhYTj+d9ddwx67FWVmhmODP/4YevVKf9+vTfmu+kqEPOEJ03+svqKIiEgToRxekQRNngzHHRf2z61ss83g0UfD0cG1Zd7vyygqKa22XnFpGYuWJ7Vbi4iI1AXXlGx90QyvSDVKSuC666B//9jB7vDh8MUXtRvsArRplkdmAovRsjIyaJmXW7uDERGRpCmlof5ohlekCt99B8cfD598El224Ybw0EMwcGDdjGXPXt0ZM2EipWVV5+eWlJWx2+bd6mZQIiIiaWZmbYDNgHbAOkAhsBiYD3zrnvxUuQJekRjKysKJaBdeCKtXR5cPHgy33QatW9fdmDZdb116du7AlJ9/oaSsLGadnKxM9u7VnTb5zepuYCIiUj1H25JVwcx2B44E/gJsQVjnF8tSM/sYeBN4zN0XJXJ/pTSIVPLzz7D33iFVoXKw2749PP982Fu3LoPdcrcf81fWa92CnKzo7R/ysrPo3mFdrjh4UN0PTEREJElm1sLMLjKzGcDbwFBCsAvh7UEhsAD4DSiLXGsF7AXcAswxs+fMbOfq+tIMr0iEOzz+eAh0ly6NLj/oIBgzBjp0qPuxlVunRT7PDz+WsR9+waMffsGqwmIcZ50W+Zz4l378rf9W5Gbrn7WISENksT+ca3LMLBs4FbgU6EAIZH8EXgMmAp8BM929oFK7dYCtgb7AzsDewMHAIWb2CnCxu38Vq0/9ZRQBFi0Kh0g8/3x0WatWcPvtIZc3HUcDp6plXi6n7bEDQwdsz7KCAsyMVnm5WEMYnIiISPUuBK4GlgOjgSfdfWJ1jdx9CfBu5DHSzFoABxFmhvePPGLugK+AV5q8l16CU06BhQujy/bYIyxM69yplAnjPuGZm19i9jdzMDM27dONIy84kO3335aMjLrPDsrIMOXqioisTZTDW24pcBVwm7vH+Ew1Me6+AhgLjDWzXYDL49VVwCtN1rJlMGJECGgry8uDm26CYcOgqKCQ8/a4jh+nzKZgZeEfdb7+6DtuOO4Oeu/eiyvGnfkfs9IAACAASURBVEtmjLxaERGRctpGLHD3O2vhnu8De8Yr16I1aZLGj4ettood7G63XdhX96yzICMDbj7hbmZ8MXONYLdcwcpCvnhnKmMufLwORi0iIiI1oYBXmpTVq+Gcc0Kqwk8/rVmWlQVXXw0ffgibbx6uLZrzKx+/+jlFBcVx71m4qohX73+blctW1eLIRURkreaE1dHpfkhCFPBKkzFxImy7LYweHV225ZbhcInLLguBb7kJ4z6iqqQrz8+ldIP2lGzQngfvfIOioqoPhRAREZE1mVlmTR7J9KGAVxq94mK48krYcUeYPn3NMjM47zyYNCkEw5Ut+eV3igujg1jPzaZk2+6U9Nucso07UdipPS+//z2HHHEHz73wWe28EBERWavpaOG4imv4SJgWrUmj9vXXYTuxSZOiy7p2hUcegV13jd++TftWZOVkUVJh5tZzsijp1wOyMkOSb0RxaRnFq4u4/6H3WLW6iOP+vlMaX4mIiKz1Gk+Amm5lxD9ZLS00wyuNUlkZjBoVZm1jBbsnnwxTplQd7ALsevgOZGSs+W+wdJPOIe8hzlZkBQXFPP7EhyxavLymwxcREWlKugAbVvHoD5wEfAMsIxxBvGEyHSjglUZn1qywKO3cc6Gw0sYK660Hr7wC990HLVtWf6/1u3ag9x69yM7NBsAzM/D2bSCj6jei7vDSy1/U8BWIiEhjYyilIR53n1fNY5K7PwxsBbwDPAi0TqYPBbzSaLjDAw/A1lvDhAnR5YcfDtOmwf77J3ffix8bzoY9OpGbn4s3bwZl1f+GKS4u5fPJs5PrSEREROJydwdGAM2By5Jp2yADXjPLMLNzzGy6mRWY2c9mNtLMmqezvZm1NbOzzeyNSJ3VZvatmY0xs6SmyqV+/fILHHhgSFVYXimToE0bGDsWnnkG2rVL/t7NW+Vz+wfXcPqtx9Ouc9vG85ZaRETqTm1sSdYEtyVz958JJ7UNSKZdgwx4gVHArcDXwHBgHHAW8LKZJTLmRNtvD4wkpJHfCQwDXgOOBaaa2ZZpeTVSq559Fnr1CqkKle29d5jV/fvfw44MNZWTl8N+Jw3k/g+vJSc/t9r62dmZbLO13jOJiIikk5m1BFpFHglrcLs0mFlPQpD6vLsfVuH6TOB24CjgiTS1nw70cPcfKt3jVeBN4Grg8DS8LKkFv/0Gw4eH2dvK8vNh5Eg49dTUAt3KWrTIY5edezB+wjeUVZHaYAYH/zXGPmciItJk6QPC1JiZAbcQUqK/TaZtgwt4gaMJL6Ty8QD3ATcSZl/jBrzJtHf3WbFu4O5vmdkSoFeSY5c68sYbcOKJMHdudNlOO4XtxjbdtHb6PvWUAUycNJMVKwpiBr15udn87Yjt6NAhqTefIiLS2CngjcnMrqmmSibQAdgd6Ba5NjKZPhpiwLsdYT+2TytedPcCM5scKa/N9phZa6AlMC2JcUsdWLkSLrwQ7r47uiw7G665Bs4/HzKTOn8lOe3bteRfdw7mimteYPZPv1JaWkZJSRnNmmXjDoOP25kjD+9fewMQERFpXC4h8X14lwOXuntVk59RGmLA2wlY7O6FMcrmAjuZWY67F9VSe4BLgWzgkWQGLrXro4/CIRIzZkSXbb01PPZYeK4L66/fmn/dNYQfZy7ik4k/UlRUQqeObdj1L5uRG9nCTEREpCKlNMR1P1UHvB2BnsBGhPTUO5PtoCEGvPlArGAVoKBCnXgBa0rtzexw4Dzgv8BDVQ3UzIYCQwG6dOlSVVVJQVFROBr4/9u78zC5qjr/4+8PWQkRBBK24BAWWUfZwiIoiyAgizpAZEcYAUdkCaKyDIKK4PIbiIj4YBDFCCIEwiargAF+ArKNiuwIAUxAEgKBAAlJ+jt/nFtS6VRV13Jr6crn9Tz3qfS959x77jfd1d8+de45P/hBWlCi2FJLwUknwRlnwJC+nyXL3VprjmStNUe2/sJmZmZdIiK+VE05SfsBFwLrAZ+v5RqdmPC+QxqnUcrQojK515e0O3AZ8DDw+Wy+t7IiYgIwAWDMmDH+u60J/vrX1Kv7l78sfmzttWHixDRm18zMrKMFVc3jbuVFxBXpuTUul/RfEXFhtXU7cVqy6cAISaX660aRhitUGo5QV31JuwGTgceAXSLizdqbbnlZuDD16I4ZUzrZPfrotN/JrpmZ9RvRhG3JczXpk/wja6nUiQnvg6R2LfLUj6ShwCbAQ3nXl7QrcA1pmrKdI+L1ehtvjXv2WdhuOzj5ZJg/f9Fjo0bBrbfCBRfAMlUtQ2JmZmbdIiIWAPOBdWup14kJ7xWkv1nG9dp/JGns7b9mXZW0tqT1662fnWMX4FrgaWCniJjV6A1YfSLgwgth443h3nsXP37QQfDoo7DLLq1vm5mZWaMU+W9VXbfxFWxPkTRJ0nOSQtLUPspvJel2SW9JelPSLZI2qa61fbbl46Slhcs9r1VSx43hjYhHJV0AHCNpMmnlsw1IK6XdxaJz8N5BemJP9dSXNAa4Lqv/S+DT6rVKQURcmvc92uKmTYMvfjH13va24oopEd7XS4CYmZnVYzwpD7qGNH9tIS/aVNLOEdFTqTJwNjALeAT4YKWCkrYGppBmxjo9230McI+kbSLi0RJ1+ppMdACwMrAT8D1S3lYiYyiv4xLezDhgKmkGhD2AmcD5wOlV/KfUUv/fef9BtvFlzuWEt4ki4Le/TWNy33hj8eN77gkXXQSrrNL6tpmZmeWq8rPwTdHoCraZtSPiuaze34DhFcr+mDQT1nYRMS2rcyXwBCnZLvU57fwS+8oJ4HngGzXU6cghDUTEwog4JyLWi4ghETEqIr4aEXN6