diff --git a/LICENSE.txt b/LICENSE.txt
index 40651d4f1..e0136804c 100644
--- a/LICENSE.txt
+++ b/LICENSE.txt
@@ -3,10 +3,11 @@ pycalphad, a Python library for the CALculation of PHAse Diagrams
 
 The MIT License (MIT)
 
-Copyright (c) 2014-2019 Richard Otis and Zi-Kui Liu
-Copyright (c) 2016-2019 Brandon Bocklund
-Copyright (c) 2016-2019 California Institute of Technology
-Copyright (c) 2018-2019 Materials Genome Foundation
+Copyright (c) 2014-2023 Richard Otis and Zi-Kui Liu
+Copyright (c) 2016-2023 Brandon Bocklund
+Copyright (c) 2016-2023 California Institute of Technology
+Copyright (c) 2018-2023 Materials Genome Foundation
+Copyright (c) 2020      David Walz
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
diff --git a/docs/api/pycalphad.codegen.rst b/docs/api/pycalphad.codegen.rst
index de0f99a16..34b798f60 100644
--- a/docs/api/pycalphad.codegen.rst
+++ b/docs/api/pycalphad.codegen.rst
@@ -12,6 +12,14 @@ pycalphad.codegen.callables module
    :undoc-members:
    :show-inheritance:
 
+pycalphad.codegen.phase\_record\_factory module
+-----------------------------------------------
+
+.. automodule:: pycalphad.codegen.phase_record_factory
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 pycalphad.codegen.sympydiff\_utils module
 -----------------------------------------
 
diff --git a/docs/api/pycalphad.core.rst b/docs/api/pycalphad.core.rst
index 545b3b827..f889fe614 100644
--- a/docs/api/pycalphad.core.rst
+++ b/docs/api/pycalphad.core.rst
@@ -20,18 +20,18 @@ pycalphad.core.calculate module
    :undoc-members:
    :show-inheritance:
 
-pycalphad.core.cartesian module
--------------------------------
+pycalphad.core.composition\_set module
+--------------------------------------
 
-.. automodule:: pycalphad.core.cartesian
+.. automodule:: pycalphad.core.composition_set
    :members:
    :undoc-members:
    :show-inheritance:
 
-pycalphad.core.composition\_set module
---------------------------------------
+pycalphad.core.conditions module
+--------------------------------
 
-.. automodule:: pycalphad.core.composition_set
+.. automodule:: pycalphad.core.conditions
    :members:
    :undoc-members:
    :show-inheritance:
@@ -124,6 +124,14 @@ pycalphad.core.phase\_rec module
    :undoc-members:
    :show-inheritance:
 
+pycalphad.core.polytope module
+------------------------------
+
+.. automodule:: pycalphad.core.polytope
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 pycalphad.core.solver module
 ----------------------------
 
@@ -148,6 +156,14 @@ pycalphad.core.utils module
    :undoc-members:
    :show-inheritance:
 
+pycalphad.core.workspace module
+-------------------------------
+
+.. automodule:: pycalphad.core.workspace
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 Module contents
 ---------------
 
diff --git a/docs/api/pycalphad.plot.rst b/docs/api/pycalphad.plot.rst
index 980b66ca3..b692f492c 100644
--- a/docs/api/pycalphad.plot.rst
+++ b/docs/api/pycalphad.plot.rst
@@ -20,6 +20,14 @@ pycalphad.plot.eqplot module
    :undoc-members:
    :show-inheritance:
 
+pycalphad.plot.renderers module
+-------------------------------
+
+.. automodule:: pycalphad.plot.renderers
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 pycalphad.plot.ternary module
 -----------------------------
 
diff --git a/docs/api/pycalphad.property_framework.rst b/docs/api/pycalphad.property_framework.rst
new file mode 100644
index 000000000..cfa957822
--- /dev/null
+++ b/docs/api/pycalphad.property_framework.rst
@@ -0,0 +1,53 @@
+pycalphad.property\_framework package
+=====================================
+
+Submodules
+----------
+
+pycalphad.property\_framework.computed\_property module
+-------------------------------------------------------
+
+.. automodule:: pycalphad.property_framework.computed_property
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycalphad.property\_framework.metaproperties module
+---------------------------------------------------
+
+.. automodule:: pycalphad.property_framework.metaproperties
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycalphad.property\_framework.types module
+------------------------------------------
+
+.. automodule:: pycalphad.property_framework.types
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycalphad.property\_framework.tzero module
+------------------------------------------
+
+.. automodule:: pycalphad.property_framework.tzero
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycalphad.property\_framework.units module
+------------------------------------------
+
+.. automodule:: pycalphad.property_framework.units
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: pycalphad.property_framework
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/docs/api/pycalphad.rst b/docs/api/pycalphad.rst
index 043dc8b0d..d850dd77f 100644
--- a/docs/api/pycalphad.rst
+++ b/docs/api/pycalphad.rst
@@ -12,6 +12,7 @@ Subpackages
    pycalphad.io
    pycalphad.models
    pycalphad.plot
+   pycalphad.property_framework
 
 Submodules
 ----------
diff --git a/docs/conf.py b/docs/conf.py
index 233eabf6f..160162aaf 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -47,7 +47,7 @@
 
 # General information about the project.
 project = 'pycalphad'
-copyright = '2015, pycalphad Development Team'
+copyright = '2015-2022, pycalphad Development Team'
 author = 'pycalphad Developers'
 
 # The version info for the project you're documenting, acts as replacement for
diff --git a/docs/examples/LegacyEnergySurface.nblink b/docs/examples/LegacyEnergySurface.nblink
new file mode 100644
index 000000000..3cea2979d
--- /dev/null
+++ b/docs/examples/LegacyEnergySurface.nblink
@@ -0,0 +1,3 @@
+{
+    "path": "../../examples/LegacyEnergySurface.ipynb"
+}
\ No newline at end of file
diff --git a/docs/examples/LegacyReferenceState.nblink b/docs/examples/LegacyReferenceState.nblink
new file mode 100644
index 000000000..124ecb423
--- /dev/null
+++ b/docs/examples/LegacyReferenceState.nblink
@@ -0,0 +1,3 @@
+{
+    "path": "../../examples/LegacyReferenceState.ipynb"
+}
\ No newline at end of file
diff --git a/docs/examples/Metastability.nblink b/docs/examples/Metastability.nblink
new file mode 100644
index 000000000..473bb9d28
--- /dev/null
+++ b/docs/examples/Metastability.nblink
@@ -0,0 +1,3 @@
+{
+    "path": "../../examples/Metastability.ipynb"
+}
\ No newline at end of file
diff --git a/docs/examples/index.rst b/docs/examples/index.rst
index e98ac9f24..8827811c0 100644
--- a/docs/examples/index.rst
+++ b/docs/examples/index.rst
@@ -6,10 +6,20 @@ Examples
 
    BinaryExamples
    TernaryExamples
+   Metastability
+   ReferenceStateExamples
+   EquilibriumWithOrdering
+   CementiteAnalysis
+
+Advanced Examples
+=================
+
+.. toctree::
+   :maxdepth: 1
+
    UsingCalculationResults
+   LegacyEnergySurface
+   LegacyReferenceState
    ChargedPhases
-   ReferenceStateExamples
    PlotActivity
-   EquilibriumWithOrdering
    ViscosityModel
-   CementiteAnalysis
diff --git a/examples/BinaryExamples.ipynb b/examples/BinaryExamples.ipynb
index 2171e3687..32a33deb0 100644
--- a/examples/BinaryExamples.ipynb
+++ b/examples/BinaryExamples.ipynb
@@ -73,14 +73,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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zZjRwJ/Bm9P1E4LykJs8yN56wD3PyZjJWTQ4ZrckuRRx53kkMGKR5p0QkXKHOJJsKNJOsiIjUlWaSTZ6wL/GIiIiI/A8VKFJvL0+Zx5ylP4QdQ1JE6ef/oWz29LBjiEiaC7sHRdJcaVmE4//xOvvt2ZYR5y+ueQPJeKff/Cg5XXvS/S412opI3alAkXrJy81h2i2/pVu7lnSgWdhxJAVEbj8JmujvgojUjwoUqbdBPTuFHUFSSE6PbcKOICIZoE49KGbW2My2MrN2iQ4kIiIiUqsCxcxamdmZZvacmeUDhcASYLWZFZvZ52Z2s5ntltS0knY+nL2ECx9+l9KySNhRJA24O0V3XUPpp++GHUVEQlbtJR4z6w78iWAitJbRxeuAr4E1BBOodSB4yN8w4BIz+4Lg+TpPJyWxpJWP5yzjoQ9m0OzXH9G0RdhpJNXllpRx5r8fYaMtpe/uB4YdR0RCVOVEbWZ2LcG0802At4FngEnu/m0lY1sAuwCHACcAPYDPgDHu/mVyoieGJmpLvuKSUpo0UruT1I6XbIG8RgTP/BRJbZqoLXmqu8RzCTAe6Onuh7v7Y5UVJwDuXuDuH7j7FUAv4EigEXBUogNL+lFxIvGwRo1VnIhItZd4tnP37+PdYfQBf68Br5lZ1zonExERkaxV5RmUuhQnAGY2IGYf+XXZh2SP/LWbGHzxBP41fUHYUSTNeVkZBef8kuLnx4cdRUQSoKYm2evd/era7szMtgPeAbrXN5hkh5wco2mjPGblfMVnPBZ2HEljRoSjGy8jJ28hg8IOIyL1Vu3TjM0sApzr7vfWuCOznsBHQA93z01cxORSk6yIiNSVmmSTp6Z5UBYC48xsVHWDorcjvw9sDdyQoGwiIiKSpWoqUA4hmO/kSTPbp7IBZtYZeBfoA/zD3f+U2IgigQ2bi8OOIBnKCwvwsrKwY4hIjGoLFHefDxwOlAKvmNmOsevNrD1Bz8n2wH3ufnGygkp2e33qt7Q9eRyfzF0WdhTJMB6JsOnYoRT9449hRxGRGDVOUOHu06KXeF4D/m1me7j7YjNrQzCB2yDgUXc/J8lZJYvt1KsTRx7SmVe2eoC3wg4jmSUHdj9mWzoP3IqhYWcRkR9V2yT7s4FmvwGeAOYBhwFPA7sBzwKjvbY7SjFqkhURkbpSk2zy1HqKT3d/Otpv8g9gLtAYmAicmK7FiYiIiKSmWj3NuJy73wHcTFCcvAEc5+7qLJOU9MHMxWwuLgk7hmSosnlfElmtuShFkqXaAsXMyiq+gMsAJ7jDp6iSMaUNEVykOl99t4r9xz7LvW9+EXYUyVAFZx1O0bhaz2MpInGq6RJPXZ7Ypad8SegG9ezIUxf9khaDlzGWsWHHkQzU66aD6d/tAEaEHUQkQ9W6STZTqUlWRETqSk2yyRNXD4qIiIhIQ1CBIlKJjYVb+OuLk1m1fnPYUSRDlLzzMqVT3g87hkjaqLIHxcy6u3u9pu00s27uvrw++xAJw7Rv87n66Y/Z0m05tseXYceRDHDqA4+T27UXPXbbP+woImmhyh4UMysE7gNudffva71DMwOOAMYCL7v7tQnImTTqQZGqfLN8LX27tQs7hmSIyNpVWKMmWMvWYUeRBFIPSvJUd4nnFmAM8J2Z/dvMTjWzvpUNNLOWZnaAmd0MLAFeAoqif4qkJRUnkkg57TqpOBGJQ5WXeNz9GjN7APgzMBo4GMDMNgArgLVAU6AD0I2g2DHgC+ASd38mqclFREQkY9X0NOOl7j4G2Ao4F3iZ4MzI9gTP4RkMdCYoSv4O7OHuQ1WciPyvJz+czWl3/zvsGJIGSia/w+Y/nY4XF4UdRSQ0tXoWj7tvIOhHuQ/AzBoRnDkpdPf1yYsnkjnmLlvDO1/P55rIWEz3z0k1Bi//ir2/+opmW4qhSdOw44iEQhO1qUlWRETqSE2yyaN/x4mIiEjKUYEiIiIiKUcFikgG+Nf0BXQ+7W7mfb8m7CjSwApvuICCC0eFHUMk4WrVJCsiqa1r2xZsu10jxjcfR8uww0iDGtbnO/q37kClk1SJpLFQm2TNbBHQq5JV/3L3X0THnANcSjDXyizgInf/KGYfTYC/Ab8BmgHvAue4+9LaZFCTrIiI1JWaZJMn7Es8uxAUHuWvoYADzwGY2a+BO4AbgJ2BT4B/m1nPmH3cDhxDUKDsDbQGXjez3Ib5CCIiIpJooRYo7r7K3fPLX8DhwAaiBQrwB2CCuz/g7nPc/XxgOXA2gJm1AU4HLnX3t919OvBbYCfgoIb+PCKZZktJGUt/2Bh2DEkALysjkr8k7BgitRZ3gWJmnczsd2Z2h5k9WGH5rmbWrC5Bog8ZPB14wt0LzawxMAx4q8LQt4A9oj8PAxrFjnH3JcCcmDEiUkdXP/MR2577AOsKNKNputvyzD1sOm4Yke+/CzuKSK3E1SRrZqcD4wiewWMEl2POiK7uAkwmeMDgQ3XIMhLoAzwQfd8RyCV47k+sFfx0dqQrUAasrmRM12o+x5hoTnr27FnVMJGsd8LeAylom88/mt+EhR1G6qXtvus4JPIb+nfuHnYUkVqpdYFiZiOB8cCXwDXAIcDvyte7+0wzmwUcRd0KlDOBz919Rh22jYu7jyf4LAwfPjy7p9IVqcbg3p25u/fxYceQROhBcAFcJE3Ec4nncoL+j33dfSKwspIxXwID4w1hZp2BI/np7AkEZ0XKCM7MxOoC5Ed/zic4y9KxmjEiIiKSZuIpUIYDr0cfHFiVpVRzaaUapwDFwNPlC9x9CzCN4NJPrJEEd/MQXV8SO8bMegADYsaISApYsGIdb/x3YdgxpAZlc6ZTNmd62DFE4upBaQwU1DCmLcFZj1qLNseeATzj7psqrP478LiZfQZMIriktBXRpyq7+3ozewi4xcxWAj9Et/kSeCeeHCKSXNe9MJnHP5zFFU9sIrdR2GmkKife/AwtvQVdH50WdhTJcvEUKIsI7pqpzm7A13Fm2A/oC5xYcYW7P2tmHYCrCeZJmQkc7u6xbegXAaXAs/w0UdtJ7h5XoSQiyXXTCftw9sFD2LVRt7CjSDXK/nJC2BFEgPgKlFeBy8zsOHd/vuJKMzuVYP6Rq+IJ4O7vQ9U3CLj7PcA91awvBs6PvkQkRXVp24IubVuEHUNqkNtLk+ZLaoinQLkFOB542syOBdoAmNl5BDO4jgK+Ae5MdEgRERHJLrVuknX3tQSXYz4GjgMOJjjzMS76/hPgQHevqU9FRCRuy9du4oKH3mXFOv0nJmxbXplAyZv/cyJdJKHimqgt2vuxn5ntBIwAOgDrgU/dXR1VIpI0Mxev5q43prN+10n0aRsJO05WO2HiszRrsxU9Djku7CiSwWr9NGMzew+Y5O5/Sm6khqWnGYukj3UFRbRt0TTsGFnPiwshJxdr1DjsKKHT04yTJ555UHYnmBRNRCQUKk5SgzVppuJEki6eAuUbYOtkBREREREpF0+B8iDwCzPT0/VEJG2sLyhmnz89zSuffRN2lKxR/Nz9bL78f6a2ajBl875k05kHU7Z4fr33VbzgDLYsuToBqSRe8TTJvkYwpfwkM7sZ+JzgeTf/08Ti7osTE09EpH4i7mwoLGZ6yZd8wZNhx8kKuxRPY0jBBpqHFaC0lM0FK3i4bBxraF+vXf2y7FPa5Q5g2wRFk9qLp0k2QlCMGJUUJTHc3eO6OyhMapIVEZG6UpNs8sRTSDxG9YWJiIiISELUukBx91OSmENEJKWUlJbRKE83LiaDl2yJ6y4gLy3B8hruCZMeKQHLI3iWrYQlbS7FiIg0lGuf/4R/vD6Vxff9jlbNdDttIpV9O5uC0w6k2djxNNr//2oc74UFbBo1hEajTqPpmVckPZ97hMIvepHb/jia9L6jTvuYNm1a57y8vAeBQcR3M0o2iQAzS0tLzxg2bNjKygaoQBERqWDPft35YsM33Nr4Bv12SbCmHQvZ55DtsG02MKI2GzRpRqNDjmPWTs14jbFJTgcY7NuxD+1admJIHXeRl5f3YNeuXQd06tRpbU5OjlojKhGJRGzVqlUD8/PzHwSOqGxMPE2yD9fyuO7up9dybOjUJCsiInVVWZPsjBkzFuy4444qTmoQiUTsq6++ajd48OBtKlsfzxmUU2pYH3uHT9oUKCIiIgmWo+KkZtHvqMqTlPGcvexTxWtnYAywFHgWqLQSEhFJd0VbSvl8/vKwY0iSeNlmygr03NtUEc9dPN9Vseo7YIaZvQl8CbwDPJSAbCIiKeWOf07jj09+yNw7Tqdf9/pNACapp2T5LZQs+wvNhiwip0mvhO2346l3Dv5hY1HCej47tGpauvqR82dUNyY3N3dY3759C8vfv/rqq/P79eu35f33329+6aWXbr169epGzZo1i+y0004FDz744JJWrVpFnnvuudbXXntt98LCwpzGjRv7XnvtteGBBx5YWt1x+vfvP3C77bYrev311xeUL3v44Yfb3XDDDVstWLCg6QcffDBnn3322VyXz5mwL8zdl5jZa8CFqEARkQx00n47sLr1Yp7qNg7dgJp5WnbawCFNLmfHBBYnAIksTmq7vyZNmkTmzp07O3bZkiVL8k444YRtH3vssQUHHXRQAcAjjzzSbt26dTlz585tfPHFF/ecOHHi/J133rmotLSU2267rVN1x5g+fXrTSCTCZ5991nLDhg05rVu3jgAMGTKk8MUXX5x/5pln9q7Hx0z4XTwrgL4J3qeISEro1q4ltx54HHBc2FEkGZoA1f5KTm+33XZb51/96lc/lBcnAKeeeupagAsuuKDHxRdfvHznnXcuAsjLy+Pyyy9fVd3+Hnvssfa/+tWvfpg7d26zp556qu3vfve7NQBDhw4tSkTehN1BZ2a5wAHA+kTtU0REROJXXFyc079//4H9+/cfOHLkyG0BZs+e3Wz48OGVXm75+uuvm+22225xXYp55ZVX2p988slrR48evea5555L+DXPWp9BMbN9qtnH1sCpwBCCpx6LiGSMb/PX8vaM7zjr4MGaXTTDla59HcttSW7r/cKOUi+VXeJJpA8//LB5+/btS/v27bulT58+W84+++zeK1asyO3SpUtZoo4RzyWeD6j+WTwGfAhcWp9AIiKp5tEPZnH9i5P5ZpdXadVed49mstMW30XjRt3pOrDaHtS0NGDAgMKpU6c2P/HEE9dVXLf99tsXTZkypfmIESMKK9n0fzz++OPtFyxY0LR79+47AhQUFOQ+8cQT7S6++OLVicobT4FyLZUXKBFgLfCZu3+WkFQiIinkylG785u9BjCgfYewo0iSRQaciVnDPfenIV1yySUrd9tttwFHHHHE+gMOOKAA4NFHH217wAEHbLriiivyjzvuuG0POOCATTvttFNxWVkZt912W6fLLrvsf/pQysrKeO2119p/8cUXs3r37l0C8Nprr7X661//2i2UAsXdxybqoCIi6aRp4zwG9FBxkg1yGndP+D47tGpamujbjOuy3dZbb1362GOPLbj00kt7/PDDD41ycnJ899133zRq1KgNW2+9deHNN9+85De/+c02hYWFOWbGyJEjK+0pfeONN1p26dJlS3lxAnDYYYdtPPXUU7f57rvvGv3nP/9pcemll/Zcu3Zt3tFHH913wIABmz/++ONv4s0bz1T3+wCL3H1xNWO2Bvq4+4fxBgmLproXEZG6qmKq+0WDBw9O2JmETDZjxoyOgwcP7l3Zunju4nmfmqe7Pyk6TkQk7T307pdc9dRHYceQBuQlqyma/xsim2eGHSXrxXPKqTat6+XP4hERSXsfz13GRwu/pdHod8OOIg2kQ8kqfrv2Bea234EhzQeFHSd0l19+eddXX331Z7cQH3nkkWtuvvnm/GQfO9ETtfUCNiZ4nyIioXjk3MPCjiANrTmwy90MCTtHirj55pvzG6IYqUy1BYqZ/bnCov2qmAMgF+gJHA98nJhoIiIikq1qOoMyNuZnB/aLvqqyDPhjvRKJiIhI1qupSXb/6OsAgv6SCTHLYl/7ADsAvdxdz6qWBrfnVU/yp6cz5+Tdr26byGl3/zvsGFlpY+EWBl70MA++82XYUSQkW76/lcKvdsa9TnfzSoJUewbF3f9T/rOZPQq8ErtMJFX07tSGDW2XMfZnJ/3S15KOjenbuGfYMbJSXo7Ru1Nrlrf6lrG8FHYcCcEOjb5gRJNWdE/c4+qkDuKZqO3UZAYRqY8nL/pl2BESauzJYSfIXs2aNOJfVx0bfXd0qFkkJJ1I6FONC6Z1HEzpD4m7KSWvQ2mLYaurnYu/efPmO2/evPm/5e/HjRvXYerUqS0ee+yxxQB33XVXhzvuuKOrmXlubi7HHXfcD9dee+2KY445pvenn37aqlWrVmU5OTk+bty4xbFPP66opKSEzp07D/7Nb36z+p577llWvvxXv/pVrxkzZrRwd7bZZpuiZ599dlGbNm0i8XxMlYciIiLJlMjiJAH7e+6551rfc889nd9+++158+bNmz19+vQ5bdq0+fEhf9dff/3SuXPnzr7++uuXnXPOOb2q29fLL7/cuk+fPsWvvfZau0jkp/rjvvvuW/L111/Pnjdv3uwePXpsufnmmzvHmzOuAsXMupnZ3WY238wKzayskpcu2knc1hUUUbRFf3USbdX6zZSVxfWPlqxVXFLK2k1FYceQFOElP+CRkpoHpqFbbrml20033bS0fKr6Zs2aeWXP0Dn00EM3LlmypEl1+3r66afbn3POOSu22mqrLe+++26L8uXt27ePAEQiEcqnzo9XrQsUM+sOTAXOAgqAJsBi4BugjKCJdgagaRclbjv8/hFOuUtNoYm0cn0BPc66j7++9GnYUdLC2ePfZtvzHqC2j/+QzOWRYjZ/0Ycti38fdpQ6Ky4uzunfv//A8teNN964Vfm6b775ptmee+65uaZ9PPPMM2379u1b5dONN2/ebJMmTWp9/PHHrzvuuOPWPPHEEz+b0O3YY4/t3alTp8Hz589v+sc//nFlvJ8hntNEfwa6Aoe4+ztmFgEecfdrzawH8ADQGzgw3hAiVx69GwVdl2RMk2sqiLSEEaMasdXwdWFHSQsn7jOQ5r3W8xf7S9hRJGw5sGv33ejcqh/Dax6dkpo0aRKZO3fu7PL35T0otdn26quv7nHzzTd3a9++fclDDz20qKpxzz77bNvdd999Y8uWLf3EE09cO2TIkK1KS0uX5OUFpcULL7ywqLS0lFNOOaXnww8/3O7CCy/8IZ7PEE+Bcgjwhru/U3GFuy81s+OAmcBfgAtqu1Mz6wbcBBwOtAIWAGeX3y1kwXmha4AxQDtgCnCuu8+K2Uc7YBxwRHTRROB8d18Xx+eTEJ172FBgKHBk2FEyRy7wq7BDpI8DduzFATtWe7ldsslWNQ9JV9ttt13hpEmTmh9xxBGVzvx+/fXXLz311FPX1rSfZ555pv3UqVNbdu/efUeA9evX57722mutjz766A3lY/Ly8jjhhBPW3HLLLV3jLVDi6UHpCsyKeV8GNCt/4+6bgLeJ4zeMmbUFJhFcHvoFMAA4H4g9FXQZcHF0+S7RdW+bWauYMU8R/HY7NPoaCjxe2xwiIiLZ4rLLLsu/4ooreixevDgPoKioyP7+9793jGcfa9asyfn8889bLl269Mtly5Z9tWzZsq9uuummxU899VT7SCTCzJkzm0DQg/Lyyy+37du3b9wNXvGcQdkANI55vxboXmHMeuK7OesyYLm7nxSzbGH5D9GzJxcBN7n7i9FlJxMUKaOB+81sAEFRspe7T46OOQv4yMz6ufvXceSRBFq4Yh3L1xawR/+Kf00kVS1ZvYFFKzew98AeYUdpEOsLivl47lJ+MWzbsKNIivGyTZRteJ/ctr+kLg2eP5PXoTTRtxnXZ/Nf//rX6/Pz8/MOPPDAfu6OmXHCCSf8T5NsdZ588sl2e+yxx8ZmzZr92LR1/PHHrxs7dmyP4uJiO+mkk/ps2rQpx91twIABmydMmPBdvDmttg1hZvY5sNjdj4m+f5vgjMf27r7ZzHKAL4Cm7r59Lfc5G3iDoNDZH/geeBC4293dzLYBvgV2dffPY7b7J7Da3U82s9OAO4DWHv0w0cJmI8FlnkeqyzB8+HCfOnVqrb4Dic8xt77CmzMWsemJi8KOIrX023H/5NlP5lL45O/Jzc38WQhueWUKlz/xITP/cSo7bB3XPyAlw5Xkj2PLdxfSdNBUclsMq3KcmU1z95+1qsyYMWPR4MGD4/qFn61mzJjRcfDgwb0rWxdPRfcuMMbMGrl7CfAo8BjwSbRY2Ytguvsb4tjnNsA5wD8I+lCGAHdG191FcFkJYEWF7Vbw09mbrsAqj6m0osXNypjtf8bMxhD0tNCzp2brTJa/nbQf+639SI2vaaTjb4xbDxqaFcUJwFkjh1DaYznPb30Xz4cdRlJKo05b+EWT6xncfGjYUbJWPAXKQwSXdToSXJZ5wsyGEfSG7BQd8wzw1zj2mQNMdfcrou//a2Z9gXMJCpSkcPfxwHgIzqAk6zjZrk+Xtpzf5f+A/ws7itRWgmfQTHVtWjThyuFHAUeFnERSTi7BbRnCb3/7256ff/55y9hlZ5999op4m17jFc9U998AN1dY9nszu4HgTMgid694pqMmy4HZFZbNAS6M/pwf/bMLwZwrxLzPjxnTycyswiWezjFjREREpA4ef/zxxTWPSrx4Jmo7ycwOqbjc3Ve5+5Q6FCcQ3MHTr8Ky7YHyZpqFBEXGyJgcTYG9gU+iiyYDLYERMfsYAbSIGSMJVloWYeyzk5i1RJdZs9HMxasY++wkStNwltpp3+Zz/QuTiUR08lSqV7JqAqVrXg47RtaK5xLPwwT9IW8m8Pj/IOhhuQp4FtiZYA6VK+HHXpLbgSvNbC4wD7ga2ERwazHuPsfM3iC4o2dMdL/3A6/rDp7kyV9XwF9f+pTlTRbTbWt9zdnm42mN+OClJpxx0E706NCq5g1SyEtTvuHW16aw8dA3aday5vGSvU5Zfg+NG/Viq/Z6aGQY4ilQ8knwwwXd/XMzO4qgsfZPBJdx/gTcEzPsFoL5Vu7mp4naDnb32AlmRvPz4mkicF4is8rP9ejQiqX3/45OrZuTQz1vwZO0EznSWbX/Zrq0rdXElCnlL7/ek4t+MYxOLZuHHUVSnA/8PVhin/MntRdPwfEGsH/0duKEcfd/uvtgd2/q7tu7+7iKd+S4+1h37xYds6+7z6ywj7XufqK7t46+TtQsssnXpW0LcnJUnGSjnBxLy+IEIC83h05tVJxIzSyvDZabnn/PmzdvvnPFZX/4wx+2+vOf/9wFggnULrvssm69evUa1Lt370G77LJLvylTpjSravtx48Z1OOmkk3pW3M8xxxzTu3v37jv269dvYO/evQcdffTRvb/99ttGifgM8ZSGVwGfAg+Z2aXuruYDERGRGmw4rO9gNqxN3KmY1u1KW//7mxn12cVNN93UacqUKS1mzpw5u1WrVpGXXnqp9ahRo7abM2fOrNatW8fVXFY+NX4kEuG6667rfOCBB/abO3furKZNm9ar0SuesyFPE8wUexKwxMzmmNn7ZvZehde79QkkqWPl+gIOHPssH89ZGnYUSUPXPPsxf5jwXtgxiEScY//2KhPen1nzYJEYkcK5FM7en0jh3PrtKJHFSYL2N27cuG733nvv4latWkUARo0atWGXXXbZNH78+PY1bVuVnJwcrrnmmpUdO3YseeGFF9rUN2M8H3K/mJ+bENx9U/EOHAC1xmeIoi1lLFi5no8KpvIOD4YdR9LMxB8a03pzZ+CAUHNE3Pk2fx2T133FIl4INYukly5l33Nk8X9ZWvYfdqV/2HESZs2aNTmFhYU5AwcO3BK7fNiwYQWzZ89uVtV2tbXTTjttnjNnTtP67ieeeVCyY2pJ+VHPTq1ZeE/5jVFHhRlF0tDYc8JOEMjLzeG/fzs57BiSjloCO4/P5Acbx8XManUCoraP0KmJig4REZEs0r59+0izZs0is2fPjn0AMNOnT28+fPjwAoAmTZpEioqKfrwLYs2aNXkdO3as1UMKv/rqq+YDBw4srG9OFSgiIiJZ5rzzzss/99xze27atMkAXnnllVbz5s1rdsopp6wF2G233Tbed9997QE2bdpkL7/8cruDDjpoY3X7jEQiXH/99Z1XrVrV6JhjjtlQ34xxFShmlmNm55vZp2a23sxKY9btbGb3mFmtnmQsqWPS3KU0G/0PJs1VM6w0jM3FJXQ9427++uLkpOy//wUPcc4Dbydl35JdiuaNonD2/mHHiFtRUVFOly5ddip/jR07tkvs+iuvvHLlsGHDCnbYYYcdunfvvuOYMWP6vPfee/OaN2/uAPfee++SV199tV3//v0HDhs2bMBRRx219rDDDttU2bGuvvrqHv369RvYp0+fQVOnTm3x3nvvfV3fO3ggjh4UM2sM/JugWXYNsJHgCl25hcBpwCrgmvoGk4bTvX0r/m/YtixpP4uxaoaVBhDJg62GNSand12ekFGzXw7bhka9ftCTtKXehrbeTL+ybSu9I6TWWrcrTfRtxjUNiUQi06pbn5OTw9/+9rflf/vb35avX78+57DDDtv2uuuu63rXXXctA+jTp0/J+++/P7+ybf/+979/X/7ziy++uCjO9LVmtW1miU5Hfx0wFrge+DPwJ3fPjRnzFtDa3XdPfNTkGD58uE+dOjXsGCIikobMbJq7D49dNmPGjEWDBw/WXGG1MGPGjI6DBw/uXdm6eC7xnABMcvdr3T1C5bcTLwR6xh9RRERE5CfxFCh9CGaSrc4aoM6TvEjyrdlYyJLV9e5dEkmqb5avZXNxSa3GFm0p5etla5KcSLJZZPOXCbt1VmovngKlCGhbw5iewLq6hpHk++2d/2K3K54IO4ZIldYVFDHwooe56qmPajX+uhcmM/D3D5O/ttL+PZF6KdvwAYVfDabsh6fj2SwSiUT0oLIaRL+jKqfVj6dp5wvgYDNr7O5bKq40szbAIcAn8YaUhnPlqN14Z91nah6U1NUCDj0jjyEDims1/JT9B7G5w3Lubfs3PVdbEi6vRQlDeh5Mm7Yt2bv2m81ctWrVwE6dOq3PycnRqZdKRCIRW7VqVRugymdQxFOgjAeeBJ40s9NjV5hZW+ARoB1wX/xRpaHs2b8He9IDGBV2FJGqjaz90L7d2vGPbr9KXhbJbrlAt/g2KS0tPSM/P//B/Pz8QWi+sapEgJmlpaVnVDUgnqnunzazkcApwBHAWgAzmwrsQPB8nrvd/V/1SSwiIpLOhg0btpLg96TUQ1yVnbufRjDXyWygE2DAUGA+cLq7n5/whFJn/12wgn9O+zbsGCIiaa1s46eUrX8n7BhZJ+6JY9x9AjDBzJoRXNJZ7+4FiQ4m9Tf2uU/4YO5Cfj9Bd+2IiNTV8Usm0GlLCR2GLAs7Slap88x27l4I1PthQJI84393MOsKiumnO79FROosst2ZECkKO0bWibtAMbOWwNHAzkAbYD3wX+Bld9d9fimkS9sWdGnbIuwYIiJpLadx97AjZKW4ChQzO47gLp228LM7+hy43czOcvcXEhdPREREslGtm2Sjd/A8DbQCHgNOBQ6L/vl4dPnTZnZQEnJKFZ76aDbXv5CcJ8KKiAh4pIjihWdTtmlK2FGySjxnUP4MFAN7u/v0CuseNbO7gA+j49Tu3ED+NX0BH367kNJj3ww7iohIRmpRuokzVj/E8maNGNhyt7DjZI14nma8CXgueqtxVWMmAMe6e8vExEu+dH+asbtTFnHycjUXkIhIsnhkC5bT+H+WV/Y0Y0mMeM6gFAPLaxjzfXScNBAzIy9XE3yLiCRTZcWJJFc8/+z+CNizhjF7ElzmEREREamzeAqUy4GdzOwmM/vZvatm1sLMbgEGAX9MZEAJfD5/OQMvephZS1aHHUVEJOuUrHqcwpnD8TLNptFQ4rnEcznwJXApMMbMpgMrgC4E0923ITh7crnZzy45uLufjtRLXk4OkSaF3JtzNx3RwzFFRBpSv5yZ7JdTQlc9M7vBxNMkG6njMdzdc+u4bdKle5OsiIiER02yyRPPGZQ+SUshIiIiEqPWBYq7f5fMIPJzGwu30KJJI3JydDpRRCQVeFkB5DTFLGUvCmQUTZ6RgtYXFNPtzHv4y/OTwo4iIiKAexmbv+jFlkUXhB0la9TlYYE5QHegB9CosjHurluN66Fl00acf9hQOg5ey1jGhh1HREQMRnTZkU4tezE07CxZIt6HBV4KXAJ0rGGozn/VQ25uDjeesE/03f+FmkVERKJ6hB0gu9S6QDGzsQTP2fkBeBRYBpQmJ5aIiIhks3jOoJwOLACGufv6JOXJWv+ZtYSh23ShVTNNpywikoq8bBORgqnktt4v7ChZIZ4m2Q7ARBUnifdt/lr2u+YZbn9d87GIiKSqkvxxFM3Zn0jhvLCjZIV4zqDMB9ol8uDRy0bXVFi8wt27RtdbdP2Y6LGnAOe6+6yYfbQDxgFHRBdNBM5393WJzJpM23Ztx3N/OIJGOyxWU6yISIpq1nkzhzb9M4ObbR92lKwQT4FyD3CdmXV19/wEZvga2C/mfVnMz5cBFwOnRMf9GXjbzPq5+8bomKeAnsCh0fcPAo+TZt2lx+3RD+jHUYwMO4qIiFSmEcG1BGkQ8UzUdp+ZbQ9MMrNrgelApZd73H1xHBlKKyt4omdPLgJucvcXo8tOBlYCo4H7zWwAQWGyl7tPjo45C/goWsR8HUcOERERSRHxTtQ2g+BSy8PAF8DCSl4L4tznNmb2vZktNLNnzGyb6PI+QFfgrfKB7l5I8EDCPaKLRgCbgE9i9jcJKIgZk5Len7mYxz6YGXYMERGJQ8mKeyjb9HnYMbJCPLcZnwHcT3Br8QfA99T/NuMpBJdv5gKdgauBT8xsB4LiBIInJsdaQTBRHNExqzzmiYfu7ma2Mmb7yj7LGIK+Fnr27FnPj1A3d/xzGpO+/Y4F+70QyvFFRCQ+OZEyLlh8E2s7HkaflhPDjpPx4ulBuYTg8soe7r4wEQd393/HvjezTwnOwJwMfJqIY1Rx3PHAeAieZpys41Tn8Qt+webiErrQIozDi4hIvHIgMvh3tMlrH3aSrBDPJZ5ewAuJKk4q4+6bgFlAX6C8L6VLhWFdYtblA52i/SrAj70rnWPGpKRWzRrTpa2KExGRdJLTuDuW0yzsGFkhngJlGVU8eydRzKwp0B9YTtDPkg8/3dYSXb83P/WcTAZaEvSilBsBtODnfSkiIiKSRuIpUB4DDjezVok6uJn9zcz2NbM+ZrYb8AJBcfFotK/kduByMxtlZoOACQRNsU8BuPsc4A2CO3pGmNkIgj6Z11PxDp5LH/uAv7+m5ioRkXRVuuZliuaPxl1Pekm2eHpQbgB2At4xs8uBaTFzkdRVD+BpgocPriLoO9nd3b+Lrr8FaAbczU8TtR1c4bijgTuBN6PvJwLn1TNXUkz9Np+mnQvZwD/DjiIiInWwS/En7L5pJk0iWyA3ruftSpws5gaY6gealU+gZkB1G7m7p83/asOHD/epUzXFvIiIxM/Mprn78LBzZKJ4ComPqL4wEREREUmIeGaS3S+JOURERER+FO9MshKns8e/xR5XPhl2DBERSYBI8WIKpnWh9Ifnw46S8erUK2JmLYDtgZbu/lFiI2WWHbbuyKpG+XpKsYhIBmiaW8ihLdtR0nglu4QdJsPVukkWwMx6AHcQPCk4l5iGWDPbi2B21nPc/YPER00ONcmKiEhdqUk2eWp9icfMuhHc5nsk8DrBJGkWM2QKwQyuv05kQBEREck+8fSgXENQgIx091HA27Er3b2E4E6fPRMXL33NX7427AgiIpIEXroGL10TdoyMF0+Bcjgw0d3fr2bMYmCr+kVKf+999R19z3+QFz9NuclsRUSknormjKRo7uFhx8h48TTJdgG+qWFMCejxvMO37cp1x+9F3sDFjOXpsOOIiEgC9e2+HTvYTgwJO0iGi6dAWQNsXcOY7Unxpwg3hNbNm3D1scHzC4/86VmHIiKSCdqHHSA7xHOJZxJwhJl1rWylmfUFDgWquwQkIiIiUqNqCxQzO8nMdoq+vRVoCvzHzA4DmkfHtIi+fw2IALclMW9KW7RyPa9P/TbsGCIikkReslITtTWAmi7xTADGAl+6+xQzOwu4l+A243Ibon+WAqe5+6xEh0wXN708hQfencHljxXQuGnYaUREJBn2WPEBey77DzktFoQdJaPFNZOsuz9sZh8B5wC7Ax2A9cCnwF3untW3rVx3/F6cuv8gdmua9TcyiYhkLO+6gUjrL8hp2ifsKBkt7qnu3f0b4PdJyJL2OrVpTqc2zcOOISIiSWR5rcltvU/YMTKeHhYoIiIiKac2Z1DamlnPeHbq7ovrmCctvTxlHt/mr+OSI3cNO4qIiCSZl21ky5KradTlnLCjZLTaFCgXRl+15bXcb8Z4fvI8Ppi3gE1H/ivsKCIikmQdildxyop7mddCl/STqdqnGZtZhKAJdl08O3X3tOkcSsTTjEvLImwpLaN5k0YJSiUiIqnMS9djeW30NOMkqs2Zjn+4+7VJT5LG8nJzyMtVO4+ISLawvDZhR8h4+q0qIiIiKUcFSj3c++Z/OfT654lEqr5MJiIimado3jFs+f7msGNktKxqZk204pIylhevZqz/RZWeiEgWOSbyJU0ircOOkdFq0yQ7NpN7UBLRJCsiItlJTbLJU+0ZFHfXiQERERFpcCpA6mhLSVnYEUREJCTuETxSEnaMjKYCpQ4mfj6fFifezvQFK8KOIiIiIdiy4BQKv9oh7BgZTU2ydTCwRwdG7tueZzrey8Sww4iISIPr36aAoU32BL4JO0rGqrZJNhuoSVZEROpKTbLJo0s8IiIiknJUoMRp9pLVrN6wOewYIiISIi/bTNkmnX1PJhUocXB3hl3+OBc8/F7YUUREJEQl+bdRNGuXsGNkNDXJxsHMePqiX7K26zeMZWzYcUREJCQtO23gkMaXA5ruPlnUJKsmWRERqSM1ySaPLvGIiIhIylGBUkvfr9nEHf+cRkmpZpAVEcl2XrqBkuV/DztGRlMPSi29+Ok8LnrkPeYOep0uvbL7spiISLbbZuM8jln8dNgxMlpKFShmdgVwA3C3u58XXWbANcAYoB0wBTjX3WfFbNcOGAccEV00ETjf3dclKtvZhwxh/0FbM6hnp0TtUkRE0lU7KBt0OTAk7CQZK2Uu8ZjZ7gRFyJcVVl0GXAycD+wCrATeNrNWMWOeAoYCh0ZfQ4HHE5kvLzdHxYmIiPwot8XgsCNktJQoUMysDfAkcBqwNma5ARcBN7n7i+4+EzgZaAWMjo4ZQFCUjHH3ye4+GTgL+KWZ9WvQDyIiIiIJkRIFCjAeeMHd36+wvA/QFXirfIG7FwIfAntEF40ANgGfxGw3CSiIGVMvd/5rOtc9/0nNA0VEJCt42SaK5p8YdoyMFnoPipmdCWwHVPa/dNfonysqLF8BdI8Zs8pjJnRxdzezlTHbVzzmGILLSfTs2bPGjP+ZvYRv1n9P2XFv1ThWREQyX8uyDZy27oWwY2S0UAuU6CWYG4C93L2koY7r7uMJztowfPjwGm/JeeGSI5OeSURE0khjYPjfAQs7ScYK+xLPCKAjMMvMSs2sFNgXOCf68w/RcV0qbNcFyI/+nA90ivarAD/2rnSOGSMiIiJpJOwC5RVgR4L7tMpfU4Fnoj/PIygyRpZvYGZNgb35qedkMtCSoNgpNwJowc/7UkRERCRNhHqJJzpPybrYZWZWAKyJ3rGDmd0OXGlmcwkKlqsJmmKfiu5jjpm9Adwf7S0BuB943d2/rk++SMTZ9Y+Pc/RufbnqmBE1byAiIlnBvZSimXqacTKF3iRbC7cAzYC7+WmitoPdfWPMmNHAncCb0fcTgfPqe2Az2KZLW9a1WaqnF4uISAznyCbFYYfIaHqasZ5mLCIidaSnGSdP2D0oIiIiIv9DBUo1Vq4voKwsEnYMERFJQV66PuwIGU0FShVWri9g67Pu54aXPg07ioiIpKDCWWqSTaZ0aJINRYeWzbjqmN3ptss6NciKiMj/2LFbf+CbsGNkLBUoVcjNzeHPx5U/yufwULOIiEgK6gyaSTZ5dIlHREREUo4KlCr8e/oCVm/YHHYMERFJUZHNs8KOkNF0iacSS1Zv4PAbXuSEUd3YbvT8sOOIiEgKOmHBg2FHyGgqUCqxdcfWvHH1sQzftisdaBZ2HBERSUGRbX4F7BB2jIylAqUKhwzpE3YEERFJYTnNB4YdIaOpB0VERERSjgqUStz08hQmzV0adgwREUlhpeveCDtCRtMlngoKi0v464uTOfiAdrzdf2HYcUREJEX936oXwo6Q0VSgVNCsSSMW3XsWrZs1phG5YccREZEU5dv+EXQjRdKoQKlEh1b6CyciItWznKZhR8ho6kERERGRlKMCpYILH36Xa579OOwYIiKS4kpWPhx2hIymSzwVLP1hI98WL2Ys74QdRUREUtiupfrHbDKpQKngxUuPCjuCiIikg61ATzNOHl3iERERkZSjAkVERERSjgqUGNc8+zHdzriHoi2lYUcREZEUt+X7m8KOkNHUgxJj2DZd2WWX+dyYd72uKoqISLV6N58fdoSMZu4edoZQDR8+3KdOnRp2DBERSUNmNs3dh4edIxPpEo+IiIikHBUoMWYsWkm2n1ESEZHa8UhR2BEymgqUqPdnLmbIJY/yzKS5YUcREZE0UKIm2aRSk2zUbn278feT96flkGWM5dmw44iISIpr03Ft2BEymppk1SQrIiJ1pCbZ5NElHhEREUk5KlCi3p6xiMlfLws7hoiIpInIluVhR8ho6kGJOvuBt2nRrpSjr1sRdhQREUkDu67W04yTSQVK1Ft/Oo4mebl0p1XYUUREJA141yKgWdgxMpYKlKhturQNO4KIiKQRy2kadoSMph4UERERSTkqUICvl63htLv/zQ8bC8OOIiIiaaKsYEbYETKaLvEAs5as5vGPZtHkF5/RpVV2zwsjIiK1s8NmFSjJFGqBYmbnAmcBvaOLZgHXu/s/o+sNuAYYA7QDpgDnuvusmH20A8YBR0QXTQTOd/d1tc0xavftKXj89zRulFuvzyMiIlmkE4CFnSJjhX2JZylwOTAUGA68B7xiZjtF118GXAycD+wCrATeNrPYW22eim5/aPQ1FHg83iAqTkRERFJHqGdQ3P3VCouuMrOzgRFm9hVwEXCTu78IYGYnExQpo4H7zWwAQVGyl7tPjo45C/jIzPq5+9cN9FFEREQkgcI+g/IjM8s1s+OBlsAnQB+gK/BW+Rh3LwQ+BPaILhoBbIqOLzcJKIgZU6PRt7/OhQ+/W6/8IiKSXcrWvx12hIwWepOsme0ITAaaEhQbR7v7V2ZWXmBUnNp1BdA9+nNXYJXHPPHQ3d3MVkbXVXXMMQR9LfTs2ZOhTRuxusn3jGVsIj6SiIhkgR62KOwIGS30AgX4GhgCtAGOBR41s/2SeUB3Hw+Mh+BpxuN/d0gyDyciIpmoNZzJo2GnyFihX+Jx9y3uPt/dp7n7FcAXwO+B/OiQLhU26RKzLh/oFL3bB/jxzp/OMWNEREQkzYReoFQiB2gCLCQoMkaWrzCzpsDe/NRzMpmgZ2VEzPYjgBb8vC9FRERE0kjY86DcBPwTWAK0Irg7Zz/gF9FektuBK81sLjAPuJqgT+UpAHefY2ZvENzRMya62/uB13UHj4iISPoKuwelK/BE9M/1wJfAYe7+ZnT9LQSPirybnyZqO9jdN8bsYzRwJ1C+zUTgvORHFxERkWSxmBtgstLw4cN96tSpYccQEZE0ZGbT3H142DkyUSr2oIiIiEiWU4EiIiIiKUcFioiIiKQcFSgiIiKSclSgiIiISMpRgSIiIiIpRwWKiIiIpBwVKCIiIpJysn6iNjPbSPBEZambjsDqsEOkMX1/9afvsH70/dVPP3dvFXaITBT2VPep4GvNAlh3ZjZV31/d6furP32H9aPvr37MTFORJ4ku8YiIiEjKUYEiIiIiKUcFCowPO0Ca0/dXP/r+6k/fYf3o+6sffX9JkvVNsiIiIpJ6dAZFREREUo4KFBEREUk5GV+gmNk5ZrbQzIrMbJqZ7V3D+H2j44rMbIGZ/a6hsqaieL4/MxtlZm+Z2Soz22hmU8zsiIbMm2ri/fsXs91eZlZqZjOTnTHV1eH/w43N7NroNsVmttjMLmiovKmmDt/faDP7wsw2m1m+mT1hZl0bKm8qMbN9zGyimS0zMzezU2qxzY5m9h8zK4xu92czswaIm3EyukAxs18DdwA3ADsDnwD/NrOeVYzvA/wrOm5n4EbgTjM7pmESp5Z4vz9gX+A94BfR8f8CXq7tL+VMU4fvr3y7dsBjwLtJD5ni6vgdPgMcCowB+gHHAV8mOWpKqsN/A/cEHgceBXYAjgIGAk82RN4U1BKYCVwIFNY02MxaA28DK4BdottdCvwhiRkzVkY3yZrZFOBLdz8zZtk3wAvufkUl428GRrl735hlDwI7uPuIhsicSuL9/qrYx2fAR+5+cZJipqy6fn9m9hIwAzDgWHcflPSwKaoO/x8+GHge2Nbds3521Dp8f5cA57t7r5hlpwJ3unvLhsicqsxsE3Ceu0+oZszZwM1AF3cvjC67Gjgb6OGZ/As3CTL2DIqZNQaGAW9VWPUWsEcVm42oZPybwHAza5TYhKmtjt9fZVoBaxOVK13U9fszs3OALsD1yUuXHur4HR4FfA78wcyWmtk3ZjbOzLLul2sdv79JQDcz+z8LdASOJzgbKjUbQfAPstizLW8CWwG9Q0mUxjK2QCF4vkQuwam2WCuAqq6ndq1ifF50f9mkLt/fz5jZuUAPglPG2Sbu78/MdgSuAU5097LkxksLdfk7uA2wFzAYOAY4j+Byz4TkRExpcX9/7j6ZoCB5EtgCrCI4k3dy8mJmlKp+h5SvkzhkcoEiIYr27dwKjHb378LOk+rMrAnwLHCJuy8MO08aywGc4O/dFHd/k6BIOcbMuoQbLfWZ2UDgTuA6grMvhxL8Yr0/zFySnTL5YYGrgTKC0+WxugD5VWyTX8X4UrLvaZ91+f4AMLNjCZo8T3L315ITL+XF+/11AwYAj5jZI9FlOYCZWSlwuLtXPFWf6eryd3A5sMzd18csmxP9syf/+6/bTFaX7+8K4DN3vzX6/kszKwA+MrMr3X1pcqJmjKp+h5Svkzhk7BkUd98CTANGVlg1kqCTvTKTqxg/1d1LEpswtdXx+8PMfkVwSecUd38heQlTWx2+v2XAjsCQmNd9wPzoz1V+55mqjn8HJwFbVeg52T76Z1adyavj99ecoKiJVf4+Y39fJNBkYG8zaxqzbCTwPbAolETpzN0z9gX8muA66hkE/zq9A9gE9Iqufwx4LGZ8H6AAuD06/ozo9seE/VnS5Ps7HighuLWua8yrfdifJR2+v0q2HwvMDPtzpNN3SHBb6BKCO3l2APYkuE30+bA/S5p8f6dE/z98NkE/z54ETcfTwv4sIX1/LfnpHwybgT9Hf+4ZXX8j8G7M+DYEZ0qeAQYBo4ANwMVhf5Z0fIUeIOkfEM4hqFyLCf41sU/Mug+ADyqM3xeYHh2/EPhd2J8hXb6/6Huv5PVBQ+dOlVe8f/8qbJv1BUpdvkOCuU/eiv5CWQbcDbQK+3Ok0fd3PjAr+v0tJ2iY7RH25wjpu9uviv+mTYiunwAsqrDNjsCHQFH0+7uG6JQeesX3yuh5UERERCQ96ZqiiIiIpBwVKCIiIpJyVKCIiIhIylGBIiIiIilHBYqIiIikHBUoIiIiknJUoIiIiEjKUYEiIiIiKUcFioiIiKQcFSgiIiKSclSgiIiISMpRgSIiIiIpRwWKiIiIpBwVKCIiIpJyVKCIiIhIylGBIiIiIilHBYqIiIikHBUoIiIiknJUoIhkETNrbGbfmNm/Qs7R3MzyzeyJMHOISOpSgSKSZszsdDNzM/t3NWP+GR1zToVVFwDbAVfHjN0vOra2r/1itv0guqzMzHasIsuE6JiDype5+2bgRmC0me1Spy9CRDKauXvYGUQkTmb2KnAEcJ67311h3dnAPcC/3f3wmOUtgKXA5+5+cMzy3sApNRxyV+AwYBOwo7svim77AbBvdMyb7n5oJVknACcDI939nZjlTYHvgamxeUREAPLCDiAidXImMAK4xczecfevAcxse+BvwA/AaRW2GQ20BSbELowWG2OrOpCZ9QHOBRw4obw4qWA+cIiZjXT3t2vzAdy9yMyeBc4ys77u/k1tthOR7KBLPCJpyN1XEhQpzYEnzCzPzPKAJ6LLxrh7foXNTge2AK/U9jhm1hp4DegIXOnuE6sYeiVBAXOrmcXz35VnAON/iykRyXIqUETSlLu/CjwMDAf+HH3tAkxw95dix5pZm+i46dH+jxpFC42ngR2AJ9z9pmqG/5egOBpMcDmntj4DSoCRcWwjIllABYpIersIWEhwBuNKYBFBI2xFI4BcYGoc+74VOBz4FDijFuOvAoqA68ysWW0O4O6FwCxgZzNrFUc2EclwKlBE0pi7bwSuJSg+coGzo8sq6hn9c3lt9mtmpwF/AJYAR7t7cS2yLAFuB7oDF9fmOFH5BP8t6h7HNiKS4VSgiKSx6JmKy2MWHVfF0A7RP9fWYp/7APcBm4EjK+llqc6NwCrgMjPrUstt1kT/7BjHcUQkw6lAEUlvtwD9gTuAL4DTzOz/KhlXGP2zaXU7i96x8yLBHX6/dff/xhPG3TcAfwFaUc2dQRWUXw4qrHaUiGQVFSgiacrMDia4/fcrgrMovwWKgQfMrOLZiJXRPztQhWgPSPkdO9dUbLSNw/3APOAMM+tfi/HlmVZWO0pEsooKFJE0ZGbtgUcI7oA50d2L3X0m8CegC3BvhU2+jP5ZacFQ4Y6dZ9z9urpmc/dSgoIpj6DRtib9COZtWVrXY4pI5lGBIpKe7gO2Aq529y9jlt8GfAQca2YnxiyfRdAbsnsV+7sV+AXwOQmYk8TdX4nm+CWwZ1XjopeUugAfuKa1FpEYmklWJM2Y2W8JmmE/JChIfuTuETM7meCMyZ1m9oG7L3V3N7OXgTFmtoO7z4rZ37EEd+xA0MdyuZlVF+EVd/+iFlEvIbhFebtqxpRPcf9iLfYnIllEBYpIGjGznsCdwAbgJHePVBzj7gvN7PfAA8DDZnZI9OzEPcAY4CR+fufPoJifz6xFjEUEhUy13P2z6FT2x1cz7GSCMzsqUETkZ/SwQJEsYmZvAjsB20QnSQszy07ADOBP7n59mFlEJPWoB0Uku1wCdALOCTsIwQRzS6hwmUpEBFSgiGQVd/+KoAm2KMwcZtac4Pk9J4V9JkdEUpMu8YiIiEjK0RkUERERSTkqUERERCTlqEARERGRlKMCRURERFKOChQRERFJOSpQREREJOX8P9nIR9qyHkMHAAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
-       "<Figure size 648x432 with 1 Axes>"
+       "<Figure size 900x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -127,14 +125,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -182,14 +178,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 648x432 with 1 Axes>"
+       "<Figure size 900x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -243,16 +237,24 @@
     }
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/rotis/git/pycalphad/pycalphad/plot/binary/map.py:129: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n",
+      "  eq_ds = _solve_eq_at_conditions(start_point, prxs, grid, str_conds, statevars, False)\n",
+      "/home/rotis/git/pycalphad/pycalphad/plot/binary/map.py:154: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n",
+      "  eq_ds = _solve_eq_at_conditions(start_point, prxs, grid, str_conds, statevars, False)\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 648x432 with 1 Axes>"
+       "<Figure size 900x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -300,14 +302,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 648x432 with 1 Axes>"
+       "<Figure size 900x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -330,80 +330,11 @@
     "axes.set_xlim(0, 1)\n",
     "plt.show()"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Calculating Energy Surfaces of Binary Systems\n",
-    "\n",
-    "It is very common in CALPHAD modeling to directly examine the Gibbs energy surface of all the constituent phases in a system.\n",
-    "\n",
-    "Below we show how the Gibbs energy of all phases may be calculated as a function of composition at a given temperature (2800 K).\n",
-    "\n",
-    "Note that the chi phase has additional, internal degrees of freedom which allow it to take on multiple states for a given\n",
-    "overall composition. Only the low-energy states are relevant to calculating the equilibrium phase diagram."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2022-02-19T19:16:49.958474Z",
-     "iopub.status.busy": "2022-02-19T19:16:49.956474Z",
-     "iopub.status.idle": "2022-02-19T19:16:50.747368Z",
-     "shell.execute_reply": "2022-02-19T19:16:50.748370Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 648x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%matplotlib inline\n",
-    "import matplotlib.pyplot as plt\n",
-    "from pycalphad import Database, calculate, variables as v\n",
-    "from pycalphad.plot.utils import phase_legend\n",
-    "import numpy as np\n",
-    "\n",
-    "# Load database and choose the phases that will be plotted\n",
-    "db_nbre = Database('nbre_liu.tdb')\n",
-    "my_phases_nbre = ['CHI_RENB', 'SIGMARENB', 'FCC_RENB', 'LIQUID_RENB', 'BCC_RENB', 'HCP_RENB']\n",
-    "\n",
-    "# Get the colors that map phase names to colors in the legend\n",
-    "legend_handles, color_dict = phase_legend(my_phases_nbre)\n",
-    "\n",
-    "fig = plt.figure(figsize=(9,6))\n",
-    "ax = fig.gca()\n",
-    "\n",
-    "# Loop over phases, calculate the Gibbs energy, and scatter plot GM vs. X(RE)\n",
-    "for phase_name in my_phases_nbre:\n",
-    "    result = calculate(db_nbre, ['NB', 'RE'], phase_name, P=101325, T=2800, output='GM')\n",
-    "    ax.scatter(result.X.sel(component='RE'), result.GM, marker='.', s=5, color=color_dict[phase_name])\n",
-    "\n",
-    "# Format the plot\n",
-    "ax.set_xlabel('X(RE)')\n",
-    "ax.set_ylabel('GM')\n",
-    "ax.set_xlim((0, 1))\n",
-    "ax.legend(handles=legend_handles, loc='center left', bbox_to_anchor=(1, 0.6))\n",
-    "plt.show()"
-   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.10.6 64-bit",
    "language": "python",
    "name": "python3"
   },
@@ -417,7 +348,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.0"
+   "version": "3.10.6"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "dffc026621927c134bf51c98cc1a2a1db0bb9758f5626f77d77eb66ad657b830"
+   }
   }
  },
  "nbformat": 4,
diff --git a/examples/CementiteAnalysis.ipynb b/examples/CementiteAnalysis.ipynb
index da2059a3e..28c0d127a 100644
--- a/examples/CementiteAnalysis.ipynb
+++ b/examples/CementiteAnalysis.ipynb
@@ -52,7 +52,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Do some initial setup, including reading the database."
+    "Do some initial setup, including setting up the workspace."
    ]
   },
   {
@@ -75,6 +75,13 @@
     "matplotlib.style.use('fivethirtyeight')"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We compute the molar heat capacity at all temperatures from 1K to 2000K with a step size of 0.1K."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -90,18 +97,17 @@
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "from pycalphad import Database, calculate\n",
+    "from pycalphad import Workspace, as_property, variables as v\n",
     "\n",
-    "db = Database(TDB)"
+    "wks = Workspace(TDB, ['FE', 'C'], 'CEMENTITE_D011',\n",
+    "                {v.N: 1, v.P: 1e5, v.T: (1, 2000, 0.1), v.X('C'): 0.25})"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Compute the molar heat capacity at all temperatures from 1K to 2000K with a step size of 0.1K.\n",
-    "\n",
-    "We do this with the `calculate` routine instead of `equilibrium` because the cementite phase has zero internal degrees of freedom. Since there's nothing to minimize, we can do the computation faster with `calculate`."
+    "The isobaric molar heat capacity is defined as the derivative of the total molar enthalpy (`HM`) with respect to temperature (`T`). We use \"dot derivative\" syntax to specify this as a property. In addition, we give this property a legible name and specify our desired physical units."
    ]
   },
   {
@@ -109,34 +115,82 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-02-19T19:16:56.721543Z",
-     "iopub.status.busy": "2022-02-19T19:16:56.661508Z",
-     "iopub.status.idle": "2022-02-19T19:16:56.741641Z",
-     "shell.execute_reply": "2022-02-19T19:16:56.741641Z"
+     "iopub.execute_input": "2022-02-19T19:16:56.761125Z",
+     "iopub.status.busy": "2022-02-19T19:16:56.761125Z",
+     "iopub.status.idle": "2022-02-19T19:16:56.981139Z",
+     "shell.execute_reply": "2022-02-19T19:16:56.981139Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "heat_capacity = as_property('HM.T')\n",
+    "heat_capacity.display_name = 'Isobaric Heat Capacity'\n",
+    "heat_capacity.display_units = 'J/mol/K'\n",
+    "wks.plot(v.T, heat_capacity)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "result = calculate(db, ['FE', 'C'], 'CEMENTITE_D011', T=(1, 2000, 0.1), P=101325, N=1, output='heat_capacity')"
+    "Display units can also be specified inline using bracket syntax."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2022-02-19T19:16:56.761125Z",
-     "iopub.status.busy": "2022-02-19T19:16:56.761125Z",
-     "iopub.status.idle": "2022-02-19T19:16:56.981139Z",
-     "shell.execute_reply": "2022-02-19T19:16:56.981139Z"
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
-   },
+   ],
+   "source": [
+    "wks.plot(v.T['rankine'], heat_capacity['J/g/rankine'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "PyCalphad will propagate a `DimensionalityError` if an inconsistency is detected in the internal units and specified display units. Observe what happens when the temperature units are mistakenly left off the heat capacity:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DimensionalityError: Cannot convert from 'joule / kelvin / mole' ([length] ** 2 * [mass] / [substance] / [temperature] / [time] ** 2) to 'joule / gram' ([length] ** 2 / [time] ** 2)\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 648x432 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -144,19 +198,25 @@
     }
    ],
    "source": [
-    "# Note: 4 moles of atoms per formula unit (Fe3C1). That's why we multiply times 4\n",
-    "fig = plt.figure(figsize=(9,6))\n",
-    "fig.gca().set_xlabel('Temperature (K)')\n",
-    "fig.gca().set_ylabel('Isobaric Heat Capacity (J/mol-formula-K)')\n",
-    "fig.gca().plot(result['T'], np.squeeze(4.0 * result['heat_capacity']))\n",
-    "plt.show()"
+    "# try/except block is only used to suppress the full traceback in the docs\n",
+    "try:\n",
+    "    wks.plot(v.T['rankine'], heat_capacity['J/g']) # wrong heat capacity units!\n",
+    "except Exception as e:\n",
+    "    print(type(e).__name__ + ':', e)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.10.6 64-bit",
    "language": "python",
    "name": "python3"
   },
@@ -170,7 +230,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.0"
+   "version": "3.10.6"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "dffc026621927c134bf51c98cc1a2a1db0bb9758f5626f77d77eb66ad657b830"
+   }
   }
  },
  "nbformat": 4,
diff --git a/examples/EquilibriumWithOrdering.ipynb b/examples/EquilibriumWithOrdering.ipynb
index e36f8675b..1f8c21bad 100644
--- a/examples/EquilibriumWithOrdering.ipynb
+++ b/examples/EquilibriumWithOrdering.ipynb
@@ -24,6 +24,7 @@
     "%matplotlib inline\n",
     "# Optional plot styling\n",
     "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
     "matplotlib.style.use('bmh')"
    ]
   },
@@ -40,11 +41,7 @@
    },
    "outputs": [],
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "from pycalphad import equilibrium\n",
-    "from pycalphad import Database, Model\n",
-    "import pycalphad.variables as v\n",
-    "import numpy as np"
+    "from pycalphad import Database, Workspace, as_property, variables as v"
    ]
   },
   {
@@ -52,9 +49,7 @@
    "metadata": {},
    "source": [
     "## Al-Fe (Heat Capacity and Degree of Ordering)\n",
-    "Here we compute equilibrium thermodynamic properties in the Al-Fe system. We know that only B2 and liquid are stable in the temperature range of interest, but we just as easily could have included all the phases in the calculation using `my_phases = list(db.phases.keys())`. Notice that the syntax for specifying a range is `(min, max, step)`. We can also directly specify a list of temperatures using the list syntax, e.g., `[300, 400, 500, 1400]`.\n",
-    "\n",
-    "We explicitly indicate that we want to compute equilibrium values of the `heat_capacity` and `degree_of_ordering` properties. These are both defined in the default `Model` class. For a complete list, see the documentation. `equilibrium` will always return the Gibbs energy, chemical potentials, phase fractions and site fractions, regardless of the value of `output`."
+    "Here we compute equilibrium thermodynamic properties in the Al-Fe system. We know that only B2 and liquid are stable in the temperature range of interest, but we just as easily could have included all the phases in the calculation using `my_phases = list(db.phases.keys())`. Notice that the syntax for specifying a range is `(min, max, step)`. We can also directly specify a list of temperatures using the list syntax, e.g., `[300, 400, 500, 1400]`."
    ]
   },
   {
@@ -68,42 +63,12 @@
      "shell.execute_reply": "2022-02-19T19:18:13.301974Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<xarray.Dataset>\n",
-      "Dimensions:             (N: 1, P: 1, T: 34, X_AL: 1, vertex: 3, component: 2, internal_dof: 5)\n",
-      "Coordinates:\n",
-      "  * N                   (N) float64 1.0\n",
-      "  * P                   (P) float64 1.013e+05\n",
-      "  * T                   (T) float64 300.0 350.0 400.0 ... 1.9e+03 1.95e+03\n",
-      "  * X_AL                (X_AL) float64 0.25\n",
-      "  * vertex              (vertex) int32 0 1 2\n",
-      "  * component           (component) <U2 'AL' 'FE'\n",
-      "Dimensions without coordinates: internal_dof\n",
-      "Data variables:\n",
-      "    NP                  (N, P, T, X_AL, vertex) float64 1.0 nan nan ... nan nan\n",
-      "    GM                  (N, P, T, X_AL) float64 -2.858e+04 ... -1.413e+05\n",
-      "    MU                  (N, P, T, X_AL, component) float64 -7.274e+04 ... -1....\n",
-      "    X                   (N, P, T, X_AL, vertex, component) float64 0.25 ... nan\n",
-      "    Y                   (N, P, T, X_AL, vertex, internal_dof) float64 0.5 ......\n",
-      "    Phase               (N, P, T, X_AL, vertex) <U6 'B2_BCC' '' '' ... '' ''\n",
-      "    degree_of_ordering  (N, P, T, X_AL, vertex) float64 0.6666 nan ... nan nan\n",
-      "    heat_capacity       (N, P, T, X_AL) float64 25.45 26.93 ... 42.44 42.44\n",
-      "Attributes:\n",
-      "    engine:   pycalphad 0.9.3.dev40+g1bf356e4.d20220219\n",
-      "    created:  2022-02-19T19:18:13.281597\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "db = Database('alfe_sei.TDB')\n",
+    "dbf = Database('alfe_sei.TDB')\n",
     "my_phases = ['LIQUID', 'B2_BCC']\n",
-    "eq = equilibrium(db, ['AL', 'FE', 'VA'], my_phases, {v.X('AL'): 0.25, v.T: (300, 2000, 50), v.P: 101325},\n",
-    "                 output=['heat_capacity', 'degree_of_ordering'])\n",
-    "print(eq)"
+    "wks1 = Workspace(dbf, ['AL', 'FE', 'VA'], my_phases,\n",
+    "                 {v.X('AL'): 0.25, v.T: (300, 2000, 50), v.P: 101325, v.N: 1})"
    ]
   },
   {
@@ -124,39 +89,10 @@
      "shell.execute_reply": "2022-02-19T19:18:13.621652Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<xarray.Dataset>\n",
-      "Dimensions:             (N: 1, P: 1, T: 1, X_AL: 100, vertex: 3, component: 2, internal_dof: 5)\n",
-      "Coordinates:\n",
-      "  * N                   (N) float64 1.0\n",
-      "  * P                   (P) float64 1.013e+05\n",
-      "  * T                   (T) float64 700.0\n",
-      "  * X_AL                (X_AL) float64 1e-10 0.01 0.02 0.03 ... 0.97 0.98 0.99\n",
-      "  * vertex              (vertex) int32 0 1 2\n",
-      "  * component           (component) <U2 'AL' 'FE'\n",
-      "Dimensions without coordinates: internal_dof\n",
-      "Data variables:\n",
-      "    NP                  (N, P, T, X_AL, vertex) float64 1.0 nan nan ... nan nan\n",
-      "    GM                  (N, P, T, X_AL) float64 -2.447e+04 ... -1.949e+04\n",
-      "    MU                  (N, P, T, X_AL, component) float64 -2.443e+05 ... -1....\n",
-      "    X                   (N, P, T, X_AL, vertex, component) float64 1e-10 ... nan\n",
-      "    Y                   (N, P, T, X_AL, vertex, internal_dof) float64 9.807e-...\n",
-      "    Phase               (N, P, T, X_AL, vertex) <U6 'B2_BCC' '' '' ... '' ''\n",
-      "    degree_of_ordering  (N, P, T, X_AL, vertex) float64 0.009636 nan ... nan nan\n",
-      "Attributes:\n",
-      "    engine:   pycalphad 0.9.3.dev40+g1bf356e4.d20220219\n",
-      "    created:  2022-02-19T19:18:13.601354\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "eq2 = equilibrium(db, ['AL', 'FE', 'VA'], 'B2_BCC', {v.X('AL'): (0,1,0.01), v.T: 700, v.P: 101325},\n",
-    "                  output='degree_of_ordering')\n",
-    "print(eq2)"
+    "wks2 = Workspace(dbf, ['AL', 'FE', 'VA'], my_phases,\n",
+    "                 {v.X('AL'): (0,1,0.01), v.T: 700, v.P: 101325, v.N: 1})"
    ]
   },
   {
@@ -170,39 +106,26 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2022-02-19T19:18:13.631270Z",
-     "iopub.status.busy": "2022-02-19T19:18:13.631270Z",
-     "iopub.status.idle": "2022-02-19T19:18:13.821371Z",
-     "shell.execute_reply": "2022-02-19T19:18:13.821371Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "plt.gca().set_title('Al-Fe: Degree of bcc ordering vs T [X(AL)=0.25]')\n",
-    "plt.gca().set_xlabel('Temperature (K)')\n",
-    "plt.gca().set_ylabel('Degree of ordering')\n",
-    "plt.gca().set_ylim((-0.1,1.1))\n",
-    "# Generate a list of all indices where B2 is stable\n",
-    "phase_indices = np.nonzero(eq.Phase.values == 'B2_BCC')\n",
-    "# phase_indices[2] refers to all temperature indices\n",
-    "# We know this because pycalphad always returns indices in order like P, T, X's\n",
-    "plt.plot(np.take(eq['T'].values, phase_indices[2]), eq['degree_of_ordering'].values[phase_indices])\n",
-    "plt.show()"
+    "degree_of_ordering = as_property('degree_of_ordering(B2_BCC)')\n",
+    "degree_of_ordering.display_name = 'Degree of ordering'\n",
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot()\n",
+    "ax.set_title('Al-Fe: Degree of bcc ordering vs T [X(AL)=0.25]')\n",
+    "wks1.plot(v.T, degree_of_ordering, ax=ax)"
    ]
   },
   {
@@ -211,43 +134,33 @@
    "source": [
     "For the heat capacity curve shown below we notice a sharp increase in the heat capacity around 750 K. This is indicative of a magnetic phase transition and, indeed, the temperature at the peak of the curve coincides with 75% of 1043 K, the Curie temperature of pure Fe. (Pure bcc Al is paramagnetic so it has an effective Curie temperature of 0 K.)\n",
     "\n",
-    "We also observe a sharp jump in the heat capacity near 1800 K, corresponding to the melting of the bcc phase."
+    "The decrease in heat capacity near 1250 K corresponds to the order-disorder transition observed in the above figure. We also observe a sharp jump in the heat capacity near 1800 K, corresponding to the melting of the bcc phase."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2022-02-19T19:18:13.850853Z",
-     "iopub.status.busy": "2022-02-19T19:18:13.831253Z",
-     "iopub.status.idle": "2022-02-19T19:18:13.961842Z",
-     "shell.execute_reply": "2022-02-19T19:18:13.961842Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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BedvFedslaG/fQUFEykVkoYiUB2qQIz09k7epjzJRcE/3UUg/KQAc7wWFJ/b0MKIHD3kRBe9ccN52cd52Cdp70qAgIkeJyP+IyFYgAewAEiKyRUS+JyJHB2qTBXGdEAPCdx8cHmFP9yB5Aotm739SmF9ZTEN5Ed2DKTa3H9whJmzvXHHednHedrE2yY6I/Bb4NbAXeBtQBxR5fy8DdgO/8vazTlzbFEP47o1dA4woLKoqoSh/fxYQkYwipIPrFcL2zhXnbRfnbReb/RSuVdVjVPWrqvqgqnaq6rD390FV/ZqqHgNc6/dkIpIvIk+IyK3e+6tFZJuIbPCW4/weK65tiiF8923j1CekmaxeIWzvXHHednHedgnau2CiDap6q4jMU9W9E+0jIq9V1VuzON8HgWeByox1H1fV67M4BhDf5mMQvvvW0aBwsMfq+RUI8ExTHwPDI5QU7L9vCNs7V5y3XZy3XWw3Sb1TROrG2yAilwC/8XsiEVkIvBb4iX+9iYnrhBgQvvu2SYJCZUkBK+tKGRpRNjb1HrAtbO9ccd52cd52Cdp7wicFjzsxgeEsVe1KrxSRfwL+D3hzFuf6LvAJYOyMEF8Rkc965/qkqg5mbmxpaWHdunUUFBSQSqW45JJLuOKKK9ixYwfDw8Pk5+fT3d1NfX09HR0dqCr19fU0NzdTXm4aSvX29tLQ0EBraysiQk1NDa2trVRWVpJKpejr62Pu3Lk0NTVRWFhIVVUVbW1tVFVVkUwm6e/vH91eVFRERUUF7e3tVFdX09/fz8DAwOj2kpISSktL6ezspLa2lp6eHpLJ5Oj29LZEIkFdXR2JRIKhoaHR7WVlZTN6TZWVlWxp2wfAgvJ8GhsbD7qmw2bn82IbPLqjk/pU5+g17dixg4KCgnGvqaioKLRrmur/NDg4yODgYNb/p7Cvac+ePSQSiUDzno1r2rNnD6Wlpda+T0Fd0/DwMD09PZH4jcjmmnbu3ElPT89B15QrouM0PTxgB5EfAWuAc1S1R0TeifmB/0dV/auvk4hcCLxGVd8vImcDH1PVC0VkHtCEqcD+EbBFVb+Y+dmHHnpIV61addAx+/r6KCsr83P6yBGme8e+Id78642UFeXzx8uORkQO2ueJPT38+22bWV5Twg8vOXx0fVzT3HnbxXnbZSLv9evXP7527doTsj2en34K7wGeAW4XkQ9jAsLr/AYEj9OAi0RkO/Bb4FwRuVZV96phEPg5cKLfAyYSiSxOHy3CdB+tT6guGTcgABzZUEZxvrC1Y4COfUOj6+Oa5s7bLs7bLkF7T9VPIQ8Q4F2YJqifw9QL3C8ied72KVHVT6nqQlVdiilyuktV3+Y9KSDm1+n1wEa/4kNDQ1PvFFHCdN8+SX1CmqL8PI4eHfJifyukuKa587aL87ZL0N5T/agPA0PAAPCPmFZD93jr0tumw69E5GngaUz/hy/7/WBc2xRDuO5bvSk4JwsKAGvme01TM+ZXiGuaO2+7OG+72J5PYRmw3FuWjfN+ebYnVNW7VfVC7/W5qnq0qh6lqm9T1d6pPp8mrm2KIVz3beMMbzEe+4fS7iZd7xTXNHfednHedrHWTwFAVRvHrhORhaq6K1CLHIhjhVCasNyHR5Qd3pPC0urJx2BfVlNCdWkBHfuGaewaYGl1aWzT3HnbxXnbJWjvXEZJ3RSoQY7k5+eHrZAzYbnvTgwwNKLMrShiVtHkDiLC6vkH9m6Oa5o7b7s4b7sE7R3bobO7u8cf8z8OhOWenkNhqvqENGOHvIhrmjtvuzhvuwTtnUtQGL8do2Xq6+vDVsiZsNz91iekSQeFp/b2MpQaiW2aO2+7OG+7BO2ddVBQ1bE9kkOho6MjbIWcCct9W0YfBT/UlRWxZHYJA8MjPNvSF9s0d952cd52Cdp7qmEuRhGRJcCxwAF9qFX114Ea+WSqnthRJiz3bZ1T91EYy5oFFTR2DfD47h7Oa4hnmsc1rzhvuzhvg6+gICKfAj6DqWTOnH1FMXMuWCeuj3oQjnvv4DAtvUMU5QvzK4t9f+74hRXc8Ewr63f38E9HLZlBw5kjrnnFedvFeRv8Fh99FDhBVU9Q1TMyljMDtcmC5ubmsE49bcJw3zbaFLWU/Dz/1UJHzy2nIE94sW0fW3dNOIp6pIlrXnHednHeBr9BoR3YHuiZp8l0RwIMkzDcJ5tYZzJKC/M5Yk4ZIwrb9/kubYwUcc0rztsuztvgNyh8CPiRiJwgIoszl0BtHDPGZHMoTEW6FdLTLQOBOjkcjujhNygUAecDj2KeGNLLtpmQ8kNvr+8RMSJHGO7bsuyjkMmx882dyHNt/VPsGU3imlect12ct8FvUPhf4D8wA+IVZiyhTVXU0NAQ1qmnjW33EdX9LY98NkfNZGXtLPIF9vSm2JdMBa0348Q1rzhvuzhvg9+gUAD8XFV7VTWVuQRqkwWtra1hnXra2HZv7k3SPzRCTWkBs0sLs/58cUEeK2pnocDz3qxtcSKuecV528V5G/wGhW8Cn5SJZmUJgQipZI1t9+nUJ6Q5fM4sAJ5r6QvEySZxzSvO2y7O2+A3KPwb8HmgV0R2ZC6B2mRBTU1NWKeeNrbdsx3zaDxWzTEjMT4bw6AQ17zivO3ivA1+g8LbgPOA1wCXjVlCIa6PemDfPdsxj8bjcC8oPNeyL3Y9P+OaV5y3XZy3wVfDc1W9J9CzBkBlZWXYCjlj2z3XPgqZzKsooqIoj66BYZp6k8yr8N8rOmzimlect12C8h4cHiE1EtyNU2lh3qRFREGnt99hLgqBKzFPBvOBPcAvga+oajJQI5+kUvFrBZPGpvvA8Ah7ugfJE1g0O/egICKsrC7iieYBnmvpi1VQiGtecd52ycVbVdndPcgzzX1s8pbGrmD789x6+bEUFUwcFIJOb79dVL8BnAi8F2gElmDGQqoEPhyokU/6+vqoq6sL49TTxqb7js4BRhSWVJdQlD+96TMWlwtPNMOzLfs4Z0V8yl/jmlect138eA8Oj/Bi2779QaClj8TA8AH75AsUFdibqibo9PYbFC4FjlXVdu/98yKyHniSkIJCXCfZBrvu6f4J06lPSHPCsnpu2rIjdi2Q4ppXnLddpvL+ywvtXHX/TobGFA1VlxZwZEMZR8wp44iGclbWlU77Biwbgk5vv0FhomeX0NpwNTU1sWRJPEfttOm+1atPmGpOZj9UDncjwJb2fpKpEasZfzrENa84b7tM5f2n59oYGlEWzy7hmLnlHNFQxpENZcytKAq1OWvQ6e03KFwH3CIiXwB2YIqPrgR+H5hJlhQWZt8JKyrYdA+i5VGaqlnFLJ5dQmPXAFva+0dbJEWduOYV522XybwHhkd4oXUfeQJXXXQoZVPMcW6ToNPb763eJ4C/At8HHgf+B/gb8O+B2mRBVVVVWKeeNrbcVXVaYx6NpaqqilVeJ7Y49VeIa15x3naZzPvZlj5SCitqSyMVECD49PYbFGpU9bOqulJVZ6nqIar6GaA6UJssaGtrC+vU08aWe2f/MImBYcqK8qkvm/7dRFtbW0Z/hfgEhbjmFedtl8m8n95rBp07am70htcOOr39BoUXJli/KSiRbInr3QjYc9+a0T8hiDLPqqqq0aDwbEt8xkCKa15x3naZzPvpJhMUjo5gUAjrSeGgXxQRqQRGArXJgmQylO4RgWDLPcj6BDDei2eXUFqYR3Nvko59Q4Ecd6aJa15x3naZyHsoNTL6ZHxUQ/Tq0YJO70mDgojs9MY3Kh1nzKO9wI2B2mRBf388x/YHe+7pKTiDqE8A452fJxxa5w2O1xqPIqS45hXnbZeJvF9s62cwpSyZXZLTKMMzTdDpPVXro7dhnhJu48BxjhRoVtXnA7XJgri2hQZ77i+0miKeoJ4U0t6Hzynjyb29PNuyj1OXzA7k2DNJXPOK87bLRN7poqOj5kbvKQGCT+9JnxRU9R5VvRuo816nl3vDDAhg2ubGFRvu7fuG2NE1QElBHod4d/bTJe0dt8rmuOYV522XibyjXJ8Awae33wHx9onIccAZQB0ZdQyq+tlAjXxSVBTapG/Txob7U3t7AHN3U5AXTMeatPeqehNknm/dR2pEyQ/o+DNFXPOK87bLeN6pEeWZZq8+IaJBIej09lXRLCLvBh4AzsX0TTga+CiwMlCbLKioqAjr1NPGhvuGPebu5rj5wZ0r7V09q5CG8iIGhkdo7Ax28K+ZIK55xXnbZTzv7Z399CVTNJQXMac8msEu6PTOpvPaBar6D0C/9/cNQGjNT9rb26feKaLYcN+wxzwpBBkUMr3TM7E9G4PK5rjmFedtl/G8n/L6Jxw9L5pPCRB8evsNCnNU9T7v9YiI5Knqn4HXBWqTBdXVofWbmzYz7d7ck2RvT5LyonxWBFTJDAd6x6leIa55xXnbZTzvp5tM/o5qfQIEn95+g8IuEVnqvX4BuFhEzgBCa5Ac12ZvMPPuT3r1CUfPKw+0vD/Te1XGTGxRJ655xXnbZay3qrLRq2Q+JqItj8B+k9Q03wAOB7YDXwSuB4owczeHwsBA9MuyJ2Km3UeLjgJ+5M30XlFbSmGe0Ng1QO/gMOXFfrOSfeKaV5y3XcZ670oM0jUwTHVpAfMrozupVNDp7bf10dUZr/8sItVAkar2BmqTBXFtCw0z666qM1LJDAd6F+XnsaK2lOda9/F86z6OXxjdKRjjmlect13Gemc2RQ1zaOypsNpPYTxE5JOqmgwzIEB820LDzLrv6R6kbd8QVSUFLAlgDoVMxnqP1iu0RrsIKa55xXnbZax31PsnpAk6vXOZJeU/cj2ZiOSLyBMicqv3fpmIPCIim0XkdyLiu81XSUmwP3g2mUn3J9JPCfPKyQv47mas96qYVDbHNa84b7uM9d7YlO6fEN36BAg+vXMJCtP5pfkg8GzG+68D31HVlUAnsM7vgUpLg2tVY5uZdH/Sq084NuCiIzjY+/CMuRVUdbyPRIK45hXnbZdM7+aeJM29pgXf0upoX0/Q6Z1LULg2lxOJyELgtcBPvPeC6Qx3vbfLNcDr/R6vs7MzF41IMFPuqsqGven6hOAfecd6N5QXMbukgO7BFHu6ozsyZlzzivO2S6Z3uujoyIayyPfYDzq9s24yoqrvy/Fc38V0gkvfwtYCXao67L3fBSwY+6GWlhbWrVtHQUEBqVSKSy65hCuuuIJkMklbWxv5+fl0d3dTX19PR0cHqkp9fT3Nzc2Ul5sfxt7eXhoaGmhtbUVEqKmpobW1lcrKSlKpFH19fcydO5empiYKCwupqqqira2Nqqoqkskk/f39o9uLioqoqKigvb2d6upq+vv7GRgYGN1eUlJCaWkpnZ2d1NbW0tPTQzKZHN1eWlpKUVERjY2N1NXVkUgkGBoaGt1eVlaW8zVt2LaXxMAwNSX5DHc2MVgyL9BrSiaT9Pb2HnBNSyqErgFY39jKcFUq8GsK4v9UUlJCc3NzTv+nRCIR2jXl5eXR2NgYeN6b6WtKJpMMDg5a/T4FcU3l5eXs3LkTEeGJXeYmZ2VVPm1tbaH8Rvi9plQqxc6dOw+6plyRiR77ReSXmNFQJ0VV3z7lSUQuBF6jqu8XkbOBjwGXAw97RUeIyCLgz6p6VOZnH3roIV21atVBx9y7dy/z5s2b6tSRZKbcb9jYwg8e3s15K6v5xNlLAz/+eN6/2dDEzx/by8VH1HHFqYsCP2cQxDWvOG+7ZHqvu24TOxODXHXRoZGfi3yi9F6/fv3ja9euPSHb4032pLA524NNwmnARSLyGqAEqASuAmaLSIH3tLAQ2O33gHGdyANmzj1ddDQT9QkwvnccZmKLa15x3nZJe3f2D7EzMUhxQR4ra6NdnwDBp/eEQUFVvxDUSVT1U8CnANJPCqr6VhG5DjOG0m+BdwA3+T1mXNtCw8y4p0Z0dJyW4+bNTFAYz/vQulkIsKV9H4PDIxQX5FJNNbPENa84b7ukvdOtjo6YM4vC/Ojl57GE1k9BRM4WkZ+JyO3e33MCOP+/Ax8Rkc2YOoaf+v1gXNtCw8y4b+kwoznOqyiioWJmRnMcz3tWUT5Lq0tIKWxui+bTQlzzivO2S9p7/6Q60e6fkCaUfgoi8i7g90AT8EfMVJy/EZF/yfaEqnq3ql7ovd6qqieq6kpVvVRVB/0eJ67N3mBm3NNDWxw7Q08JMLF3ur/CsxHtxBbXvOK87ZL23hiTTmtpgk5vv62PPgG8UlWfTK8Qkd8BfwB+HKiRT+I6kQfMjPv+obJnLiNP5H34nDL+/Hx7ZDuxxTWvOG+7FBUV0ZdMsaW9n4I8Gb3ZiTqhTLKDKdrZNGbd80BNoDZZkEgkwjr1tAnafXhER8tBZ6qSGSb2zuzEFkXimlect10SiQTPNPeimLqykgjWj41H0Ont96rvB74tIrMARKQM+C/gwUBtsqCuri6sU0+boN2fb+1jYHiExbNLqJ1VGOixM5nIe9HsEmYV5tHaN0R7X2jzLk1IXPOK87ZLXV0dT6cn1Yn40BaZBJ3efoPCe4FjgYSINANd3vv3BGqTBXG9G4Hg3dOjoh47w7NDTeSdJ8Jh9el6heg9LcQ1rzhvuyQSif2T6kR4prWxhPKkoKp7VfVMYDlmtrVlqnqWqu4J1CYLhoaid0fql6DdZ2LqzfGYzDtdhBTFeoW45hXnbZfegSQvtO1DgCMb4hMUgk7vbIe5SAApABGZDxBWYIhrW2gI1j05PMIm74d4pp8UJvNOd2LbFMGgENe84rztksivZHikkxW1pZQV5Yet45tQ+imIyHkishUzkumujGVnoDZZENe20BCs+6aWPoZSyvKaUipLZnb2s8m8D59TRr7ApuY+dnRGa+atuOYV522Xh7e0APFpipomrPkUfgp8FagCCjOW0NqelZXFpyJoLEG622iKmmYy78qSAl59WB0jCr9Yv3fGXbIhrnnFedtla88IEL+gEHR6+w0KJcDPVbVXVVOZS6A2WZCfH5/Hu7EE6f7k6FDZM1ufAFN7v2V1A0X5wr3buiLVuzmuecV522MoNcKLHWYMoahPqjOWoNPbb1D4DvAJidBEpd3d3WEr5ExQ7v1DKZ5r6SNP7NzdTOVdV1bE6w43zeOueTw6TwtxzSvO2x6b2/tJppSFVcVUl85cs+6ZIOj09hsU/gD8C6ZJ6tbMJVCbLKivrw/r1NMmKPeNTX2kFA6pm2WlYsyP9xuPbaCkII9HdnazqTkalc5xzSvO2x77+yfEq+gIgk9vv0HheuA+4C2Y4JC5hEJHR0dYp542Qbnbaoqaxo93dWkh/3CUyaQ/fyy0FssHENe84rxnHlVlS/s+7tlmZi+LY1AIOr39NldZBqxW1ZFAzz4Nojwn8FQE5T5an2Cpo41f70uPnsMtm9p4cm8vT+zuYfUCO0FrIuKaV5z3zLG3e5C/benkri2d7OgyreXyBI610GAjaIJOb79B4SbMfMp/DfTs0yCOj6hpgnDvGRxmc/s+CvKEIy3d3fj1Li8u4NJj5vDzx/Zy9eN7OG7+oYRZHRXXvOK8g6Wzf4h7t3bxty2dB/SnqSzO56zl1ZyztJz6svgN5hd0evsNCsXAzSJyH9CcucHPdJwzQXNzM0uWLAnj1NMmCPenm3oZUTiiwd7AXdl4v/7Iem7Y2MqzLft4ZGc3Jy+ummG7iYlrXnHe02dfMsWDjQnu2tLB+t09jHg31cUFeZy6pIq1K6tZs6CSgjyhsbERqA7VNxeCTm+/QeEZb4kM052cOkyCcE+PdzRTs6yNRzbepYX5vPm4Bn748G6ufmwvJy6qJC+kp4W45hXnnRvJ4REe3dXN3Vs6eXhHgmTKRIJ8gZMWVXLuympOXlxFaeGBjTPC9s6VoL0nDQoich5wT5BTczqC4UmLndZy5cJVdVz/VAtbO/q5b1sXZy2P312YIx6kRpQn9vRw95ZO7t/exb6h/dWfR80t45zl1Zy5vJqqGe71/1JgqhT6GGaGtQeAPwG3qerumdeamt7eXmpra8PWyInpujf1DLKtc4DigjyrE4Fk611UkMdbVs/lvx/YyTWP7+X0pbPJz7P/tBDXvOK8J0dV2dTSx91bOrlnaxddA8Oj21bWlnL2imrOXl7NnHJ/9QQuvQ2TBgVVvcCbQ2Et8BrgShHpAm7DBIkHw2qR1NDQEMZpA2G67vdu7QLg5MWVFFmcWDwX7wsOq+W6p5rZlRjkzs0dnH+o/S9dXPOK8z4YVeXF9n7u3tLJvds6aendP0Lowqpizl5ezTkrqlk0uyTrY7v0Nkz5i6Kq+1T1FlV9n6ouAd6KGRjvy8BeEfmtiJwUqJUPWltbbZ8yMKbrfvdW06badnFMLt4FecJla+YB8Mv1TQyl7N9DxDWvOG+DqrKto5+fP7aHd163iX+98Xmuf7qFlt4h6soKecPRc/je6w/jp284nLcfPy+ngDAT3rYI2jvrAjZV3QhsBL4hIlXA+UBloFY+iNCIG1kzHffdiUE2t/czqzCPExfaTfZcvc9ZUc3vnmymsWuAPz/fzkVH2G2yGNe88nL33tk1wD1bTdFQY9f+kXerSws4c9lszlpezRENZYE1YHi5p3eaqSqal0/xeQVuV1Xrg53U1IQ2PfS0mY77Pd5TwqlLqiiyPIdsrt75ecLbj5/Hl+7cxq83NHH+obVW57+Na155OXrvSgxw79Yu7t3WxdaO/tH1FcX5nOEFgmPmls9I3dTLMb3HY6onhc2YH/7J/gMqIluAdap6X2BmU9Da2hqZttDZMh33e0IqOoLpeZ+2tIqVtaVsbu/nlk2tXHqMvfLbuOaVl4v37sQA927r4p6tBwaCWYV5nLZ0Nmcvr2b1ggoKZriRwsslvadiqormKW/nRKQSeBPwA+CogLympLLSeolVYOTq3tjZz7bOASqK81kTwtAR00nzPBEuP2EeV96+ld8+2cyJiypZUl0aoN3ExDWvvJS904Hg3m1dbGk/MBCcunQ2Zy6bzZoFFVYbUryU0zsbpt1o1ys6+rGIXD59Hf+kUqFN5TBtcnW/x2t1dOqSKgotflnSTDfNX7GwkuMXVPD47h4+ePMLfGbtMo63UC8S17zyUvNu7Oznvu0J7t928BPBqUuqOGNZNccvtBsIMnmppXeuTBgUROTbwDdUdcK53kRkLvAJVf2Iqp4WqNkU9PX1UVdXZ/OUgZGLu6qG1uoozXTTXET43CuX81/3NHLfti4+ffsW/vXURVx4+Mz+H+OaV+LuraqjHRfv354YHXgOTCA4ZUkVZ4YcCDKJe3oHxWRPCs8Dj4rIs8A93vseoAI4FDgbOAzTNNU6cZ0cHHJz39rRz67EIFUlBay2NFT2WIJI85KCPD597lKueWwvv3mymf9+YCe7EwO868QFM9axLa55JY7eqkp3QRU/eXQ392/vYk93cnRbRXE+pyyu4vRls1kzv8J6Q4mpiGN6Q/DeEwYFVf0/EfkZcDHwauD1wGxMH4WngB8Ct6jq8ETHmEmamppiWSkEubmni47OCKlXMASX5nkivPMV81lQVcx379/JHza2sqc7ySfPWXLQeDRBENe8Ehfv4RHl6b29PNjYxQONCdr69ncom11SwKlLqzhj6WyOnT/zlcXTIS7pPZagvaeqaB7CTLBzfWBnDIjCwnhNmZdJtu4HFh3NngEjfwSd5ucfWsvciiK+8NdtPLQjwUdufZEvnr888OGL45pXouzdP5Ti8V09PNjYxSM7u+kZ3F+uPbs4j7NW1HLGsiqObJiZ5qMzQZTTezKC9o7t6FBVVeENxTxdsnV/oW0fTT1JakoLOCrEmaFmIs2PmVfBVRcdymdu38qW9n7+7aYX+ML5yzm0blZg54hrXomad1f/EI/s7OaB7V2s390zOvoowKKqYk5bOptTl1SxYJZSEcMRR6OW3n4J2ju2QaGtrY2yMnuDwQVJtu6jRUfLqkO965qpNF9YVcJVFx3KF/66jaebevnorS/yybOXcNrS2YEcP655JWxvVaWxa4CHdyR4uLGbZ1v6yJzj6/A5szh1yWxOWVLF4oyhJRobG2MZFMJO71wJ2ju2QSGuUR2ycx9RHe2wdnaIRUcws2leWVLAf756BVfdv5O/vNjBF/66jTOWzeZtq+eyrGZ6/RnimlfC8E6mRnh6by8P7+jmkZ0Jmnr2VxQX5AnHzivntKWzOWVxFbVl4xdbuPS2i3tS8Egmk1PvFFGycX+2pY/WviHqywo5vCHcu5iZTvPC/Dw+euZiFs0u4ReP7+W+bV3ct62L05dW8dbVc1lRm1uRUlzzii3vjn1DPLarm4d3dPP47m76M+YiqCop4KRFlZy0uIrjF1Qwq2jqhgAuve0StLevoCAiNwDXAH/yKp9Dp7+/f+qdIko27ndv6QJM34SwZi5LYyPNRYQ3HdvA2pXV/P6pFv70XBv3b09w//YEpyyp4m2r53JIlvUNcc0rM+U9PKJsau7l77t6eGxX9wE9igGW15Rw0qIqTl5SxaF1s7IusnTpbZegvf0+KdwHfBb4qYj8Hvilqj4YqEmWxLVNMfh3T40o920Lv9VRGptpXldWxPtPWcibjm3g908186dn23ioMcFDjQlOWlTJZWvmcWi9v+AQ17wSpHdzT5K/7+rmsV3dbNjTc8DMZMX5wjHzKjhpcSUnLaqioWJ6rb9cetvFWj+FTFT128C3ReRI4G2Y2diSwC+BX6nqlkCtfBDXNsXg331jUy8d/cPMqygKtDVOroSR5rWzCnnfyQt50zENXP90C7c828YjO7t5ZGc3Jy6q5LI1czmsfvJitbjmlel4dw8M81RTL0/u6WX97m52JgYP2L5kdgknLKzghIWVHD23PNCOZC/H9A4Tq/0UxqKqzwCfEpHbgO8BnwM+KiJ/Bz6qqk+O9zkRKQHuBYq9c16vqp8TkauBs4CEt+vlqrrBj0tRUbBt2W3i1z3d6ujM5dWRGOs9zDSvmVXIu09awKXHzOEPT7dw86Y2Ht3ZzaM7uzl5cSXvOH7ehHUOcc0r2XjvS6bY2NzLhj29bNjTw5b2/gNaCs0qzGPNAhMETlhY6XuKylx4OaR3lAja23dQEJHDME8JbwHSTwkXAq3A+4EbgWUTfHwQOFdVe0WkELhfRP7sbfu4qmbdOa6iIpyhHoLAj3tqRLlvexcQfqujNFFI8+rSQt514gIuPaaB659q5sZNbTy8w1SSnr50Nm8/fi5Lx4y+GgXvXJjMuy+Z4rmWPp5uMoHg+dY+MroNUJgnHD6njOPml3Pc/ApWzSmz1pv4pZjeUSZob78VzY8BS4HfAW9R1UfG7PJtEfnARJ9XVQV6vbeF3qIT7e+H9vZ2ymPYFhr8uT+xp4fEwDALq4pZPs0mmUERpTSvKilg3YkLuOSoOfzuqWZuebaN+7d38cD2Ls5eUc1la+aysMq0nY+SdzakvVWVvT1JNjX3maWll20dAwd8gfIEVtXP4rj5FRw3v5wjGsqtTmQ0nnfccN4Gv08K/wncrKoTtn1S1YmeEgAQkXzgcWAl8H1VfURE3gd8RUQ+C9wJfFJVByc7Tprq6nBGCg0CP+77+yZEo+gIopnm1bMKee/JC7n06AZ+82QTtz3Xzt+2dHLP1k7WrqzhbavnRtJ7MvqSKbZ29LOhGba8sJVNzX10DRw4xFi+wMq6WRzZUMZx8ys4em45ZT6ai9ogbumdxnkb/AaFT49XxCMij6nqCX4OoKop4DgRmQ3cICJHAZ8CmoAi4EfAvwNfzPxcS0sL69ato6CggFQqxSWXXMIVV1zBrl27mDNnDvn5+XR3d1NfX09HRweqSn19Pc3NzaPRs7e3l4aGBlpbWxERampqaG1tpbKyklQqRV9fH3PnzqWpqYnCwkKqqqpoa2ujqqqKZDJJf3//6PaioiIqKipob2+nurqa/v5+BgYGRreXlJRQWlpKZ2cntbW19PT0kEwmR7eXlpbS19dHZ2cndXV1JBIJhoaGRreXlZWhksd9XlA4rGyQnTt3RuKadu3axeLFi8e9pqKiIhKJxITXNNP/p1QyyesWjHDxYSu4+tGdPLBnkDte7OCuzR0cWVvIEfWlLKuAEw9ZQFtLs6//00xf09DwMDvaeugtqODpnW3s6lN296Zo6Tt4jMmKQuGQmiKOnFvOvMIkxy6uY2Ro0PyfGkpo2ruL3ghcUyqVoqWlhRUrVlj7PgV1Tfn5+SQSiUj8RmRzTXv27CGRSBx0TbkipmRnip1EulW1csw6AdpVNesJQr0ng32q+s2MdWcDH1PVCzP3feihh3TVqlUHHaOxsTGWLQVgavdHdiT4zF+2srS6hB/94+EWzSYnTmm+p3uQa59o4q7NHYxkZPHigjyOmFPG0fPKOWZuGYfVl1E8g8UsqkrPYIqm3iRNPYM09yTZ25NkW0c/2zr6D2gamqYwX1haXcK84hFOWtnAEXPKmV9ZFJknxqmIUz7J5KXmvX79+sfXrl3r66Y9k0mfFETkF97L4ozXaZYCz/g5iYjUA0Oq2iUipcArga+LyDxV3esFmNcDG/2Kx7VNMUztHuY8zJMRpzSfX1nMJ85awjtPmMdjOzp5rm2Qp5t62ZUY5Ik9PTyxpwcwFbKH1s9iWU0plcX5lBcXeH/zqSguoLwon8riAsqL8ynKFwaGRxgYGqF/eIT+oRQDQyPsGxqhf9i87k2maO5J0tSbpLlnkKae5Lg//GlqSgtYXlvK8ppSVnh/F1aVkJ8nDA4OUlxcbCvJAiNO+SQT522YqvhoywSvFXgAuM7neeYB13j1CnnA71X1VhG5ywsYAmwA3uvzeLFtUwyTuyeHR3iw0bTQjUqrozRxTPP6siKOmDXAq88w3p37hni6uZen95qWO9s6+nmmuY9nmvtmzKG0MI+55UXMrSimoaKIuRVFLJ5dwoqaUqpnTTzscRzTG5y3bWzPp/AFABF5WFVvz/UkqvoUsHqc9efmesySkpKpd4ook7nft72LfUMjrKwtZUFVtK4xrmme6V09q5Azl1Vz5jLzFNY7OMwzzX009STpGRymZzBFTzJFz8AwvckUPYMper31QyNKcb5QUphPaWEepQV5lKZfF+ZRUpjPrMI85pSZH/50EKgszs+p6OelkN5xwnkbJpuj+UxVvdd7OyQi4/6Aq+pdgRr5pLQ0Gs00c2Ey95ueaQXgtTM8b3EuxDXNJ/MuLy7gpMVTjzKpqowoVocufymmd5Rx3obJnhT+FzjKe/3TCfZRYHmgRj7p7OyksrJy6h0jyETuz7f28VzrPsqL8jl3RbTqEyC+aR6Et4iQb7me9+Wc3mHgvA2TzdF8VMbrSfsghEFtbW3YCjkzkXv6KeGCw2pnZK7i6RLXNHfednHedgna21dbPBE5TkQWjVm3SESODdQmC3p6esI69bQZz72zf4h7tnYhwOsiWHQE8U1z520X522XoL39NtC+FjM0RSZFmPGPQiGuE2LA+O63PdfO0Ihy0uJK5lVGsxliXNPcedvFedslaG+/QWGxqm7NXOENl700UJssiGubYjjYfXhEufXZNgAuPqI+DCVfxDXNnbddnLddgvb2GxR2iciazBXe+z2B2mRBU1NTWKeeNmPdH9jeRfu+IRZVFbNmQXRHaoxrmjtvuzhvuwTt7Xfso+8AN4nINzCd2FYAHwO+EqhNFsS1+Rgc7J6uYL74yPpID2UQ1zR33nZx3nax2SR1FFX9sYh0AeuARcBOzKQ6Wc+DEBRxnRADDnTf0r6Pjc19zCrM47yVWQ8jZZW4prnztovztkvQ3r5HAlPV61T1AlU90vsbWkAASCQSU+8UUTLdb/SeEs4/tJZZERn6eCLimubO2y7O2y5Be2cz81oDcCJQhxmrCABV/VmgRj6pq4tms00/pN27B4b52xYz+N1FR0T/euKa5s7bLs7bLkF7++2n8HpMXcIXgf8DPuD9vSxQmyyIa1SH/e5/fr6dZEo5YWHF6CxhUSauae687eK87RK0t9/ioy8D71TV1UCf9/fdmJnUQmFoaCisU0+boaEhUiPKzZtM0dHrj4xuM9RM4prmztsuztsuQXtn009h7DDZ1wBvD9QmC+LaphiM+0ONCVr7hphfWcwJC+Mx3kpc09x528V52yWsfgotXp0CwHYROQXTLDW0mtG4tikG436T95Rw0RF15EW4GWomcU1z520X522XoL39BoUfA6d7r78D/A14EjOSaiiUlZWFdepp0z5cyJN7eykpyONVh8ZnEK64prnztovztkvQ3n77KXw94/UvRORuoExVnw3UJgvy86PdfHMy7thuZvk675AayiLeDDWTuKa587aL87ZL0N6++ymISL6InCYil2I6sL0QqEmWdHd3h3n6nOkZHOa+xl4ALo5BM9RM4prmztsuztsuQXv7elIQkWOAG4ESYBewEBgQkX9Q1ScDNfJJfX08WuyM5fbn20mOwOr55Sypjle3+rimufO2i/O2S9Defp8UfgZ8H1igqicCC4DveetDoaOjI6xT50xqRLk5PRpqTJqhZhLHNAfnbRvnbZegvf0GhUOB76qqAnh/rwIOCdQmCzyVWPHozm6aepLUleZx0qKp5wWOGnFMc3DetnHedgna229QuA24aMy61wF/CtQmC+L2qKeqXPvEXgAuOrzO6gTwQRG3NE/jvO3ivO0SVvFRPvBbEXlQRH4nIg8CvwPyReQX6SVQsylobm62ebpp89COBC+29VNTWsCa2fHsORm3NE/jvO3ivO0StLffAfE2ekuaTcDtgZpkSXl5eZinz4oRVX7xuHlKeNOxDdRWxbPpW5zSPBPnbRfnbZegvf32U/hCoGd9mXH/9i62dgxQN6uQ166qoyfRGbaSw+FwjEs2/RSKRORoETlHRM5NLzMpNxm9vb1hnTorUiPKLx833dD/6bgGigryYuM+FudtF+dtF+dt8NtP4XTgOqAYqAS6gQrMDGzLAzXySUNDw9Q7RYB7tnbS2DXAnPJCXnWYGdIiLu5jcd52cd52cd4Gv08K3wG+oao1QI/390uEOPZRa2trWKf2TWpEufYJ85Tw1uPmUpRvkjsO7uPhvO3ivO3ivA3Z9FO4asy6/wQ+HKhNFkR5gvs0d27uYFdikHkVRbwyY+C7OLiPh/O2i/O2i/M2+A0KCUyxEcBeETkCqAZCq66vqYn2JPfDI8qv0k8Jq+dSkNEvIeruE+G87eK87eK8DX6Dwh+B13ivf4YZOvtx4PpAbbIg6o96d7zQzt6eJAurilm78sB/WtTdJ8J528V528V5G/w2Sf1QxutvisjDmIrm0PoqVFZGd7ayZGqEX20wTwmXrZl7UO/lKLtPhvO2i/O2i/M2TPqkICKlInLU2PWqej+m5VFRoDZZkEqlwjr1lNz+fDstvUMsmV3CmcuqD9oeZffJcN52cd52cd6GqYqPPgGsm2DbO4GPB2qTBX19fWGdelKSwyP8ZoPpdn7Z8Qc/JUB03afCedvFedvFeRumCgpvAr45wbZvA/8UqE0WRHWS7T8910bbviGW15Ry+tLZ4+4TVfepcN52cd52cd6GqYLCAlXdPd4Gb/2CQG2yIIqTbA8Mj/DbJ81TwtuPn0veBE3FoujuB+dtF+dtF+dtmCoo9InIovE2iMhiYF+gNllQWFgY1qkn5JZNrXT2D3No3SxOWTzxfAlRdPeD87aL87aL8zZMFRRuA746wbYv4XM+BREpEZFHReRJEXlGRL7grV8mIo+IyGZvSG7fFddVVdGapKZ/KMXvn2oBzFPCZB1KoubuF+dtF+dtF+dtmCooXAmc7v2Yf05E3u393QCc4W33wyBwrqoeCxwHXCAiJwNfB76jqiuBTiau1D6ItrY2v7ta4cZnWkkMDHP4nFm8YuHkTcSi5u4X520X520X522YNCioahOwBrgFuAD4mPf3FuB4b/uUqCE9lF+htyhwLvs7wF0DvN6veJSienvf0GhdwuXHz5+y23mU3LPBedvFedvFeRum7Lymqp2YJwK/TwXjIiL5mF7QK4HvA1uALlUd9nbZRRYV18lkcjo6gfLjR3fTPzTCqUuqWL2gYsr9o+SeDc7bLs7bLs7b4HfmtWmjqingOBGZDdwArPLzuZaWFtatW0dBQQGpVIpLLrmEK664gqamJvLy8sjPz6e7u5v6+no6OjpQVerr62lubh6dkai3t5eGhgZaW1sREWpqamhtbaWyspJUKkVfXx9z586lqamJwsJCqqqqaGtro6qqimQySX9//+j2oqIiKioqaG9vp7q6mg27urhrSydF+cKFi8zUeKWlpXR2dlJbW0tPTw/JZHL08+lt/f391NXVkUgkGBoaGt1eVlYW+jX19/czMDAwur2kpITS0lKampqYNWvWuNdUVFREIpGI5DUNDg6iquNe02T/p7Cvqa2tLaf/U9jX1NTURFVVVaB5z8Y1DQ8PMzg4aO37FNQ1NTc3Mzg4eNA15Yqo6rQOkNNJRT4L9AP/DsxV1WEROQX4vKq+KnPfhx56SFetOjh+DA4OUlxcbMV3IlIjyhU3Ps/Wjn7etnoubz9+nq/PRcE9F5y3XZy3XV5q3uvXr3987dq1J2R7PN8zr00HEan3nhAQkVLglcCzmIH13uDt9g7gJr/HjEKb4j8918bWjn7mlBfyxmP9T3QRBfdccN52cd52cd4GX0FBRC6dYP0bxls/DvOAv4nIU8DfgTtU9VbMk8JHRGQzUAv81OfxKCoKbdglABIDw1zz+F4A3nPSQkoK/MfXsN1zxXnbxXnbxXkb/NYp/BQzHedYfoSP4bNV9Slg9TjrtwIn+nQ4gIqKqSt0Z5KrH9tDz2CK1fPLOX1pdrX/YbvnivO2i/O2i/M2TDVK6nIRWQ7keR3Nlmcs5wEDgdpkQXt7e1in5sW2fdz2XDv5Au8/ZWHWMx+F6T4dnLddnLddnLdhqieFzZj+BIJpQppJE/D5QG2yoLr64CGpbaCqfP/BXShw8ZH1LKkuzfoYYblPF+dtF+dtF+dtmKrzWp6q5gP3ea8zl/mq+qNAbbKgv78/lPPeubmTTS19VJcWcNkaf62NxhKW+3Rx3nZx3nZx3gZftaOqelagZw2AgQH7JVd9yRQ/edQMGrvuFfMpK8rP6ThhuAeB87aL87aL8zb4qmgWkQLg/cBZQB2mOAkAVT0zUCOfhDH2+a+eaKKj34xvdN4huU+W7cZtt4vztovztovt+RTSfAd4D3AvcDzwB2AOcFegNllgu03xjq4BbtjYggBXnLJowrkS/ODaQ9vFedvFedsllH4KwCXAq1X1KmDY+/t64JxAbbKgpKTE2rlUlR88tIuUwgWH1XJo/axpHc+me5A4b7s4b7s4b4PfoDAL2Om97heRWar6HOP0PbBFaWn2rX5y5cHGBI/v7qG8KJ93npBb5XImNt2DxHnbxXnbxXkb/AaFZ4FXeK8fAz4vIlcC407VaYPOzk4r5+kfSvHDh81lvv34ecwunf4sR7bcg8Z528V528V5G/z2aP4gkPJefwT4AVABvDtQmyyora21cp6f/X0Pzb1JVtaW8rrD6wI5pi33oHHednHednHeBr9NUv+uquu91y+q6nmqepKq3heoTRb09PTM+Dmeburlpk1t5At89MzF5OflXrmciQ33mcB528V528V5G3yP4iYirxSRn4rILd77E0Tk3EBtsmCmJ8QYHB7h2/fuAOBNxzawonZ6lcuZuMk87OK87eK87RK0t99RUj+AKTJ6EUj3S+gHvhyoTRbMdJviX67fy+7uQZbMLuEtq4M9l2sPbRfnbRfnbZew+il8CDhPVf8TGPHWPQccFqhNFsxkm+LnW/u4/ukW8gQ+cuZiivKDnXbCtYe2i/O2i/O2S1j9FCrY3yQ1PVVbIRDa89ZMNR8bSo3wrXt3MKJwyVFzOHxOWeDncE3f7OK87eK87RJWk9R7gU+OWfdvmJnTQmGmJsT4zYZmtncOML+y2Pf0mtniJvOwi/O2i/O2S9DefoPCB4B/EJHtQIWIPA+8EdM8NRQSiUTgx9za3s9vNphHsY+csSir2dSyYSbcbeC87eK87eK8Db76KajqXhF5BWaWtMWYoqRHVXVk8k/OHHV1wfQZSJMaUb51XyMphQsPr+OYeTM3C1PQ7rZw3nZx3nZx3gbft8JqeERVr1PVh8MMCBB8dPzD0y282NbPnPJC3vWK+YEeeyzujsQuztsuztsuVp8UROQ+9lcsj0tYQ2cPDQ0FdqydXQNcs34vAB86fTGzcpwnwS9ButvEedvFedvFeRumKj76ScZrAb6PmVchdIJqmzuiyrfv28FQSjn/kBpOWFgZyHEnw7WHtovztovztovVfgqqek3GcjUwOGbdNYHaZEFQbXNv3tTGM8191JQW8J6TFwRyzKlw7aHt4rzt4rztElY/hchRVjb9/gN7ugf52d/3APCB0xZRUex3fMDpEYR7GDhvuzhvuzhvQ2yDQn7+9Mr9UyPKN+9tZGB4hLOWz+a0pbODEfPBdN3DwnnbxXnbxXkbJg0KInJu5gIUiMg5Y9aFQnd397Q+f8MzrWxs6qO6tIAPnLooICt/TNc9LJy3XZy3XZy3Yarykp+Oed8O/CzjvQLLAzXySX19fc6f3dE5wM8fM8VGHzp9MZUldoqN0kzHPUyct12ct12ct2HSX0NVXRbo2QKko6ODWbOyH846NaJ8455GhlLKqw6t4ZQlVTNgNzm5uoeN87aL87aL8zbEtk5BddLuExPy2yebeaFtH/Vlhbz35IUBW/kjV/ewcd52cd52cd6G2AaFXB6ZtrTv41qvk9pHz1xM2Qx3UpsI95hqF+dtF+dtl6C9YxsUmpubs9o/mRrhG3ebsY0uOqKONQtmvpPaRGTrHhWct12ct12ctyG2QaG8vDyr/a9d38S2zgHmVxaxbobHNpqKbN2jgvO2i/O2i/M2xDYoZMOzLX38/qlmBPj4mUsoLYxne2SHw+GYaWIbFHp7e33tNzA8wn/d08iIwhuOnsORc8O/G/DrHjWct12ct12ctyG2QaGhocHXfj//+x52JQZZMruEd8zQTGrZ4tc9ajhvuzhvuzhvQ2yDQmtr65T7bNjTww3PtJIn8PGzl1A0QzOpZYsf9yjivO3ivO3ivA3R+JXMARGZdPu+ZIpv3bsDgLccN5dD66LTKWUq96jivO3ivO3ivA2xDQo1NTWTbv/jxhaae5OsrC3lLaujNU76VO5RxXnbxXnbxXkbrAQFEVkkIn8TkU0i8oyIfNBb/3kR2S0iG7zlNX6POdUj05uObeCtq+fy8bOWUJAXrTsA95hqF+dtF+dtl6C9bY0ENwx8VFXXi0gF8LiI3OFt+46qfjPbA1ZWTt75rDA/LzIVy2OZyj2qOG+7OG+7OG+DlaCgqnuBvd7rHhF5FpjWNGepVCoItVCIq7vztovztovzNlivUxCRpcBq4BFv1b+KyFMi8jMRqfZ7nL6+vpnQs0Jc3Z23XZy3XZy3wepEAiJSDvwB+JCqdovID4AvYeZl+BLwLeCfMz/T0tLCunXrKCgoIJVKcckll3DFFVeQSqVoa2sjPz+f7u5u6uvr6ejoQFWpr6+nubl5tPt3b28vDQ0NtLa2IiLU1NTQ2tpKZWUlqVSKvr4+5s6dS1NTE4WFhVRVVdHW1kZVVRXJZJL+/v7R7UVFRVRUVNDe3k51dTX9/f0MDAyMbi8pKaG0tJTOzk5qa2vp6ekhmUyObi8tLWXWrFk0NjZSV1dHIpFgaGhodHtZWVlkrymVStHb2zvuNRUVFZFIJCJ5TWVlZTQ3N2f9fwr7mgoLC2lsbAw079m4plQqxeDgoLXvU1DXVF1dzc6dOyPxG5HNNQHs3LnzoGvK+Xfa1nCxIlII3ArcrqrfHmf7UuBWVT0qc/1DDz2kq1atOuh4jY2NLFmyZIZsZ5a4ujtvuzhvu7zUvNevX//42rVrT8j2eLZaHwlmFrdnMwOCiGTWBP8DsNHvMW+88cbA/GwTV3fnbRfnbRfnbbBVp3AacBlw7pjmp98QkadF5CngHODDfg/4xz/+cYZUZ564ujtvuzhvuzhvg63WR/cD43UWuC3XYw4PD+cuFDJxdXfednHednHeBmt1Crly5513tgKNY9d3dHTU1dTUtIWgNG3i6u687eK87fIS9F6ydu3arKdli3xQcDgcDoc9Yjv2kcPhcDiCxwUFh8PhcIwS2aCQyyB6IvIpEdksIs+LyKtCdN/utaraICKPeetqROQOEXnR+1vtrRcR+W/P+ykRWROS82EZabpBRLpF5ENRTG+v93uLiGzMWJd1+orIO7z9XxSRd4Tk/V8i8pzndoOIzPbWLxWR/ox0/2HGZ4738tdm79pmfMTHCdyzzhsicoG3brOIfDIk799lOG8XkQ3e+kik+SS/fXbyuKpGcgHmAWu81xXAC8ARwOeBj42z/xHAk0AxsAzYAuSH5L4dqBuz7hvAJ73XnwS+7r1+DfBnTOusk4FHIpD2+UATsCSK6Q2cCawBNuaavkANsNX7W+29rg7B+3ygwHv99QzvpZn7jTnOo961iHdtrw4pzbPKG96yBVgOFHn7HGHbe8z2bwGfjVKaT/LbZyWPR/ZJQVX3qup673UPMNUgehcDv1XVQVXdBmwGTpx5U99cDFzjvb4GeH3G+l+o4WFgthzYqS8M1gJbVPWgVl8ZhJbeqnov0DGOTzbp+yrgDlXtUNVO4A7gAtveqvoXVU23KXwYWDjZMTz3SlV9WM03/xfsv9YZY4I0n4iJ8saJwGZV3aqqSeC33r4zxmTe3t3+G4HfTHYM22k+yW+flTwe2aCQifgbRG8BsDPjY7uY5kis00CBv4jI4yLybm9dg5rRYsHchacnVo2Sd5o3c+AXJerpDdmnb9T8wYz79eeM98tE5AkRuUdEzvDWLcC4pgnbO5u8EbU0PwNoVtUXM9ZFKs3H/PZZyeORDwoyZhA94AfACuA4zHDc3wrPbkJOV9U1wKuBK0TkzMyN3t1GJNsCi0gRcBFwnbcqDul9AFFO34kQkU9j5h35lbdqL7BYVVcDHwF+LSJRG/A/dnljDP/EgTc/kUrzcX77RpnJPB7poCBmEL0/AL9S1T8CqGqzqqZUdQT4MfuLLHYDizI+vtBbZx1V3e39bQFuwDg2p4uFvL8t3u6R8fZ4NbBeVZshHuntkW36RsZfRC4HLgTe6n3Z8Ype2r3Xj2PK4g/1HDOLmMLM59nmjSileQFwCfC79Loopfl4v31YyuORDQpeeV82g+jdDLxZRIpFZBlwCKZyyCoiUiZmdjlEpAxTkbjR80vX/r8DuMl7fTPwdq8FwclAIuMRMQwOuHuKenpnkG363g6cLyLVXrHH+d46q4jIBcAngItUdV/G+noRyfdeL8ek71bPvVtETva+I29n/7VaJYe88XfgEBFZ5j2RvtnbNwzOA55T1dFioaik+US/fdjK4zNVgz7dBTgd83j0FLDBW14D/BJ42lt/MzAv4zOfxkT357HQImMC7+WYVhVPAs8An/bW1wJ3Ai8CfwVqvPUCfN/zfho4IcQ0LwPagaqMdZFLb0zQ2gsMYcpJ1+WSvpgy/M3e8s6QvDdjyn3TefyH3r7/6OWfDcB64HUZxzkB8wO8Bfge3sgEIbhnnTe87/AL3rZPh+Htrb8aeO+YfSOR5kz822clj7thLhwOh8MxSmSLjxwOh8NhHxcUHA6HwzGKCwoOh8PhGMUFBYfD4XCM4oKCw+FwOEZxQcHhiDkicoSIPOZn5E4RaRCRZ0Wk2IabI364oOAIDRHpzVhGxAxbnH7/1rD9ckHMUMznWT7tl4BvarrR+hgHEXmziHSKyFlqeqr/DXj3BMdyvMxxQcERGqpanl6AHZjOQul1v5rq87bxhkaI1Dm8XsXnADdOsP0dmI5Nr1XVe7zVvwLeMw1Nx0sYFxQckUNE8kTkkyKyRUTaReT3IlLjbVsqIioi7xSRnd4d8HtF5BXeaJ1dIvK9jGNdLiIPiMj3RCQhZkKbtRnbq0TkpyKyV8yEMV/OGOog/dnviEg78HkRWSEid3lebSLyK9k/Mc4vgcXALd7TzidE5GwR2TXm+kbv5MVMVHO9iFwrIt3A5ZM5jcMrMWNVDYyTju/BDFL3KlV9MGPTI8ByEVmS5b/G8TLABQVHFPkAZqz4s4D5QCfmbjeTkzBj07wJ+C5mWIXzgCOBN4rIWWP23QLUAZ8D/pgOMpjhDoaBlZghis8H3jXms1sxwxR/BTOkwNc8r8MxA459HkBVL+PAJ55v+Lzei4HrgdmYu/ipnDI5GjOUxFjeB3wRWKuqj2VuUDN/w2bgWJ9+jpcRLig4osh7MePi7FLVQcyP7hvGFK18SVUHVPUvQB/wG1VtUTNC7X2YH9M0LcB3VXVIVX+H+RF9rYg0YMaU+ZCq9qkZ1fY7mIHa0uxR1f9R1WFV7VfVzap6h5oRNVuBb2OC13R4SFVvVDPaaKUPp0xmAz3jrH8lZtKepyf4XI/3WYfjAGa8jNThyIElwA0iMpKxLsX+SUUAmjNe94/zvjzj/e50JaxHI+ZOfwlQCOzNaLiTx4ETk2S+xgskV2EmaKnw9u/0dVUTk3kOP06ZdHoeY3kfcCXwExFZN+b68T7Tlauw46WLe1JwRJGdmJE1Z2csJd5TQC4sGNNcczGwxzvPIGY+7fR5KlX1yIx9x/6YftVbd7SqVgJvwxQpTbR/HzAr/carG6gfs0/mZ/w4ZfIUZsz/sTRjplU9A/jfzA3eE9dKzEi+DscBuKDgiCI/BL6Srgj1xrmfzly+c4B/E5FCEbkUUxdwm5ox5/8CfEtEKr0K7hVj6iPGUgH0AgkRWQB8fMz2Zszw6WleAEpE5LViJk65EjOh/bjk4HQHsEZESsY51h5MYLhARL6TselEYLtOPge342WKCwqOKHIVZnz+v4hID6Zs/KRpHO8RTKV0G6ay+A3qzbCFmTClCNiEKYq5Hpg33kE8vgCsARLAn4A/jtn+NeBKrxXUx1Q1Abwf+Alm1qs+Dpzvdzx8O3n9Du7CVFaPt30HcC6mTuZr3uq3YgKvw3EQbj4Fx0saMVNdvktVTw/bZaYQkSOAa4ATx6k7GLvvHOAeYPV4zVgdDlfR7HDEHFXdBLzC574tmOIzh2NcXPGRw+FwOEZxxUcOh8PhGMU9KTgcDodjFBcUHA6HwzGKCwoOh8PhGMUFBYfD4XCM4oKCw+FwOEZxQcHhcDgco/x/4iFKn6So+q8AAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "plt.gca().set_title('Al-Fe: Heat capacity vs T [X(AL)=0.25]')\n",
-    "plt.gca().set_xlabel('Temperature (K)')\n",
-    "plt.gca().set_ylabel('Heat Capacity (J/mol-atom-K)')\n",
-    "# np.squeeze is used to remove all dimensions of size 1\n",
-    "# For a 1-D/\"step\" calculation, this aligns the temperature and heat capacity arrays\n",
-    "# In 2-D/\"map\" calculations, we'd have to explicitly select the composition of interest\n",
-    "plt.plot(eq['T'].values, np.squeeze(eq['heat_capacity'].values))\n",
-    "plt.show()"
+    "heat_capacity = as_property('HM.T')\n",
+    "heat_capacity.display_name = 'Heat Capacity'\n",
+    "heat_capacity.display_units = 'J/mol/K'\n",
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot()\n",
+    "ax.set_title('Al-Fe: Heat capacity vs T [X(AL)=0.25]')\n",
+    "wks1.plot(v.T, heat_capacity, ax=ax)"
    ]
   },
   {
@@ -260,36 +173,52 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2022-02-19T19:18:13.981235Z",
-     "iopub.status.busy": "2022-02-19T19:18:13.981235Z",
-     "iopub.status.idle": "2022-02-19T19:18:14.121543Z",
-     "shell.execute_reply": "2022-02-19T19:18:14.121543Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot()\n",
+    "ax.set_title('Al-Fe: Degree of bcc ordering vs X(AL) [T=700 K]')\n",
+    "wks2.plot(v.X('B2_BCC', 'AL'), degree_of_ordering, ax=ax)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that the plot abruptly ends around 0.8 fraction of Al, corresponding to the (metastable, because only liquid is entered) limit of stability of B2 on the phase diagram at this temperature. Because we want to plot in the metastable region, we will now tell pycalphad that we want to remove liquid from the calculation and then repeat it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
      },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "plt.gca().set_title('Al-Fe: Degree of bcc ordering vs X(AL) [T=700 K]')\n",
-    "plt.gca().set_xlabel('X(AL)')\n",
-    "plt.gca().set_ylabel('Degree of ordering')\n",
-    "# Select all points in the datasets where B2_BCC is stable, dropping the others\n",
-    "eq2_b2_bcc = eq2.where(eq2.Phase == 'B2_BCC', drop=True)\n",
-    "plt.plot(eq2_b2_bcc['X_AL'].values, eq2_b2_bcc['degree_of_ordering'].values.squeeze())\n",
-    "plt.show()"
+    "wks2.phases = ['B2_BCC']\n",
+    "wks2.plot(v.X('B2_BCC', 'AL'), degree_of_ordering)"
    ]
   },
   {
@@ -301,7 +230,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2022-02-19T19:18:14.121543Z",
@@ -310,41 +239,12 @@
      "shell.execute_reply": "2022-02-19T19:18:16.781182Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<xarray.Dataset>\n",
-      "Dimensions:             (N: 1, P: 1, T: 110, X_AL: 1, vertex: 3, component: 2, internal_dof: 5)\n",
-      "Coordinates:\n",
-      "  * N                   (N) float64 1.0\n",
-      "  * P                   (P) float64 1.013e+05\n",
-      "  * T                   (T) float64 300.0 320.0 340.0 ... 2.46e+03 2.48e+03\n",
-      "  * X_AL                (X_AL) float64 0.1\n",
-      "  * vertex              (vertex) int32 0 1 2\n",
-      "  * component           (component) <U2 'AL' 'NI'\n",
-      "Dimensions without coordinates: internal_dof\n",
-      "Data variables:\n",
-      "    NP                  (N, P, T, X_AL, vertex) float64 0.3637 0.6363 ... nan\n",
-      "    GM                  (N, P, T, X_AL) float64 -2.526e+04 ... -1.944e+05\n",
-      "    MU                  (N, P, T, X_AL, component) float64 -1.719e+05 ... -1....\n",
-      "    X                   (N, P, T, X_AL, vertex, component) float64 0.25 ... nan\n",
-      "    Y                   (N, P, T, X_AL, vertex, internal_dof) float64 2.229e-...\n",
-      "    Phase               (N, P, T, X_AL, vertex) <U7 'FCC_L12' 'FCC_L12' ... ''\n",
-      "    degree_of_ordering  (N, P, T, X_AL, vertex) float64 1.0 6.377e-15 ... nan\n",
-      "Attributes:\n",
-      "    engine:   pycalphad 0.9.3.dev40+g1bf356e4.d20220219\n",
-      "    created:  2022-02-19T19:18:16.761326\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "db_alni = Database('NI_AL_DUPIN_2001.TDB')\n",
     "phases = ['LIQUID', 'FCC_L12']\n",
-    "eq_alni = equilibrium(db_alni, ['AL', 'NI', 'VA'], phases, {v.X('AL'): 0.10, v.T: (300, 2500, 20), v.P: 101325},\n",
-    "                      output='degree_of_ordering')\n",
-    "print(eq_alni)"
+    "wks3 = Workspace(db_alni, ['AL', 'NI', 'VA'], phases,\n",
+    "                 {v.X('AL'): 0.10, v.T: (300, 2500, 20), v.P: 101325})"
    ]
   },
   {
@@ -357,53 +257,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2022-02-19T19:18:16.811073Z",
-     "iopub.status.busy": "2022-02-19T19:18:16.791567Z",
-     "iopub.status.idle": "2022-02-19T19:18:17.031526Z",
-     "shell.execute_reply": "2022-02-19T19:18:17.031526Z"
-    }
-   },
+   "execution_count": 10,
+   "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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11U3Yvrldo8bFBtMfGc1cowZo1xXFQTcU0Lt8iGlQNmyjfNgldTZ0xYboqa1uvVJTVd7aqHFytcX0h6+Bs6udiSvrXFvDRs7JFRtCCCEDQw0bC+Pv79/lczVVjfjsv7+jqqK1UePsZovpD4+Gk4utVOX1mTjGhpMrNt3lQ0yLsmEb5cMuqbPh42xAeq2xsbHTx2urm/QaNS5udsw3aoArd/fmZYxNV/kQ06Ns2Eb5sEvqbKhhY2EqKio6PFZX04TP/ntMbNS4utth+iPsN2oAAH91RfEw1RvoPB/CBsqGbZQPu6TOho+zAem3+lo1Pvvv76gsv3KlZtrDo+HobGPiynpHnBXFwVRvQgghA0dnAwvTfmnr+jp16zo1Za3r1Di72mLaw9eYTaMGuDJ4mJeuKFoWnl2UDdsoH3bRLRWIUWVkZAD4695PW46jvKR1RWFHFxtMe/ga8+h+aufKOjZ8fJTb8iHsoWzYRvmwS+ps+DgbkF5raWlBs1qDz7ceR2lRLYDWxfemPzya2Snd3eHtik1LS4upSyBdoGzYRvmwS+psqGFjYWxt7LD745MozGu9oWXbvZ9c3MyvUQO0nxXFx0fZwYGd21UQfZQN2ygfdkmdDR9nA9IrOq0OfxwqRe6lcgCAysYKdz04Cm4MrijcW1fWseHjio2np6epSyBdoGzYRvmwS+psqGFjIQRBwN4vziErvbVRY2WtwNRZI+Hla943jhO7ojiZFZWVlWXqEkgXKBu2UT7skjobPs4GpEe//JCB1JOXAQAKhQx33Dcc/sGuJq7KADgbPEwIIWRg6GxgAc6eyMevP10Sv//btHiEDPIwYUWGo+Ns8LCfn5+pSyBdoGzYRvmwS+psqGHDuZyMcuzf9af4/ajr/RE1xMeEFRmYlq9bKjQ3N5u6BNIFyoZtlA+7pM6GGjYcKyuuxZefnIROJwAAhl8bBM9AviIXOLulQllZmalLIF2gbNhG+bBL6mz4OBuQDhrqm/HFByfQrNYAAMKjPXHjbTGQyWQmrsxw2qZ6A/w0bAghhAwMnQ04JOgEfLfzLGqqmgAA3v5OmPSPeMjlMkRFRZm4OsMRp3qDn64onvLhDWXDNsqHXVJnQw0bDv1+OAtZ6aUAAFt7JabcPwJKpRUAIDs724SVGVZbNxQAgJPp3jzlwxvKhm2UD7ukzoaPswERFeRU4tD3F1u/kQGTpg2Fg9OVm1qq1WoTVWZ47Rs2vHRF8ZQPbygbtlE+7JI6Gz7OBgRA67iabz49DeGvwcLX3hDeYVq3vb35rjJ8Nf2uKD4+yjzlwxvKhm2UD7ukzoa5s8HGjRsREhICGxsbJCQk4NixY91uv2HDBkRFRcHW1haBgYH417/+haamJomqZUfbuJra6tafPTDUDdeNj+iwnY8PP1O99Rs2ViasxHB4yoc3lA3bKB92SZ0NUw2b7du3Y+nSpVi1ahX++OMPxMfHY+LEiSgpKel0+23btmHFihVYtWoV0tLS8N5772H79u148sknJa7c9DLTS8VxNXb2SkyaPhRyeccZUJcuXerwmNnisCuKq3w4Q9mwjfJhl9TZMHU2ePXVV/HII49g9uzZiI2NxaZNm2BnZ4ctW7Z0uv2RI0cwZswY3HPPPQgJCcGECRMwY8aMHq/y8CjtdKH47+S/x+qNq+EVTfcmhBByNWbOBs3NzThx4gSSk5PFx+RyOZKTk3H06NFOX3PdddfhxIkTYkMmMzMTe/bswd/+9jdJamZFS4sWl863XtWysbVGeIxXl9vydLmWx+nePOXDG8qGbZQPu6TOhpmBCWVlZdBqtfD29tZ73NvbG+fPn+/0Nffccw/KysowduxYCIIAjUaDuXPn9tgVVVtbC3m76cEqlQoqlWrgP4SJZF8oQ0tza7dMRKwXFN1cvdC1u8ph7vRmRXEy3ZunfHhD2bCN8mGX1Nkw07Dpj5SUFLz44ot46623kJCQgIyMDCxevBjPP/88nnnmmS5fFxcXh4aGBvH72bNnY+HChfD19RX7Ar29vSEIgji+Z9CgQcjPz0djYyNsbGwQGBiIixdbp1V7eXlBLpejqKgIABAeHo6ioiLU19dDpVIhJCQE6enpAAAPDw8olUpcvtx6p+3Q0FCUlpairq4O1tbWiIiIQFpaGgDAzc0Ntra2KCgoAACEhISgoqICNTU1UCgUiIqKQlpaGv74pVT8WWydm5GamoqgoCDU1NSgqqoKMpkMMTExSE9PR1VVFZqamuDq6oqcnBwAQEBAABoaGlBRUQEAiI2NxYULF6DRaODo6AgPDw/xtvN+fn5Qq9UoLy8HAERHRyMzMxPNzc2wt7eHt7c3MjMzAQC+vr7QaDQoLW2tLzIyErm5uWhqaoKtrS38/f2RkZEhHm8AKC4uBgBERESgoKBAPN5BQUG4cOECAMDT0xNWVlbIO58u/twNTY1ITU2FUqlEWFiY2Bh2d3eHSqXSO95lZWWora2FlZUVIiMjkZqaKh5vOzs75OfnAwCCg4NRWVnZ4XgLggAXFxc4OTkhNzcXABAYGIja2toOx1ur1cLJyQlubm7iWg7+/v5obGwUj3dMTAwyMjLQ0tKCxsZG2Nvb6x3v5uZmcUnyqKgoZGdnQ61Ww97eHj4+PuJn1sfHBzqdTu8zm5eXJx7vgIAAvc+sTCYTj3d4eDgKCwvR0NAAlUqF4ODgDse7sLC1uzMsLAwlJSWoq6vr9Hjb2Nh0+pm9+ni7urrCwcEBeXl5AICgoCBUV1ejuroacrkc0dHROH/+PHQ6HZydneHs7Kx3vOvq6lBZWdnhM9vZ8W5qaur0M+vg4AAvL69uP7M5OTlQq9VoaGiAo6OjWf6OaPvMOjo66h3vzn5HtH1mze13RENDg5hFZ5/Z4uJi1NfXm/3vCAcHB3h6eprV7wi1Wi2+Z39/RwhC62zf3pAJfdnaiJqbm2FnZ4edO3fijjvuEB+fNWsWqqqq8OWXX3Z4zbhx43Dttdfi5ZdfFh/7+OOP8c9//hN1dXV6V2UAQKPR4ODBgwgLC+Pmik1LixZv/ftHtDRrYWNrjXlP3tjtFZvU1FTExsZKWKHx1Gfm4dB10wEAvndOQPzGZ01bkAHwlA9vKBu2UT7sMkQ2Wq0Wp0+fRlJSEqysur8mw8z1e6VSiZEjR+LAgQPiYzqdDgcOHEBiYmKnr2loaOjQeFH8Ndaiu/aao6MjnJycxC9zbdQAfeuGAlpb6LzQX6DPrC8+injKhzeUDdsoH3ZJnQ0zDRsAWLp0KTZv3owPPvgAaWlpmDdvHurr6zF79mwAwMyZM/HEE0+I20+ePBlvv/02Pv30U2RlZWH//v145plnMHnyZLGBw7v0s1dmQ0UN6XmAVttlaB7wuEAfT/nwhrJhG+XDLqmzYerP3OnTp6O0tBQrV65EUVERhg0bhr1794p9q7m5uXpXaJ5++mnIZDI8/fTTKCgogKenJyZPnox///vfpvoRJNU6G6q1b9rG1hpB4e49voarxQs5nO7NVT6coWzYRvmwS+psmGrYAMCCBQuwYMGCTp9LSUnR+97KygqrVq3CqlWrJKiMPVnppWI31KDB3j12QwGAra2tscuSjP6sKD6u0PGUD28oG7ZRPuySOhs+/sy1UBfOFYn/jozz7mbLKwICAoxVjuR47IriKR/eUDZso3zYJXU2fJwNLFB/uqEAiFP5eMDj3b15yoc3lA3bKB92SZ0NH2cDC9Sfbije8LjyMCGEkIGxvLMhJ7IulIn/7m03FNC66BIv9K/Y8NGw4Skf3lA2bKN82CV1NtSwMUOCICA7o7Vho7CSIyDUrdevlck63vHbXLW/YgNOrljxlA9vKBu2UT7skjobPs4GFqayvAG1Va3T5wJCXGFt3furFW3LY/OAx7t785QPbygbtlE+7JI6Gz7OBhYm++KVbqjgiN4NGuYRj9O9CSGEDAw1bMxQTka5+O/gCI8+vTY8PNzQ5ZiMoOFvVhRP+fCGsmEb5cMuqbPh42xgQbRaHfIyWxs2tvZKePk49un1bXdf5QKHXVFc5cMZyoZtlA+7pM6Gj7OBBSnKr0azuvVKRXC4O2Tyvg3KamhoMEZZJsHjdG+e8uENZcM2yoddUmdDDRszoze+ZlDfx9eY853Mr8bjdG+e8uENZcM2yoddUmdDDRsz0358TUgfx9cAQHBwsCHLMSkeb6nAUz68oWzYRvmwS+ps+DgbWAh1UwsK86sBAG6e9nB0tunzPi5cuGDoskym/XRvXtax4Skf3lA2bKN82CV1NnycDSxEbmYFBJ0AwLKnebeh6d6EEEKuRg0bM5JzcWDdUADg6elpqHJMjsfp3jzlwxvKhm2UD7ukzoaPs4GFyPnrNgpyuQyBYb2/jUJ7VlZWhizJpPRWHrbi44oNT/nwhrJhG+XDLqmzoYaNmaiubERleeuUOb8gFyhV/fug8LTWg97gYTkfH2We8uENZcM2yoddtI4N6VTb1RqAxteIOJzuTQghZGCoYWMGBEHAuRMF4vd9vY1Ce2FhYYYoiQk8TvfmKR/eUDZso3zYJXU2fJwNOJeXVYHLuVUAAHcvB/gGOPd7XyUlJQaqyvR4vLs3T/nwhrJhG+XDLqmz4eNswLnfUjLFfyfcENbn2yi0V1dXZ4iSmKA3K4qT6d485cMbyoZtlA+7pM6GGjaMK8yrElcbdnGzQ/QQnwHtT6lUGqIsJrRfx4aXBfp4yoc3lA3bKB92SZ0NH2cDjv360yXx36OTQiEf4Amcp37o9l1Rck6me/OUD28oG7ZRPuyiMTZEVFJYg0vnSwEAjs42GDzcf8D7PH/+/ID3wYr2g4fByXRvnvLhDWXDNsqHXVJnw8fZgFPtx9ZcMy4UCiuKqz0e7+5NCCFkYOhMyajykjqknysCANjZKzHkmgCD7NfdnaM1cDicFcVVPpyhbNhG+bBL6mz4OBtw6MiBDKD1fpcYNS4E1taGuSJhY9P3O4Kzisd1bHjKhzeUDdsoH3ZJnQ0fZwPOZKSVIP1s69UaG1trDEsIMti+CwoKet7ITPA43ZunfHhD2bCN8mGX1NlQw4YxTY0t2L/7T/H7GydF9/u+ULzTG2PDyawoQgghA0MNG8ak7DmP+lo1ACA00gOxw/0Muv+QkBCD7s+UeFx5mKd8eEPZsI3yYZfU2fBxNuBE9sUy8Z5QSpUCN98xGDJZ/1cZ7kxFRYVB92dKPN7dm6d8eEPZsI3yYZfU2fBxNuBAs1qD73edE79PuiUKTi62Bn+fmpoag+/TVPRXHuajK4qnfHhD2bCN8mGX1NlQw4YRB79LR01VEwAgMMwNQ68JNMr7WFnxM16Hx64onvLhDWXDNsqHXVJnw8fZwMydPZ6P08fyAABW1gpMnBI3oBtddicyMtIo+zUJDruiuMqHM5QN2ygfdkmdDR9nAzNWkFOJ/V9emQU1fnIMXNztjPZ+qampRtu31HQa/lYe5ikf3lA2bKN82CV1NtSwMaHa6iZ8+clJ6LStK/ENvzYIQ0YZZoVhi6Cj6d6EEEL0UcPGRFpatNj98R9oqGsG0Dqu5oZJ0UZ/X1dXV6O/h1R4XHmYp3x4Q9mwjfJhl9TZ8HE2MDOCTsC+z8+iuKB1pLiTqy0mzxgGhQQnZwcHB6O/h1R4nO7NUz68oWzYRvmwS+ps+DgbmBFBJ+D73X/i/JnWWyZYKxWYcv8I2NkrJXn/vLw8Sd5HCjze3ZunfHhD2bCN8mGX1NlQw0ZCgiDgx2/ScPZ4PgBAJpdh0vR4ePo4mrgy89R+ujc46YoihBAyMHQ2kIggCDj4XTpO/poLAJDJgEnThiIixkvSOoKCDHdDTVPTvwkmHx9lnvLhDWXDNsqHXVJnw8fZgHGCIODw/os4fji79QEZcMudQxA91FfyWqqrqyV/T2PRG2PDyeJcPOXDG8qGbZQPu6TOhho2RqbT6vDDl6n4LSVTfOzmvw/G4BH+JqmHp//8Qvvp3px0RfGUD28oG7ZRPuySOhs+/sxlVEuzFt98egqXzpeKj900OQbxo41zu4TekHPSZQNAf+VhTho2XOXDGcqGbZQPu6TOhho2RtJQ14xdH51AYV5rS1WukOGWO4cgdpifSeuKjjb+WjlS0Z/uzcesKJ7y4Q1lwzbKh11SZ0NNXCMoK67Ftnd+FRs1SpUCd84aZfJGDQCcP3/e1CUYDI9dUTzlwxvKhm2UD7ukzoau2BhY6qnL+H7Xn9C0tJ507R1VuPOBkfDydTJxZa107adImzkeVx7mKR/eUDZso3zYJXU21LAxEI1Gh5++TcPp364sROTp64g77hsBZ1dbE1amz9nZ2dQlGEz76d7gpH+dp3x4Q9mwjfJhl9TZUMPGAKorG/H1/06hKP/KyO+4kf4Yf3ssrK3ZGvvB03/+tis2MoUCMpnMxNUYBk/58IayYRvlwy6ps+Hjz1wT2/v5WbFRY2Ulx8SpcbjlziHMNWoAIDc319QlGIw4xoaTbiiAr3x4Q9mwjfJhl9TZ8HNGMKGb7xgMpUoBFzc73DP3WgwZFWDqkizClSs29DEmhBDSirqiDMDNwx5TZ42Ch7cDbGytTV1OtwIDTbeGjsG1NWw4meoNcJYPZygbtlE+7JI6G/pT10ACQlyZb9QAQF1dnalLMJi2riiertjwlA9vKBu2UT7skjobfs4IpFcqKytNXYLBtM2K4qlhw1M+vKFs2Eb5sEvqbPg5IxCL035WFCGEEAJQw8bixMbGmroEgxF0/DVseMqHN5QN2ygfdkmdDTVsLMyFCxdMXYLBCFr+uqJ4yoc3lA3bKB92SZ0Nc2eEjRs3IiQkBDY2NkhISMCxY8e63b6qqgrz58+Hr68vVCoVIiMjsWfPHomqNT8ajcbUJRiMeEsFTlYdBvjKhzeUDdsoH3ZJnQ1T0723b9+OpUuXYtOmTUhISMCGDRswceJEpKenw8vLq8P2zc3NuPnmm+Hl5YWdO3fC398fOTk5cHFxkb54M+HkxMY9qwyBx3VseMqHN5QN2ygfdkmdDVMNm1dffRWPPPIIZs+eDQDYtGkTvv32W2zZsgUrVqzosP2WLVtQUVGBI0eOwNq6dap1SEiIlCWbHTc3N1OXYDjidG9+xthwlQ9nKBu2UT7skjobZv7UbW5uxokTJ5CcnCw+JpfLkZycjKNHj3b6mq+++gqJiYmYP38+vL29ERcXhxdffBFarbbT7QmQnZ1t6hIMRsfhdG+e8uENZcM2yoddUmfDzBWbsrIyaLVaeHt76z3u7e2N8+fPd/qazMxM/Pjjj7j33nuxZ88eZGRk4NFHH0VLSwtWrVrV5XvV1tZC3m5chkqlgkqlMswPQqRD070JIYRchZmGTX/odDp4eXnh3XffhUKhwMiRI1FQUICXX36524ZNXFwcGhoaxO9nz56NhQsXwtfXF5cuXQLQ2qASBAElJSUAgEGDBiE/Px+NjY2wsbFBYGAgLl68CADw8vKCXC5HUVERACA8PBxFRUWor6+HSqVCSEgI0tPTAQAeHh5QKpW4fPkyACA0NBSlpaWoq6uDtbU1IiIikJaWBqD18p2trS0KCgoAtHazVVRUoKamBgqFAlFRUUhLS4MgCHBxcYGjoyPy8vIAAEFBQaipqUFVVRVkMhliYmKQnp6OlpYW5Ofnw9XVFTk5OQCAgIAANDQ0oKKiAkDr1LwLFy5Ao9HA0dERHh4eyMrKAgD4+flBrVajvLwcABAdHY3MzEw0NzfD3t4e3t7eyMzMBAD4+vpCo9GgtLQUABAZGYnc3Fw0NTXB1tYW/v7+yMjIEI83ABQXFwMAIiIiUFBQIB7voKAgcWS9p6cnrKysoP1rQJogkyEnJwf19fVQKpUICwsTG8Pu7u5QqVR6x7usrAy1tbWwsrJCZGQkUlNTxeNtZ2eH/Px8AEBwcDAqKyu7PN5OTk7izd0CAwNRW1vb4XhrtVo4OTnBzc1N/KvF398fjY2N4vGOiYlBRkYGWlpaYG1tjcbGRr3j3dzcjLKyMgBAVFQUsrOzoVarYW9vDx8fH/Ez6+PjA51Op/eZzcvLE493QECA3mdWJpOJxzs8PByFhYVoaGiASqVCcHBwh+NdWFgIAAgLC0NJSQnq6uo6Pd42NjadfmavPt6urq5wcHDQ+8xWV1ejuroacrkc0dHROH/+PHQ6HZydneHs7Kx3vOvq6sSFv9p/Zjs73k1NTZ1+Zh0cHODl5dXtZzYnJwdqtRoKhQJqtZr73xFtn1lz+x3h4+MjfrY6+8wWFxdz8TvCwcEBnp6eZvU7ws/PTzyG/f0dIQgCeksm9GVrI2puboadnR127tyJO+64Q3x81qxZqKqqwpdfftnhNUlJSbC2tsYPP/wgPvbdd9/hb3/7G9RqNZRKpd72Go0GBw8eRFhYmMVesSkuLu5wVcxc7QsYB0GjhdPQKFz3/fumLscgeMqHN5QN2ygfdhkiG61Wi9OnTyMpKQlWVt1fk2FmcIJSqcTIkSNx4MAB8TGdTocDBw4gMTGx09eMGTMGGRkZ0P21UBvQOl/e19e3Q6OmPUdHRzg5OYlfltKoASD+FcUDcVYUR9O9ecqHN5QN2ygfdkmdzYC6olJTU5GZmYnKyspOLxPNnDmzT/tbunQpZs2ahVGjRmH06NHYsGED6uvrxVlSM2fOhL+/P1566SUAwLx58/Dmm29i8eLFWLhwIS5evIgXX3wRixYtGsiPRcyAoNMBbZ85jgYPE0IIGZh+NWwuXbqE++67D8eOHeuy30smk/W5YTN9+nSUlpZi5cqVKCoqwrBhw7B3717xElZubq5eF1JgYCD27duHf/3rXxg6dCj8/f2xePFiLF++vD8/lkWIjo42dQkGIS7OB74GD/OSD48oG7ZRPuySOpt+NWzmzJmDs2fPYsOGDRg3bhxcXV0NVtCCBQuwYMGCTp9LSUnp8FhiYiJ+/fVXg70/7zIzMxEREWHqMgZMaDeln6euKF7y4RFlwzbKh11SZ9Ovhs0vv/yCJ598EgsXLjR0PcTImpubTV2CQehdsbHi54oNL/nwiLJhG+XDLqmz6defuh4eHnB2djZ0LUQCDg4Opi7BMHTtu6L4uWLDTT4comzYRvmwS+ps+nVGmDt3Lj7++GNa4dcMdXbPLXOk3xXFzxUbXvLhEWXDNsqHXVJn06+uqMjISGi1WsTHx+PBBx9EYGAgFJ0M4Jw6deqACySGlZmZidjYWFOXMWD6g4f5uWLDSz48omzYRvmwS+ps+tWwmT59uvjvxx57rNNtZDIZXdEhRiNw2hVFCCFkYPrVsPnpp58MXQeRiK+vr6lLMAhB064riqPp3rzkwyPKhm2UD7ukzqZfDZukpCRD10Ekovnr/krmTm+MDUcNG17y4RFlwzbKh11SZzPga/ipqan47rvv8N1334k3riLsarvZnLlrP8aGp5WHecmHR5QN2ygfdkmdTb9vqfDll19i6dKl4t1I24SGhuLVV1/F7bffPtDaCOkSjbEhhBDSmX6dEfbs2YM777wTAPDiiy9i165d2LVrF1588UUIgoCpU6di7969Bi2UGEZkZKSpSzAIXqd785IPjygbtlE+7JI6m341bJ5//nkMHToUZ86cwfLly3H77bfj9ttvx/Lly3HmzBkMGTIEq1evNnStxABycnJMXYJhcDrdm5t8OETZsI3yYZfU2fTrjHDmzBnMmjUL9vb2HZ6zt7fHAw88gDNnzgy4OGJ4arXa1CUYBK9dUbzkwyPKhm2UD7ukzqZfZwQbGxtUVFR0+XxFRQVsbGz6XRQxHjs7O1OXYBBCu1H2PM2K4iUfHlE2bKN82CV1Nv1q2Nx000147bXXcPTo0Q7P/fbbb3j99deRnJw84OKI4fGy1oP+ysP8NGx4yYdHlA3bKB92SZ1Nvxo2a9euhY2NDcaOHYvExEQ88MADeOCBB5CYmIjrrrsONjY2+M9//mPoWokBXLp0ydQlGASvXVG85MMjyoZtlA+7pM6mX2eE0NBQnDlzBosWLUJlZSW2b9+O7du3o7KyEosXL8bp06cREhJi4FIJuYLXdWwIIYQMTL/XsfHy8sL69euxfv16Q9ZDjMzb29vUJRgEr9O9ecmHR5QN2ygfdkmdDf2pa2EEQTB1CQbB6929ecmHR5QN2ygfdkmdTa+u2Dz44IOQyWR49913oVAo8OCDD/b4GplMhvfee2/ABRLDKikpgYeHh6nLGDD9e0Xx07DhJR8eUTZso3zYJXU2vWrY/Pjjj5DL5dDpdFAoFPjxxx8hk8m6fU1PzxMyEPoNm373qBJCCOFMr84IV98P6urvifkYNGiQqUswDE67orjJh0OUDdsoH3ZJnU2/zgi5ublobGzs8vnGxkbk5ub2uyhiPPn5+aYuwSB4ne7NSz48omzYRvmwS+ps+j3de9euXV0+/9VXXyE0NLTfRRHj6a5Bak54HWPDSz48omzYRvmwS+ps+nVG6GmEc0tLC+Ryfk42POHlVhd6s6I4mu7NSz48omzYRvmwS+psej3qsqamBlVVVeL35eXlnXY3VVVV4dNPP6XlrRkVGBho6hIMgtcF+njJh0eUDdsoH3ZJnU2vzwjr169HaGgoQkNDIZPJsGTJEvH79l/Dhw/Hnj17MHfuXGPWTfrp4sWLpi7BIHjtiuIlHx5RNmyjfNgldTa9vmIzYcIEODg4QBAELFu2DDNmzMCIESP0tpHJZLC3t8fIkSMxatQogxdLSBua7k0IIaQzvT4jJCYmIjExEQBQX1+PqVOnYsiQIUYrjBiHl5eXqUswCF5XHuYlHx5RNmyjfNgldTb9+lN31apVhq6DSISbQd2cTvfmJh8OUTZso3zYJXU2/Xq3p59+GsOGDevy+eHDh2P16tX9rYkYUVFRkalLMAheb4LJSz48omzYRvmwS+ps+tWw2blzJ2699dYun//b3/6G7du397soQnrCa1cUIYSQgen3ysPh4eFdPh8aGoqcnJx+F0WMp7vczImg4XNWFC/58IiyYRvlwy6ps+nXGcHBwaHbhktWVhYtlsQoXi7X6t1SwYq6oojxUTZso3zYZRZdUTfccAPeeecdFBQUdHguLy8P7777Lm688cYBF0cMr76+3tQlGASvY2x4yYdHlA3bKB92SZ1Nv2ZFPf/88xg9ejQGDx6Mhx56CIMHDwYAnDt3Dlu2bIEgCHj++ecNWigxDJVKZeoSDILXlYd5yYdHlA3bKB92SZ1Nvxo2UVFROHToEBYuXIj169frPXf99dfj9ddfR0xMjEEKJIYVEhJi6hIMgte7e/OSD48oG7ZRPuySOpt+nxGGDh2KgwcPoqSkBL/++it+/fVXlJSUICUlBUOHDjVkjcSA0tPTTV2CYXDaFcVNPhyibNhG+bBL6mwGvBa9h4cHPDw8DFELIb1G070JIYR0ZkANm/z8fJw8eRLV1dXQtesaaDNz5syB7J4YAS+NUF5vgslLPjyibNhG+bBL6mz61bBpamrCrFmz8Pnnn0On00Emk0EQBACtN8JsQw0b9iiVSlOXYBB6V2w4mu7NSz48omzYRvmwS+ps+vWn7pNPPokvvvgC//73v5GSkgJBEPDBBx/g+++/x6233or4+HicPn3a0LUSA7h8+bKpSzAIXqd785IPjygbtlE+7JI6m37fUmH27NlYvny5ONXb398fycnJ+Oabb+Di4oKNGzcatFBC2qMxNoQQQjrTrzNCSUkJRo8eDQCwtbUFoL8Az5133okvvvjCAOURQwsNDTV1CQbRfro3T+vY8JIPjygbtlE+7JI6m36dEby9vVFeXg4AsLOzg6urq950rpqaGjQ1NRmmQmJQpaWlpi7BIHjtiuIlHx5RNmyjfNgldTb9GjyckJCAw4cPY/ny5QCAyZMn4+WXX4avry90Oh3Wr1+Pa6+91qCFEsOoq6szdQmGwWlXFDf5cIiyYRvlwy6ps+nXGWHRokUICwuDWq0G0HqLBRcXF9x///2YNWsWnJ2d8frrrxu0UGIY1tbWpi7BIHTtr9hwNCuKl3x4RNmwjfJhl9TZ9OuKzdixYzF27Fjx+8DAQKSlpeHs2bNQKBSIjo6GldWA1/4jRhAREWHqEgyj/RUbOT9XbLjJh0OUDdsoH3ZJnU2fzwgNDQ2YOnUqPvnkE/0dyeWIj49HXFwcNWoYlpaWZuoSDEJ/gT5+rtjwkg+PKBu2UT7skjqbPjds7Ozs8MMPP6ChocEY9RDSKzTdmxBCSGf6dUYYO3Ysjh49auhaiATc3NxMXYJB8Hp3b17y4RFlwzbKh11SZ9OvM8Kbb76JQ4cO4emnn0Z+fr6hayJG1LbukLlr3xUFjqZ785IPjygbtlE+7JI6m341bOLj45Gfn4+XXnoJwcHBUKlUcHJy0vtydnY2dK3EAAoKCkxdgkEIGj5vgslLPjyibNhG+bBL6mx6Ncr3zJkzCA4OFhsrd911l1GLIqQnel1RHE33JoQQMjC9atgMHz4cH330Ee655x4AQE5ODp566imMHz/eqMURwwsJCTF1CQYhcDrdm5d8eETZsI3yYZfU2fTqjGBra6s3CyolJQXFxcVGK4oYT0VFhalLMAxOp3tzkw+HKBu2UT7skjqbXl2xiY+Px6uvvgqFQiF2R/3++++wsbHp9nVTp04deIXEoGpqakxdgkHwOiuKl3x4RNmwjfJhl9TZ9Kph89prr+Guu+7CQw89BACQyWR47bXX8Nprr3X5GplMBm37mSuECQpOrm7wuo4NL/nwiLJhG+XDLqmz6VXDZtSoUcjIyMClS5dQXFyMG264AU899RSSk5ONXR8xsKioKFOXYBC83t2bl3x4RNmwjfJhl9TZ9PpPXSsrK0RFReH666/HrFmzcNtttyEpKanbr/7auHEjQkJCYGNjg4SEBBw7dqxXr/v0008hk8lwxx139Pu9ecfLsuMCpzfB5CUfHlE2bKN82MX8LRUA4P3330dCQoKhawEAbN++HUuXLsWqVavwxx9/ID4+HhMnTkRJSUm3r8vOzsZjjz2GcePGGaUuXgiCYOoSDILXrihe8uERZcM2yoddUmfD3Bnh1VdfxSOPPILZs2cjNjYWmzZtgp2dHbZs2dLla7RaLe69916sXr0aYWFhElZrflxcXExdgkG0b9iAo+nevOTDI8qGbZQPu6TOhqkzQnNzM06cOKE3dkculyM5Obnbe1M999xz8PLyEgc396S2thY1NTXil1qtHnDt5sLR0dHUJRiE2BUll0Mmk5m2GAPiJR8eUTZso3zYJXU2vRo8LJWysjJotVp4e3vrPe7t7Y3z5893+prDhw/jvffew6lTp3r9PnFxcXrr8syePRsLFy6Er68vLl26JL6nIAhiF9igQYOQn5+PxsZG2NjYIDAwEBcvXgQAeHl5QS6Xo6ioCAAQHh6OoqIi1NfXQ6VSISQkBOnp6QAADw8PKJVKXL58GQAQGhqK0tJS1NXVwdraGhEREWJ/pJubG2xtbcXlqENCQlBRUYGamhooFApERUUhLS0NgiDAxcUFjo6OyMvLAwAEBQWhpqYGVVVVkMlkiImJQXp6OqqqquDv7w9XV1fk5OQAAAICAtDQ0CCuNRAbG4sLFy5Ao9HA0dERHh4eyMrKAgD4+flBrVajvLwcABAdHY3MzEw0NzfD3t4e3t7eyMzMBAD4+vpCo9GgtLQUABAZGYnc3Fw0NTXB1tYW/v7+yMjIEI83AHF9pIiICBQUFIjHOygoCBcuXAAAeHp6QtPc0hqeXIampiYUFxejvr4eSqUSYWFh4ufF3d0dKpVK73iXlZWhtrYWVlZWiIyMRGpqqni87ezsxPufBQcHo7Kyssvj7eTkhNzcXABAYGAgamtrOxxvrVYLJycnuLm5ITs7GwDg7++PxsZG8XjHxMQgIyMDLS0taGxsRGxsrN7xbm5uRllZGYDWQXjZ2dlQq9Wwt7eHj4+P+Jn18fGBTqfT+8zm5eWJxzsgIEDvMyuTycTjHR4ejsLCQjQ0NEClUiE4OFjveFtZWaGwsBAAEBYWhpKSEtTV1XV6vG1sbDr9zF59vF1dXeHg4KD3ma2urkZ1dTXkcjmio6Nx/vx56HQ6ODs7w9nZWe9419XVobKyssNntrPj3dTU1Oln1sHBAV5eXt1+ZnNycqBWq9HQ0IAhQ4Zw/zui7TNrbr8jGhoaIP/r6m1nn1lefkc4ODjA09PTrH5HqNVq8Q/Q/v6O6Et3lkxgqGPy8uXL8Pf3x5EjR5CYmCg+vmzZMhw8eBC//fab3va1tbUYOnQo3nrrLdx6660AgAceeABVVVXYvXt3h/1rNBocPHgQYWFh4n8AAFCpVFCpVMb5oRiTmpqK2NhYU5cxYL/cNBO1qRmQ2ygxITvF1OUYDC/58IiyYRvlwy5DZKPVanH69GkkJSXByqr7azJMXbHx8PCAQqHosKpxcXExfHx8Omx/6dIlZGdnY/LkyeJjur8WbrOyskJ6ejrCw8M7vM7R0dFi1zwICgoydQkG0XYTTJ6megP85MMjyoZtlA+7pM6GqTE2SqUSI0eOxIEDB8THdDodDhw4oHcFp010dDTOnj2LU6dOiV+33347brzxRpw6dQqBgYFSlm8WeFmdU9D91bDhaKo3wE8+PKJs2Eb5sIvJlYeltHTpUsyaNQujRo3C6NGjsWHDBtTX12P27NkAgJkzZ8Lf3x8vvfQSbGxsEBcXp/f6ttHXVz9OWlVVVcHPz8/UZQxY26wonqZ6A/zkwyPKhm2UD7ukzoa5hs306dNRWlqKlStXoqioCMOGDcPevXvFgWO5ubl642NI3/Ayg0hs2HD2WeAlHx5RNmyjfNgldTZMDR42trbBw/Hx8RY7xoYXKSOnoKmgGCovd9x45mtTl0MIIcSI+jJ4mK8/d0mP2qaUmjvx7t6cdUXxkg+PKBu2UT7skjobvs4KpEfc3HGd064obvLhEGXDNsqHXVJnw9dZgfTIycnJ1CUYhK5tujdnXYq85MMjyoZtlA+7pM6GGjYGUn74OJrLq0xdRo9cXV1NXYJhcDrdm5t8OETZsI3yYZfU2VDDxgDq0rPwx8zlODJhNqr+SDV1Od1qWyLd3PE63ZuXfHhE2bCN8mGX1NnwdVYwkdQnXoG2oRFNBcX47e9zkbPlc8lv025peJ3uTQghZGDorGAAQzeugsvooQAAoUWDtCdfwZlHn4WmvqGHV0ovICDA1CUYhLjyMGdjbHjJh0eUDdsoH3ZJnQ01bAzAxtcToz9/EyFz/iE+VrhrP47e8jAasvNNWFlH7e9qbs547YriJR8eUTZso3zYJXU2fJ0VTEhubYXo1Ysw7L0XoXCwAwDUX8zGr5P+ieqT7Iy7qaioMHUJBtF2E0xw1hXFSz48omzYRvmwS+ps+DorMMBn0g247vv34RAZCgBoLq/CsakLUPrDERNXxg9BEIC/xjDxNiuKEELIwFDDxgjswwKR8NXbcL12GABA29iEP2YtR/420y/9Hxsba+oSBkxot9gTb2NseMiHV5QN2ygfdkmdDTVsjMTaxQmjPl0Pn9vHA2g9GZ9b+hKy3tpm0rouXLhg0vc3iL/G1wD8zYriIh9OUTZso3zYJXU2fJ0VGKOwUSF+02oEz5kuPpb+3JvIeW+nyWrSaDQme29DEdo3bDgbPMxDPryibNhG+bBL6mz4OiswSCaXI2b1Ygxa/oj4WNpTryLv4y9NUo+jo6NJ3teQ2qZ6A/x1RfGQD68oG7ZRPuySOhtq2Egk/F+zEf6vB8Tv/3x8LQo++07yOjw8PCR/T0Pj+YoND/nwirJhG+XDLqmz4euswLiIZY8gZN49rd8IAs4u+TeKvv5R0hqysrIkfT9jEKd6g78xNjzkwyvKhm2UD7ukzoavswLjZDIZolbOR9CDd7U+oNPhzKLnUX0m3bSFmRlB1+6KDU33JoQQ0g41bCQmk8kQ88IS+E37GwBA16jGydkroC6VZgEjPz8/Sd7HmHie7s1DPryibNhG+bBL6myoYWMCMrkccS8vg/PIwQCApoJinHzoSeiaW4z+3mq12ujvYWztx9jwtvIwD/nwirJhG+XDLqmz4eusYEbkKiWGb3kJKl9PAEDVsTNIffIVo98VvLy83Kj7lwLPg4d5yIdXlA3bKB92SZ0NX2cFM2Pj7YERW16CXKUEAOR//BXyPthl4qrMAMfTvQkhhAwMNWxMzHl4LOJeWSF+f37V66jLyDHa+0VHRxtt31IROF55mId8eEXZsI3yYZfU2fB1VjBTfnfdgqCHWmdK6dTN+PP/1ujN/DGkzMxMo+xXSnrTvTmbFcVDPryibNhG+bBL6myoYcOIyCfnwTa4deR45W+nkfv+F0Z5n+bmZqPsV0p60705G2PDQz68omzYRvmwS+ps+DormDEre1vEvfKE+P2Ff7+NhpzLBn8fe3t7g+9TajxP9+YhH15RNmyjfNgldTbUsGGI+9iRCJw5BQCgbWjEn4//x+CzpLy9vQ26P1PgeYwND/nwirJhG+XDLqmz4euswIGoZx6FjX/rh6D8599R8L9vDLp/Hvqh9dax4awriod8eEXZsI3yYReNsbFwVo72GLx2mfj9+dVvoqWmzoQVsYfnu3sTQggZGGrYMMhzfCJ875wAANBU1yL3/c8Ntm9fX1+D7ctUeL4JJg/58IqyYRvlwy6ps+HrrMCRiMceFm8XkP3Op9DUNxpkvxqNxiD7MSW9MTacTffmIR9eUTZso3zYJXU21LBhlH1oAHzvSAYAtFRUI//jLw2y39LSUoPsx6Q4nu7NRT6comzYRvmwS+ps+DorcCZs4f3iv7Pe3gZtE93kDbhqurecrys2hBBCBoYaNgxzjAmH163XAwDURWUo2L5nwPuMjIwc8D5MjeebYPKQD68oG7ZRPuySOhu+zgocCl88S/x31psfQ9cysL7K3NzcgZZkcjw3bHjIh1eUDdsoH3ZJnQ1fZwUOOQ+LgceNCQCAxrxCFH7x/YD219TUZIiyTKr9dG9wNt2bh3x4RdmwjfJhl9TZUMPGDIQveUD8d+YbH+qNMekrW1tbA1RkWu2ne8s5a9jwkA+vKBu2UT7skjobatiYAdeEeLheOwwAUJ+Ri6oTf/Z7X/7+/gaqynR4XnmYh3x4RdmwjfJhl9TZ8HVW4Jj/tL+J/y47eKzf+8nIyDBEOSalv/IwXx9hHvLhFWXDNsqHXVJnw9dZgWPuSdeI/y7/+XcTVsIAvZtg8tUVRQghZGCoYWMmbP29YR8RBACo/iO13/eP4uEOuDzPiuIhH15RNmyjfNhFd/cmXXK/fjSA1gXqKo78YeJqTIfnhg0hhJCBobOCGfG4YbT47/KD/euOKi4uNlQ5JiNor6zlw9vdvXnIh1eUDdsoH3ZJnQ01bMyI23XDxZs+llnwOBv9KzZ8NWwIIYQMDDVszIiVgz1cRsYBABou5aIxr7DP+4iIiDB0WZLjuSuKh3x4RdmwjfJhl9TZ8HVWsADu11+ZHdWfqzYFBQWGLMck9Fce5usjzEM+vKJs2Eb5sEvqbPg6K1gAvWnf/Rhn09jYaMhyTELgeLo3D/nwirJhG+XDLqmzoYaNmXEeFgMrJwcAQPnh4xB0uh5eoc/GxsYYZUmL464oLvLhFGXDNsqHXVJnw9dZwQLIrazgPnYkAKCloho1Zy/06fVBQUHGKEtSPK88zEM+vKJs2Eb5sEvqbPg6K1iI9uNsyn/u2+0VLlzoW0OIRe1vgilTWJmwEsPjIR9eUTZso3zYJXU21LAxQ+5JV9azKevnejbmjOdZUYQQQgaGzgpmyC7EH7aBvgCAymNnoG1o6vVrPT09jVWWZHhu2PCQD68oG7ZRPuySOhu+zgoWQiaTibOjhOYWlP9yotevtbIy/64b/TE2fM2K4iEfXlE2bKN82CV1NtSwMVOeydeJ/y7+5qdev66wsO+L+rGm/RUbyPn6CPOQD68oG7ZRPuySOhu+zgoWxOOGBCgc7AAAxXsPQdfcYuKKpMNzVxQhhJCBobOCmVLYqOA1cSwAQFNdi/JerkIcFhZmzLIkwXNXFA/58IqyYRvlwy6ps6GGjRnzmXyT+O+ir3/s1Wt4uAOu/nRvvho2POTDK8qGbZQPu+ju3qTX+tMdVV9fb+yyjI/jrigu8uEUZcM2yoddUmfD5Flh48aNCAkJgY2NDRISEnDsWNeL0G3evBnjxo2Dq6srXF1dkZyc3O32POlPd5RSqTR2WUbX/jYSvDVseMiHV5QN2ygfdkmdDXNnhe3bt2Pp0qVYtWoV/vjjD8THx2PixIkoKSnpdPuUlBTMmDEDP/30E44ePYrAwEBMmDDBYu706nv7ePHfhV/13B3FQz+0oOW3K4qHfHhF2bCN8mGXxY+xefXVV/HII49g9uzZiI2NxaZNm2BnZ4ctW7Z0uv0nn3yCRx99FMOGDUN0dDT++9//QqfT4cCBAxJXbhruSaPF7qiSvT/32B11/vx5KcoyKv27ezP3ER4QHvLhFWXDNsqHXVJnw9RZobm5GSdOnEBycrL4mFwuR3JyMo4ePdqrfTQ0NKClpQVubm5dblNbW4uamhrxS61WD7h2U1HYqOB9yzgAgKamDmUH+e+G01vHhrOuKEIIIQPD1FKNZWVl0Gq18Pb21nvc29u71y2+5cuXw8/PT69xdLW4uDg0NDSI38+ePRsLFy6Er68vLl26JL6nIAhiF9igQYOQn5+PxsZG2NjYIDAwEBcvXgQAeHl5QS6Xo6ioCAAQHh6OoqIi1NfXQ6VSISQkBOnp6QAADw8PKJVKXL58GQAQGhqK0tJS1NXVwdraGhEREUhLSwMAuLm5wdbWVuxWCwkJQUVFBWpqaqBQKBAVFYW0tDQ0xw8Cdu4DAKR/vAtl/q4ICgpCTU0NqqqqIJPJEBMTg/T0dKjVauTn58PV1RU5OTkAgICAADQ0NKCiogIAEBsbiwsXLkCj0cDR0REeHh7IysoCAPj5+UGtVqO8vBwAEB0djczMTDQ3N8Pe3h7e3t7IzMwEAPj6+kKj0aC0tBQAEBkZidzcXDQ1NcHW1hb+/v7IyMgQjzdwZfR8REQECgoKxOMdFBQk3kitqd1AtEtZWYgK8EZxcTHq6+uhVCoRFhYmfl7c3d2hUqn0jndZWRlqa2thZWWFyMhIpKamisfbzs4O+fn5AIDg4GBUVlZ2ON6CIMDFxQVOTk7Izc0FAAQGBqK2trbD8dZqtXBycoKbmxuys7MBAP7+/mhsbBSPd0xMDDIyMtDS0gKZTIbGxka9493c3IyysjIAQFRUFLKzs6FWq2Fvbw8fHx/xM+vj4wOdTqf3mc3LyxOPd0BAgN5nViaTicc7PDwchYWFaGhogEqlQnBwsHi8PT09YWVlJS6yFRYWhpKSEtTV1XV6vG1sbDr9zF59vF1dXeHg4IC8vDwArXcArq6uRnV1NeRyOaKjo3H+/HnodDo4OzvD2dlZ73jX1dWhsrKyw2e2s+Pd1NTU6WfWwcEBXl5e3X5mc3JyoFarIQgC1Gq1Wf6OaPvMOjo66h3vzn5HtH1mze13hIuLi/jZ6uwzy8vvCAcHB3h6eprV7wg3NzfxGPb3d4QgCOgtmdCXrY3s8uXL8Pf3x5EjR5CYmCg+vmzZMhw8eBC//fZbt69fs2YN1q5di5SUFAwdOrTD8xqNBgcPHkRYWBjk7bowVCoVVCqV4X4QiWmb1PhpyG3Q1NbDyskBN539BnJV54O1qqqq4OLiIm2BBnZqzjMo+rK1q/H6Y5/DLsjXxBUZDg/58IqyYRvlwy5DZKPVanH69GkkJSX1eIsGpq7je3h4QKFQdJjzXlxcDB8fn25fu27dOqxZswbff/99p42a9hwdHeHk5CR+mXOjBrhqdlRNHYq/O9jltm1/lZgznlce5iEfXlE2bKN82CV1NkydFZRKJUaOHKk38LdtIHD7KzhXW7t2LZ5//nns3bsXo0aNkqJU5vjecbP479QnXkFjAceLVXE83ZsQQsjAMHdWWLp0KTZv3owPPvgAaWlpmDdvHurr6zF79mwAwMyZM/HEE0+I2//nP//BM888gy1btiAkJARFRUUoKipCXV2dqX4Ek/AYnwivW68HALRU1uD03JXQtWg6bBcaGip1aQbH83RvHvLhFWXDNsqHXVJnw1zDZvr06Vi3bh1WrlyJYcOG4dSpU9i7d684cCw3N1fvTqFvv/02mpubcdddd8HX11f8Wrdunal+BJOQyWQYsv5J2AS0dtlV/X4WF9e802G7tgFm5ozn6d485MMryoZtlA+7pM6GqVlRbRYsWIAFCxZ0+lxKSore922jyAlg7eKEYe++gN/+PhdCiwZZGz+B67XD4HXzGHGb2tpaE1ZoGDyPseEhH15RNmyjfNgldTZ8nRUIXEbEImrlfPH7s4ueR2N+kfh9T6PJzYFeV5QVX11RPOTDK8qGbZQPu6TOhho2HAp+eJreeJs/Zi6DprZ17ZfIyEhTlmYQeg0bOV8NGx7y4RVlwzbKh11SZ0MNGw61jbexC/EHANSmZuDUP5+GrkUjLnxkznjuiuIhH15RNmyjfNgldTZ8nRWIyNrFCSM/eQXWrk4AgLKffkPqipf7tHojq9rf3ZtuqUAIIaQ9OitwzD48CMPfXwOZ0hoAkP/J19B82fXifeaC5+ne3d3jjJgWZcM2yoddUmdDDRvOuV07DENee0r8vvD1j3H5i+9NWJEBtHVFyWSQyWSmrcXA7OzsTF0C6QJlwzbKh11SZ0MNGwvgN2UCBj0xR/z+7KLnUbL/FxNWNDBtV2x4G18DQLy5HmEPZcM2yoddUmfD35mBdCps0UwE3P93AICg0eLUI0+h4shJE1fVP22Dh3mb6k0IIWTgaOK/hZDJZBi85jE0VVSj7NsU6JqacWLm4xi98w04D4sxdXl9Il6x4WyqNwAEBwebugTSBcpGGg0NDWhoaACAPnU1u7u7o7y83FhlkQHobTZtk1vs7OwG1H1FDRsLIlMo4PX0PyFrUqP0wFFo6xpw/J6lSNj1FhyizOc+K22zonjsiqqsrIS9vb2pyyCdoGyMr7q6GjKZDO7u7n0eP9fc3AylUmmkyshA9CUbQRBQW1uL6upqODs79+v9+DszkG7VNTZg2OZ/w/XaYQCAlopq/H73ItRl5Ji2sD4Qu6I4bNjU1NSYugTSBcrG+FpaWuDk5NSvSQHadrMlCVv6ko1MJoOTkxNaWlr6/X78nRlItxQKBRR2Nhj50ctwio8GAKhLyvH7XQtRn2Ueg+/E6d4cdkUpOJu+zhPKxvgGMsuRtxmSPOlPNgPJkxo2FiYqKgoAYOVoj1GfboBj3CAAgLqoDL/fuQANOQWmLK9XeL5i05YPYQ9lwzYbGxtTl0C6IHU2/J0ZSLfS0tLEfytdnXDN9tfgEBMOAGi6XIJjUxegIbfQVOX1ijh4mMNZUe3zIWyhbNjW2Nho6hJIF6TOhho2FubqWyoo3V1wzWevwSGydfBwU0Fx65Ublhs3bVds5Px9fHm45QWvKBtCzAN/ZwbSLRcXlw6PqTzdcM3nb8B+UOt01sa8Qhyb8igaci5LXF3vXFmgj78rNp3lQ9hA2bDNmGOg4uPjMXToUL0BrUuXLsWaNWtw+PBhuLu7IzAwEEFBQRgzZgz27NnT7f62bduGKVOmdPqcm5sbCgquDAk4dOgQJk6ciMDAQERERGDBggWoqKgQn58/fz7WrVunt4/Dhw9j5MiRne5z/vz58PHxQVBQEIKCgnDTTTdh48aN0Gg0vT8gfST1+DRq2FgYJyenTh9Xebrhmp1XGjdNBcU4NnU+k2NueJ7u3VU+xPQoG7YZ++RZV1eHTz75pNPnQkJCkJeXh+zsbDz44IN4+OGHUVlZOeD3PHLkCO655x7cd999uHDhAg4dOoS6ujpMnToVzc3N/d7vY489htzcXKSmpuLpp5/GBx98gDlz5vT8wn6ihg0xqtzc3C6fs/H2wOjP34T9oBAAfzVupsxnbrYUz4OHu8uHmBZlw7aBnOh7Y968eVi/fn2305DlcjlmzJiBpqYmZGdnD/g9V69ejZkzZ+L++++Hra0tfH19sWnTJhQXF+Ozzz4b8P4dHBxw00034b333sPu3btx7ty5Ae+zM8bO5mq0QB/Ro/Jyx+gv3sTvdy1EXXpW64DiKY/imp1vwCGCjZVXxYYNh9O9CSFXzN99HpUNvesiEQShX1OEXe2ssPGO6B63GzduHFJSUrBt2zbMmjWr0220Wi22bdsGe3t7hIWF9bmW9urr63HixAmsXLlS73EbGxvcdNNN+Pnnn3HfffcN6D3aDBkyBAEBAfjtt98QFxdnkH2aEjVsLExgYGCP27R1S/1+9yLUnc9snQo+dQGu2fE6EysUC7q/1rHh8IpNb/IhpkHZSK+yQYOyhv4v1GZoy5Ytw+LFi3HPPffoPZ6Tk4OQkBAoFAqEhoZi69at/V41t01VVRV0Oh28vLw6POfl5YXTp08PaP+d7bOqqsqg+2wj9YrQ1LCxMLW1tXB0dOxxO5WnG0bvfAO/T1+C2j8vQl1SjmNT5+OaHa/DMTZCgkq7JmhaGzZyDqd79zYfIj3KRnqudr0/RQkQIEP/rtj0VlJSEnx9ffG///1P7/Hg4GCcOHGiz+/dHRcXF8hkMpSUlGDQoEF6z5WUlMDd3R1A6/iVq7vHWlpaYGXVt9N7cXGx0QbIa7VaScfZUMPGwlRVVcHPz69X2yo9XHHNzjdwfPoS1Jw5j+byKhy7cwFGbX8NzkNNt1hZW1cUOJzu3Zd8iLQoG+n1pouoTWNjI2xtbY1YTatly5Zh6dKlGDt2rFHfx97eHiNGjMCePXswZswY8fGmpib8+OOPWL58OQDA39+/w/ivvLw8BAQE9Pq9zp07h4KCAiQkJBim+KtIfbsL/s4MpFt97YNWujrhmh2vwXnEYABAS2UNfr97Ear+SDVGeT0SBAEQZ0Xxd8WGloVnF2VDAODGG2+El5dXj1O6e6LT6dDU1CR+dTbAtm3G0scff4zGxkYUFRVh7ty5cHJywt133w0AmDx5Mr799lv88ssv0Ol0yMrKwqZNm7qcTt5efX09UlJS8NBDD2Hy5MlcjK8BqGFjcWJiYvr8GmtnR1yzfQNcRg8FAGiqa/H7tEWo/P2socvr2V+NGoDPWVH9yYdIg7JhmxRXa9osW7ZswNO5Dx06BD8/P/ErMTGxwzZJSUn46KOP8MEHHyAiIgKxsbEoLCzEF198Id5pPjY2Fm+++SZWrFiBkJAQ3HnnnZg2bVqHcUDtrVu3DkFBQYiOjsbq1atx3333YfPmzQP6ebojZTYAIBMsaDlNjUaDgwcPIj4+3mJvaJeent7ve95o6hvwx/3LUHHkDwCAws4WIz9eB7frhhuyxG7pmlvwfVASAMD12ngk7H5bsveWwkDyIcZF2RhfeXm5OHakr5qamri/X9TevXuxdOlSfPfddwgOZmOWam/0J5urPwtarRanT59GUlJSj+OH+PuTl3RrIH2dVvZ2GPnxOrgnXdO6r4ZGHL93Kcp+/t1Q5fVIHF8DPqd7S90XTXqPsmGbJfyNfsstt+Df//43jh8/bupS+kTqbKhhY2EGunqqws4GIz5YC8/k6wAAukY1/rj/cZT+cMQQ5fVI0F5Z04LHm2DS6rbsomzYxtpV+Ndffx2BgYEdvl544YUB7XfKlCm48847DVSlNGjlYWJUbm5uA96HwkaF4VtegvffWruEdOpm/DF7BYr3/jzgffdE74oNh2NsDJEPMQ7Khm2sNWwWLVqEvLy8Dl9PP/20qUuTHDVsiFEZYplvAJArrRH/zvPw+ft4AIDQosGph59C0Vc/GmT/XdHviuLv42uofIjhUTZsk3rZftJ7UmfD35mBSEZubYWhG1fB765bALQunHdq7kpc/uJ7o72n0H6cA2N/oRFCCDE9athYGH9/f4PuT25lhSGvPQX/Gbe1PqDT4cz81SjYPrA1HroicD7d29D5EMOhbNhmbW1t6hJIF6TOhr8zA+lWY2OjwfcpUygQ98oKBM76a0EoQcDZJf9G/ravDf5e4Lwryhj5EMOgbNhmCbOizBXNiiJGVVFRYZT9yuRyxK55DMEPt66GCUHAuaUvIfeDXQZ9n/ZdUTyuPGysfMjAUTZs02h6dxdwIj2ps6GGDTEYmUyG6OeXIGTuDPGx1OUvI+e9nQZ7D72GDYfTvQkh7ImPj8fQoUP1bja5dOlSrFmzBocPH4a7uzsCAwMRFBSEMWPG9Hi7hW3btsHT01NvGvg777wjPv/NN98gOTkZAQEBiI2NxcyZM/Hnn3+Kzx85cgS33347AgMDERUVhalTp+Lo0aPdvuf8+fOxbt26Do9rNBrMmjULcXFxcHNz63DfqaeffhojRoxAUFAQxo4di3379nW6/82bN2Pt2rUAgISEBFRXV4vP/frrr7jtttsQGBiIyZMnd1unIVDDxsIYe1l4mUyGqFULELrgPvGxtKdeRfa72w2yf95nRdGy/eyibNhm7FWH6+rq8Mknn3T6XEhICPLy8pCdnY0HH3wQDz/8cI+3XBg7dqzeNPA5c+YAALZv34758+djzpw5SE9Px6lTpzBlyhT88MMPAIDDhw9j2rRpmDRpEs6dO4fU1FTMnz+/ywZHbyQmJmLLli1QqVQdnnNwcMBnn32G7OxsvPTSS5gzZw5ycnI6bHfmzBnEx8ejtrYWGo0Gzs7O4nPOzs6YNWsWHn/88X7X2Bf8nRlItzIyMoz+HjKZDJFPzUP4vx4QHzu/8jVkv/PpgPfN+zo2UuRD+oeyYZtarTbq/ufNm4f169frXbW5mlwux4wZM9DU1NSv5QF0Oh2ee+45rFixAnfffTfs7e2hVCoxZcoULF68GADw3HPP4f7778ecOXPg7OwMhUKB8ePH49lnn+3Xz2VlZYW5c+di9OjRnT6/YsUKREREQC6XY9y4cYiKisLp06c7bHf27FkMHToUZ8+exeDBg/Wei42Nxd133y3ZAPzub7hAuNPdf0pDkslkGLT8n5ApFMhY9x4A4Pyq1yEIAkLbdVX1Fe9jbKTKh/QdZSO9IxMehLq0vFfbCoLQrzuwqzzdcd33W3rcbty4cUhJScG2bdswa9asTrfRarXYtm0b7O3tERYW1udaLl68iMLCQkyaNKnT5+vr63HixAmsXLmyz/s2hKqqKqSlpendM23w4MGoq6tDfX09EhMTxTVrQkJC8MYbb2Dy5MmSDx6mho2FcXBwkPT9Ih57CJDJkPHyfwEA6c++AQgCQud1fefZ7rSf7g0Or9hInQ/pPcpGeurScqgLS01dhmjZsmVYvHhxhztn5+TkICQkBAqFAqGhodi6dateV0xnfvnlF4SEhIjf//7772L3lZeXV6evqaqqgiAIXT5vTDqdDgsWLMDkyZP1GjZ//vknPv30U5w7dw4vvPACJkyYgHfffVfvZ5NLPGyAGjYWxtPTU/L3jPi/B1sbN2s3AwDSV78JAP1q3PA+xsYU+ZDeoWykp/Ls352+jfUeSUlJ8PX1xf/+9z+9x4ODg3HixIk+ve+YMWOwa5f+rNG2mXclJSUICgrq8BoXFxfIZDKUlJQgMjKyT+83UI899hhqamrw3nvviY+tWLECn376KRoaGmBjY4MPP/wQjY2NuOGGGzB+/HhxW6nXsaGGjYXJyspCbGys5O8bsXQ2ZDLg4n+uNG5kcjlC5vyjT/sRNHzPijJVPqRnlI30etNF1KaxsRG2trZGrKbVsmXLsHTpUowdO9bg+x40aBB8fX2xZ88ezJ07t8Pz9vb2GDlyJPbs2WOU9+/KqlWrcPr0aezevVtvgPGaNWuwZs0axMXF4dSpU9i9ezeOHz+ONWvW6L1erVZLkk0b/v7kJcwK/9dsDHpijvj9+VWvI/u/n/VpH4KO7zE2hBC23XjjjfDy8upxSnd/yOVyrFy5EmvWrMHnn3+O+vp6tLS04Ouvv8brr78OAHjmmWfw4YcfYvPmzaipqYFOp8PBgwexevXqHvev0WjQ1NQkfrWtL6NWq9HU1NTh3wCwbt067Nu3Dzt27ICjo2OHfebn58PLywtWVlbizKir6XQ68f3a/m3MMWvUsLEwfn5+Jn3/8MWzELHsEfH7809vQM6Wz3u/A867okydD+kaZcM2Kbs7li1b1uN07v6aPn063nzzTbz99tuIiopCfHw8duzYgeTkZACtg5i3b9+Or776CoMHD0Z0dDRee+01TJw4scd9r127Fn5+fuLX0qVLAQCjR4+Gn58f1Go1EhIS9D7rL774IrKzsxEfHy+uubNjxw7x+TNnziAuLg5A68yozho2x48fh5+fH+bNm4ejR4/Cz88PS5YsGchh6pZMsKB1qDUaDQ4ePIj4+HjmbnEvlZKSEpMMPLvaxbX/xaVXr1xmjv3P4whquyVDNyqOnMSxqfMBAKHz70XUM/ONVqMpsJIP6YiyMb7y8nK4u/dvXE1LSwvdL4pR/cnm6s+CVqvF6dOnkZSUBCur7kfR8PcnL+lWWVmZqUsAAEQ8/hDCllyZMpm6/GXkb/umx9fx3hXFSj6kI8qGbXRLBXbRLRWIRWhb56b9CsXn/u8lXP7i+25fx/sCfYQQPrz++ut6t0xo+3rhhReM9p4zZszo9D3bVi22FDQrysK0X3/A1NpWKNY1tyDn3e2tdwVf+DzkSmv43HZjp69p37ABh2NsWMqH6KNs2GbsWyr01aJFi7Bo0SJJ3/PqaeiskDob/s4MpFv9WebbmGQyGaJXL0LgX+NrBK0Wp+euRMn3hzvdvv10bzmH071Zy4dcQdmwzdi3VCD9J3U21LCxMCz+55fJZIh96f/g/4/WZcQFjRYnH34K5YeOd9i2/RgbcDjGhsV8SCvKhm0WNA/G7EidDTVsLIy9vb2pS+iUTC5H3Csr4Dt1AgBAaG7BH7OWo/L4Wb3teF95mNV8CGXDOqmX7Se9J3U29EmwMD4+PqYuoUsyhQJDXnsaXreMAwBoGxpx4t7HUHPuwpWNOB88zHI+lo6yYRtN9WaX1Nnwd2Yg3bp06ZKpS+iW3NoK8Zueg/v11wAANNW1OD59CeoycgDwP92b9XwsGWXDNuoqZBeNsSEWT2GjwvD318BlVOtqls3lVTg+bTEa84u474oihBAyMHRmsDDmcjndyt4WIz95BY5xgwAATZdLcPwfS6AuKRe34fEmmOaSjyWibNhmzO6O+Ph4DB06VO/+RkuXLsWaNWtw+PBhuLu7IzAwEEFBQRgzZkyP95Hatm0bPD099daaeeedd8Tnv/nmGyQnJyMgIACxsbGYOXMm/vzzT/H5I0eO4Pbbb0dgYCCioqIwdepUHD16tNv3nD9/PtatW9fhcY1Gg1mzZiEuLg5ubm7Izc3Ve/7pp5/GiBEjEBQUhLFjx2Lfvn2d7n/z5s1Yu3YtACAhIQHV1dXic5s2bcK1116LoKAgXHPNNfjkk0+6rXWgqGFjYXQ6Xc8bMcLa2RGj/rcedmGBAID6jFxcXPOu+DyPY2zMKR9LQ9mwzdgzb+rq6ro8IYeEhCAvLw/Z2dl48MEH8fDDD/d4L6mxY8ciLy9P/Jozp/UGwdu3b8f8+fMxZ84cpKen49SpU5gyZYq4yN7hw4cxbdo0TJo0CefOnUNqairmz5/fZYOjNxITE7Flyxa9O3e3cXBwwGeffYbs7Gy89NJLmDNnDnJycjps13YDzNraWmg0Gjg7O4vPyWQybN68GVlZWdi6dSuee+45/Prrr/2utyf8nRlIt0pKSkxdQp+oPN1wzfYNUPl4AAB06mbxOR7H2JhbPpaEsmGbsZftnzdvHtavX9/tXanlcjlmzJiBpqamfq17pNPp8Nxzz2HFihW4++67YW9vD6VSiSlTpmDx4sUAgOeeew73338/5syZA2dnZygUCowfPx7PPvtsv34uKysrzJ07F6NHj+70+RUrViAiIgJyuRzjxo1DVFQUTp8+3WG7s2fPYujQoTh79iwGDx6s99ycOXMwZMgQKBQKDB48GNdffz2OH++4nIeh0MrDhHm2gb4Y9ekGHLtjHlqqaq88QWNsCOHaRxuPoL62dwNPBUGATCbr83vYO6pw//zretxu3LhxSElJwbZt2zBr1qxOt9Fqtdi2bRvs7e0RFhbW51ouXryIwsJCTJo0qdPn6+vrceLECaxcubLP+zaEqqoqpKWl6a3CPXjwYNTV1aG+vh6JiYlobm794zMkJARvvPEGJk+erLePlpYWHD9+HNOnTzdandSwsTCDBg0ydQn94hgdhhEfr8PxuxdD29gEoHUGFW/MNR9LQNlIr75WjboadmY7LVu2DIsXL8Y999yj93hOTg5CQkKgUCgQGhqKrVu36nXFdOaXX35BSEiI+P3vv/8udl91dRf5qqoqCIJgkrvM63Q6LFiwAJMnT9Zr2Pz555/49NNPce7cObzwwguYMGEC3n33Xb2frX0X1zPPPIOgoCCMHz/eaLUy+Sfvxo0bERISAhsbGyQkJODYsWPdbr9jxw5ER0fDxsYGQ4YM6XHgliXLy8szdQn95jpqCIa99yKsXRxh7eYsTgnniTnnwzvKRnr2jio4OPXuqy/bXv263kpKSoKvr2+HezIFBwcjOzsbly5dwg8//NCrk/aYMWOQnZ0tfnl6esLV1RVA192eLi4ukMlkJukWfeyxx1BTU4NXX31VfGzFihUICQnBokWL8OGHHyIoKAgnT57EDTfcgIceekjcrq37bv369fj555+xdevWfl1d6y3m/uTdvn07li5dik2bNiEhIQEbNmzAxIkTkZ6e3mkr9ciRI5gxYwZeeukl3Hbbbdi2bRvuuOMO/PHHH4iLizPBT8C2pqYmU5cwIJ43XYukE7sgt7KCXKU0dTkGZ+758IyykV5vuojaNDY2wtbW1ojVtFq2bBmWLl2KsWPHGnzfgwYNgq+vL/bs2YO5c+d2eN7e3h4jR47Enj17jPL+XVm1ahVOnz6N3bt36119WbNmDdasWYO4uDicOnUKu3fvxvHjx7FmzRq91+t0Ovz3v//FRx99hG+//VZswBkLc1dsXn31VTzyyCOYPXs2YmNjsWnTJtjZ2WHLli2dbv/aa6/hlltuweOPP46YmBg8//zzGDFiBN58802JKzcPUvzHNzYrezsuGzUAH/nwirJhm1TL9t94443w8vIySs+AXC7HypUrsWbNGnz++eeor69HS0sLvv76a7z++usAWrtyPvzwQ2zevBk1NTXQ6XQ4ePAgVq9e3eP+NRoNmpqaxK+2AddqtVpsuLf/NwCsW7cO+/btw44dO+Do6Nhhn/n5+fDy8oKVlZU4M+pqO3fuxPr16/H555/D19e3X8emL5hq2DQ3N+PEiRNITk4WH5PL5UhOTu5yjv7Ro0f1tgeAiRMndjunv7a2FjU1NeKXJa1YGRAQYOoSSDcoH3ZRNmyTctn+ZcuW9Tidu7+mT5+ON998E2+//TaioqIQHx+PHTt2iOe5cePGYfv27fjqq68wePBgREdH47XXXsPEiRN73PfatWvh5+cnfi1duhQAMHr0aPj5+UGtViMhIQF+fn7ia1588UVkZ2cjPj5eXHNnx44d4vNnzpwRe0fOnj3bacPmlVdeQVlZGa6//npxH+27tAxNJjB0S9TLly/D398fR44cQWJiovj4smXLcPDgQfz2228dXqNUKvHBBx9gxowZ4mNvvfUWVq9ejeLiYr1tNRoNDh48iNtvvx0NDQ3i47Nnz8bChQvh6+srLpvu7e0NQRDEvsxBgwYhPz8fjY2NsLGxQWBgIC5evAigdaCXXC5HUVERACA8PBxFRUWor6+HSqVCSEgI0tPTAQAeHh5QKpW4fPkyACA0NBSlpaWoq6uDtbU1IiIikJaWBgBwc3ODra0tCgoKALSOMq+oqEBNTQ0UCgWioqKQlpYGQRDg4uICR0dHcRxAUFAQampqUFVVBZlMhpiYGKSnp6Oqqgr+/v5wdXUV1yIICAhAQ0MDKioqAACxsbG4cOECNBoNHB0d4eHhgaysLAAQP/zl5a0L5UVHRyMzMxPNzc2wt7eHt7c3MjMzAQC+vr7QaDQoLS0FAERGRiI3NxdNTU2wtbWFv78/MjIyxOMNQMwsIiICBQUF4vEOCgrChQut94zy9PSElZUVCgsLAQBhYWEoLi5GfX09lEolwsLCcP78eQCAu7s7VCqV3vEuKytDbW0trKysEBkZidTUVPF429nZIT8/H0Brv3llZWWXx9vJyUlczCowMBC1tbUdjrdWq4WTkxPc3NzE6Z/+/v5obGwUj3dMTAwyMjLQ0tKCxsZGxMbG6h3v5uZmlJWVAQCioqKQnZ0NtVoNe3t7+Pj4iJ9ZHx8f6HQ6vc9sXl6eeLwDAgL0PrMymUw83uHh4SgsLERDQwNUKhWCg4O7Pd4lJSWoq6vr9Hjb2Nh0+pm9+ni7urrCwcFB7zNbXV2N6upqyOVyREdH4/z589DpdHB2doazs7Pe8a6rqxNPLu0/s50d76ampk4/sw4ODvDy8ur2M5uTkwO1Wo2GhgYMGTKE+98RbZ9ZU/yO8PT0hJOTE3Q6HeRyOaytrcU/PNsaLm3jNVQqFVpaWsRt268zZGVlBZlM1um2MpkMKpVKvCrR2bYajQZarRYymQw2NjZobGwUt5XL5eLMH6VSCa1W2+m2CoUCCoWi022B1iuATU1NEAShw7bW1tYQBEG8omJjYwO1Wg1BEDo9Lv3d9upj2H7bgR7D9tu2b2ZcvW1Xx7CkpARKpVL8HSEIAtRqNZKSkmBl1f0oGots2ISFheldtlSpVJ0uTMSj1NRUxMbGmroM0gXKh12UjfGVl5fD3d29X6+VaowN6bv+ZHP1Z0Gr1eL06dO9atgw1RXl4eEBhULRoUFSXFzc5XLmPj4+fdoeABwdHeHk5CR+WUqjBuh6GiFhA+XDLsqGbT2d7KT2+uuv690yoe3rhRdeMNp7zpgxo9P3bFu12FSkzoapT4JSqcTIkSNx4MAB3HHHHQBaR1MfOHAACxYs6PQ1iYmJOHDgAJYsWSI+tn//fr0rPuQKY06xIwNH+bCLsmEba/ksWrQIixYtkvQ9r56Gzgqps2Hqig3QemOxzZs344MPPkBaWhrmzZuH+vp6zJ49GwAwc+ZMPPHEE+L2ixcvxt69e/HKK6/g/PnzePbZZ3H8+PEuG0KW7uqrW4QtlA+7KBvjG8jIiO5udUBMqz/ZDOSzwNQVG6B1RHhpaSlWrlyJoqIiDBs2DHv37hUHl+bm5uqNj7nuuuuwbds2PP3003jyyScxaNAg7N69m9awIYQQMyOTycSBrMRytQ067i+mBg8bW9vg4fj4eCg4vIFib6jVaosaU2RuKB92UTbG19zcjJqaGri5ufW5cUMNInb1JRudToeKigo4OTlBqbyyXllfBg8zd8WGGFdhYaHePTwIWygfdlE2xqdUKuHk5ITKyso+39SSZkWxq7fZtGV+daOmr6hhY0HUajXeeOMNvPjii/SXJ4MoH3ZRNtJRKpV9nvKtVqvx4osvUj4MMkU2dN3OgqjVarz//vsWtdKyOaF82EXZsI3yYZcpsqGGDSGEEEK4QQ0bQgghhHDDosbYtE0Aa7tXh6XR6XSws7ODTqez2GPAMsqHXZQN2ygfdhkqm7bX9mYit0VN925qasIvv/xi6jIIIYQQ0g9jxoyBjY1Nt9tYVMNGp9OhubkZCoWCueW3CSGEENI5QRCg1WqhVCp7XBPHoho2hBBCCOEbDR4mhBBCCDeoYUMIIYQQblDDxsw9++yzkMlkel/R0dHi801NTZg/fz7c3d3h4OCAO++8s8NdinNzczFp0iTY2dnBy8sLjz/+ODQajdQ/Chd+/vlnTJ48GX5+fpDJZNi9e7fe84IgYOXKlfD19YWtrS2Sk5Nx8eJFvW0qKipw7733wsnJCS4uLnjooYdQV1ent82ZM2cwbtw42NjYIDAwEGvXrjX2j2b2esrmgQce6PB/6ZZbbtHbhrIxjpdeegnXXHMNHB0d4eXlhTvuuAPp6el62xjqd1lKSgpGjBgBlUqFiIgIbN261dg/ntnrTT433HBDh/8/c+fO1dtGsnwEYtZWrVolDB48WCgsLBS/SktLxefnzp0rBAYGCgcOHBCOHz8uXHvttcJ1110nPq/RaIS4uDghOTlZOHnypLBnzx7Bw8NDeOKJJ0zx45i9PXv2CE899ZTwxRdfCACEXbt26T2/Zs0awdnZWdi9e7dw+vRp4fbbbxdCQ0OFxsZGcZtbbrlFiI+PF3799Vfh0KFDQkREhDBjxgzx+erqasHb21u49957hXPnzgn/+9//BFtbW+Gdd96R6sc0Sz1lM2vWLOGWW27R+79UUVGhtw1lYxwTJ04U3n//feHcuXPCqVOnhL/97W9CUFCQUFdXJ25jiN9lmZmZgp2dnbB06VIhNTVVeOONNwSFQiHs3btX0p/X3PQmn6SkJOGRRx7R+/9TXV0tPi9lPtSwMXOrVq0S4uPjO32uqqpKsLa2Fnbs2CE+lpaWJgAQjh49KghC6y97uVwuFBUVidu8/fbbgpOTk6BWq41aO++uPnnqdDrBx8dHePnll8XHqqqqBJVKJfzvf/8TBEEQUlNTBQDC77//Lm7z3XffCTKZTCgoKBAEQRDeeustwdXVVS+f5cuXC1FRUUb+ifjRVcPm73//e5evoWykU1JSIgAQDh48KAiC4X6XLVu2TBg8eLDee02fPl2YOHGisX8krlydjyC0NmwWL17c5WukzIe6ojhw8eJF+Pn5ISwsDPfeey9yc3MBACdOnEBLSwuSk5PFbaOjoxEUFISjR48CAI4ePYohQ4bA29tb3GbixImoqanBn3/+Ke0PwrmsrCwUFRXp5eHs7IyEhAS9PFxcXDBq1Chxm+TkZMjlcvz222/iNtdff73e3W8nTpyI9PR0VFZWSvTT8CklJQVeXl6IiorCvHnzUF5eLj5H2UinuroaAODm5gbAcL/Ljh49qrePtm3a9kF65+p82nzyySfw8PBAXFwcnnjiCTQ0NIjPSZmPRa08zKOEhARs3boVUVFRKCwsxOrVqzFu3DicO3cORUVFUCqVcHFx0XuNt7c3ioqKAABFRUV6H7S259ueI4bTdjw7O97t8/Dy8tJ73srKCm5ubnrbhIaGdthH23Ourq5GqZ93t9xyC6ZOnYrQ0FBcunQJTz75JG699VYcPXoUCoWCspGITqfDkiVLMGbMGMTFxQGAwX6XdbVNTU0NGhsbYWtra4wfiSud5QMA99xzD4KDg+Hn54czZ85g+fLlSE9PxxdffAFA2nyoYWPmbr31VvHfQ4cORUJCAoKDg/HZZ5/Rf1JC+uAf//iH+O8hQ4Zg6NChCA8PR0pKCsaPH2/CyizL/Pnzce7cORw+fNjUpZBOdJXPP//5T/HfQ4YMga+vL8aPH49Lly4hPDxc0hqpK4ozLi4uiIyMREZGBnx8fNDc3Iyqqiq9bYqLi+Hj4wMA8PHx6TCzoO37tm2IYbQdz86Od/s8SkpK9J7XaDSoqKigzCQWFhYGDw8PZGRkAKBspLBgwQJ88803+OmnnxAQECA+bqjfZV1t4+TkRH8I9kJX+XQmISEBAPT+/0iVDzVsOFNXV4dLly7B19cXI0eOhLW1NQ4cOCA+n56ejtzcXCQmJgIAEhMTcfbsWb1f2Pv374eTkxNiY2Mlr59noaGh8PHx0cujpqYGv/32m14eVVVVOHHihLjNjz/+CJ1OJ/6iSExMxM8//4yWlhZxm/379yMqKoq6OgwoPz8f5eXl8PX1BUDZGJMgCFiwYAF27dqFH3/8sUN3nqF+lyUmJurto22btn2QzvWUT2dOnToFAHr/fyTLp09DjQlz/u///k9ISUkRsrKyhF9++UVITk4WPDw8hJKSEkEQWqdIBgUFCT/++KNw/PhxITExUUhMTBRf3zYFb8KECcKpU6eEvXv3Cp6enjTdu59qa2uFkydPCidPnhQACK+++qpw8uRJIScnRxCE1uneLi4uwpdffimcOXNG+Pvf/97pdO/hw4cLv/32m3D48GFh0KBBelOKq6qqBG9vb+H+++8Xzp07J3z66aeCnZ0dTSnuQXfZ1NbWCo899phw9OhRISsrS/jhhx+EESNGCIMGDRKamprEfVA2xjFv3jzB2dlZSElJ0Zsu3NDQIG5jiN9lbdOJH3/8cSEtLU3YuHEjTffuhZ7yycjIEJ577jnh+PHjQlZWlvDll18KYWFhwvXXXy/uQ8p8qGFj5qZPny74+voKSqVS8Pf3F6ZPny5kZGSIzzc2NgqPPvqo4OrqKtjZ2QlTpkwRCgsL9faRnZ0t3HrrrYKtra3g4eEh/N///Z/Q0tIi9Y/ChZ9++kkA0OFr1qxZgiC0Tvl+5plnBG9vb0GlUgnjx48X0tPT9fZRXl4uzJgxQ3BwcBCcnJyE2bNnC7W1tXrbnD59Whg7dqygUqkEf39/Yc2aNVL9iGaru2waGhqECRMmCJ6enoK1tbUQHBwsPPLII3pTUwWBsjGWznIBILz//vviNob6XfbTTz8Jw4YNE5RKpRAWFqb3HqRzPeWTm5srXH/99YKbm5ugUqmEiIgI4fHHH9dbx0YQpMuHboJJCCGEEG7QGBtCCCGEcIMaNoQQQgjhBjVsCCGEEMINatgQQgghhBvUsCGEEEIIN6hhQwghhBBuUMOGEEIIIdyghg0hhBBCuEENG0IIMSGZTCZ+rVu3rl/72LBhg95+ysrKDFwlIeaDGjaEmLn2J7TuvlJSUkxdqsm89dZb2Lp1q6nL6NKUKVPw0UcfYdKkSeJjKSkpkMlk2Llzp962zc3NuO222yCXy7FlyxYAwC233IKPPvoIU6ZMkbRuQlhkZeoCCCED89FHH+l9/+GHH2L//v0dHo+JiZGyLKa89dZb8PDwwAMPPGDqUjo1dOhQ3HfffT1u19LSgrvuugt79uzB5s2b8eCDDwIAoqOjER0djYyMDOzatcvY5RLCNGrYEGLmrj4h/vrrr9i/f3+vTpTmSBAENDU1wdbW1qLqaGlpwbRp0/DNN9/gnXfewUMPPSTJ+xJibqgrihALoNPpsGHDBgwePBg2Njbw9vbGnDlzUFlZqbddSEgIbrvtNqSkpGDUqFGwtbXFkCFDxG6sL774AkOGDIGNjQ1GjhyJkydP6r3+gQcegIODAzIzMzFx4kTY29vDz88Pzz33HK6+325fa9q3b59Y0zvvvAMAeP/993HTTTfBy8sLKpUKsbGxePvttzu8/s8//8TBgwfFbrkbbrgBAPDss89CJpN1OF5bt26FTCZDdnZ2r+qoqqrCkiVLEBgYCJVKhYiICPznP/+BTqfrXUA90Gg0+Mc//oEvv/wSb7/9Nh555BGD7JcQHtEVG0IswJw5c7B161bMnj0bixYtQlZWFt58802cPHkSv/zyC6ytrcVtMzIycM8992DOnDm47777sG7dOkyePBmbNm3Ck08+iUcffRQA8NJLL2HatGlIT0+HXH7lbyStVotbbrkF1157LdauXYu9e/di1apV0Gg0eO655/pVU3p6OmbMmIE5c+bgkUceQVRUFADg7bffxuDBg3H77bfDysoKX3/9NR599FHodDrMnz8fQOvA2oULF8LBwQFPPfUUAMDb27tfx7GzOhoaGpCUlISCggLMmTMHQUFBOHLkCJ544gkUFhZiw4YN/XqvNhqNBjNmzMCuXbuwceNGzJkzZ0D7I4R7AiGEK/Pnzxfa/9c+dOiQAED45JNP9Lbbu3dvh8eDg4MFAMKRI0fEx/bt2ycAEGxtbYWcnBzx8XfeeUcAIPz000/iY7NmzRIACAsXLhQf0+l0wqRJkwSlUimUlpb2u6a9e/d2+FkbGho6PDZx4kQhLCxM77HBgwcLSUlJHbZdtWqV0Nmvwffff18AIGRlZfVYx/PPPy/Y29sLFy5c0Ht8xYoVgkKhEHJzczvsvz0AwqpVqzo8/tNPPwkAxPfduHFjt/tp//O0HWdCLBF1RRHCuR07dsDZ2Rk333wzysrKxK+RI0fCwcEBP/30k972sbGxSExMFL9PSEgAANx0000ICgrq8HhmZmaH91ywYIH4b5lMhgULFqC5uRk//PBDv2oKDQ3FxIkTO7xP+/Et1dXVKCsrQ1JSEjIzM1FdXd3rY9RbndWxY8cOjBs3Dq6urno/S3JyMrRaLX7++ecBvWdxcTGsrKwQGho6oP0QYimoK4oQzl28eBHV1dXw8vLq9PmSkhK979s3XgDA2dkZABAYGNjp41ePiZHL5QgLC9N7LDIyEgDEMSt9ramrk/ovv/yCVatW4ejRo2hoaNB7rrq6WqzRUDqr4+LFizhz5gw8PT07fc3VP0tfrV27Fhs2bMBdd92F77//HmPGjBnQ/gjhHTVsCOGcTqeDl5cXPvnkk06fv/qErFAoOt2uq8eFqwYFG6OmzmYeXbp0CePHj0d0dDReffVVBAYGQqlUYs+ePVi/fn2vBu52NnAYaB0n1JnO6tDpdLj55puxbNmyTl/T1qjrL19fX+zfvx9jx47FpEmTcPDgQcTHxw9on4TwjBo2hHAuPDwcP/zwA8aMGSPJ1GSdTofMzEy9E/qFCxcAtM4sMlRNX3/9NdRqNb766iu9q0xXd2MBXTdgXF1dAbTOanJxcREfz8nJ6XUd4eHhqKurQ3Jycq9f01dhYWHYt28fkpKSMHHiRBw6dAiDBg0y2vsRYs5ojA0hnJs2bRq0Wi2ef/75Ds9pNBpUVVUZ/D3ffPNN8d+CIODNN9+EtbU1xo8fb7Ca2q4gtb9iVF1djffff7/Dtvb29p3uMzw8HAD0xsHU19fjgw8+6PH920ybNg1Hjx7Fvn37OjxXVVUFjUbT6311Z8iQIfj2229RV1eHm2++GQUFBQbZLyG8oSs2hHAuKSkJc+bMwUsvvYRTp05hwoQJsLa2xsWLF7Fjxw689tpruOuuuwz2fjY2Nti7dy9mzZqFhIQEfPfdd/j222/x5JNPil1MhqhpwoQJUCqVmDx5MubMmYO6ujps3rwZXl5eKCws1Nt25MiRePvtt/HCCy8gIiICXl5euOmmmzBhwgQEBQXhoYcewuOPPw6FQoEtW7bA09MTubm5vfp5H3/8cXz11Ve47bbb8MADD2DkyJGor6/H2bNnsXPnTmRnZ8PDw6N/B/MqiYmJ+OKLLzB58mTcfPPNOHToENzd3Q2yb0J4QQ0bQizApk2bMHLkSLzzzjt48sknYWVlhZCQENx3330GH4yqUCiwd+9ezJs3D48//jgcHR2xatUqrFy50qA1RUVFYefOnXj66afx2GOPwcfHB/PmzYOnp6d4q4E2K1euRE5ODtauXYva2lokJSXhpptugrW1NXbt2oVHH30UzzzzDHx8fLBkyRK4urpi9uzZvfp57ezscPDgQbz44ovYsWMHPvzwQzg5OSEyMhKrV682+ADmCRMm4KOPPsKMGTNw66234sCBA3B0dDToexBizmRCf0b+EUJIJx544AHs3LkTdXV1pi7FbMhkMjz++ONYtmwZ7O3t+zXmqKmpCXV1dVi7di1efvlllJaWGuwqESHmhsbYEEKIib388svw9PTExo0b+/X6TZs2wdPTEy+//LKBKyPE/FBXFCGEmND+/fvFf/d3avidd96JuLg48XtDd38RYk6oYUMIISZkiGnigYGBHRZQJMRS0RgbQgghhHCDxtgQQgghhBvUsCGEEEIIN6hhQwghhBBuUMOGEEIIIdyghg0hhBBCuEENG0IIIYRwgxo2hBBCCOEGNWwIIYQQwg1q2BBCCCGEG/8POupcNKw4qPoAAAAASUVORK5CYII=",
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x1ebe70d6dc0>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "execution_count": 9,
      "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "from pycalphad.plot.utils import phase_legend\n",
-    "phase_handles, phasemap = phase_legend(phases)\n",
-    "\n",
-    "plt.gca().set_title('Al-Ni: Phase fractions vs T [X(AL)=0.1]')\n",
-    "plt.gca().set_xlabel('Temperature (K)')\n",
-    "plt.gca().set_ylabel('Phase Fraction')\n",
-    "plt.gca().set_ylim((0,1.1))\n",
-    "plt.gca().set_xlim((300, 2000))\n",
-    "\n",
-    "for name in phases:\n",
-    "    phase_indices = np.nonzero(eq_alni.Phase.values == name)\n",
-    "    plt.scatter(np.take(eq_alni['T'].values, phase_indices[2]), eq_alni.NP.values[phase_indices], color=phasemap[name])\n",
-    "plt.gca().legend(phase_handles, phases, loc='lower right')"
+    "wks3.plot(v.T, 'NP(*)')"
    ]
   },
   {
@@ -415,65 +284,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2022-02-19T19:18:17.051366Z",
-     "iopub.status.busy": "2022-02-19T19:18:17.041984Z",
-     "iopub.status.idle": "2022-02-19T19:18:17.481394Z",
-     "shell.execute_reply": "2022-02-19T19:18:17.481394Z"
-    }
-   },
+   "execution_count": 11,
+   "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\rotis\\AppData\\Local\\Temp/ipykernel_7844/3149017277.py:7: RuntimeWarning: invalid value encountered in greater\n",
-      "  (eq_alni.degree_of_ordering.values > 0.01)))\n",
-      "C:\\Users\\rotis\\AppData\\Local\\Temp/ipykernel_7844/3149017277.py:10: RuntimeWarning: invalid value encountered in less_equal\n",
-      "  (eq_alni.degree_of_ordering.values <= 0.01)))\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "plt.gca().set_title('Al-Ni: Degree of fcc ordering vs T [X(AL)=0.1]')\n",
-    "plt.gca().set_xlabel('Temperature (K)')\n",
-    "plt.gca().set_ylabel('Degree of ordering')\n",
-    "plt.gca().set_ylim((-0.1,1.1))\n",
-    "# Generate a list of all indices where FCC_L12 is stable and ordered\n",
-    "L12_phase_indices = np.nonzero(np.logical_and((eq_alni.Phase.values == 'FCC_L12'),\n",
-    "                                              (eq_alni.degree_of_ordering.values > 0.01)))\n",
-    "# Generate a list of all indices where FCC_L12 is stable and disordered\n",
-    "fcc_phase_indices = np.nonzero(np.logical_and((eq_alni.Phase.values == 'FCC_L12'),\n",
-    "                                              (eq_alni.degree_of_ordering.values <= 0.01)))\n",
-    "# phase_indices[2] refers to all temperature indices\n",
-    "# We know this because pycalphad always returns indices in order like P, T, X's\n",
-    "plt.plot(np.take(eq_alni['T'].values, L12_phase_indices[2]), eq_alni['degree_of_ordering'].values[L12_phase_indices],\n",
-    "            label='$\\gamma\\prime$ (ordered fcc)', color='red')\n",
-    "plt.plot(np.take(eq_alni['T'].values, fcc_phase_indices[2]), eq_alni['degree_of_ordering'].values[fcc_phase_indices],\n",
-    "            label='$\\gamma$ (disordered fcc)', color='blue')\n",
-    "plt.legend()\n",
-    "plt.show()"
+    "dis_degree_of_ordering = as_property('degree_of_ordering(FCC_L12#2)')\n",
+    "dis_degree_of_ordering.display_name = '$\\gamma$ (disordered fcc)'\n",
+    "L12_degree_of_ordering = as_property('degree_of_ordering(FCC_L12#1)')\n",
+    "L12_degree_of_ordering.display_name = '$\\gamma^\\prime$ (ordered fcc)'\n",
+    "wks3.plot(v.T, dis_degree_of_ordering, L12_degree_of_ordering)"
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.10.6 64-bit",
    "language": "python",
    "name": "python3"
   },
@@ -487,7 +324,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.0"
+   "version": "3.10.6"
   },
   "pycharm": {
    "stem_cell": {
@@ -497,6 +334,11 @@
     },
     "source": []
    }
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "dffc026621927c134bf51c98cc1a2a1db0bb9758f5626f77d77eb66ad657b830"
+   }
   }
  },
  "nbformat": 4,
diff --git a/examples/LegacyEnergySurface.ipynb b/examples/LegacyEnergySurface.ipynb
new file mode 100644
index 000000000..d06cc1727
--- /dev/null
+++ b/examples/LegacyEnergySurface.ipynb
@@ -0,0 +1,178 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Calculating Energy Surfaces [Legacy Method]\n",
+    "\n",
+    "**Note**: Since pycalphad 0.11, we recommend using the Computable Property API for calculating energy surfaces. This method remains supported, but is not recommended for new users.\n",
+    "\n",
+    "It is very common in CALPHAD modeling to directly examine the Gibbs energy surface of all the constituent phases in a system.\n",
+    "\n",
+    "Below we show how the Gibbs energy of all phases may be calculated as a function of composition at a given temperature (2800 K).\n",
+    "\n",
+    "Note that the chi phase has additional, internal degrees of freedom which allow it to take on multiple states for a given\n",
+    "overall composition. Only the low-energy states are relevant to calculating the equilibrium phase diagram."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-02-19T19:16:49.958474Z",
+     "iopub.status.busy": "2022-02-19T19:16:49.956474Z",
+     "iopub.status.idle": "2022-02-19T19:16:50.747368Z",
+     "shell.execute_reply": "2022-02-19T19:16:50.748370Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "from pycalphad import Database, calculate, variables as v\n",
+    "from pycalphad.plot.utils import phase_legend\n",
+    "import numpy as np\n",
+    "\n",
+    "# Load database and choose the phases that will be plotted\n",
+    "db_nbre = Database('nbre_liu.tdb')\n",
+    "my_phases_nbre = ['CHI_RENB', 'SIGMARENB', 'FCC_RENB', 'LIQUID_RENB', 'BCC_RENB', 'HCP_RENB']\n",
+    "\n",
+    "# Get the colors that map phase names to colors in the legend\n",
+    "legend_handles, color_dict = phase_legend(my_phases_nbre)\n",
+    "\n",
+    "fig = plt.figure(figsize=(9,6))\n",
+    "ax = fig.gca()\n",
+    "\n",
+    "# Loop over phases, calculate the Gibbs energy, and scatter plot GM vs. X(RE)\n",
+    "for phase_name in my_phases_nbre:\n",
+    "    result = calculate(db_nbre, ['NB', 'RE'], phase_name, P=101325, T=2800, output='GM')\n",
+    "    ax.scatter(result.X.sel(component='RE'), result.GM, marker='.', s=5, color=color_dict[phase_name])\n",
+    "\n",
+    "# Format the plot\n",
+    "ax.set_xlabel('X(RE)')\n",
+    "ax.set_ylabel('GM')\n",
+    "ax.set_xlim((0, 1))\n",
+    "ax.legend(handles=legend_handles, loc='center left', bbox_to_anchor=(1, 0.6))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Triangular projection plots\n",
+    "\n",
+    "Importing the `pycalphad.plot.triangular` module automatically registers a `'triangular'` projection in matplotlib for you to use in custom plots, such as liquidus projections or contour plots of custom property models.\n",
+    "\n",
+    "Here we will use pycalphad to calculate the mixing enthalpy of the FCC phase in our Al-Cu-Y system. Then we will the triangular projection to plot the calculated points as a colored scatterplot on the triangular axes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-02-19T19:24:35.941781Z",
+     "iopub.status.busy": "2022-02-19T19:24:35.941781Z",
+     "iopub.status.idle": "2022-02-19T19:24:36.721283Z",
+     "shell.execute_reply": "2022-02-19T19:24:36.721283Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from pycalphad.plot import triangular\n",
+    "from pycalphad import calculate\n",
+    "\n",
+    "db_al_cu_y = Database('Al-Cu-Y.tdb')\n",
+    "comps = ['AL', 'CU', 'Y', 'VA']\n",
+    "\n",
+    "# some sample data, these could be from an equilibrium calculation or a property model.\n",
+    "# here we are calculating the mixing enthlapy of the FCC_A1 phase at 830K. \n",
+    "c = calculate(db_al_cu_y, comps, 'FCC_A1', output='HM_MIX', T=830, P=101325, pdens=5000)\n",
+    "\n",
+    "# Here we are getting the values from our plot. \n",
+    "xs = c.X.values[0, 0, 0, :, 0]  # 1D array of Al compositions\n",
+    "ys = c.X.values[0, 0, 0, :, 1]  # 1D array of Cu compositions\n",
+    "zs = c.HM_MIX.values[0, 0, 0, :]  # 1D array of mixing enthalpies at these compositions\n",
+    "\n",
+    "# when we imported the pycalphad.plot.triangular module, it made the 'triangular' projection available for us to use.\n",
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(projection='triangular')\n",
+    "ax.scatter(xs, ys, c=zs, \n",
+    "           cmap='coolwarm', \n",
+    "           linewidth=0.0)\n",
+    "\n",
+    "# label the figure\n",
+    "ax.set_xlabel('X (AL)')\n",
+    "ax.set_ylabel('X (CU)')\n",
+    "ax.yaxis.label.set_rotation(60)  # rotate ylabel\n",
+    "ax.yaxis.set_label_coords(x=0.12, y=0.5)  # move the label to a pleasing position\n",
+    "ax.set_title('AL-CU-Y HM_MIX at 830K')\n",
+    "\n",
+    "# set up the colorbar\n",
+    "cm = plt.cm.ScalarMappable(cmap='coolwarm')\n",
+    "cm.set_array(zs)\n",
+    "fig.colorbar(cm, ax=ax);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.10.6 64-bit",
+   "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": "3.10.6"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "dffc026621927c134bf51c98cc1a2a1db0bb9758f5626f77d77eb66ad657b830"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/LegacyReferenceState.ipynb b/examples/LegacyReferenceState.ipynb
new file mode 100644
index 000000000..bdbc85708
--- /dev/null
+++ b/examples/LegacyReferenceState.ipynb
@@ -0,0 +1,182 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Special `_MIX` Reference State\n",
+    "\n",
+    "pycalphad also includes special mixing reference state that is referenced to the endmembers for that phase with the `_MIX` suffix (`GM_MIX`, `HM_MIX`, `SM_MIX`, `CPM_MIX`). This is particularly useful for seeing how the mixing contributions from physical or excess models affect the energy.\n",
+    "\n",
+    "Below is an example for calculating this endmember-referenced mixing enthalpy for the $\\chi$ phase in Nb-Re. Notice that the four endmembers have a mixing enthalpy of zero."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-02-19T19:18:32.010452Z",
+     "iopub.status.busy": "2022-02-19T19:18:32.001422Z",
+     "iopub.status.idle": "2022-02-19T19:18:32.461953Z",
+     "shell.execute_reply": "2022-02-19T19:18:32.461953Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pycalphad import Database, calculate\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "dbf = Database(\"nbre_liu.tdb\")\n",
+    "comps = [\"NB\", \"RE\", \"VA\"]\n",
+    "\n",
+    "# Calculate HMR for the Chi at 2800 K from X(RE)=0 to X(RE)=1\n",
+    "result = calculate(dbf, comps, \"CHI_RENB\", P=101325, T=2800, output='HM_MIX')\n",
+    "\n",
+    "# Plot\n",
+    "fig = plt.figure(figsize=(9,6))\n",
+    "ax = fig.gca()\n",
+    "ax.scatter(result.X.sel(component='RE'), result.HM_MIX, marker='.', s=5, label='CHI_RENB')\n",
+    "ax.set_xlim((0, 1))\n",
+    "ax.set_xlabel('X(RE)')\n",
+    "ax.set_ylabel('HM_MIX')\n",
+    "ax.set_title('Nb-Re CHI Mixing Enthalpy')\n",
+    "ax.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Calculations at specific site fractions\n",
+    "\n",
+    "In the previous example, the mixing energy for the CHI phase in Nb-Re is sampled by sampling site fractions linearly between endmembers and then randomly across site fraction space.\n",
+    "\n",
+    "Imagine now that you'd like to calculate the mixing energy along a single internal degree of freedom (i.e. between two endmembers), referenced to those endmembers.\n",
+    "\n",
+    "A custom 2D site fraction array can be passed to the `points` argument of `calculate` and the `HM_MIX` property can be calculated as above.\n",
+    "\n",
+    "The sublattice model for CHI is `RE : NB,RE : NB,RE`.\n",
+    "\n",
+    "If we are interested in the interaction along the second sublattice when NB occupies the third sublattice, we need to construct a site fraction array of \n",
+    "\n",
+    "```python\n",
+    "# RE, NB, RE, NB, RE\n",
+    "[ 1.0, x, 1-x, 1.0, 0.0 ]\n",
+    "```\n",
+    "\n",
+    "where `x` varies from 0 to 1. This fixes the site fraction of RE in the first sublattice to 1 and the site fractions of NB and RE in the third sublattice to 1 and 0, respectively. Note that the site fraction array is sorted first in sublattice order, then in alphabetic order within each sublattice (e.g. NB is always before RE within a sublattice)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-02-19T19:18:32.481334Z",
+     "iopub.status.busy": "2022-02-19T19:18:32.471893Z",
+     "iopub.status.idle": "2022-02-19T19:18:32.731301Z",
+     "shell.execute_reply": "2022-02-19T19:18:32.731301Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Site fractions:\n",
+      "[[1.00e+00 1.00e-12 1.00e+00 1.00e+00 0.00e+00]\n",
+      " [1.00e+00 1.00e-03 9.99e-01 1.00e+00 0.00e+00]\n",
+      " [1.00e+00 2.00e-03 9.98e-01 1.00e+00 0.00e+00]\n",
+      " ...\n",
+      " [1.00e+00 9.98e-01 2.00e-03 1.00e+00 0.00e+00]\n",
+      " [1.00e+00 9.99e-01 1.00e-03 1.00e+00 0.00e+00]\n",
+      " [1.00e+00 1.00e+00 0.00e+00 1.00e+00 0.00e+00]]\n",
+      "Site fractions shape: (1001, 5) (1001 points, 5 internal degrees of freedom)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pycalphad import Database, calculate\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "dbf = Database(\"nbre_liu.tdb\")\n",
+    "comps = [\"NB\", \"RE\", \"VA\"]\n",
+    "\n",
+    "# The values for the internal degree of freedom we will vary\n",
+    "n_pts = 1001\n",
+    "x = np.linspace(1e-12, 1, n_pts)\n",
+    "\n",
+    "# Create the site fractions\n",
+    "# The site fraction array is ordered first by sublattice, then alphabetically be species within a sublattice.\n",
+    "# The site fraction array is therefore `[RE#0, NB#1, RE#1, NB#2, RE#2]`, where `#0` is the sublattice at index 0.\n",
+    "# To calculate a RE:NB,RE:NB interaction requires the site fraction array to be [1, x, 1-x, 1, 0]\n",
+    "# Note the 1-x is required for site fractions to sum to 1 in sublattice #1.\n",
+    "site_fractions = np.array([np.ones(n_pts), x, 1-x, np.ones(n_pts), np.zeros(n_pts)]).T\n",
+    "print('Site fractions:')\n",
+    "print(site_fractions)\n",
+    "print('Site fractions shape: {} ({} points, {} internal degrees of freedom)'.format(site_fractions.shape, site_fractions.shape[0], site_fractions.shape[1]))\n",
+    "\n",
+    "# Calculate HMR for the Chi at 2800 K from Y(CHI, 1, RE)=0 to Y(CHI, 1, RE)=1\n",
+    "# Pass the custom site fractions to the `points` argument\n",
+    "result = calculate(dbf, comps, \"CHI_RENB\", P=101325, T=2800, points=site_fractions, output='HM_MIX')\n",
+    "# Extract the site fractions of RE in sublattice 1.\n",
+    "Y_CHI_1_RE = result.Y.squeeze()[:, 2]\n",
+    "\n",
+    "# Plot\n",
+    "fig = plt.figure(figsize=(9,6))\n",
+    "ax = fig.gca()\n",
+    "\n",
+    "ax.scatter(Y_CHI_1_RE, result.HM_MIX, marker='.', s=5)\n",
+    "ax.set_xlim((0, 1))\n",
+    "ax.set_xlabel('Y(CHI, 1, RE)')\n",
+    "ax.set_ylabel('HM_MIX')\n",
+    "ax.set_title('Nb-Re CHI Mixing Enthalpy')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.10.6 64-bit",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.10.6"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "dffc026621927c134bf51c98cc1a2a1db0bb9758f5626f77d77eb66ad657b830"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/Metastability.ipynb b/examples/Metastability.ipynb
new file mode 100644
index 000000000..a17306db3
--- /dev/null
+++ b/examples/Metastability.ipynb
@@ -0,0 +1,353 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " # Export phase energies as a table"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This snippet can be used to export the results of a calculation to a Pandas `DataFrame` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Temperature [K]</th>\n",
+       "      <th>X_AL [fraction]</th>\n",
+       "      <th>X_ZN [fraction]</th>\n",
+       "      <th>GM(FCC_A1) [J/mol]</th>\n",
+       "      <th>GM(HCP_A3) [J/mol]</th>\n",
+       "      <th>GM(LIQUID) [J/mol]</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>300.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1.000000e-10</td>\n",
+       "      <td>-8496.605675</td>\n",
+       "      <td>-3555.605675</td>\n",
+       "      <td>-1043.275310</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>300.0</td>\n",
+       "      <td>0.98</td>\n",
+       "      <td>2.000000e-02</td>\n",
+       "      <td>-8564.717990</td>\n",
+       "      <td>-3674.761325</td>\n",
+       "      <td>-1250.180921</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>300.0</td>\n",
+       "      <td>0.96</td>\n",
+       "      <td>4.000000e-02</td>\n",
+       "      <td>-8573.037243</td>\n",
+       "      <td>-3733.289523</td>\n",
+       "      <td>-1394.468476</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>300.0</td>\n",
+       "      <td>0.94</td>\n",
+       "      <td>6.000000e-02</td>\n",
+       "      <td>-8564.854312</td>\n",
+       "      <td>-3774.752790</td>\n",
+       "      <td>-1519.171542</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>300.0</td>\n",
+       "      <td>0.92</td>\n",
+       "      <td>8.000000e-02</td>\n",
+       "      <td>-8549.523829</td>\n",
+       "      <td>-3808.760080</td>\n",
+       "      <td>-1633.419774</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3495</th>\n",
+       "      <td>990.0</td>\n",
+       "      <td>0.10</td>\n",
+       "      <td>9.000000e-01</td>\n",
+       "      <td>-54233.504407</td>\n",
+       "      <td>-54769.630291</td>\n",
+       "      <td>-58354.955825</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3496</th>\n",
+       "      <td>990.0</td>\n",
+       "      <td>0.08</td>\n",
+       "      <td>9.200000e-01</td>\n",
+       "      <td>-54199.685094</td>\n",
+       "      <td>-54895.795230</td>\n",
+       "      <td>-58409.376195</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3497</th>\n",
+       "      <td>990.0</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>9.400000e-01</td>\n",
+       "      <td>-54123.191596</td>\n",
+       "      <td>-54986.670187</td>\n",
+       "      <td>-58424.306318</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3498</th>\n",
+       "      <td>990.0</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>9.600000e-01</td>\n",
+       "      <td>-53989.616338</td>\n",
+       "      <td>-55028.419738</td>\n",
+       "      <td>-58385.478971</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3499</th>\n",
+       "      <td>990.0</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>9.800000e-01</td>\n",
+       "      <td>-53768.693811</td>\n",
+       "      <td>-54991.395316</td>\n",
+       "      <td>-58262.766288</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3500 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Temperature [K]  X_AL [fraction]  X_ZN [fraction]  GM(FCC_A1) [J/mol]  \\\n",
+       "0               300.0             1.00     1.000000e-10        -8496.605675   \n",
+       "1               300.0             0.98     2.000000e-02        -8564.717990   \n",
+       "2               300.0             0.96     4.000000e-02        -8573.037243   \n",
+       "3               300.0             0.94     6.000000e-02        -8564.854312   \n",
+       "4               300.0             0.92     8.000000e-02        -8549.523829   \n",
+       "...               ...              ...              ...                 ...   \n",
+       "3495            990.0             0.10     9.000000e-01       -54233.504407   \n",
+       "3496            990.0             0.08     9.200000e-01       -54199.685094   \n",
+       "3497            990.0             0.06     9.400000e-01       -54123.191596   \n",
+       "3498            990.0             0.04     9.600000e-01       -53989.616338   \n",
+       "3499            990.0             0.02     9.800000e-01       -53768.693811   \n",
+       "\n",
+       "      GM(HCP_A3) [J/mol]  GM(LIQUID) [J/mol]  \n",
+       "0           -3555.605675        -1043.275310  \n",
+       "1           -3674.761325        -1250.180921  \n",
+       "2           -3733.289523        -1394.468476  \n",
+       "3           -3774.752790        -1519.171542  \n",
+       "4           -3808.760080        -1633.419774  \n",
+       "...                  ...                 ...  \n",
+       "3495       -54769.630291       -58354.955825  \n",
+       "3496       -54895.795230       -58409.376195  \n",
+       "3497       -54986.670187       -58424.306318  \n",
+       "3498       -55028.419738       -58385.478971  \n",
+       "3499       -54991.395316       -58262.766288  \n",
+       "\n",
+       "[3500 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from pycalphad import Workspace, variables as v\n",
+    "from pycalphad.property_framework.metaproperties import IsolatedPhase\n",
+    "from pycalphad.plot.renderers import PandasRenderer\n",
+    "\n",
+    "wks = Workspace('alzn_mey.tdb', ['AL', 'ZN'],\n",
+    "                ['FCC_A1', 'HCP_A3', 'LIQUID'],\n",
+    "                {v.X('ZN'):(0,1,0.02), v.T: (300, 1000, 10), v.P:101325, v.N: 1})\n",
+    "\n",
+    "props = [v.T, v.X('AL'), v.X('ZN')]\n",
+    "for phase_name in wks.phases:\n",
+    "    prop = IsolatedPhase(phase_name, wks)(f'GM({phase_name})')\n",
+    "    prop.display_name = f'GM({phase_name})'\n",
+    "    props.append(prop)\n",
+    "df = PandasRenderer(wks)(*props)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Plot energy curves for several phases"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pycalphad import Workspace, variables as v\n",
+    "from pycalphad.property_framework.metaproperties import IsolatedPhase\n",
+    "\n",
+    "wks2 = Workspace('alzn_mey.tdb', ['AL', 'ZN'],\n",
+    "                ['FCC_A1', 'HCP_A3', 'LIQUID'],\n",
+    "                {v.X('ZN'):(0,1,0.02), v.T: 600, v.P:101325, v.N: 1})\n",
+    "\n",
+    "props = []\n",
+    "for phase_name in wks2.phases:\n",
+    "    # Workaround for poor starting point selection in IsolatedPhase\n",
+    "    metastable_wks = wks2.copy()\n",
+    "    metastable_wks.phases = [phase_name]\n",
+    "    prop = IsolatedPhase(phase_name, metastable_wks)(f'GM({phase_name})')\n",
+    "    prop.display_name = f'GM({phase_name})'\n",
+    "    props.append(prop)\n",
+    "wks2.plot(v.X('ZN'), *props)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Driving force calculation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pycalphad import Workspace, variables as v\n",
+    "from pycalphad.property_framework.metaproperties import DormantPhase\n",
+    "\n",
+    "wks3 = Workspace('alzn_mey.tdb', ['AL', 'ZN'],\n",
+    "                ['FCC_A1', 'HCP_A3', 'LIQUID'],\n",
+    "                {v.X('ZN'):(0,1,0.02), v.T: 600, v.P:101325, v.N: 1})\n",
+    "metastable_liq_wks = wks3.copy()\n",
+    "metastable_liq_wks.phases = ['LIQUID']\n",
+    "liq_driving_force = DormantPhase('LIQUID', metastable_liq_wks).driving_force\n",
+    "liq_driving_force.display_name = 'Liquid Driving Force'\n",
+    "wks3.plot(v.X('ZN'), liq_driving_force)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Calculate T0 (t-zero) as a function of composition\n",
+    "\n",
+    "The T0 (t-zero) temperature is a thermodynamic condition in which two specified phases have the same value of the Gibbs energy. Below T0, a so-called \"massive\" phase transition is thermodynamically favored to occur, without a barrier to diffusion."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pycalphad.property_framework.tzero import T0\n",
+    "\n",
+    "# For T0, conditions must be one-dimensional (step calculation)\n",
+    "wks4 = Workspace('alzn_mey.tdb', ['AL', 'ZN'],\n",
+    "                ['FCC_A1', 'HCP_A3', 'LIQUID'],\n",
+    "                {v.X('ZN'):(0,1,0.02), v.T: 300, v.P:101325, v.N: 1})\n",
+    "tzero = T0('FCC_A1', 'HCP_A3', wks4)\n",
+    "\n",
+    "wks4.plot(v.X('ZN'), tzero)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.10.6 64-bit",
+   "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": "3.10.6"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "dffc026621927c134bf51c98cc1a2a1db0bb9758f5626f77d77eb66ad657b830"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/ReferenceStateExamples.ipynb b/examples/ReferenceStateExamples.ipynb
index 56de80c62..37adf46eb 100644
--- a/examples/ReferenceStateExamples.ipynb
+++ b/examples/ReferenceStateExamples.ipynb
@@ -10,7 +10,7 @@
     "\n",
     "By default, energies calculated with pycalphad (e.g. `GM`, `HM`, etc.) are the absolute energies as defined in the database and are not calculated with respect to any reference state.\n",
     "\n",
-    "pycalphad `Model` objects allow the reference for the pure components to be set to arbitrary phases and temperature/pressure conditions through the `shift_reference_state` method, which creates new properties for the energies that are referenced to the new reference state, `GMR`, `HMR`, `SMR`, and `CPMR`.\n",
+    "pycalphad allows the reference for any property to be set to arbitrary phases and temperature/pressure conditions through the `ReferenceState` meta-property, which creates new properties for the specified property that are referenced to the specified reference state.\n",
     "\n",
     "### Enthalpy of mixing\n",
     "\n",
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2022-02-19T19:18:27.132381Z",
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2022-02-19T19:18:28.232251Z",
@@ -47,48 +47,30 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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qNhMREQnL7MdL2bu/rtzW3eybo4SonsKCPDoG35VtO7VIo4iISHsrKa/gL6+9Fy1PGVWQ9G21lBA14ECNAVC1zyleWR5yNCIiItnl6odepna79UG9D2P2hOSNHaqlhKgBPz73pOjxjTFT/URERCS5ileWs6mybr+y2y8Y3i6fq4SoAbGDtpzIwC4RERFJvu8vqWuImDNxWNK7ymopIWrEcX26R49/8dTrIUYiIiKSHYpXlnPA68rJnFVWnxKiRsy/fBQ9czoBULKpMtxgREREMtyWyqq4YSozRg9u189XQtSI/NwcjuoRWZNoS0WV1iQSERFJou/c/xK1jUOdO8AN409o189XQtSEn59/Ml07GXsPODMffTXscERERDJSSXkFr23bFS0nc0XqxighakJhQR59glaiquoDIUcjIiKSmb730MvR4xP79WjXsUO1lBA1I6dzRwDerahiWem2kKMRERHJLMtKt7E5Zpr9fZeeGkocSoia8fPzT6YDUANc/4dXwg5HREQkY2yprGLag3W72ffo2pH83JxQYlFC1IzCgjy6do4cV+9Xt5mIiEiizH68lP01deX2WoSxISmZEJnZbWb2upm9amZLzCw3qB9oZlVmtiZ4/TrmPSPMbK2ZlZnZnWZmiYqnS8fI9Puqfa5uMxERkQQoKa/gL+vr9iubNe54xg7tG1o8KZkQAcuAk9z9ZOANYFbMubfcfXjw+m5M/T3AFcBxwWtcooL5xTc+Ez2+/n/UbSYiItJW0x9YTU0wz37c0KOZOubYUONJyYTI3f/i7vuD4otA/6auN7N+wBHu/qK7O7AA+Hqi4hk7tC89ukYGV+/8ZL9aiURERNqgpLyCrR/tjZZv/trQEKOJSMmEqJ5vA0tjyoPMrMTMnjWzM4O6Y4DNMddsDuoS5rOf7h09nrVEG76KiIi01rQH6gZSjxp0ZGgDqWN1CuuDzexpoKHOwpvc/bHgmpuA/cCDwbmtQIG7f2BmI4A/mtkhpZVmdiVwJUBBQcvXObj5nKG8vKmC7buq6Z8X/oMTERFJR/OWl7EtpnXojknDwwsmRmgJkbuf3dR5M7sM+CrwxaAbDHffC+wNjleb2VvAEOBd4rvV+gd1DX3uvcC9AEVFRd7QNQ3Jz83hQDASfs2mnRSvLA9l4SgREZF0taWyilue3BAtzxp3fEq0DkGKdpmZ2Tjg34Fz3H1PTH0fM+sYHA8mMnh6o7tvBT4ys9OC2WVTgMcSHdetE+uWEv/+YnWbiYiIHIrpMV1l/Xp2DX0gdazQWoiacRfQFVgWzJ5/MZhRdhYw28z2EVkr8bvu/mHwnunA/UAOkTFHS+vftK1ipwNqRSIREZGWK15ZzprNO6PluReNCDGag6VkQuTuDaaM7v4o8Ggj51YBJyUzLoCjDu/C9o+rAdRtJiIi0kLfj5mQlNPZKCzICzGag6Vkl1kqmzelKHr8oz+VhhiJiIhIeiheWc6BmFG7d04+JbxgGqGE6BAVFuQxalAkq+1gkbUUREREpGFbKquYGTPudsbowaGuSN0YJUStMOSoIwDYXV3DzEdfDTkaERGR1PWLmFllBtww/oTwgmmCEqJWmPb5T9O9S+Rbt+uTfSFHIyIikpqKV5azeE3dKjg3jjs+xGiapoSoFfJzc+jSKbKVx4cfV7OlsirkiERERFLPDx9bFz3OO6xzSk2zr08JUSv9/LyT6dwB9h5wZi1Wt5mIiEis4pXlVMeMpP75eSeHGE3zlBC10tihfenQwQB4ceMHIUcjIiKSOuoPpP7NJSNSciB1LCVEbXDaoF4A7N3vFK8sDzkaERGR1DArZsKRQconQ6CEqE1uOe/k6Dfw5sfXNXmtiIhINigpr+DZN9+PllN5IHUsJURtkJ+bQ+8eXQDYv9+1JpGIiGS9Kxesih4PyOuW0gOpYykhaqPcnEhCdAC49pE1ocYiIiISppLyCnYE21sB3Hlh6q1I3RglRG005/y6UfNbNf1eRESy1JbKKi6+78VoecbowSm3X1lTlBC1UWFBHhYcf6JuMxERyVKzHy9ld3UNENnaKlVXpG6MEqIEiB0wpq08REQk2ywr3caTr70XLc/8cnoMpI6lhCgBpo45lgG5XQF4872PWVa6LeSIRERE2s91//NK9HhQ78PSZiB1LCVECZLTpTMADlwf84MhIiKSyW5bup6PPtkPRNYcuv2C4aHG01pKiBJkzvknR8cS7a+pCTUWERGR9lBSXsHdz26Mlu+9ZERaDaSOpYQoQQoL8uif1w2A6v01GlwtIiIZb/qDq6PHh3XukBYrUjdGCVECnfqpyFYe+2rif0hEREQyzbLSbWzduTda/q/JhSFG03ZKiBLo+pjZZlt37lUrkYiIZKyrF70cPe7eJb1bh0AJUULl5+YwamBd3+nVD5eEGI2IiEhyFK8sp2qfR8u/mpTerUOghCjh7phcGB1cvW2nVq4WEZHMsqWyillL1kbLM0YPTvvWIVBClHD5uTn07RlZk2h/DVqTSEREMso1i0qoCRqHBuR1S7sVqRujhCgJTh/UG4isSfTvWrlaREQyRPHKcla8Uzc+Np02b22OEqIkuH7c8XTrFOk462DNXCwiIpIGtlRWcePiuq6yTkbarjnUECVESZCfm4MHzYkf7N6n2WYiIpL2vjP/JTym/NMJw0KLJRmUECXJ7HNPih5rw1cREUlny0q38drWXdHyrHHHM2lkQYgRJZ4SoiSZNLKAQb0PA+CN9z6meGV5yBGJiIi0zvcerltzqFf3zmm5eWtzUjIhMrMfmdm7ZrYmeH0l5twsMyszsw1m9uWY+nFBXZmZ3RhO5PEK+9f1rd78+LoQIxEREWmdZaXb+GR/XWfZfZeeGmI0yZOSCVHgDncfHrz+DGBmJwKTgaHAOGCumXU0s47A3cB44ETgwuDaUF0/7nhyOkdGVffM6RxyNCIiIodmS2UV0x6o24rqxH49MmogdaxUTogaci6wyN33uvvbQBkwMniVuftGd68GFgXXhio/N4f+ed0BeH9XtdYkEhGRtHJt8RpiGocytnUIUjshusrMXjWz35lZbTp6DLAp5prNQV1j9QcxsyvNbJWZrdqxY0cy4o7z8/NPBqAGuPaRNUn/PBERkUQoKa/gxbc/jJZnjTue/NycECNKrtASIjN72szWNfA6F7gH+DQwHNgK/DJRn+vu97p7kbsX9enTJ1G3bVRhQR6dg+/yrr0HNLhaRETSwvQH67rKRg06MiMHUsfqFNYHu/vZLbnOzH4D/G9QfBcYEHO6f1BHE/WhO+PTvXn2zfcB+NGfSjNuqqKIiGSWkvIKtu7cGy3fMWl4eMG0k5TsMjOzfjHFCUDtFK3Hgclm1tXMBgHHASuBl4DjzGyQmXUhMvD68faMuSm3nHcyuTmR3LN/XuY2N4qISPorXlnOhLnPR8uZ3lVWK7QWomb83MyGE9kO7B1gKoC7l5rZI8BrwH5ghrsfADCzq4CngI7A79y9NIS4G5Sfm8Mn+2oAeHP7bopXlquVSEREUtKsmO05hhx9eMZ3ldVKyRYid7/E3Ye5+8nufo67b40591N3/7S7H+/uS2Pq/+zuQ4JzPw0n8sb95zlDo8dak0hERFJRSXkFNTHlOeedHFos7S0lE6JMNGlkAf16dgVg737X4GoREUk5//6Huq2munbKrM1bm6OEqB2dPrhX9PgHf1zbxJUiIiLta97yMt7c/jEAHQzuunBEyBG1LyVE7ej6L/8LFhzvq4k0TYqIiIRtWek2bnlyQ7T86LQzGDu0b4gRtT8lRO0oPzeHs4b0jpa1UKOIiKSC2M1bRw06Mqu6ymopIWpnt0w8ObpQ4/u79jZ9sYiISJLV37w1G9YcaogSonYW2d/sMCCycvW85WUhRyQiItlqS2UVMx6qax2aMqogK9YcaogSohDcHpN93xrTZysiItKeZj9eSvWBSOtQz26dmD1hWMgRhUcJUQgKC/LoGIyudjS4WkRE2t+85WU8+dp70fIvvvGZEKMJnxKikHz2uLrB1Vc/XBJiJCIiko1iZ5UN6n1Y1s0qq08JUUhunXhydAr+5ooqtlRWhRqPiIhkj/qLA99+wfBwAkkhSohCkp+bw1lBK5ED92hwtYiItIPileXMjNmvbNa447Nymn19SohCdMt5JzMgrxsA5R/uCTkaERHJBjfF7JQwekifrNm8tTlKiEKUn5vDnurINnr/ePN9Da4WEZGkKimvYH/M7q23TMzeWWX1KSEK2a3BD+MBh6tjVgoVERFJpGWl2zjvnuej5Vnjjs/aNYcaooQoZGOH9qVLMAd/U8UnaiUSEZGkuOrh1dQEC1IPOfpwdZXVo4QoBZz26V7R4+kPrg4xEhERyUQ3L1nL3v115TnnnRxeMClKCVEKuHVi3Q/m1p17NQVfREQSZktlFQtW1E2z79W9s2aVNUAJUQrIz81h1MC6H87ZfyoNMRoREckk1xTHL/5736WnhhRJalNClCLumFwYHUv0wsYPQo5GREQyQUl5BSverhubOmfiMLUONUIJUYrIz83hqB5dAdhZtZ95WqhRRETaKHZc6qhBRzJpZEGI0aQ2JUQp5NRBR0aPb43ZY0ZERORQzVtextade6PlOyYNDy+YNKCEKIXc8OV/iR47aHC1iIi0Skl5RdzmrVpzqHlKiFJIfm4Oo4P9zQBmLX41xGhERCRdTX+grqusX89uWnOoBZQQpZhbzjs5+lD+/sb7aiUSEZFDUryynK0f1XWVzb3olBCjSR9KiFJMfm4Ox+RGNnytAX7x1OvhBiQiImljWek27WTfSkqIUtCd3zwlOgW/ZFNluMGIiEjauOrh+N0O1FXWckqIUlBhQR6f6tUdgLff36Mp+CIi0qxpC1bFbc8xY/Tg8IJJQymZEJlZsZmtCV7vmNmaoH6gmVXFnPt1zHtGmNlaMyszszvNzEL7AhLg5+fXbeehKfgiItKUkvIKlr72XrQ8ZVQBN4w/IcSI0k9KJkTuPsndh7v7cOBRYHHM6bdqz7n7d2Pq7wGuAI4LXuPaLeAkKCzIiz4cJzJITkREpCHXFK+JHh/RrSOzJwwLL5g0lZIJUa2glecC4OFmrusHHOHuL7q7AwuAryc/wuTKz6tbM+L7S9Y2caWIiGSr4pXlvPPBHgA6GMz/9qiQI0pPKZ0QAWcC77n7mzF1g8ysxMyeNbMzg7pjgM0x12wO6tLaf19YGD0+4FqoUURE4pWUV8TNKpt38QjNKmul0BIiM3vazNY18Do35rILiW8d2goUuHshcC3wkJkdcYife6WZrTKzVTt27Gj7F5JEhQV5HHV4l2h59p9KQ4xGRERSTexeZQPychg7tG+I0aS30BIidz/b3U9q4PUYgJl1AiYCxTHv2evuHwTHq4G3gCHAu0D/mNv3D+oa+tx73b3I3Yv69OmTnC8ugeZNKYo+pGfWv6dWIhERAQ7eq+zOmF4FOXSp3GV2NvC6u0e7wsysj5l1DI4HExk8vdHdtwIfmdlpwbijKcBjYQSdaJEp+IcBUF2jViIREYkMoai/V5m6ytomlROiyRw8mPos4NVgGv4fgO+6+4fBuenAfUAZkZajpe0UZ9LdHrND8d/fTO1uPhERSb7v3P9S9Lhfz65agDEBOoUdQGPc/bIG6h4lMg2/oetXASclOaxQFBbk0aWjUX3A2V1dQ0l5hf4nICKSpW5bup7Xtu2KludeNCLEaDJHKrcQSYwfn1uX631n/ktNXCkiIplqS2UVdz+7MVo+sV8P/Qc5QZQQpYlJIwuixx/s3qeFGkVEstCsR1+NHud0Nu679NQQo8ksSojSyJRRdUnRD/6ohRpFRLLJstJtPPvm+9HyQ1ecTn5uThPvkEOhhCiNzJ4wjA7BDm37arRQo4hItigpr+CKhXVrDs2ZOExdZQmmhCjNdO/SMXqsKfgiItlh+gN1yVAH4odRSGIoIUozt18wPHr8ZOl7GkskIpLh5i0vY+tHdQswzhx3fIjRZC4lRGlm7NC+jDvx6GhZY4lERDJX/QUYRw06UmsOJYkSojR08zlDo8f7aiJ9yyIiknliF2Ds3qUDd8Qs1CuJpYQoDeXn5tCja91YoqkLV4UYjYiIJMOy0m1xCzA+8J3TNKssiZQQpanYsUTbd1VrxpmISAZZVrotblbZlFEFmlWWZEqI0tTYoX3pGFOetVhjiUREMsVVD9clQ106GrMnDAsxmuyghCiN/Wxi3V+Qf2rTVxGRjFC8spy9++vKsVs3SfIoIUpjk0YW0Dl4gvsdTcEXEUlzJeUVzIxp8e/dvYvWHGonSojS3E++XtdK9MPH1oUYiYiItNW3fr8yrvybS4tCiiT7KCFKc5NGFnB4MOOs+oCrlUhEJE3NW15GZVVdX5m252hfSogywB0xM860UKOISPopXlketwDjcUd1V1dZO2tTQmRmnRIViLTe2KF9o2OJ9tVEpmuKiEj6iP3PbKcOMP/bo0KMJjs1mxCZ2Z/M7FMN1J8NrElGUHLoYscSzVqiViIRkXRx3aIS9tXUle+5aIQWYAxBS1qIFgF/M7ObzKyzmeWb2SPAT4FLkxuetNSkkQWMHtIbgPc/rmbe8rKQIxIRkeaUlFfw6Jot0fKcicMYO7RviBFlr2YTInd/ECgECoD1wAvA08Bp7r66qfdK+yrI6x49ju2LFhGR1HT1Qy9Hj3NzOmncUIhaOoboRGAksBLYCxwNaPxQipn2+U/HlbWdh4hI6pq3vIxNlZ9Ey7//1sgQo5GWjCH6LXA3MN3dv0mktagn8IqZfSnJ8ckhyM/NoddhnaPl2X8qDTEaERFpzLLSbXEt+ZpiH76WtBCtA0519xcA3H23u18PTAJ+mMzg5NDdd9mpdLDI8d9e365WIhGRFPRvxSXR41GDjlRXWQpoyRiiO9z9QAP1a939zOSEJa1VWJDHl044GoC9B1ytRCIiKaZ4ZTm7q+umld0xaXh4wUhUs+OAzOxPgDd23t3PSWhE0mY3nzOUv77+Hvtq4K/r32NLZZWmcIqIpIDileVxe5XNGne8/n1OES0ZGP2LpEchCZWfm8NhXTqx85P97KuBa4pLKJ56RthhiYhktfobt/bo2pGpY44NMSKJ1WxC5O7Ptkcgkli/+MZnuGJhZFWEFW9XqJVIRCRk0x+MX6nm9phtlyR8Lekye7Wp8+5+cuLCkUQZO7Qv3bt0iPZTT39wNX+c8bmQoxIRyU4l5RVs3bk3Wp417ngtwJhiWjLLrAY4ACwELgC+Vu/VKmb2DTMrNbMaMyuqd26WmZWZ2QYz+3JM/bigrszMboypH2RmK4L6YjPr0tq4MsmvJhVGj9ds2klJeUWI0YiIZKctlVVM+e2KaHnUoDx1laWglswyGw5cCBwOPERky46hwLvu/n9t+Ox1wETgudhKMzsRmBx8xjhgrpl1NLOORNZDGk9kocgLg2sB5gB3uPuxQAVweRviyhhjh/ZleP+e0fK1j6wJLxgRkSx1zaISdu2NTNbuYHBHzH9WJXW0aKVqd3/d3f/D3U8B/gQsAK5pywe7+3p3b2h/iXOBRe6+193fBsqIrJI9Eihz943uXk1kj7VzzcyALwB/CN4/H/h6W2LLJHMvHkGXjpHjt9/fw7LSbeEGJCKSReYtL2PFO3Wt87dMGKbxnCmqRQmRmR1jZteZ2T+Ai4kkQ/ckKaZjgE0x5c1BXWP1vYBKd99fr75BZnalma0ys1U7duxIaOCpKD83hy8cf3S0fP0fXgkxGhGR7FG8sjxuNepZ447XAowprCVbdzxLpFWoM/AtIjvcPwF0MbMjm3nv02a2roHXuYkIvjXc/V53L3L3oj59+oQVRru6+ZyhdApWr95ZtV9jiURE2sGNMVPsO3VA44ZSXEvWIfoUkYUZpwJXxtRbUD+4sTe6+9mtiOldYEBMuX9QRyP1HwC5ZtYpaCWKvV6ItBINOPIw3v5gDwBTF65i5U1jQ45KRCRz3bZ0fdyKxj/9+rDQYpGWacmg6oHuPih4DY55DXL3aDJkZkMTFNPjwGQz62pmg4DjgJXAS8BxwYyyLkQGXj/u7g78DTg/eP+lwGMJiiVj3B6zNPz2XdXa40xEJEnmLS/j7mc3RsvnDc9XV1kaaNEYohZaeCgXm9kEM9sMnA48YWZPAbh7KfAI8BrwJDDD3Q8ErT9XAU8B64FHgmsBZgLXmlkZkTFFv03EF5RJCgvy4maczVrc5PJSIiLSClsqq+LGDR3Vowu/nKxZZenAIg0sCbiRWYm7p9VTLyoq8lWrVoUdRrvZUlnF5259hhoimfA/bvyCZjuIiCTQpF8/HzerbMn0MygsyAsxouxiZqvdvaj5Kw+WyBaixGRWkjT5uTkck9sNiKy2qVYiEZHEqT/Ffs7EYUqG0kgiEyJJA3d+85To8bNvvK8ZZyIiCbCsdFtcV9moQXkaN5RmEpkQVSfwXpIkhQV5DMir6ybT6tUiIm1X/99SrUadflqyDtEpTb1qr3P305IbqiTKnRcWEixLxDvv71ErkYhIG0xbsCq6NQdEFmDU+Mz005J1iFYR2Xfs/aBsMeecyLYZkkYKC/IY2CuyLpEDMx99lb9cMzrssERE0s6y0m0sfe29aHnOxGHqKktTLekyuxb4CKgCfg98zd0/H7yUDKWp2ycNj+5xVvbex9rjTETkEG2prGLGQy9Hy106omQojbVkYcZfufvngO8RWSn6r2b2iJkNT3ZwkjyFBXl8dVhky7ca4KqHX276DSIiEmf6A6upPlA3wfrub44IMRppqxYPqnb3jURWgP4LkZ3nhyQrKGkf1487Pnq8d79TvLI8xGhERNLHtAWrWLN5Z7T8m0tGMHZo3xAjkrZqyaDqwWb2fTNbAfwn8Apwgrs/kvToJKnyc3M4b3h+tPyDP65t4moREYGDxw3NGD1YyVAGaEkLURlwAZFtNF4ACoBpZnatmV2bzOAk+X45uZDOwU/Bvho040xEpBn/uqgkenxE147cMP6EEKORRGlJQjQbWEJkqMnhQI96L0lzP4nZhfni+17Uxq8iIo24bel69uyriZbnXz4qxGgkkZqddu/uP2qHOCREk0YWMGvJWmocdlfX8P3Fa7n/2yPDDktEJKXctnR93C72M0YP1tYcGaTZhMjM7mzqvLtfnbhwJCwzv3x8dNn5f5btCDkaEZHUsqWyKi4ZGj6gp7rKMkxLusxWx7zOqVdenbzQpD1NHXMsh3eNLEy0rybyPyEREYkkQ1+/6x/RckeDuRdpin2maUmX2fzaYzP7t9iyZJY7LhjOFQsjOe7dz25keEGeZk6ISNab8eBqtn9ct13nry8eoa05MtChbu7qzV8i6Wrs0L4M798zWv634pImrhYRyXzLSrdRsqluvSFNsc9cidztXjLA3IvrmoF3V9doGr6IZK2S8gquXFg3MkTjhjJbSxZm3GVmH5nZR8DJtce19e0Qo7Sj/NwcOsVs33vtI2tCi0VEJEzTH1gd7RYxNG4o07VkL7Me7n5E8OoUc9zD3Y9ojyClff10Qt26RG+/v0cbv4pI1llWuo2tH+2Nlm+dOEzjhjKcuszkIJNGFjDuxKOjZY0lEpFsMm95WXSCCcCsccdrF/ssoIRIGnTzOUOjx7ura7Txq4hkhWWl26JrsgGMG9qXqWOODTEiaS9KiKRB+bk5zBp3fLQ8c/FadZ2JSEarP4i6k8HNXzsxxIikPSkhkkbFLtYI8O+PvhpiNCIiyfXt36+MW1vmHq03lFWUEEmT7rhgePR4V9U+TcMXkYxUvLKciqr90fKcicO03lCWUUIkTRo7tC/HHXU4APsdpj+o3VpEJLOUlFcwc/HaaDk3p5MGUWchJUTSrJ+ff3L0eOvOvRpgLSIZY1npNs6b+3y03LkD/P5bI0OMSMKihEiaVViQx6iBedHyDx9bF2I0IiKJM+2B1dQExx2AR757BoUFeU29RTJUaAmRmX3DzErNrMbMimLqx5rZajNbG/z5hZhzy81sg5mtCV5HBfVdzazYzMrMbIWZDQzhS8pod0wupHOwgnX1AVcrkYikvZuXrGV/zCjqeZeMUDKUxcJsIVoHTASeq1f/PvA1dx8GXAosrHf+IncfHry2B3WXAxXufixwBzAniXFnpfzcnLh9zmYuXqsB1iKStpaVbmPBirr/2J02ME+DqLNcaAmRu6939w0N1Je4+5agWArkmFnXZm53LjA/OP4D8EUzsyaul1YYO7Qvsd/UqQtXhRaLiEhrFa8sj1uJulf3ziz67hkhRiSpINXHEJ0HvOzue2Pqfh90l/0wJuk5BtgE4O77gZ1Ar/YNNTvcGLNY4/Zd1WolEpG0sqWyihtjZpQB3HfpqSFFI6kkqQmRmT1tZusaeJ3bgvcOJdL1NTWm+qKgK+3M4HVJK2K60sxWmdmqHTt2HOrbs97UMcfS5/AudWW1EolIGrlmUUnc4oszRg/WuCEBkpwQufvZ7n5SA6/HmnqfmfUHlgBT3P2tmPu9G/y5C3gIqJ0b+S4wIHhvJ6An8EEjMd3r7kXuXtSnT5+2folZ6d4p0THwaiUSkbQxb3kZK96p+/dqzsRh3DD+hBAjklSScl1mZpYLPAHc6O7/jKnvZGa9g+POwFeJDMwGeJzIAGyA84Fn3D32PwGSQIUFeQzv3zNavuCe55UUiUhKq79p66hBR2rxRYkT5rT7CWa2GTgdeMLMngpOXQUcC9xcb3p9V+ApM3sVWEOkVeg3wXt+C/QyszLgWuDGdvxSstLci0fQIRjBtc/h2kfWhBqPiEhjtlRWMeOhukHU3bt04I5Jw8MLSFKSZXNDSlFRka9apTEwrVW8sjy63L0Bi6drQTMRST1jfv4M73xYBURaAR7Vv1UZy8xWu3tR81ceLOW6zCR9TBpZwLgTjwbAgasffjncgERE6ileWR5NhkCLL0rjlBBJm9x8ztDoD9Gmik+0grWIpIzrFpXEbdo6/sSjtfiiNEoJkbRJfm4OnxvSO1qetXgtWyqrmniHiEjyLSvdxqNrtkTLY4b04Z4prepJkSyhhEja7NaJJ9Oja0cAaoBZi18NNyARyWrLSrfFrUTdtaPxs4nDQoxI0oESImmz/NwcFlw+Klp+9o33WVa6LcSIRCRbbams4rsP1CVDBiyaejr5uTnhBSVpQQmRJERhQV7cPmdXPby60WtFRJLlyvkvcSBm8vS9GkQtLaSESBImdp+zvfvRAGsRaVfXLSph3dZd0fKM0YM1iFpaTAmRJMzUMccyZVTdyq8zF6/VCtYi0i7qD6I+qd8R2pZDDokSIkmo2ROGxXWdfWe+Fr4UkeQqXlkeN4g677BO3HupZpTJoVFCJAkX23X2we5q5i0vCzEaEclkWyqr4tYa6tIRnrj6LA2ilkOmhEgSrn7X2S1PbtCsMxFJiukPxE/guPubI5QMSasoIZKkqN91ps1fRSTRbl6yljWbd0bLU0YVaBC1tJoSIkma2K6zXXsPqJVIRBLmtqXrWbCibibrjNGDmT1Biy9K6ykhkqSZOuZYBuR2i5avXLhas85EpM2KV5Zz97Mbo+Ve3TtrRpm0mRIiSao7v3lK9IfMUdeZiLTNstJtcYOoOxrcd+mpIUYkmUIJkSRVYUEe8y4ZES2/8/4etRKJSKuUlFdw5cL4bTn+MO0MrUQtCaGESJJu7NC+DOp1GBBpJbroNy+ypbIq3KBEJO1MXbiKmF05tC2HJJQSImkXt08aHj3es6+G64rXhBaLiKSf25auZ/uu6mh5zsRhmlEmCaWESNpFYUEeM0YPjpZfePtDzToTkRa5blFJ3CDqGaMHM2lkQRPvEDl0Soik3dww/gRGDaxr3v634pIQoxGRdHDzkrVxe5TNGD1YM8okKZQQSbu6Y3Jh9Hh3dY229RCRRs1bXha31tBxR3VXMiRJo4RI2lV+bg6zYhZsvOXJDUqKROQgJeUV3PLkhmi5V/fOzP/2qBAjkkynhEja3dQxx9KvZ9do+ZYnN2jWmYhELSvdxoS5z0fL3bt04E/fO1N7lElSKSGSUMy9aERc+cr5L4UUiYikki2VVXy33oatD3znNCVDknRKiCQUhQV5zJlYt+/Quq27tGCjiDDjwdUciFlsaMbowVprSNqFEiIJzaSRBZzYt0e0fNnvVqrrTCSL3bZ0PSWb6nav14wyaU9KiCRU9112Kl07GQA7P9nPrJg9ikQkezS01pCSIWlPSogkVPm5Odx14SnR8rNv7OC2petDjEhE2tu85WVaa0hCF1pCZGbfMLNSM6sxs6KY+oFmVmVma4LXr2POjTCztWZWZmZ3mpkF9Uea2TIzezP4Ux3OaWTs0L5x44nufnajxhOJZIllpdviptdrrSEJS5gtROuAicBzDZx7y92HB6/vxtTfA1wBHBe8xgX1NwJ/dffjgL8GZUkjk0YW0K2zRctTF65u4moRyQTFK8u5Iubver+eXbXWkIQmtITI3de7+4bmr4wws37AEe7+ors7sAD4enD6XGB+cDw/pl7SyH9Prus6275rL9ct0tYeIplqWek2ZsaMGezS0Xh02mc1vV5Ck6pjiAaZWYmZPWtmZwZ1xwCbY67ZHNQBHO3uW4PjbcDRjd3YzK40s1VmtmrHjh0JD1xab+zQvnEbwD66ZotWsRbJQPVbhgDu/uYpSoYkVElNiMzsaTNb18Dr3CbethUocPdC4FrgITM7oqWfGbQeeRPn73X3Incv6tOnT4u/FmkfN4w/geP6dI+WtYq1SGYpKa+IaxkCmDNxGGOH9g0pIpGIpCZE7n62u5/UwOuxJt6z190/CI5XA28BQ4B3gf4xl/YP6gDeC7rUarvWtifj65H2Mf/y+DEE39Eq1iIZYVnpNibGbMkBkWRo0siCkCISqZNyXWZm1sfMOgbHg4kMnt4YdIl9ZGanBbPLpgC1idXjwKXB8aUx9ZKG6m8A+9rWXZqKL5IBvvfwy3HN90qGJJWEOe1+gpltBk4HnjCzp4JTZwGvmtka4A/Ad939w+DcdOA+oIxIy9HSoP5WYKyZvQmcHZQljU0dcyxTRtX9Q3n3sxspXlkeYkQi0hbzlpfxyf66dGjWuOOVDElKsciQm+xUVFTkq1atCjsMacLInyxj+8fV0fKS6WdoXyORNHPb0vVxq1BPGVXA7AnDmniHSOuY2Wp3L2r+yoOlXJeZSKx5U4qwmPL0B7U+kUg6qb8lx6xxxysZkpSkhEhSWmFBHvdeMiJa3rpzLzcv0X5nIungukUlB23JMXXMsSFGJNI4JUSS8sYO7Rs3yHrBinKtTySS4m5esjYuGTpveL625JCUpoRI0sLUMcdyYt8e0fItT27QfmciKeq6RSUsWFE3CSI3pxO/nFwYYkQizVNCJGnjvstOjStrfSKR1DNtwaq4lqEOBr//1sgQIxJpGSVEkjbyc3OYM7FuMOYHu/dpKr5ICpm3vIylr70XV/foNM0MlfSghEjSyqSRBXHrE81cvFZJkUgKmLe8jFuejN+v+zeXjFAyJGlDCZGkndkThtE55idXSZFIuG5esjYuGerXsyvP3/gF7U8maUUJkaSln3w9fh2TWUvWahNYkRDUH0Ddr2dXHp32We1cL2lHCZGkpUkjC1gy/YxoS1GNwzXFa0KNSSTb3LZ0fdwA6q6dUDIkaUsJkaStwoI8HvnuGdHyirc/ZPKvn2/iHSKSKPW34zBg0ZVnKBmStKWESNJaYUFe3KKNL75ToZWsRZJs2oJVcckQwGLtMyhpTgmRpL2pY46lb48u0fKCFeVKikSSZNqCVXFT63M6m2aTSUZQQiQZYfGMz9EhZhfYBSvKtZK1SIJd9tsVccnQ+BOPZv2Pv6LZZJIRlBBJRsjPzWHexSPi6i6+70XNPBNJkNuWrmf5m+9Hy+NPPJp7phSFGJFIYikhkowxdmjfuJWsd1fXcOnvVoQYkUhmqD9maMxxvZUMScZRQiQZZdLIgrhB1m9u362ZZyKttKWyii/dvjyum2zG6MHcf/moEKMSSQ4lRJJxpo45lvOG50fLL75ToZWsRQ7Rlsoqvn7XP3hj++5o3aBeOdww/oQQoxJJHiVEkpF+ObmQfkd0jZZnLl7LvOVlIUYkkj5KyisY/6vn2P5xdbRuyFHdefCK00OMSiS5lBBJxnp0+mcZkFe3SNwtT25gWem2ECMSSX1bKquYPO95dn6yP1o3Y/Rg/nLtGC26KBlNCZFkrPzcHIqnnh7XUnTFwtXqPhNpxLzlZZxx6zPsPVBX95tLRqibTLKCEiLJaPm5OTw6/bN06Vi3SNHMxWvVUiRSz7LSbXE71gOcNzxfawxJ1lBCJBkvPzeHu795SlzdjIde1hpFIoFpC1ZxxcLVcXVzJg7jl5MLQ4pIpP0pIZKsMHZoX35zSd3CjdUHnLPmPKOWIsl685aXxU2rB5g17ngmjSwIKSKRcCghkqxRPyna75ExRdriQ7LV5F8/f1A32YzRg5k65tiQIhIJjxIiySpjh/aNW7gRYPK8F9R9JlnnukUlvPhO3X8GDHj+xi9oALVkLSVEknWmjjmWKaPqugP2HnDOu+efSookK2yprOLztz3Do2u2xNXfOnGYptVLVgstITKzb5hZqZnVmFlRTP1FZrYm5lVjZsODc8vNbEPMuaOC+q5mVmxmZWa2wswGhvNVSbqYPWFY3GrWW3fuZfyvnlNSJBmtdvXptz+o+zk/b3g+79z6/zRmSLJemC1E64CJwHOxle7+oLsPd/fhwCXA2+6+JuaSi2rPu/v2oO5yoMLdjwXuAOYkPXpJe7+cXBjXfbbzk/2c/cvlGlMkGWlZ6TbOvPWZuNWnx594tGaSiQRCS4jcfb27b2jmsguBRS243bnA/OD4D8AXzcyauF4EiHSfjT/x6Gh5z74aJsx9XkmRZJSbl6zlioWriVlvkRmjB2vHepEYqT6GaBLwcL263wfdZT+MSXqOATYBuPt+YCfQq/3ClHR2z5QiZoweHFc35bcr1H0mGWHe8jIWrIhfnX3G6MEaPC1ST1ITIjN72szWNfA6twXvHQXscfd1MdUXufsw4MzgdUkrYrrSzFaZ2aodO3Yc6tslQ90w/oS4lqJdew/whV/+TS1FkrZqB083NK1eyZDIwZKaELn72e5+UgOvx1rw9snUax1y93eDP3cBDwEjg1PvAgMAzKwT0BP4oJGY7nX3Incv6tOnT+u+MMlI90wpYs7EYdHyJ/ucCXOf1+KNknaWlW7jzDnPxA2e7tezK0umn6FkSKQRKdllZmYdgAuIGT9kZp3MrHdw3Bn4KpGB2QCPA5cGx+cDz7i7t1/EkikmjSw4qPvsioWrmbe8LKSIRA7NvOVlkfFCMf8Cjj/xaF6YdTaFBXnhBSaS4sKcdj/BzDYDpwNPmNlTMafPAja5+8aYuq7AU2b2KrCGSKvQb4JzvwV6mVkZcC1wY7Ljl8x1w/gTDkqKbnlyg1qKJOUVryxvsItMg6dFmmfZ3JBSVFTkq1atCjsMSVHFK8uZuXhtXN2cicO0XouknC2VVVz2uxW8sX13XL1+XiXbmNlqd2/V/wA6JToYkUxR+4skNimauXgtlXuqtdeTpIyGEvceXTuy4PJR6iITOQRKiESaMGlkAUd278KVC1dT25Z6y5MbeGPbLi1oJ6G7blHJQVtwHHdUd+Z/e5S24RA5RCk5qFoklYwd2pfF08+gU8xSn4+u2cLkXz8fXlCS9S777YqDkqEZowez7NoxSoZEWkEJkUgLFBbk8T/Tzoj7C/PiOxWM/eVyLeAo7WpZ6TZO+OGfWf7m+3H1s8Ydryn1Im2ghEikhQoL8ni0XkvRmzt28/W7/qGkSNpF7ZT6qn11k2EO79KBJdPP0Lg2kTZSQiRyCAoL8nhu5hcYclT3aN32j6v57K3PaFq+JM2WyipO/o+lDU6pXzd7vAZPiySAEiKRQ5Sfm8Nfrh0Tt1aRowUcJTlKyiv44i//xkd7a+LqtQWHSGJplplIK90w/gS27fwkbmDrLU9u4IW3PuD+y0eFGJlkituWrufuZzfG1fXo2oGnrtHAaZFE08KMWphR2qikvIKJc58n9m9S/55deWTaZ/VLS1qleGU5Ny5eS/1/ndUqJNK0tizMqC4zkTYqLMjj3ktGEDPWms079/I5jSuSVrhuUQkz6yVDRx3eRRuziiSZEiKRBBg7tC//vPELFOR2i9bVoHFF0nIl5RWM+PFfDlpb6Lzh+az8wVgNnBZJMiVEIgmSn5vDczd+kSmj4veOuuXJDQy88QklRtKom5esZcLc5/lg9764+hmjB2tFdJF2okHVIgk2e8Iwhh7Tk5v+uJb9MRODbnlyA7mHddFmmxI1b3nZQVPpAaaMKmD2hGEhRCSSvTSoWoOqJYkamiU0/sSjuWdKq8b8SQZpaB+yDsAt2qFepNW0271Iirph/AkckdM5rhVg6WvvMejGJ7hVv/iyUmOtQmOO663lGkRCpIRIJMmmjjmWwX0OZ8aDL1NdE2mRdWDm4rUsf327WouyxLzlZdz65IaDptJDZB8ybb0hEi51manLTNrRZb9dcdCmnKD1ZTJZY2sKgZ67SKKpy0wkTdx/+ShKyiu49Lcr+GjvgWj93c9uZOOO3WotyjDTFqxi6WvvHVQ/IK8bd154iqbSi6QQtRCphUhCcvOStSxYUX5QvVoN0l9j44S6doS7vjmCsUP7hhCVSOZrSwuREiIlRBKi4pXlzFq8lpp69b0O68x9l52qFoQ001iXKGickEh7UELUSkqIJFU01lp0Ur8e3HvpqdoTLcUtK93G9AdXs69+ZgsU9u/Jkqs+1/5BiWQhJUStpIRIUklJeQVXzH+J9+utVgxaqC9VNbTOVC11fYq0PyVEraSESFKRfsmmvoYWVaw1sFcOD11xulr1REKghKiVlBBJKmtshhJoPEpYileWM3Px2gbPde4Acy/SgGmRMCkhaiUlRJIOmhqoqxaj9tFUi9BhnTvwX5MLlQiJpAAlRK2khEjSxZbKKmY8uJqSTTsbPH/e8Hztip5gJeUVTF24mu279jZ4Xi1CIqlHCVErKSGSdFNSXsGU377Irr0NTGdCG8cmQlMrSwMMH9CTuReN0BghkRSUlgmRmd0GfA2oBt4CvuXulcG5WcDlwAHgand/KqgfB/wX0BG4z91vDeoHAYuAXsBq4BJ3r24uBiVEkq5KyiuYct8KdlUfaPB8l45wtxYAbLGmZvjVUiucSOpL14ToS8Az7r7fzOYAuPtMMzsReBgYCeQDTwNDgre9AYwFNgMvARe6+2tm9giw2N0XmdmvgVfc/Z7mYlBCJOluS2UVl/1uBW9s393oNRrj0rgJd/2Dks0Nd0PWUiIkkj7Sci8zd/9LTPFF4Pzg+FxgkbvvBd42szIiyRFAmbtvBDCzRcC5ZrYe+ALwzeCa+cCPgGYTIpF0l5+bw1+uHQM0Pl1/z74arli4GtAvd2h6gHSto3p0Yd4lRVopXCSLpMrmrt8GioPjY4gkSLU2B3UAm+rVjyLSTVbp7vsbuF4ka9ww/gRuGH8CWyqruGL+S5Ru3XXQNY+u2RJNBob378ncizN/LMyy0m1cvaiEqoaWka5HyxmIZK+kJkRm9jTQUDv9Te7+WHDNTcB+4MFkxhIT05XAlQAFBQXt8ZEi7So/N4cn/vUsoOlFHtds3skZtz4TLWfSFP6WtALV0kB0EYEkJ0TufnZT583sMuCrwBe9bjDTu8CAmMv6B3U0Uv8BkGtmnYJWotjrG4rpXuBeiIwhavEXI5KGaluNoPH90mrd/ezGuOQpnbrXmlrEsiHp9LWJSPsIc1D1OOB2YLS774ipHwo8RN2g6r8CxwFGZFD1F4kkPC8B33T3UjP7H+DRmEHVr7r73OZi0KBqyVaHmkAAdDC4ZcIwJo0Mr2W1ufWYmpJJLWAi0rB0nWVWBnQl0sID8KK7fzc4dxORcUX7gX9z96VB/VeAXxGZdv87d/9pUD+YyLT7I4ES4OJgUHaTlBCJNN2tdigSNf5m3vIybnlyQ5vvo1YgkeyTlglRKlBCJHKw4pXl3PTHtexvfgxySpkyqoDZE4aFHYaIhCgtp92LSGqaNLLgoG6xRLUiJYq6v0Qk0dRCpBYikTZpapp/a405rjf3Xz4qYfcTkeygFiIRCU3sNH8RkXTVIewARERERMKmhEhERESynhIiERERyXpKiERERCTrKSESERGRrKeESERERLKeEiIRERHJekqIREREJOspIRIREZGsp4RIREREsp4SIhEREcl6SohEREQk62X1bvdmtgvYEHYcEtUbeD/sICSOnklq0fNIPXomqeV4d+/Rmjdm+273G9y9KOwgJMLMVul5pBY9k9Si55F69ExSi5mtau171WUmIiIiWU8JkYiIiGS9bE+I7g07AImj55F69ExSi55H6tEzSS2tfh5ZPahaREREBNRCJCIiIpIdCZGZjTOzDWZWZmY3NnC+q5kVB+dXmNnAEMLMGi14Htea2Wtm9qqZ/dXMPhVGnNmiuecRc915ZuZmphk1SdaSZ2JmFwR/T0rN7KH2jjGbtODfrAIz+5uZlQT/bn0ljDizhZn9zsy2m9m6Rs6bmd0ZPK9XzeyUFt3Y3TP6BXQE3gIGA12AV4AT610zHfh1cDwZKA477kx9tfB5fB44LDiepucR7vMIrusBPAe8CBSFHXcmv1r4d+Q4oATIC8pHhR13pr5a+DzuBaYFxycC74Qddya/gLOAU4B1jZz/CrAUMOA0YEVL7psNLUQjgTJ33+ju1cAi4Nx615wLzA+O/wB80cysHWPMJs0+D3f/m7vvCYovAv3bOcZs0pK/HwA/BuYAn7RncFmqJc/kCuBud68AcPft7RxjNmnJ83DgiOC4J7ClHePLOu7+HPBhE5ecCyzwiBeBXDPr19x9syEhOgbYFFPeHNQ1eI277wd2Ar3aJbrs05LnEetyIpm+JEezzyNobh7g7k+0Z2BZrCV/R4YAQ8zsn2b2opmNa7fosk9LnsePgIvNbDPwZ+B77ROaNOJQf88AWqlaUpiZXQwUAaPDjiVbmVkH4HbgspBDkXidiHSbjSHSgvqcmQ1z98owg8piFwL3u/svzex0YKGZneTuNWEHJi2XDS1E7wIDYsr9g7oGrzGzTkSaPD9ol+iyT0ueB2Z2NnATcI67722n2LJRc8+jB3ASsNzM3iHSH/+4BlYnVUv+jmwGHnf3fe7+NvAGkQRJEq8lz+Ny4BEAd38B6EZkjzMJR4t+z9SXDQnRS8BxZjbIzLoQGTT9eL1rHgcuDY7PB57xYGSWJFyzz8PMCoF5RJIhjY1Iriafh7vvdPfe7j7Q3QcSGdN1jru3er8gaVZL/s36I5HWIcysN5EutI3tGGM2acnzKAe+CGBmJxBJiHa0a5QS63FgSjDb7DRgp7tvbe5NGd9l5u77zewq4CkiswV+5+6lZjYbWOXujwO/JdLEWUZkoNbk8CLObC18HrcBhwP/E4xtL3f3c0ILOoO18HlIO2rhM3kK+JKZvQYcAG5wd7VqJ0ELn8d1wG/M7BoiA6wv03+qk8fMHibyH4Lewbit/wA6A7j7r4mM4/oKUAbsAb7VovvqmYmIiEi2y4YuMxEREZEmKSESERGRrKeESERERLKeEiIRERHJekqIREREJOspIRIREZGsp4RIRNKGmQ0ws7fN7MignBeUB5pZPzP736B+jJntNLM1Zva6mf0i5h6XmdmO4Fzt60Qz62NmT4b1tYlIuJQQiUjacPdNwD3ArUHVrcC97v4OcC3wm5jL/+7uw4FC4Ktm9tmYc8XuPjzm9Zq77wC21rtORLKEEiIRSTd3AKeZ2b8BnwNqW3/OAw5q4XH3KmANLdjtmsiWGBclIkgRSS8Zv3WHiGQWd99nZjcQSX6+FJQHARUNbQRsZnlENj59LqZ6kpl9LqZ8epA4rQJ+ksTwRSRFqYVIRNLReGArcFJQ7sfBm2meaWavENnl+il33xZzrn6XWVVQvx3IT2bgIpKalBCJSFoxs+HAWOA04Boz6wdUEdlhPNbf3f0zwFDg8uB9zekW3EtEsowSIhFJG2ZmRAZV/5u7lwO3ERlD9AYwsKH3uPvbRAZfz2zBRwwB1iUkWBFJK0qIRCSdXAGUu/uyoDwXOAEoAt4ys2Mbed+vgbPMbGBQnlRv2v0ZQf3ngSeSFLuIpDBz97BjEBFpMzObAIxw9x+04R7PAee6e0XiIhORdKBZZiKSEdx9iZn1au37zawPcLuSIZHspBYiERERyXoaQyQiIiJZTwmRiIiIZD0lRCIiIpL1lBCJiIhI1lNCJCIiIlnv/wO1rYZO0wpTpAAAAABJRU5ErkJggg==\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 648x432 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "from pycalphad import Database, calculate, Model, ReferenceState, variables as v\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "dbf = Database(\"nbre_liu.tdb\")\n",
-    "comps = [\"NB\", \"RE\", \"VA\"]\n",
+    "from pycalphad import Workspace, variables as v\n",
+    "from pycalphad.property_framework import ReferenceState\n",
     "\n",
-    "# Create reference states\n",
-    "Nb_ref = ReferenceState(\"NB\", \"LIQUID_RENB\")\n",
-    "Re_ref = ReferenceState(\"RE\", \"LIQUID_RENB\")\n",
-    "liq_refstates = [Nb_ref, Re_ref]\n",
+    "wks = Workspace(\"nbre_liu.tdb\", [\"NB\", \"RE\", \"VA\"], [\"LIQUID_RENB\"],\n",
+    "                {v.P: 101325, v.T: 2800, v.X(\"RE\"): (0, 1, 0.005)})\n",
     "\n",
-    "# Create the model and shift the reference state\n",
-    "mod_liq = Model(dbf, comps, \"LIQUID_RENB\")\n",
-    "mod_liq.shift_reference_state(liq_refstates, dbf)\n",
-    "calc_models = {\"LIQUID_RENB\": mod_liq}\n",
+    "ref = ReferenceState([(\"LIQUID_RENB\", {v.X(\"RE\"): 0}),\n",
+    "                      (\"LIQUID_RENB\", {v.X(\"RE\"): 1})\n",
+    "                     ], wks)\n",
     "\n",
-    "# Calculate HMR for the liquid at 2800 K from X(RE)=0 to X(RE)=1\n",
-    "conds = {v.P: 101325, v.T: 2800, v.X(\"RE\"): (0, 1, 0.01)}\n",
-    "result = calculate(dbf, comps, \"LIQUID_RENB\", P=101325, T=2800, output=\"HMR\", model=calc_models)\n",
+    "ref_enthalpy = ref('HM')\n",
+    "ref_enthalpy.display_name = 'Enthalpy of Mixing'\n",
     "\n",
-    "# Plot\n",
-    "fig = plt.figure(figsize=(9,6))\n",
-    "ax = fig.gca()\n",
-    "ax.scatter(result.X.sel(component='RE'), result.HMR, marker='.', s=5, label='LIQUID')\n",
-    "ax.set_xlim((0, 1))\n",
-    "ax.set_xlabel('X(RE)')\n",
-    "ax.set_ylabel('HM_MIX')\n",
-    "ax.set_title('Nb-Re LIQUID Mixing Enthalpy')\n",
-    "ax.legend()\n",
-    "plt.show()"
+    "wks.plot(v.X('RE'), ref_enthalpy)"
    ]
   },
   {
@@ -102,7 +84,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2022-02-19T19:18:30.561221Z",
@@ -114,228 +96,55 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 648x432 with 1 Axes>"
+       "<Figure size 900x600 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "from pycalphad import Database, equilibrium, Model, ReferenceState, variables as v\n",
+    "from pycalphad import Database, Workspace, as_property, variables as v\n",
+    "from pycalphad.property_framework import ReferenceState\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "\n",
-    "dbf = Database(\"nbre_liu.tdb\")\n",
-    "comps = [\"NB\", \"RE\", \"VA\"]\n",
-    "phases = dbf.phases.keys()\n",
+    "dbf_nbre = Database(\"nbre_liu.tdb\")\n",
     "\n",
-    "# Create reference states\n",
-    "Nb_ref = ReferenceState(\"NB\", \"BCC_RENB\", {v.T: 298.15, v.P: 101325})\n",
-    "Re_ref = ReferenceState(\"RE\", \"HCP_RENB\", {v.T: 298.15, v.P: 101325})\n",
+    "wks2 = Workspace(dbf_nbre, [\"NB\", \"RE\", \"VA\"], sorted(dbf_nbre.phases.keys()),\n",
+    "                 {v.P: 101325, v.T: 2800, v.X(\"RE\"): (0, 1, 0.005)})\n",
     "\n",
-    "# Create the models for each phase and shift them all by the same reference states.\n",
-    "eq_models = {}\n",
-    "for phase_name in phases:\n",
-    "    mod = Model(dbf, comps, phase_name)\n",
-    "    mod.shift_reference_state([Nb_ref, Re_ref], dbf)\n",
-    "    eq_models[phase_name] = mod\n",
+    "ref2 = ReferenceState([(\"BCC_RENB\", {v.X(\"RE\"): 0, v.T: 298.15}), # NB\n",
+    "                       (\"HCP_RENB\", {v.X(\"RE\"): 1, v.T: 298.15})  # RE\n",
+    "                     ], wks2)\n",
     "\n",
-    "# Calculate HMR at 2800 K from X(RE)=0 to X(RE)=1\n",
-    "conds = {v.P: 101325, v.T: 2800, v.X(\"RE\"): (0, 1, 0.01)}\n",
-    "result = equilibrium(dbf, comps, phases, conds, output=\"HMR\", model=eq_models)\n",
+    "x_re, enthalpy_of_formation = wks2.get(v.X('RE'), ref2('HM'))\n",
     "\n",
-    "# Find the groups of unique phases in equilibrium e.g. [CHI_RENB] and [CHI_RENB, HCP_RENB]\n",
-    "unique_phase_sets = np.unique(result.Phase.values.squeeze(), axis=0)\n",
+    "# Find the groups of stable phases in equilibrium e.g. [CHI_RENB] and [CHI_RENB, HCP_RENB]\n",
+    "unique_phase_sets = np.unique(wks2.eq.Phase.squeeze(), axis=0)\n",
     "\n",
     "# Plot\n",
     "fig = plt.figure(figsize=(9,6))\n",
     "ax = fig.gca()\n",
     "for phase_set in unique_phase_sets:\n",
     "    label = '+'.join([ph for ph in phase_set if ph != ''])\n",
-    "    # composition indices with the same unique phase\n",
-    "    unique_phase_idx = np.nonzero(np.all(result.Phase.values.squeeze() == phase_set, axis=1))[0]\n",
-    "    masked_result = result.isel(X_RE=unique_phase_idx)\n",
-    "    ax.plot(masked_result.X_RE.squeeze(), masked_result.HMR.squeeze(), marker='.', label=label)\n",
+    "    # composition indices with the same stable set of phases\n",
+    "    unique_phase_idx = np.nonzero(np.all(wks2.eq.Phase.squeeze() == phase_set, axis=1))[0]\n",
+    "    ax.plot(x_re[unique_phase_idx].magnitude, enthalpy_of_formation[unique_phase_idx].magnitude, marker='.', label=label)\n",
     "ax.set_xlim((0, 1))\n",
-    "ax.set_xlabel('X(RE)')\n",
-    "ax.set_ylabel('HM_FORM')\n",
+    "ax.set_xlabel(f'X(RE) [{v.X(\"RE\").implementation_units}]')\n",
+    "ax.set_ylabel(f'Enthalpy of Formation [{as_property(\"HM\").implementation_units}]')\n",
     "ax.set_title('Nb-Re Formation Enthalpy (T=2800 K)')\n",
     "ax.legend()\n",
     "plt.show()"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Special `_MIX` Reference State\n",
-    "\n",
-    "pycalphad also includes special mixing reference state that is referenced to the endmembers for that phase with the `_MIX` suffix (`GM_MIX`, `HM_MIX`, `SM_MIX`, `CPM_MIX`). This is particularly useful for seeing how the mixing contributions from physical or excess models affect the energy. The `_MIX` properties are set by default and no instantiation of `Model` objects and calling `shift_reference_state` is required.\n",
-    "\n",
-    "Below is an example for calculating this endmember-referenced mixing enthalpy for the $\\chi$ phase in Nb-Re. Notice that the four endmembers have a mixing enthalpy of zero."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2022-02-19T19:18:32.010452Z",
-     "iopub.status.busy": "2022-02-19T19:18:32.001422Z",
-     "iopub.status.idle": "2022-02-19T19:18:32.461953Z",
-     "shell.execute_reply": "2022-02-19T19:18:32.461953Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Gxk+21ireR/p93YM5Dg+TqZg1axZOnz6Nc+d4WetwkJaWhlmzZo32MDgjwPTsdJzq7FMse/lAMzbevHCURjS+UIssvvKjawAA4Yjyhxgvsb84YJOnZ9ht8Lp9cbbmGKU3ODyOC24MqbBYLJgzZ85oD4PDGfM8t7pELmdleaHmOO4qnzsKIxpfsCKLmalRJ30gpPQV+IMDaPX4uXTBRcSMSWk4xhhD77jascw5fFpyE4XhTD7nYTIOhzMoSgtyoCXm8cSuoxd8LOMNdTix+vuL5dcPrFBqcoUFrkY93mn1+OHpi3qG2rr7cTUTFr17a/0ojGr889NXldWWeZMGn5LBjSEOhzNo9BzUvEVHfF4+EL0/BEpVb3XoEeChsvHOM7uOwheIevxmTErDc7dHm6v7gjyJejD0h8OK95vWlOlsmRhuDHE4nEEzPduquZy36NDn6Z2fKd4/uUqZY1WxpBBZVuVXczjC02zHM3Ut5xXv27r7edhziNQ1d0FtQ6pbBSUDN4Y4HM6gqfrOlZrLBXDvkB7qhpKrrypQvM+z25ChUvj2hwQuzjeOCUnGLBXUvCQ3YxRHc3Ew3KFFbgxxOJxBU1qQgxSdddw7FIvaK7Tu+ks0t+vtF93/XI364oJIWXZH3d4Y/Sj+4yE5znmHV3+LG0McDmdITM6yyK8nMR4N7h2KRe0VuvfGL6DV48fDbxzCw9sPyxNkZpp4TydnWGGziBNoMMzzSsYjdc1dOOcV20UMSLIJJzv6UL2/CQ+umC9v99Ab/MeDUVo9fvSpSuqXzJk8pGNyY4jD4QwJqzn6NZJtsyjWce9QFD2vUNWeRlTvb0b1gSZU1TQCgKxCbTYRpBDxDQ+VjU/ue+1TBMICUk3A5IxUAEB+jg0ViwsVEhQhnhZmmI1vumKKN36zumRIx+TGEIfDGRI2S9QACoQHsGBGtvxegKg7xNH2CgEACPu1Lr62pYoetjOeflzqyJTX8lDZ+IOKLVotKXBLHiJbagpPoB4CWtWVQ72f3BjicDhD4te3Xi6/PusN4l+/eqliPdcdilWbZnOFKsvnomJxASoWF6JS8hT8+pYrYAIQEYATZ3uRIgk68VDZ+IMqiqeYCKzSB7kgb5LmtrwPXWJaPX70jYAbjRtDHA5nSJQW5ICNjv34T3W4pSRPsc1E71nGqk0DjFcI4i/ax25aiMduWiD/ui0tyIFjkihbkJ6WgnRLNFRW19x1gUbNGSqtHj/O+0RvULc/jMCAgNzMVKy/IZorxNYN/pSLLyZk45suDKhiZFrir8nCjSEOhzNkJqWnyq/94QimqVzWrMjgREPdmZ5Nmo0Hmzd07dyp8vJ7XuWhsvHCM7uOylo4NF/ImZetCOn8ktGZ2ndSqUfEiUUrRPaAwf9T8eDGEIfDGTJZ1qgxlJFqQsXiwphJX20UTBTYzvRmAs2+ba0eP57aeUQ3TLLhG07QPPWzPTyUMl6gYouT0szIsokiFG3d/Ypt1DpTnPiEhVjd++HohciNIQ6HM2TYvKHeYATunn7cVT5X4b5mjYKJwtd/+77ivZ5IZfW+JlTtbUT1/iZ5Gc01CUcE5NltsJrFuxkIRXhuyTiBii1m28womCwKLc4YQv+sic47rnb4Q8PTpV4NN4Y4HM6QKS3IwVRGb+ihNw4DAH5foZz8b/6Pv13QcY0m77ja4Wr3yu+d07N0O5NXLClEZXkRKhYXAhA9RZ1S5ZFPEmCcmiWGVnjj1vFJu2TAqj1Darg2lz7qxqzDCTeGOBzOsGA2Rb9OcjPFsNky53RMtkVTROtOd08YrZwfVB9UvL9u/jRdj06e3Yb7V1wm55JU72uSdWcC4Qjqmrvw869Fk65549axT11zF872BAAA5hQT7FLOUE66JWZbtscfF1/UJxSJrabMTNXTwE8ObgxxOJxhoa8/2kH6xLle+fX//dfrFNvdpTISLkbUpfT5ObaYMFg8KpYUYlXpTKSagOCAgO/98e94/1g0zOgPDvBQ2Rjnvtc+lZOnS/Nz8Llb9BIec/titmXDp1x8URu9ENlzQxRbpHBjiMPhDAtLinLl1y1dfty9tQ6tHj/y7DYsnh3tJh1BrLFwsaEupd961xJULC6Erz8kGzHxkqbz7DY8u7oEk7NEb0JXXwggArKldic8VDb2oWKLWdYUrL9hPi51ZAEA5jEimpShdFufKOiFyPRCz8nCjSEOhzMsbPiGE+mp0ZTp7XWtsifklR9do9hWbSxcTCz/TY3ifWm+KLCXaTWLbTeke1K1pxFVexvlFhxa0IatFpMozvjS966Sk9J5qGx8YE+3IM9ug6dXzAHr6gsl3Id7/WIJDIQTbzQEuDHE4XCGhTy7TdZSAYBJNrOcEAzEdmhf+uv3LtjYLhRP7/wMx5gQoTWFoK6lG9X7m2KSpKNtOPSrY2jD1twsK/LsNpQW5CBNatwajvB4ylil1eNHj2TImlNMaPX4EZKUAi/JzdDchxVf/OctF38oORnqmrsQ0LCFhkNskcKNIQ6HMyJMZoQYAVF1ec7kqNjcyfP+i06ZWt1/7BUmPAZAkSRdWT4XleVFcgsOLVKYb2gaUrOYxIRR3rh17PLMrqPo8YsJQ6X5OaiqOY6TnX0AgJbzfk3PDyu+WNvSfWEGOk64W0eZezjEFincGOJwOMOGhakoO9nZh2feVvYl23PfVxTvLyZlarWm0CVT0uHITosJj1Hy7GLn8up9TbphEao11OMPyQnYv7ntCnk9b9w6Nvn7KTHZneYLnfWKVWWTbGa42no0E+m5+KI+57za/z+GQ2yRYk68CYfD4RijNH8yTnaekd///VQnntp5BMudDmw7eAYgAm4sdmBng1veZumv34sxksYbL9QcV2gKmQCc6OyTw2MgUIQMKVRsEUT0GqnxB0TvQjAckZu55tltsFkI/CGBN24do3j80bygPLsNJ8+JFWSTM1Lx7asLNZ8FjjatHj/6R0hokYV7hjgczrCxfsV8ZFmjXytt3f2o2tuIR3c0oPpAE6r3N2P2tExkpTIepPN+VL788WgMd9h4YpfSA/ZCxZVyfpBaQ4glJo9IBa3QC0WA3sCAfAwzEbMleKhsbEJzvTKl6r/p0udWMDld91lQw8UXRR58/dOYxqwjATeGOBzOsJFnt2ESIyoXjoiNKR9ZWYyKxYVYVZoHX38IL39/sWK/nQ3ucTupf/mJdxXvZ2Rb4Zw5ydCkF89QAoANK52YZBUn1LqWaLd6tnErD5WNLeqau3BeqhzLkowio+rTJbMmya9/fpHl0w2WD49rt/Epy5+kuXywcGOIw+EMK2zeECAKDpYW5KCyvAgnOvpQvb8Z22pPY940ZVXND8ehGON3/+sAWroD8nszAdp6AobFFRORZ7fBlibez/5QNCS24RtOpEilNDxUNra4e2s9AmEBqSbgqVvEnn3UM5SoL9lmRnwxfAG8IWOduuYu3fvwH2u0+/wNFm4McTicYaU0f7Li/VFJebd6XxPqWzwozbcDAsGxs72KkJqA8VVu/0LN8Zjms/9f5TVxw17JotWjrNXjR/W+JtgsvKpsLNLpE70/AgBHtmj8GPUMGQmfTSTu3lqvKzwx3PeKG0McDmdYUecNhQdEPRyaH7NpTRkqlxahsrwIBZOVarwnz/tx+/MfXtDxDoZWjz8mT2jBjCw4stMM54QYge1RRvNPaNL1ly7lobKxiC1V/JxCEcgeQrVnKJ76OMtEN3L1qshGAm4McTicYUWdN3S2px91zV2K/Bj6+vGbF2COSoRu/6muMa8/VK7yYOVmWHC4zTts4TFKxZJC2bA0mwhaPX74+sNYVZqHqVmpkNKJeKhsjFDX3IUOn+jJs5qJ7CFUe4aoQav1vEzNjOpz/etF3rYmHq0ePwI6MTJHVqrm8qHAjSEOhzOiBAaAh944rLmutCAHe9aX48Zih2L5yweax2w1zVWP7UaQEX9ONxO8+S9fHtbwGIU1LD19IVTVHEf1gSb87Xgnqvc3g4pQ81DZ2OC+1z5FRBAn1v/4VpnsIVR7huJVEf7+jkXy674J3LX1wdc/RVjn8p+vWKS9YghwY4jD4Qw7NotF8T43M/4vudlTY5tX3r/tEOqauzS2Hj2+/MS7ONur7C317vqlCavChgINu3gDAzjnDaAk346z3gBK8+24hmmOy0Nlo483IHqFHJOsigaias9QvOeFN20V0asiA0bmHnFjiMPhDDu/vvVyRd+g5vN9utvS0I9zRlbMups3fzhmmlYu/fV7isoxAFjhnD7iSa+/vuUKUFmmo24vNq8pk3Ovnrjlcl5VNoagjXVpsjsghs7OSknwC/KSLwefiB6/eFVkIwU3hjgczrBTWpADx6SoN6i1q0/Xy1O9rwnVB5pQVjgZleVFsFtTFOuvefK9UfcQffmJd3HyvNIoWzJnMjasLB7xc5cW5GCylCPRHxqIyb3KkDxHPFQ2uohGu2iZ2pm+fA9tPwSPP4TczFSsvyG2l5ZWMnW6JTo1T8S8oXhVZJmpKTprhgY3hjgczohAGN9QYAC4//VPNbej+ROV5UW4f8VlqH90BbJTlV9NN2/+cNRyiK56bHeMR+iOqwvwp7uWXLBSaC2PA4XtVXbPq/UXZDycWDa+6YJXap/yxdlReYkpWVYAovio1vOilUz9u9tL5dcTMW+orVvfk/zc6pIROSc3hjgczojgD0ZU72MnciA2f6LV48eaJXOQruqceP+2Qxe8yuzyDTtjcoRuKcnDxpvFDuP0V31dc5ehUmkWLY+AXsm1ur0DyzLndNDfyn0BHiobLfY1ijkuqSlQeIASaQxpJVOz+UYTjbrmLgS0vyoAjNy94cYQh8MZEZ6+9XKYiHLZw9sP4+E3DqHV49ed+Okv5Tu/VBTjIXr5QHNMd/iR4B1XO2Y/8BZ6VAbdLSV5+A3zq52O9dEdDZql0vH0ZOi+67bUyuv1Sq5TpNtgVt9QCZsUOhjAxMwxGQukS4bqlEyrwgOUSH1a/WOgrrkLN236QLHNRPpM795aPyrn5cYQh8MZEZY5p2NadjR3wt3dLzdrrappxNottZoTP/tL+dONNyItRWkAuNq9WPDwn0ds3Df/x9/wA53WINNUYQ461kdWFmuWSsfTk6lYUojSfDvqWjzyer2S63BEkP9qGVhs6IBXlY0OegZr8/le6a9+6Ifl0R0NqG/xgH3sJ1LekLtH/z6NhL4QhRtDHA5nxPD6o/7uYATIsqagYnEBAEFuzbG82KGY3Nlfyq0eP/6fRQXIsCi/qnwhAbMfeGtYvUTUG1R3ujtm3YtMF3oWOtbSghzNUmm1ccMaMnl2m6jGzazXK7kOSHkj7p4Ann77SIyBtcw5XRZg7A/FiTFwRoR3XO1o9Yh5ZVQKgUKSrIp6ZGUxSvPt+JUUigUmTt5QXXMX/CH99SOhL0SJDUBzOBzOMJFCTACUX+SP3bQQrR4/MtMsqFhcKHtPQID7V1ym2JZWmlWWF+GzM90xvcBc7V7MfuAt3FjsQNUdg/+iXPDwn+ELxc5admsK6h9dAWBwuQrUuKGor1W9Xo9f33IF7qo+iIgA1Dd7NA0zQbrNgbA4OU/kvJMLzb2M2CJtzkrJn5KBk51+FExON3Ss0oIcbF93LQAxT24iwYbIUiCGfVlGUoOJe4Y4HM6I8ZvbrlDoDQVCA3JrDmoILXc6ULG4EL7+UExuTcWSQnndr265HB8+8BVoFdbubHBj9gNv4ctPvGt4bBu2H8LsB97C7Afe0jSEFs/OkQ0ho7CeH61w1nKnQ/aGJcMy53RMk6QK+sMDMQnnT+08gp8ujybt8lDZhcUm5bY5JlljJmyjTVoT8ULN8SHtPx5ge5ER7fS4EYN7hjgczoixzDkd0ydZ0SaVpgcjYon97ruvV3hJMq1mVO1tRGaaJcZTcuhMN+pbPPK6H15fJO6nQUt3ALMfeEt+PyMrFft+vgyAaPy8fCBxeT7rDdKCdo2vWFIYE85irwkCYjxeu11u1LV4sLvBjdKCHNQ1d+HRHQ1iaCTBr1698np6zsryItgsBP6QAJ8/LIfiOCNLq8cPb78Y29FKcLdnpALoRU66JWZdouPaLCb4pRDZk7uO4q7yuUMe71jlHVc7eoPRHyVq0cV0y8j6brgxxOFwRhRfvzIJgJbYVywpBAii4R72tUT1viY5t4iuY/db8/t9MWKILG3eoMI4iocJwN8e+EpCAyJeWC/RNanX02TZjTsa5NCIHplpFngDgZjyevaYW/Y1wY8wBgBs3NGA5yuuNHTtnMHzzK6j8AVEg0WdL9Tq8ePTFjEHrfFsr+Fjtnr8WLulVjaEAOiKEF4s/PTV+N5MVntpJODGEIfDGVHsNnESp/T0h1HX3IXdLrfCu6KVO8NO9HQ7Ns9mz31fAWDc66NFVqoJb99TbtiLEmPwSGh5jGgokC5T5wg9srIYG3c0YG15EZ7aeUTT20QRdKZD9pi/ue0KuRLug+PnDF0PZ2jUtZwHAKSaYvOFnn77CPrDEVjNBE+sWqi1uwL6DPn6w6hv8cCZlw1Xa8+IjHusEYrE18ga6Rw4bgxxOJwR5Yuzc9FSf0Z+3+0P4/7XP8Uxt0/hXdEyJrQSjFs9flTVHAcEgsqlRciz27Dx5oWyECIAPL3zM2zae0JzPGzobDDoJT1reYzieZEAMSF005oyrN1Si/oWj+52QGyYTOt+LXNOl0NlfYEBHiq7APilnnBTsmLzhepaxDYyeXZb3MlcNoICIVTvb0bF4gJUlhfB5w8rjKGnd36Ge2/8wghcxejyjqsdfilvT4owX3C4McThcEaU9Svm4/8eOoMg88PP2x+KSSROZDiw21XvF71AmTaz5rb33viFmEnjqZ1iSfpXnTMSemGA+LlBWmh5jCqWFMIXCMvJ4XrtGNShQC1ys9LgDfQhEI6grrkL22pPo3p/M3yBEB67KWoIWkwpPFR2AYnXKsVoWT199otnZEuG0FxZWsLV6kGtFGrbtPfERWkMJQqRLdBo4jzc8GoyDoczouTZbZicKVZC0fRSX38YdS0ebKs9I1dcGa20EivMClCxuFDTeNBTfaaaP4CgK4TI7l+1p9HQdlr6SOy1Z1rNqN7fjOr9TZpjo+PatKYsrtH17G0lSDUBwQEB92ytBwTxbtY2eRTHY3uV/fXzs7rH4wwdsXWEaPHkZsUqTOdPyQCAhGX1FUsKUZJvR0NbDzLTLArP6LZ1XxrmUY89AgNRQ1LLfvz9nV8c8TFwY4jD4Yw4NrNYEC9/0QlAxeIC+AIhVO1tRFXNcbnSauOOhoQ9vjKtFlSWFxlufAlEjZXK8rmaOj3q/UEEQ9slasHBCi9q7aMntKimtCAHVqmipsMXQOXSIpTm2+Fq64kRYLRIVmdvUPQicUaGu7fWIzggINUEPMsYoRSjZfV5dhs2qwQ49bjYWnO842qP24sMwAUJ9XJjiMPhjDil+ZMV773BAfQGBpCZSsuNiWZ7CiDWuIjX4gLQbmmhVn6OZ3zQ/VeVzYqbvKDXOkPdc4w9n94+8XqYsbANW7UUrCnXzM2VX9+TIATBGTydPtHIsVpSNKUREvUlY2G1t9TPAautdbG15mCFFqeouzMDCp2ykYQbQxwOZ8RZv2I+1DIhfz/VCQBysqje5K42ftThNL1wFQB5uVEvDrv/bpc7rtEFQNNY0jPq2GPr6RPFPRdiK8r0jvfELZfLva3au431xOIkD2ucamHUMySHZmuOaz4H962ICmpeTK05Wj1++KRkwhQCOGfZY7Z5gLn2kYQnUHM4nBEnz25DTkYqznqD8jJPX0hutRGvekydmKwWLtRLvGaX65XDJ6UZpCLevgtmZmPBzEkJQx5Gz0XRStbVq8IzQWxn4A8JqGvuGtFWBhORd1ztcPeIkhFZadqCitPtNhw7Kwouxkvap8/SqtKZKNHIm/P4L85+cxvfdMmv0ywmfKIR0r1QQpPcGOJwOBeEQFCpI+ILDGCFc7quAcBO8qyxoTYc9AwJtUZRIh0jNYn6htF9aaNZOtHRaje9nCYtjPYo0xJepBOpLxBGptUsj+OaubnYK/Vy+/Gf6vDX+79iaCwcY8TrR0ahHeuPtHtx4GSXbqUkfZZ8/SHUM4Y+u55VXb9YSuzZBP8FeZNw4NTo5bfxMBmHw7kgLCnKVbwXAHzcdF4uIVaHq2jIoErVk0kdGtLLtTCSmBxvm0R9xvTCaXp5QcMBTaDu9AXlxGi9KrknbrlczrdoS5CLxEkevX5k7LNCS+uz08y6z4Rs9C8u1E3uz7PbcMfVBfJ7PQ2t8USrx48+SVvITICz3thQ4rxpmRdsPNwzxOFwLggbvuHEX4+fQ28wAgsBQgJQNFUsPY56N0LItFrEX8oCncoTp1Bq7T/UCpREfcYoau+SUS/PYCjNz8GpDj+CAwLuf+1T7L7nevl8rR4/MtMsinGkWwh6QwLCAu9kP9zoKYLT5+Zkhw+tHnGC/+LsKbrPhDrcqrfdxpsXDlplfSzy4OufyndwarYVoUhsLtQfv3fVBRsPN4Y4HM4FIc9uQzAsfuHRJvFnvWLOBTUo3N1+WUiwculcZNrMCT0srR4/3N39cM7IQm//gLS/MmQ0GIz0TqPXpW67wY6tag8t059reCx6go/33nAZ3m1wwxsYgD+kzCPRMsK+fOk07GpwAxA72X/CjaFhoa65C2d7xPy3flVC83KnA/tPdOJvx88hMCAgNQXIsKboim4azRdTs/WjZqy+qiDxhmOUD493yK/nO7Kx70RHzDYXUj2dh8k4HM4Fw56hTDRt6uiL6qYIkL1BB06cR1XNcUVPMj2q9zVhW/0ZuNq8yEhLMSSsmAg2dKHuKaZVAq9XDVa9rwnVB5pk0UWjxNNKykgTC63DkcTyxhu+4ZSrymgne87QuXtrPejtf0BlgNIEf+rPtJpT4n7+RnWmWj1+xYT9s+2HBjn60ecdV7v8g8hsAo6f9crilZSUxA7hYWXUPEOEkKcBrAQQBNAI4P8VBMEjrXsQwD9BLIb4sSAIb0vLVwD4HUTZhf8UBOFJafkcAK8AmALgIIAKQRCC4HA4Ywq7zYpz3mgX+wiA+17/FLd/sQBVextRsbhALks/dtaHzDRLwpATbXkBCIo2BmzIKFmMVKjFS+rWGlsyY4nnLYjX/kFNnt2GjFQzegK8PcdwQvWFAODUeaV0Af3sDjadx0cnu1Ccl42ywslDziGr3tcE1gc1MI7b2LPtN66dOxUHmzpjtvkV02vwQjCanqF3ACwQBOFyAMcAPAgAhJBiALcDcAJYAWAzISSFEJICYBOAGwEUA/iWtC0APAXgOUEQ5gLogmhIcTicMcavb70cqZKCHP3hl2YxyUnAleVzsWlNmdRqo8DQBJJnt+GxmxbgsZsWxpToDyVEVrG4AG5PPx5+45CmmrR6DFrn0xobS11zF27a9EGMSrTe8Vo9ftkjoadtQ7ejHizenmP4ofpCGakm3WfB0yv+Hu/qCw3pWaREE+WjjFd1cbb9xpSMVM1tLnQIcNSMIUEQdguCQO/IfgCzpNffBPCKIAgBQRBOAjgO4Crp33FBEE5IXp9XAHyTEEIAfAXAa9L+LwG46QJdBofDSYLSghxMybQCiBpD530BuHv65QmDGhCV5XM11XjjYVTJWW87uhwQW35sqz+jCHEM1chS8+iOBtQzLUge3n5YYXypqd7XhN5gfNG9Vo8fa7fUymG2Zc7pMPP2HMNGXXMXOn1irtu07DTdZyEZ9WkjaOWE/dMf/z4sx76QbP2oWW6/YQLQFwjDFxh9IcmxkjP0PQA7pdczAbQw605Ly/SWTwHgYQwrulwTQsgPCSEfE0I+Pnfu3DANn8PhGKVPCu+YAKSmEAQGgPtf/zRmO6OqzIPZJ16OD10ueoeMe6gGwyMri1Gab8eGlcWG8osqlhTCKlk27p6ApmFTva8J9S0elObb5XFPZybkH//p4mrncKG577VPQSWzSvP1hSypxlCzFEYzaqgn4kFGkfl8XyjOlmOTh9+I5jrdv2K+ZuL0uusvuZBDAjDCxhAh5F1CyGGNf99ktvk5gDCALSM5FoogCL8XBGGRIAiLpk6deiFOyeFwGKjekGgSiTEfb3/sl3oivZ54HeDjGS+tHj98/WFNI4fuv7zYgep9ojq2XojLCIkmwNKCHGxaU4bdLjeWOx26xhfrsfqPb5XBBCAiAPe/FmtE0mvYtKZMHve/f7tMcSzO4PEGxPBXRqoJ62/QbxVBVDk9gzHutVArMo8nT1+rxw+aJ201i8ra3f0DMduNhqDkiBpDgiB8VRCEBRr//hcACCHfBfCPANYIgkAfnTMA8pnDzJKW6S3vBGAnhJhVyzkczhhkwzecct4Q/YXd6Q3GTNLxGlcC2pOLeh8tY4R6YDLTLJo5Pob7kiGxMKORCZBus7vBrZtfxB5nmXM6pk0S8yy8gVgjUiuUV1qQI1fLDAjjawIda9AEdhMhmjld9BnInyJqaBVMTgcwcmKcP3jp42E93kiy8U2XnPOWZjFj9pT00R0Qw2hWk60AcB+A6wVBYNPx3wTwP4SQZwHkAbgUwEcQUwwulSrHzkBMsv62IAgCIWQPgFsh5hHdCeB/L9yVcDicZMiz22BPF/uUpZqAYAQIRgQ88/ZRPLu6RLHtYHqHJRJLNKLrYlT7ZbjO5QuE5NJ3I1o0dELu8AZ091Fz7aW8PcdQecfVjl4pvyU3KzYXiBX/PNTSDSDapHU4xTjXXX+JrELd0Tt+CqfZBP5nbr0C92mEx1ml7QvJaOYM/QeALADvEELqCSHPA4AgCC4ArwJoALALwDpBEAaknKB/BvA2gM8AvCptCwD3A7iHEHIcYg7Rf13YS+FwOMlA+5QJApBlFd1EdS1Kb0W8cBagn8zM/gLX+jWeTJsOQFtXiHoAljsdCc8Vz7tFt8m0WqR8ofhaNHQ8qWbxnoUiMBx2YdtztHT5ebhsENz72qeIQLR9S/InxdxDmmd24GQnuvyi125B3qThH4cqjDQePst3XO3olcSFLCZgmXM6rJZYMaGNF7iknjKa1WRzBUHIFwShRPr3I2bdLwVBKBIEYb4gCDuZ5X8WBGGetO6XzPITgiBcJR3z/xEEIXChr4fD4RiH5g2FBCA4IBpG/aEBRZghXjgL0M/HYY2doVZ/JUq03t3g1jyX+jqq9jZi7ZZa3UlLy5CKF3brkSZaiymx94rNN8qg8UkAP9s2fkX7Rgvajyw91YTtda2aopiZVjOOucXk6Tm56XJe0XAlUGvxw3EQKrt7a738+srCyahr7kJX79hJAOftODgczgVnwzec+KCxA97AgFxm6+4O4Om3j2B7XasYEpJCQ2X5dnz9d3/F/OmZWH9D1LCJF0Izgl7LC5Z4Yorxwl/s2CqWFGLfiU7Ut3hQvb9Jc6xaIRSt66Pnff1gC856g8jNshpS6KZd7b90aS52ucT2HB8c59W0yUL7kWVYzbjjmnzNRHexP55JLheP97waeQb1uOPqArlX2eG2niFd14XAH4wmSl/qyMR9r30aozq9YEb2hR6WzFgpredwOBOIPLsNEUGcLFJTABMR1ajrWzyyh4QaCJtqGuFq68E21S/x5U4HSvLtWF7sAJD8L28jyc3xxBTjeZxYT0+e3YbNa8qSTp5lj8F6d+5fcRnSpdBihzeAu7fW6V4zG2oEBOxyueU2B6EIT6ROBrYfWWhAiPn8qb5T9f5mpJDY8I+W90/vGUyUmA/EhpPGcqhs60fNoKaQxQRUls+FPxTrFfr9nYsu7MAYuDHE4XBGBZM0YYQGgJx0UdG3PzQQk6uzrrwIuRmpWCGVnlN2u9yob/Fgt9SItKrmOKr2NqKq5rih8+tV9wxHOENtLA06XCf9cFZPmlTfJhQBtte16l4zG2qsLJ+LyvIihQgg1xwyDu1HRgD8+pbLY9az+k7zJQ8HrSQDtJ8BvfAoK5pptCT/Oy/uH+IVjhwPMX3UNq8R28H0aLSTuZCNWdXwMBmHwxkVri2ail0NbggAOqXcAVpir67S6ugNYs7UzJiJRBGqEuivcWMdHvW6zRsJvw0lvGEUdaiNvdZ7b7gMu13tjBq19jWz+9HJeHmxAzdv/hAA0DaGvQljDdqPLNOagmXO6THr2Xt92wsfAABOnPPFPaZeeFQtmqkXkmVDZSc6+2LWjwVaPX65KWsKxMTpe16pR49fqS+Uq9OW40LBjSEOhzMqbPiGE+8ecSMcEV3UEURL7NffMF85AWhMBuqJpHJpETJt5qRCUfHycuIdZ6j5SkbQMmSAqCFG3UZi2KFI8xh0P+rtqlhSiNKCHGSmpsAXHEBYEEMYF7oP1HjElmqGNxCEjUlCZ2E/I69f9HpoeT8Sof7cAf1nbOPNC2VjCBArtrQMtdHkQaZ8Pt2aglaPH3Ut52O2e3EUQ2QAD5NxOJxRIs9uk1tLWC0E2bZoib1WRRigXeauIE4n73iK1cuLHfI6IyEtrf2Gm0TVadQbRJOo44X31KGW5xg9p4fe4FVliahr7kKHL5ovlAgaJrtselbS5xpKBeS6/6lNep+R5oPPo+02vIEBVNUch9vbH7NdaYF+a5MLATeGOBzOqDE5XXSN94cEpEnaOR09gZhmpWwehV6JeqJk1Ko9jZqK1VqK00YauRpRqh5q/pE8dikfivZMqywvkpOoE10/EJub4pwZ1b7hidSJSZQvxNLq8eNIu1jdddY78iovbK+yoAFD7UKy9aNmUN+YWfJ2/e14B/qCynGOltAiCzeGOBzOqPHF2aLekADgvPTL2xscUDQrpYZQfYsHjiyrXKKupqzAjikZqTh5zqepzQMi6FZ0qY2FRPpC6ySDLFGLhaH2o2K9QOoqu36pVNknhWLijUXtbRDDbFF4InV8znnF5yk91ZQwDFW9r0nOhwkPREbMc0hR9yrbsH3sePrYxOl7b5iPQ2e6cbIjNrcpw2a5kMPShOcMcTicUWP9ivn430/OYEAAzCkEJkFAcEDsZs8aJjShdMPKYmyrPa3ZumJTTSM6e4PY5XJjztRMzRwgdSk0mwTN5mXE0xd6/9g51LV48MzbR+HITpP310qqNtrWQ4/lTgf2nejEqrKZMWGE3Kw0eAN96A0MyLkiRvOXKpYUwt3Tj211YhvH1i6eSK1Hq8cfo4cTj7ICO6wpBIEBAbZUM6r2NuL9Y2dRVjAZlUuLRiThPsuaAm9ANMBePtA8airOLHXNXYrEaY8/jPoWDywm0RvJMtz92gYD9wxxOJxRI89uQ5okyZ9iIpicKYbNpkh/2ZYXm9aUicm/Oq0rHllZDOeMbKxwOuDrD8m/xvVyMOJ5bfTydfLsNpQV2gEAR91exf56jWOHooBN5QO21Z6O8TA8e1uJnHh+n0b3eopWqC7PbsOzq0tAuyEMQAxpcGJ5ZtdRhKXJe1p2bD8yNb/efQSBAQE56RY8dcvlKMm3w9XmjdtuZai8/E9XK96PhbBn5X8flF/n5dikViUFMKmsjrL8SaNaUk/hniEOhzOqTE5PRW8wgGAoAnOGODuf9ynVqI14bUoLcvDWv34ZD79xCNX7m1Hb7MGLdyxKWl1ajbpyrLJ8LjLTLFhe7MDuBre8/2C8QIlK9Okxff6wYgytHj92u9zIybCgszcEi1lfTiBe5dvjNy/E/VJbjofeOMSryjT4+ykxAdhCRAM0EWHJcrKnW1BakIPNa8okHSgyYh4Qtdfwu3/4CJ/84oYROZdR3D3RfKl/+1Yp8uw29PZHFecpyXjdRhLuGeJwOKMKzRsKCcBABLCaCQIDSjXqGOJ9f0p6Q67WnsS/xA18D6tzcai3p7Qgx5CwYjJVXurt6TErlxZp5jTRHmW9gbDuOeLlEq2+qoArUifgfJ+Yy5ZqMRmqeMqfkgEgKriYZ7fhsZsW4rGbFgyLB0TveWKTkLsHUdI/nGz9qFn+r5WRaoIjOw1P7TwiG5aUNDPB4zcvuPAD1IAbQxwOZ1RZv2I+LNI3ka8/jKlZtMJsIMZYMVJVVrlU6iK/uCBu6bvR5OZ4oS4j1WLJVHnpba8eA90vhxGqYxO72fFV72uS86VaPX48vP0w7t5ah4e3H0arx88VqePQ6vEjKHkuTBotNrRoPt8r/R0ZEUS950mdJ/TTV0bvs2QTp3+7ulQec2u3srruvfVLR72knsLDZBwOZ1TJs9swJSsV7d1BRISo9dPpDcaEd2gyNVtVpg79iL/ExV+bT+08ohsiGmpyMx1PIvHFeOfRUiA2Mi663/a6FgBi49DZU9JQ1+JBVU2jfP3q8dH2HJRMmxn//u0yWZGaJ1Ir2fimS072vXbuVEP7hAciir9DgRqzy50O7Ha5xbybOM9HRqpJViV/vb4Vv7m9dMhjSBZ14vQy53TkZlmxy9UWU0k2FnKFKNwY4nA4o06WNRXtCKI3GIGJiC7+YERMQl1e7FBMCiCIydfRQ2viYPN0jFRfxcvrScZwqWvuwtottXhkZbHi17D6+FoGkh69UjjEHxxAWaEdrrYesO409fgqlhTCFwijNxBGhtUse4wsRApTgitSs+xrFMM6qSnAhpXFcbelnyORbr85ZeiBF2rM7jvRifoWj2zU6j0f//39xbJhCwAv1ByPKb0faf6FEX7MyxH/v+x2uWMNoUmJk9EvJDxMxuFwRp1f33q5HCrzBgbk1119IexucMuig9tqT6NicaH4K3lx4r5gbP8xGj5ShxkShbqMVp0l4tEdDahv8WDjjgbDx09EbpY4ofQGBrAgbxIqy4tQyUx+Wg1jH7tpAZ5dXaLIYXmcCbFwReooVqn1hj09NeFnHPXCiQ8v26TVCPEU0h9ZWRxXz4qiDjk9setoUmMYDlo9UXXpDf9YjKd2HkFZgT1mu8tnTYpZNppwY4jD4Yw6pQU5SLNEv45SzeLrLGuKOAEwTViTNR7U3ezjCSzGm5AGG06jx1xXXiRrJbGwx09WsZotr39y55FB5zatvqoAVM+aJ1KLJNuCg36OAsQwVbI5Q/FyxdTJ+vFgFakBsV/ZhWLrR81gg4O/ffcYqvY24p5X62O2nZrFPUMcDocTQ0aaGLU3m4AM6Rd5f3AA7p5+uZqqsrwoeeNE1c1e7S1i+4zR5Ozh1Aqik1xtiwfb110b8+udPX6yhl5pQQ5ypYRzI+X18Y47w84TqVmSacEBiJ/j8mIHzkkl5Ra1oE4ChmJ0s8auOiy2dstBnb2Gn59vU3oVXW1eOLKssiAkZVXpTN3mwqMFN4Y4HM6YIMsqTurhCOQvz5AA3P/6p5qNW40aJ6whBYgTx/df+ljyFjUq+pNRpevlxQ48vP1wTI+0wRBvklN7bAYzIfqD0byhZMagPve/f7tMXtfS5Z/w3qFkWnBQHtp+CIEBwGYx4albExtQLEaeaz0Pn9rYZcvsQ5EL4x16oeY42IJ+WtF5z7J5mtuPpeRpgBtDHA5njPDrWy+Xv5BCTCWOpy8YMwEkE07S6svV0NYjrVUmG1Ol690ut6QY3KwbPjOKnpo1HQs7iQ3GC2VLNUt/U3S30Tqu+tylBTnIZI7BvUOipy0Qjhj63Fs9flnf56o5k0ekZFzPw6c2dtVl9qwa9EjxJJOftHjOZDx20wI8dtNCzRy0o+3eER9PsnBjiMPhjAnYkM8kmwV0Xu6QSuzZCWAoScdiWwDxV6tesvFypwPOGdlYVTpTDqkZOV+yydhDzUcC9D1Dicaide7nVpdE95/AZfbvuNrhl0rUwxEYes6q9zXhtHTPTpzzGTKekzWy2ZAuu5+WsXtjsUN+HRZGtt1KXXOXQhLsWek5esfVHtOHzJFlHTNCiyzcGOJwOGOGgDShn+8NoUT6ZR0B5BJ7Cu1zRBu2xkM94dCKqsduWqjrgdntcsPV1gPHpDQxx8ig0ZLIaNI8jkZurtFJUlwvejD6QwOK0BYdi1qckh4bQMwEusw5nfcrg5QvJL2mBnEiljsdoGlb53tjDXgtkjXq2ZBuov2q7likeP/AtpGrEmTL6fNzbPIzdVe10iNVWV6kmTc3FuDGEIfDGTMsKRJbcwgAznkDyLaJ7iFaYs9y6ExP3OaXdNKv2tMYd+IwUkFmNHyVqDJMK2SnNTY65qqaxrjnq97XFM2vigD3Mw1bK5YUoiTfLotTsvvEux+8zB7oD4v31GwSvRxGwpa7XW7QNlsmQgwZz+rnzKgRbNQ4v6UkT34tAHh652cJr2MwtDHl9Itm56DV48cLNccVlWUEMCSHMVpwY4jD4YwZNnzDiYxU8WupPzSAKeli2CzVRBSeIapEXZpv150QorovQtyJQ6u03t3Tb6hvmSaC8rg/eOljufWFGt1JjSr3JRgE9ZDZJHeONxCS1+XZbdi8pizm+Ikm0tVXFchqvBcq+XYs0erxwyI1bJvMtDtJRMWSQsyYZAUAFOdlGzKejRrHifbTQ61AvWnviUSXkTRbP2oGDdASANvrWlG9vylG40iAsXDjaMEVqDkczpghz26DSQo19PhDyLKLwnXBiIDqfU2ye51VVjbald6IijSdjPaf6EQdo/hrFLb9RcWSQlk52NXWg0ybWbN1iNbxV5XNwuEzPVhVNitmnVqx+rGbFuKNutMABPhUDTq1jm9E4fraS3Ox93NRffnurfU4vHGFkcu/KHhm11H4pX4S9nTjxlCe3QarpJV11htIsLU2Q2kRo6eUvmBGFg63RROWb3/+Q7zyo2sGNT4t2HL6B1bMh6c/jP3HO2K2W+F0DCk3bqThniEOhzOmmJIpqSoHIzh21id/SdW1RPNhjPwyZvWE9EJl6kam1GuygVH8TSbJdbnTgRKpNJ96ZioWF0gJ28Yngt0uN+paPDGhQUDbe6CuKGv1+PHwG4d0PVJ0G73reoLR1fEFByZUmT3trG4hwFMG9IVYhtqXLNlqQvYz1PMq/f7OLyre7z/VNWS5CMrWj5rlcnoC4K7yuahYXIi6092K7bKsKdiw0jlmQ2QAN4Y4HM4Y47nVJfIXE1VXBsQJxohhojVB6IXK9ErbWcVfI6ELes5ttadRzxgx1HPDtr4wQrxQltY6dUVZ9T5JFiBOTlWiNiPTs63y+4lUZt/RK3p1zGaSdKIvFVqkfcmGIsmQiFaPXyESqvfM5NltWOF0KJbd8GzNsIyB7U7/gKR8vew3e2K28wYGxnSIDOBhMg6HM8YoLciB1ULgDwmwmIH0VAu6+kI409WPjTtc2OVyK8JX6vCAOlQVL5ymDku0evyo2kONp7mytyhR6IKes2Lx0EvlAWUoy0gjV1uqGd5AUPYMiQ1ZQwCI7lgSXVfVd66Um362dPnR6vGP6V/2w8E7rnb4pbSrZJ07dc1d6OoTjdLSfNGIYp/FZMKtRqja04j6Fg+cM7Ll51vvHBtWOrH36Fn4pQxvbzCC7/7XAfzxn64e9PnfcbXL3elNADz+MG5//kP0hpR5bikAcjIsKMu3D/pcFwLuGeJwOGMOMxEThyIDYisEExE9RPsaO2OMDXUJOfsLOV7YQR0io8dixRZlBMDd068r/rhczocQFMcbDs8A7a32zNtHdI9FRSo7fEHUNXcZ8kjFE4MERKOUltkDwM9GsDR7rHAvU4332DeT08K57/VP4PGHkJNuwfobRC9JMurjRqH79QZFq62s0G4oUfsv65cqltV83oEXpH59g+HurfXy6wxriphrdyo2nBoB0NEbwuYElZGjDTeGOBzOmOPauVMBiO041r/2CbLSRI+H1WKKMW7UJeRaBpDWxKMuX2/1+OELhCRdmYKYRq4bdzToij9uqz2NQ2e6Y4yoeKGoRJOhPOkFRG/D+8c6dI/161uuAAEQEYB7mEnKKHrjZMvs//b5uaSPO95IlYSCpmWlYvVVBQm2VkJDlZnWFNnAVBvbLFr33IiBRPfLsJqlNjNzdbdlybPbsO76SxTLBtvVvq65Cz5G5PPZ20p0txUgCi2qGxSPNXiYjMPhjDk2fMOJvxxxIxQBuv1hucKswyt6PthcDpqoXL2/KeYXOJ2QfIEQqvc3K8MVqvJ1mmdTWV6kCDfQcNLyYgd2N7hjytRBAJ8/rCj1p+dd7nTohqIShVDo+lWlMzEty4qz3oCulMAy53RkWk3wBiLo8MWvZNKqOtILma2+qgAPbT+EkCCqGL9QczymEejFQqvHj65esUt9vD5vRkn0+WrdcyNhNSOVlHrce+MXUHP0HFxMO4zLN+zEpxtvTOo4bHuP/BwbfrpVO6fMZiG4JDcTj9+8cEwKLbJwY4jD4Yw58uw2pJoJQkEBBMD9N8zHU28fFT0fr9ZjhXOGYjLXy5eIl8tTWT4XmWkWeZmeQcBWpanLlul5Wz1+ZNrM8gT11M4jSU1q8db7+kOyIbRpTZnuBEjzhgYiQtz8Hq0JN16+yeM3L8T9UojsiV1HL1pj6JldR+XWEblZaUnvr06eTvT5at1zvX3UBuxQ8o/e+sl1uOqx3TjbK4bZeoKRpAyiVo8f7T1Rg7vL1w+fKk+IEh4Q4GrzYneDmxtDHA6HMximZtnQ29kHALhkaiby7Fac7gqgwxswnJQa71e0elKJN8kk+sWu3tdI0nWiSU1haElGWzxPAA3T9IUiqN7fNGgjTM3qqwpkYwgQy6mTDSGNB9iS+mdvuyLp/fOnZOBkpx8Fk0VtrMEYLXqJ84NNxFYbUfT9G//yZVzz5Hvydj3BCIofegsNj3894THvfkXpBdIzhABg+iQb/vGKvDGtL0ThOUMcDmdMQkvsBYh5Q/SXNwQocnrioc4fGmzSqtH2B/H6fg0JA2rYVGso3WIyZIQlM7arZ0d/1V+sLTrO94khMoEAjmylZ8jIc9N8vlf625fwXEaOx+a0DbahrzoviX2/fa1SeLEvDMx+4C389BXtkBflgEaSNOXqOZPlBssEgDMve1DhvNGAG0McDmdMQkvsAcDrD4M2JPUGB9AbGEjKwJH7lEmVWcl2nzdqQNDJZh3THHWoFWVGWzQEQmKeSziibTkNZRzP3V4KWlgWilx8DVzrmrvQFxTvm1aXeiOJ8DTRvT80EHOv1e8NfaZSTltvIBw3EVtvPOrKSkBp1JcW5GD72mvkz5Xyen0rZj/wlmYblg3b9Q3hFc7peG51CaZJ+lQCgF0u95jXF6LwMBmHwxmziCX2AiIATnb2wWISJ+O/nzovb2MkhEC3ETVZCrC82IGndh7RbM9h9JhaVCwplFt50FDVULVmEoW1aOijpCAHe491IDgg4MFtn+Kl7109LKEWQDQGr2NadDz0xqGLKlR232ufys63/BwbfP0hRd5VvM+A3lfqWegLDsTca/V7UQcqrDiPOqRFc9p8/nBSn5v6XPFCwaUFOTj55NdR/NBb6FN2csEPVB3n43FzaR7uWDIbT799BO5uMZ8oy5qCm0pnjYsQGcCNIQ6HM4a5du5U7JLUnGdMSkNHj9gd290dFQHUEk5U50n4+sNw5mXD1dqD6+ZPxW6XO+lKHz3o+coK7NhU04h15UWobfEofo37AiH4/OFBCReqJzA9kcmKxQWyYve+xk4Asb3Sku17xZ7rJ8vmycYQbeC6zDk9qWsZq3gDYogsI9WE8vlTUb2/GZlpFkMJ5hVLCvGXI24cc/sAAEsumRJzr9Xv8+w2ZFrNqNrbKJ9HbcRoJecbYTDP7rvrv4I1v9+Hk+eT8xpmpZrgDUawva4Vpzr6xH5+EjPsNlSWF42LEBnAjSEOhzOG2fANJ979zI2wAJzz9kMS0EUwAjy47VMUz5gklq8LgOtMN9ZuqcUlUzKwrf6M4ld59QExzHDdvKnRSSLJSh+9Rph0EqPl75trGrF93bWKY2VaLeLEp9GsNVm0vAz0Wv58qA2dvSFkpYlf7eoE8mTPzZ5Lnbd0sTRwbfX40ekVjSETITFVhonIs9sAQbw5OekWuQdXouT8RAZTvH0TjSee8czCfr577vsKAKDkkV3wBOJLC5gA/O0Bcftn3j6Ko24v1pYX4YfVB+XH5JjbFzeRf6zBjSEOhzNmybPb5C/XSERMnH7lo2aEIqL3Y++xDjksRY2R0EAkJk9Cq6LMyJe0kTATPX5Zvh2baxo1xeW0Jrp4k1S8dVpeBjqeYHhA+huJWZcsVISSbTLr7unHtrozAKINXMd6yXQiNr7pUpTUD+aeTbfbcOxsLy6fNcmwJ0TLYNKTcBgK8cKjWs9l/aOxBu7Wj5oVFYUnnoxWnWVYU+Bq7cGvdn6msJdtZhOWFyt7oo1luDHE4XDGNDZLCnzBAUQAXHfpVNQccaPFE4DFRLD6i/lYVTYL22rP4Jy3Hy1dfjx+04IYUUatyS2ewUFRh5m0wl3s8fXCRlq/1tduqUV9i0dzkoo3gcWbrNU9yrSuWd17TQ9WhJJu9+zqEnzY2CHrzKzbUosPH/wH3WOMBz44HlXWHkxJPWCskizZ5224PCqJwshGzvNzxhBaMmeycqUgpmCf7FBeuz8cGRf6QhReTcbhcMY0z60ukV/f9/qn+OLsXABAbyiC3sAAHNlpqG06j10uN8oK7Ia+fNUdv/VQ9znLtFridoI3SvW+JrHJZl62bFzpnTcZegMh6W9Yc31VzXG59xpb8aaF3hiqvnOl/Lq1u1+z6mg8QfuxZqSaDD876kqxc5Jx2C9V9GlV7hmpIBvs5x4PdSWkug1NvGsDRNVx9mm678bLFNtULi1CxeICZKRG69JyM1KltjbjI3ka4MYQh8MZ4yxzTse0rFQAgCWFYP2K+ciyip6Pv586j+p9TXC1ie0FegMDhvp90Y7feu0tKOqJRG+ySrZsnR6nLD9H07gajBYQAJhTxPviD0ZQ16zUg2n1+FHb5AEgTlZ1LR65ua0WemMoLcjBdKl8GhAVwccrdc1dckgx22Yx9BmqjZqqPY3oDYrH8PWHNbcBjBk6g/3ck0Iu2Q8ZMtjY/mUPrpgvFx9U1TTKmloL8iahV5ImsJiAjt4gHJPSxk3yNMCNIQ6HMw4ISL2iznmDcPf0y19c7m6/1DFeFGGk3bP1fn1HwxACKsuLNNtbtHr8ePiNQ3h4++GYiVFvsjKqBaQ+TuXSIlSWF8ml/kPpbg8Az9wqNWyFsmEr9YS52npQmm/Hi3cuUjS3TRbWO+QNDMQYXuOFu7fWy/lCWWmWhJ+hVh5VtMcdkMkkrlcsLlB4/S6IoWOAyvK5qCwvQoZUzcZe73KnA6X5djnXh9WTIgBWlszEcqcDJfl2+AIhef8Nbx6Wt0u3mpX3Z5zAc4Y4HM6YZ0lRLnY1uCEAuOfVT+T3wYhoiDwrhdLY1hUsWo1T4+VtVO8XJwG2+stIUnMi/SI1dII00svMCHoNW2lYjvY3A4CFeZNwSW7GoEr+SwtykJmaIncuH6+5Q+e8oqFiJsBTt1we04hXjVYeVWX5XLxRdxreQARmqaPwcFcQasE+j3Rsy50O7Ha54z5/Wm1eKLtdbtS1eORcHzZXSIAkRikA9S0e9AXCmOfIRMOZboTCUYOw2x9GZpp51I2+ZOHGEIfDGfNs+IYT7x5xIxwBzvb4seX7V+PDkx3o8Q+griXqlVD3dqqqOS4meBIhtmu9DjRRGiCKiaJqTyOqDzTBFwjjsZsWKPYZilGj6W0YAlpJ1KyxVr2vCb7+MKoPNKE03466Fs+gJuznVpfIwnyt3f3jrrKMVZ22pabAkZ0GX38IVXsaUblUWx9Hq/oqz25DbqYV3oBfbtKqt+1wopY9qNrbKFdWGnn+tKrX2DFv/ahZkSvEtsCRzwPI+koAMCnNjG+PQ68QwI0hDoczDsiz22A1E4SDAgJSXCM7zYwe/wDCAxHFtvQXszjhix4e0dAoMOQFybPb8NhNC2NXyOEQ/UZhg5kAtbwNWtejbrap5wVgk6jZfVljjSpxryqbJXtDtDwN8TwMy5zTkW4xoU/6PMabd+jurfXyJ1lWOFnXI8iiVX1V19yFLkm+uTQ/cRXjcMGqWK8qmyUbu7sb3IY9lJrVa9JNYb1CeZPSFP8nNq0pQ1VNI3oDYfxv/RkMSPtk28xYXuwYd14hgBtDHA5nnEC72IcF4MFth9AbECfh0139+FH1x7jr+iLsdrnhC4RQvb9Z+iVbAICgsrxIrqQCEbSNHR2okbCqbFZCMT69ruPxJodEBpRea4d9Jzo1S/MFQXQVCIK2QCP9VX/d/KkoLciRvTmsV4t6Gthja13P724vVXiHxpMqNQ2RAUDBZJuuR1AL9l7c9/on8PhDyEm3YP0N80d41FG0VKwBMYRJP0tfIIRMq0X3GVQ/e/R5qW/uUniFHv2GM+bc1Du68/AZ+EX7Gy1d/di4o0EhOjpe4MYQh8MZFzy3ugSrNn8IAcCHx8/Jia+0IWRtswdnvQFULFaWw8sItPRX3ZoyPoPVfjG6XyIPgp5SMfUCqCduGqnxhyIoK7ArKpjy7DZsWlOG6v1NMfuxx91WezombKfQXGLCK9OzrbLu0HhRpW71+BGQ8lzMJsiaS0aNZPZe+IOi2ZBpTbngHhE9Q5ouZ/uaaQk6qp89up+67L62xYNlzukx3sOn3z4iG0IpBLhsRram6Oh4gBtDHA5nXFBakIP0VILeoABzCsH3r7sErx1sxjlvCGYTcNYbQGm+XTfcVLm0yFCPJ7UHRCs5GkgcRoondmdkf62xyAiAIztN04i6du5U7HKJyeZPv30Uu++5XrFez/hi8560wnbs9bDGQNV3rsTNmz8EIKpSjwfv0DO7jkKqqEf+5IykjRhlDtbJhNsb9RImi55qtVZfM/qZ+QJhZFrNmmPJs9tgT1OaBawxzebNZVrN2F7XKm+3rHg6nq+4EuMVXlrP4XDGDZPTRb2hUEhAe7cfGakWAOIX2arSmZql8hSjpc3qMnm6H9VXWbulFlU1xxOWYavPxx7XaCm+1naJ9t2w0omMVPGr3SvlDyWDnh4Oez1sCbZad+hupqR/rPL3U2LDWQsZnOo0+0z4ApGE29PPrKrmeIxgoxFdrHiSC/GeB/Yzo58rIMRsz56H1RWaNy0D7x89B7fUIJnmzf3fT1pxssMHm0X0smakmsatR4jCjSEOhzNuoOrTYQDb61pROCUDJiI2bj3c2j0sv7r1jIGKJYWyNg/NQ0omUZo9rlrPhaKe/LTGkki8T7wH4qTV409sDKnPacRoZEuwAaXukC84gBdqjic872hyvk9szJpqMaY6rUfFkkJkWRNPo/Tz7u0fUBgiiQxbvfXsZ2ZUtVrWtpJ0hrRCoP+85aBin2Nne+Fq68HGHQ0AgFVls5BmNqHLH8Iulxv+kPichSP6RQXjBW4McTicccP6FfNhZr61XGe6Qb+Hvf0hw0rQWi0V6Hs9YyDPbsPmNWWoLBeFEpMV0GOPqzYmWGVsLa8Uex42NNLq8etcs/G8qGQFI4FYg6y0IAdXz44aFU8y3oWxBltSbyLJ5Y+pybPbMCndknA7+nlnpKXENBGOZ8iw69nPmf3MkhVz1Nq+YkkhVpXORG1Lt7ysLH8SVjgdcOZF84B2u9zol+KL7J0LhAU88/bY/cyNwHOGOBzOuCHPboPNbIJXan/Q0RuECaLisrs7gKffPiLmMcRJWtZqkjpcyc5a59LKFdGr4qlYXGDoV76WxgybC5JhNaM3GESGNfFXfLxqtnh5TurGs/OmZ+HAKVHzSYDY0+qu8rkJz3+hYUvqr507VV4+Unk9gPIeqw1bo88bW+033BpGeXYbPmzskN+nmQmuviRXlm9wZKfJ1+ELhAEIeOtQK873RmvOjrq9wzKW0YIbQxwOZ1xx7dyp2CV5VADgy/Om4q+fn0NEUsZNZEywaszqCi2jk4vRiVPPyFKX4Pv6w5IhFL+TPCVmvASiYKCqwskvKUTHI96ErGV0aRmMVKNnyZzJ2HfyPACxp9XKkpljTnOGVZ1m81wGUzVY19wlN2m1WfSn0+HQHFIbVFqJ0yxGn9FWjx8bd7jkikAAKJ8/TSHf8P6xcygrtIvyElYzZk9JlzWZLCZgniMbj6uESMcb3BjicDjjClaN2mwCfvLVS3GwqRO+QAQdUml9vC//5U4H9p3oxIaVxbolxokwOnEaMbKq9zWh+kATcjNS0ds/gPUr5ivEFfWqftjzqtsr7DrcBm+gD72BoVV3aRldaoFGtXLxNU++J+//0631+NNdSwZ17pGgrrlLbihqtZjieuuMcN/rnyAwAFjNBE/devmwj5dF/ZknegaNPqPV+5qwyxX9cWExiUn4c6ZmYnmxAxt3NKCuxQNXWw9qm8S/qSnRINnjNy3E6qsKhukqRw+eM8ThcMYVVI0aAAYiQPW+U3JFjzcwgO/+n4/i5gztdrlRz+TrGKXV48fD2w/j4TcOYbnToeuBMpJ/xFKxpBDTsqzo6A1iW/2ZuMm18XKi2HM9e1uJ3LB1/aufJHWdesdkX+tV3OXZbXhwRVR4cN/J82OqiStb6TYtW9vATMaTFZbyZ/LstgveiiSZfKN42G1Kn8jjNy2U70VpQQ42rSlD8YxsAMD86ZmoWFyIMJWcBnDqfN8Qr2RsMCRjiBDCPUscDueCMzVLnLAEAH8/1YVVpTNhkb7Njrl9MSXMLLTrtrqSKxHUg1O9vxm7G9wxE6ecBG2g7J4lz27DCxVXwpmXjVWlM3WTa2muk5Fji5pM4g0JC4lLv5NFL7EXAO4qn4t0S3Rq+VH1x8N+/sFCQ2QEgyupV5M/JQMAUDA5fcjHSpZExlu89fQzq2vuUpTSL5kzOcbLk2e34Zc3L0BJvh0VS2aj+Xwv6BOVmjJyvdcuNAmNIULIDkJIzNUSQr4KoH4kBsXhcDjxeE7qUg8A7h4/TnT0wp4hVvVkWVMAgciaQHXNXYrJWs8zlKgSrWJJodzjTGsCkMMSTNm90eq20oIcvPXjL2P9DfPlKjEtnSJ1rlM8aPK0kSTqZGFDNloG2u9uL5Vfu71BbP2oedjHkCys6nR66tBK6iknznmlv74EW44eWs8gfVbVhupvVpcoPKB0n221p1Hf4sG22tPY19gpb/+Vy5L7QTGWMfK/5BUAewgh/wXg1wCmAvgtgEIAd47c0DgcDkeb0oIcmE1AOAIEB8TEaZrH0BsYwKycNEzLsqK+xYNHdzQoKsfY3BCtzvZ6/ZzYfkxaaFUMaXWxj5cLpJXnwTZm1apI0jsm27B1pNAz0JY5p2NqZirO+UQ9n4feODTqeSUb33TJqtPqENlg8fSFFH/HIlptVJY7HXIPP0pp/iR5++oDomF7+EwPNq0pk1vZ9AbCMJkEYABIt5iwy+XGnKmZI9qQ9kKR0DMkCMIWAKUACgB8BmAfgHcBLBYE4WC8fY1ACPkpIUQghORK7wkh5N8IIccJIZ8SQsqYbe8khHwu/buTWX4lIeSQtM+/ETJE8QgOhzPmmS6V+1KskgBRBMCz7x7DWW8Ajiwr1km6QLSdBgBF7kv1/mbpy1/06FCvUjyhOy309Fv0BO60Ql3xttcKzam3Wbullhlf8l+DRj1Z6vFuWiN+TbP7/v6ORfJ2oQhGXYjxg+Pn5NfDESIDgEypdUVmWny/gpa35UJBP6PlxQ7Zi7e7wY1Dp6OaQgRAXUu32LNO8oA6Z2SjrsWD6v1NqFwq/h/qCwygX7L7QgMRrCrNmzhhMoliAFcB+AhAAIADw1CJRgjJB7AcAOtDvRHApdK/HwKokradDOARAFdLY3mEEEL9nFUAfsDsN/Y7BXI4nCHx798uU7z3BgZkQcZJNgtK8+1wewOobfEo2mmwRoj4xV8gN3e9f8Vl8he/+kt+MOKEWgaSnvq03vZGEmFZdWw6vmmSsegPRvCOq93QeI0oHuuNV71vaUEOSmZNkrd9YtfRUUumbvX44ZfcQhlSiIy9pmSNQIrZRBR/9WDzzZJ5foYDtnUI9eLZ08yoZ4yhB1bMVzQ3riwvQlmhXW7WS4/havXI+4QiwFG3b8xJJwyWhAaNFB4rBbBWEIR9hJAMAI8C+IQQ8hNBEHYP4fzPAbgPwP8yy74J4GVBEAQA+wkhdkLIDADlAN4RBOG8NK53AKwghNQAyBYEYb+0/GUANwHYOYRxcTicMU5pQQ6sZoBGgWwWEzKtZpzzBdHhDeKny+ZjcdGUuFpCWp3K9crsh0vojlWfNpK3YqTsP89uwyMri7FxR4NsZD17WwlWbf5Qrij75NHE5fVaTWlZQ0erTFsrjEdZOGuSYtL9UfXHOPDzZQnHMdw8+PqnMSEyoxpKRvD0heQ8Ly1YscJknp/hFIJkx/AUkzSdbjHhqkumYLfLDXdPP6r3NaG9x4/tda1wSlVkgChLcNoTUBxzviNzSGMaSxjx7hwG8ENBEAYAQBCEXgDrCSEvAdgMYFDGECHkmwDOCILwiSqqNRNAC/P+tLQs3vLTGss5HM5FjjXFjEBYEhgMRZA/ORWdvUFEBODJXUdQt2G5vO1ghe/YCcno/vEmMbXBQVV+6V+6T7ITodrIohVlvcGIoYoy9nxq4yeeIRgvz2lV2Sy0nPej5pgYonJ7g6PS1f7D41F15ZL8SXJPLy0NJaOwgovewACq9zfpPh9a+Wbx1L0pgxGC1CPPbkOm1Swl+UfpC0Xw0PbDcLX1YP+JTtS1eORSeldbj3xd97/2qazcDQBzctOx/obxnytESWgMCYLwnM7yQwC+HG9fQsi7ALSe+p8D+BnEENkFhRDyQ4jhNxQUjH+hKA5nIvOb267AD6rF1EUC4KlbLkfllo/R3h1EmsU0LL+sq/Y0ovpAE3yBcNwEapZ4kxg1ymhyNVX5pRNRsi1CKFRMkg2/JdOWgz2f+ljxDEktQ4k91h+/dxW++Pg7cjL1v75Sh4bHbkw4nuGi1eOXS8FTiNjgd/ok8XrUwpXJQAUXU1OAf7x8pq4hpfUMqlvC6HmmEnkjk32+lzsdMcYQIOoHXTd/KpYXO7C7wY3lxQ5sqz0NgMiFBmc8vYp9LCmmiyZEBhgLk+0AFAahAkEQvhFn3Vd1jrkQwByIoTYAmAWglhByFYAzAPKZzWdJy85ADJWxy2uk5bM0ttcb0+8B/B4AFi1aNP5b7XI4E5hlzumwWQj8IQFpFgJHdhoCUifttu4Abvzd++j2hw0bFJqTC6FfE8a/LoyE1MSwRQi9/QNYODMbq8pmYXeDe9AtQljJABp+8/YHpb/Kaiet62TPR6vEjITytAwldcXely7NFXvGQfREbNh+CBtvXqh1uGFn45suUI3APLsN/3hFbNLvYIxm2vLEkZ2GZxmpBzVaRi29v44sK5YXO8TeXxqfdSJvZrIG88+3HVK8r1hcAFo4QK/bkZ2Gqj3iMenye7bWycrdlAV5k3AxYSRM9sxwn1TyKk2j7wkhpwAsEgShgxDyJoB/JoS8AjFZulsQhDZCyNsAfsUkTS8H8KAgCOcJIT2EkMUADgC4A8C/D/eYORzO2MRiSoEfYfhDAn627RC6mDLnbn8YjiyrYpJRT3xa4aH9JzqxaU2ZlEw6V25zwZJMuwwtxLCFBdX7m1FZXiSHtpI5BouW8STN1wiGlROZ1iTKnk9tzCTbA0vdYHR7XSvK8ifJXdFfPtCML8+bekHCZWwV2b99q1TTuBtKOMpIvpD6c6lYUih7AmmVYKLzat3n5U4H9qu8gXq8UHMcDe3RZqoPrpiv2Ui3quY4qg+INU2ZaRbcv+Iy1DV7FNs487Kx/ob5MfuOZ4yEyfZeiIEw/BnA1wAcB9AH4P+VxnGeEPIYgL9L222kydQA1gL4IwAbxMRpnjzN4UwQ2FAZnfgyUgl6gwIIgHuWzVNMImrdFTZcwU5SNFdC3VRVL69mMOh5fwYd3lM5r9JTU+ANDCAiiDku1BBI5HXS65Y+mB5Y7LmWPv0eAlLv2AsVLgsL4k2xWYiul2soyfFG8oXYdfSz3bCyWOEJTISWXpAvEDKUjN/q8SuUpnMzUrGyRCe1VtIUmpObjr80uOHu9qPHH1RsUlaQc1GFyABjYbJP460XBGHI3ekEQZjNvBYArNPZ7g8A/qCx/GMA47tlLofDGRRsqMxkAiqvK8LJsz7sanBDAPD8+ydwsqNXMxlYLRqYZ7dh05oyUW9Fw0BRG05DNWToRElLuxNVb8VLutXa59nbSvDD6oMQANyztR577l0aPbnBqJ9ehZl6fSLDqtXjx9LLHHJT0L5QBC/UHNf0TgwX77ja4ZfCpqnmFN3thtJVPsuakpQRNVgjmoZVff6wnMdGJSESnX/tfyslATt6g7oGXOXSImTazPhLQzuOnfXh2FmluvY8RyZWlV18NUpGwmQRiP9t/gfADgAXVjGKw+FwEkBDZYEwMHtyOioWF+K9o24EB4CzPX6Frg878dEww9ryIoVhoecBqW/xwDkjGz4pB8eIp8SIcWS0eiteObjWPsuc05EuecnOevtjjrPvRCc2S+FALfQ8YdSQjHe/tK5xl8uNaVmpOOsVPQ1P7DqKS6Zmjli4jG3M+sytwyO0qMaebonpURfv845nPCYKvWZaLaja24hVpXkoybdjVdnMhDldL9QcV8gbrLv+EkQI0TVu8+w2VCwuxF90Ghkfc/sMy0KMJ4yEyUoIIZcB+BZEg6hB+rtbEISR03nncDgcg7Chsge3HcLra6+B1WxCcCCCYDiiG0qg5eibaxoVlVxqWj1++PrDUsIpUL2/Wc6nUKOe7Ix4AtT7GNY6Uu1TsbgQVTXH0ds/gIy0FFSWz0VE8gBFBEFxHFrFFi/EozCAVB41I94NdnJnPUyrNn8oO6ZGKlzW6vGjNyjG5MwEugbXYEKSrR4/uvu0p79E9yae8ZhoX1rlByAmwV2vao0Nj5kJsGbJbOTZbYpqRrVBXL2vCcfO+jDPkYljbtEzZAIwyWbG1ZfkXjSq0yyGVKQFQTgCUf35EULIagAvA3gKwNMjODYOh8MxxDLndFmAMQLgnq118AbEgupQBJiTm6H5BU4n6LJ8OzbXNOomolIF4WhIgsDXr504qw59aYkRqjHqXVFvp96HthehZKZZYCKiG8nE6Lnl2W3YrBMOBPT7oSWbYK2e3Ol9uX7eVFl7qC8UwdM7P8O9N34h4fUnw8Y3XbLBNSOOkWNEUFJ9bU+/fQReKfnJZlFOo0Mph9fal92eVgwunJmt27bFFwgj02pGxZJCfP+Pf1ccPywAVTWNyLSaZcNKyyCm42jv9svGUARAlz+MOVMzLrp8IcCgMUQImQngdgA3A+gCcDeA7SM4Lg6Hw0kKVoDR3ROAc0YWjrZ7ERaA8z5RHE8dFmA1f+IloqqbsFLxusw0C5YXO/DojgY8srJYse9gvCfDozIcQm9gABlSLsvrB1s0tYaG4qEwmmCtp0FUc+ycIly2ae8JFEzJGNZmruoqMj3iGS9VNcfl5r2sUnldi9hWJDWF4KlblWmzyZTDq8ONWjlket45PUPK1x9C1d5G1Dd3KarH7ri6ABk2i7weBAqDWP0cViwuxOoX9inOkZuZelF6hQBjCdR7AWQBeBViZVentCqVEDKZqejicDicUYMNlfWFInC1eWE1A+Ew0NsfxtNviyXeiSZttpN95dIixSSltT1Nqt64owHb112ruU08hltlWN1eJDQgesjOMurPrR6/pCUjoLJ8blwPxVByYOJpEC0vduDWqg9lDaAHth3Cl+dNHRavQ6vHj76QeN3xqsj0xigjUG8aUSwOS/fUkW1NOneGvX42IZ8dQzwDKFHVWqvHj3PeIHap+tEJTA4bNeLZz5Uate8fO4eyQjsAoKVLmSK8qPDiqyKjGGnUWgggB8BdAN4G8LH076D0l8PhcEadZc7pMCvnLAjSZBYG8N6Rs6hYXCgnjtY1d8nNOdmGo1V7GuVO9tX7mzSbeLLbP7KyGKX5dmxYWRw7KI2KLXo8ev7lToehiiC9/RM1F/31LdHE4fWvfgIgtnGo+hrjNWDVJAn5Wnrs0oIcPP+dKxWH+OctB/V3TAJWaDFeFVkiaNPeyvKiYRkXoN04Vf3Zs818tZr3akE/QwDYcyS2MW+vFNZjz89+rsudDjiyrFILjmY0n499rqZmWQd1zeMBIwnUs40ciBDiFATBNeQRcTgcziCZPikNpz1i1dQkmxk/u/EL+PkbhxCOiAKMh890AxBQvb9ZzpdQ99SqbRad3c68bEPJwqUFOQqPEEVvP7aSS+v8RivPtPZXI16PR5YeCEYGZAPM3ePH0Xaf7CFIJtQ1XHpLy5zTse76S7Bp7wkAQG1L97CU27MhsqFUkQ2l5D4R8UJeyTbzBaLPxV+PnZO1nAAgb1IaWrv7kWFVGoUVSwrh7unH3qPnxDYcLjfc3gByM1Jx3bypeJepJrt+Xi4KJqejcgRlEEYbQzlDBqkGUDaMx+NwOJyk+Pdvl8mVSpGIgNVXFeCJnUfg8Yul8HUtHiyYOQmV5UVyHyZ1PourzYvSfLusQK02Bozm+OiFj9gwidb5jVaeae3PwuoipVtMAAQQEPn4jmwbttW1isdIMtSlF8ZJhuh9nI3//NsJeQIfjnJ7VmjxQjeFNUqy/d4SUbGkEPXNXdh3Mpq5kpthwaY1ZYrnhH1+j7Z70dDWg4feOIwX71gkC45mWFMQikQtquK8SSNmFI4VhtMYIok34XA4nJGjtCAHaZIXxBsYQF1zF56+9XJZeHBSmjmmD5MiaVRKQI7misROWkY9IVqTndqQKi3IUSTLGpkE2ePG8xqwgpJH2j0AAAGCIiRHRfyA5AUAjeSxaF0zOz56H1kxRgD48Z/q8Nnjgyu33/pRc0KhxaEmrYclvQL6d7gZjEfK3dOvMITSzARv/suX5eeMwladUYMnP0cMh97+xXy0evw41u6N3sMUctEmTbMYyRkyysg8FRwOh5MEFlN0Avzxn+qkMJH4VReORDQnZJo3IfcLk/KFtKhYolT91cop0kMr/4ZdFi8/RH2eROel49y0pgzsb1V6fCPXqnd+o3ksetfMjq9icSE2rHSifN5UeZ0/HMFPX6lLeGwtHn4j2oxUL0RmKA8qDr7+sOLvhSLeZ/7dP3ykeP+nHy7RTXivLC+CLxDCMXcvnHnZmJplRdXeRjz7zjG4vQHUNnfJ28/MuTgTptUMpzHE4XA4o85vbotOgK0eP6r2NsrVP73BCO7eWidPJmrDhi6rWFwo6wjFSy4GkptY9c5nJIFafZ5E52XHST0k/pCAd5gqI6PnTvY6WfTOwY4vz27DH793Fe64Olpa/3p9K16oOZ7UueqauxAUP+q4IbJkrlvzHFKlWm7mhU0o1vsMNmw/hG7GMFt3/SXY7XJrGk30vmemWgCIfcYqy+eisrwIj9+0AM68bFB9ThOAkx19gzYaxxPDGSYLJt6Ew+FwRhZaVRYWgAEBqFhcgAV5k/DgtkOIANhe14rpk6ITsTocodYRUre9YGn1+OELhFCxuNDQxKp3PiMhEXUILZm8kmdujcoO3Pfap6iTjIRkwjHJnE8dhjJ6jo03L8T/ftIqT+xP7DoKe3qqYf0htv3G9Enputtp6fkAMBQ6u+/1TxCMAFYzwbOrSxKOaTh0pLREMClP7/wMLx+ICm3On5aJCJMbxibnV+1phC8YQmaqBauunIlMmzkm1Pn6wdNwtfYAEL1C5fOnTogwmRGdobhJ0YIg1Ep/Fw/XoDgcDmcosFVlfzveicryuciypqA7MAAToKs0TWEnfndPP/ad6NTchyo+09JrrV5PyaI3eaqNimSMjGXO6ZiSYUFnbwimQcYDqBCfkYndSF6V3nX+8XtX4ebNH8rv7992CPOmZxmqqjrnjXpCnr3tioSGCCuqSPt+JcoFo6GxyRmphsY0HDpSesfY+lGzXIkHANYUgqsumaxpNFE5BcqfD7fhvhvmY+2WWjyysljOn2Mr8Vq6RE2ii1VbiMWIZ+hjAIcBdEjv2URpAcBXhntQHA6HMxTYqrKTHb145u2jmJxpRXegDxEAv333c7z0vat092cNDZqIvK32NHa73IqJ1YiAXrIMpj2EHuz23VJFnacvNGijzejEbsSLpHes0oIcPLVqIe7fFs39+dbv9+G99UvjjreuuQt9QTG+k5FqEo8TRx0bgEJU0ajnizbpVecLqT+beN6cZNEaW11zl+IeAcDXLp+B6v3N2O1y4/mKKxU9ynz9YawqzcPh1h4cc/vQ2RvEoztc6AtF8NAbh2FJMaG+xaOY4HNslgnhFQKM5QzdA6AHYrf6/wNgpSAIS6V/3BDicDhjjtKCHIUA4wfHO3Cys09+v6+xQ2MvbWh+CQQSk69BjaZttafFjvaSNtFQiJfPUlVzHFV7G0WFbAOwOSYp0re9IIX9BpMHEm9sySZY6x2r1ePHqc4+Rf5Qf1jAd17cH3dsd2+tl6t4pmVHjdV4uUGsqCI75niJyhlpog/Blpqi2EYvp4uWtVfvazKUZK+F+n7WNXfh1qoPFds8tWoh7r3hMjiyrHB7A9i4o0FeR71Cjkk2PHXL5ZgzJR1Z1hRkp1swz5GJfLsN9S0epKYoK6GmZlsnhFcIMCa6+FsAvyWEXAKxP9lfCCFNAH4lCEL9yA6Pw+FwBsc1c3Ox93PR6HF7A4p1kQEBdc1dio7feu0p2PwSmmOh3qe3XyxRLisYersCvfBXq8ePg00e6Z0xJRPWo/DyhycRQASEABVXFwzKaBtKPzOjx6LHqSwvwh1XF8j5MCc6+/DTV+rwm9u1+4y5e6LG7rNSEn2iUGKiMWhdi9kk3nt/cEAqURdDbGoPEHvvh7PlCiBWjg0wVsu66y/Bl+dNRfW+JtyzbB6efecY1jKq2eqx0B8G3sAAZmTbMDVbTARPMRFgQAABYE+34N7l84c81vGC4QRqQRBOEEL+F4ANQAWAeQDqR2hcHA6HMySeuOVyXPvke/Iv3XSLCQtnTsKBU10IA7j/9U+x++7rASjzKTLTLJq5MVp6Q3SfVaUzB12dlAgabvH1h9HQ1oPSfDtWlc00FOpixzwtOw0nO/owIADnvIFh/8UfL8yUTHhPrWF0zhvATkkN+fX6VmRYzdh4s7L/2juudkhRQKSmxO9FNtRroWSmmfGdJbPh84cVhg6blE3v/WCFKVnoPfystVtROXbH1QW498YvyCHB0nw73N4A3v+8A7XNnpgkdlFLK4xz3n60dPmxQcoX6g0MYHvdGQCid6irL4TaFs+YFa0cbhKGyQghlxBCfkYIOQDgUQCfAPiCIAivjvjoOBwOZ5Dk2W1It0Q9KI+sdOK520uRkSp+7Xml3A8gWk5fsbhA8Uuehjy0wiYVSwrhnJENAMiwmuOGhZLRIlIT9SoIqFhciAUzs7Ht4BlU7W3E2i21ho/57G0l8usPjhsPEwLGxh8vNGbkfuodp+qORZiUFv3d/vKBZmzYrsyVYavIHvvmgrjjH8q1tHr88PaJXsA0SwruX3GZHGqjho5W+Xsyukz0PA+/cQgPbz8cE4ar+Tz62d1SkicbhjQkuGFlMSoWF6K26bxmOFRs5rsAG1Y6cd2lU+HITkOe3Yba5i5FiCwnfeLkCwHGPEPHAXwK4H8h5g4VAKgkRPySEQTh2REbHYfD4QyBL186Dbskr8LjbzXgO52zkUJMACJwdwfkUBmdICjqX/LqMAf9lf74zQvitsSgRFV/oyEVdTK2HlrhlorFBSjJt6O+xYPq/U2GEq0d2Wkwm4BwBIgIgtapDKlFDybMo3c/fYEwMq3mhPfhj9+7StHhnobOqCHgD4oGSgqAL8+bKvdfo/eYHb+eVEK8UCll4w4XugOiV6Y0X/Q+qT2Gw+EFolWKAJBpEw3tz1q7FduUX5qLn664TOEhpM/moTPdclsZ2phYfY9pJd1fjrixIC8bZ7rE0Jl0i7B0/rQJky8EGDOGNiKaU5U5gmPhcDicYWXDN5zY/ZkbEUHMj6ja24gVzunY7WpHBMA9r36CPevLFftoGQSJjKNE0P1pSIX2gDKyPzvZLnc6sO9EJ1aVzUJleRqq9zfJY6pr7sKjOxrwyMpiuSM5e/zqfU0IS4KEGVbtr3696xrqBK9nMPj6Q4buY2lBDl6rvEZRcv/ygWZkpZlRMCUDtItWujVFvgb2HtPQkK8/hFVlszSvxUiolCbep6YQrL9BO5+GNUoefuMQIBBULi1KyrCQ28JAbIXx01fqFB4hAPjCzEnKHnGLC1G1pxG1zecV/fX0DHma53bM7cMxt08+rgCxSbHe9V2sGEmg/sUFGAeHw+EMO3l2G8wEkCqukZ9jw4aVxfjg+Fl4AxGc7fHH/GrWmzzUxhHt60WrpxKNg03CLsu3Y3NNI5YXO5LKp9ntcqNe6mZ+/4rLFBP2ozsaUN/iwcYdDWILDtWEX7GkEC/+tRHhCNAb0G4joWf0JKNpZOR6FPcjzVg4prQgBy9WXCmLRwJQaOwAYijQOXNSTCNbtZCm1rVQgwkQdJOebdYUdPcPYEpmqqJ8Xutatbw7Rj9r0VMper0qX/5YzpkCAIsJuP0qJgGe8RpSY86Zl40FeZPk62I/U+oRWlU6ExWLC9AbCOPN+laEGWfhfEfWhPIKAcZEF/8t3npBEH48fMPhcDic4eWxm6KaNW3douEyJTMN3kAfeoMROXxVWT5XUxdGzziCQFB9oEme6IxAjYCndh5BnWTUxFO4VhPPQ/XIymJs3NGADSuLdZWurWYTwsEIAuGIphE3mAah6gk+Ga9Zsudb5pweYxBRrGbIyb5ajWwTebcShUqBaCUZ/RvvWtXenUTba/Hd/zqg8AhZzQSv/HCJ4rq0EqP/fqoL1a3RZ5NN7D4nVVb2BcNwZKfhukunYntdq+rME6/VqJEwGfvUPQrgkREaC4fD4Qw7q68qwM+2HcIAxHyZd1zteG51CW7Z/CEi8lbEUIio1eOXxRUrFhcMuoIsZqI1GIKKl5+SZ7fJYRGaFKuGVpSFI2L+y/MVi2K2ScZTxd4Pet+MhtQG26ZCzyAKJeiZmqzhZWT7eNfKeneMbM9S19yF7/7hI0XVWHZaCnb95Pq43rbHblqAmzZ9gM7eIBxZVkW+EH2+5zkyAAAnOnqxy+VGagqJMX0yrJa447sYMRIme4m+JoT8hH3P4XA444EZ9mh7jvWvfYJPHrkBGakmeIMREACrymbCkZ0m55WwXhO2DUV7j18WV9RLso0HawCwE+1gtWe0yv3jeR6eva1EzrvRqyhLxntB1bnVibp6OklaHiSjSdQsWgZRBMCXnngXr1ZeOyLtUABA6veLcEQ0H4bTwKLnnT0lPUZZepbdild/lPi6Wj1+XJKbjtBABI/ftECRO0YNsZNnfTjm7kUoHIFzRjZcbT2KY+RmpsrtZSYSyXapmXi+Mw6HM+75929HWyx6/WHUNXchNysNgPil9tt3P5fzSqr3N8eUI9OJ+2i7mGg6WHFFepxkSuK10CsPFyUCCuRcJjWlBTmwSXID4UgkZj09hlGPF91205oyeeLVU7ZWl5zLyt4QBq2IreZ0dwDXPPketn7UnHhjaN9Hvc7wrR4/OqQQE9uKI16pvpEyfroNVRdXG0I3Fjvwtwe+qlnmrz52Vc1xbKtrRVmBHY7sNLh7/HDOyEZZvh1VNcfh7vbjREcvAOBkZx/O9ynFSOfkpuPFOxZNuHwhYHi71nM4HM6YpLQgBxYChATRg3BX9cc46w3K62mVENtrjE2spstp4vOqspmDGkfFkkLsO9GpWxJPSRRC0vPeiAad2HA0US5TICzg4e2HYyqdkvF2qMX82HBiooq8wSRRs8dltYXSzAT9TAbw/dsOobmzF/fe+IW4x9O6j3qhrKffPoKQZD/mZlrjHsPIOvU2l0xJj1l3Y7EDVXfEhjJ1j830Wqve14RtUi7Qg9sOoaM3+rznZqRicmaqoooMAOw2Y81nL0aMJFB7EfUIpRNCqE+NABAEQcgeqcFxOBzOcMG256CGUIrYfQDBAQHvuNqxzDldTnBmJxqtxGe9SaPV4xd7h6lKqulE/sjKYuxucOvqvwCJJ9F4uSfqdXqGVURATAL4YPN41Ps9/MYhuRs8zZthDSf19kaNL/a+UG0hAPjTD5fg/tc+wbGzvfKyTXtPwNXagz/+09W6x9O6j3rjqWvpAgCkpgDPri6Jewx2XaKqw7ICO1JTCE4wvfMAscXGvTd+QfczWe50YP+JTiwvdsjLKpcWKVrG+AIh1DZ55FDYPEcmzCYTGtp6MDkzVXG+vElp2LCyWPdeXewYyRnKuhAD4XA4nJHkiVsux5eefE9OmnbmZSM3MxV7j4kG0vrXPsEnUjWS3gSn5zli0SqppstZAydeR/VEBo16wlavZ0vu1edNNafAL2Ub5+fYFNeoFodUX59hUUbGQ6HFYEUc6X0xCYKsLZSRasJD2w/j2Nle5GZY0NEbVRav+bwDsx94Cw+umI+7yufGHC9eHzj1dQYkt9CUTKvCEI5nzCXy1G3YfkgWkGR5cMV8eCQDSq0lRMe12+WOMczVY3nspoVR4xxEzgVat6VW1GBiCA5EJqxXCOBhMg6HM0GgOUE9ksZOjz+Ex29agA+PdyAUAfr6o7/etSY4doKMpyitVVINxBo4rBCg2muQbGK0er1iAlWFr5ZcMgW7XKJujac3qDivWhxSfT69cai9FGoPhfr+xbt2uq3au8buf+2T78nbfvnSqfL1dPSGsMI5HX/5rF0OaQHAE7uOonrfKcPJ1VrX6ZPat7D5QkbQMqzfcbVj7ZaDijECQLY1BS/909Vy/hWre8RqH+070YlHVhZrGuzsfaLXQpP96bq15UUx1XiLCieuIQRwY4jD4UwgfnPbFfIk0NLlx7baM/iHyxzY1eBGSAA27mjA8xVXAoidkLUMDGo00DwgOjlRrwqAmHYJFHdPP952teOsN6ArBEhJVJKtZWixJff02E/tPIJdLrccHgwLytmYblvX3IVDrd2KEEyrxw9ff1ju38ai9lJoGZNqAyOeCCLrXTvc2q1QUnb39Mt5GyYAd11fhJbzfuRPtmFqVhoqy4vwfMWVMRo9