lRsdEYvN21ZD/UsiQpW2Zt/rkuy112D//eHAAxdPdocPh4svhuuvd7JrZmbWgEor0L5DWoG2okKy2xdJ65AW+JpUSHaz+tOAScDOkkr9Vu+h78fzekjDT78HbFZ8/mp0ag+vdbkbb4QjjoBXXln82HbbwSWXwJprLn7MzMysv2rTwhMNr0Bb47UA7itx7H7gP4HNgRt7HetrMYP5wOw+ZumqyAmvtdRbb8FXvwo///nix4YMgbPPhnHj0oISZmZmXaN504iNkFQ8A9WEbJ2AgjxWoK3WakXnLXUtSFPELiIipudw7Yqc8FrL3H03fOELMHXq4sc22ywtDbzhhi1vlpmZWX82MyLGVDje6Aq2tRiWvZa63txeZVrKCa813dy5cNppcO65i4/XHzAgHfvv/4ZBg9rTPjMzs2YToDY8tEbjK9jWei2AUot/VXUtpemyNgc2A0Zmu2eQZoh4uK9VcMtxwmtN9cgjcMgh8Pjjix9bf/20NPAWeY4eMjMzs2LTgQ0lDSkxrKGaFWxrvVbhvL0V9pV92EzSgcCZQLmneJ6XdEY9U8Z6pKQ1xYIFcOaZsNVWpZPdcePgd1e9wv0TL2bvEYfz6aEHMHaVI/jlaZfz2ste6M7MzLpQTxO2vjW6gm0tHsxeP1bi2NakUcwPl6oo6WzSVLCjs3Kzgb9m55ye7RsNTJT0/Vob5oTXcvfUU7DttnD66SnxLfZv/wZ33gn7bv8Ax2/1NW6a8HvemjWHBe8t4I1XZzPpnBs4fP3jefz+p9vTeDMzsyZRRO5bFRpdwbZqEfEsKYEeK6nwABvZv8cCd0bEYvMzSfokcFLWzquBzSNi+YjYJCK2iojVgfWBX2dlviFpp1ra5iENlpueHvjJT+Ckk9K43d4OPxzGj4dZL73IsXudx7x3Fv8EZf68+cyfN59Tdvsulzx9PsuvtFwLWm5mZtadGl3BFkDSIdl+SONqB0s6Lfv6hYj4dVHx44E/kFZWOz/bdyypk/XEMs08NrvmeRFxQpn7eAY4TNJrpOT9mKy9VXHCa7l48cWU0N555+LHVloJJkyAz342ff3Tr1zL/HkLFi9YZMF7C7jxZ7dx8DfHNqG1ZmZmLda8acmq0egKtl8Etu+178zs9S5SzysAEXGvpB2A72ZbAPcCYyPiL2XO/zHSAI3vVNGW75KS6q2rKPsvTnitIRHpwbPjjoM331z8+N57w4UXwsjsOcuenh7uvuo+ehZW/vl6b+58brzoDie8ZmZmDYqIhaRlfc/po9zoMvt3qPF69wG1DDlYnrSwRJ8P8UTE65JmAyvU0iYnvNawyy5bPNldbrk0vOGgg0BFH4zMfXse0VPdn7hvzZrTdyEzM7N+IRafm9MKZgEjJa0YEa9VKihpBWA50lRlVfNDa9YQCX7xi5TgFuy8Mzz6KBx88KLJLsCQYYN7jQwqb5nlls6voWZmZtap7iXlpNUMafh2VvbeWi7ghNcatvrqcMEFsPTSqVf31lvhQx8qXXbAgAFs85kxaKnKWe+gIYPY9bAdm9BaMzOz9lDkv3WJ80hjfb8s6UpJG/QuIGkDSVcAX8l2/biWCzjhtVwceCA88wx85SuwVB/fVfuf/B8MHlp5WbUBAwfwmaN3zbGFZmZmbRaR/9YFIuJu3n/AbV/gMUkvS3ow214GHiNNbRbAmRFxVy3XcMJruZBgVKl1VUpYd/O1Oe6nRzJk6cEs1aund8DAAQwdNoRvX/N1RoxasQktNTMzs04TEWcA+wHPkJLalUjLC2+W/TuAZ4EDsrI18UNr1ha7HLoDa310Da784XXcM/lPLJy/gCHDhrDzwdux74l7MWqdVdvdRDMzs/wEqLqV0ZZYEXEVcJWkQqKbzfHEDOCRiHik3nM74bW2WWeTNTn1N+OICBYuWMjAQf52NDMzW9JliW3dyW0pzjCs7SQ52TUzs+7XJWNu+yOP4TUzMzOzruZuNTMzM7NWcAdvSZJeyutUEbF6qQNOeM3MzMxaQB7SUM6qVL0sVUVlz+GE18zMzMzaabtmX8AJr5mZmVkruIe3pIioaZngejjhNTMzM7OWkrRV9s8HIpr/l4BnaTAzMzNrtgB6mrD1X7sC9wGvSLpE0j6ShjfrYk54zczMzJpMBIr8t37svmxbATgEmATMlHS7pOMlrZXnxZzwmpmZmVlLRcTvI2Jb0gwNh5MS3rnAjsB44FlJT0j6H0nbSRrQyPU8htfMzMysFfp3j2xTRMRMYCIwUdJA0owNewB7Aetm21eB1yXdBtwA3BwRr9dyHffwmpmZmVnbRcSCiLgzIk6MiHWBDYCvA38AhgOfBy4FXpV0j6RvSNqwmnM74TUzMzNrhYj8ty4WEU9HxLkRsROwEnAgKeF9A9gG+D7wN0l/l3R+pXM54TUzMzOzjhYRsyPiiog4lJT8bkdKeB8DRgNfqVTfY3jNzMzMmq0wLZk1LJu394/ZdqqkNYA9K9VxwmtmZmbWAv18GrGOFREvABdUKuOE18zMzMzaptEpxwoiYmG5Y054zczMzFrBPbzlvAcoh/OUfTbNCa+ZmZmZdYJG/iKomDA74TUzMzNruu6fRiwHXwbmNOPETnjNzMzMmi1wwtu3qyPitWac2PPwmpmZmVlXcw+vmZmZWSt4Ht626dgeXklLSTpB0pOS5kp6SdI5kpZpRn1Ju0u6V9LbkmZJmiRpzXzvyszMzMxarWMTXmA8cC7wOHAsMAk4DrhBUjXtrrq+pL2B3wFLA18H/h9pybo/Slotl7sxMzOzJZoict+sOh05pEHSRqQkdXJE7FO0/3ngx8D+wG/yqC9pEHA+8BLwiYiYk+2/GXgY+BZwVI63Z2ZmZmYt1Kk9vAeQ5lP7Ua/9FwHvAAfnWH97YDXg54VkFyAi/gxMAfbLkmIzMzOz+kXkv1lVOjXh3YI0tPuB4p0RMRf4c3Y8r/qFf99X4jz3A8sC61bbcDMzM7PFBNAT+W/d4dlsK7s0cKM6NeFdDZgZEfNKHJsGjJA0OKf6qxXtL1UWYFSpi0g6StJDkh6aMWNGheaYmZmZWSkRsV5ErBsRbzTrGp2a8A4DSiWrAHOLyuRRv/BaqnzFa0XEhIgYExFjRo4cWaE5ZmZmtmRrwnCGfjqkocrJB3I9b6cmvO8AQ8ocG1pUJo/6hddS5au5lpmZmZlV7yRJl0haL4+TSRoo6T+BJ8uV6dSEdzpp2EGpJHQUabjCeznVn160v1RZKD3cwczMzKx67uEteBc4BHhM0nWS9pe0dK0nkbSBpO+Qxv/+HFi5XNmOnJYMeBDYBdgSuKewU9JQYBPg7hzrP5i9fgy4vdd5tgbeBJ6u+Q7MzMzMivXfBDVXEfEjSU8BZwN7AnsBb0u6i5SXPQhMBV7PtqHA8sAIYGNgDLAtsFF2yrmkmbnOKnfNTk14rwBOBcZRlLACR5LG015W2CFpbWBQRDxZT33gLuBl4AhJ44vm4d0Y2AH4ZUTMz+e2zMzMzCwibgZuzhb/OhL4FPBpYPdqqmevrwC/An4aES9VqtCRCW9EPCrpAuAYSZOBm4ANSCul3cWii07cAaxBmne35voRMV/S8aQk+R5JF5GmIjsBmAGc0bQbNTMzsyVDYVoyW0RETAYmS/oQMBb4BPBxYIUSxXuAR0mdmbcBN0dETzXX6ciENzOO1J19FLAHMJO0ItrpVd5c1fUjYpKkd4HTgP8hzdhwB3BSRHj8rpmZmVkTZT2052YbkkaQhjCsSBrzOxN4NVtToWYdm/BGxELgnGyrVG50I/WLyv8O+F1trTQzMzOrRkB1nZEGRMRMUpKbi06dpcHMzMzMLBcd28NrZmZm1lU8S0PbOOE1MzMzazY/tNZWHtJgZmZmZl3NPbxmZmZmreAhDW3jHl4zMzMz62ru4TUzMzNrBffwto0TXjMzM7OmCye8beQhDWZmZmbW1dzDa2ZmZtZsAfR4pbV2cQ+vmZmZmXU19/CamZmZtYLH8LaNe3jNzMzMrKu5h9fMzMysFdzD2zZOeM3MzMyaLqDHCW+7eEiDmZmZmXU19/CamZmZNVtAhKclaxf38JqZmZlZV3MPr5mZmVkreAxv2zjhNTMzM2sFz9LQNh7SYGZmZmZdzT28ZmZmZs0WAT1+aK1d3MNrZmZmZl3NPbxmZmZmreAxvG3jHl4zMzMz62ru4TUzMzNrgfAY3rZxwmtmZmbWdOEhDW3kIQ1mZmZm1tXcw2tmZmbWbIFXWmsj9/CamZmZdTFJS0k6QdKTkuZKeknSOZKWybu+pCmSosw2Jv+7q457eM3MzMxaIdr20Np44DjgGuAcYIPs600l7RzRZ8NqrT8TOKHEeZ6r/xYa44TXzMzMrMkCiDYMaZC0EXAsMDki9ina/zzwY2B/4Dc51387Ii7N7SZy4CENZmZmZt3rAEDAj3rtvwh4Bzi4GfWzYRDLSlLNLW4CJ7xmZmZmzRaRhjTkvfVtC6AHeGDR5sRc4M/Z8bzrjwLmALOBOZImS1q/msY2i4c0mJmZmfVfIyQ9VPT1hIiYUPT1asDMiJhXou40YBtJgyPivTLnr7X+88Afgb8CC4GtgGOAnSR9PCIerf7W8uOE18zMzKwFmjSGd2ZEVJr9YBhQKlkFmFtUplzCW1P9iDi8V5mrJF0PTAHOBT5Voa1N4yENZmZmZt3rHWBImWNDi8o0qz4RcQ9wN7CjpKUrlW0WJ7xmZmZmrdCeMbzTScMeSiWto0g9xOV6d/OoXzAVGAAsX0XZ3HlIQ04efvjhmZJeaOAUI0jz1ln1HLP6OG71cdxq55jVx3GrT7VxW6PZDSnlLV6/9fa4akQTTt3XPT8I7AJsCdxT2ClpKLAJqee1mfULPgwsAGZVWT5XTnhzEhEjG6kv6aE+xuBYL45ZfRy3+jhutXPM6uO41afT4xYRu7Xp0lcApwLjKEpYgSNJY28vK+yQtDYwKCKerLP+csCciFhY3ABJewDbAjdnszu0nBNeMzMzsy4VEY9KugA4RtJk4CbeXyntLhZdNOIOUg+46qy/I3CupBtIq6otIPUMH0zqiR7XlJusghNeMzMzs+42jjSG9ihgD1LyeT5wehXLCtdS/yngYWBPYGVgEPAP4ELg7IiYlsO91MUJb+eY0HcR68Uxq4/jVh/HrXaOWX0ct/o4bmVkQwzOybZK5UY3WP8JYGx9rWwuRbR+XWczMzMzs1bxtGRmZmZm1tWc8JqZmZlZV3PCa2ZmZmZdzQlvgyQtJekESU9KmivpJUnnSFqmGfUl7S7pXklvS5olaZKkNfO9q+ZrVdwkLS/peEm3ZWXelfSUpAmSPtScu2uOVn+v9ap7paSQ9LfG76S12vAzOlDScZIeyX5OZ2f//lK+d9ZcrYybkgOz97aZkt6S9Jik0yUtm//dNUcOMTsle09/Lvt5m9pH+a0k3Z7F601Jt0jaJJebaaFWxU3SUElHSrpO0tTs98Fzki6XtEGuN2WdJyK8NbAB5wEBTCZNwnwuMB+4E1gqz/rA3kAP8L/A0cApwD9Jy/6t1u5YdGLcgN1I8wDeCpwEfBEYT1r3+w1gw3bHotNiVqLensDCLGZ/a3ccOjluwGDgFmAe8AvSFD5HZ99zZ7c7Fh0ct7OysncAxwL/Bfw223c/2QPWnb7lELMAXgN+T1qNamqFslsDc4G/Aydk29+Bt4CPtDsWnRg3YP2s7D3AN7PfB2dldeYBO7Y7Ft6at7W9Af15AzYiJaBX99p/bPZDdWBe9Ulz2U0DXgCGF+3fhJSMTGh3PDo0bqOBtUucY+es7FXtjkenxazX8eHAi8CPSXMw9quEt9VxA84k/YHVr39xtvhndCDwNmnuzt6J8KVZ+U3aHZNmxywru1bRv/9G5YT3AeBNYFTRvlHZvtvaHY9OjBuwYqnvJWBDUsL7ULvj4a15W9sb0J834LvZD+Qneu0fmr2B35RXfd5P0L5Z4jx3ALNJywG2PS6dFLc+zvMa8GS749HJMSP1vEwHlqV/Jryt/BldJks2rs6+FvCBdsegH8Rt6SzhubHEeX6YnefD7Y5Js2NW4nyVErd1smtdXOLYxVk8V2l3TDotbn3UexiY2+54eGve5jG8jdmC9MbyQPHOSOtE/zk7nlf9wr/vK3Ge+0kJybrVNrzNWhm3kpTW+/4AaUhIf9DymEnaEjgGGBcRb9bd8vZqZdw+QfqeeljSeaTk901JMySdLak/LfTTsrhFxLvA3cBukk6StI6k0ZIOIw0HuTQinmnsdlqi4felGq8F5X8fCNg8x+s1UyvjVpKkpYBV6T+/D6wOTngbsxowMyLmlTg2DRghaXBO9Vcr2l+qLKSPs/qDVsatnNNIw0R+VU2DO0BLY5YlZxeRPhq9soF2t1sr47Ze9joO2Af4BrAfcC9pvP3FdbS/XVr9M3oQ8Afg+8AzwPOkMdDjgUPraH875PG+VMu1CuctdS1Ycn4f5OHLpIS3v/w+sDr0px6HTjSMNO6nlLlFZd7Lof6w7OtS5YvL9getjNtiJO0LnEh6kO2XfTW2Q7Q6Zl8HPgz8R23N7DitjNsHsq9XAP49Ip7Mvr5S0h+AQyX9ICIer7bxbdTq77d5wHOkBOcW0kfc+5D+MJ1LerCo0zUas1qvRZnrLWm/DxoiaRvScrl/Bc5uxjWsM7iHtzHvAEPKHBtaVCaP+oXXUuWruVYnaWXcFiFpd+Ay0nitz0ekwVv9QMtiJmkd4HTgrIh4rsZ2dppWfq+9m73eX5TsFkzMXrevcK1O0srvt2GkXvBlI+ILEXF5RPw2IsYCVwDfkbRemXN1kkZjVuu1KHO9Je33Qd0kbQ7cSHpOYfdsGIV1KSe8jZlO+ril1A/rKNLHNJX+Kq2l/vSi/aXKQumPtzpRK+P2L5J2I0178xiwSz8bl9rKmJ1Dmqbnmmw85TpZEjwQGJx9vWr9t9JSrYzbP7LXV0qUfTl7Xb6KNneCVsZtX9KnCZNKlJ1E+j318apb3j6NxqzWaxXOW+pasOT8PqiLpM1I05jNJs2q0l/iZXVywtuYB0kx3LJ4p6ShpOnCHsqx/oPZ68dKnGdr0gMyT1fb8DZrZdwKx3YFrgGeBHaOiNfrbXybtDJma5DG1T1GGk9Z2EaREpNnSON7+4NWxq3w0M3qJc5T2PdqVa1uv1bGrZCgDShxnoG9XjtZozGr9VpQ/vdBkD7F6g9aGbfCuTclJbtvkZLdF/K+hnUeJ7yNuYL0xjKu1/4jSWOOLivskLS2pPXrrQ/cReolOkLS8KLzbgzsAEyKiPl130lrtTJuSNoFuJb0B8FOETGr0Rtog1bG7GvA2BLbDOCl7N/fa+BeWqllcYuI54E/AltmvUeF8w7Iyi8AbmvkZlqold9vhTHNXyjRjsK+B0sc6zSNxqxqEfEsKREcK6nwABvZv8cCd0ZEqU8aOlHL4padY1PgdtKUZztmP7e2JGj3vGj9fQPO5/0VYo4gfRw8H5jCoit+TU3hrq9+VnYsi660djJpGpVXKJp8vD9srYobMIY0tnIu6Q314N5bu2PRaTGrcP2p9LN5eFsdN2BTYA5pSMi3SJPn//+s/rfbHYtOjBupZ/dPWdm7geOzn9W7s31XtjsWLYzZIaQH9U4jvbe/XvT1Ib3KbkN62OvvWbzGZf+eA2zc7lh0YtxIn17NJP0ePYMSvw+AZdodD29N+j5rdwP6+5a9WZ8IPJW9+UwjLYs4vFe5cj+oVdUvKr8naZ7Fd7If6qsosZJYp2+tihtwWPZGWnZrdyw6LWYVrj+V/pnwtvpn9KPA9aSlq+eS/kA9rN1x6OS4kWa4OJs05GheFrdHSVO7DWx3LFoYsykV3qumlCj/MdLCQ3NIH8/fCmzW7jh0atxIn4ZW/H0AjG53PLw1Z1P2TWBmZmZm1pU8htfMzMzMupoTXjMzMzPrak54zczMzKyrOeE1MzMzs67mhNfMzMzMupoTXjMzMzPrak54zczMzKyrOeE1M1uCSPqWpJB0SbvbYmbWKk54zawuki7JEqeQNF/SSn2U/1xR+ZB0WIuaamZmSzgnvGaWh4HAgX2UObQVDTEzM+vNCa+ZNerF7LVsQitpBWAPYA4wqxWNMjMzK3DCa2aNug/4O7CppI3KlNkfGAxcDbzbqoaZinHmgAAABndJREFUmZmBE14zy8evs9dyvbyF/RP7OpGkvSRdJ+kVSe9JelXSDZJ2rVBnK0nfk3S/pGlF9W6RtG8f1/uspJsk/TMbizxL0lOSLpe0X6+yO2Tjj6dWON9hWZkpJY4Vxi+PlrSBpF9Jeim77rV5xCKrt17W/lclvSvpSUlnSBpSqZ6ZWbdywmtmeSgkvAdJWuR9RdK6wFbAS8CUcieQNEjSpcD1wGeAlUm9wSOBPYFbJP2wRL3hwP3Aydl1RgJzs9ddgUmSflbmmmcB1wKfBlbKrrc0sC6pV/q8vm+9Lp8AHiL9IbAcsKBXu+qKRVZ3O+CRrP0jgfeANYFvAX8g9bSbmS1RnPCaWcMi4jngj8Ao4JO9Dhd6dy+LiJ4Kp/khcBAwlfQA3AciYjngA8CXgDeBr0s6oFe9HuAm4IDs+kMjYllgeeBY0rjhoySNLa4kaTQpSQb4HjAyIpaNiKVJCea+wI193Xudfgo8CHwka+sw4MSi43XFQtLywKTsfI8Am2T1hgNfADYGjm7SPZmZdayB7W6AmXWNicC2pAT3dgBJAg4uOl6SpA8DxwFvADtlCTQAETEHmCDpDeAK4L+By4uOv0N6IG4REfEG8BNJs7NrH01KBgu2JP3R/2REnNqr7quk8cZXV3PjdXgV+HREvJtdL0jjoBuKBXAMqaf6NWDXiJiZ1ZsPTJTUw/u98WZmSwz38JpZXq4kDSXYW9Iy2b7tgTWAhyLiiQp1DyW9H11bnOD1MhmYB2wkadUa2nVD9rq1pAFF+9/MXpeTNKyG8+XhJ4Vkt4RGYlEYr3xRIdnt5TLghXoabGbWnznhNbNcZD2qNwDLAPtku6t9WG2b7HXf7AGtxTbgH8CgrNyHiitLGijpi9lDai9Lmld4QAx4PSs2lDTMoeBPpCnSVgXuk3SUpDVrvO163VfhWF2xkDQYKMyScVepE2c9yXc33nwzs/7FQxrMLE8TgbHAIZImkRLf+Sz6sXsphV7K4dnWl3/1yGYPrd3K+4kipAe8ZpDG90IakwspGS98zP+6pENIvZ4fBX6Wne8V4DbgFxFRMnHMwYwKx+qNxQpAoQd7eoXy06o4p5lZV3EPr5nl6RbS+NRPksaTLgvcXObj9WKF96LjI0JVbFOK6n6TlOzOJD2YtXJEDIuIlSJiFdKDbAUqvmhE3ASMBo4iDcmYDqxC6pmeImlCzRGozsIKxxqJRTXUdxEzs+7ihNfMchMRC4Dfkt5bzsp2V/OQ1D+z1w3ruGxh9oVjI2Ji9sBZsZV7VygWEbMj4qKI2C8iRpGGBVyUHT5SUvEDcYXpw4ZWOOVy1Ta8jHpjMYv3E+nVKpSrZfyzmVlXcMJrZnkrjNcdRBo/e0OFsgWFMa17SRpUseTiVs9e/7fM8Z1rOVlEPB4RR5Hm9oX04F3BG9nrStmY2VK2qOV6JdQVi4h4D3gs+3K7UmWyWTNKHjMz62ZOeM0sVxHxMGmRg3OAcRExr4pqvyKNt10NOKVSwWyu2WKzs9ePlCg7nDR1V6nz9LUAQ2EWheLVyZ4mzY4gYK8S51yH9x/Yq1cjsShMu3akpBVKVNmfNITDzGyJ4oTXzHIXEd+OiK9FRJ9LCWflnwB+lH35bUkXSFqrcFzScEmfkvRrFp1LF+D32eu5krbPejGRtAVwBzCizGW/LOlWSQcWT+0l6YOSTgV2yHbdWtTO94Drsi/HS/q4pKWybZesLeWmG6tKg7G4gDSGegRwq6SPZnUGSTqYNFRjNmZmSxjP0mBmneIbpGV9v0xaJOJoSW+RxqUux/sPW03pVe804FOk6bmmAHMlLSTNyPAu8DmKktYiAnbJNiS9TZpR4oNFZSZkD7YVOwXYKbvePcA7pM6DocCfgfE0viRxXbHIZp74PHAzMAb4S7bwxlBST/V9pCnLTsbMbAniHl4z6wgRsTAijgY+DlxKWiBhMCnxexG4hjQLw+d61XuOtGrapaTezQGksbaXAVtExG1lLvkb4EjSimVPkJLd4cDLwPXAZyPiSyXa+RywFWmqtRnZ9f5BekhvW95f0KJu9cYiq3sXsGl2XzNIie5U0jCTT5KGZJiZLVGU5iE3MzMzM+tO7uE1MzMzs67mhNfMzMzMupoTXjMzMzPrak54zczMzKyrOeE1MzMzs67mhNfMzMzMupoTXjMzMzPrak54zczMzKyrOeE1MzMzs67mhNfMzMzMutr/ATIQq+A65XMQAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 743.226x572.185 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 743.226x572.185 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 743.226x572.185 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "feed_cols = []\n",
+    "labeled_complete_data = pd.read_csv(\"../../data/csvs/PC88A_HCL_NdPrCeLaDySmY.csv\")\n",
+    "complete_exp_df = labeled_data.drop(labeled_data.columns[0], axis=1)\n",
+    "estimator.set_exp_df(complete_exp_df)\n",
+    "exp_data = estimator.get_exp_df()\n",
+    "for col in exp_data.columns:\n",
+    "    if 'aq_i' in col:\n",
+    "        feed_cols.append(col)\n",
+    "exp_data['total_re'] = exp_data[feed_cols].sum(axis=1)\n",
+    "for species in species_list:\n",
+    "    fig, ax = estimator.parity_plot('{0}_org_eq'.format(species),\n",
+    "                                    c_data=\n",
+    "                                    exp_data['total_re'].values,\n",
+    "                                    c_label='Feed total RE '\n",
+    "                                            'molarity (mol/L)',\n",
+    "                                    print_r_squared=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}