NLk6Xh5OvPudkWaGNxBEZppy2kwUWlSHB7/z4v6YkBgg9hn7ze2lAMTWKSDAyXM+7HK5sap0ppy7Rp81KrypRqv1yP4TnVhbXoT7Xv8EXX1hzMi2KvbJz0nDhpXOuPfkYocbQxwOZ8KwzDld7r0EiN3b7yovwtsNbggAPjh+LtolPhCSJ2Qx2hP9hc4m/4II6O0fwMKZ2Ypf79tqT+PauVPEJFYNj86jOxpw1huAzWKC29Mve0i0Jlcj4ZyY9Rq1v3SS/8PfGjGg4eCQr70/LE+4NHRCFY4ry2NbS9A2IazxpHduLYMtJswneddqmzyok5LN6fY76s/Ix5yZY8Nulxuuth5cN3+q4h788Z+uxtaPmmOqs3Y2uDH7gbdwx9UFcm8z9b3Uut9kiCE/tZI0xWICXv3RNZqht6/97q8AgKPt3picMy3DuNXjhy8Qkp9Td08/ttWeRl2LBz9/4zC6+sQPva1HWUWWbUudkBVkLMmW1nM4HM645qrZ0Unn76c68eiOBtlu8AUG8PTbUi4PCFaV5mGeIxMHTnSgen8zMtPMMeGsTKsF2+rP4NAZMUm1YkkhHFlWuL0BHHX7UFleJOcYsWXQj6wshiPLCn8ogm31Z+RSbr3SbiC2nJqWY4s5IVH0jkEnWRMhmseMTuxCTHl9vJJ7tk2IHurO7ex79XipYOGLdy6Sz0m37+ztl4/5b98qjTuu1VcVoPJ6bc2clw80Y/YDb+G7/3Ug7j2j6IXJ4p2/rrkLCx/ZhdkPvKVpCC2YkY29931FNoToZ1HX3IWndh7B3V+9FKX5djx+8wJ5fBt3NMj3Qw31qNHndLfLDbc3gNJ8O3550wLMc2RixqSoV8hmIeLxGeXtiQr3DHE4nAnFc7eX4hop56SzN4TOXg/m5GbgVEcvBAAfn+qSJze2PLk0365bMaQul3++4kq5NUZpQY5msnRpQQ6er7gSD20/hPnTsw3lEhlNVE4UVhONIQGhAUHuVaXOL6Jeqoe3H5Y7uet5PoxWsOmh51lSh5h+tu0Q/FJ+dGoKkY2IePldFUsK5aatZhPkZrUUmmQNAJdMSdc1amgCNVtWz46RGjIVSwrxs9c/jWmsynLJlHS8d+/SmOVsTlB9iweV5UXYvu5aAGLojF6HniyD2tvm6w9jVelMZKSa4Zw5Cbvvvh4LHtklb3/9PIccFp7ocGOIw+FMKPLsNpgB0N/3+Tk2bPn+1bjhuRp4AxG0d/ujpcl0MrGaNcND9Hib15TJHeRbPX7sdrmxaU2ZrsYORQzxeHHd/GmKYx860436Fk9MPo36OFqJynRM8UI2bFuO5vN9Md4XirqTe7yqKa1E6/c/P4ey/JyEXdupZ2lb7Wk5EV2rSq/m2Dn5/WPf1PdmqI3GTWvK5AqqVaUz8barDb3BWNHJE519sqEMRPN47nv9EwQjYm8wWlbPGlyPvemSPT+iV1GbWXYrll7mQCVT2aY23NRNZtn1m5jnjFLX3IVHdzTgEcnwZvPDqg80oSTfLj5L0nPiC0QlCe66/hLdsU40iCBMXFHpRYsWCR9//PFoD4PD4Vxg2FySFACNT34d5U/vwSkpsZX+mq4+IE48bAPPRF4P6gWqLC9KWDbe6vHLkxs9Ft2/NN+ODSuLdY0Do+PRoq65S27LkWVNwaFHV+geu6qmEYBguP0ITTxn9W0S3Qt6H3z+sJyXpN7+HVe7nPxOP7NEx2MbpC53KrvYy966C4A6P4mFjqMk347NjAGtXk/vibpa7KZNH+CsFAqjXiQgeg+oUOiGlcV4YW+jXHkHAKtK8/Ds6tIRuOLRgxByUBCE+BnyGnDPEIfDmXCsvqpANoYGEG3eetvzHyIUAf5+6jzK50+Vtlb+YByKIKIaLQ8Ou7+R5Fwa5mIFDhNB23L4Q4JuWw46vseSzCeheSsViwswf3omjrb74iZW0/PIatQani4AeHSHS36dlmrSVM9mj0fvn69fNLDiNeD967FzeHDbIejfieS5ZEo6TnT2JTQEtcKsWt4i1rBjq8XOegNwZFmxYWWxZhI4KxT6PuNZA4CjKgXqiQxPoOZwOBMSNpH0nlfrUVqQg3RLCgDgdJcf1106FZXlRYqQBpC4/5c6UThZ2P2XOx1yE1RdEggcGumPlYhk+m/RhOLK8rlwZNvgauuJm1jNonfvWj1+nPFEE6d7gxFUH2iSk51pbtPDbxySxxEvGVx9rlOdfYgAKMm348MHvoLta69BljXF8P0BgPJLc/HhA19BZXkRPnzgK/jvHyzWTZ5Xj2PzmjLFGGOSuYXofWDlDSqWFGJVaZ6cx6Tej60umz05HX1S3pMJ4InTKrhniMPhTEg2r7lSDhV5AwOiIGFRLnZJZfa/2vkZVjhnxOyXTP+voaKlMqxGL2+IMli1Z6PHUK/T61c2FB58/VPZP5eRasKqslmgopatHj/WbqkVdZ4QzW1SJ4PHQ8s7c+jRFbKn5eV9J9AbFEfAenq0wpzs/dFSGlfn+Ki9OXXNXdh77BxWlc6M8Q7SMGLxDLELVp7dhhMdfXC19eCh7YdRVmiXy+rrmrtwV/VBnPUGUJJvx/a60/K4Mqwpipw2DjeGOBzOBKW0IAcWE2QV4Hu21uPZ1SX4oPEcvIEITp/v02yDwDZuHcwkn0yOj9Z5ErXmMHIMAHJbDn9IwNaPmrH6qoKkj5FoXTJ9x+LxAVOZ9dvVpVgmtU0BxJya+haP2J29MFrxp65E0+oXx45TnQTPhtgyUk0ABGSkmmKu09cfQtWeRt2Q3XKnA+8fO4eT53y4e2sd3j/Wgc7eIDbuaMD2ddfGtNugBszZnn5kWFNEw09a9/TbRwAADW09uHnTB3i+4ko8srIYG3c0YHZuOqr3N8uJ/mu31OKsN4AUAtS3eBRhIG9gIG6j4IkID5NxOJwJy+NMjs3+k+cBANMnid3DQxFgTm6G4tc5q4Mz2FAYPdbaLbWK0IlWKIo9D11ftadRHktdcxdu2vQB6pq7dM+nN9Znbr1Cfv3wG4c196XnBKIGoTrcM9SwYCLecbXLlX9mIgpnsveKhuVevHMRHrtpYVz9HTa0pkZL9whEwKrSmQhIlWeTM8TmpqwuU6LjUlHIXS43tte1orM3qMjxYUUSq/c14aw3gHSLCR29QVTvb5aVpvPsNmSmWgAANrMJbm8AG3c0oLQgB9vXXYt7b7hMDt/WNXfhkikZyEm3YEByqbH5UI4s65C9dRcb3BjicDgTltVXFYDNDPnZtkP49a2Xy9k3HV4xT0WvDYUaI/k5y50OTMuyyiEZSiLRP7q+NxiW84ge3dGA+hYPNu5oMHS9LMuc02GVYgPEpF1VzI5Jb3yDyUlKZp+7t9bLr7906dSYcWkZjOrj0jwvanQkGhuVVIBAUNtyXjbGbKlmxbmNHHe50wHnjGyscDrk0Nf2ddeitCAnqvEEQc4Rc+Zl47p5U7FCel2Wb5ev6bp5uZiWZcVPJDHGteVF8joavq0+0ISNOxqwrf6MQlyTMic3A89XXMlDZCp4mIzD4UxovnRpLvZKYZi/HjuHP37vKmRJDV29gQGsfuFDtHT1G8o9MZKfs9vllkuh4zVyBbQ1aHz+MOpaPNhWewZzctMRCkewYWWx5j6JxkvFF7UmTc0xaYTDkslJUoefEu3T6vHDF4zq4vxqlejJUzeGTTQWqmhtBKqtRFXExRAZYDYBT91yudw3jD4P9Lis6CK9760ePx7d0QBXWw9SzSZsWuNUfCa9UtPg3kA4um1rD1ytPSjJt8PV2oPNNY1ih3kC7GvsxFlvQPQyrbtWzkfyBcLItJqx3OmQdYo27mhAXYsHWdYUeCVtoYxUE/asLzd0HyYa3BjicDgTmiduuVwW2hsA8ELNcUxKj3a3b+kSvUO1Tec1O6yzGMkl0kvsNdLcNNoPTRyPq82LyvIiRXI13ccXCMkNY4da2QaIE3u8fmdGwi50bBWLC3RbWFBaPX7csvkD+X2WNUX2/lCPmDqx3EiPtETGIptMXZpvx5F2DwDAajbFKF6zydC00zz9rNjEbkeWNUY5utXjx9F2sbT9aLsPVXsaxdynvGyUFeRgVdlM7G5wKwQYqZFDjV9679u7xUTu94+dw4t3LoK7px/B8ADmTcvEsbPR8vmIICR8hicq3BjicDgTmjy7DdOzrWiXmlc+uetozJyfk26Bq80bM5kZbagag0GtWy1DQwyHmOFq8yq8S3Q81Dvg84cTemzMJhOAAfSHBNQ1d+lWrAHxvS5GE3GTqfCqqjmuaCj67G0l8jiooaKl6K1lJAGMV4o24JU8KI/uaMC68iLUNnvkz5JNpl72bA2gUiCix3r/2Dm42nqwcUcDNq0pi9EDouPcIDVXpWX2FUvE/CBXWw8cWVa42npQVmiXS/G31Z7GtoNn5KRsei15dptCWJHe+3u21gEAXG09qN7fhH2NnXC1eTHPkaloTOwPCTxxWgduDHE4nAlP1XeiZfYCxHAC266hqy8UY3jQX/1siCKRWjRgLKwUtxs9tI0K9XGpB0mrxxnl2rm52OUSpQR+/D+1+OsD/6A77nhd5o2SyHBij3us3Ssvt5gA58xJuteuNUY1Ua9UtLEq/Qwf2HYInb1B+AJhPHbTAsU4M6xm9AaDyLCaY461qjQPqWYTNqwsjrk29TjVPeq0Wm9QZWwxjwg43NqdsAS+1ePHEcnDNG9aJnz9IawrL8Jv3/0c53sDCrs7PyeNJ07rwI0hDocz4REFF02yKJ26b5XFBGxYWSxP1OyvfkBA1d5GuYlmojwYOgmW5dvx9d/9FfOnZ2L9DcpqrEQGUyLlarpNptUs6iExfcVYg+Ou64vk9gxt3f2K48Ur4ddqPKsmnsGkt4697oOnohVyoYjoKaLVYkZ7pOndH3rOR1YW46E3DqM3GEJnL6DlsvMHw9LfAcWxfIEwAEH0CEn3JJGXMMZYE8QGrGojyhcIobbJE7cpK3vPGtp6UJpvx4KZ2VLXeguumzc1pt3IF2dP4SEyHbgxxOFwOAB+d3up3PsKAObkpqOlsw9hQZyM5b5OJHZSy0yzKH7h68EaAWu31MLV1gNXWw8yrGZFfs9gdIwSTb5aycvs3J+iElyOZ5AZGV8yQo3q49rTzHIFVxTtJG/AWNK4Vnf50oIcXHfpVLkXnLqB6sYdLngDsd3q1YYmBMjd5ml/Md0xCfHvAU3KVvdX00PrWaQtRtSw3i2OEn5nOBwOB2KpuYUAIWmyCg8I+OXNC+UeZh8c74jp7q6e8OLl3ADKCZB6JeY7MgGB6Ko4a6E30bJ5QzRkp/bm0FJwX38Iq8pm4ffvN2JAQExFWUKDJ0He02CEGul1X/rgW/KyxXMm41JHFgD95N9kKtrU2+qF3ar3NcleMxNBTLd6mptFr0GtYK01JoXAouRd0gtj0ntR19yFtVtqZcVqNWwftuVOh/y5/PKtzxTbzXNkorK8KO69mchwY4jD4XAkHmeMn5YuPw639iAr1QRvMAJvYABl+XbNsM7+E52G2hso8kRcbrx4xyLZixCvpYYavcmfLn+99jTOegMK9Wx28qbbZaZZkGYR86NCA4Kir5devlK887NoGXSJcqEAUWQxxBhaz64uQdWeRlQfaAZAUFlepOjarmWYxEMrnKj2GFHv3EsfnkRfKILczFTZENG7djbpWus86mV6YUw1rJbUpjVlccOLf/nMjWNnfWho65GrIWUE8BBZHLjoIofD4UisvqpA8QvRdcaDa+dOld+v+59aPLz9sKIpaWm+Xc7t0IIVAqQTLy3DVitaA1CIBsYTEdQqTafjoTpGy4sdWLulFlV7G+UQHjUe6P7TstMAAOEIsHGHSyEqmMz5jQgpGhFu/NdX6uTl5fOmIs9ugy8YAiDq8WgJQbIqzfGQjbHFsR41ep+oMnie3YYUaYZU5wslavyq9V5rmfpYWvfwkZXFckWa3v2jxwERrcgPjkdDZHSSXyAloHO04cYQh8PhMFx7aa78uralG1OzrbCJXRAQHBAUrRfy7DZsWFmMOVPS8cpHTXjH1R5zPNo+o6ommsyqN6FW1RyXtj0OQN940GuBkWe3YZPUAX3TmjK51JxWwmkZD7RkHQD++vk5xdiSOX8iBe141033/e7/+UhOYgeiIou0DQUgyO0rlhc7FK0sjKA3RpoQr1YGt6Wapb/RhKpE7UeSUddWH0ttiD78xiFUf9iEBTOz4chOkxWvff6wfHwaRlte7MBTt1wBZ142wkz+fwRih/r1N8w3dI8mKjxMxuFwOAysCCMAsTrHmgJRklEU/2Mn320Hz+BkZx8A4KE3DiuaiAKQf62zoo26OUECzdsR/xpNpFbnEGl1jddLzC4tyAG9uv5gRHf/ROhtqx4b652ieU0VSwqj1XgSeZPSZCOhcmkRMm1msWv7/iZUlhdht8utaEyqdS51DpVeOI0qWt/+xXz86e8tsmijViVZIrQkDgbTmJf2PaPQUFqm1SIfP9Nqxt5j59DQ1oMfVR/E9nXXIlfqn0aZ58jkHeoNwI0hDofDYVCLMALA1CwrfAHR4AlHIorJrVcK4aSmiL/A65q7FHpDleVzcehMjyK5loXtqL7qypmK3CGjgoZGBRH1jmc2AwNh8a/6XhgV6FOrVbMyBGzLCCp6SAUL3T1+OLJtuP2L+QpjiJass8eOya1SGTbq+0Df0+RmvRyn3S436lo8aDnfh47eIB7afhiP37wAESl3KTPNHHNdesaF2ihMJrkbgJwATUvsewMDyGAMcKqyLd7HJqxwTsepDh/c3gCqao7jr1JrGUAUC33qlsu5IWQAbgxxOByOClaEEQAcWWk41xOALzgAf0iIVgQtLsRRSRzw0mnZ2NXgRrs3oJh41YrG6gmV9QBk2syGJswYb8sgSvFZzCYTAohgIIJhadegrpqi4o+s6KG72w9XWw+Otvuwra4VWdZoKEqviF5tnCUq+Wc1nTbVNGq26aCd450zsuFq6wEgKjnf82q9rDeVZbUohDbVhg37eQBQVNpptQgxorN0/4rLNPup0dDnwpmTUFleBJ8/jL5QBKX5dhxr98pa2WYiioVqqXFzYuHGEIfD4agoLcjB1bNzcEAS/tt/8jyunzcVeyXtFotJanexp1Fui0FbLmhNvPEEC1kPAC2zBhDXA6GeNFnPycPbDwNEQGX53ITKxfQc07LTcLKjD+EI8OC2T/HEqssHpTBNUYfnZK+OpIFDQ1iOSTYsL3agel8TttWdkfcXAGzc0aBoPaE39nj93ej7p3Ye0W3TQY3RisUFKCvMQW8gjAyrGW/UtQAQE2ufuvVyhdAm21aDGrT081BrDmm1CNEKpVXtaURvMCRLHxi9t3XNXaht6YIjy4q3G9zydl+6dCq+kJfNFacNwo0hDofD0eC520sVuUNef1Ausw9FgOoDTXLDUVoqz6pT6/0i1yrtfuymhbKRxIr46YVW9DxBtOM6gLjl2nRbeo5nbyuRPWEfHu/QDO2ovR/xjCWt8Fq8kF3lfx9UbJubmYo5UzIM6QrR/Bq9sdQ1d+Hdz9yY58jU9AzRfKFVZbMUn9fbrjZ4A0HkZoll9Wy3+nhaRYBScyhRiT29Hvq50RwoowbfttrTcqd7ljSLifcgSwJuDHE4HI4GeXabokdZbUs35kxJh1dKls6ypgACiZkcE4Ws4unaUA2ibbWn41ZJ6eXysG0iklEuzrPbYDUDgTBgMhlTrtYz1oy04WCTp909/Yr8rPJ5U3HWG8C2+jNw2NM0jR11onG8sdxVfVDUXIJoOKj7x9F8IbXxqk6ejhE3lD4r+hmy6y7JTUcoHMHyYochw3C504G/HBG9OmX5djy18wjc3f3YVn8G7p5+OLLTFPcMiBqjvf3ayd0nOnr1PnqOBtwY4nA4HB1+u1rZooNWjQGALzCA6gNNYkKvFOry+cUJVMvYUaMX6qKNOtkqKaOIXqYFhrdlDQ31Oi3lakVZvMrg0zKa1NVUrEgl7eO2oz4aHku3mODxh+ReW1QnSZ2nY7TirXpfE856A8jNSMWi2ZNxsMmDhrYexbH09relmuENBBVl9erPjA15suGxeikRPFG+jnzPAiEcc4vNVp9793M0tPVgniMDgNisdVvdGTnhfP+JTrkHGQjkakUK7bIyYxJPmk4GbgxxOByODsuc0xXeIRMgJ6gKAObkZjBqwmLJM02C1vNYsN4RakCx4aChJkOz50iU80PH6O7pBxUsDoahOx72WHr9xtRGk5bXjPZxW17sUOgvXTdvKna53HDOyMaCmdnYdvAM6ls8cGRZNUNcgPHmrGJ7jXZZc0m9P2u8unv6cc4bBBAruKiVoM0aiGX5dvzqzw3w+MMoy7fH/Syi90zUD+oNhHH4jBjuWpA3Cf/whek4cc6HY24f+kJhOGdko67FgwVS8nTF4kJZk4piNom99Dq8AXCMw40hDofDiQPrHYoAWDJnMvafPA8BQFNHL9w9/ZoVXfHyemipOS25Z6vIkiln1yNRzo/a0Nl7NKpYLCYvu/B8xaKkxqOVB0M1hLSSqSEAj77pkve3mIANK52YMzUTvn6x/H5V6Uw4sqxwewMKL0sy2j1sqbqWUUepqjmO6v3N8AVC2H+yUy4IUzdo1cp7ognQIALe//wcTnaKSfCbaxqx+JIphhre5tlteHj7YRw768M8RyYyUsVk86o9orF4sqMPFYsLcN38qYrk6f0nOxXHDEUAR5YVj99szEPIERlVY4gQ8i8A1kHU+3pLEIT7pOUPAvgnafmPBUF4W1q+AsDvAKQA+E9BEJ6Uls8B8AqAKQAOAqgQBCF4gS+Hw+FchCxzTsekNDO6+0XXyaEz3Sicko5TnX2IAPjxn+rwj5fnxfTbipfXAwK4u/2ob/EgNyNV1+sBJDnxq86hyPmR9H18gRAyrRaFQOPyYgfuf/1TOVTz18+jxpGekGG8xF51xZzaIKNjYaFtT9jKM58/DLfUWkQvf0nPUNMqhY+3bW2TR3pH4JM+azOJNmiNB5sAXbG4EKtKZ+JwazfmTMmI2zdN7ZWimlXnfUE5BFu5tAi9wTCOtvfISd50+73HzuFzdzQ3KCPVhFVlsxJWEnJiGbV2HISQpQC+CeAKQRCcAJ6RlhcDuB2AE8AKAJsJISmEkBQAmwDcCKAYwLekbQHgKQDPCYIwF0AXREOKw+FwhoU/fu8q+bUvOIBvfTFfft/S5U/YhoLC9sainoeO3iB2SyXRWq0cjLS5UMO2eZC9RFKTUwgk5nilBTnYfff1oNkx/cFoPwf1+bXai6iv0dcf1iwRZ8eSkRqdflIA1Bw7J7ehoPeocmmR3FpEWcIuaLb1YKna04j6Fg+cM7TLy2m7i4e3H0ZVzXG4pDylyvIi9PhFowQEcGSnyZ/LO6523LTpA9Q1dymORROoaYhw/Q3zkZ5qxrb6M4b6ptHryrCaUZJvR0dvUDYA8+w2OLLT4Grzys8J3d4fUjZjFcO5hBtCg2A0PUOVAJ4UBCEAAIIgnJWWfxPAK9Lyk4SQ4wDoN9FxQRBOAAAh5BUA3ySEfAbgKwC+LW3zEoBfAKi6IFfB4XAuekoLchS5Q8++cxTZVrPcGTw/x6YpqKiGDV8RqfXGvGmZcdWKtUT7kkEdiolRcWbQUqKOzYuhZpwQsz+9huoDTZoJ4PRY9jSzfC8BIC/Hhn+8Ii+2Mm9xYYyAoVYZvCZEe3yAymsEyEKQ0RCauG84AtEIlJKjp2VZcdYbwJoX9+N3t5eittmD5U6x+g8CQeVS0SNDdY3Y/KS65i48uqNBbLyqGntMiHG/spksm2v11M4j8n1gFbtTCDAgAHqfCyc+o9modR6ALxNCDhBC9hJCvigtnwmghdnutLRMb/kUAB5BEMKq5ZoQQn5ICPmYEPLxuXPn9DbjcDgcBb9dXSq/DgwAy74wTX7fJiUcJ/LisI1KM9JEP8zVl0zW7WIOQCHap8ZIU1CtburU6FDvZzaJU0IwDNn7od6/snwuKsuLUFk+V3Mces1Y2WM9/fZReRkB8MXZObL44nKnQ7NRbKvHj4e2H0Zdiwfbas/EHFt9XwDIqtJ6jVmdkihhZXmR4hozrGJj2HSLSWwK63SgJN+Ony6bh3SLCX2hCB564zCq9jZi444GVO9vVjTwpfeAthR5aucRPLT9EOpbPNi4o0F70EL0Hqk/H3rfdrvccqPdteVFit3TLClYVZqn+Fw4xhlRzxAh5F0A0zVW/Vw692QAiwF8EcCrhJBLRnI8ACAIwu8B/B4AFi1axE1oDodjiGXO6Ug1AdShsa2+FRapcicsAO+42uNWgqm9RpXlc2VFZopWnpH6mGyyLgSSMHdGC71KN6pELQC4Z2s99ty7VPsAAuDu6ZeNl0d3NBjOzfnZtkMIM9+8Nzgd2FbXig+Od8LtDeiWvVfva5LbZcTzfrBeHzbhWF3FR40gLQ9er+TxIwSykVvf4sGSoinY8oPFeGj7YcyabMO0LCtWlc0SPUMgCt0hdf7UqtKZSDWnYMPK4pjz6fVU23+iU1Q2Zxra0vux5sX9yjEHB3Cyo4+HyAbJiBpDgiB8VW8dIaQSwDZBEAQAHxFCIgByAZwBkM9sOktaBp3lnQDshBCz5B1it+dwOJxh4yuXObBL8tAIAGZkp6HZ0w8AWP/aJ/jkkRt0RQjVSb9Gq7TUDVC//9LHolYOgFWlM2UvymC7o7NGQkm+HSc7RC2l1u4+PPzGISn8EzUa1M1P90t/0y0mlOXb4563el8Tao5FPfJL5kzGhpVOtPcEFGEl9lpogrGvP4wVzuloPt+HVWWz4p6DHotNJKZGCR23niH0jqtdDuGZiSnmfuXZbbhu3lRU7W2UzpEm9xBTJ46z+1KVckd2mnwu2qS3t38AFYsLNMNg1NCk7T1oOf0pRvMKEMNkWoYWxxijmTP0BoClAPYQQuYBSAXQAeBNAP9DCHkWQB6ASwF8BNGbeqlUOXYGYpL1twVBEAghewDcCrGi7E4A/3uBr4XD4UwANnzDib3HzsIvuTaaPf0wm8Tckm5/GHdVf4xpWVbFJExLtlnDxShaTV0b2qJtFzKsZt0KrnhoVX6pc1ACYUQrvoggV6BRleveQAiX5GYAEORu75trGrHMqRUMEDGpPDq/WV0iN7IVE7LF9WyZ+2M3LZTzkErz7Who64kRM2TvExXAhJSTRVFrHOl9Dutf+0R+/bOvf0E2TujQqWHmzBM1f6r3Nynyu/Yz+V1agpPs58M26S2ekS0uk8QU15YX4edvHJaT9Wl7j/ZuP7bXtSrGnJpCsOnbZbwh6xAYTWPoDwD+QAg5DCAI4E7JS+QihLwKoAFAGMA6QRAGAIAQ8s8A3oZYfPAHQRCoSMX9AF4hhDwOoA7Af13YS+FwOBOBPLsNsyZn4POzPnmZzSz2KwOAt12i10jRF0yalI+0e+VJ0WiZvFYPLNEQEZuJVjJ5I4MVa6QT+NryImyqaZSTii0m4ParCgCQmPYbmVYzqvc3oSTfjvoWD1aVzsTJjt64nolWjx+b9p6Q36+7/hJFHlOm1cz0ZqOGDFFcm54ho75PVADzcGu3XIkGABDE6jDW01ZVc1zh/QqGxRCZzUJwqrNPqSotyTuLfekKcd28qTH5XWJOk9j2Q5YQINFEeNpuo2KJmIv0yt+b0dUnqm5fWZgjG8w/eOljnPUG8PL+Jrx4xyJU1RyHzx8VZWT5x8vz4hqhnMSMmjEk6QB9R2fdLwH8UmP5nwH8WWP5CUQrzjgcDmfE+PWtl+PbL+6DPyS6CrzBCEwEiEiegxXO6YpQz6orZ6K2uQuuth48tP2wmPei471Re4K0wll64Z3BijXSCby2xYPNa8pw7ZPvQYCYC8Xq2rAVaFrGSSLj7u5X6uTXBMC9N35Bcd3qcnz2fOy1GWl+Syu8WM9NIs8MFb4kks44AdE3wnTEG+n2Pn9YVpaWlaKlUv/fvvu5/AxAALr6xDJ+54xsrCqbid2SQT1/eiZcbT2Y78hUKJznZqTGXH+GNSVmGSc5uAI1h8PhJEFpQQ5uvTJfIRoYYaI/R91eubyaTr5lBTnixDY9U07o1UKvXxmgHQZLlCek1RiVbseuYyf36+dNlfN6fvw/tfjrA/8Qt+O8kdDMO652HDgV1eZ5YMX8mOtWl+MnagCrfs9uv9vlhtsbwJSMVLR3++UqN63u8b5ACACRDc5AWPTyCRBiFLPV166Grqtr7sKh1m6sKpsZvT9SqX/+5HRYzCYsL3bAkZ0mn39V2Uw5P4gmTjsm2eTxlhXYkW4xoaNXqSc8z5HJK8iGAW4McTgcTpJUls/F34534qRGZ/BTHb2oa+7S1ucRxIojWg2mVgpW68nodWpPpMRMwz+0MSkb5mG9ImJbEFGRmvKrVQtxzZPvAQDauvt170EyCdv/8qda+XVmagruUk3eWoaKVr6UVsWVXpsLmgO1va4V0yfZ5Co3dQNdmvwMAA+/cUjS6gFMJJpzFO9cWuNl5RCoMbSqbBYOn+lBeqpJLrHftKZMkXxNFcmpfADbdHdTTSP6QpGY8149ZwqvIBsGuDHE4XA4SZJnt+FLRbmaxpAAYO2Wg3i98lqFN4GGOQ61dst5OTS3SF09peUFMtRJXoIN/5Tm28XybFWujTqkw3qjUlOA4ABgiqNEx1aVbWbzclS8UHMc/Uwt/XMa7S3UFXNqQ69icaFcDq8VqlMbjnl2GzYxSdnsdcfr23bM7ZW3m5YVrfoyko+lLodXb0/DkQtmTkJpvj0m+Zqe4+Q5H3a53Djn7Vdc1yMri3Hz5g8V55w3LVORN8YZPNwY4nA4nEFQubQIb9afQXcgHLOurTuAp98+Ilb9qJKffVIVVoY1RVd5OtHkqy7X1lpPwy809KQVzvL1h9AbGFDk6rR6/KCN2gcGYnZRnIN6nNZtqVXo4bChuCd2RQUWy/InxU30VWsEsfk21QeaFSraNPF5W+1pOUlZHcpjPSvsuN09/dh79ByWFztQWpAj338LY/yVFNjxjqsdm2oa8cjK4oQ5XhVLCvH+sXOaXh16XvYzpQnRdc1d8n27f8VleHj7YQBAy3k/drnc2H+iE5vWlGl2obeYTdwrNExwY4jD4XAGQZ7dhq8UT4spc6Z8cLwDjiyrrL1D2zxsr2tFxeICRXhGXZKdKBmartcrp2fDP7QHl5ZeEPUesbk61fuias0DALZ+1IzVVxVojmHzmjKs21KLuhYP7qo+iLMq0cS1/31Qsc9/rLlS95roubU0gtQtQNixs0nKRsiz23CioxcNbT3YuKMB29ddKxuqW6RmqykE2C4JQZ71BuTtKLS1xpzcdIXBW1Zol4QhY0Uh1Z9pjKdQOkbl0iJk2swoy7fj52+Iittrt9TiWLuyisyRZcXjGsYeZ3BwY4jD4XAGyb03XIZTHX0KfR7KWa+Y6Prbv3we9YaoSsYpNISi1s9JhOxt6g+hVWoJooY1HNhSc3XyMHvMkx292OVqByDm0WgZQ0A0HPWDlz6Gq61H0RR160fNqD/dLW/LltLHux7qPXH39GPtllo8srJYVutmRQl9gZCoFC1EvS3qcJle0vW68iJsrmnEhpXF8jJAkBPhU80E3732EpTl2+XtWGiic18wjNJ8O8ry7Xh4+2H0BsOSR0uZE6UeR11zF/7yWTvmOTKxrrwItS2emMq5p3YewVlvAI4sqxxWpThnZOPFOxdxr9Awwo0hDofDGSR5dhs2rCzGbc9/CI3cVgDAfEem/Jr+6ld7MQarEQQAh86IngWt/CPW6Klt8ijyVNTJw0B00t6wshjvHWkXw2VE+7zsPaAekbJCuzxB/2zbIcV2EZLgQFB6T2i4TPbeLC6MNlclwGM3LZQ9Y5k2M3x+McfIFwjLIapokngYmVazrPtTWV6E7euuVYXlCuW+Y+mpZkAAnDMnKTxClEdWFmPjjgY4sqzY1eCOlssDmtIHbD7RpjVleGj7IRw7K+abUUOI1TsCIOdIrSqbGfN8sfeZMzxwY4jD4XCGwG6XW9cQAoC+4IDstWFLtVkvhlYCccWSaD6RXsUWG1bSyz+iRk+rxy93Q9eD3Tcs5QuFBxJXjqn7rG3YfghsutGq0plJG3rU4FhbXoSndh6Brz8cc62sEVlVc1zaMxqikpPE+0NSf7A8lObb5XAke/9Wlc3En6QwWXdfKG71WGlBDravu1YMPwKYPz0LZYU56A2EFF46Vr6A5hNV1TRiviMbrjYv5jky5b5rrN4RBFGJumJxAX777jHF85WfY4vbjoQzOLgxxOFwOEOgYkkhttWeFpuMarDL5cacqZmKSTVeqTZd5+7px9+Od8Tk4ajPzXqU5NYR0jKa2/LIymKUFuQkFGVkj/f7mkYAQATR9hjxNI7o8hdqjuPlA1ENpnXXX6IQWDRKaUEONq0pk3uxiR3Zi2LFDiXbR6/xLU3ArlhcAAhEEY5kr/fBbZ/KDWRtqSn4zpLZCRvusuG73S63ZMQ0yV469nNm84nWr5gPhz1NvhbNkCUBTp71Ye+xDsX5W7r8SYdTOYnhxhCHw+EMgTy7Dc9XXInvv/QxOlWCeBS3JPxHvQXqMnEgVgRx79Fzcs6InlclkSijOtRk5FrkUv4cG1q6/ACAY25vTJKy2qCjFV6sGGVuRuqgDCF6P9ZuqZV7sbF92PTGoGXssYKOFYsLdZWtPzweNTqunZsrb/Pw9sMKTSitc+pJHbDGlutMN952uXHdpVM1PYTqkOX9Ky7Dl596T7Hs6jmTZW8SZ3jhxhCHw+EMkdKCHHxt4XRU72/GghnZONymrPzZVtcqqwlTA0WdW6KeZJcXO7BxR4OcvKtODlajlXdEQ03JdDOX84b+sRg/qBarwT4+2YWtd10T93xsqIfy4p2LDJ1LS1WahrCcM7JRVmjXVFmm/b5o2EsLdcd5tTglPZ9J7MIBAtGbNzUrTe5DBkQ1ofSUrNlzsEYODYPtPSYatw9uO4T3Pz+HyvK58mf+/rFzimus2tOIs75+2Rhlx7v1riVx7ylncHBjiMPhcIYBOpH19g+goa0H6jSisny7Zo4PRT3J0rwUwFhHeq1yfHoMdY4SRSs/iU0ypmjJDam9G739IcX6B1fMjwnltHr8ePrtIzja7sMvb16A3S63rqq02sBQH4eOVa30rHVteveMPV8sgrzcmZet6U1SePOYanrFcaUmt6tKZ6LTF4DbG5CMRnGlc0Y2XG09cLX1IDPNIieCa3HWq68Izhka3BjicDicYYCqTKu9I5QNbx7G65XXirkhQuzsS/NbtDwlNHSmpbZsBIWRwZyDhrVoSw51c9HnaxrlOZ4VB2RL1+UKL4bcjFS55Yba20N1mWg7CtYAjOfF0boedqzq/Cg29+pIuxfzp2fi3hsu021/UiXlSBEC/Oj6aKiL5iHpJbDTKrG6Fg/cPf040dGLddKYfP0hMdlZOkeGNQW9gQGpsaogJ0mXFeaAKmVHE8GjzJliQ3tPAL9Y6TT0eXOShxtDHA6HM0yIGj0+7G5wK5q3AqIqtbunXzY6aJd0dhJXe0pE5eVoubgRD5HeuFhDKOq1iOoeyUm8gjhxV+9rQnqqCb1B0cf14z/ViWEbVX8ztSEEiOExagS5e/zYVtcKXyCEyvL/v723D4vyPvO+Pz8YGJAXB0VGUfAdU9BdQZto2hqybYxtH1tD99lkNyXJdjebEp/tttn0SLvNy90kezx1m822fW41abrZNtzZbXbvoHeyWROTrWibio2CjQxVFBUQcABlmAGG4e16/rheuK5hQJJGRTk/x8EhM3PNzO+a69D5ev7O8/tdhj/Yz4nzIR7fnD9hAGwszNdcNHsGWWluVmanc/ZCHzDq/WM3UjR7r+ragtS1Bak/3+Pw5zHf76vlh633SHbFjRvKGl1Js4+/763zW++1o7KBdUtms3N/A3t9fh66Lc8ypVyd42HH3UWALrSKcjyWy3W2J5my4mVUVJ+zPneAFXPT2ffNibcchd8PEUOCIAgfEdke/X/wI5q1Q+Lgyz+p4u2Hih3VEPuXeHSlxHRe7o0MjpkU+6Dris7AsiofRkNxrPy0TQVzLfPF1q7wmCbq0vUL+flvGukKj0aSbCtZRWFuhiXcCualG48osj3JPBsjmwwmF/xqCrmsNDftoQjPvl2vT/EpvT/qkVffpzcySE1Tlz4ttm4h/kA/bd1huvoG8bUFLZ8l+/u9a2ue/lTenEtO+lWdvsAiw326pHC+VTHbmO/l0V21LMqcwcYCrzVl+N3Xfbp/UYIe0mpZHGjwT++cdLhhAw4hBHDozMVLXGHh90XEkCAIwkfIE5vz+Wr5EfyhCIU5Hk61hwhF9K6b3oERXj54lkdsE1b2JufoPpwNy+dQ2xJEA4dp33hiYTKCIrraYf/dkXlmBLu+5TuPhj5iHy0Ontt3yiGE7rkpl7MX+qhp6qKnX3dj3rB8DtsrGygpmj/hGieTDG+uz3SGftDm3pztSWZGostRHSo/2EjF0RZDwGnYR9ftQbMDRghbcoLigVuW4g9GYjZll65faG2JDQzpgmV/fQcXegesrcaihR7Kq5qYO1OfMnzy9Tru+ngOP3+vmQeLl3LgZIcejmtsUS7OnEHGjASy0t20BsJ8+9X3x7zvTYtnxfw8hI8OEUOCIAgfIWbT8jNGo/Cjn8/nEZsb8/b9p/lMwVyr4dfeKG1iflGb6ebjJZ1HMxlBEU0sv6DWQDhWvJaD7+/5ncNPaEVWKilJCY4emtJ1uWyvbOBoc4CK6nPs9fmtBu0DJzsoysmw8tIm48JtF3K3FcylNRCmuilgPR49PWdOm5UUzR/TYG0Pmo03dgtHNM2q1MVqyjbjR8qrGtmY77WOLczxgKYcfUym95ApXj+VN0eP/dAU5Yf0ypB5TQHe8vlJSXTxy5OjVaqstEQ+uWwOD9++YuKLIfzeiBgSBEH4iNHDQPvwtQX5+XvNbL1lCdv3n7Yev2PHr9n14M3jTkAV5XoozPFYlY+N+fq00sr56WPEQnRy+gfdRosloOzbQXHo02Qa8LbvPLcVzOX5ylOO84lX8PDtKzhQ30npulxKihZQUX2OI40B6tqCY8SCKQJ8rUGrdypWv9ClKl3RMRfe9CTWLZmNNz0J0N3BJxI2Owxh8+KvGhge0t22jzYHKMhOnzDvDQ286UnsuLvIaLzWKCma79hyNLcJ/cEwpzv7mJvu5k2fn00Fc1ltOF6XFS/l/p+9h68tBMCe2lZrCjHNHc/urZ+U2I0rhIghQRCEy8ATm/N5dHct3jQ3wcgQ7niI2GbU/+Kn71H9+EbHc6IrQuYW0HjeRPbnTGQ8OBHj+eaY1Z0cm/niQ/9+lEc/n8//++YJx2sMa/Cd3bW0hyKUFS+lMDeDvT6/JYS2mw3DhljwB/t5dNcxVszVxd0H3TqzT9mZ63zw5WpWZc/Ux9KN4ycjDs909BAxdvqSEuK45+bF+ni7zUnajn0C76ktq0h1u/SGeFs2nL2/681jbZy50EdinP780x091Lf3UFF9jqe2rOKFez/OzspT/OpUJ2c6+6z3CUWG2Vl5aowZo3B5iLvaCxAEQbgeKczNYEVWGm/W+SmvamIoynjoYt8gZS8ddtynm/Tlsnh2CiWF2Xqm1r6G0WpFeEjfwop6TnRj8wfBrMjYRYi5HVRWvJQf/WmhdX8oMuzY8gPDTyjHQ3soQlaam435XloDYc509pCZkshdH88xUuFHs9LKD541qiGaw9W5vMrprxN9bqbQ2Fl5Sp/UqtO3oVbneIwAV83aotq257j1nqB7Ndk/O9MW4E2f37rvU8vn8MimGyi7dWnMz7Q1EOZIY8C4pe+tbSzwstrIOzNf01zbI5tuIMGlHzcwovsVmfRGhq01lhUvo8MW52Ju210yJVf4yJDKkCAIwmXihH/UiXo4Rg/Onjo/ZS8dZuc9+ti03avI/IIvKZxPYY6HxbNTKD/UaFVX7CnnH7QaNBns21axJuNAnxy788ZcNq+eb1Wv9tbpGV2myPjHt+vH5KudON8DQG1rcMIpueits1j+QvbtrugtqlhmjuPZAsQreNzu4xPjhMsPNlrVLtOU0r4Vh8YYU81tX/pDHt1dywpvGimJLsoPNVKY4yHFHW+t6Xx3mB5b2XBY04WT3fhSuLyIGBIEQbhMPH3HKv7kuV8zOKJ/2Q5rejneXiTaU+fnU9t+wSsPrHc0EuteP2etaaWV89MtAWDPADN7biZiMlNmExFLCG29ZQl33phr3V6VPZNVtp4m00/o659Zbm33mXz9M8t5dHctizNTJt3wbWa6lRTOxzQojPYLMrFvj7UGwtZkW/RWoNlADeCKZ9SMMsrfKdbrmu8dvRVnmmr6g/3WVt6G5XMoyvXwg3fqKSmcz8O3r8Af7Ke2JcjGfC9l/+uI9R6uOBgagaLcDOkXuoKIGBIEQbhMFOZmkJqUQFffIPEK/uymXFZmzxyz1dTcFeaWf/gF//7Vmx3p8t99vY4LvQN409xWUChgmSUWzJu4odo8fqIps0sJpefHcUSurO/kMwVdltuzGYZqvobdT+i2grmO51c3BfCHIjRfDI8RKeNhZp+Z/VTR/TxmUGx0tWzbnuPW2szb5rnuuLuIT277BSMaDAzp9gU9kSHL36m68eLYJuooZRgtxEyvpr115/GHIpbgykxJpLN3AJSytgZrmgNs23Oc88HRLbKhEf26SlXoyiI9Q4IgCJeRf/jSHzAjIY6BETjWom+bxeoEGRzRp8zue/E3PLarltZAWI+WyPHwXOkaxxey2dNid1M22bmvgZ37G3jmrRNWn8xEfUXj9esAPL7r2JhmaYAzF8L4DKPAmqYuDtS3U1I43/H6Zn9PdI+TefzyrFTqjDyuyVRANhZ4rQm70nW5Y/qnTLFUfqiR8qpGWgNhHvr5Uf77+HlKCrMd7tvmufpausc4hYOeUF8wLw1fW8iKx7D3A8X6rExK1y9kdY4HfyhCQXY63nQ3eVmpJCfGA7DCm0prIMz5YJi8rBSqbIaK82bqU3BFCz1SFbrCSGVIEAThMnJbwVze+dti7v5JFUebA/y2OTChhU9lfQcwuv31+OZ8R+YWXCK2wqhqnPCHqKhpGXfKLDr3LFoolb10mD11fmJRUpjNCX+PtTZfW4hEV3zMSbCDpy+ww2YU+Z1dtdS1hcjzpo5pjp6oQrXX57cm7KIjTYDROBHDWNE0XARISUywtiB7IoOWkHro349ar/+tTSsI9A9ZW2BFubPwtYWsRmczFNZsZLdntZnnu7FA9xZ6YnM+e+v8Y0JXC3M8PHz7DeysPGVltNnZcXeRZXYpXFlEDAmCIFxmsj3JBA2n5lhCyOwnsvO7lm5aA2Er08qM67hU709Z8TJSkxIcLtKxGG/rrKapi/te/A3d/UMxnwfwsC3wNNro0MTek2OPwBgc0RuFV2bPjNkcHR0mawbW+oNhCualszHfq/sIRQm4bE+yNYZe09TF/voOblqcQb2/B2+a29rusgupPqNpOQ4IhIccn2vZrUtJTXbpE322pm1T4JiRJfZk+p+/10hX3xD+YJhn7yzUK1dKoyMUobkrzF0fz+H+n71Hd3gw5udqTqAJVx6laZewGb2OWbt2rXb48OFLHygIgvB78rbvPA+UH7Gap2cmuRyCI9mlCA85/z02p7i8aW6eK11jBbmWFU9ugmyiaktrIOyYwALdVdpupmiyrWQVT79RZ8WK3LQ4g1ceuHly7x9jysv0HrKvqaapyxJV5nmax5kNzaALIHtTcyy2bH+Xo80BvGluPbsMvZrlTU/Wqzd1fjbmeynZ8WsjpEP/nAuy01nhTSUlMYGyW/WeHX2bTOlhrD6/4/l2sbn15WrLTbogO503vvap0c/POG8zUy0aBdxRmO0QmcKHQyl1RNO0D5xqK5UhQRCEK8BtBXPZUphNRU0rCkhPcjHfk0zded19OFoIwWgV6WNz06wtsqrTF2LmZsViosZp+1bb277zbP3XagaiylMLPG7+/at6VMgnTmRa4/KHznQ5jotOc4+O9zCJNY1lYm6D7a3TBcer1ees+BFz68/5qYyPWa16sHgp3997gnp/D7UtQSpqWq1tu/KDjdYraeBwxQZ9mxINyquaKCteagm08SpXK7Nn4k1309wV5mmbWLNPs63Mnsn33zpOVnoSdYbrNMD3DIsC4eohYkgQBOEK8fDtN/CrUxdoD0VoDvTjjo8Qjx53AXpQaGZqEhd7I47k8sqTnSz61hssnp3MmQvhmPESsdB7ZIbGjZaoaerinn8+ZFV8otm8eoFV0bGbE0Y3gNtH0U1H5ksJsFhrtQuNdiPo1rnNpyY1ZWXPeztQ30m9v4eV89OZ4XZZ23aeZOfXnxmi2hsZIsWd4BiV7wkPUbJmvmN90T5G5sTac6XOooT9sbMX+ujsHSTQ59wmq20Ncuclz0q4nIgYEgRBuEJke5J5vnQN9/3Lb+gODxEZ1shKS6Q9NABAeFCjuStMVpqbDcs8YxqYz1zQp6d+19INTGIb7GAjPZFBq1nX7Kl523eeB18+wmCUK7bJt23NxOCM5wC9klLT1DUqyIzKTW9kEDQmPS4f/dnYm6HtPUHlBxud1gIfgJI186lt7aZ0/SK86UnsrGygp3+QnxxocBy3o7JhTGAuMG6zNgqKcjxs2f4uW4vHd6zuiQxSum4hG/O9VBxpYf3iWRy0TZDpTN92lamCiCFBEIQrSGFuBusXz+ZNQ+h0GELITnsowvkYvSUmZqXIIkYVxqxe5M/TIyCaLoZZ/ndvjCuAAL60Opt/vKtwzP1mPMft/7TfqiJ97V+reeWrN1N+sJGSogWkJiXQ0z9IeVVjzAy1SxEt7Ow+QZfKJzOnuiaaQvvu63XsuLvIqlzZfWXyvKmOBvBJhd9q8IN3TuJrC/Lo7lpLSNl9jMxxf3ObzT5ZBpCZksiGvDmUFS/7QJ+V8NEjYkgQBOEK09w1GsipoSeU27eqsmcm8fjmfO7/2WE6ewdYnDmD9mC/Y+vMzs7KBiM9fSx1bXoPzH5jZD8WK7JSeeuhW4CJ+39e+oubuGPHrwFoCfSP2S5qDYRJTUqY9Li8/fHoAFST8cSI+d4V1ef0JulxXKzN/iNze8x8veeNzyse2PsN57n3RAZ1h28z8NUWf1KyZj6P7jqGry3EpgIvnT0R/KGI3ttkTJXZg2LNbcqSogX85JcNDjH6f388R6bHpggihgRBEK4wT9+xyhI6mSmJvHDvWn5z+oJlcNja3Q/AC/euHTNh9VFjT5U3jQWPNgfoiQxyrCVojY8/sukGR5/SCLrQOGhr6M72JDuaiydq4IZRQdMTGbICUKsbA47+pvH6jOyj+2N7i5zCpt0wQOzp13t1NuZ7LfGYmDDaAWWuR3f2zrUCX3v6hyg/pMefHGvtNkJmYUaii435c0FpY3qMTEPIYy36CH7zxbBDCOV5UyfdCC9cfkQMCYIgXGEKczN47a8/yc7KU/RGhqg40kLJmvm4FJhDZVtfrubX3/60tf3iTU+yTAXNJuIv7XyXtu7xt9Nicc9NuWhK0RMZJNWd4NjSMsNLC3M8oCmH0DDFhT209aWDZ62QUlMomYLi1epz/P2WlY4U+ehqk1ml6ekfpK4tSJIrDl9bkPtfOswL9zjdtaOrTLECWu1Eh7r2hIcor2okNSmB//ztqOHh3PRkx5ab2Ru1YcUcXjp4ll01rRTnzaFgXho5s2YwIzGeJZkppLhdgObYFmwNhC0Beay1m1XZMy2jxujKXL2/Z9KN8MLlR8SQIAjCVcCeUA9Q3dSFfbq+tbuft33nrVwvu6kg6L0pphAyc6/ML/7xBIJVLekfYldN65jentL1C/EH+zl+PsSGvExSk11jPILsW3rvnuwc0zhcun4hr1afoz0UYUdlA9vvLrKqTXaDQrszdmsgTG1L0GrQ9rUGLaNGc93ma1SdvmB5FE00nWYKm5Ki+RTmZuhbeMb5/Ni2pfh3n/uYY33bbQLrL3+m+9DVtnTT2TtAoiuemuaA5fNU09TFsZYgRTkeh0t1Vpqbo80BlmSmsDrHA5o2pkXam+YWp+kphIghQRCEq4QZD9EbGaa2JQDo4/XhQf2r86/Kj1Dx4M0xqwemcDnhD7EgI5m3fH5Am1AgjFZLci0RE11xOX4+RF1bkB+8c5I3/mbUONDcEvvTj+dYQbPtPQMxx+efL11jbe+Z1SZvmntC9+jtdxexs7KB3sggKW6Xo+/IFCtZaW5qmgNW/w5KG3fKzO5bVGgkwJtrtQuTPcfOW9UbU/iZx/39HSstv6Lq5sAYo8WKIy0cbQ5YjdSmGC3K8fCDd05S29JNfXuPY10zEuJYMGsG2770B2KwOIUQMSQIgnCVMKs92/Ycp6Kml8IcD3fZxIYGfLX8MIe+c5vjeaaASUl04WsNssKbxuocDxuWz3FMM0UTy/TQrPgcqO+gKDeD3FnJ1LUFWTE3zfHcvT4/R5sDrF86+5LnZff58aYnWb09ZtxErKmw0vWxnaVNMWW5Q7tdoClrMis6vd4kup/JjiseBox+9RPthvmhNnY99vMwK3QOYWpYCgyOjFhCyKyi+YzG9Wj6Bkf49Me8sj02xRAxJAiCcJWxQkY1RdWZC47H/KEBvlp+mAduWWqNkEdXeHr69e2ZHZUN1BhbSWbTtV0Yxaoala5fyIH6DnxtQXxtQfKyUo3XXTbmOFNI2SfXnq88xQPjjIabwsIMLo21LXSpJmurr8jIBCtdtxCURknhfFLc8Y5+JLsANMWbWRky1+JJdllCKA54eote/bHcro1tPNNA0uxz2rmvgd4BvWpVUrSAvT4/G5bPYa/PT72/h5REl2PNPZFB3qo9T3uP0zohY0aCbI9NQUQMCYIgXGXsAaKzUxIBcLsgYkSXvenzc/D0BbrDQ5zp7NU9cQxh4g/28+iuY5QUzqd0/ULri90MeB1PZNjfuyg3w6pk1Lf38Ol8ryUsWgNhdu5rcGxJbb1liZVh9r03T4wrhkyhY+/zid6WG9fHx7Y+a2zfCE41vXse2XTDuD5EsYwbo6fxNuTNwZue5OgTAqymblMUmRNhJmZ/0+ocD/5QBK+xfffgy9U8sTmfiupzNF3sGyOEAG5dkSXbY1MQEUOCIAhTgOhR8QeLl3J/+RHr8W4j9f7w2YuOCs+DL1fjawuR6IqnMDeDxzfn89XyI/jNcXJjzDuWEDEpu3UpKI3eyBCgHPEdZpwEjG5JffOzH7PEkAZjKjOxprPMhmhTlJgZYRP1OMUyYoz2MhpPTMXKRTM/X5PK+g6eees4pzv7eGJzvrV++/uYVTezSb0gO53HN+dTUd1Cb2SQVfPTKSlawHdfr+Noc4Dv7Kq1vJ1Mkl1xfHbVPFLcrknFiQhXnrhLHyIIgiBcbsxR8ZLC+fQMDPLqkXMU5sx0HJMQB8uyUvn8D39JTZMelvrE5nxLPG3bc9wyISzM8VCU66H8UKO+/cNodeTBl6stHxzzvcuKl+FNT0YfF29iZ+UpK06ipHD+hBEbO/c3GOnuON5nb52f7XcXOSbOStcvZHWOxzJBnIhY6zVFjl3cbcz3snNfA4/tPuY4r1ifr52y4qWcON/D0eYAT75eR2sgzLY9x6lp6tJF2LqFlBUvo6x4KS/cu1b/8561eNOTONbSTUVNK6lJCRTmZrDDOM/cWWOrPuGhEVLc8Ty1ZaVUhaYoUhkSBEGYImR7kjlxPshJfy8n/b0ApCTGWc7TcXGKKiPX6pFX3+fTN3gpXb+QXVs/YW0XmY28pviwV1Gi3ZjtlRPTATovKxWA3siwNcVVui6XVHcC/mC/VfFxxcGQwxBbOSpC9kZt+/uYomRn5SlH1SoW9mrOlu3v8nzpGkfjsX0bzhzLH6+hGuCXNq8fBVZmmDkxZp6v+Z4V1ef0BHpNbwS3R4QcbQ5QMC8df3eYb7xSgwJSEhPGhLDieEdhqiJiSBAEYQqxwptuORwr4Ad3FlrbZZEhjcyUBDp7B/F39xs9LXp8RaxJMXD20ez1+XU3ZuNL/LHdx6w+oI6g7lk0z5PEp/O91vaQacBobm2ZYmEoKhmkrHjpJZuh7dtesQJQY7EkcwYn2oK0hyI8+Xqd3t8TZdq4Md9LRXULoDlG8qO3BB/dfcx6XQ145q0TPHvnaktMmudrRqH4QxG+XXGMzt4Bq6G6KNfDgfp2NhV4ab7YR0VNa4xVj5IYD390g1e2x6Y4IoYEQRCmEA9vWsHr77cwOKJ/Yb965Bz33JTLS0YcRGfvIN60RPxGwKs9vmK8kXVTDBTleshKc5MzK9n6EjcrKWZeWqfhHRTdm5Oa7LJ8dsw/7VNl9mbo8Sa87GJpvF4f+7rLDzZa65yREMeDMQSXec7Ro+qxhFl0SG1tS7f1e7SY3JCXSUVNK2sXZbB4Tirnu8OUVzWS7FKEhzQ6ewbwhyIkxsHAiC56BoYZw8Aw+IMR2R6b4ogYEgRBmELo20hr+KvyI2jAm3V+lmelkpWaaE0n+UMDlBRmc+J8CF9bcMyWlzNjbHREfHtlA+2hCM0Xw4YIGa2kPH3HKsso0VyH/TWjRUdhbgbPVTZYBobmiP1kJ7xiNU7b120KpjOdPew/0UHf4AjVzQFrbH0yW2yXOm5wZMR6LHo9D99+A96ZydZaH9tVC0B4SCMzJZGnt6y0zBZhrBDypiUyw+0iIT7O+kyFqYs0UAuCIEwxbiuYy49L1xBvtJmcbO+haKGz8vHuqU6evmPVmDgM0D1xzJ4W0Ni5v4HyKt3vp2BeOnlzUykpmk+qO8F6jmkwOJEZoNlgbDYpf2vTCusxM2QWdCFSui7XEVgKzubnWK9nz0Yzz6nq9EXCQyOWP48VY2JrDI9F9HGv/KZpzDFnOvsor2ocsw77WgEe232M3oFB8rx6P5U3PYmC+TMpWuiJ+d4pifE8vWUV+x6+lb3fuEUMFq8BpDIkCIIwBbmtYC7/u+xmK92++WKYdYtnWQ3U/tAAP3znJD/9yo3Wl/nGAi97fX56BvQmXtNF2mwU3uvzU7TQQ3lVE2c7+yyDxgeLl7K9skGfTDO+uGuauvju63WO+6K3nh4oXuYQQfYqS3RPUGsgbMVolKyZr68zonsGma8XXTnatuc4gbB+LnPS3ADWeUZvscXyQ7Jv292x49fWsYU5M6lp7raS4+3VqPEqbADLs1LJTEnE1xbUo0P6h1mcOYMznX3Wc+IV9A4M84P/Pmm5VgtTHxFDgiAIUxQz3f7O5w/iawuSkugs5lfWd/C27zzbKxusENOa5gCl6xZSUpjN/voOLvQOUFasu1fbp83MKaqa5gB//W819A+N8OjuWt742qdoDYQt08YHyo+we+snyPYks7HAy/76Ds53h6lp6mKvz+9Yz5Ov1/Fc6RpgbE+QOa0GeiitPcvLPCaWN5CZv/b0lpUTNmjH8kMyX2/bnuOOYxfPTqWmuZuV2TOtfLGCeeljKmz2KBBNw/IP8qa56YkMsuvo2ObpbE8SzV39rDCqSMK1gYghQRCEKUy2J5nOUD8AvQMjzEx2WQaMAGUvH2FoBGsKymxwfqD8CBd6B5zp6EaVZK/Pbzkv2ysf5hd4+cFG2kMRZiTE0R6KWD1Je31+6tqC1LUFrcrSjIQ4+ozO5P0n/I51OwSLNjpanjMrmQRXnJUoP9G5P3vnakCv0vT0Dzn8juzN1nqP0BC9kSF6wkOWWCtdv5CNBV6H+7RmZIrVtnaDpv9etNAzpq9orKBroLpRF3Kp7gRHpQ5g3kw3zV39FOZ4ePj28SfkhKmHiCFBEIQpzv/4wkq+XXGMEXQn6pTEeHqNjt2hEUhOiCMr3c1LB8+SmphARfU52kMRMlMS+cQyPVjVFCeP7aql/FAjPZEhntqy0vD8aaA3MkRKoovWQNhqPu418kDM3h9TcICm53PV+Tnd0cNbRoUoPDSaB2/3HKqoPkfvwJCVJwZ6xIiZG2Z/TvRWl/k6Pf16NpkZhgp6b5T9XJ7astI6vyNNXXolR0FNY5fj80x1J1jGjwAF89LHZLFZGKekh+qu1NdT1UhRjmdMz9IfLvCwpTB1jL2BMPURMSQIgjDFufPGXGpbu61tpt6BYUdFJjw4YgkSgJLC+RTMS2NwWKOiphXvTFuVxqiKHDpzgYdeqSElMYGyW0dH1s0en1R3AuVVTRTmeKhpDnCstZsddxc5kuXNAFT7e7/tO091U8ASL/YIDDNPrKapi2MtwTGJ8rG2uqJDae1j+71Gb1RvxGZ0aJxf7qwZuF1xFOV4HBYAmwrmUla8FH+wn6/89Dd09Q0xODJiGUqawax2AWbflsv2JFOU4+GvbFEpJnPS3BN6JglTFxFDgiAI1wBlxcv4z/db6erTqzV9gyNWXpadvKxUTpwPWsaNZi+MWWEpKVpgBY3W+3sA3UPIviVk35Ky526Z22X27SnA4UZd9r+OMKThEC8V1ecAZW03RSfKm9grT/b4Dvt99r6h0TrU6BaceX4zEuKpaQ6wvdIZzmr2NJUfbLQ+y3p/D1/5l/foCg/iD4Y53dlnOW+XrsvFH+jnsd3HrLT6n/76jO29dQqyJ6guCVMeEUOCIAjXANmeZF6870buev4gkWH9qzhaCIHuIL2/vpPkhDjCgyNWL4zp/dMTGWLl/HQWZ84AIMWdMMb3x9xqKl230MrdMreGtmx/lyWzU6g42qJrEM0ZyzGk6f1LJUULqDjSQkX1OWvLy8QUOPZAWPMcra0om2FkqtulV62SEthY4OXg6QsU5Xj4fp1ZkRqVJnt9fmqaAyzOTGF1jodPLJllVaZMyVTT1MX++g42FXg53dlLvb+HLmNqrbY1SL2/h8yUREv8mBW5Yy1BR9Ar6NNjwxoU5WbI1tg1jIghQRCEa4TC3Aw+vypbFyIG9uwygF+d7KQgOx1fa9ASJfZx9J5+fZy9pHA+pzt72Wq4OptVnp37Gjh0thOA6saLDnfrLdvf5WhzgMHhEae/kcKxFfWgMb1mbnnVtgTZbiTUm31B1U0X8bWFYmaJOZyq1y10NE6bE147Khuob9crWyk2vySz3+lIY4C6tiC/tYmXBZ4kAL77eh11bUEUcNOi2aBBfXsPs1MSmZeeRL2/h87eAUvImY3ZJ8470+hNIZSZkihxG9c4YrooCIJwDfHwphUszhytQPQOjHBL3hzr9rAGHcF+Stfl8vjmfL77eh079zfw5Ot1bMz30hsZomBeOrUt3RxtDvDtimNW6rzZkFzv7yUzJQFfW8jRJPzE5nwKczw8vWWlJUxg7Jj74/+nltL1CykpnE9mSiI1toR6sy/I1xaiIDvd6gGyGx6Wrh8duTePT03SBU9PZJDSdQt5sHgpBfPSKSnMpqx4qeW15A/2c6xFn3jLSnM7trN+9GdF1nkUzEtnYGSY8kON3LRkFqtzPFzoHaCzJ2J7hjJer5valoC19Wj/rAE25M2RqtA1jlSGBEEQriGyPcm8/JfrufWZfUSM6a3OUD9bb1nC9v2nAWjvGeC/f9dOb2SIo80BZhuCxOz9ASgpzCbF7SIr3W00QCvLrBFgQ14W3plJ1tbY1uKlVDcFrArPY7uPUV7VxIGTHbxwz1rLyBCgzcji8qYn0dk74HCULl2/kP31HdS1BVnhTbXWZPYFoSnKbl1q9SaZ4scSRlVNlBlr8bUFSXTp/6c3q0lmw3ZhjodNBV6HKaQ9SmRD3hx27m+wqmdoLayan65v71Wf0yfpNHh017ExIsgkyaX43KpsHr59RczHhWsHEUOCIAjXGNmeZP7nnxbx7V3HiFPgawuRMyvF4XvT2t3Pu6f07a7ZqYl8btVcNiyfww/eOcmKuamUrl/EXp+fjQVelszRx8F37tO3ugrmpfPw7SvI9iRbW2OP7q7FH4qMTlYZvkG+VsONOeIM53rbd36Mo7S59p/cu5adlaesrazCHA+gWb05ta3dVjp9eVWTJYTMrb6N+V4qjrRQMC/dqjqZ71WU42FHZQOPb87nyz85ZK0nJWF0I6Q1EMYfDFMwL133ZjK29MqKl1KYm0FhbobVY3XT4tg+SMkJcfzr/eskauM6QcSQIAjCNchtBXO5rWAua556G4B9x/3cuiLLMXLvDw3gUvq01E2LZ3GgvhNfW5CihRmWI7V9bLzs1qX6ZJlNvDyxOZ8nX6/jweKlelCqUeEpu3Wp0ZGsAZrVv2PytX+r5ndPfy5mGGv5wUbQFHVtQQrmpbPSqMgAVDcGxggcMzHeH+zHm55ERfU5yg/pfU+Jrjg25nv1+I11C7nvxd9Q397DI6++b3kxAfzgrkLr/e1Gk3vr/GMauv3Bfg7Ut3NLXib76zvHfPYZMxJ48b6PixC6jpCeIUEQhGuY75Wswh0PkWGNN+v89A2OOP5hN30QeyPDVDfpVaPqxosU5XpYneNxeP1EB6nCaIBrwfyZ9PQPsnNfg9Xfk+p2UVa8jLLiZZSuW0ixrXcpPKRR09TFlu3vUtOkmx6aQmTnfr2BuqQwG4Dyqib21vl5assqXrh3rRXyCrpQS03U+4WOnw8ZTtKKsuKlpCS6rO0/szHbFGWmbYCJmRNmj9goKczGH+hnZ+Upeo2ctGfeOsED5UfwtYU4ECWEjB05sj3JIoSuM6QyJAiCcA1zW8FclmWlWX0tCXGQlBBPKGrb6r2zF2nuCpOV5sbXFuLbFe/T2TtIRfU5CnMzqGnq4ju7alkxN5Vv3u5Mlt9ZeYpDpy9aQiM12QUajsqSaca46FtvWO/5tX+robkrzKO7a9mwfA49/XoPU8YMvTl7cFivKNl7irI9yaApy+zwqS0rrYpVUY6H7+89QeWJdpIT4vjm7TdwrFVvBL//Z4fpG9AF1MwkFwnxis7e0R4oU8CZPUhlxaNGk6BPhAGcOB+iPRTBpUaFJOhJ9F/7o2W86fPz+Ob8j+biCVOGq1YZUkqtVkpVKaWOKqUOK6VuNO5XSqkfKaVOKaXeV0oV2Z5zr1LqpPFzr+3+NUqpY8ZzfqSUUrHeUxAE4Xrk6TtWkRg/+s9etBACaO4Kk5ORzPOlayiYl2YJhd7IMI/tPsZf/uwwdW1BdtW0OibIzL4dUwjNTklkY76XjQVeCqMqS62BMDNsvTnnusIU5nhY4U21qkGl6xYyJ1UXHijdlXr73fo/89v2HKemqctRwbKP9h+o76Te30NzV5j69l5+8M5JVs1Pp2BeOr62IGcu6Onxn/6Y1yGEAMqrGq1zSU1yWcGz+fPSyfOm0tk7QFaam69/Zjk5niSHEHIZSfRv+vzs2voJqQpdh1zNytA/AN/VNG2PUupzxu1i4LPAcuPnJmAncJNSahbwBLAWfZP6iFLqNU3Tuoxj7gcOAf8FbAL2XNnTEQRBuDoU5mbwygPr+Wr5Eb3J2SAnQ09QN2nuCvPDd06ywpuOry1kVENGG5czUxLZkDfHkd5elOshMyWBpVmp1PtDXDD8d1LdCdQ0B6iobrECUcsPNlr9SqD/Q20KHdPVecOKLLb98R/y5Ot1PL453xIWZsPywdMXrLX52kLsrDzFU1tWATim3dwuRd7cVMqrmsiYkaCbSGpw5kKfVSGyPh9b5aknMoi/O8xju2pBadS1BSldt9DacttZeYrmQL/j+Q9sWMKvT1+UitB1zNXsGdKAdOP3mUCr8fsXgZc0nSrAo5SaB9wOvK1p2kVDAL0NbDIeS9c0rUrTNA14CdhyJU9EEAThamP29pjbPYnx4IqLoyhnpuO4yvoOLvYNsDrHQ2fvAPtOdJCTkUSeN5UX7l3Ls3eudvQMba9soLN3kLOdfVZ8xaHTFzkfDBsCQzN8ihqsLSg7D758hGxPMjvuLrK8g8y12issZqVpa/FSyoqXsmG52X+kLA8hs/aV5o5nRqKL7PQkXAq6+gY509nHjEQXpetyOXz2omMNj2/OJ9uTbJ1XRU2rYQip9x6Z1amV89KpNuwBnJ9Zp1SErnOuphj6OvB9pVQz8AzwbeP++UCz7bhzxn0T3X8uxv0xUUr9lbEtd7ijo+P3PQdBEIQpQ7YnmRfuXUtWmpuBYb1K4muN9eXewSeWzGJGQhxdfYM0d/VT7+/hydfrqGnqcpggOo0Wc8nzplLf3sOumlaOtXRTUrSAsuKl9BgNyKCx9ZYl1nsdbe4e7dcJD7Gz8pTDYNHEjNGobg7wyKYbeHjTCkrX5eq9SZWn2Lm/gRR3AmXFS3G74unqG+T5A6cZ0iDZFUeeNxVfW5BjLcExW2R7jdiO1kCYI40BAPK8qaBhibf7XjxEbZvTYdpkxdy0D3YhhGuOy7pNppR6B5gb46HvAJ8GvqFp2qtKqT8B/hn4zOVcD4CmaT8Gfgywdu3a6Kw9QRCEa5rC3Ax2b/0E9/3Lb6j392C2DxXlzHRUPUyDxsyURNYuyqD5Ypia5gB3v1Clb3UZ/kB7fX7LaPG2grk8tqvWyu4yw1Yf2XQDD71SA0BvZIintqyyXh/gzucPUpyXZcVz9EaG8aYnsWj2DP7x7Xr+fsvKmHllqe4EI7F+1JHaTI1/dHctX/nEIquh2Zue5BiZNzEn08yR/rq2IN40N0syUyg/1EjvwCD7T3RwoW9UQMUpGDG+HfK8qWKqOA24rGJI07RxxY1S6iXgb4yb/wH8xPi9BcixHbrAuK8FvafIfn+lcf+CGMcLgiBMS7I9yfz0z2/k/pcO42vVqx09kSGHS7TJnWsX8M3PfozWQJjN/9+vuNA7QGZK4piE+NJ1C40R9GErzX5vnX90W0wzN7H0P/Vxf/2e5q4wvQODeJITCIQHqW3ppqKmxfJE+puf1/DO3xY7Alkf2XRDTNNGGPVYag2ECYSH8KYnWVtxG7b9wmp+TnYpS1ClJrvYWODl1epz+EMR9vr0alFFTSt20t3xFOZm8H5LNzctnsXjmwskamMacDW3yVqBW4zf/wg4afz+GnCPMVW2DujWNK0NeAvYqJTKUEplABuBt4zHgkqpdcYU2T3A/7miZyIIgjDFyPYk88I9aymYp7dm1rf3MjCksW7xLMdx2/efZuOzlTzz1nHSk/X/H5tZW2MywqqaqDjaQnVjgPJfN9LTP4g/2M+2PcfRlK5AUtzxAPzPP1vjeJ++gWECRjL8yvnplBUv5YnNBZYgsvccOfqOJqjfm2LNnH7L9iQ7psBuWpKJ3+htKl23kIojLbSHIsQDI7FfksjwCPtPdnLXjbk8V7pWhNA04WpOk90P/FAp5QL6gb8y7v8v4HPAKaAP+HMATdMuKqWeAt4zjntS0zSzS+5B4KdAMvoUmUySCYIw7TF7iHZWNlB5oh1fW5DivDncUZjNa0dbraDR+vZe6tt7AX3y6uHbV1jbSqXrF1rCqCcySHWjngnmM/pr/vVQE8NG740pnFoDYaqbAo61VDVcoKQwmxPneyhdvwhvehLlBxt5+f517K3z09Ov9xwVzEtnZ+UpSooWWLllVacvWFt1MOpibcZzmOLp8V3HHO9ZWa/3hZYVL9Wfawi2Ge44QpHYcigypOFNc49pBBeub5Q+gDU9Wbt2rXb48OGrvQxBEITLzpqn3uZC74B1O1bUREIc7Lh7DbcVzOWB8sO85fMzM9nFT//8RmuSyjRh/NWpDs506o3QLgX/UXbzmDH56D6l1TkejjYHKCteapk2lhWPhrLe/7PDlsgyj/WmufGHItZx9tfXxVWIFd50Ht60gpu/9wvrvfKyUlg530OK22WJodZAmDu2/wp/aPRzMElJjGPlfA89kSGe3rJSJseuUZRSRzRNW/tBnycO1IIgCNOA75Ws4v7yI9btgw0XxhwzOAL3lx9h6y1LOHxWj9DoDg/xQPkRni9dY/kJPbVlFY/tquVMZyPx6E7N5sTWd1+vY6tRIapudI64m2nyG/O9VFSfc2yJZXuSKVrowdcWpCA7nSc257O3zs/GfK/152O7j4GmKFkzHxQcONGBry2Ery1EW7dzQq2+vZeblmRaztg1TV3c/7P3xkyaJcTp553nTeeVB9b/fh+ycM0iYkgQBGEacFvBXLaVrOKJ12qJU8oyR0xJjMMVF0d3/6hR4fb9p8nxuBlKcoHSaA9FrC2rnsgQqW6XJUh6IoOkuhMoXbfQmub6/t4TBPoGaQ9FmDUjgYu2SS17Gr21fWVQUrSAYy1BnjCmw9DAm57EI5tuYNue45Y5ZGqyi0c23cDGfC+P7q4lJyOZN42GaNBbuPU9D43WQJiHXjlK1RmnMAOYN9ONS0GyO0EMFac5IoYEQRCmCXfemMudN+ay8dlKq0eod2CERbOTSIiPo9O2jdYc0J2st96yhBGlKMrxsKOygY5QP+VVfvzBfk539nLUSLIvP9jI1uKl7KhsIBAeoD0UIdkVxz/f93Hu2PFr63Wfes3HY18ocPT6gL79ZgquJ1+vY3HmDCpqWumJDOk+Rv1DlBTOJ8Xtsp7nTU9iw/I5/C7KH+hbm1bw2vttdIQifP5HByyzSDvxCtq69XMszJHg1emOpNYLgiBMM7b98R+yOHOGlWd29kKfQwjZ2b7/NPuPt3PgZAc1zQFr+6ym6SJHmwO6eaHhQl3dHNCdmnM8AISHRqioPkdeVor1enuM7TRTQL3tO8+W7e/y/beOc7Q5QJIrjprmALUtpsDR2LmvgfJDjRyo72DD8kzKDzZaTdQ79zdYjdImgfAQvtYgb/r8Y4RQmlv/2hvWICM5gYLsdKkKCVIZEgRBmG4U5maw7+FbaQ2EufWZfUSGNBLidLPBGBmv1J0PUXc+RGZKAp29AxTmeOiN6CIjIT6OsuJlpCYlWJNkKW4Xmwq8NF/so7d/2KpCmfzJc++SmZbM0eYAWWlu2kMRBodGKMzxUGOaJqrRCbWdlacA6Owd4OH/+C3d/UOc6ewhOTGe5IQ4wrY8tC+tzubFXzWMe+7mFNmMhDhe/POPS0VIAKQyJAiCMG3J9iRza14WAPMzZjiEkD193qSzdxAFpCe7+ObtKyjITmeFNxWARzbdQLYn2eoHaroYxtcW4kJfhNkpicxLd1uvcy4QsapKf79lpR73ccdKtt9dROm6hRTMS6Pe3wNolB9spKRogVGBGmWvz8+umlaHEAJ49WjrGEE3M8n5//6MGQm8fP86EUKChVSGBEEQpjGPf6GAxVmpbMz38rV/q6G5S5/KSk1y0T84MMacUAP213eyv76T4rw5VNS04p2ZbI29byzwUnX6At50N3VtQXwtQcdIv52E+Dgy09wOX8VUt4un71hFRfU5jjQGqGsLgoJtX/oDnny9jpuXzGLH/tPjmiZGk+xShI0U+7ysVG5aMouy4mVipig4EDEkCIIwjcn2jAqZvoHRkkpkaNgSHDkZSTR39Y95rtmrE2fzq6s40kJNc4CSwvmUrsulIxjh9IVeBoc1znQ6t8vmpCTy6K5afG1BHnn1fQaHRzjT2ceZzh4WZ6ZS1xakMMejR4Hsa6CmOUDTxT40dG+joQls8syR+bBx0IyEOH76lRtFBAkxkW0yQRAEAdC9iDJTE9lU4OWGuXqMR4KCL9+0kDxvKt60RFSM523ff5pF33qDJd96g5PtIUCP5Uh1J/BmnZ9Pf8zLJ5dmjnle5clOEl36K3YE+znT2QfAvuPtnOnsoWCe3tzscI9O1OM+xhNCaYn615q5e6bQG6V/eFehCCFhXMSBWhyoBUEQxvD5Hx7A16YLGzM/zCQnI9naTpuIe27K5f3WIFuLl3LgZIcV5RGN2UQ9M9lF/8AwkeHR76VNBV4On704xiwxFkmuOEY0jQHj+fEKnvuy7qgtTA8+rAO1iCERQ4IgCGOoaerikVffpyMUoatvkMzURDp79N6fxZkzAKxKzgfBHQ+e5ET8Pc4+Ivvrf1AS4yE5wWUZR7pdimxPMs/+yWppkp5miBj6EIgYEgRBmJjWQJjyqkY25nv5zq5avaEZ8CQnWCn0U4GC7HSKcjMATRqkpzEihj4EIoYEQRAmjx7S2gBodIQivOnzkz0ziVD/IKFYBkVXiJlJLn76lRulCiRIUKsgCIJwecn2JFvBp62BMP5gZNQkMQoFpLrjCUWGyfOm4g+E6f4QgsmcChuPwhwP2+8ukkqQ8HshYkgQBEH4wGR7ktl+dxHPvHWC2tZulmSm0Hyxz2q61oBQZJicjGSC4UG6I8PEA0mJcfQO6OpmU8Fcmi/2Uu8PjSt4Yt3vjld8/g+ySXHHy5aY8JEgYkgQBEH4UGR7knn2ztXW7dZAmGfeOs6+E+1WJlhnKEJ4SFc0w0BCfDwYDkb//bvzDI7oxoiDI5Nr2cj1JPHzr94sAkj4SBExJAiCIHwk6OKokNZAmCdf93H4bBffvH0F/1Xbxi9PdjKiQSA8SLzSg1IHR8aO7Y9H3pwU9v5t8eU/CWFaIg3U0kAtCIJw2dGbr08BipXZ6XzvzeN4ZiTwd5/9GAdOdtARinC6s5eV2TN5+PYVUvkRPhQyTfYhEDEkCIIgCNcPH1YMSRyHIAiCIAjTGhFDgiAIgiBMa0QMCYIgCIIwrRExJAiCIAjCtEbEkCAIgiAI0xoRQ4IgCIIgTGtEDAmCIAiCMK0RMSQIgiAIwrRGxJAgCIIgCNMaEUOCIAiCIExrRAwJgiAIgjCtETEkCIIgCMK0ZloHtSqlQsCJq70OwSIT6LzaixAs5HpMPeSaTC3kekw9VmialvZBn+S6HCu5hjjxYdJthcuDUuqwXI+pg1yPqYdck6mFXI+ph1Lq8Id5nmyTCYIgCIIwrRExJAiCIAjCtGa6i6EfX+0FCA7kekwt5HpMPeSaTC3kekw9PtQ1mdYN1IIgCIIgCNO9MiQIgiAIwjTnuhdDSqlNSqkTSqlTSqlvxXjcrZR6xXj8kFJq0VVY5rRiEtfkIaVUnVLqfaXUfyulFl6NdU4XLnU9bMd9SSmlKaVkeuYyM5lropT6E+PviU8p9a9Xeo3TiUn8m5WrlNqnlKox/t363NVY53RBKfWiUqpdKVU7zuNKKfUj43q9r5QquuSLapp23f4A8UADsARIBH4L5Ecd8yDwnPH7XcArV3vd1/PPJK/JrcAM4/cyuSZX93oYx6UBB4AqYO3VXvf1/DPJvyPLgRogw7iddbXXfb3+TPJ6/BgoM37PB85e7XVfzz/ABqAIqB3n8c8BewAFrAMOXeo1r/fK0I3AKU3TTmuaNgD8HPhi1DFfBH5m/P6/gU8rpdQVXON045LXRNO0fZqm9Rk3q4AFV3iN04nJ/B0BeArYBvRfycVNUyZzTe4Htmua1gWgaVr7FV7jdGIy10MD0o3fZwKtV3B90w5N0w4AFyc45IvAS5pOFeBRSs2b6DWvdzE0H2i23T5n3BfzGE3ThoBuYPYVWd30ZDLXxM5foCt84fJwyethlJhzNE1740oubBozmb8jeUCeUupdpVSVUmrTFVvd9GMy1+N/AF9WSp0D/gv46yuzNGEcPuj3zLR3oBamMEqpLwNrgVuu9lqmK0qpOOBZ4L6rvBTBiQt9q6wYvXJ6QCm1StO0wNVc1DTmT4Gfapr2j0qp9UC5UmqlpmkjV3thwuS43itDLUCO7fYC476YxyilXOglzgtXZHXTk8lcE5RSnwG+A3xB07TIFVrbdORS1yMNWAlUKqXOou+/vyZN1JeVyfwdOQe8pmnaoKZpZ4B6dHEkfPRM5nr8BfDvAJqmHQSS0HPLhKvDpL5n7FzvYug9YLlSarFSKhG9Qfq1qGNeA+41fv9j4Bea0YElXBYueU2UUoXA8+hCSHohLi8TXg9N07o1TcvUNG2RpmmL0Hu4vqBp2ofK/xEmxWT+3dqNXhVCKZWJvm12+gqucToxmevRBHwaQCn1MXQx1HFFVynYeQ24x5gqWwd0a5rWNtETruttMk3ThpRS/w/wFvpEwIuapvmUUk8ChzVNew34Z/SS5in0hqy7rt6Kr38meU2+D6QC/2H0sjdpmvaFq7bo65hJXg/hCjLJa/IWsFEpVQcMA9/UNE0q2peBSV6PvwVeUEp9A72Z+j75T/XlQyn1b+j/Gcg0+rSeABIANE17Dr1v63PAKaAP+PNLvqZcL0EQBEEQpjPX+zaZIAiCIAjChIgYEgRBEARhWiNiSBAEQRCEaY2IIUEQBEEQpjUihgRBEARBmNaIGBIEQRAEYVojYkgQhGsGpVSOUuqMUmqWcTvDuL1IKTVPKfWfxv3FSqlupdRRpdRxpdQztte4TynVYTxm/uQrpeYopd68WucmCMLVQ8SQIAjXDJqmNQM7ge8Zd30P+LGmaWeBh4AXbIf/UtO01UAh8H8ppT5he+wVTdNW237qNE3rANqijhMEYRogYkgQhGuNfwLWKaW+DnwSMKs+XwLGVHY0TQsDR7lEarXBbuDuj2KRgiBcO1zXcRyCIFx/aJo2qJT6Jrrw2WjcXgx0xQr1VUploIeYHrDdfadS6pO22+sN0XQYePoyLl8QhCmIVIYEQbgW+SzQBqw0bs9jbDDmp5RSv0VPq35L07Tztseit8nCxv3tQPblXLggCFMPEUOCIFxTKKVWA7cB64BvKKXmAWH0pHA7v9Q07Q+BAuAvjOddiiTjtQRBmEaIGBIE4ZpBKaXQG6i/rmlaE/B99J6hemBRrOdomnYGvdH6kUm8RR5Q+5EsVhCEawYRQ4IgXEvcDzRpmva2cXsH8DFgLdCglFo2zvOeAzYopRYZt++MGq2/2bj/VuCNy7R2QRCmKErTtKu9BkEQhN8bpdQdwBpN0x79PV7jAPBFTdO6PrqVCYIw1ZFpMkEQrgs0TdullJr9YZ+vlJoDPCtCSBCmH1IZEgRBEARhWiM9Q4IgCIIgTGtEDAmCIAiCMK0RMSQIgiAIwrRGxJAgCIIgCNMaEUOCIAiCIExr/n9xxq/93UpEgQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 648x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from pycalphad import Database, calculate\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "dbf = Database(\"nbre_liu.tdb\")\n",
-    "comps = [\"NB\", \"RE\", \"VA\"]\n",
-    "\n",
-    "# Calculate HMR for the Chi at 2800 K from X(RE)=0 to X(RE)=1\n",
-    "result = calculate(dbf, comps, \"CHI_RENB\", P=101325, T=2800, output='HM_MIX')\n",
-    "\n",
-    "# Plot\n",
-    "fig = plt.figure(figsize=(9,6))\n",
-    "ax = fig.gca()\n",
-    "ax.scatter(result.X.sel(component='RE'), result.HM_MIX, marker='.', s=5, label='CHI_RENB')\n",
-    "ax.set_xlim((0, 1))\n",
-    "ax.set_xlabel('X(RE)')\n",
-    "ax.set_ylabel('HM_MIX')\n",
-    "ax.set_title('Nb-Re CHI Mixing Enthalpy')\n",
-    "ax.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Calculations at specific site fractions\n",
-    "\n",
-    "In the previous example, the mixing energy for the CHI phase in Nb-Re is sampled by sampling site fractions linearly between endmembers and then randomly across site fraction space.\n",
-    "\n",
-    "Imagine now that you'd like to calculate the mixing energy along a single internal degree of freedom (i.e. between two endmembers), referenced to those endmembers.\n",
-    "\n",
-    "A custom 2D site fraction array can be passed to the `points` argument of `calculate` and the `HM_MIX` property can be calculated as above.\n",
-    "\n",
-    "The sublattice model for CHI is `RE : NB,RE : NB,RE`.\n",
-    "\n",
-    "If we are interested in the interaction along the second sublattice when NB occupies the third sublattice, we need to construct a site fraction array of \n",
-    "\n",
-    "```python\n",
-    "# RE, NB, RE, NB, RE\n",
-    "[ 1.0, x, 1-x, 1.0, 0.0 ]\n",
-    "```\n",
-    "\n",
-    "where `x` varies from 0 to 1. This fixes the site fraction of RE in the first sublattice to 1 and the site fractions of NB and RE in the third sublattice to 1 and 0, respectively. Note that the site fraction array is sorted first in sublattice order, then in alphabetic order within each sublattice (e.g. NB is always before RE within a sublattice)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2022-02-19T19:18:32.481334Z",
-     "iopub.status.busy": "2022-02-19T19:18:32.471893Z",
-     "iopub.status.idle": "2022-02-19T19:18:32.731301Z",
-     "shell.execute_reply": "2022-02-19T19:18:32.731301Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Site fractions:\n",
-      "[[1.00e+00 1.00e-12 1.00e+00 1.00e+00 0.00e+00]\n",
-      " [1.00e+00 1.00e-03 9.99e-01 1.00e+00 0.00e+00]\n",
-      " [1.00e+00 2.00e-03 9.98e-01 1.00e+00 0.00e+00]\n",
-      " ...\n",
-      " [1.00e+00 9.98e-01 2.00e-03 1.00e+00 0.00e+00]\n",
-      " [1.00e+00 9.99e-01 1.00e-03 1.00e+00 0.00e+00]\n",
-      " [1.00e+00 1.00e+00 0.00e+00 1.00e+00 0.00e+00]]\n",
-      "Site fractions shape: (1001, 5) (1001 points, 5 internal degrees of freedom)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 648x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from pycalphad import Database, calculate\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "dbf = Database(\"nbre_liu.tdb\")\n",
-    "comps = [\"NB\", \"RE\", \"VA\"]\n",
-    "\n",
-    "# The values for the internal degree of freedom we will vary\n",
-    "n_pts = 1001\n",
-    "x = np.linspace(1e-12, 1, n_pts)\n",
-    "\n",
-    "# Create the site fractions\n",
-    "# The site fraction array is ordered first by sublattice, then alphabetically be species within a sublattice.\n",
-    "# The site fraction array is therefore `[RE#0, NB#1, RE#1, NB#2, RE#2]`, where `#0` is the sublattice at index 0.\n",
-    "# To calculate a RE:NB,RE:NB interaction requires the site fraction array to be [1, x, 1-x, 1, 0]\n",
-    "# Note the 1-x is required for site fractions to sum to 1 in sublattice #1.\n",
-    "site_fractions = np.array([np.ones(n_pts), x, 1-x, np.ones(n_pts), np.zeros(n_pts)]).T\n",
-    "print('Site fractions:')\n",
-    "print(site_fractions)\n",
-    "print('Site fractions shape: {} ({} points, {} internal degrees of freedom)'.format(site_fractions.shape, site_fractions.shape[0], site_fractions.shape[1]))\n",
-    "\n",
-    "# Calculate HMR for the Chi at 2800 K from Y(CHI, 1, RE)=0 to Y(CHI, 1, RE)=1\n",
-    "# Pass the custom site fractions to the `points` argument\n",
-    "result = calculate(dbf, comps, \"CHI_RENB\", P=101325, T=2800, points=site_fractions, output='HM_MIX')\n",
-    "# Extract the site fractions of RE in sublattice 1.\n",
-    "Y_CHI_1_RE = result.Y.squeeze()[:, 2]\n",
-    "\n",
-    "# Plot\n",
-    "fig = plt.figure(figsize=(9,6))\n",
-    "ax = fig.gca()\n",
-    "\n",
-    "ax.scatter(Y_CHI_1_RE, result.HM_MIX, marker='.', s=5)\n",
-    "ax.set_xlim((0, 1))\n",
-    "ax.set_xlabel('Y(CHI, 1, RE)')\n",
-    "ax.set_ylabel('HM_MIX')\n",
-    "ax.set_title('Nb-Re CHI Mixing Enthalpy')\n",
-    "plt.show()"
-   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.10.6 64-bit",
    "language": "python",
    "name": "python3"
   },
@@ -349,7 +158,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.0"
+   "version": "3.10.6"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "dffc026621927c134bf51c98cc1a2a1db0bb9758f5626f77d77eb66ad657b830"
+   }
   }
  },
  "nbformat": 4,
diff --git a/examples/TernaryExamples.ipynb b/examples/TernaryExamples.ipynb
index 68d0fd7d3..6b546f02e 100644
--- a/examples/TernaryExamples.ipynb
+++ b/examples/TernaryExamples.ipynb
@@ -34,13 +34,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Wall time: 5min 56s\n"
+      "CPU times: user 1min 21s, sys: 3min 1s, total: 4min 22s\n",
+      "Wall time: 25.4 s\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<TriangularAxesSubplot:title={'center':'Al-Cu-Y'}, xlabel='X(AL)', ylabel='X(Y)'>"
+       "<TriangularAxesSubplot: title={'center': 'Al-Cu-Y'}, xlabel='X(AL)', ylabel='X(Y)'>"
       ]
      },
      "execution_count": 1,
@@ -49,14 +50,12 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -73,76 +72,6 @@
     "%time ternplot(db_al_cu_y, comps, phases, conds, x=v.X('AL'), y=v.X('Y'))"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## triangular projection\n",
-    "\n",
-    "Importing the `pycalphad.plot.triangular` module automatically registers a `'triangular'` projection in matplotlib for you to use in custom plots, such as liquidus projections or contour plots of custom property models.\n",
-    "\n",
-    "Here we will use pycalphad to calculate the mixing enthalpy of the FCC phase in our Al-Cu-Y system. Then we will the triangular projection to plot the calculated points as a colored scatterplot on the triangular axes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2022-02-19T19:24:35.941781Z",
-     "iopub.status.busy": "2022-02-19T19:24:35.941781Z",
-     "iopub.status.idle": "2022-02-19T19:24:36.721283Z",
-     "shell.execute_reply": "2022-02-19T19:24:36.721283Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Dkk6Nc9Vll/CQ+1zFk5/85FuB3Xf5hjucHRCQ87yl7Pn9FXFuYaHI6Mwx1wJ/Fn9/x4L9c3h8tvbvLzxwkCmTJ8K++90zGEgI2ezEUN56WzCg4xFsrqPeoUDVW2Fz98W8+uu/ljf9n9/eJSKrd+1WO5xN6JbbHc4VLBIZfejMMX8PfBVhSf6VwKqI7FHV2xYNaEWuf/BwVR6xtpPR4TFJL0FE6O9exaYLstvjMa4/xHgPGMrVvZQ79jX7V/fs48XPfhof+PQdd9C9t7YHApv8TM/ijOL8vvvth28GHi0ifwc8miAh5WYPqlsy5GLec93qCja1qILNEsQakkE+e0p9IjJcRXfvR3ftpVyZX1U/5yufzPvf924rIvc8pXfW4YzhfPckOyN57uCELRdU9d9V9atU9YHAa+O2Q7MDqeotrzQHLr5xzx5efMnFiICvPKMjBYO9q9h8sRNo19YAAe9APeiCY6zlpd/8eh75mBv/6STvs8PZBmO29tqm2L53tv3wHqLIaGyR+SyCIGkDEdkrk1TkawiZ7jmIyOB308MHvu4eF4eeyYlBDOy+5wF2XHqAwd6d8+cMh5grr4G6RE2V/Ojn545bl1Ue+JBHkGepvOK13/vGk7/dDmcDREJZ4lZe2xWdkTxHsExkVEReLyJfEQ/7EuBjIvKPwAEWaPABPDvfc/gpB/ezlqXkqzm9nX3SQcZg3yomy5AsxeQZkiZIYkn27iW917Vz5WnGlWhVogSnciQ9bkuCivlLv/n1vP1tb3meiHSxyXMc5/tyu3sDn0NYJDKqqt/V+v03mbTSXAgRufSa3iB52UX3ZO2inVMJmtGhdbKdOybH1kbRlSxcWwNlMqDKBozJOcYaisV7WNl3Lx50w5N45BOedhgY3slb7XC2oEvcdJ7k+YZH5av/8nUXXMjKzsFcBnvz1sP4spo/qXJw+NDcZi8Wn2QgwlgGSPQpN6sMEJ75wm/mT3/vNwYi0vEmz2UY2dprm6LzJM8jWJFH3rC6JvfTPnYRQVihGhVk6czbwgi4CtSjCCKCk4Tx6l6Mr1AR1CgikKij9GHs/mDI05//Kj74/nd/nu69ds7ifK+4Ob/v/jyCiNh7pf13ft2uC9h77W5svsBICthBb3qbMUiSwOpOSHPK4S6O7b6Ujb2X4XqrIAYnKUPWSShQwOvEq3jsk5/Npz7xD1ZErr1bb7DD3QPhvPckOyN5nuAldv9t9+sNOegSbv3IHYiZV5xeuWAPxjswgiQJJsuwg34wkjt2AlDlK2gSShUBDJ7cbSIoqY45VvYAqSsbERG+/tU/zAMe+vgPda0ezkWc3uy2iLxaRFRE9sa/RUR+QkQ+LiIfEJEHtY69SUT+Kb5uam1/sIh8MJ7zE3f1fdctgc4DiMjaFVlv7b+uXE7ST1i7dBUxBpMlJP2ctJ+TrQ5I+kE3V8cFdm3H5I2/c29jFFUMog7FNNsExajjdrcfjyUkeSaG8ur7XM/ufRfw3G/6Xx8A7nt6777DXcJplEoTkUuAJwCfam2+kdBr+ypChdkbgIfGOPd3A9cT3nDvi1oGd8RjXgy8m5DofBLwByc7r86TPA/wVb1dt3313n3sOzjkwusPsnJwJcQVRyXqPIP9OxsDWcOXFaztggsvgR1rqHcU/TWMgPUV1heInxTzjMkoNQv7zXQm3Cs8++u/n7/4nZ+4TkSWlPN0ODshMcO9hdddx48D38o0leIpBNEWVdV3ATtF5ALgicCfqOrt0TD+CfCkuG+Hqr5LVRV4I/DUuzKpzpPc5hCRq+/TH9hv3HcZqxetzi2xi6MbeOenWjUAyM5dyO5aGk2pTIrLBpP9gNEKpwZnbPAsI1LjsaI4FZyHI5sZa3su5IbHfjX3/qKnHwZmAp8dzmbcifYNe0Xkva2/b1HVW7Z0DZGnAP+mqn8/szpepFlw0Qm2f2bB9pNGZyS3OR4+XP3o8y64ACOCzRZntN3mGLPSn2xLEkyvF8oPjUXF4HurmKpERfDGhkoMAFUOs4tNv4KgMfsNVhSLcvsoQxFQuPEZr+Q7XvzFuchrD6jqf5yuZ9DhLkC4MzzJW1X1+qVDibwdOLhg12uB7yAstc86dEZyG+PV9oJ3DlcTud9KaMlQrhekvWkdSAQ27zhCunsNEQnJmh2rSFWgRw6hO/dQ7NgPNkFQRBVxHmdTwje+ss4qnoREHJUaVAWnwrFxyqiaGGZJenz5c7+L9/7F2z5LF+o5R3DqMteq+riFVxC5L3A5UHuRFwN/KyIPYblmwb8RKsza2/88br94wfEnje6Nuk0hIunvpocf+ZKLJiuN9S+sU25Ot2VI8pTezlXS3bvIDuwn2bVzkrCpKnySg502rAJI5Ex6k5BQxmuC98KxIuXIKKNwhnZ4SYD73/BUjh36N7E2efDdcd8dTi1EuNuz26r6QVXdr6qXqeplhCXyg1T1cwR9gufHLPfDgMOq+llCee4TRGSXiOwieKF/FPcdEZGHxaz284G33pVn0BnJMwwReZKIfCzSFb59wf5LReQdIvJ3kQLxZVsZ90XJvsMP762yp7LohI8DqtjUYlJL0kvo7V4l6echuwJheZ3m+D0XoAcvRdPF4UNRqLCs21UKMlRhVFqOFjleDdZAniiZVUYFHDoqHD4meA9Pe/GPccnVN7y3owSdC4hliVt53T34feCTwMeBnwW+AUBVbwe+lyD88h7g9XEb8Zifi+d8gruQ2YZuuX1GscWWDN9JELN4QyRk/z5w2QnG3XvPtNf/iR33xI09buzZeflO9l93EO8cWnlMmpDtGJLtCMkY7xymNwjVM6u7G7UfqYqgRj5jz7wxVEnOIb8bsHiEjSqdnQqpVY5ugG91vdl/yXVcfPl1XPWgZ3+WxTGqDmcTTvN3WfQm698VeOmS436BBUpXqvpe4LpTNZ/Okzyz2EpLBgVq1Yk14N9PNOiXZWv/8awd+1hdzVi9aMjKwQE7LllDjGDThKSfMTiwqzGQGIPdtTt8GGzSGEifZFSDtTCF6I0q4IzFmxSMxeLwChtViur8hymGLefwpGe/jg+88w0HRGQwv7fDWYXzXE+y8yTPLLbSkuF1wB+LyMsJajoLg981ROS+90sG5jUX7Gf3lWvNdptOv4lHtx8h37WDZNjD7tw1iSnVxlAMVW+15UUoqkqRDPBJhjMTrzHUagsiOmcoR8V0mWKN/soe7vPwm7jH4c8fArK5AzqcHehUgDpP8hzAs4FfVNWLgS8DflkWKA7ULRn6Yj5wXW+AKx3j9TGucKhq0IUc9IJR7OesXHYR/YP7SFdXMdY0zp54B1UZEjYzyywBMAZng/KPKpSkOCc4H4QvRGp1ySAedOuRxQH9zU3Pg7/0Zj71sbenInLpqXpYHe4GWLu11zZFZyTPLE7YkgF4EfBmAFX9awIRe64Btqre8k324K4v6e/ghWsHKY9VjA+XuNKR7xwy2LsjiKOKYKyhOrY+ObkooJxkvWX9MFTFwglPFt3KEQmeaoWBmh9pwktEWR9bernMMUiKwlOUSq+f8eiv/B6ueuBX/cuJHlSHM4jTV3FzVqIzkmcWJ2zJQKhjfSyAiNybYCS/MDuQiPTeyh1XvHDHJA9SbQRtyP6ulbkLu80R6nzz5va33Rq8AWMRY0g2DgcyeQuK4JIcUWWDIZs6ZLNKUZ2p1onGMrFKYmHnKvRz8N5z+HDF4SOunjNX3e/xlMWmGGMfufXH1uG0QaSLSZ7pCZzPUNVKROqWDBb4hbolA/BeVX0b8GrgZ0XkVQQX7gXacHomeF6693Cvbzm4q8/KhUPECFURjdyCxInp97Brq0iSoN6jRQVVBcZOsi0u8CTFO5xJKLIh4pUN6bMuq6CRMxkrbabvbTqjXTk4dNjhZno3GiN88Zd/H5vHbn2niFhV9XQ4u7CNvcStoDOSZxhbaMnwYeCLjzeGiFx4ZdLLfvYe17Lrsh3U9MO0n+ArT7E+Ih22dCUEBpdfGiTQCLW50svwVYXftRvyHqiSjI9xbLiHMh0QI5JsyoARQyyKtRWpqTg0HjBy2dRnyalgDBxZDyt5Rdi5M+X220vaJt57ZXX3FRy87GFccvVTbwd23vmn2OFuRZe46XCu43H5js88f7ifvfeYGMgaJjHkqzkmTZvYUbp7FyZZ8P2Y5TBYgSSNr4zK5KGyRgxeDGOdJpeLwCAdM64MVUzgFJVQOcEaKEppvMzECsPBJMCvqqyvh1jogx/7zXzyg7+2JiI76HD2oFtud57kuQ4r8pAvWt0hj7vX/qUd67wLPMd8Z9CINIP+wuMwE43IRitSHSqTTPcKR2Ot9sTYJRLMYOWnPyhVXFp7r1SVogppKqgqVeVZP1ZSlmF1nearXPuwF/P5z3zodrr35dkFs30z11vB9jX/5wFExNynN3j3yy66iKTd1EuE/u4VVi/azfDAGjZNgj5kkmDyHK0q1E+H/hTwq7umt4mhSvtTMSkjSs7m1HGVGvrpdE24KhzZMDinjMeKcyFG6RykiVAUvkmKeq+sHxlx6b2fxu2f/VsrIledmifU4a7jtOpJnpXojOQ5jJcnBz5/r9Uhl/f6VGPX1GivHNxJb+eQJE/Jhj3UebLdO0n6PcQaTJLgR2O0eWMLfnU35NMepjfJwniUZZJ98Sp4NfSSCq+hBLys4Mhm2FdV81mjft9SVQ7nFGOEqigDl1MMD3zMd3LhlY/72Kl7Sh3uEmpl8vN4ub1972ybQ0RWfl8O7/naAxcA4CvP6PAYk1nS/nwBS7W+Mb2hKNBxCUmGZhniSphJLIuvYD6RjiMBFEXZcCkOEz8jSuUFFaGXBWK5X5CrbrQo67lVk4P2X/IQ0nQoD/iSH37nFh9Fh7sRCqGefwuv7YrOSJ6jeIbdfceT+7vYu7NPf1eP3lqOzSxJvrjCT928tVLv8Str6O6DsLYHyjL00pZQVVPmq3im7aRHGEsvxPMlxCNBqLw0fbdV4eiG4L2QzWteUFV+ysNUP22IH/iY1/Kpj/3aI0VkwdkdTi/OuArQGUcXID8HISKXXW17yf+695Wk/WBH+rsG9NbCcjksXae/2ZOVQXsAZPdeZGUF8FCO0TTH9Ya4/mr7SnibhVgioUVDRQpTfqByZJRReosxkIlyeEMoq3BMlgnOa8OPVFWOHJmOX67u7FOMKjY3QpXPcO0iLr7ysRy45ImHgU4A40xjGxvAreD8vvtzFI9IVz/5orWDDHYEr1GMkO+YUHPcqJhKzCTDHsYKGi2V7NmLWV1tlr3iSnAlmqSId9EfjBRx9RgTltizBlIVjpV5FLgIEKExkOFvIUuFslSKUilKGAxTjJk+Ju+n5P00Mk6E+9zwUv7tE2/t161FO5whiKDGbum1XdF5kucYXmEP/qL0RW64x+5mm7FmynNU76k2x9g8Y3jhXsQYtHJU6xvYnTtJhvNliuIqrK+oxOKSoCtZmYzEFTiTkjFmTC82/JJoIDPWi/nlfWKUsmVM1zc87RW1c4r387HOLEuaKqGqEK68/4u59d/f8x/A9v0EngvYxvHGraDzJM8hiIh9e3Lkpm+48CJ84fExzuhK1/zeRtLPm053kmWk+/Zg0uX2xoulzFZQEcbJgCrJUWMxeFJKEkq8QuEtpSZ4tSz6+Ax6nnYtZDVTirjsM1dn56uyYvPYiAuuuJH1w/9sYg+UDmcKXXa7w6nGFloy/LiIvD++/lFEDm1l3K9P9h968NoqF/VyhvsGpIO0Wf2u37pOu6Tb5hn5zkl8UasyLK97/YVWyueDRgLNSzK3fHJqKX0WDWOo2O4lFVamDSIEcYuVvmKMxj7ccQ6qOOdRhcFgfhFTFhUIrB9er58T1z7sW9hz4cM+0LV6OFPYWmZ7O2e3u+X2KcZWWjKo6qtax78ceOAWxt15z7y38tP7r6a31qO/M8QgbWJRr9ieJVsdoJUj27VKOgjG0O5YxfTyiR3bsTMauYlh8zbF9VdREcRXiFHEpcFQilBqygbDMB6KqqPwlo2qR544tArVNl6FooIiliimSfjgDAfCoSOesvRNprzXTzHWcOxogas8xbikqjzqA1+SyMVc23sfesMLuOYh3/1PwJUn+W/pcLK4cy1ltyXO77u/e7CVlgxtPBv49RMN+pR0163P2LefgbVkwxTVoBSerfbYcdFuVvat4YsqKIzHeF+ytort94IHGRMlsrkBaOilnfXxvRXUJlT5CtgkLK19Sb88SlqNAkFcBMNkOS8CifF4DbHQfurJE8f6SNgYJ1QuxC0rp6xvKl4Fa3SOcpllll4/oSwDsbzWusz6E+aPKysuv/Zm/u2f3nRPEVnclazD3YgucdMZyVOPRS0ZLlp0oIjcg9Bv+M+ON6CIXPOJZGyftGs32WqGzWyte4tN7FTNti8ryo0RxZF1JM/nxtLNuJRVj5RjsAY33Dn3Jjd4jFZkbkwinr5s0vY+rWiITzrDqLJ4Naz2fVQmD8mZY1EByHtYEDIN41jD2q4+w9Ucaw2o4iqPMYJ3nqooyft7OXDpE7ns2psPH+85dbh7oGK29LqrEJGXi8hHReRDIvIjre2viaGrj4nIE1vbF4a1oj7ru+P234harSeNzkieWTwL+E1VdYt21i0ZBmI+cr/hCtkwZbhnMJXJ3rjtGH4mM5Ku9Mn27JzjSs6Nrx7KcqkXICgGHwyqaNNfWxUOjfsowUM0kVCeGFjrVzivjMY0x3ofjOEsVJWyDOWUWZ4gRtg8NsIYQ9bPsYlp4qyXXP1Mbvvs/81E5ILj3lSHU4/TULstIl9KWHHdX1XvA/xo3H4t4XNyH+BJwE+LiG2FtW4ErgWeHY8F+GHgx1X1SuAOgrr/SaMzkqceW2nJUONZHGepraq3vNIc2HzE2hrfeO8r6K8tXm2Wo2lytojBLqm8IZkuYhF1mGWtGiQssisSKrXUnuTRskepafPZSKxixLfaNyjORwOp4aw0FZJk+oNUFA6dNGLEOUeap6S9BFQx1mKSYMCNzbj8ups5cI8vW/YsO9wdqBuB3f0VNy8BfkhVxwCq+vm4/SnAm1R1rKr/TOil/RCWhLVigu8xwG/G838JeOpdmVhnJE89ttKSARG5BtgF/PWygUQk+317+BEvv/Ky0KxrgcI4MLXcltSS7VpD0gQtpknliEB/OHWu2pC7aw+tgJMELwlH7B4KepTkeCylg7GfrxY0JozgVUgjI6Q9XxFhMLAMh5aicKyvFxTF9BrcWotNLUma0F/tR5J53njEey98JOX4dhFjv2jZM+twanEaa7evBh4Zl8n/V0Tq//Gy8NWy7XuAQ6pazWw/aXTZ7VOMLbZkgGA837SoFUONF9q9h83OhIPD4EG60pFk0zFIk1qylT6CIknC4KL92EHL46wq1BhMlkGaoYMhPuuFb371YCzj/k5ULKqKF6G0PSqTU5q8UfzxJIgIqVkYGQj5coXSC2kKKz24bcZBFQnepPfzwhfeayCSx6dhjKG30mPz6Cb9HUNc5VDvueqB38hH3/NDfyMi5njPrsMpxNa9xL0i8t7W37eo6i3NMCJvBw7On8ZrCbZoN/Aw4IuAN4vIFSc34VOLzkjeDThRS4b49+uON4aI7LvC5L0fXb+Ucr0iW0lBodgoSXKLWEM2yNhx6T6SfOLZJcMFgrreo7v2YowBF8oOwzIqGEa1aX1RSttnnA5RTNO/xlBRaShNtAYyU1HMeJPOC6MqZLWNgTxX+rmwOZ57NvT7thHbrbG5PmbW5mV5yubRzWBc04RyXLCydk9Wd9+LfRc+/gss6BrZ4VRD8LLlzPWtqnr9sp2qurRnvIi8BPg/8Yvvb0TEE/6/xwtfLdp+G7BTRJLoTR4v3LUldMvtsxRPStc+96xsDzmGjc9tNh6WeqXcrDDGINZgZypoZhV1gCBoUSuNe4dsHGvWwiJCMj42dexsUy8AS1i9qMJKuokVR634My6F9cLGIeO4wI4htLtEBDK5kqaWLDNT24tRxSza1CXvPK6qsFnCVQ98CZ/91G/vEZHh3EkdTj1OT0zyt4EvBRCRq4EMuJUQqnqWiOQicjlwFfA3LAlrRSP7DuDpcdybgLfelYlta09SRERVtf55puezVYjIA+5vB+ZRKzvo7++TDhNsbvBeEQSTGNQr6TDH9nJ8UQbrJYIfjTHp9L9VVlanMt2iHvUOYjzS+Mn51o3BDllYbxhhJHiTpTMcGvfwapokTaCpB26kCOQpjEaKSLjEeOwZjVxYuqcmJm401JfP8IRUlTRLKUZFMJDR4pr+Li65+mkcu+Mzh4BOTu3uhHC6qml+AfgFEfkHoABuip/ZD4nIm4EPAxXw0poNsiisFcf6NuBNIvJ9wN8BP39XJratjWRtGM8xAyn3t4O/+7rBAXbfZxc2ely+UoyVII1mhSRL6O1cDRngflg2m16OXRnGb3aCB9kfIqtrS68XyDuQFceokh6aJAsNZPAuPYphvczYdBmlMygmZLLjSUo0ekDloSjDo6+N4Xjs8V6jTZbIHBF27Oxx6LZpYeCqdCBQjqeDmyLCJfd6On/9O89NROQyVf2Xk3jUHbYARU4JB/KE1wkZ6ucu2ff9wPcv2D4X1orbP0nIfp8SbFsjKSK/DvwD8ADgt4C31PSCsxmvMgc+9VEZcd2FOxsDWcM7xTvPyt41hvuD4atGI2yek6wMsMMovag+VM+shsZfs1BjwSYhK2mTwJeMsmhW3cRyts8REIWNKmfT9YLR84KR+Z7bIgISMt1GaBHMQ9Y7ibQe57SJTea9hN4gpRhVtVNLkhrKQkmyBFe6qZiltSlXPuBlfPpjv/lJurDR3YttXJe9FWznN9ePEeIcvw68EvjfItKUB56Nggki0n+rHLr4RfsvwC9xftNhzmBvW7jC4cbj+Q6IVYVubKDOocUY9T40+8p6uB278UmGJlkTS1IEbxKsOjI33ehLCdltRSh8FpfWgrXQSz07eg5r5ktqnA9VOXkm5JlgjJIkBmMEY8Jyu45N1nzJrBd0JdMom2aMpTfsM9gxnEpQeefZe+HDEKMixj76JB53hy1Bolr9iV/bFdvOkxSRXcD9VPX/xk0fAv6PiLwAeJ2IPBl4haqOztQcl+Fr0j2Hd6xk7EpT3NhRHC3JVqdDbvkwZ3ToGPmOYUO0BmHO5osgO9aQbFKaqCL43mCqRagCZdKjzFfR2LrBagne4UwQ2fUYVCVmtBXVkPduXYpB6jk6nmwvHVQVpGn46T2U5bzhtzYcX4yr6bipEbJeyvqRzSYZlWUZRoTRxggfBYSvftCrGG984c9jNnMxP6nDXcLpWG6fzdiOd/8Y4CdF5PUicm8R6QOo6i8CXwx44JUiZ9d/XkQu+hvW06esTsR0y/VWJY3Ajot2YvMEFMqNSeRAqwqdKU2UwXDKQAKIKnYzZLJVDN5mqM0o8tXwu7HRUAkJFaKOSg2lTxi5HKdJ1CufRy0nWFYwLsMbyxiwJvS5OZ7f7pxntLk4uz2brbdJ4GvaJMFYy2D1YnYduJ4rrvvGQ8uv0OGkEWPbd3dZ4tmMs8pQnAqo6m8BXwnsB/478IK6BYCqbhACwBcC9z5jk1yAx2Q7Pv2CnQdIW7bbppb+7j6DvUP23esgvR19TJJg8xRtdTYUY6iOHpuursnmxS0AcBXeJrhsiE97+LRHXm5gXMhwe0z0ICGlJKPAq8ETKD7OL37LBCpQWIaDUDrB+5ppJKQJpIlp9Fnrz5RzOtUtsY1iPNvLWxlvjhq1IJtYkizl0mtu4j8+/bsrIrLzeM+4w8kgMGW38tqu2JZ3pqofV9WvJxS6PxH4eRG5V9z3aeCNHJfkcnphRR5+DC8PG+yY2j48OCTJEoZ7WxQeDcXQJklCFtlabL+P6feQNIUsQwZDzIKEDYDaFG/zqW9+AdJqE2eSoEhuc7xJUSxGlMyUCA6HkKeOfjJmVmh3szTUbyfvoSjrkQO8Bg80SQxJYkhTAyjr60EMOM3m+Z5VOe0de+fnFNhFhN5wJxdf9SwuvOLptx7vOXe48+haym4jIykiXywiz2knZFT1T1X1qYS45Pe0tr9PVf/hDExzDiJirjH9//dC3Uu17lCvmETId2aAkvTSpWo+Sb9P0u+R7d9NsrqCGIOogqvQKmqUtaAiqJGpmGQzD/UUtje9bIq/OzWUOmkCllplmI4bbmTpAqFcJLQPCyv/6TmPF/AK0tSQ5wl5HnQl816d9fZsbIznZOBUF3ucSZpw4B43cuzwx2z9Zdjh1OF0SaWdrdhOd3Yt8CXAD4jIw2f2fQ+wS0T2nezgy7TrZo55hoh8OOrh/dpWxn2ZOXDrVZJziWaUhyp0rPT39Uj6lnKzbHrULIJ6h+RZqMuu59AfIDt2hnhkVUJRhDdxkkCWI8aG7ogz8GKXVk3MliCWLsQpU1NhcTgPiYV+Br0sGNFZVG5+W13LbW0wltaGzHeSmkZaLckSbGox1pAuUzYikOyvuO/L2HXgho8se14dTgZddntbGMmoMfhnBD7kUeCFIvJtInJVPORlwOdV9QsnOf7xtOvqY64CXgN8cdTD+8YtjLv6exza9Wz2xA3QPziJJYoRXDGf0ACwkQ5jezl+PA7tYm2C9PoTzzPyFdUmaG+IT3N8PkCqYopzqECV5IFfOQOndsopLCqL04RQox3qtCtvm2uKQC8Pkaw2EjvvDasGdXKgIZhneUKep6ztGtAbZDFJYxtjmfdnklFGQpw2TVjbc1/SbEWuesBr/mLhQ+twUuiW2+c4RORKQknTPYC3x9//AFgFfkpEfoegJ/ed8fiT+W9upSXDi4GfUtU7YEoPbymebnbd/uUru1kbpmS7Unbee5Wk1SDLppZys5iLw9l+Tjbsh+W1MeA8Oi7AWlrZkgm8Q22C66/i8wEkCcYXqHpKm1NkK2AsuducOldVKUlJxIEGI+Zn3jKVD6IWU/MzsDqcnkO/J3MJUFUavmStLVlDRBiutuhLqlEFKWWwOqQ36NFb6WNjbDbJUrJBj6uvfyWf+9e3PEJEunLFU4AggC9bem1XbAee5H8BfldV6xYInwPeIiJ/A/wvQvOov1fVz0V5rSWNBI6LRdp1D5055moAEfkrQi3p61T1D5cNKCJXXJP1k5dccAk2Wg+V2JMBQQwM9q0w2LuKzYLArbEGk6XTSZwIs7KCJElYYkMQ162X6mmOmmQqFimA9SWV5jiTNJ5f4gtcXDo5Enz8AKSmijqSW/swmJnDjBGGA2FcRJGOloMsAq6V4a7796jCcEePjaPjqS8KMYI14a1bUeHiYCLCYPVC9l/6pey94DFHgAWSSB3uFOT0lCWezTin715EvgS4RFV/qrWtvqdjQKmqf6SqnwM4SQO5VSQEhZIvITT3+tlFlBSJLRmGmE/cbzhsDCSAGzu8U2xm6e8aMNy3ik2Cd6jO44pqmjdY99Tu9TD9/nSCpwqUHrUJ5D10SWxT8DibxYx2QmVSKsmpTA+VhPAWCW0a+rZgNqudWT+3tIZaL1Kbmu3KKc6FMkWvkUMZbbb3iolWVVWbpTdA3ktZ2z1Y+tBdNR+OuPy6m/jCZ9/euysx6A4TnO+e5DltJIEjzCt715/YfcDNp4g7t5WWDJ8hSDWVUWb+HwlGc3pyqrd8g9n/oQdlQ77hwIxgcnyfmcSQruQLkza+mCRdkuEAsXZhw696PF1dC9U3fnExioptPFKHxUnWZLVFIJdA92lawZpp+o/TUL8trYWZIfAlNSoBiQjWCHkWGoKJSFOamCTBSKat8sRZGGsYrvXI8nTKka0N6ixs0uPy617Ewcu+4nOLH0yHO4Muu30OohVXvB24UUReKCIJTCn+PBS4r6oeOgWX3EpLht8meJFE8vrVwCcXzD35Uzny/K+1+wLlp56uwHD/kLWLd9Jb62FTs5D6433oTZ3s3EG6cweS2OXf4cYiRai+FO8QN+11OZsFIQxfhHjjggylCBj1OA0Jm8RqMJTqcR42CxuNICQmxCOBWLOtJMnEa2yTyFvPg+EwoddLSFKztHAjTRMGO/qs7hoG75rQE8cu4YNecPkTGG/8mxGR+y97PB1ODO2y2+emkSRqCEaJrFcQDOLzROR+ACKyB/h2Apm8vQQ/KUSF41q77iPAm+uWDCLyFfGwPwJuE5EPE0Q/v0VVb5sd62az79ADzIB9LmH8hZLyaIVJhD1X7WbtojXSfkraz8Azp9QNgUSe7d9DuroymV8xr+rdWCUkZL4VpBxBVYbKGSxV2g/9bEwWvM6FBYeBJzlx4YTEQi+pMAJ5Mh/BKCpiIzCZomoaI6wM2rxHpaoU5yDLDL2eXdpVMbR8ULxTesOcwY4+eS8nSROyPJv6QjHGkCQJ937ot7Bz//XvPxvFTM4lnO/L7XM1cfM1IvIEQoe1vwAuJRjKR4rIQ4D3AX+kqv8vCu7e5VjkiVoyRA/2m+JrIURk9+U2H/5Yfg+sGLL9gdZi84R8Zb4TYrk+Jh1OGmGJNfQP7sHOiOriPbq+DsNhSN7YwIn0eR/yfvP2VSKR3FgMkIzXGfXWGn5koo4CnXL3nApuwdvESnikqfVAyaiaHFP5UJq4SCTdWsHaYBinw4nCYJBSVZ6q8o3Rr43jLB0qVNrkeO8pNoNhLItp/ufa3mtZWbuUA5fc+CmmwyUd7gS2M71nKzgnjaSq/i8RuRC4IRqvn4mVFgNgjaAjeceZnOMi/Kd05+fvbfv0o1Hym4pNwWaLlyqurEjJQ7LGCMOL9odyRO/nv7e9gyyDvB8ykgDZxPAq4PLhTIZbMa5qtL0FJdURjhSPoSSNRPIFHMf6pzai5g3yVCkrJUvCdu+D8EXb21imgzwcpow2XZPwcZXH2KDKvsjRzfs55bicayxW46oHvYz3/NHXXyzyvX1V3Vx8VIfjIag+nb8454xkqxXD+4AfixqRr1LVj80cZ+HsUSUXkWvvk/TtS5IDzbY6U93f2YstDGRK+SbJA91H0djvpQ74edS5aUHdvIfkrbLCGWUWb7O5ckQBUrdJ4YZNa1mDIlpQklLRxzChJTXzBsZVAnFeWaKIc4xdEMEQwNrJOdaGqYzLsC3PFpcpQuRNZoay8FHIQpvbWQSbGFZ3rVCMCo7cNl9JlPf2cNGVX85488ghYEmGq8Ny1Km48xfn3N23WjL8IfBIgvf4bhF5lYik0cPkbNIWFBG5Ph1+6MXZfkz9aTcgibBycIXBngFpL43xyPBK+hlJP6vPx/Yy2utXLUv8eIwvy2DC8nyqrLCp4SYSgpdRgBR65VGML6O4ruAkoZIMK4o1IVETGoGF0YrKUMWqmxqp9RiCO1e6ab1JCOHRCXdS6PeFRa6hAv1BbCEhNNQgY0zzexvWhgRX3s/prfQba1pzLQHucc2zuf2zf5mJyF3qv3w+IrwnzJZe2xXnnCdZQ0Ssqt5OSNh8CfA64Abg30XkCPBDqrp+5mY4wavNwQ/8vd3kmnyAekWsYHJDtiMlHSSBJJ60DJyZF9G1aYorCmwyiTEigt2xhqRpMAijTWh7k2UBSaD0iPoFbMYwhglZopDAIdB32nFIEchshVdH5S1OFztk1gQu5DLCeVh6h3ikESHPhaKY0H5qDqW1wspKynjsMEYarcmsl1KVLhDPBRIbKo5cFZbng9U+eS9j/cg65WjiVRqbcuUDv4HP/vMffJpz0DE409jOSZmt4JwxkiJyRWzwA0x7iqr658CXiMg1hDaUa8DG3CBnACKSXZn2rvve4aWY1CA21FOLgZX9g7kOgQ1UQ+zR2lB+aA1aV5akIYhoVlYbRraIBKMoEsjjgPYGE86jd4grmx7bQQLLoCJUkgS+ZH1plhk6RQWsuJjxnobXiUpQ8CanboeqglFrmS0EQd4pVKDekOcGYwybmxV5L6EsI9HemsZ7rJH3MkabReOYDlYHlFlJVVSU4xJV5cClj+ZTH/0NEWMfpt69a/FD7zCP7Z253grOiW9VERkAfywi/yn+bWb21/HHj6rqrar6ibMlFnlTuvfII7Md7LFpoPVUis0Nw4MDxEpTk7wIIoJYQzLIY3bE4EZjJE2R/qDuiYAWE8EKjbHIhWWI1RgpR3ixqElCQzAEERMagEWEZfP84/ORCpQtqLxxvq7jDopAvbRdiaOIaPQSw5ay1IXJlqTmU5ogmQZBg7LfT+n1g8c862WLCSK8LrZ0EBGyXsZgx4BskNNb7dNb6XP/L3ktu/bd/6/vKiXsfIPGwoATve4KROQBIvIuEXm/iLw3slSQgJ+I6lsfEJEHtc65SUT+Kb5uam1/sIh8MJ7zE3eVAnZOvFmiovj3AY+Kf3uYkMrPpvhjGyJy4C/1WP60wZ7WNthx6QrZSho7DsL46GjOJElim0SNGEOyukK6tkrS7we1n/b/XTW0IhzuQHoDEIPU22mLFBANqA0VEtGTVDFYrUjcuJljwnQSxCuUmuDVImIZJGMkGlPvYaOcjlFmCaz0PL3Uk6fx736YT5ZKUApa8NYVCU3DBOacWWsNeW+xboV6P2fXVRXT8jp37L6S3Rfcn2sf9v914rxbxGkUuPgR4HtU9QHAd8W/IShvXRVfNwNvgECnA76bQP17CPDdEvpbEY95ceu8J92ViZ0TRjLit4FHi8iX1p5A7S3WnuTZhscnO/79a4b7yMRgUmH10iG77r02ZeDCLQha+RA7tAabpSS9SdImWV3B1OvSWvlnFqqQtOKIKFKOwxvYJEEuzSTTxhJwLZXyzG2Suk2MK0goSRjhVCjVMvYZ7beLNTBIg0dZ+CQO0aSJEFHayXeR4CGmkRaUJIZZuufkWCFLpemk2B4jyyfbktSQZgYxUFXz35OL1hLXPORlfOaf3rxLRFbm93ZYhNNkJBWopfnXgH+Pvz8FeKMGvAvYKUEa8YnAn6jq7VF560+AJ8V9O1T1XdE+vJGgAnbSOOtjkiLy/QTO4z2AfyV8S/yuiNwT+CxwT4J241kltmpFHnT/ZGC+OAvtX9fuuTqVnKkhRujvDGI1vnRkq/1WF0QwWYKpxWajIfLOzbdnWKA2zngT7a1M0YIMinElmmQIYHyFk1AvqGIwKIrimfAS/Ux3xGbugKrBiMeIYXbFpTPURhsz3C5yK9M01HZXVeAN1ZxLiFSgxDAYJow2KpLMYozE6humlt1JarHJClXpKMcVxbgK/MoFIYOst8bl930Oxw59+g4ahmiH5ZAYZtkS9orIe1t/36Kqt2zx3G8E/khEfpTwbXxD3L5IgeuiE2z/zILtJ42z2kiKyCrhW+WTBI7bO4Hr4t9/SzCSl6vqSRlIEXkSoVmYBX5OVX9oZv8LCFJstZjFT6rqz21hXLmv7b/vZZdeTN5L6e/qLTSQAMO9K5jEYntpqKRpVdfYLA3GUBVay2+KMd5aTLvZ16LGX8niNoXGFbg0HG/weF/hbNrU3wo+xD9JMECKAwmeWqWWykePVCGzjtrXKHyIcUKYck0FUo0yl8CwD2WlbIyCx5imwVgCFKVOtZ1VglFN87aor5AkZq55WJKEZXWaJRhbsnF0hE0NztmpXjmqymX3fgbv/D/PTGaTgR3mUdPCtohbVfX6ZTtF5O3AwQW7Xgs8lsB3/i0ReQbw88Dj7uR07xac1UZSVY8SaqYbiMjFwFNU9Ylx0zvi9ppkviXIRG388YRvm/eIyNtU9cMzh/6Gqr5sboDj4JXmwKc/2Su4sh8kvpZV1NjcBn7ksIfNpv8VgVTuSVZ3BErQ7BLbucCPTFJIoh5kVU0tuZcps8hMlabRCrzgTZ35rrlvSZzL5NhUHF5NaC8rLSMpQmIqKm+oPGyWtvlwhfzLRFkoS0MCZ2NTcX5xFU5dYVM55hI1fsG/ORwTY569hM11Ic0T0jxhvFmwuT4K4yuIsVx3w7fw8Q/88sc5t0JOZwSnKrutqkuNnoi8EXhl/PN/A7UzskyB69+IgjKt7X8et1+84PiTxjn3BlHVHwduj9zI9vY7m83eitr4nYaIDN7GoYu+hj2Uhxe3XqjRW+vFDLdndvqB/mMwaQpLlG4g0IEkSYJxVAdVAS4QwzlOf5y5cQhxTAUcxKZbSxpvyXT8LzUea2pjqaQWdvRdNLWL/y1GglcpEpNLGviT9bQrB0kiDIeWXm7o98Irzwx2Aam8/fxEIM1CcktE6A1ydu7dwXB1QNZL6a/0uPjqR2ONlQc8+nt+acsP6XyEnp7sNiEG+ej4+2OAf4q/vw14fsxyPww4rKqfJQjKPEFEdsWEzRMIeg2fBY6IyMNiYvf5wFvvysTOKSPZSuX/KfBqERneheGWxTRm8bRIPfhNETmhSMKzZfehJyRr7B7m5Dsz0kGCK+aTCsO9Q3qrQSjXFxXVxmjBaDIv8V0j7wUaUF0cXVXhFde2apPFmQtATYJURRME1GjKPIZKUtRmqCTYmL0WfMxkz41EE3XUabUg1ZDRXpacgTqBEzxDr5DnQr9vyDIhTUNbBxO1J2uDZ60wHKZT3u2srqR3OledE6pyMvorfXqDnLyfcf0TvpvP/NNvP1+izF6HxThNiZsXE8qM/x74AUImG4KozCeBjwM/C3wDQCwk+V6CjOF7gNfHbcRjfi6e8wlCO5eTxjn15mh5i28C/kVV1+/sMvtO4neAX1fVsYh8HfBLhG+5hRCRS66yvfTHB5excmm/aYfqS08lFTYNsbUdF62R9hpVCcRYUI8vK0y0KmJNNHIho9EkKsQEFfJ8pv0rBBpQjENKMYL+auhv00rq1PxJox7nXUMur0yGFxP2x3pdI2CYeMNePQ5LqaFue3LfSjUlpxaUgOoPTmKgWuyUklkYM72k9n5SjrhIVDdNDYNhSlUF4zgeVc33gQgMhinWCBvrxdR5imKTCSVo98EruOCKR3HJ1U85BHTZ7oU4JV7iCaGqfwk8eMF2BV665JxfIPS0mt3+XkLu4pTgnPIka6jqMVV9e/z9ZA3kCdXGVfU2Va1rRH6OBf9EAIktGVYwn7q36bN6oNcYSDFCviOnt9ojyRMGewYkeRSTSCxJv0fSy0j6weipKmJNqLRJk4kn6X1YgudRsXwZP7ZOZCNBGai+F5uhST6VBTcaaEcmZs2FUJnT8CtnnqwRRTTGQmX61U+mQwvtD1aWhBrw2vNsj5sksHMF0kQxJpDJT0T9rWlTSWLIMsvKakavF4R7+/0Uaw39YUrSigXXXzJtYywi3O9Rr+RfPvK/hy2OXYcWlFBEsJXXdsU55UmeYjRq4wTj+CzgOe0DROSCGOMA+AqW0IxU9RYr8uFrk/5fvGLfRWRrE2ZJb2cPE4VkBaHcKMmGOTaz0029CKRxkyaNXmS+ey0IVdRaZKoT5R8fWshOQUwwgrWRS+L4M3XbQfAiEspVcZKAWKw6rLogk2YTFtFPTcPynragVhwhhmnicYpreXe9NHh9o7LdEaeVzEkm3mYK1NKQ1kqgCLXgfIhXGgn9vMty8fK6108Zx+frnV/olQ5WdnDfG17Cf3zm777A+f15WIq7szHUuYDta/5PgC2qjb9CRD4U4ySvAF6waCwRMVfb/l98w4UX0dufNdttZhsD2UaxUYR67AUuk/pgEJOV/pQ0GoCkk7GpKqbq+kSg4VMKKtGMeR+8w+hVKuCTLCyzjY3RpOmPQdCVnF6qzswyHjf9Sk1J7S1a4zEyk0U3MOwpK73p5mGq08vxxE5quq0VkmS6HW1ihX7PkueW4SAhXUKvEmi8x0X/Bwj9c6592HM49PkPWRG593Fu+rzFaUrcnLU4r785t6A2/hrgNSca56Vm/+1fWPVcNpjEIU9w4blsdg0RwebZNAcyTTFZFug+qnG5K1AW4ffBMPAka6Max55UwYCtRnjN8GkfZqhBVh1OHV6SkLwxaYhHaoVv5zRU8Sok4nA6S1mC3HocGhuDBfm09SLBaygzNGZCMM8SAfGoQlEJ9fe1jZGElYHgfeRTpiFhowqj8XQ7WoB+f75vN8DqjgyOFhRjhzEG9drUgwOxGVm47sOf/Hre96c/8mEJbYfvrhj3OYft3pphKzivjeSpgIjsuDzJ1/7HvtAYsRo7sujZuMIFabQZw2mzJJDE4xK6DZMm2F4+laypVX9UPUKwNI2kvrHTBjJMKl5osuwXwLoCl0yTzhVQsZErqZR2ohyUUOLV4QmcR0eCiEGWmJDEKJmWeJKQZEdIjY9ag9Jcr3QymW5cim8WoWqnrRlsrbAyCMtr52IWe8G1jTGsrCSMRh7nNLb3CZ766mpONQiMTecU9cpoVFGWrpkTwMHLvoj+cC+PeOpPfgC47+I7PD+xnb3EreC8XW6fKnxVsuv2pw52sxJjheqUYr1sPMViY1oxx/YS0kEeBSwkGFAJP8UapC5JVEXLalr1xjnqdj0aUsChZcPSJM709pgrn/rbmxQ1oeOiVUderU88UcDig7Gk7sENRuYpTfXlMuswErw60bp7Yn1VnSKWt88b5rr0NkxjOIUlq2ayzDIYJKyupgwGKVlm0Oh1GyO4SAtKUsvKas7azj7GTi5orfCor/wePvo3t1wnoSNmB4jULtnSa7ui8yTvAkTkqmtMz960vpfqmCNZmRjKpJ/QX+s1Bq42mtkgJ8nTRmVcjJkyGUm/1ZdGFR2XSK/C1JU0zqG4sGTO6iZhs+YvwlVTyR2f9IIH6iowBpVkzpBafFxmT5c1t5dcFo+Vam7JHZbShkQ8FbblKIcluBGmKmymrhskSxbeR51BV4V+L/bL0elz60ROUU5CDcFAAgjqZWpsY4ThMOPokTHWCllu6fUv5l4PeiqX3/cph4H+womeZwgR5u1rALeCzpO8C3ioHX7sBbIXK0J5dDpQZoyJXmL4uyFD58ukbyDpR6MngmQpJk0xWYofjYPn2Ib64FUmCYuEGQVBxptT1sTbJMQG1YcGYEvyllNlizEOaZh4jyKQm5JMxu3DcFMfpvB7MI7aeIOJmdeqjFKZc15iPfU6wW8EUmtYWw1UIWsDWT1JwuDtwiTV0KZ20LcM+pYdO1LyXJplPIR678EwJe9NpOce/LhX8M//8Ns9Edm/8OGch+gSNx1OCl9v9/9yhpHrzGBun1ihtzZpBYu0stYxbZv00iZ73cQeE4tWFSafoQap4jc2McPBVA23JFHDUeNFGuMzcb1UPUgUnqi7dEUY53AzLB8lKAOBBK1JhEQU0REjGdJeOlvjwHlUDW7u+zZ4ha3vCSAYwjzxjKvYE1xrUy30MmVcxFpvCe0gqgqcN+TZZNlt1TAaT/Mt20gSMGJwU7ZeSVOLteFZqypF4SlLmaIGJWmPh934bfzLh//4swThk/Me53saq/MkTwIikvwZR5/7QtnbbEt3xO8bgZWDw+gREonhoW2DWKHcHM8JVtQG0dftGRbVaqtikiQsz42ZyHejNEXPUUi3WUIbCyZptomfjiUadZhq0mRGAScJYmyIRaoj0dAAzIqSskkwh6EuOxHPwG5Si+9O7gfSKHwR/p7+lKVWGWQOI74xkPVxvdwz7CvDntLPYdAPZPR27ssI820fCB5irydkqZmLb07opvW1gl7lovL2qx7wFI7e/mljbPrA+b3nG+qi1BO/tis6T/Ik8J/NvsMb1rOfNCwThxYMpMMUkwhpPw2eUBRZQCDJ86ATqVCNikZUFxFMNlnu1V3+FnEo25A0a3mqy47VqX2LmoFZV+BRfNJvBHqnrkNYnnsxpOrwM0bFitJPxpQ+YeRTrATSiDGhNa4S1M/nKEMEY5eYULtdz3I2ARDijZPvgRp5GkQw6u31cX5JzHORNxSk2izeu8abFAm9v2987o/w+7/86r893ylBSpfd7ozknYSI7LkiyQf/be89ydtuiAo2NfR29oJzV3hUK5I8IRvkEzKzAKpURUGS59EZVLC2kUeTRQkMCRntxgOdUnjwMN4MnmM6yXaHnEkwlEpc96qf4kkGzmVc9p+oHrA13iwSceR2khyxKIkpcGpRFcaVY+xSQCLRXEmM4ViRNm12l/VEM6Iz8c6QTMhS0KQ2bjLFPTUGWmHURWyrME8rDAZJ1AnRpsnYBZc9gP0XXcO9r3/2p5mW3jrv4M9zI9ktt+8knpysff6Zw330Z9Zp6TAhHaRTlR2+9LjKLaz28EVFnZjWyqHlpKeMd5HeU6d1rcX0eiHG6KOsWlkw5+B4B1WrlaqrkDKqC8UsunEF4qtgLH2FtyH+aXyJqJuzJAp4qcsqPblOJ4NiA4243Jqn9giB2pNYJUs8WeKwJmyzxpMaP3X8IkjzU0msklgfa8FD0qbfE/o59DIhab5DhJaG8cKxVTUSymuFoekqqMc8/XX8/V+84SIROX8z3RqIGFt5bVd0nuSdgIhcd99kYF6R7cAk0iRj0pWEbDVphCvasNbiY59oEZnKeLehLniJktiaOd3EHiVvLa3r4NoyjUnvGuMqIthyTGUnnRMFgpGMqJ0tg2J8iVNHZYIQRohR1kmf2gx6Eh1TSU6IVYae3RWLe3E301rgjYSabYc4pfQGi4AJXEpj6qS9kCUg4pvvDYAsVfJSqVrUHmthxyrccSRmw43QTyBJFDBULiSGvJ8IaRw5Gj7ddWuINoY79vGARz2fjaN3HAbOS+5kt9zehp6kiDxJRD4moZ3ktx/nuKeJiIrIUrn5mePlgcngg1+3ejAs7ZzS25PR252SDhYbrHw1JxtG4xFtW9LPyVYG2DzDV256iZjEWm9V/OYm3jmwS2q8l4bJhClrAphiDK6sT5x62Wo8dbb1DusrxEdCvBhE/RQtyFJhtKLWHre4qVrsqXk2VKAl00VJLGTRO+ynjtXcsZKFn/20wojGrPQklmkklKr3s8Dka+5ehNVhiFn2csiyuvQw/Oz3DMNB6OudZZZez5AkURZuwSQf+rhv4OMf+P10K1qi2xUzb5mlr+2KbeVJbrUlg4TeOa8E3r3VsV9lDnzww+mYa1YGYaVaKsWhknQtabxEVUUmxEiSmfan6TBvmnyF+KMCPlTfJDZoSLZRloGGkyTzhnLZm7LNm6w9Sl8how10ON32FcBUY5zNoqep4D2WApUeKop1RaMurgiVCcvzlDJIZEmI5xl1+KhD2dCN6qw1nlQqKvK4n2Z7L1E2K4sRUPFTxjRkycEax9Hx4rdqLa/WbpaYp0o/F7yHURGoQmkizYfZ+XC8NbBzNXiiSlAUuuOQn/rA9wcZNz7ne3nPO375X9mGTsVWsJ0z11vBdvunb7Ulw/cCPwwskgOfg4jkb5VD9/nPew4iRrC5YXBBTm9fTtILy+zerh42NdShObFMGzahkUBrQ33Qj0yH83xL1UjvqeLy2JiwpjS2McwzMw0VNrU1iBqUCpDmLFrnC5AWGxhXYLyLy+4KqUqsKu32C4JifTnxDvFYDbFMK46UKlZ4l2RSkpuCvtnEigsKQOmYzJRYqbDiSEzwHnvJmDwpyewScruEuvD691nU8cn41JrnYkzwKPMsxCezNLz6OQx74fc0NaFaJ3ZnXBmGvt9ZGmKd1grXPOhGxqOjYmzyxQsnuM3ReZLbC4taMjy0fYCIPAi4RFV/T0S+ZSuDPs/sOdJbSdhtA7WntzubiFZoELKoNkqStdAVsZZIm6LyKCyi9ogxoWXsgqVeQzb3iphWt8SYIdeiQLJWqCxdEDaLHp34aqnzKRKSPD7JmuEnHEamPgFh/pM4oBCMJXhELKn66cR7JLbXm/IkqIiHzoom8tsFK+BbGpSz6CWe9SJUdswaypA00Ka0cUKnmi+pq7PcxoC6OrsfvMrKQZbKHN1IVXnyc3+U0frtfykiVlWX5OG3H1RlaSnp+YLtZiSPCwlcl//KEl3ImWNvJvTZSPZKkv3y2r0AsLlZKIdWjSu8KlkahCuSfJ7t7Es31xXR5GlY5cbWDY0RrUnjgCxabovAaBNNW9U5iwxtfayrltJ3gJDZbs818iXreGRMxFOY+XpvAfqM2cQGF7o9zoLFikig9fjGiIXxEuOpvJkxbIHIjhF25I7NMqFSO7U/T4KSEMComERHT+jcyOSgRjjJCv0ejIvQ39uIkuXCldfci3te+3Ae9Oibbgd2nmjo7YTt7CVuBdttuX2ilgyrhN4Xfy4i/wI8DHjbouSNqt6iqtc/zu643zcMDpJuIS6jlacal0EGbdF+VbzzDWHc5Bk2lo6o87hR1IdM04mgha2X8Iuur7C5EWg/ZbHQKtQGR1QndKA2WpxJ40L1TWD1yJSBhJpYvvwTkzFidhLLEjqpeHJTBuX1+liBXlKRmFjRYxy9xJEnnl7iSKyy2qtYzQushAho+9EEz7EdHmBuPm20b6X9e5IIqyvC7p3C2g7DoB8SPV/5gu/ib/7kDWsismPpoNsQp6kR2FmL7WYkm5YMUe7qWYSWlACo6mFV3auql6nqZcC7gK+IjYPmYEW+6I7EySN3r01KB918+1cAMYJJTLOsXorIdazPmbJAImhR4SsXuJJZGpbTfvE1a2pOoxBR/06kblgbshq1WnixiSlqnqNQlyvWI4v6yKN0IS654K1vfTnvWqiCTlQn24bJ4OePRzHiseLJbUliKnI7JrcFiXGkxpOnjtRO5NOM1GIZnsw6VvMCI9NLe5EQgzQS7leiuMa0oQx8zTz1pHaa7xnCIaHfTj+v816TCwxWdvLYr3wpj3/at9zOeQLl9PAkReSrYxcAP+u0iMhrIlvlYyLyxNb2hUyW+Pl/d9z+G7X0nYjk8e+Px/2XbWVu28pIbrElw5YgInLvpP83L9l9YaNeDaAluHKmBjoxoYd2XTkyLpmDCCa1mDwlGfRI+kFY15fVhEMpQGLDsjtNQq12LVRRlVOGUmuxiqAwG6zIeBOKEeqqsH2B9ynlGDPehBij1Nb8IHh+4h1JuTnFp2zuFSVx0WOsjaOWJFphfEmiY1IKLCUQOJuJuKkq3/aC2hglsxXWhJYPeVKSmLYg7mSWUXqzIaev5CWZdWTWNf6MEQ3Z7UxJjWN14FkbOIa5o59VrPQ8vSwcszrw9HNPYpU0UYZ9GPRabXAXfPAf+aQX8YkPv9OKyJXze7cnTlPi5h+ArwLe2d4oItcSnJ37AE8CflpEbIvJciNwLfDseCyEpOyPq+qVwB3Ai+L2FwF3xO0/Ho87IbZdTPJELRlmtn/JsnFeYQ589l97JZdnvbl9fqwkqxZJhSRLmlaxQGPw6kqbmkCeDkNDMJPYKe9EnUetb4la6Jw6EABVhVZViEHG7DZimhayDVyFiFn0+Y7De7AmtJRFp5bb8aIYPB5LWm5SZkNmyxitOrwvJ8vduM+gZFrgjQBBeLdAqSQhiTNyahqa1KyPVyOxFVWVhAy4uFAyqFC4ZIpClBjF29Cjp59WTXuIojKUzpD3Yi24QGY0cPRnrtXPPMM8tMDdHE/7zotKGY21PP3mH+Htv/lf/5Ft5mQsQqBM3f1LaVX9CCxibPAU4E2xa+k/i8jHCSwWiEyWeN6bgKeIyEcIbZ/rpn6/BLwOeEMc63Vx+28CPyly4pbU2/6ffDIQkeHvcOjA81f2LdyfrmYkvQRrg2BF20Dmqz3SQRaIyfHZ1/zIpc2/YkpXrEXE4MsqUHfKae9RsgxjbUOqxnsoxovLExf932shidhRUdy8pxhuQ+OxSlpuBMMao30q4R6sr+YKEYWoZO42Y+MxJWfEkHX6rJMyZt58zz8Pg2KoSI2bWm7ntoqcS0dm4n6CJ9guac/TYDRn1YcWfxIkVv4og3y+cdki83DN/R+FSTJ5zsv/xxsXDrnNcCc8yb0i8t7W6+ZTcPlFjJWLjrN9D3Aorirb26fGivsPx+OPi23nSZ4KPNPsPrQ7TRmODZrN0HYE+msTqo16pRpXmNRMC1k0+0Mr06BjseRjamKypmkX6yciu17RJMHk2ZS8WhhcA4/SVcGjnOwI22TGyyQairKI1J0SdRWa5mirH7cColU82GDZxKe9SOcxiC9JAPFBc9KZBJVJ2WP46SfE+qAFREKFkQ3G2seHIsT6alNzNKKkCziTIpAZN+FBijJMCwqdJ8lbqwxMRaWO0llKbyLhf3bUSdwzsWHetX8sooGbH1tPOk80vMIzv+6H+env+ernibzia1sfyG2JO7GUvlVVl1awicjbgYMLdr1WVd96ElM7LeiM5AxE5B5XmV7y4+k90EqbtgwiQQ8yW1tMCPeVR+wiv4OJC+N1WskHwvI86kTOnVbXc8/oT86hrUyuCpVDEhcM3+x5NpvOFbkgdqE2wSd5UAJq8RoFj7gxXh2CUJkETes6bQ1Zc1dQ2hziMj9kx6cz/MEoK4jSN5s4L1QkoAmO2sjp5B6WICRvXHsDuAXiksQWQISYZ+mEjdLivMFrLdihpDY0Oqux2q+TPh7vBedN7OYYdS1NMJZrV1zKAx/+JG54/HMOA8OlE94GOFXiFar6uJM47XiMlUXbbwN2ikgSv7zax9djfUZEEmAtHn9cdMvtGTzCrPzzC/N92JqQXCo2NWRrCXZg8JWP5YTTMDbsWwRtZad9UeHLCu9cTNaY+FpgYFWRNBjQufYNbYhprXvitrJAosfYLIvbgrxTl9GwPF5EEQpHNF7foq8BIdR8T7DsbRXDCupJxJGKI5eSoWwwkA36bJITyiAzmW6gFuCbjHYjkGQ0ktmPj9Qqw7RiJasYZiWDtGQ1L+kljuk2FkJqPXkSasoTC4NcGeThbyOBlWUMPPV538pf/OEvDUTkhEu2cxUKnOH2DW8DnhUz05cDVwF/wxImS4wvvgN4ejz/JuCtrbFuir8/HfizrWiFdp5kC1bkUQ9NVuSBycQxEAu2H7sEGiHpJ1RF0ImsDVtD/yFQhNpLbpNY8Iobl43QrrrQGrZW/TGJna/GkdDOofEEnUOtnfFCJWa27cSetHtvl0XohFi3pF1245FkblDcCY5ZxntkStBXm+OnDiFwLw0+qgIppi43JGTrLWWMh3pElcqneCQYR3QqcRPOg15SMKqySFwPykQWHz3U6cclENpRoFHXElJbsFkmjKoklFWacC9WFj+PLKlIjZLvyvnq57+aj3zg/32e7drq4dRkrk8IEflK4H8A+4DfE5H3q+oTIzvlzcCHgQp4qWqofBCRmsligV9Q1Q/F4b4NeJOIfB/wd8DPx+0/D/xyTP7cTjCsJ57beSy6PAURsfcyverbdl7MBS4BT1he70lIh7H6xArpIMWmoeTQpME4DXYPpgxjOshjJ4VpDzEZTPre2DxFrMXmGabmnMTjxdqpqh7Js0n22wajJ43SjyB5b+JazXyhKwIrOyZ8yCSbzmhHGg/GoEmKTzLUpnNj1IrlClTZcM4Aiq8YJ4NwXHxPVSZrrhukPxSr1RS5xyM4yfCSxKN8k1WuNKFqKZRV3kwtjWmNoyp4lYZcrgpjP1OmqZ7Sh+ukxs851aUjxkKDZ1Q4w0aZNstzACueXjYxnd4rr3rh4/nQ3/3Vda0P6bbBZfe6Xr/7ZxbSiOfwtY+R9x0vJnmuovMkI15i9t9+W99xSX+a8lNX6ZrUkO+YGDmBQP9JwlJYo1ELNJ/jyJuJYLNo5GLlTQMfuh/Olj1qUSK9+EGNVCAZrkTXMNRwkyTB86xDAU1sUdGqgli+qK6c9L2JN1hnytV7pCqCYY16kohpkjL1fYsrUJs11qiuykmrzWZcFUviC7xYKrF4kzBbcFjHKUULSiTSpybTT6XCe9sYRiM+GqwWhao13nI5tnBuYhxOzcL6bwhybcaE/4ki5Al4rai8xWuIhyZ22qkwRnjxq3+IN/7k6/5Btmmrh+13R3cOnZEERGTtcpPv+PEdV8zvjG+QbJjNGT5XOLJBFLuo30i1GzPradW6kF5xRYlZ6QffakYrQasq5FfthE8pSTK3VlbvJ/NxoVnXlJWoVRxqQ+l946ni3eKwoYYwgHEVGmOMmuSTcetlua9QY5sPT31Vi0KsAVcNCREjDoviTIZLFgjzaqjKyXSEMxbPdG24lQqvtZGElJJKbcyXy9xzqZGaikRKRi7QsUykAw3SksrJQo+0Rrh8MJZh6S1YJHq589b1Afd/IH908AK+8Xve8EFC2eu2wnZWHd8KusQN8FS787anZrsY6PzjsHkgyy3KXAdl8snfYqRJ4LiiCqWFgCQ2LK8jsRzVEJe0E2L11LiVQ2spGmMWd0+clapJF2R462SPc0hVzp8zi3pJHrPbBrDVGDtexxSbmHIDcRWmKsFPN2yY+hxpyHpbLTG+DBJsWi10SUIFjsfiyHxB5janBH4TSlJGJIxJGZHJmBW7zg57lDV7hNXkKCv2GKmMm4qeRELVTmo9w3Q01TMcggGVuWSPTvEqgzxbxUo2Zldvg9VskzypZu80zN3Azd/4On73199wHxE5vkT7uYYtciS3s7d53nuSInKve9u+vbl3EDdy2NwiSVj6JX1L0rdLqT1iZcLZS8ykO2KEOo/pZ43QbtNfW0LDLzcusHk2n7SBYCSTecWdBjPZ7oXZ8bAnjOE9aBHLFZnzdlU1XFMJy/GpERRRF+hBvgyyuuOjgTspsc9CTQvSOgI5OVdRcCXOuhh7bC46lwgyKJnbpLChe6NRR95qA6aEWGVNN5JIBs9N1TAv27DiGNoN1n3oGW6lIrMluRZsuh6Vr0WQdeZRB/WheltqQwsOxVB5QcSTGRezupa9+w7y2BufxqMf/xWHgfkyrXMUytxb7bzDOeFJLitkb+3/ehH5oIi8X0T+slXDeUJ8kR1+5ObVgySZxWYWX3pwSm9PSjoMCZR0kC60VaYVh5wqTWwhVM9Eao4yRx/yy1oEtrLVOhu7jNunDl/2Tm6+6j3iPYw2gmJQVYRlunPgSqQqsMUY46pYLbMI0rAZxRhsNSYpN7G+mARvmcsdBa9UHdYV2CigET55x6MUFRh1C6t6Ui3jNSd7zFTvb8VSxlfIVu+wRzGU9Mw40I+MYzVZZy07xjAJwsC5GdMzYwyOBDdXsZMax450nT35MXZnG6ymY3p2Uqf/jOe/jHf+ydtyEVlEmD5n4f3WXtsVZ70nKVtryfBrqvoz8fivIGhGPulEY3+TPfjOYWrlPtkAkwuSCMZLk3ixmSHphUdUN/NqjGETptPgeS6B+rB8DsvmmNSoP8wCNvbinstK12pBVdkst0MBjEWsDWO0PdCyRLOZhmENf7JlwCI1KJSv9NoXREWQqsT4Cp8PJyrnrWU49U/vEO8ao2kxuGwAUUkIVdQY1KRNXDQr1ynTAUZ8mJNIaB2xAEaVpdQ7EUw8v7WJVEschpwx0vr/OLWIWIZ2kliqzzHqsQKrSdmck5uQvKrUUmpGzO8HJ3tmTqlxFD7EX7Ms5wUv+Xb+3zt+5985RxyQE0G1i0me9UaSVksGoClkJ/CmAFDVI63jhxyHElhDRNIrTf7I163cA9M3mDQmSeLS2jsly2digXFUmwXDZqICeSPY4MMHt+1R1vzJkDiJ+6JXaevltI+lcFMGLpifuUob79A0wXgPvgxZdVVIUmQ8Qm3SHEeaBwO7IJHUeJi1yhCB5B15OVg9FqtvLL42pjUZ3UeqThxHUGy5EVSDYutaIHA7vcOlfeq68axYDwWLElrVOjFzy/vmUdfL8SVzt34cjamhkhQjkGg0kKqNd2lxeDUUCzo6igQvdIaiGsbH4anITYERjQZDpmhJIjBMRhSaUDnLox77ZbztN26RJEmur6pqa9yZsxzbMGF/p3AufNstK2Sfgoi8VEQ+AfwI8IoTDfq1Zu/hh9lV9idpYyDbcIWfM7U2s2SDrKH54IOIrp8pCwzGkiZZM9nXzLXhVbZrtHE+vFRDHfeSShxprqdxnVO3qo2Zax+lxsoi0IOOtxaKBmd2SVsnZsAjvsKijXSaaPAiiSWNoorRELecW2qrD8v51jajDvEe6x398RFMNZ6K/It35NUmg+IISTnd57u+b6uOnh+R6Zjcb9Jzx6LRr++hJcpLEN7IGDMLVRaIbtQMKg3L77jsrit8ggzc9LGZKRkkI3akG3zHa7+DBzzwwe+R5YHicwrne+LmXDCSW4Kq/pSq3pPAtv/OZceJyM0i8v5f8bf1h16QFLRS1M3H/cqNaRWetLe4JcOSCU0I5nWJY12WN7vd+8kSPOw4ris8/80+7W3VZY4iocKF0YyhqX8vxlCMoByHGOWCYyRmqoGQpS7HmLJAqnJBvHCxTWirmYt6jPpoKCtEldSNSctjWFeSVAWJK7G+xPoxPbdOvzgUDHQdW1WdM2wWT+Y3m7kueoKLBIAtVVj+zz7RaG8XmTk7c7zG66WmomfHXHOvq7nXNdfw/33/D3524QM5x3C+xyTPBSN5opYMs3gT8NRlO1X1lifJ2n1flR3kqy86EPtcE8p3q4kBMlZCPLEhZ7Owt422jWF7u9eGPB7Oj8vUGO/0lcMVJepcjD9OGygtSvyMVFoDN92ve5qUvuBT7T2Mx6HrYlWFdg++neiIx9TSaa1leCh9maiRhzhk4xKH7a4KOpYLRHoVQgY8PJS5BI+oDyK/6rG+RNRjqxG2GmOcC3XevmIwPhTU0md6gLeRaEniizlD2EbOJokWWK1IdUxCgWWanlT/qku+qiTyQVNGDDhKn3WsODI2SahIKPmmV76MN7/xlw6IyDktfrFVL3I7e5LnQkyyKWQnGMdnMRHUBEBErlLVf4p/Phn4J5ZARO53X9M3L88PLqbNKJhUmiW4OmJVrgTJsxlDOSuiO3OxJdvjLu/xhUdSxWbzCQytHH5chrJFE2OXGoy11AkVH72j0QiyDLEL4nuqMB4hlZ20pV2UbPIOyliDbUKsUJIEUY+W41CuqD726I7HV5OltLoKaAkBN3HPYJAXSgFraFPrTY7xJeJ8S7AiZA3USIg3uhGI4E26KNcVKEN40modNUlTStk6IPAfqdqbEJS+P4IGOjwJoceOQ9g0O6b/j6r0/DEGeFRN+AIQIaOg6TkO7N61i2c+46v5/G1fOAQslik6R7CMgHG+4Kw3kqpaLSpkF5HXA+9V1bcBLxORxwElQa79pkVjiYjcXwZ//7XpvtAje9ExRjCZmcpgu8LT39UHQq1uGCvGFpPF4ywznmaB8K6WFSwwkrWR8UUZ6sBtTIp4xa9vQNZqGOY9OhqheY6dbS2rSiPb7Vx4Zcs5z3VMEh/KHYFI23FNXbdAKI9s3xsg43UoJCRj1KNJyLj7urSxvncfl9sET9PUBnWmbjwkhzyKoTc6TJUNUVcGz8XYkB2XyeLauOhNOqhsHurHCR6rF0FbFT31wlzUkaijbjdRw6L0/FHGZhjEhtWT+01sNOKCDx0fxTbztNGbdibla57zbJ7+1c9IRORSVf3U0gd+lmOR6tX5hLPeSAKcqCWDqr5yK+O8yhz4+IdkkytND18u/sebRFAXlsUmGlJjzXTpIXF5YXQSX2wjJm3mssqtxMLcPXqdJ623hSgWtYtdVIkzHofscW084zWnzjYmLLsXVenMoijCcY1xq2K5o1kY95sysMaGZXiSYl0ZHMOsj7gKccWEcykh1qhi8HbBnBSIvXGScmPqXpxYSpuTuDFSnx/nmroxqRuj8ThvMzw6qf+OMdskim4sekckeNLqEIrgFyTSBI94jaGCSRjC+hKxPb711d/Ib/zGm/+FcyO0NYeOAnSO/uNOBiLS+209dMXz09CSIVmxc/xGSaUxVDabeIJJP2mNE2hCdXNCWfQG0ta370zQZhl5fGH80Qbprzj/Rfe0cCwtxvjNDbQYhyXxLL/FmEkb2nbkfdF45Tg0GGsSIkQi+oKuifX91sfFGKSMNjCjDWyxid043BhI6iMb6tN0zLI1KMaVkaY0k3hRR684SuLGWFcsPF8gtJtQxXhHXq4zKA7RHx8mK46G5JSvyMp18uJoq2VFuB/jS0yrte7U9X1FXm2S+AqrDqOOWjQkcQWPfvhDKYqxWGsfueD0cwJdTPI8wXPNnsOZFXYnKWJoFMY1BVSitFnkShoJxkyEbJAiiaCq2MS0kjeCGqUsSkw642GIUG2Oo2TadFMvX1ZNg7D28ajiRuMogQY2zxq+HlFsYW6ZPqty3uwI79qmdW27c2J7jDqRAzFW2Z8fB8LyfP0o9AZBbUgk9NZxVSCcp1nTNyc8mcn5Uo6iyqOAsVjvUFfikyycK4B3oR5cPcaVVPnqZDz1TSxTZ+ff/CfCclrRQIQ3s/zWEA9Nyw2MVoGIXu8CSg2tJeqRjXpM6RinK1hXkLoxMGaUrqAzZZV2Jlkl8XwvBlFH5ka8/tUv5dZbb3tnVMs+QQH92Qd/nruS54WRFJEL72ny7L+tXIGJHzI39ti+wcx2CwTEghhDOkgwVsAp3jnwGmq7a3UeEfCtpXKLSK5ANS5J8hjDs5GIrVCNSmyeNlnxtqGrDZsrStJ08u+pa7nbJqItgQYsNqRVFZI5dex02Ve+czAeTWKVs+6BKjLeRKskVP3EeCLehaROmkPWm6YE1ZxNMaHDY71dFVOO8THpYaqJZylVSVrdQZX10SRFprQv/XQIYrKjIbZbF4SG1SSxNe4GxpV4m1Il+ZSBRD3Gu5BVmTGsRn0wqkwMf1ZuUKTDJltvZvifszCR7nTFPS7l4Q++P1/15MfeTmgZcM5A2d5e4lZwXiy3v9SsfuZ5/X2kLQNSHln8hW6skPQSsmGKsdPVM975+RYNqrFkcfrYurLG11U47YRN7eXJcXrXzL4x6+6J0PQQEAjliPG1cPntPaq+oRq1Pcz2PQDBq6zJ57OcSe+gqpDxaK7LogBSjpG2ypAqVEXYVnsitdHUSPL2VYhPtraH8YJHaMox4sqQ5KkK0vExzGyLiRlKkKiSlCPS0RGy0aFQK04wntnoKKYcYcfHsMV6UDiKtKJFMDr9HCwe60Zk1QZ5tUGqi0MOQcItUJrqf+Q3fd3X8mu/9Xs7ROScMpKo4vzWXtsV296TtCIPfVAylIdmq9M7FLRSpFVtYxLBZhZrDd57kgWJEe88Nm1tP06Gu7mOC1lQ0zrPlw6xJraIXURFCt6rac9Bg8CuAqY/U3etGqpkFjUUq7mMVainxns0TTGzWXBAXRXmU1+35kk6F7ytVta/DQG0GMUkjwmGrTYg3sM4JlwkdFgkzZFyjPEthR+RoF9Ze+oQjWQgnwMk5SaVOjTpARPB3/ojGig9fnH1T6tqqH0TxlW4OTX2yW/t8EHi3VRnybCUaPUOUsUQKoqkdfbqypCbn/sMPvqxj93GOfa5W/Idct7gnPpn3VmIiLlWeu+6ebiYE9nOJtvMkA0mHxStPKQLssczaAQvZol7Mk0DUu/xTlrEc0XLID/WCFi0Cc3ew+YYja1km3hpHM8XJSZNJ9f0Hh0X0G49q2FSU8mO2ossy3DtVitaje1pm1hnrCufnOsm487VU4elsqLT9duxxrz5WwQxFi2LufYWogquRG2KuApNarrRTLKmKkLluJ20lKivZYp16M98ITbPooWWUbNuhPgytKVoYquBGmRdga1GMakUY6NicElOlQ0Qsaj4oNmpfiqsQC0AEg3ws57yZL78eb9tReRqVf3H+UmefQjL7e3rJW4F29pIvtwc+Py/MObSJJ9bvtq+TFXQpL1WBrsRufBz1TTBYdNGKWhKwKJFEVnEk1TnY8a6lZnWJW9C1bCvKCdTTxNsYsMHMXqOMyeFVg+RcxkUimyMp/qJcauX+UURDGP0LnEhEy7JCVKW4xHUfXXiPdRVPFKV0B80ySj8jEfXpEM9Igu+hGLduRiLeBc/pH72+wdbjtCxD43OBKSqmgy4uhKf9uIyOnq+qsFYtb9A1GPKUUi2AHa8gY/XbXzZmdVE6NXjMcUGabEeDWYfo2VLrV1QYzFaBQp9NcZWI4wr+f6XfA3/9Vd++6OcK6GuRW+z8wznxj/qJCAiK7/j79jzNWZPiIFZMD3B5EKyYrB9G4xWfbwJH6a2d+krj6v8lBETE1ouNDHH1hjqfCN4sWwJXY/RznovvoFgeNvtaKm1KVvxTnW+aVGrqmhZ4kYj/HiMH43wGxvoeBwMYG0IqwotCnwdfyxbquWqaFUunlPzYHyIXVYuxjHHk0+SaiiBbN3voucgxzPCrkLWDyNHb8ceuQ2zfiQYT+cmJZLeYVyJLUfYYoRpxDc8xlXY8XqgHBUb2HGkH20eQaqimUM7YRTOVayroliHspiSpKFuvRwjZYGpCmy5jjiHLTbJxsfIxkfJ1u/Abh5rkkc2JnluuP+1rPRzecN3vPSdx3/IZw9qPdMTvbYrtq2R/GrZfceXmZ3sWssn+pB9QzK0mMxg86AVaVODSYSqcHNLSGNNyG43GySIOHhpPFNf+mnuY50cWfamMRIKX8oqvIqqSai0MZFfm060+DL07Q4GsQr8SufC70UZjq/i784tpc2Ewfzk/Pb1q0mPm4Wox6tFe2fhqiCqUcyr7sSbi9efPlddBaMNzGh9IqRBrOQ5dgRz7BBm/Qhy5PZo2KsoIFzNhQAWea8CmPFm9KpnDOCC/1fI3k8bycCpnIgBi/eYqkRcgamTT1WFuBJTFaTrh+YSXd/7Dc/nlrf80SNF5KwvV1QF53RLr7sCEflqEfmQiHgRub61/fEi8r4oqv0+EXlMa9+D4/aPi8hP1KpLIrJbRP5ERP4p/twVt0s87uMi8gERedBW5rYtjaSIXP5+1pMbe7umPEONVTYmkamyxMBf9FPlV2In4ruNVxj3q9cpebRqVOGq+IGXMJ4uIY27cRn2BfojWjnK9dG0BtCCeF9jxFTxZYkbjZdkqXUqi6HlvOhEuBfffKC1LNHxOFCK6lOLAq3KSa+d5hLh+loWwatbAK0qdDwKRnLWiKqG7XW4oIyk9vEoZM5ns+plEZNA0xlsGW8gxbjx6ihGccyWsYznSzkOpPiyCHFDVy4U41h8MzN/L1Ju936idBTn2hhRFFMWU4dfcnAfX/bF1/P9Nz/z8NYmcWYxSxpf9rqL+Afgq4BZD/tW4MtV9b6EcuNfbu17A/Bi4Kr4qoW2vx34U1W9CvjT+DfAja1jb47nnxBnzEhuoSXDN4nIh6PF/1MRucdWx75BVj7xgmQfNpEpw+dHihv5pXetzkfPbZ5v2BzTfBimt9fK5U1ipXL4asEH0Su+mvHcai3J473T6l1tGtGsAZL6ozk14aCMPjUHPz1WfWhVNfPyoxHuyBHc0SO4Y0dxmxu49WP49WMwHqGjTdRVaDGeTji5Cnw1eX61sayi1zfaDEv1Y4fDvtE6bBybqKXP3o/3E8J7PffZOCfBowtx0Zaijysb41oTzqlK7GgdU4xC/PREn+72hVwVzpupNqo5mlPPdurBzv9vv/GZ/4lf/5O/6ovI3uNP4MzDe93S665AVT+iqh9bsP3vVPXf458fAvoikovIBcAOVX1XbOP7RibqX08Bfin+/ksz29+oAe8CdsZxjoszkriRrbVk+DvgelXdEJGXEMR0n3misV9uD/yigtxfB0EnMgmGzVjB9A3JYGYJHWFSM5FNA9zYIb3p5E6YPPH8aEzNhMg9W3/tK4/iQouGNuqv3ikOpsMuqgOvT5F5D3PLcSDn0HamXY/zJVCWk0QOTOKYLYOs3iNJMhG4qELSopFfS9L5MaNQbTthxWgjZJOXcUUnFwRME4tcTCinyTCr24D+cKnBEu+QItyPj9cWr6ivoLcyNR/xDspx1NVsxZ/b/z9XwsYmrKwtmb8ixQhNM0DAlQyt8i3P+jLe+f6P/Accp7/tGcadjDfuFZG2GvstqnrLKZzO04C/VdWxiFxEsB012mLcB1S11vL8HHAg/r5MwPu4up9nKru9lZYM72gd/y7guScaVETs1dK76VtN6MOkhaJ5yEeagZAMwvJZPaiZNhSLVIFc6Ujy1iMSSFLbeBeqHnxMwviw7DapmTaKS95gwcBO/valCwpESdJyGkM5pLpIMenl0x7UzLf3sjJFBSgiPccrki7vwqjjAm2JAi8re1TViUOuGuvE/WQZ3euH+VdVWJrXRiWLLSWaoG7MrusM37B+durB6VTZ47JnGgxaILBrUUC/P3/M7N9liRSjZp+2qo4EwnXnMtyt+bmYxBFBj94B+XDu+JAEcsiojF5x+CJ5+qO+iJ/7nXcYEbmvqn5w4U2dBbgTPMlbVfX6ZTtF5O3AoiZpr1XVtx5vYBG5D/DDwBO2PBtAVVVmO7rdSZwpI7nIoj/0OMe/CPiDEw36dWbfodvVcVAmJOnau5utr/aVIib0kJmtrJmcPP2nSczUknpyDY9JJ/1upjw15xerCc6uir1SbZYkPZDYHEyhZRhCOEDq9rQ+xAa9c9GbnSR6TCxnVNUgzeZnPKCyQrJ5lSJ1od93+3jyxY26cA6NRHid8TSJYh2a96aTN6phmW4M0m5aVi+rx+vBsEnLayR8GUlVtjy8aIjbBrwqw1K+fryuCo+kN28oQ3UP4dM/Ey8UCNtsEj3qaMTbSSHvkGp9esyYEdfRMUiyUM+OTBTfrYWNo82zVUDE8EP/+el85y/81gdExOhZmiL2p2haqvq4kzlPRC4G3gI8X1U/ETf/G0GAu0ZbjPs/ROQCVf1sXE5/vnXOnRHwBs4BnqSIPBe4Hnj0cY65GXhJhqy8MN0bBCmqSLeprdEiG+gBoyBm4RJ0dqmtTpfKpzb13DMGV2MfnDbfUlWDDq2NlKKWRxjqulmodu7L6IUoTR8dnEf9hGju3RhftKgtvRybtUnyDu/KYOxbfb21rCaxy/bSvHKLJdmcDxzONJkuR2xupALdXBxndA4djZB+v+FTahmlxjY3g4GRcKxqrDJKonZmLfpbt6UVQmxzdmntPWysB+Odx3r0cUzg1LAJkmbzc2wbhWIcxD3ETOraa8EQ36pCquldEAxjbRyribqT1PffHFfyoAt2c+m+XfzsS57xceCe8w/yzCJkt88cUVJEdgK/B3y7qv7VZF76WRE5IiIPA94NPB/4H3H32whJnh+KP9/a2v6yuHJ9KHC4tSxfijNlJLdk0SUI6b4WeLSqLuGTgKre8hS766evTHs8PtsZtjVOhyKzKj0L4EolSBFOjKqZWYIfL5bnncfOqs/UY49LyJIpo6uquHHwYuyMtxaUghZ4cMI0t5OJcWwf056hH41DeaMJpZaiINZMjGLttcU5BYOlgQxvTDBobtpQqtdYhligm5uYXj63LFcIIYJkyVvM+5DYclEAoy6F9D4YJmMDg2AzypaJ4G2CybMQ86xFhOvkSyt2USe0BIIBHY+aZzNlEF0V/p4pz1QIiuvOhax5jXIMaR6z8tEDreftPAyH02T1zXUmDdUIBrXW8KyqxrB/zzOeyJf/wM9e8WKRnqrOFKefeZwO/1ZEvpJg5PYBvyci71fVJwIvA64EvktEag3ZJ6jq54FvAH4R6BNWmvVq84eAN4vIi4B/BZ4Rt/8+8GXAx4EN4IVbmduZMpJbacnwQOB/Ak+KD2QpROSae5ue/br0wGRbXcZnJQjpemWhO9nwEA2+AhtrudtqPw30OIZSFVc6vPMkCwyidx5RWeghzg3lFVdWTdWOqs6V8NXjqvcQaT6SWGy64F9aOXzdvKqtVOQnGfm69ru+F1+E6iBjLToao0bqwhVsmoae2tEYqXMLWt/6Zo5T827zRUbBHvh4bbuygkSjraONQA9qnkmId2qWhqX3cR/gkv/Rgm0ak07S9go31mNccYF1qIrpfgaRRgWg68fqjcFLnfNuHfjaC53sO7BzlWc94oF85YPvfRgW9L09wzgdyuSq+hbCknp2+/cB37fknPcC1y3Yfhvw2AXbFXjpnZ3bGaEAqWpF+Ib4I+AjwJvrlgwi8hXxsP8CrAD/W0TeLyJvWzbe9TL8yIt7BxoZtDZqYV0/mq6cgbg0nlEcB8DKHMUHQIzBFfPjxL0xbqiUm5MlVjBkk8qc2mD6KhjUxtBNPyB8UVFtFlSjgmpzHJqGzSQu1Pmp5I1WDj9L95lBnYWHmTd/e77Oo5VDixK3OcKNRsFQjgsoCtzGxjSnsqzwbfpQVTUerB+NgjGtjWO7Mider/aq3Pp6IMJHxaPmeZVV8HzHBe7IkSmOahhm+gtpWaZh0f9NyxIO34EePYIevgM9fIi6FcZWxwjPMnqXZTHhfi4eYOHml974SN76vo9mInLh4hPPDFQVv8XXdsUZi0nqiVsybCnI+4324F+sYrjG9Ob2Sdb2mgiqP1lYjzbCFEze+FPZZq9o5SceoUISDa4rPSaRhmi+KAPsCodpZbnVKWp1hmSuzec5iOzK9IdIQ6tbCNlvtMBmk6RMzesMcxfEGnw1c10NPXKAIDwRdS01LmEhxiZrqseUl1Q/DA3H12LBGpJGprWUDjXmZeRJxhOjoXMbm5h+H8nShfcYnk8Ug5ip0tHKTccbK4c/dBjZsRqfj0NtEsIBIYAWnutMq4XGU27PeZaDGTaiZajGUZ/O/2+dX+iRzhnmRQZZNXakTObWNHma8J1PeyxvedcHPsNZVuRxvve4Oav+GXcWIpL9rj/0iJvMXrSc5nNJGpe2ErxJkws2NxgjQSSina1sjCWBJuOCgfTxVffkbrxDr/hScUWo7V6E2c/IotLDsIPoOZah5HCZmoBGErqfGM32m1d9NMAaJNZCwsgFA1lfO5YvutGYamNEtb4Zrlsbitb8NI7jyzCvWW9Xx8XchycYIm284/b/w29u4tbXQ7nklNfqwthecUWJGxeTOvSlz0xxm6PgzdbJkbKIBqg2utWkXW+kBGkZ2/T6qK85Xhzm1mjwdDxd1aQx4TQ7o4WrgVh3P7uN8Rg2N8L82s+nqnjSfS7n80fWJbHmIQsndoZQF1ic6LVdcdZnt4+HF5q9h0uUvZKijph9VtKVpDF8JpemRWw7KbMIxsokGw4TjygSxGsVnykPxSmuci0ieqDVmDrOR/wmViV0O5hP7qiGKdWVOI5QErlISagcjbFLkiF1LNMVwejVtKQpzIznxgUYCbqVbSNZL5HjBLWscDH2aJIEVaU8th48WBFMmoYkr3P4YuKdSRoSQOoD9chvjsPzydLYViLGJIvYKCx6vZKn2AU8x8nEq8DndD4YS2MwUSZOvcePxqHc0ntMv9/QoqiqxrhqVSGLmqGpNnFD3QyUJSD8rYo/fBgzGDQ8UL+5EShN+XQ4MczLolWFW1/HDgbBk48hBtUSV5ZoEQVGVPmBL38Er9zYfLeIWNU7wVC8mxC+W7evAdwKzlkjKSL7Lyfv/ahtJcktTb315MCZn8cfc26bqjZJ0dogzMK72SZRitO6DWxrye904RMPBPf2t7HG5bViszQaW2m8E+d1itYzc+nJvMoQnzxRssiXsVug15CoqbPcdRiiVbuu3oU69VrFKFIOg3EL9CRpCQlr6VDc9JeLKn5cYHp5NBgT1XRUw9jjMnDIl2XHnccdPRYMTH0fSYJkaSTPT2KCfmMDWRlOMvT1/fmwBJ9qn6GKjguk1zJ4bY/Q+VCyubnZiCZD9DqtbbL56j2+KILXuhH4m1VVkqysIGkato+Dd9se/5oL9nK/i/fzrAde/XngLChZ3N4KP1vBOWsknyhrn32gGZBHgrfJTPAYc9Nka21uJtqQhWJMKCOc9QhD9ni5FVXvF3MFIxYljNQrGJ3qoaM+eJ21NxmWtBrswiJeZOXB1MkYRSSK70aP5kS0pnqMExlJtzFGe2HJq2mKGAmJohifFGsx6YxXW3tbUzdN46E1DcNgKqM+NbdxEQQ40iTIrjUfRgeJxa+X+DTFzlCMfFmGipqZ8kStKny9hBZB0qSJgbqjx6CXx/9HbKyGBPk5axoqlEbBDw9IpGY12X9VqvX1Jm47S2/yGxtgTPCk45dq24hTOarDR6aJ9AsM0GtuvIEn/4/f2PNKkRVVPTZ3wOmE0jUCO9MTOBmIyAPuJ33zMrs/xB7TYDxMPvkwtg1kjWrsSfuxmqUOwIsAirGLCeW1i9iUDcr0MaoaRckXBfPnN/kyxDknH7zag1tsyNpLV8WjjujBuFBe2JrHQk/YOXwlSJtONFUz7kPs8ZjDpLaJK84uvb0wTS86kYH2fq48b9HcgIXiHjWxXcdFWOYntllKH1cFtk4MzSyn1Xv8oSO4+joi2GEfk6TxPxeZBtEDrfsGIdLUsofKoll5tZk1hHPBg6yqEFJYUJ2lMU4sSTJZfrewe9jnhTfcn3/7wu13sLR84fSh8yTPMYiI3E/6f/efs/0YYyZyZmaaCD5rIGu0M9NAeIPaeskLKsrUrnaljFMUHxpxSaT8qA/ct0U2bpkd0ek3nkahXjNLyK493NkkkPNU4wLjfPiQRa6nzeezsaHcsUASg1iLTSxVHW8UiR/6aCZKB9VMD5/mmi54fM24C5apMewgRqa7Weh8S1yNiSjTFtNY9Ki8xxehq6Es6AfUXDcKGdexVLF2ih7ly2nlHvWe6vBRACTGUxHBZjMk/iZjzlxywlcVxkwbQl9W097jzFz9uJhK6Jheb6Ex/dovvj+P+6+/mojIZar6L0sf0N0MZf6+zzecc0byG82BT31URtzT9DCZOe4yeSE01G3brJXdVpnaP8VQaSn91Ku78KbRhojtnZ8rR0RCEqh9fhheF3qdvnSQTEjw6kMXxrrmvB6nvkaIWYLRiUFrWti2yeK1gdLwAXbjYqJnGQniYkzjadJ4xsvhyopqXJKthuSFryrc5iQGaBKL6WXhudRzdj4kQCQYeTceB+PW7y2NBbvRJPvsRuH+k5VBqB5yQTDYF+PmS8RkaeOBSmKbSqbGkM6GC+I+vzmi0aFcFWzL+xQTaFXVaIRNZ76EvOLHZbMaUeciKyJU8kgrTKOquKII9+Tje8daiCryJs8xiW2+TKzCdz3xofzcX33gk5xJForqGS1LPBtwThlJEelfTn7x9/cvDr2x24K6rrXkVOaMUzi/PjgYSpO0kiZLlqu1EEa7RDHQU8C2uiS6wjUGoWnfkCYgE+OmGsr9ltWRz7WrJcqttd6kxprgFRKy4dKOrUaS+qIlXrleTvEn2+WL6gM30MQe3svUhKpxMRENBsZ3HAnnzM3ZoaNiJvMtgW7USqggEiTiFiRnavX1IKAR21gAxe1HQoy0l6LFNMfRF2UT/9TKUWkBozG+KEkGrZ480Hx5aDndBrc6so4f5OELrqpwpWsSZt4WpKsrU7Hsan1jqbCxd4500Kda34h19+GexQQmhFZ1IzgTWm00jyU8+0fe4wJu+cu/lx99wkN/8Zv/+N0vWHiR04Dz3ZM8p3iSzzF7Dj/W7GCnTRYbtLHiq+AK+hneZCMV3UY7E7yU7xgrZiqdHB8dydlYTS1mURvpquYEamvfkmv4BSIRtQc0dY8tAnl9zfAzxhZnWjFoFPkN49WliQsy+THpAuCKaqoaqBqXwfC5aYONMvnwz8AVJeWxTYqjG4zuOEI1HjdVOe35uVFBNRpTHNugXN/ElRXlxiZuPG4M9izf0FcV1bHN2UvWD21yXFHiNkZo5XCbE3HghsO55MOv4zIkcHzweN2owBfBWx4fOhK4nJFvulT5HcB5isNHp5+R1nzXcE/eOcpjm00d/+y9fM8TH8avf+iTN8nCrml3P8Lb4vzucXPOeJIicvE9ydMfyy5tVdHMeItK9Pri+ykUNyDJjFGQ2sBN8x3VLshyK5FgDlUdQ6POeJ4gf6FhXK8e74KxrCixadLEGtuG0Fhz4gZhBI8RE5Zm1bjEGMFFcnk1KrFZQhIrc2qjGuK2stCLbc/Xe0+1MUKzFJNYqs3JktdkyVQf8MbQLEotTMUslOLYJsaaqUQUhKV56UKHxEawKT7UpL94vrXxPBHayu2+rPBH14NnHr80bD9fzFv1nmo0Dl5zXdFUe4BFRVEdCx5yXB7XdC1J7Jza06LsdTC+DpMm2Cxp9S2CJM9QPK4IoZFL+jk3XHqQr7znRYeABb1y72Z02e1T70nKidsyPEpE/lZEKhF5+lbHfbSsfup5Zu90QZfTZqmsqsEYGoOvgkcZKmN8I5vWQAHPnEF0RaueuubRteCLaU8qEMl9KGFc8kbyTqe8UFd4ys0SX7rGO6tRjUqqUaiAOa4RqBNLLnxAq3E1ub6CG1eUGwXF+jj03ynCMeV44tkunGtVUW2M4xgl5cZoZv+Ml1q58AGfqRdf5AHjdc5A1mPWX0R1mWfzPDaXCj81Xu4cWkvh4JWOKddHof59VE61snCjYn41oMr48DrVxripYGrilzWZvKyoNseUmyPK9RFuXOCKkmpjhFuStFkEX1ZUo4kHWW2M2PiP29n4jzsY336Ean1EcWSdl1x7Ob/24X9ZkSAbdtrRVdycQsjW2jJ8CngB8M1bHdeK3PAgGcr1ZohJZlyLuhogkUbMoob6mGxZ4u5VhSPJpj0JX0YOnJHmVaNu+1AL+QZPKhgrEeaEfWHRslajcZWpmOZkzh71NtD26gD/3DFKNT5+IysfKU5TYbhKceMKmydBwEMmMVm/qMfObAbn/2/v3INkuav7/jndPbOP+9DrCsRLCMkIEJjYPByccgwhYGQcgwEZo9gECgHGduzCxDFJSFUUx0mIcVDZgZBSwGVDVZBtEheyceKALQJWjAkY8RAQHkEBxYCsi+7u3Z1Hd/9+J3+cXz+nZ3Z2d650L7e/VVs70/Ob07/+Tff5nffxWh6rPxj5eFpJmWqS84xzag91bF7t6PT0mMHm2oxnvGDkUWLtH9Q5XGYxqCrGcMw+W8SWAq1q9KiSj6ck61WmTjaedkt/hX204TGftf+6aYbEJoH7NGewuVZpNgWt+kbraw4l1Yazq9hsjg4SbvjOq7jz5Na93O/a37e3Kr0MVr3gy7RluCt8tpTLTESix7F++w3Rpc3Qnvq9Oa+yOJgHd568HJwlUSezMiZg6YGtz7VmXywZiVSB2y21sfpa8wHrqoheSkghuLmYX73QRnH9ewWTd33uMkee5UQhVVCx1hVdamcbCky2xyRryUytTZ/meIJXN46gFpLlneuU3Ju0tdMS4NIMn2YMjm4YQ2xJ+NZwzfLIXZqRtyVDXKg9ISQd1+hzx3Rrt4qvrK1rqUoLREVgOlo6koDODTjd3i0ZYbo7JtlYC2FV0si/L+eoiktNOlWvDDbWyo2owIuuvpx3v/dDsYg8VlU/P3chVwxVqk6g5ylWzST325ZhT/z96EH3fk1THiHDBmOUDsbWhsQQDaS49zuxl5rgvScKVolG4y/nTY0uzyXEgxjnqzhMr0pUF5FaD7BLmz10CgnJ5x7ywnFi4+Kh2buU0JKhLll1ePLnoYhP9Frd+EICAvFML5dmuFI+MTNBGrzIa8fmhO8Ery2RlGprNIjt4Z8zJ/UWIlQxVk8+Sa0yehSRbo9srrGQrK8ZI6amCs6x/4ULAey62/U2S6mu+G7Y9Lxz5KNKFXaTjMHRdZM4A/OUOOrcgNvOrXw0JfO+MW+buyfdnVRqfUA2mlgs/jAuN69IhDc87Qn8+sc+/zkWW5ZXjl6SPEsh1pLhNUPkopfJJXbQ19SerpAdT6PvXKl+a011bX13z98/1IKUSPAZKN6EvHaFLaf4yMa5aVUBR+LIGFy7FYQq+djhsirGMk6acZ/1ubnU4XBB1YdkzQKgC8dMHAr91uMpZwK4a973OvJpho488TBheGQtOITywFRioiSy9MlaTUxCkHqyMaStwhb/s9OTcg4+c6Q6tZjMSMpSdd77MoaxYDzqHNluZY9U70KWUYQ6JZ+kNckuSGITC9KOFkjERUWkwuGSj1NUIE6SGYeLm8zaFrPRlDiJycZTi0cFY+y132y+TVLIRhOiYRLqkmblOTo3uMCI6xL+ky+7hAs2hvyrJz/+w//k43f+zbkXukp02ObPN6yaSR6o0U4XVPXm66KL3/oQGXBtdGF53Odq1cOdovGs17pJpPayFBjUVMHas2TOGhsvEQ0mA0UMpD0M+cQjwpyslNmwEs09DpBWpfOiTmQhCcWD2DzgXolCxop3VRZOIQGiQB5a3kYSGJgnGxUxguYxHh4ZIlGosKMm3bgs765cHuDSnEmthqbLHS61HtrxMK5iJ8Ml5lOLIyyYaDyMiQcxqpCN09JmZ50ohWxnUn5XYmG4aWplFGpcomrxjR0MSp236ugFYwzSasUg1crMpbnRnXuNGdPtUSPky8URyebQint4JRtPu73ezpMF55N3ShyF4iAhA8s7j0/z2Tx3KmnMpzkuOOwWmkvENApXo6eq/OKTHsOr3v/R73uDyFBV51T2XR2UvbWtb3esmknu2ZZhWYjIVVeznvy9qFUIJQhC89IO6yiLGLSOkYIfhPgdae7kGqTVqFW93+xQQRKdc+p6323vajtwUJ2jcD6f+6Cq1SSQLA9hQibxWgiPoIlWzcJKIS2o5U5NEi3sf2pB7i7NmXizj5aMVaxMmyaLC2OUtDNf2l1FzHY5WO+23brQw3py33Q2JRFwKY2gd1tPZXJ6DF5J1gckG0MzYRQ1MUNIU1EyDmB6esrasXWiIIH6NG88wMUa5WlOMkyCyp7hnWewMUQEsvEsA7ZNZlpuXABRNFtM2WJf82CnTNm4cNOkUl/Fk0ZxVDK2KKjmdQ85BDt4q71Hl4MPbCMizW1TVuWyjXV+4PLLeM6DL93CeruccfgHvmLbA4qVMklVzUWkaMsQA7+hoS0D8DFVvVVEnor1srgI+GER+eeq+vg2rafJkS8+Ty4ibt88QrAmE3Koa+dv2dBEQ9fEQijw4Q/QDEi0s992mbEzj5kUn8+kG9oEZ0KCFNzEmUQZBTXYmURr2Y0SJKrC3qmko8wYhLOWt3UHQSH1lu87PLeWD96aQ+ZApNlLHHsQ606kOoMssoskc7DeDIjUGjOrUiGDAbgtSdXa4dbn5HNPujMlm2QlU9G8VrB3CvGaxX2q84zvs1auyVpSOtQq77D9c9OcfJLhahEAxeuGRNyaSx0uc8T1yvaqZKNpTQJVRid3SinP1QuRhDVI1gbB+16ZUoqNQGg644oQqOL7db6kzuNUyzoFr7zmSl74vtvXbxJ5kO7R/+nQKExV5zFWbpPUvdsy/C+a/XJn8NPxg98ZI/LEaHP2Q3Mw2p9TY5QF7Rx00A7zwILK52E/JvCaiORS1+iR44IXdLg5WHhTlRkyLjB6CEU1xCrU1Bigcw4fe6LYqqxHSVRKerN0myaEeYHuFodptkbCw+1zD8NQDq3mufetDJvp7pTB2oA8zYPttZLkoiRaqsnZ3HXJPTqoUkTrqJ/HrtWTjlKbt9q5BxsDvPelmuyz7gwm701Nbh9v9zSyMKtQCNgr2Ti1DVfCtUbFhjLH86sh7jUbEw+S0rTgwtp5IJJ6SqnivCvTW0vbamBS2TgrN2bvPK/+jsu5/Zsnv07DCr96KPMzxVYJEflR4EbgccD3qDX5qn9+ORYlc6Oq/mo4di3wa9gavF1V3xiOPwq4BbgE+DjwUlVNRWQNeCfwZOAk8GPLFA8569ISRST5gN966SviS42FJ9gSRPa6yHQp0GBIDvxES6/znl65HHw2O2ZeHncbPrfMCZe6StWbuk4mthDFeE/juxpCgLz3llGzDy+jS3Omp6dMT0/LB6w4lc8toD0rGE045jNPNsnLQPp2QLib5Ey2x6Q7KenOtLH2y6xX45J19uGrz3NmfKEKKxbAn1UOKJ97JtsTptuTSgKeqdhj1zY9PZ0JaXGZY3zfaGaNfe4Zb41JT0+rhIRgilgoXYldX57maK7k44zRyR1GJ3fMdqy2AbnMChjnmdl2feZId6dMtsaMTu4w3R6X6aqF7brYGJ592aXcPZlGIvJdi9b50FCL8Fjm75D4DPBC4ENzPn8zVcvYekz2DwLXANeLyDXh438D3KSq3wHcB9wQjt8A3BeO3xTG7Ymzzrv96ujSU1vqeHBSKx/V4ZApGZlTtBUzqJniRREvZluM5j/ENtYjob2D3YiK0BE/OccxVDIVj1WCGWinY2c/ME90MPYHCSZPHWtHhx0TaR4y5l3dtD7zZD4j2UjM4dBV4NdZAYl8nBGvJTPe+Gpc4Xlpfu59re94W30MAfQWXVAVvW3Y6ZwnDww66Vq7QoLP3YzUZ9MxW+hka2xtLzDVGmCyU9kbRYTxt8YMNhOS9QE+ZCMBpDvTEMqVkE/y8rhEwXFVu2YXCo8MNrraPxhTr9s4i+M+d2STjHxiEnw8iInXQhB+vX6AhoiGbGIahGt67kWEX3jcVbzpM1/6hIhEegbjdO4PdVtVPwfdz6mI/AjwFWC3drgzJltEPgc8k8oX8luYhPo2LGb7xnD8PcBbRET2WruzikmKyMVXMDzypsHl+5NMujzdORDZQy0DZrJx6vCpolNzzEQDQWLBpSbpRKH1SzyIyocfKNWgIifbLsDUf+8UkdnyaWVY0B7XZmExrYMKeEh3MisZtl719i4cVD6kYRaOlOq8WFpgZmk22s48IVQbCqqjzzzSZaut0wz549V7xakLqZ1WNamy99o6mO0zKk0mpQ3UK/nEGJKbOuJ4tgReUdV90QObT/Lg7Alrn5gTRfPZONJsZOmaM15op2R51ljDwjlVt+W6NCefmOc/WW/FusJCycpMHjbOZZYpBBAPm9XyC/U6m+S4qWft6Fq5casqVw3WePjakGuvuOKrNKNKVgZF2UernRMiUleTb1bVmw9zfhE5Crwey+KrZ+nNi8m+BDilWgbp3R3GNr4T/CdbYfy9i+ZwVjHJvyMX3vMYWWdjbopMhVm7o9rD35KSVNSqeUctR4vXUoWSRMoH16dKtBYcLE6Leqt4ZxkyFHatYTQTK1lKuL56sKSw1dXizWbSF6W4CCtU4VJfSkEN8sEnol7JRhkSWfiMD6p+NIjmVjMq10mqLKMy9a3laHK5Jx1lrB8bzqxZsTnkkxw5ItVD65XJdlXbkYnNZ7g5sHN6Y3BZrYWr1BxV9fOMtycIQhSbdJisJ2iuZJOi3Jgy3GzGZ5r9NDCozBFJTLqTVo6kBevRXGOLGJgZ6iumZXGkDp8r41MTkrWYeBiXm2lXBlebVvdxsyvn45x0nIeNxFqT4GGyNSkl2jzNUaf85CMezs999vMP/2WRDVWdUxrpENif4+ZeVX3KvA9F5APAZR0fvUFV3zvnazdiqvPOfk06q8JZwyRF5PHXsBG/Orp0T5ve3KJRHjQqDFW1PNukUFnDw+i1weDUWXGM0kNbe2jVVYxB68UXnJ/vGSdIgwo+dThvTp1ivEutu6JzDp9WkqgkhPYMc26G1mEXYhoL5uWmrgyY71we75mcmpYtA6I4Il6PcalH1VRHEZOy1HlGmWN4dGjqXu5Jd01lz0ZZmT8+2BwQDyN8Ohuk7jOzu81z6MyoowQmNWlKwtk4L3PqAaJESDU1D3cclaE+bdqLtIfiXPVoCPVKni1IoZRgFmjNJ5868qnZM+OhBd935eX73ONyk7SLKk11uDxndDKtYkbD/eudL5ljW0u4ME547oNOsLWbbgHDGaKHhuI6yvgdiJLqsw7wtb8OXCcivwJcCHgRmWAOma6Y7JPAhSKSBGmyHqtdxHHfLSIJcEEYvxBnBZMUEXmSbH7mx6NLLJYw2HS6bIkzBS7aqBW8KO2WOaV6I0m3YKE5SGFeCkzRjbw9MHHYzetYpIEolbqstWPBM+0m3moMikmxxTUVjNtPvXm16w+atiRhNUk48yZxlDbQlj0QAu8XYbI9NbtpUcswdyadKaH515TBRoIGaVSdMt2alrGk3nn86SpdUtWTT53Z9zokXzCGGw0iu8aOAFMr8QbJuvUeahfuKJ00FLZBk4Dd1JGNcktT7Nis7Bqr4Pf2mtj6gXpztLnMAvJ9bgxpeLTJb7zzTE41qyK1ISKM75sw2Bww3EwapoU8daS7NQ953IwGyMZWuamtdRS/Q7prMZjJWjzDwF/8kMt4xSfvHIjIw1T1QMkb86D7kyRXDlUtM4tE5EZgR1XfEpjcTEy2qqqI3AZch3m4XwYUUuqt4f2fhc//ZBlb7lnBJF8XXfapT+qIx0gtNjbY4JSKWS6hhVfOlOAF1FYtySJucsYWBWhqtixE8GNfMcIuqaKo/tOW+gpmWLwOar1EghdjgHUGqpni0apveKn2Z8QbMXERwK2KqDQk3EKCNPXLBWYZMoNyF5xaxuxdGvrPDKPGRiMhU0SDAwyfl/GajWtyik8VSRS0Gdye7eS4gWewmcwwonQnMyko2GezUYbPautWu0ejQbC1BhqF6aGkV5grNoo0SXvfDpBX1VCJ3cK0rP+4lHGpRTytquKmZsMtbL0SmaSf7qblXHzuS3U7n9qPN9iwghWNWyLcc9mupT/Gw9jWLfdMt7MyvTVKhOn2NJhUzFbrQ+8lhZmsLfudTFPwmWN4bFg7LiTADZddxh/dc/JrnIGIlWVqdx4WIvIC4N8BlwLvE5E7VPU5c+c0JyY7fPx64BYR+WXgE8A7wvF3AO8SkS8B38IY695ze6CT10Vk7UrWJv8sfhiXyByeHVGWJrNCLMGGGLLZ5toqhLmFMNoSaT2AV9V6wmiQ3uKNaKbqDYCbeuJNa2Vb2LcawcenHX4SJNjYpMa4LZFC0/lTTL1g5OE642E0+91wCelpZypqMYdYSgYvHRtCvNHsJOlzDSYGsyPOMMlijNfujaGgO4wYbA7K609PZ8G+amp9tpPPSCX1+alXooEwPGJMIA3hMnVo2CzyUehlEwvD4wmDjaSyF6bO1H+CHTe2jcFNHN57krWYZD0hG+XkoxxfSxyQyDptqlNcWmPgiTQKmhDB+gXDUhr0XslHWSl5uUmRoUWZmGAxoPYbxaGzp8+12ozFfpsuM44F7tv547WotIEKJgVPT2X8429+hY9Pdv6GU/2zzh/oADh+8TX6Pc9551Jj//iWp358kU3yXMUDLkm+LDqxpTCfQUJZ2KKSDGvH0SqcVs34berhgrCf0JSq7h1u5HbnpQ/bpIddx+B4s7akz0z6cjseXa95sWOrDuR22k83nSr6XsHh6hU/UhyeaCgkx2uhKArpVh5yxsMXhYaA07UGPlPien+gIoZSjRm2maTW1T+BfOxINmbV62zkyMeW5+5TV66pwyT0rhJhPjUneZTY+qbbDjRjsJl0r0umTenSKel2bn1igsQWJ0K260xrUCCGeC0ORZkFH3l2vjXCT5X4SLuikhVGbkcX+HbhZg+T+9IgqUqlwaiS7VRhSuV9pDWaAvm0yXCjoTWOcyNPdLyjhF7t3sl2HJPd1I7FwbQTKz95/DLe7P/f/xSRWPfhkl6MfXm3vy3xgDJJEXnwo1hb+7fxEtELxT3VqvSjSpm5Uh9rN6SamtpSxXym+NQhiYTWDvUOeB3n9uDGwRaXhTS9SIiG7RtZ8RMlH+ez1WiCGj1T1mwvQb42H58q6VbO8Jj9bOl2btdep7GUSaJgilo6poq5aMgHL8q91eM17RqbD2xjqllRbLcVghQYWTSQxrF8x5WMQwbC4HhUMn6febPdtetWtplVoDU5leInPoQKhU2vGOqwEnZDAVHS+3LwEA27N5H98AR1ipdaSmldMmyMo7HOzYuy70lYH58q8VorEqP2G7hxtQHhzDmIh4dpwtWyzjMHx+8FLl7+KhZcH5z37RuWYpLz0n9qnx8o3efZcvzrT5UjDJcyNgYED3apHonlZ3d5MtWD5IpGGPPwNDI1LARIKoeNHe08rZs220BoEZ8BeGcSgOZBFQ3ZQZ3TT5VoUJkPinm1YQ5Onc0ICq0psu2cfMcRb0TVQxYLUasIRVf2kBXBmPVGl3PMzFanXonXooY6PD1pXRDj9aZaXswpHkbku8b8oqGQHE3Mzjf2yAWVFFyMKeeUKdlpX9LMxz44nCgdWG7icBM/Y3ZQVfzIl0xSkmhWTc8VHytutwoVUomIl8yu6kIZElRoHkU1+TljzQk4T7uh7BWkqoz/KsWNPclmRHIsLm2j6cnc1jK236C8f8Jp/+7gBK8fffWit4gcU9XTB7qwxsSqRnLnK/ZkkrJcS4Yy3UdEXoKl+/zYIrqxyJOfKJvyvXL04LOH5VL1wk7ckC6K75fxlXuEi3RIMH4aMkfy2XF+4BveS5+FXt9qjJKQdaIhwDvaiEqbWlGIYyagHMi2HfnYo4F55juuIbhFmSe5oFZr0YGKqdDqgp009QyOx8ZQoxBukmvZx1xzJTttkp9OC4eOku8WG4UyyaYkmzHRILLUu5Ez+rXwHZ8q2akMYsxkMPUMLxmYGaEjn15ThY3gUNlxxGsR6XYGvpLSoqGpt40YydSYR/HbRusd0QhYyqrWNh0/8riBIzlaCwb3NufBRYOmBtKKtlCv5Ns5ybEEic226FNPtpUTH5ktleYzcwS2CxuXKP1Ytlmk95o91u040pMZ0dEIt+OrfP9wvuRY3Linj0nMDw0u5BtZ+i26W7TtG726vTf2bMnAPtN9REQez8bHXhldeuBdHAKDDGpMZ7514WkubJVzUMb4ha9bXxzKYOuFc5ynek48siFlHKPbcbCpRGvB0D/1Nftf+LyoChRJ90Oe+cZDXl5/fcxUcSNHVKS6TbyFMgV1v0B6suJSUWlTdQ3J1gWvvJt0BFdn4HY9rrYAXWusPjA/wI8903tS4vX5jKIwaeAJjJfGxH2qZFs5yZHYtAe1jaN+bfOkOT+dnWC25coNQr2psgS7pFWR8vjMTAPJRmQbi1oYFx7SLCM+FpOfyqsMopFvbHp+6mdNQs1VAjHHkht7G1+/Hgduq2tx7fduK2LXDi7kden/TUTkyuK5PTB0/nqeL1iGSS7TkmFf6T4CL36kDLlC5hdHXYjcpKOGVJiDxloxg+Ao0Zy966QUdk0xZ4nPw+twAr/IcLiIf6a+VJsRUznd2FffiUDjKpy5kEI117K4BUiZ/aOZVjZHocmgaw4bk1Q9blTLc25fglT/GxWDguOrCPkpPfYyS6O+B4pIEQNd2V+Lz2vnKkKnqosmJAeYmcJPfcnMykiqmapAJpVKbOE0hZRX0NO86A1ULUrpiuv4vXxqNuoSEWQn85K5189bVv2J7HolFvIdV5pRCuSn88ZvJJGUabKaW4vhKLGYSBdsuQWzK9ewPt8yHK0590ZLkSAQRMDL4xP8vjv1RQ5ZJUiZ05XyPML96rgRa8nwaiD+Q936xh/mWysNfA1q7wnazHn5Lp/zxs7SXG4ue+EEk33SXQYpJxjtg+7y9a33vw5daMZkr4ZmG+kZoDvv/qrOeRiciXU4flgCu1tf+KPb/+AZJ/YeCZyJ3/EswDJMcpmWDEul+4Rk95sBRORjZyKm6kzQPZfmeq7RPZfmeq7RbRWbOBBU9dpVzOVcxjJu5bIlg4gMsSj1W1tjinQf2Ee6T48ePXqc7dhTkpyX/iO1lgwcMN2nR48ePc52LGWT1L1bMkyAH93nuQ9VZ+5+pnsuzfVco3suzfVco3um5npe4QHP3e7Ro0ePsxlnXY+bHj169Dib0DPJHj169FiAM84kReRaEfnfIvIlEflHHZ+vichvh8//XESuWAHN7xeRvxCRXESuW+FcXycinxWRT4nIH4vII1dE9zUi8mkRuUNE/lSqrm+Holsb9yIRURHZM8Rkibm+XET+Ksz1DhF55armKiIvDut7p4j8p1XQFZGbanP9goicWhHdy0XkNhH5RLgfnrsCmo8M99WnROSDIrKw9XLte78hIveIyGfmfC4i8uvhvJ8SkSctQ7dHgKqesT/MG/5l4EqstPwngWtaY34a+A/h9UuA314BzSuAJ2JFN65b4Vz/FrAZXv/UXnPdB93jtdfPA/7bKuiGccewNp0fAZ6ygrm+HHjLGbgPHo0VSL0ovH/QqtagNv5nseiMVcz3ZuCnwutrgLtWQPN3gZeF188E3rXk+n4/8CTgM3M+fy7WjlWApwF/vp/f73z/O9OSZJn3raopVk79+a0xz8faPoLlff9tWZzQvSdNVb1LVT/F4iYLB6F7m6qOwtuPYIH1q6C7XXt7hLn1efZHN+BfYAVHFvce2B/N/WIZuq8C3qqq9wGo6j1nYL7XA+9eEV2lymi5APjLFdC8BviT8Pq2js87oaofwkLv5uH5wDvV8BGsB8xDlqHd48yr21153w+bN0atcU+R930YmgfBfuneQK1Z+mHpisjPiMiXgV8Bfm4VdINa9QhVfd8S9JaeK/CioLa9R0SWaWW6DN2rgatF5HYR+YhYeb5VzZdgGnkUFRM6LN0bgZ8Qkbux8LifXQHNTwIvDK9fABwTkUXPwrI4U8/MeYHecXMAiMhPAE8B3rQqmqr6VlW9CuvP8U8PS0+skvCbgX9wWFot/D5whao+EXg/lRZwWCSYyv0MTOL7jyJy4Ypog5ly3qPaVYDuQLge+E1VfTimzr5LZD+FUTvxC8DTReQTwNOxdN/zu5jjWYAzzST3k/eNLNfmcRmaB8FSdEXkWcAbgOep6nRVdGu4BfiRFdA9BjwB+KCI3IXZom7dw3mz51xV9WTtut+OFVo+7FzBpJtbVTVT1a8AX8CY5mHpFngJy6nay9K9AfgdALWeMutYkYoD01TVv1TVF6rqd2P3GKp6ask5L8KZembOD5xJgycmHfwfTM0pjNWPb435GZqOm985LM3a2N9kecfNMnP9bsz4/ugVr8Gja69/GEv3PDTd1vgPsrfjZpm5PqT2+gXAR1a0BtcCvxVen8DUw0tWsQbAY4G7CMkTK5rvfwVeHl4/DrNJzqW/JM0TQBRe/0vgl/Zxn13BfMfND9F03Hx0Wbr9n55ZJhl+oOdiUsGXgTeEY7+ESWJgO/DvAl8CPgpcuQKaT8Ukk11MKr1zRXP9APBN4I7wd+uK6P4acGegeVvXg34Quq2xH2QPJrnkXP91mOsnw1wfu6I1EMw88Fng08BLVrUGmP3wjSu+b68Bbg/rcAfwAyugeR3wxTDm7cDaknN9N/B1rNDf3ZiU+xrgNbW1fWs476eXuQ/6v+qvT0vs0aNHjwXoHTc9evTosQA9k+zRo0ePBeiZZI8ePXosQM8ke/To0WMBeibZo0ePHgvQM8nzDCLyCBH5iohcHN5fFN5f0TF2Q0T+h4jEtWOvFZGJiFxQO/YMEfmDju/fIiJ7BYT36HFWo2eS5xlU9WvA24A3hkNvBG5W1bs6hr8C+C/aTOW7HmsO98KO8W28DfjFg8+2R48HHj2TPD9xE/A0EXkt8H3Ar84Z9+PAe4s3InIVcBTLLb9+ifN8GHhWSDft0eOcRM8kz0Ooagb8Q4xZvja8b0CsffCVLQnzJVhu+YeBx4jIg/c4j8cyqf7aiqbeo8f9jp5Jnr/4QSyV7QlzPj8BnGodux64JTC//8xyHTLvAR56wDn26PGAo1eDzkOIyHcBz8aKHfypiNyiql9vDRtjefXFd74Tq8rz/lATeQh8BXjLHqdbD7R69Dgn0UuS5xlC1fe3YWr2V7GamDM2SbUK4bGIFIzyeuBGVb0i/D0UeOgSfX6uBjp7r/TocS6gZ5LnH14FfFVV3x/e/3vgcSLy9I6x/x1z7IDZI3+v9fnvheNgbTfurv19b7BZjlX1Gyu+hh497jf0VYB6zEVo//DzqvrSA37/54FtVX3HamfWo8f9h16S7DEXqvoXwG31YPJ94hSra+/Qo8cDgl6S7NGjR48F6CXJHj169FiAnkn26NGjxwL0TLJHjx49FqBnkj169OixAD2T7NGjR48F+P9q/1brPHeQrAAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "from pycalphad.plot import triangular\n",
-    "from pycalphad import calculate\n",
-    "\n",
-    "# some sample data, these could be from an equilibrium calculation or a property model.\n",
-    "# here we are calculating the mixing enthlapy of the FCC_A1 phase at 830K. \n",
-    "c = calculate(db_al_cu_y, comps, 'FCC_A1', output='HM_MIX', T=830, P=101325, pdens=5000)\n",
-    "\n",
-    "# Here we are getting the values from our plot. \n",
-    "xs = c.X.values[0, 0, 0, :, 0]  # 1D array of Al compositions\n",
-    "ys = c.X.values[0, 0, 0, :, 1]  # 1D array of Cu compositions\n",
-    "zs = c.HM_MIX.values[0, 0, 0, :]  # 1D array of mixing enthalpies at these compositions\n",
-    "\n",
-    "# when we imported the pycalphad.plot.triangular module, it made the 'triangular' projection available for us to use.\n",
-    "fig = plt.figure()\n",
-    "ax = fig.add_subplot(projection='triangular')\n",
-    "ax.scatter(xs, ys, c=zs, \n",
-    "           cmap='coolwarm', \n",
-    "           linewidth=0.0)\n",
-    "\n",
-    "# label the figure\n",
-    "ax.set_xlabel('X (AL)')\n",
-    "ax.set_ylabel('X (CU)')\n",
-    "ax.yaxis.label.set_rotation(60)  # rotate ylabel\n",
-    "ax.yaxis.set_label_coords(x=0.12, y=0.5)  # move the label to a pleasing position\n",
-    "ax.set_title('AL-CU-Y HM_MIX at 830K')\n",
-    "\n",
-    "# set up the colorbar\n",
-    "cm = plt.cm.ScalarMappable(cmap='coolwarm')\n",
-    "cm.set_array(zs)\n",
-    "fig.colorbar(cm);"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -153,7 +82,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.10.6 64-bit",
    "language": "python",
    "name": "python3"
   },
@@ -167,7 +96,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.0"
+   "version": "3.10.6"
   },
   "pycharm": {
    "stem_cell": {
@@ -177,6 +106,11 @@
     },
     "source": []
    }
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "dffc026621927c134bf51c98cc1a2a1db0bb9758f5626f77d77eb66ad657b830"
+   }
   }
  },
  "nbformat": 4,
diff --git a/pycalphad/__init__.py b/pycalphad/__init__.py
index f386b5c1c..cdf7816af 100644
--- a/pycalphad/__init__.py
+++ b/pycalphad/__init__.py
@@ -4,18 +4,21 @@
 
 from pycalphad.core.errors import *
 import pycalphad.variables as v
-from pycalphad.model import Model, ReferenceState
 from pycalphad.io.database import Database
 
 # Trigger format extension hooks
 import pycalphad.io.tdb
 import pycalphad.io.cs_dat
 
+from pycalphad.model import Model, ReferenceState
+
 from pycalphad.core.calculate import calculate
 from pycalphad.core.equilibrium import equilibrium
+from pycalphad.core.workspace import Workspace
 from pycalphad.plot.binary import binplot
 from pycalphad.plot.ternary import ternplot
 from pycalphad.plot.eqplot import eqplot
+from pycalphad.property_framework import as_property
 
 # Set the version of pycalphad
 try:
diff --git a/pycalphad/codegen/callables.py b/pycalphad/codegen/callables.py
index 86c708d68..f0645f6b2 100644
--- a/pycalphad/codegen/callables.py
+++ b/pycalphad/codegen/callables.py
@@ -1,254 +1,12 @@
-import pycalphad.variables as v
-from pycalphad.codegen.sympydiff_utils import build_functions
-from pycalphad.core.utils import get_pure_elements, unpack_components, \
-    extract_parameters, get_state_variables, wrap_symbol
-from pycalphad.core.phase_rec import PhaseRecord
-from pycalphad.core.constraints import build_constraints
-from itertools import repeat
-import warnings
-
-
-def build_callables(dbf, comps, phases, models, parameter_symbols=None,
-                    output='GM', build_gradients=True, build_hessians=False,
-                    additional_statevars=None):
-    """
-    Create a compiled callables dictionary.
-
-    Parameters
-    ----------
-    dbf : Database
-        A Database object
-    comps : list
-        List of component names
-    phases : list
-        List of phase names
-    models : dict
-        Dictionary of {phase_name: Model subclass}
-    parameter_symbols : list, optional
-        List of string or SymEngine Symbols that will be overridden in the callables.
-    output : str, optional
-        Output property of the particular Model to sample. Defaults to 'GM'
-    build_gradients : bool, optional
-        Whether or not to build gradient functions. Defaults to True.
-    build_hessians : bool, optional
-        Whether or not to build Hessian functions. Defaults to False.
-    additional_statevars : set, optional
-        State variables to include in the callables that may not be in the models (e.g. from conditions)
-    verbose : bool, optional
-        Print the name of the phase when its callables are built
-
-    Returns
-    -------
-    callables : dict
-        Dictionary of keyword argument callables to pass to equilibrium.
-        Maps {'output' -> {'function' -> {'phase_name' -> AutowrapFunction()}}.
-
-    Notes
-    -----
-    *All* the state variables used in calculations must be specified.
-    If these are not specified as state variables of the models (e.g. often the
-    case for v.N), then it must be supplied by the additional_statevars keyword
-    argument.
-
-    Examples
-    --------
-    >>> from pycalphad import Database, equilibrium, variables as v
-    >>> from pycalphad.codegen.callables import build_callables
-    >>> from pycalphad.core.utils import instantiate_models
-    >>> dbf = Database('AL-NI.tdb')
-    >>> comps = ['AL', 'NI', 'VA']
-    >>> phases = ['LIQUID', 'AL3NI5', 'AL3NI2', 'AL3NI']
-    >>> models = instantiate_models(dbf, comps, phases)
-    >>> callables = build_callables(dbf, comps, phases, models, additional_statevars={v.P, v.T, v.N})
-    >>> 'GM' in callables.keys()
-    True
-    >>> 'massfuncs' in callables['GM']
-    True
-    >>> conditions = {v.P: 101325, v.T: 2500, v.X('AL'): 0.2}
-    >>> equilibrium(dbf, comps, phases, conditions, callables=callables)
-    """
-    additional_statevars = set(additional_statevars) if additional_statevars is not None else set()
-    parameter_symbols = parameter_symbols if parameter_symbols is not None else []
-    parameter_symbols = sorted([wrap_symbol(x) for x in parameter_symbols], key=str)
-    comps = sorted(unpack_components(dbf, comps))
-    pure_elements = get_pure_elements(dbf, comps)
-
-    _callables = {
-        'massfuncs': {},
-        'massgradfuncs': {},
-        'masshessfuncs': {},
-        'formulamolefuncs': {},
-        'formulamolegradfuncs': {},
-        'formulamolehessfuncs': {},
-        'callables': {},
-        'grad_callables': {},
-        'hess_callables': {},
-        'internal_cons_func': {},
-        'internal_cons_jac': {},
-        'internal_cons_hess': {},
-    }
-
-    state_variables = get_state_variables(models=models)
-    state_variables |= additional_statevars
-    if not {v.T, v.P, v.N}.issubset(state_variables):
-        warnings.warn("State variables in `build_callables` are not {{N, P, T}}, but {}. This can lead to incorrectly "
-                      "calculated values if the state variables used to call the generated functions do not match the "
-                      "state variables used to create them. State variables can be added with the "
-                      "`additional_statevars` argument.".format(state_variables))
-    state_variables = sorted(state_variables, key=str)
-
-    for name in phases:
-        mod = models[name]
-        site_fracs = mod.site_fractions
-        try:
-            out = getattr(mod, output)
-        except AttributeError:
-            raise AttributeError('Missing Model attribute {0} specified for {1}'
-                                 .format(output, mod.__class__))
-
-        # Build the callables of the output
-        # Only force undefineds to zero if we're not overriding them
-        undefs = {x for x in out.free_symbols if not isinstance(x, v.StateVariable)} - set(parameter_symbols)
-        undef_vals = repeat(0., len(undefs))
-        out = out.xreplace(dict(zip(undefs, undef_vals)))
-        build_output = build_functions(out, tuple(state_variables + site_fracs), parameters=parameter_symbols,
-                                       include_grad=build_gradients, include_hess=build_hessians)
-        cf, gf, hf = build_output.func, build_output.grad, build_output.hess
-        _callables['callables'][name] = cf
-        _callables['grad_callables'][name] = gf
-        _callables['hess_callables'][name] = hf
-
-        # Build the callables for mass
-        # TODO: In principle, we should also check for undefs in mod.moles()
-        mcf, mgf, mhf = zip(*[build_functions(mod.moles(el), state_variables + site_fracs,
-                                              include_obj=True,
-                                              include_grad=build_gradients,
-                                              include_hess=build_hessians,
-                                              parameters=parameter_symbols)
-                              for el in pure_elements])
-
-        _callables['massfuncs'][name] = mcf
-        _callables['massgradfuncs'][name] = mgf
-        _callables['masshessfuncs'][name] = mhf
-
-        # Build the callables for moles per formula unit
-        # TODO: In principle, we should also check for undefs in mod.moles()
-        fmcf, fmgf, fmhf = zip(*[build_functions(mod.moles(el, per_formula_unit=True), state_variables + site_fracs,
-                                                 include_obj=True,
-                                                 include_grad=build_gradients,
-                                                 include_hess=build_hessians,
-                                                 parameters=parameter_symbols)
-                                 for el in pure_elements])
-
-        _callables['formulamolefuncs'][name] = fmcf
-        _callables['formulamolegradfuncs'][name] = fmgf
-        _callables['formulamolehessfuncs'][name] = fmhf
-    return {output: _callables}
+from pycalphad.codegen.phase_record_factory import PhaseRecordFactory
 
 
 def build_phase_records(dbf, comps, phases, state_variables, models, output='GM',
                         callables=None, parameters=None, verbose=False,
                         build_gradients=True, build_hessians=True
                         ):
-    """
-    Combine compiled callables and callables from conditions into PhaseRecords.
-
-    Parameters
-    ----------
-    dbf : Database
-        A Database object
-    comps : List[Union[str, v.Species]]
-        List of active pure elements or species.
-    phases : list
-        List of phase names
-    state_variables : Iterable[v.StateVariable]
-        State variables used to produce the generated functions.
-    models : Mapping[str, Model]
-        Mapping of phase names to model instances
-    parameters : dict, optional
-        Maps SymEngine Symbol to numbers, for overriding the values of parameters in the Database.
-    callables : dict, optional
-        Pre-computed callables. If None are passed, they will be built.
-        Maps {'output' -> {'function' -> {'phase_name' -> AutowrapFunction()}}
-    output : str
-        Output property of the particular Model to sample
-    verbose : bool, optional
-        Print the name of the phase when its callables are built
-    build_gradients : bool
-        Whether or not to build gradient functions. Defaults to False. Only
-        takes effect if callables are not passed.
-    build_hessians : bool
-        Whether or not to build Hessian functions. Defaults to False. Only
-        takes effect if callables are not passed.
-
-    Returns
-    -------
-    dict
-        Dictionary mapping phase names to PhaseRecord instances.
-
-    Notes
-    -----
-    If callables are passed, don't rebuild them. This means that the callables
-    are not checked for incompatibility. Users of build_callables are
-    responsible for ensuring that the state variables, parameters and models
-    used to construct the callables are compatible with the ones used to
-    build the constraints and phase records.
-
-    """
-    comps = sorted(unpack_components(dbf, comps))
-    parameters = parameters if parameters is not None else {}
-    callables = callables if callables is not None else {}
-    _constraints = {
-        'internal_cons_func': {},
-        'internal_cons_jac': {},
-        'internal_cons_hess': {},
-    }
-    phase_records = {}
-    state_variables = sorted(get_state_variables(models=models, conds=state_variables), key=str)
-    param_symbols, param_values = extract_parameters(parameters)
-
-    if callables.get(output) is None:
-        callables = build_callables(dbf, comps, phases, models,
-                                    parameter_symbols=parameters.keys(), output=output,
-                                    additional_statevars=state_variables,
-                                    build_gradients=False,
-                                    build_hessians=False)
-    # Temporary solution. PhaseRecord needs rework: https://github.com/pycalphad/pycalphad/pull/329#discussion_r634579356
-    formulacallables = build_callables(dbf, comps, phases, models,
-                                       parameter_symbols=parameters.keys(), output='G',
-                                       additional_statevars=state_variables,
-                                       build_gradients=build_gradients,
-                                       build_hessians=build_hessians)
-
-    # If a vector of parameters is specified, only pass the first row to the PhaseRecord
-    # Future callers of PhaseRecord.obj_parameters_2d() can pass the full param_values array as an argument
-    if len(param_values.shape) > 1:
-        param_values = param_values[0]
-
-    for name in phases:
-        mod = models[name]
-        site_fracs = mod.site_fractions
-        # build constraint functions
-        cfuncs = build_constraints(mod, state_variables + site_fracs, parameters=param_symbols)
-        _constraints['internal_cons_func'][name] = cfuncs.internal_cons_func
-        _constraints['internal_cons_jac'][name] = cfuncs.internal_cons_jac
-        _constraints['internal_cons_hess'][name] = cfuncs.internal_cons_hess
-        num_internal_cons = cfuncs.num_internal_cons
-
-        phase_records[name.upper()] = PhaseRecord(comps, state_variables, site_fracs, param_values,
-                                                  callables[output]['callables'][name],
-                                                  formulacallables['G']['callables'][name],
-                                                  formulacallables['G']['grad_callables'][name],
-                                                  formulacallables['G']['hess_callables'][name],
-                                                  callables[output]['massfuncs'][name],
-                                                  formulacallables['G']['formulamolefuncs'][name],
-                                                  formulacallables['G']['formulamolegradfuncs'][name],
-                                                  formulacallables['G']['formulamolehessfuncs'][name],
-                                                  _constraints['internal_cons_func'][name],
-                                                  _constraints['internal_cons_jac'][name],
-                                                  _constraints['internal_cons_hess'][name],
-                                                  num_internal_cons)
-
-        if verbose:
-            print(name + ' ')
-    return phase_records
+    if output != 'GM':
+        raise ValueError('build_phase_records is deprecated and no longer works when the output keyword '
+                         'is changed from the default. Remove the keyword, and then use the PhaseRecord.prop_* API '
+                         'in downstream functions instead.')
+    return PhaseRecordFactory(dbf, comps, state_variables, models, parameters=parameters)
diff --git a/pycalphad/codegen/phase_record_factory.py b/pycalphad/codegen/phase_record_factory.py
new file mode 100644
index 000000000..f4db7954d
--- /dev/null
+++ b/pycalphad/codegen/phase_record_factory.py
@@ -0,0 +1,78 @@
+import pycalphad.variables as v
+from pycalphad.codegen.sympydiff_utils import build_functions
+from pycalphad.core.utils import get_pure_elements, unpack_components, \
+    extract_parameters, get_state_variables
+from pycalphad.core.phase_rec import PhaseRecord
+from pycalphad.core.constraints import build_constraints
+from itertools import repeat
+from functools import lru_cache
+import numpy as np
+
+class PhaseRecordFactory(object):
+    def __init__(self, dbf, comps, state_variables, models, parameters=None):
+        self.comps = sorted(unpack_components(dbf, comps))
+        self.pure_elements = get_pure_elements(dbf, comps)
+        self.nonvacant_elements = sorted([x for x in self.pure_elements if x != 'VA'])
+        self.molar_masses = np.array([dbf.refstates[x]['mass'] for x in self.nonvacant_elements], dtype='float')
+        parameters = parameters if parameters is not None else {}
+        self.models = models
+        self.state_variables = sorted(get_state_variables(models=models, conds=state_variables), key=str)
+        self.param_symbols, self.param_values = extract_parameters(parameters)
+
+        if len(self.param_values.shape) > 1:
+            self.param_values = self.param_values[0]
+
+    def update_parameters(self, parameters):
+        new_param_symbols, new_param_values = extract_parameters(parameters)
+        if len(new_param_values.shape) > 1:
+            new_param_values = new_param_values[0]
+        if new_param_symbols != self.param_symbols:
+            raise ValueError('Parameter symbol misatch')
+        self.param_values[:] = new_param_values
+
+    @lru_cache()
+    def get_phase_constraints(self, phase_name):
+        mod = self.models[phase_name]
+        cfuncs = build_constraints(mod, self.state_variables + mod.site_fractions, parameters=self.param_symbols)
+        return cfuncs
+
+    @lru_cache()
+    def get_phase_formula_moles_element(self, phase_name, element_name, per_formula_unit=True):
+        mod = self.models[phase_name]
+        # TODO: In principle, we should also check for undefs in mod.moles()
+        return build_functions(mod.moles(element_name, per_formula_unit=per_formula_unit),
+                               self.state_variables + mod.site_fractions,
+                               include_obj=True, include_grad=True, include_hess=True,
+                               parameters=self.param_symbols)
+
+    @lru_cache()
+    def get_phase_property(self, phase_name, property_name, include_grad=True, include_hess=True):
+        mod = self.models[phase_name]
+        out = getattr(mod, property_name)
+        if out is None:
+            raise AttributeError(f'Model property {property_name} is not defined')
+        # Only force undefineds to zero if we're not overriding them
+        undefs = {x for x in out.free_symbols if not isinstance(x, v.StateVariable)} - set(self.param_symbols)
+        undef_vals = repeat(0., len(undefs))
+        out = out.xreplace(dict(zip(undefs, undef_vals)))
+        build_output = build_functions(out, tuple(self.state_variables + mod.site_fractions), parameters=self.param_symbols,
+                                       include_grad=include_grad, include_hess=include_hess)
+        return build_output
+
+    def get_phase_formula_energy(self, phase_name):
+        return self.get_phase_property(phase_name, 'G', include_grad=True, include_hess=True)
+
+    @lru_cache()
+    def get(self, phase_name):
+        return PhaseRecord(self, phase_name)
+
+    def keys(self):
+        return self.models.keys()
+
+    def values(self):
+        return iter(self.get(k) for k in self.keys())
+
+    def items(self):
+        return zip(self.models.keys(), iter(self.get(k) for k in self.keys()))
+
+    __getitem__ = get
diff --git a/pycalphad/core/_isnan.h b/pycalphad/core/_isnan.h
deleted file mode 100644
index 9e18bb8a5..000000000
--- a/pycalphad/core/_isnan.h
+++ /dev/null
@@ -1,7 +0,0 @@
-// Compatibility hack for Windows and non-Windows platforms
-#ifdef _WIN32
-#include <float.h>
-#define isnan  _isnan
-#else
-#include <math.h>
-#endif
\ No newline at end of file
diff --git a/pycalphad/core/calculate.py b/pycalphad/core/calculate.py
index 97f21c6f0..dd2f580b5 100644
--- a/pycalphad/core/calculate.py
+++ b/pycalphad/core/calculate.py
@@ -11,66 +11,58 @@
 from numpy import broadcast_to
 import pycalphad.variables as v
 from pycalphad import ConditionError
-from pycalphad.codegen.callables import build_phase_records
+from pycalphad.codegen.phase_record_factory import PhaseRecordFactory
 from pycalphad.core.cache import cacheit
 from pycalphad.core.light_dataset import LightDataset
+from pycalphad.core.polytope import sample
 from pycalphad.model import Model
-from pycalphad.core.phase_rec import PhaseRecord
 from pycalphad.core.utils import endmember_matrix, extract_parameters, \
     get_pure_elements, filter_phases, instantiate_models, point_sample, \
     unpack_components, unpack_condition, unpack_kwarg
 from pycalphad.core.constants import MIN_SITE_FRACTION, MAX_ENDMEMBER_PAIRS, MAX_EXTRA_POINTS
 
 
-def hr_point_sample(constraint_jac, constraint_rhs, initial_point, num_points):
-    "Hit-and-run sampling of linearly-constrained site fraction spaces"
-    q, r = np.linalg.qr(constraint_jac.T, mode='complete')
-    q1 = q[:, :constraint_jac.shape[0]]
-    q2 = q[:, constraint_jac.shape[0]:]
-    r1 = r[:constraint_jac.shape[0], :]
-    if initial_point is not None:
-        z_bar = initial_point
-    else:
-        # minimum norm solution to underdetermined system of equations
-        # may not be feasible if it fails the non-negativity constraint
-        z_bar = np.linalg.lstsq(constraint_jac, constraint_rhs, rcond=None)[0]
-    solution_norm = np.linalg.norm(constraint_jac.dot(z_bar) - constraint_rhs)
-    if (solution_norm > 1e-4) or np.any(z_bar < 0):
-        # initial point does not satisfy constraints; give up
-        return np.empty((0, z_bar.shape[0]))
-    # Hit-and-Run sampling
-    new_feasible_z = np.zeros((num_points, constraint_jac.shape[1]))
-    current_z = np.array(z_bar)
-    min_z = MIN_SITE_FRACTION
-    rng = np.random.RandomState(1769)
-    for iteration in range(num_points):
-        # generate unit direction in null space
-        d = rng.normal(size=(constraint_jac.shape[1] - constraint_jac.shape[0]))
-        d /= np.linalg.norm(d, axis=0)
-        proj = np.dot(q2, d)
-        # find extent of step direction possible while staying within bounds (0 <= z)
-        with np.errstate(divide='ignore'):
-            alphas = (min_z - current_z) / proj
-        # Need to use small value to prevent constraints binding one sublattice (with proj ~ 0) from binding all dof
-        max_alpha_candidates = alphas[np.logical_and(proj > 1e-6, np.isfinite(alphas))]
-        min_alpha_candidates = alphas[np.logical_and(proj < -1e-6, np.isfinite(alphas))]
-        alpha_min = np.min(min_alpha_candidates)
-        alpha_max = np.max(max_alpha_candidates)
-        # Poor progress; give up on sampling
-        if np.abs(alpha_max - alpha_min) < 1e-4:
-            new_feasible_z = new_feasible_z[:iteration, :]
-            break
-        # choose a random step size within the feasible interval
-        new_alpha = rng.uniform(low=alpha_min, high=alpha_max)
-        current_z += new_alpha * proj
-        new_feasible_z[iteration, :] = current_z
-    if np.any(new_feasible_z < 0):
-        raise ValueError('Constrained sampling generated negative site fractions')
-    return new_feasible_z
+def _jacobian_from_constraints(constraints, variables):
+    """
+    Generate the Jacobian matrix and right-hand side vector from
+    a list of constraint equations.
 
+    Parameters
+    ----------
+    constraints : Iterable[Basic]
+        Constraint equations (implicitly equal to zero)
+    variables : Iterable[Basic]
+    """
+    constraint_jac = np.zeros((len(constraints), len(variables)))
+    constraint_rhs = np.zeros(len(constraints))
+    for cons_idx, cons_equation in enumerate(constraints):
+        residual = cons_equation + 0. # force copy
+        for var_idx, variable in enumerate(variables):
+            deriv = cons_equation.diff(variable)
+            try:
+                constraint_jac[cons_idx, var_idx] = float(deriv)
+            except (TypeError, RuntimeError):
+                raise ValueError('Constraint Jacobian is non-linear')
+            residual = residual - deriv * variable
+        try:
+            constraint_rhs[cons_idx] = -float(residual)
+        except (TypeError, RuntimeError):
+            raise ValueError('Constraint Jacobian is non-linear')
+    return constraint_jac, constraint_rhs
+
+def _get_local_constraint_equations(model, phase_local_conditions):
+    phase_local_constraints = []
+    for key, value in phase_local_conditions.items():
+        if isinstance(key, v.MoleFraction):
+            cons = model.moles(key.species, per_formula_unit=True) - \
+                value * sum(model.moles(v.Species(el), per_formula_unit=True) for el in model.nonvacant_elements)
+        else:
+            cons = key - value
+        phase_local_constraints.append(cons.expand())
+    return phase_local_constraints
 
 @cacheit
-def _sample_phase_constitution(model, sampler, fixed_grid, pdens):
+def _sample_phase_constitution(model, sampler, fixed_grid, pdens, phase_local_conditions):
     """
     Sample the internal degrees of freedom of a phase.
 
@@ -120,7 +112,7 @@ def _sample_phase_constitution(model, sampler, fixed_grid, pdens):
     # Note that if a phase only consists of site fraction balance constraints,
     # we do not consider that 'linearly constrained' for the purposes of sampling,
     # since the default sampler handles that case with an efficient method.
-    linearly_constrained_space = charge_constrained_space
+    linearly_constrained_space = charge_constrained_space or (len(phase_local_conditions.keys()) > 0)
 
     if charge_constrained_space:
         endmembers = points
@@ -160,32 +152,28 @@ def _sample_phase_constitution(model, sampler, fixed_grid, pdens):
     # Sample composition space for more points
     if sum(sublattice_dof) > len(sublattice_dof):
         if linearly_constrained_space:
-            # construct constraint Jacobian for this phase
-            # Model technically already does this so it would be better to reuse that functionality
-            # number of sublattices, plus charge balance
-            num_constraints = len(sublattice_dof) + 1
-            constraint_jac = np.zeros((num_constraints, points.shape[-1]))
-            constraint_rhs = np.zeros(num_constraints)
-            # site fraction balance
-            dof_idx = 0
-            constraint_idx = 0
-            for subl_dof in sublattice_dof:
-                constraint_jac[constraint_idx, dof_idx:dof_idx + subl_dof] = 1
-                constraint_rhs[constraint_idx] = 1
-                constraint_idx += 1
-                dof_idx += subl_dof
-            # charge balance
-            constraint_jac[constraint_idx, :] = species_charge
-            constraint_rhs[constraint_idx] = 0
-            # Sample additional points which obey the constraints
-            # Mean of pseudo-endmembers is feasible by convexity of the space
-            initial_point = np.mean(points, axis=0)
+            model_constraints = model.get_internal_constraints()
+            phase_local_constraints = _get_local_constraint_equations(model, phase_local_conditions)
+            constraint_jac, constraint_rhs = _jacobian_from_constraints(model_constraints + phase_local_constraints,
+                                                                        model.site_fractions)
             num_points = (pdens ** 2) * (constraint_jac.shape[1] - constraint_jac.shape[0])
-            extra_points = hr_point_sample(constraint_jac, constraint_rhs, initial_point, num_points)
-            points = np.concatenate((points, extra_points))
-            assert np.max(np.abs(constraint_jac.dot(points.T).T - constraint_rhs)) < 1e-6
-            if points.shape[0] == 0:
-                warnings.warn(f'{model.phase_name} has zero feasible configurations under the given conditions')
+            num_points = min(num_points, MAX_EXTRA_POINTS)
+            extra_points = sample(num_points, np.full(constraint_jac.shape[1], MIN_SITE_FRACTION),
+                                  np.ones(constraint_jac.shape[1]), A2=constraint_jac, b2=constraint_rhs)
+            if (len(phase_local_conditions.keys()) > 0):
+                points = extra_points
+            else:
+                # Avoid adding redundant points for the charge-constrained case
+                if charge_constrained_space and extra_points.shape[-2] == 1:
+                    # Zero degrees of freedom in the sampler, this is probably a redundant point
+                    pass
+                else:
+                    points = np.concatenate((points, extra_points))
+            if points.shape[0] > 0:
+                assert np.max(np.abs(constraint_jac.dot(points.T).T - constraint_rhs)) < 1e-6
+            else:
+                # No feasible points; return array of nan to preserve shape
+                return np.full((num_points, constraint_jac.shape[1]), np.nan)
         else:
             points = np.concatenate((points, sampler(sublattice_dof, pdof=pdens)))
 
@@ -197,7 +185,7 @@ def _sample_phase_constitution(model, sampler, fixed_grid, pdens):
     return points
 
 
-def _compute_phase_values(components, statevar_dict,
+def _compute_phase_values(components, statevar_dict, str_phase_local_conditions,
                           points, phase_record, output, maximum_internal_dof, broadcast=True,
                           parameters=None, fake_points=False,
                           largest_energy=None):
@@ -210,6 +198,8 @@ def _compute_phase_values(components, statevar_dict,
         Names of components to consider in the calculation.
     statevar_dict : OrderedDict {str -> float or sequence}
         Mapping of state variables to desired values. This will broadcast if necessary.
+    str_phase_local_conditions : OrderedDict[str, sequence]
+        Phase-local conditions array, which are the leading dimensions of `points`, by construction.
     points : ndarray
         Inputs to 'func', except state variables. Columns should be in 'variables' order.
     phase_record : PhaseRecord
@@ -238,13 +228,17 @@ def _compute_phase_values(components, statevar_dict,
     --------
     None yet.
     """
+    plc_shape = tuple(len(x) for x in str_phase_local_conditions.values())
     if broadcast:
         # Broadcast compositions and state variables along orthogonal axes
         # This lets us eliminate an expensive Python loop
         statevars = np.meshgrid(*itertools.chain(statevar_dict.values(),
                                                      [np.empty(points.shape[-2])]),
                                     sparse=True, indexing='ij')[:-1]
-        points = broadcast_to(points, tuple(len(np.atleast_1d(x)) for x in statevar_dict.values()) + points.shape[-2:])
+        # Add dummy dimensions for the statevars between the PLC dimensions and the trailing points array dimensions
+        points = np.expand_dims(points, axis=tuple(range(len(plc_shape), len(plc_shape) + len(statevars))))
+        # Broadcast the dummy state variable dimensions to align with the size of the statevar arrays
+        points = broadcast_to(points, plc_shape + tuple(len(np.atleast_1d(x)) for x in statevar_dict.values()) + points.shape[-2:])
     else:
         statevars = list(np.atleast_1d(x) for x in statevar_dict.values())
         statevars_ = []
@@ -271,11 +265,11 @@ def _compute_phase_values(components, statevar_dict,
     if parameter_array_length == 0:
         # No parameters specified
         phase_output = np.zeros(dof.shape[0], order='C')
-        phase_record.obj_2d(phase_output, dof)
+        phase_record.prop_2d(phase_output, dof, output.encode('utf-8'))
     else:
         # Vectorized parameter arrays
         phase_output = np.zeros((dof.shape[0], parameter_array_length), order='C')
-        phase_record.obj_parameters_2d(phase_output, dof, parameter_array)
+        phase_record.prop_parameters_2d(phase_output, dof, parameter_array, output.encode('utf-8'))
 
     for el_idx in range(len(pure_elements)):
         phase_record.mass_obj_2d(phase_compositions[:, el_idx], dof, el_idx)
@@ -330,7 +324,7 @@ def _compute_phase_values(components, statevar_dict,
             expanded_points = np.append(points, append_nans, axis=-1)
     if broadcast:
         coordinate_dict.update({key: np.atleast_1d(value) for key, value in statevar_dict.items()})
-        output_columns = [str(x) for x in statevar_dict.keys()] + ['points']
+        output_columns = list(str_phase_local_conditions.keys()) + [str(x) for x in statevar_dict.keys()] + ['points']
     else:
         output_columns = ['points']
     if parameter_array_length > 1:
@@ -352,7 +346,7 @@ def _compute_phase_values(components, statevar_dict,
     return LightDataset(data_arrays, coords=coordinate_dict)
 
 
-def calculate(dbf, comps, phases, mode=None, output='GM', fake_points=False, broadcast=True, parameters=None, to_xarray=True, phase_records=None, **kwargs):
+def calculate(dbf, comps, phases, mode=None, output='GM', fake_points=False, broadcast=True, parameters=None, to_xarray=True, phase_records=None, conditions=None, **kwargs):
     """
     Sample the property surface of 'output' containing the specified
     components and phases. Model parameters are taken from 'dbf' and any
@@ -398,6 +392,8 @@ def calculate(dbf, comps, phases, mode=None, output='GM', fake_points=False, bro
         The `model` argument must be a mapping of phase names to instances of Model
         objects. Callers must take care that the PhaseRecord objects were created with
         the same `output` as passed to `calculate`.
+    conditions : OrderedDict
+        Mapping of StateVariables to conditions. Must contain state variables and phase-local conditions only.
 
     Returns
     -------
@@ -415,6 +411,7 @@ def calculate(dbf, comps, phases, mode=None, output='GM', fake_points=False, bro
     sampler_dict = unpack_kwarg(kwargs.pop('sampler', None), default_arg=None)
     fixedgrid_dict = unpack_kwarg(kwargs.pop('grid_points', True), default_arg=True)
     model = kwargs.pop('model', None)
+    conditions = conditions or OrderedDict()
     parameters = parameters or dict()
     if isinstance(parameters, dict):
         parameters = OrderedDict(sorted(parameters.items(), key=str))
@@ -452,7 +449,7 @@ def calculate(dbf, comps, phases, mode=None, output='GM', fake_points=False, bro
     # TODO: conditions dict of StateVariable instances should become part of the calculate API
     statevar_strings = [sv for sv in kwargs.keys() if getattr(v, sv) is not None]
     # If we don't do this, sympy will get confused during substitution
-    statevar_dict = dict((v.StateVariable(key), unpack_condition(value)) for key, value in kwargs.items() if key in statevar_strings)
+    statevar_dict = dict((getattr(v, key), unpack_condition(value)) for key, value in kwargs.items() if key in statevar_strings)
     # Sort after default state variable check to fix gh-116
     statevar_dict = OrderedDict(sorted(statevar_dict.items(), key=lambda x: str(x[0])))
     str_statevar_dict = OrderedDict((str(key), unpack_condition(value)) for (key, value) in statevar_dict.items())
@@ -460,36 +457,57 @@ def calculate(dbf, comps, phases, mode=None, output='GM', fake_points=False, bro
     # Build phase records if they weren't passed
     if phase_records is None:
         models = instantiate_models(dbf, comps, active_phases, model=model, parameters=parameters)
-        phase_records = build_phase_records(dbf, comps, active_phases, statevar_dict,
-                                            models=models, parameters=parameters,
-                                            output=output, callables=callables,
-                                            build_gradients=False, build_hessians=False,
-                                            verbose=kwargs.pop('verbose', False))
+        phase_records = PhaseRecordFactory(dbf, comps, statevar_dict, models, parameters=parameters)
     else:
         # phase_records were provided, instantiated models must also be provided by the caller
         models = model
         if not isinstance(models, Mapping):
             raise ValueError("A dictionary of instantiated models must be passed to `equilibrium` with the `model` argument if the `phase_records` argument is used.")
         active_phases_without_models = [name for name in active_phases if not isinstance(models.get(name), Model)]
-        active_phases_without_phase_records = [name for name in active_phases if not isinstance(phase_records.get(name), PhaseRecord)]
-        if len(active_phases_without_phase_records) > 0:
-            raise ValueError(f"phase_records must contain a PhaseRecord instance for every active phase. Missing PhaseRecord objects for {sorted(active_phases_without_phase_records)}")
         if len(active_phases_without_models) > 0:
             raise ValueError(f"model must contain a Model instance for every active phase. Missing Model objects for {sorted(active_phases_without_models)}")
 
     maximum_internal_dof = max(len(models[phase_name].site_fractions) for phase_name in active_phases)
 
+    phase_local_conditions = {key: unpack_condition(value)
+                                  for key, value in sorted(conditions.items(), key=lambda x: str(x[0]))
+                                  if isinstance(key, v.StateVariable) and hasattr(key, 'phase_name')}
+    str_phase_local_conditions = {str(k): v for k, v in phase_local_conditions.items()}
+    plc_shape = tuple(len(x) for x in phase_local_conditions.values())
+    # TODO: move state variable conditions into conditions dict
+
     for phase_name in sorted(active_phases):
         mod = models[phase_name]
         phase_record = phase_records[phase_name]
         points = points_dict[phase_name]
         if points is None:
-            points = _sample_phase_constitution(mod, sampler_dict[phase_name] or point_sample,
-                                                fixedgrid_dict[phase_name], pdens_dict[phase_name])
+            collected_points_arrays = []
+            for index in np.ndindex(plc_shape):
+                if len(index) == 0:
+                    break
+                cur_phase_local_conditions = OrderedDict(zip(phase_local_conditions.keys(),
+                                                             [b[a]
+                                                              for a, b in zip(index, phase_local_conditions.values())]))
+                # Filter down to PLCs that are for this phase
+                cur_phase_local_conditions = {k: v for k, v in cur_phase_local_conditions.items()
+                                              if k.phase_name == phase_name}
+                cur_points = _sample_phase_constitution(mod, sampler_dict[phase_name] or point_sample,
+                                                        fixedgrid_dict[phase_name], pdens_dict[phase_name],
+                                                        cur_phase_local_conditions)
+                # Collect all points arrays for all PLCs
+                collected_points_arrays.append(cur_points)
+            if len(collected_points_arrays) == 0:
+                # No phase-local conditions, can use standard approach
+                points = _sample_phase_constitution(mod, sampler_dict[phase_name] or point_sample,
+                                                    fixedgrid_dict[phase_name], pdens_dict[phase_name],
+                                                    {})
+            else:
+                # Assumes all points arrays for this phase will be the same shape (no zero-solutions)
+                points = np.array(collected_points_arrays).reshape(plc_shape + collected_points_arrays[0].shape)
         points = np.atleast_2d(points)
 
         fp = fake_points and (phase_name == sorted(active_phases)[0])
-        phase_ds = _compute_phase_values(nonvacant_components, str_statevar_dict,
+        phase_ds = _compute_phase_values(nonvacant_components, str_statevar_dict, str_phase_local_conditions,
                                          points, phase_record, output,
                                          maximum_internal_dof, broadcast=broadcast, parameters=parameters,
                                          largest_energy=float(largest_energy), fake_points=fp)
diff --git a/pycalphad/core/cartesian.py b/pycalphad/core/cartesian.py
deleted file mode 100644
index 05da53213..000000000
--- a/pycalphad/core/cartesian.py
+++ /dev/null
@@ -1,58 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-The cartesian module contains a routine for computing the Cartesian product
-of N arrays.
-"""
-
-import numpy as np
-
-def cartesian(arrays, out=None):
-    """
-    Generate a cartesian product of input arrays.
-    Source: http://stackoverflow.com/questions/1208118/using-numpy-to-build-an-array-of-all-combinations-of-two-arrays
-
-    Parameters
-    ----------
-    arrays : list of array-like
-        1-D arrays to form the cartesian product of.
-    out : ndarray
-        Array to place the cartesian product in.
-
-    Returns
-    -------
-    out : ndarray
-        2-D array of shape (M, len(arrays)) containing cartesian products
-        formed of input arrays.
-
-    Examples
-    --------
-    >>> cartesian(([1, 2, 3], [4, 5], [6, 7]))
-    array([[1, 4, 6],
-           [1, 4, 7],
-           [1, 5, 6],
-           [1, 5, 7],
-           [2, 4, 6],
-           [2, 4, 7],
-           [2, 5, 6],
-           [2, 5, 7],
-           [3, 4, 6],
-           [3, 4, 7],
-           [3, 5, 6],
-           [3, 5, 7]])
-
-    """
-
-    arrays = [np.asarray(x) for x in arrays]
-    dtype = arrays[0].dtype
-
-    n = np.prod([x.size for x in arrays])
-    if out is None:
-        out = np.zeros([n, len(arrays)], dtype=dtype)
-
-    m = int(n / arrays[0].size)
-    out[:, 0] = np.repeat(arrays[0], m)
-    if arrays[1:]:
-        cartesian(arrays[1:], out=out[0:m, 1:])
-        for j in np.arange(1, arrays[0].size):
-            out[j*m:(j+1)*m, 1:] = out[0:m, 1:]
-    return out
diff --git a/pycalphad/core/composition_set.pxd b/pycalphad/core/composition_set.pxd
index b90b3b1ce..138361d0e 100644
--- a/pycalphad/core/composition_set.pxd
+++ b/pycalphad/core/composition_set.pxd
@@ -1,5 +1,5 @@
 # distutils: language = c++
-from pycalphad.core.phase_rec cimport PhaseRecord
+from pycalphad.core.phase_rec cimport PhaseRecord, FastFunction
 
 cdef public class CompositionSet(object)[type CompositionSetType, object CompositionSetObject]:
     cdef public PhaseRecord phase_record
@@ -9,4 +9,8 @@ cdef public class CompositionSet(object)[type CompositionSetType, object Composi
     cdef public bint fixed
     cdef readonly double energy
     cdef double[::1] _energy_2d_view
+    cdef FastFunction phase_local_cons_func
+    cdef FastFunction phase_local_cons_jac
+    cdef public int num_phase_local_conditions
+    cpdef void set_local_conditions(self, dict phase_local_conditions)
     cpdef void update(self, double[::1] site_fracs, double phase_amt, double[::1] state_variables)
diff --git a/pycalphad/core/composition_set.pyx b/pycalphad/core/composition_set.pyx
index 889643e8c..16652e6ce 100644
--- a/pycalphad/core/composition_set.pyx
+++ b/pycalphad/core/composition_set.pyx
@@ -1,9 +1,10 @@
 # distutils: language = c++
-from pycalphad.core.phase_rec cimport PhaseRecord
+from pycalphad.core.phase_rec cimport PhaseRecord, FastFunction
 cimport numpy as np
 import numpy as np
 from libc.string cimport memset
 cimport cython
+from pycalphad.core.constraints import build_phase_local_constraints
 
 cdef public class CompositionSet(object)[type CompositionSetType, object CompositionSetObject]:
     """
@@ -35,18 +36,32 @@ cdef public class CompositionSet(object)[type CompositionSetType, object Composi
         other._energy_2d_view = <double[:1]>&other.energy
         other.NP = 1.0*self.NP
         other.fixed = bool(self.fixed)
+        other.phase_local_cons_func = self.phase_local_cons_func
+        other.phase_local_cons_jac = self.phase_local_cons_jac
+        other.num_phase_local_conditions = int(self.num_phase_local_conditions)
         return other
 
     def __repr__(self):
         return str(self.__class__.__name__) + "({0}, {1}, NP={2}, GM={3})".format(self.phase_record.phase_name,
                                                                           np.asarray(self.X), self.NP, self.energy)
 
+    cpdef void set_local_conditions(self, dict phase_local_conditions):
+        mod = self.phase_record.phase_record_factory.models[self.phase_record.phase_name]
+        cfuncs = build_phase_local_constraints(mod, self.phase_record.state_variables + self.phase_record.variables,
+                                               phase_local_conditions,
+                                               parameters=self.phase_record.phase_record_factory.param_symbols)
+        self.phase_local_cons_func = FastFunction(cfuncs.internal_cons_func)
+        self.phase_local_cons_jac = FastFunction(cfuncs.internal_cons_jac)
+        self.num_phase_local_conditions = cfuncs.num_internal_cons
+
     @cython.boundscheck(False)
     @cython.wraparound(False)
     cpdef void update(self, double[::1] site_fracs, double phase_amt, double[::1] state_variables):
         cdef int comp_idx
-        self.dof[:state_variables.shape[0]] = state_variables
-        self.dof[state_variables.shape[0]:] = site_fracs
+        for comp_idx in range(state_variables.shape[0]):
+            self.dof[comp_idx] = state_variables[comp_idx]
+        for comp_idx in range(site_fracs.shape[0]):
+            self.dof[state_variables.shape[0] + comp_idx] = site_fracs[comp_idx]
         self.NP = phase_amt
         self.energy = 0
         memset(&self.X[0], 0, self.X.shape[0] * sizeof(double))
diff --git a/pycalphad/core/conditions.py b/pycalphad/core/conditions.py
new file mode 100644
index 000000000..270c471db
--- /dev/null
+++ b/pycalphad/core/conditions.py
@@ -0,0 +1,182 @@
+import numpy as np
+from pycalphad.core.errors import ConditionError
+from pycalphad.property_framework import as_property, as_quantity
+from pycalphad.property_framework.units import Q_
+import pycalphad.variables as v
+from collections.abc import Iterable
+from typing import List, NamedTuple, Optional, TYPE_CHECKING
+import warnings
+import os
+
+if TYPE_CHECKING:
+    from pycalphad.core.workspace import Workspace
+    from pycalphad.property_framework import ComputableProperty
+
+class ConditionsEntry(NamedTuple):
+    prop: "ComputableProperty"
+    value: "Q_"
+
+_default = object()
+
+def unpack_condition(tup):
+    """
+    Convert a condition to a list of values.
+
+    Notes
+    -----
+    Rules for keys of conditions dicts:
+    (1) If it's numeric, treat as a point value
+    (2) If it's a tuple with one element, treat as a point value
+    (3) If it's a tuple with two elements, treat as lower/upper limits and guess a step size.
+    (4) If it's a tuple with three elements, treat as lower/upper/step
+    (5) If it's a list, ndarray or other non-tuple ordered iterable, use those values directly.
+
+    """
+    if isinstance(tup, tuple):
+        if len(tup) == 1:
+            return [float(tup[0])]
+        elif len(tup) == 2:
+            return np.arange(tup[0], tup[1], dtype=np.float_)
+        elif len(tup) == 3:
+            return np.arange(tup[0], tup[1], tup[2], dtype=np.float_)
+        else:
+            raise ValueError('Condition tuple is length {}'.format(len(tup)))
+    elif isinstance(tup, Q_):
+        return tup
+    elif isinstance(tup, Iterable) and np.ndim(tup) != 0:
+        return [float(x) for x in tup]
+    else:
+        return [float(tup)]
+
+class Conditions:
+    _wks: "Workspace"
+    _conds: List[ConditionsEntry]
+
+    minimum_composition: float = 1e-10
+
+    def __init__(self, wks: Optional["Workspace"]):
+        self._wks = wks
+        self._conds = []
+        # Default to N=1
+        self.__setitem__(v.N, Q_(np.atleast_1d(1.0), 'mol'))
+
+    @classmethod
+    def from_dict(cls, d):
+        if isinstance(d, Conditions):
+            return d
+        obj = cls(wks=None)
+        obj.update(d)
+        return obj
+    
+    def _find_matching_index(self, prop: "ComputableProperty"):
+        for idx, (key, _) in enumerate(self._conds):
+            # TODO: Use more sophisticated matching
+            if str(prop) == str(key):
+                return idx
+        return None
+
+    @classmethod
+    def cast_from(cls, key) -> "Conditions":
+        return cls.from_dict(key)
+    
+    def __getitem__(self, item):
+        key = as_property(item)
+        idx = self._find_matching_index(key)
+        if idx is None:
+            raise IndexError(f"{item} is not a condition")
+        entry = self._conds[idx]
+        # Important to use the _key_ display_units, and not the entry.prop
+        # This is because v.T['K'] == v.T['degC'], so conditions can be
+        # stored and queried with distinct units
+        return entry.value.to(key.display_units)
+
+    def get(self, item, default=_default):
+        try:
+            return self.__getitem__(item)
+        except IndexError:
+            if default is not _default:
+                return default
+            else:
+                raise
+
+    def __delitem__(self, item):
+        idx = self._find_matching_index(as_property(item))
+        if idx is None:
+            raise IndexError(f"{item} is not a condition")
+        del self._conds[idx]
+    
+    def __setitem__(self, item, value):
+        prop = as_property(item)
+        if isinstance(prop, v.MoleFraction):
+            vals = unpack_condition(value)
+            # "Zero" composition is a common pattern. Do not warn for that case.
+            if np.any(np.logical_and(np.asarray(vals) < self.minimum_composition, np.asarray(vals) > 0)):
+                warnings.warn(
+                    f"Some specified compositions are below the minimum allowed composition of {self.minimum_composition}.")
+            value = [min(max(val, self.minimum_composition), 1-self.minimum_composition) for val in vals]
+        else:
+            value = unpack_condition(value)
+        
+        value = as_quantity(prop, value).to(prop.implementation_units)
+
+        if isinstance(prop, (v.MoleFraction, v.ChemicalPotential)) and prop.species not in self._wks.components:
+            raise ConditionError('{} refers to non-existent component'.format(prop))
+        
+        if (prop == v.N) and np.any(value != Q_(1.0, 'mol')):
+            raise ConditionError('N!=1 is not yet supported, got N={}'.format(value))
+        
+        entry = ConditionsEntry(prop=prop, value=value)
+
+        idx = self._find_matching_index(prop)
+
+        if idx is None:
+            # Condition is not yet specified
+            # TODO: Check number of degrees of freedom
+            self._conds.append(entry)
+        else:
+            self._conds[idx] = entry
+        
+        self._conds = sorted(self._conds, key=lambda k: str(k[0]))
+
+    def keys(self):
+        for key, _ in self._conds:
+            yield key
+
+    def str_keys(self):
+        for key, _ in self._conds:
+            yield str(key)
+
+    def values(self):
+        for _, value in self._conds:
+            yield value
+
+    def update(self, d):
+        for key, value in d.items():
+            self.__setitem__(key, value)
+
+    def items(self):
+        for key, value in self._conds:
+            yield key, value
+
+    def str_items(self):
+        """
+        Return key-value pairs suitable for the minimizer.
+        This returns values as implementation units, magnitude only.
+        """
+        for key, value in self._conds:
+            yield (str(key), value.to(key.implementation_units).magnitude)
+
+    def __len__(self):
+        return len(self._conds)
+
+    def __iter__(self):
+        yield from self.keys()
+
+    def __str__(self):
+        result = ""
+        with np.printoptions(threshold=10):
+            for key, value in self._conds:
+                result += str(key) + "=" + str(value) + os.linesep
+        return result
+
+    __repr__ = __str__
\ No newline at end of file
diff --git a/pycalphad/core/constraints.py b/pycalphad/core/constraints.py
index 334548b0b..7be60bc4b 100644
--- a/pycalphad/core/constraints.py
+++ b/pycalphad/core/constraints.py
@@ -19,3 +19,28 @@ def build_constraints(mod, variables, parameters=None):
     return ConstraintTuple(internal_cons_func=internal_cons_func, internal_cons_jac=internal_cons_jac,
                            internal_cons_hess=internal_cons_hess,
                            num_internal_cons=len(internal_constraints))
+
+def build_phase_local_constraints(mod, variables, phase_local_conditions, parameters=None):
+    import pycalphad.variables as v
+    phase_local_constraints = []
+    for key, value in phase_local_conditions.items():
+        # Should each phase-local condition key have a `.as_equation(model)` function?
+        # That may work better as we expand to linear combinations of PLCs (fewer special cases needed)
+        if isinstance(key, v.MoleFraction):
+            cons = mod.moles(key.species, per_formula_unit=True) - \
+                value * sum(mod.moles(v.Species(el), per_formula_unit=True) for el in mod.nonvacant_elements)
+        elif isinstance(key, v.SiteFraction):
+            cons = key - value
+        else:
+            raise ValueError(f'Unsupported phase-local condition: {key}')
+        phase_local_constraints.append(cons.expand())
+
+    cf_output = build_constraint_functions(variables, phase_local_constraints,
+                                           parameters=parameters)
+    internal_cons_func = cf_output.cons_func
+    internal_cons_jac = cf_output.cons_jac
+    internal_cons_hess = cf_output.cons_hess
+
+    return ConstraintTuple(internal_cons_func=internal_cons_func, internal_cons_jac=internal_cons_jac,
+                           internal_cons_hess=internal_cons_hess,
+                           num_internal_cons=len(phase_local_constraints))
diff --git a/pycalphad/core/eqsolver.pyx b/pycalphad/core/eqsolver.pyx
index 7b80145ba..4244410ca 100644
--- a/pycalphad/core/eqsolver.pyx
+++ b/pycalphad/core/eqsolver.pyx
@@ -3,17 +3,15 @@ from collections import OrderedDict
 import numpy as np
 cimport numpy as np
 cimport cython
-cdef extern from "_isnan.h":
-    bint isnan (double) nogil
 from pycalphad.core.solver import Solver
 from pycalphad.core.composition_set cimport CompositionSet
 from pycalphad.core.phase_rec cimport PhaseRecord
 from pycalphad.core.constants import *
 
 
-cdef bint add_new_phases(object composition_sets, object removed_compsets, object phase_records,
-                         object grid, object current_idx, np.ndarray[ndim=1, dtype=np.float64_t] chemical_potentials,
-                         double[::1] state_variables, double minimum_df, bint verbose) except *:
+cpdef bint add_new_phases(object composition_sets, object removed_compsets, object phase_records,
+                          object grid, object current_idx, np.ndarray[ndim=1, dtype=np.float64_t] chemical_potentials,
+                          double[::1] state_variables, double minimum_df, bint verbose) except *:
     """
     Attempt to add a new phase with the largest driving force (based on chemical potentials). Candidate phases
     are taken from current_grid and modify the composition_sets object. The function returns a boolean indicating
@@ -92,7 +90,7 @@ cdef int argmax(double* a, int a_shape) nogil:
             result = i
     return result
 
-def add_nearly_stable(object composition_sets, dict phase_records,
+def add_nearly_stable(object composition_sets, object phase_records,
                       object grid, object current_idx, np.ndarray[ndim=1, dtype=np.float64_t] chemical_potentials,
                       double[::1] state_variables, double minimum_df, bint verbose):
     cdef double[::1] driving_forces, driving_forces_for_phase
@@ -114,6 +112,9 @@ def add_nearly_stable(object composition_sets, dict phase_records,
             continue
         phase_record = phase_records[phase_name]
         phase_indices = grid.attrs['phase_indices'][phase_name]
+        if phase_indices.start == phase_indices.stop:
+            # Phase has zero feasible grid points to consider
+            continue
         driving_forces_for_phase = driving_forces[phase_indices.start:phase_indices.stop]
         minimum_df_idx = argmax(&driving_forces_for_phase[0], driving_forces_for_phase.shape[0])
         if driving_forces_for_phase[minimum_df_idx] >= minimum_df:
@@ -129,8 +130,6 @@ def add_nearly_stable(object composition_sets, dict phase_records,
 
 def _solve_eq_at_conditions(properties, phase_records, grid, conds_keys, state_variables, verbose, solver=None):
     """
-        _solve_eq_at_conditions(properties, phase_records, grid, conds_keys, state_variables, verbose, solver=None)
-
     Compute equilibrium for the given conditions.
     This private function is meant to be called from a worker subprocess.
     For that case, usually only a small slice of the master 'properties' is provided.
@@ -187,14 +186,18 @@ def _solve_eq_at_conditions(properties, phase_records, grid, conds_keys, state_v
         converged = False
         changed_phases = False
         cur_conds = OrderedDict(zip(conds_keys,
-                                    [np.asarray(properties.coords[b][a], dtype=np.float_)
+                                    [np.asarray(properties.coords[str(b)][a], dtype=np.float_)
                                      for a, b in zip(it.multi_index, conds_keys)]))
         # assume 'points' and other dimensions (internal dof, etc.) always follow
-        curr_idx = [it.multi_index[i] for i, key in enumerate(conds_keys) if key in str_state_variables]
-        state_variable_values = [cur_conds[key] for key in str_state_variables]
+        local_idx = [it.multi_index[i] for i, key in enumerate(conds_keys)
+                     if getattr(key, 'phase_name', None) is not None]
+        sv_idx = [it.multi_index[i] for i, key in enumerate(conds_keys)
+                  if (str(key) in str_state_variables)]
+        curr_idx = local_idx + sv_idx
+        state_variable_values = [cur_conds[state_variables[str_state_variables.index(key)]] for key in str_state_variables]
         state_variable_values = np.array(state_variable_values)
         # sum of independently specified components
-        indep_sum = np.sum([float(val) for i, val in cur_conds.items() if i.startswith('X_')])
+        indep_sum = np.sum([float(val) for i, val in cur_conds.items() if str(i).startswith('X_')])
         if indep_sum > 1:
             # Sum of independent component mole fractions greater than one
             # Skip this condition set
diff --git a/pycalphad/core/equilibrium.py b/pycalphad/core/equilibrium.py
index 3235ab172..7dd71bce8 100644
--- a/pycalphad/core/equilibrium.py
+++ b/pycalphad/core/equilibrium.py
@@ -4,145 +4,17 @@
 """
 import warnings
 from collections import OrderedDict
-from collections.abc import Mapping
+from collections.abc import Iterable
 from datetime import datetime
-import pycalphad.variables as v
-from pycalphad.core.utils import unpack_components, unpack_condition, unpack_phases, filter_phases, instantiate_models, get_state_variables
-from pycalphad import calculate
-from pycalphad.core.errors import EquilibriumError, ConditionError
-from pycalphad.core.starting_point import starting_point
-from pycalphad.codegen.callables import build_phase_records
-from pycalphad.core.eqsolver import _solve_eq_at_conditions
-from pycalphad.core.phase_rec import PhaseRecord
-from pycalphad.core.solver import Solver
+from pycalphad.core.workspace import Workspace
 from pycalphad.core.light_dataset import LightDataset
-from pycalphad.model import Model
 import numpy as np
-
-
-def _adjust_conditions(conds):
-    "Adjust conditions values to be within the numerical limit of the solver."
-    new_conds = OrderedDict()
-    minimum_composition = 1e-10
-    for key, value in sorted(conds.items(), key=str):
-        if key == str(key):
-            key = getattr(v, key, key)
-        if isinstance(key, v.MoleFraction):
-            vals = unpack_condition(value)
-            # "Zero" composition is a common pattern. Do not warn for that case.
-            if np.any(np.logical_and(np.asarray(vals) < minimum_composition, np.asarray(vals) > 0)):
-                warnings.warn(
-                    f"Some specified compositions are below the minimum allowed composition of {minimum_composition}.")
-            new_conds[key] = [max(val, minimum_composition) for val in vals]
-        else:
-            new_conds[key] = unpack_condition(value)
-    return new_conds
-
-
-def _eqcalculate(dbf, comps, phases, conditions, output, data=None, per_phase=False, callables=None, model=None,
-                 parameters=None, **kwargs):
-    """
-    WARNING: API/calling convention not finalized.
-    Compute the *equilibrium value* of a property.
-    This function differs from `calculate` in that it computes
-    thermodynamic equilibrium instead of randomly sampling the
-    internal degrees of freedom of a phase.
-    Because of that, it's slower than `calculate`.
-    This plugs in the equilibrium phase and site fractions
-    to compute a thermodynamic property defined in a Model.
-
-    Parameters
-    ----------
-    dbf : Database
-        Thermodynamic database containing the relevant parameters.
-    comps : list
-        Names of components to consider in the calculation.
-    phases : list or dict
-        Names of phases to consider in the calculation.
-    conditions : dict or (list of dict)
-        StateVariables and their corresponding value.
-    output : str
-        Equilibrium model property (e.g., CPM, HM, etc.) to compute.
-        This must be defined as an attribute in the Model class of each phase.
-    data : Dataset
-        Previous result of call to `equilibrium`.
-        Should contain the equilibrium configurations at the conditions of interest.
-        If the databases are not the same as in the original calculation,
-        the results may be meaningless.
-    per_phase : bool, optional
-        If True, compute and return the property for each phase present.
-        If False, return the total system value, weighted by the phase fractions.
-    callables : dict
-        Callable functions to compute 'output' for each phase.
-    model : a dict of phase names to Model
-        Model class to use for each phase.
-    parameters : dict, optional
-        Maps SymEngine Symbol to numbers, for overriding the values of parameters in the Database.
-    kwargs
-        Passed to `calculate`.
-
-    Returns
-    -------
-    Dataset of property as a function of equilibrium conditions
-    """
-    if data is None:
-        raise ValueError('Required kwarg "data" is not specified')
-    if model is None:
-        raise ValueError('Required kwarg "model" is not specified')
-    active_phases = unpack_phases(phases)
-    conds = _adjust_conditions(conditions)
-    indep_vars = ['N', 'P', 'T']
-    # TODO: Rewrite this to use the coord dict from 'data'
-    str_conds = OrderedDict((str(key), value) for key, value in conds.items())
-    indep_vals = list([float(x) for x in np.atleast_1d(val)]
-                      for key, val in str_conds.items() if key in indep_vars)
-    coord_dict = str_conds.copy()
-    components = [x for x in sorted(comps)]
-    desired_active_pure_elements = [list(x.constituents.keys()) for x in components]
-    desired_active_pure_elements = [el.upper() for constituents in desired_active_pure_elements for el in constituents]
-    pure_elements = sorted(set([x for x in desired_active_pure_elements if x != 'VA']))
-    coord_dict['vertex'] = np.arange(len(pure_elements) + 1)  # +1 is to accommodate the degenerate degree of freedom at the invariant reactions
-    grid_shape = np.meshgrid(*coord_dict.values(),
-                             indexing='ij', sparse=False)[0].shape
-    prop_shape = grid_shape
-    prop_dims = list(str_conds.keys()) + ['vertex']
-
-    result = LightDataset({output: (prop_dims, np.full(prop_shape, np.nan))}, coords=coord_dict)
-    # For each phase select all conditions where that phase exists
-    # Perform the appropriate calculation and then write the result back
-    for phase in active_phases:
-        dof = len(model[phase].site_fractions)
-        current_phase_indices = (data.Phase == phase)
-        if ~np.any(current_phase_indices):
-            continue
-        points = data.Y[np.nonzero(current_phase_indices)][..., :dof]
-        statevar_indices = np.nonzero(current_phase_indices)[:len(indep_vals)]
-        statevars = {key: np.take(np.asarray(vals), idx)
-                     for key, vals, idx in zip(indep_vars, indep_vals, statevar_indices)}
-        statevars.update(kwargs)
-        if statevars.get('mode', None) is None:
-            statevars['mode'] = 'numpy'
-        calcres = calculate(dbf, comps, [phase], output=output, points=points, broadcast=False,
-                            callables=callables, parameters=parameters, model=model, **statevars)
-        result[output][np.nonzero(current_phase_indices)] = calcres[output].values
-    if not per_phase:
-        out = np.nansum(result[output] * data['NP'], axis=-1)
-        dv_output = result.data_vars[output]
-        result.remove(output)
-        # remove the vertex coordinate because we summed over it
-        result.add_variable(output, dv_output[0][:-1], out)
-    else:
-        dv_phase = data.data_vars['Phase']
-        dv_np = data.data_vars['NP']
-        result.add_variable('Phase', dv_phase[0], dv_phase[1])
-        result.add_variable('NP', dv_np[0], dv_np[1])
-    return result
+from pycalphad.property_framework import as_property
 
 
 def equilibrium(dbf, comps, phases, conditions, output=None, model=None,
-                verbose=False, broadcast=True, calc_opts=None, to_xarray=True,
-                scheduler='sync', parameters=None, solver=None, callables=None,
-                phase_records=None, **kwargs):
+                verbose=False, calc_opts=None, to_xarray=True,
+                parameters=None, solver=None, phase_records=None, **kwargs):
     """
     Calculate the equilibrium state of a system containing the specified
     components and phases, under the specified conditions.
@@ -164,18 +36,10 @@ def equilibrium(dbf, comps, phases, conditions, output=None, model=None,
         Model class to use for each phase.
     verbose : bool, optional
         Print details of calculations. Useful for debugging.
-    broadcast : bool
-        If True, broadcast conditions against each other. This will compute all combinations.
-        If False, each condition should be an equal-length list (or single-valued).
-        Disabling broadcasting is useful for calculating equilibrium at selected conditions,
-        when those conditions don't comprise a grid.
     calc_opts : dict, optional
         Keyword arguments to pass to `calculate`, the energy/property calculation routine.
     to_xarray : bool
         Whether to return an xarray Dataset (True, default) or an EquilibriumResult.
-    scheduler : Dask scheduler, optional
-        Job scheduler for performing the computation.
-        If None, return a Dask graph of the computation instead of actually doing it.
     parameters : dict, optional
         Maps SymEngine Symbol to numbers, for overriding the values of parameters in the Database.
     solver : pycalphad.core.solver.SolverBase
@@ -190,116 +54,38 @@ def equilibrium(dbf, comps, phases, conditions, output=None, model=None,
 
     Returns
     -------
-    Structured equilibrium calculation, or Dask graph if scheduler=None.
+    Structured equilibrium calculation
 
     Examples
     --------
     None yet.
     """
-    if not broadcast:
-        raise NotImplementedError('Broadcasting cannot yet be disabled')
-    comps = sorted(unpack_components(dbf, comps))
-    phases = unpack_phases(phases) or sorted(dbf.phases.keys())
-    list_of_possible_phases = filter_phases(dbf, comps)
-    if len(list_of_possible_phases) == 0:
-        raise ConditionError('There are no phases in the Database that can be active with components {0}'.format(comps))
-    active_phases = filter_phases(dbf, comps, phases)
-    if len(active_phases) == 0:
-        raise ConditionError('None of the passed phases ({0}) are active. List of possible phases: {1}.'.format(phases, list_of_possible_phases))
-    if isinstance(comps, (str, v.Species)):
-        comps = [comps]
-    if len(set(comps) - set(dbf.species)) > 0:
-        raise EquilibriumError('Components not found in database: {}'
-                               .format(','.join([c.name for c in (set(comps) - set(dbf.species))])))
-    calc_opts = calc_opts if calc_opts is not None else dict()
-    solver = solver if solver is not None else Solver(verbose=verbose)
-    parameters = parameters if parameters is not None else dict()
-    if isinstance(parameters, dict):
-        parameters = OrderedDict(sorted(parameters.items(), key=str))
-    # Temporary solution until constraint system improves
-    if conditions.get(v.N) is None:
-        conditions[v.N] = 1
-    if np.any(np.array(conditions[v.N]) != 1):
-        raise ConditionError('N!=1 is not yet supported, got N={}'.format(conditions[v.N]))
-    # Modify conditions values to be within numerical limits, e.g., X(AL)=0
-    # Also wrap single-valued conditions with lists
-    conds = _adjust_conditions(conditions)
-
-    for cond in conds.keys():
-        if isinstance(cond, (v.MoleFraction, v.ChemicalPotential)) and cond.species not in comps:
-            raise ConditionError('{} refers to non-existent component'.format(cond))
-    str_conds = OrderedDict((str(key), value) for key, value in conds.items())
-    components = [x for x in sorted(comps)]
-    desired_active_pure_elements = [list(x.constituents.keys()) for x in components]
-    desired_active_pure_elements = [el.upper() for constituents in desired_active_pure_elements for el in constituents]
-    pure_elements = sorted(set([x for x in desired_active_pure_elements if x != 'VA']))
-    if verbose:
-        print('Components:', ' '.join([str(x) for x in comps]))
-        print('Phases:', end=' ')
-    output = output if output is not None else 'GM'
-    output = output if isinstance(output, (list, tuple, set)) else [output]
-    output = set(output)
-    output |= {'GM'}
-    output = sorted(output)
-    if phase_records is None:
-        models = instantiate_models(dbf, comps, active_phases, model=model, parameters=parameters)
-        phase_records = build_phase_records(dbf, comps, active_phases, conds, models,
-                                            output='GM', callables=callables,
-                                            parameters=parameters, verbose=verbose,
-                                            build_gradients=True, build_hessians=True)
-    else:
-        # phase_records were provided, instantiated models must also be provided by the caller
-        models = model
-        if not isinstance(models, Mapping):
-            raise ValueError("A dictionary of instantiated models must be passed to `equilibrium` with the `model` argument if the `phase_records` argument is used.")
-        active_phases_without_models = [name for name in active_phases if not isinstance(models.get(name), Model)]
-        active_phases_without_phase_records = [name for name in active_phases if not isinstance(phase_records.get(name), PhaseRecord)]
-        if len(active_phases_without_phase_records) > 0:
-            raise ValueError(f"phase_records must contain a PhaseRecord instance for every active phase. Missing PhaseRecord objects for {sorted(active_phases_without_phase_records)}")
-        if len(active_phases_without_models) > 0:
-            raise ValueError(f"model must contain a Model instance for every active phase. Missing Model objects for {sorted(active_phases_without_models)}")
-
-    if verbose:
-        print('[done]', end='\n')
-
-    state_variables = sorted(get_state_variables(models=models, conds=conds), key=str)
-
-    # 'calculate' accepts conditions through its keyword arguments
-    grid_opts = calc_opts.copy()
-    statevar_strings = [str(x) for x in state_variables]
-    grid_opts.update({key: value for key, value in str_conds.items() if key in statevar_strings})
-
-    if 'pdens' not in grid_opts:
-        grid_opts['pdens'] = 60
-    grid = calculate(dbf, comps, active_phases, model=models, fake_points=True,
-                     phase_records=phase_records, output='GM', parameters=parameters,
-                     to_xarray=False, **grid_opts)
-    coord_dict = str_conds.copy()
-    coord_dict['vertex'] = np.arange(len(pure_elements) + 1)  # +1 is to accommodate the degenerate degree of freedom at the invariant reactions
-    coord_dict['component'] = pure_elements
-    properties = starting_point(conds, state_variables, phase_records, grid)
-    properties = _solve_eq_at_conditions(properties, phase_records, grid,
-                                         list(str_conds.keys()), state_variables,
-                                         verbose, solver=solver)
+    if output is None:
+        output = set()
+    elif (not isinstance(output, Iterable)) or isinstance(output, str):
+        output = [output]
+    wks = Workspace(database=dbf, components=comps, phases=phases, conditions=conditions, models=model, parameters=parameters,
+                    verbose=verbose, calc_opts=calc_opts, solver=solver, phase_record_factory=phase_records)
 
     # Compute equilibrium values of any additional user-specified properties
     # We already computed these properties so don't recompute them
+    properties = wks.eq
+    conds_keys = [str(k) for k in properties.coords.keys() if k not in ('vertex', 'component', 'internal_dof')]
     output = sorted(set(output) - {'GM', 'MU'})
     for out in output:
-        if (out is None) or (len(out) == 0):
-            continue
-        # TODO: How do we know if a specified property should be per_phase or not?
-        # For now, we make a best guess
-        if (out == 'degree_of_ordering') or (out == 'DOO'):
-            per_phase = True
-        else:
-            per_phase = False
-        eqcal = _eqcalculate(dbf, comps, active_phases, conditions, out,
-                             data=properties, per_phase=per_phase, model=models,
-                             callables=callables, parameters=parameters, **calc_opts)
-        properties = properties.merge(eqcal, inplace=True, compat='equals')
+        cprop = as_property(out)
+        out = str(cprop)
+        result_array = np.zeros(properties.GM.shape) # Will not work for non-scalar properties
+        for index, composition_sets in wks.enumerate_composition_sets():
+            cur_conds = OrderedDict(zip(conds_keys,
+                                        [np.asarray(properties.coords[b][a], dtype=np.float_)
+                                        for a, b in zip(index, conds_keys)]))
+            chemical_potentials = properties.MU[index]
+            result_array[index] = cprop.compute_property(composition_sets, cur_conds, chemical_potentials)
+        result = LightDataset({out: (conds_keys, result_array)}, coords=properties.coords)
+        properties.merge(result, inplace=True, compat='equals')
     if to_xarray:
-        properties = properties.get_dataset()
+        properties = wks.eq.get_dataset()
     properties.attrs['created'] = datetime.utcnow().isoformat()
     if len(kwargs) > 0:
         warnings.warn('The following equilibrium keyword arguments were passed, but unused:\n{}'.format(kwargs))
diff --git a/pycalphad/core/hyperplane.pxd b/pycalphad/core/hyperplane.pxd
index 700e71c52..bb941b6e8 100644
--- a/pycalphad/core/hyperplane.pxd
+++ b/pycalphad/core/hyperplane.pxd
@@ -1,10 +1,9 @@
 # distutils: language = c++
 cpdef double hyperplane(double[:,::1] compositions,
                         double[::1] energies,
-                        double[::1] composition,
                         double[::1] chemical_potentials,
-                        double total_moles,
                         size_t[::1] fixed_chempot_indices,
-                        size_t[::1] fixed_comp_indices,
+                        double[:, ::1] fixed_lincomb_molefrac_coefs,
+                        double[::1] fixed_lincomb_molefrac_rhs,
                         double[::1] result_fractions,
-                        int[::1] result_simplex) nogil except *
+                        int[::1] result_simplex) except *
\ No newline at end of file
diff --git a/pycalphad/core/hyperplane.pyx b/pycalphad/core/hyperplane.pyx
index 1dd19b0e5..32b477c41 100644
--- a/pycalphad/core/hyperplane.pyx
+++ b/pycalphad/core/hyperplane.pyx
@@ -56,16 +56,113 @@ cdef int argmax(double* a, int a_shape) nogil:
             result = i
     return result
 
+@cython.boundscheck(False)
+cpdef void hyperplane_coefficients(double[:,::1] compositions,
+                                   size_t[::1] fixed_chempot_indices,
+                                   int[::1] trial_simplex,
+                                   double[::1] out_plane_coefs) except * nogil:
+    cdef int i, j
+    cdef int plane_rows = trial_simplex.shape[0] + fixed_chempot_indices.shape[0]
+    if plane_rows != compositions.shape[1]:
+        raise ValueError('Hyperplane coefficient matrix is not square')
+    cdef double* f_plane_matrix = <double*>malloc(plane_rows * compositions.shape[1] * sizeof(double))
+    cdef int* int_tmp = <int*>malloc(plane_rows * sizeof(int))
+    for i in range(trial_simplex.shape[0]):
+        for j in range(compositions.shape[1]):
+            f_plane_matrix[i + j*plane_rows] = compositions[trial_simplex[i], j]
+        out_plane_coefs[i] = 1
+    for i in range(fixed_chempot_indices.shape[0]):
+        for j in range(compositions.shape[1]):
+            f_plane_matrix[i + trial_simplex.shape[0] + j*plane_rows] = 0
+        f_plane_matrix[i + trial_simplex.shape[0] + fixed_chempot_indices[i]*plane_rows] = 1
+        out_plane_coefs[i + trial_simplex.shape[0]] = 0
+    solve(f_plane_matrix, plane_rows, &out_plane_coefs[0], int_tmp)
+    free(f_plane_matrix)
+    free(int_tmp)
+
+@cython.boundscheck(False)
+cpdef void intersecting_point(double[:,::1] compositions,
+                              size_t[::1] fixed_chempot_indices,
+                              int[::1] trial_simplex,
+                              double[:,::1] fixed_lincomb_molefrac_coefs,
+                              double[::1] fixed_lincomb_molefrac_rhs,
+                              double[::1] out_intersecting_point) except * nogil:
+    cdef int i, j
+    if trial_simplex.shape[0] == 1:
+        # Simplex is zero-dimensional, so there is no intersection; just return the point defining the 0-simplex
+        for i in range(compositions.shape[1]):
+            out_intersecting_point[i] = compositions[trial_simplex[0], i]
+        return
+    #with gil:
+    #    print('trial_simplex ', np.asarray(trial_simplex))
+    if (fixed_lincomb_molefrac_rhs.shape[0] + 1 != compositions.shape[1]) and fixed_chempot_indices.shape[0] > 0:
+        raise ValueError('Constraint matrix is not square')
+    cdef int* int_tmp = <int*>malloc(compositions.shape[1] * sizeof(int))
+    cdef double* constraint_matrix = <double*>malloc((fixed_lincomb_molefrac_rhs.shape[0] + 1) * compositions.shape[1] * sizeof(double))
+    cdef double* constraint_rhs = <double*>malloc((fixed_lincomb_molefrac_rhs.shape[0] + 1) * sizeof(double))
+    out_intersecting_point[:] = 0
+    # out_intersecting_point memory is reused for plane_coefs, to save an allocation
+    cdef double[::1] plane_coefs = out_intersecting_point
+    hyperplane_coefficients(compositions, fixed_chempot_indices, trial_simplex, plane_coefs)
+    #with gil:
+    #    print('plane_coefs ', np.asarray(plane_coefs))
+    for j in range(compositions.shape[1]):
+        for i in range(fixed_lincomb_molefrac_rhs.shape[0]):
+            constraint_matrix[i + j*compositions.shape[1]] = fixed_lincomb_molefrac_coefs[i, j]
+            constraint_rhs[i] = fixed_lincomb_molefrac_rhs[i]
+        constraint_matrix[fixed_lincomb_molefrac_rhs.shape[0] + j*compositions.shape[1]] = plane_coefs[j]
+        constraint_rhs[fixed_lincomb_molefrac_rhs.shape[0]] = 1
+    #with gil:
+    #    print('constraint_matrix ', np.asarray(<double[:fixed_lincomb_molefrac_rhs.shape[0] + 1, :compositions.shape[1]]>constraint_matrix))
+    #    print('constraint_rhs ', np.asarray(<double[:fixed_lincomb_molefrac_rhs.shape[0] + 1]>constraint_rhs))
+    #raise ValueError('stop')
+    solve(constraint_matrix, compositions.shape[1], constraint_rhs, int_tmp)
+    for i in range(compositions.shape[1]):
+        out_intersecting_point[i] = constraint_rhs[i]
+    free(int_tmp)
+    free(constraint_matrix)
+    free(constraint_rhs)
+
+#@cython.boundscheck(False)
+cdef void simplex_fractions(double[:,::1] compositions,
+                             size_t[::1] fixed_chempot_indices,
+                             int[::1] trial_simplex,
+                             double[:,::1] fixed_lincomb_molefrac_coefs,
+                             double[::1] fixed_lincomb_molefrac_rhs,
+                             double* out_fractions) except *:
+    cdef int simplex_size = trial_simplex.shape[0]
+    # Note that compositions.shape[1] = simplex_size + fixed_chempot_indices.shape[0], by construction
+    cdef int i, j
+    cdef double* f_coord_matrix = <double*>malloc(simplex_size * simplex_size * sizeof(double))
+    cdef double* target_point = <double*>malloc(compositions.shape[1] * sizeof(double))
+    cdef int* int_tmp = <int*>malloc(simplex_size * sizeof(int))
+    cdef size_t[::1] free_chempot_indices = np.array(list(set(range(compositions.shape[1])) - set(fixed_chempot_indices)), dtype=np.uintp)
+    #print('free_chempot_indices', np.asarray(free_chempot_indices))
+    # Get target point for calculation
+    intersecting_point(compositions, fixed_chempot_indices, trial_simplex,
+                       fixed_lincomb_molefrac_coefs, fixed_lincomb_molefrac_rhs,
+                       <double[:compositions.shape[1]]>target_point)
+    #print('target_point ', np.asarray(<double[:compositions.shape[1]]>target_point))
+    # Fill coordinate matrix
+    for j in range(simplex_size):
+        # compositions[trial_simplex[i], :]
+        for i in range(simplex_size):
+            f_coord_matrix[j + simplex_size*i] = compositions[trial_simplex[i], free_chempot_indices[j]]
+        out_fractions[j] = target_point[free_chempot_indices[j]]
+    solve(f_coord_matrix, simplex_size, &out_fractions[0], int_tmp)
+    free(f_coord_matrix)
+    free(target_point)
+    free(int_tmp)
+
 @cython.boundscheck(False)
 cpdef double hyperplane(double[:,::1] compositions,
                         double[::1] energies,
-                        double[::1] composition,
                         double[::1] chemical_potentials,
-                        double total_moles,
                         size_t[::1] fixed_chempot_indices,
-                        size_t[::1] fixed_comp_indices,
+                        double[:, ::1] fixed_lincomb_molefrac_coefs,
+                        double[::1] fixed_lincomb_molefrac_rhs,
                         double[::1] result_fractions,
-                        int[::1] result_simplex) nogil except *:
+                        int[::1] result_simplex) except *:
     """
     Find chemical potentials which approximate the tangent hyperplane
     at the given composition.
@@ -80,17 +177,14 @@ cpdef double hyperplane(double[:,::1] compositions,
         A sample of the energy surface of the system.
         Aligns with 'compositions'.
         Shape of (M,)
-    composition : ndarray
-        Target composition for the hyperplane.
-        Shape of (N,)
     chemical_potentials : ndarray
         Shape of (N,)
         Will be overwritten
-    total_moles : double
-        Total number of moles in the system.
     fixed_chempot_indices : ndarray
         Variable shape from (0,) to (N-1,)
-    fixed_comp_indices : ndarray
+    fixed_lincomb_molefrac_coefs : ndarray
+        Variable shape from (0,P) to (N-1, P)
+    fixed_lincomb_molefrac_rhs : ndarray
         Variable shape from (0,) to (N-1,)
     result_fractions : ndarray
         Relative amounts of the points making up the hyperplane simplex. Shape of (P,).
@@ -131,11 +225,6 @@ cpdef double hyperplane(double[:,::1] compositions,
     cdef double lowest_df = 0
     cdef double out_energy = 0
     # 1-D
-    # composition index of -1 indicates total number of moles, i.e., N=1 condition
-    cdef int* included_composition_indices = <int*>malloc((fixed_comp_indices.shape[0] + 1) * sizeof(int))
-    for i in range(fixed_comp_indices.shape[0]):
-        included_composition_indices[i] = fixed_comp_indices[i]
-    included_composition_indices[fixed_comp_indices.shape[0]] = -1
     cdef int* best_guess_simplex = <int*>malloc(simplex_size * sizeof(int))
     for i in range(num_components):
         skip_index = False
@@ -170,39 +259,24 @@ cpdef double hyperplane(double[:,::1] compositions,
 
     while iterations < max_iterations:
         iterations += 1
-        for trial_idx in range(simplex_size):
-            #smallest_fractions[trial_idx] = 0
-            for comp_idx in range(simplex_size):
-                ici = included_composition_indices[comp_idx]
-                for simplex_idx in range(simplex_size):
-                    if ici >= 0:
-                        f_trial_matrix[comp_idx + simplex_idx*simplex_size + trial_idx*simplex_size*simplex_size] = \
-                            compositions[trial_simplices[trial_idx*simplex_size + simplex_idx], ici]
-                    else:
-                        # ici = -1, refers to N=1 condition
-                        f_trial_matrix[comp_idx + simplex_idx*simplex_size + trial_idx*simplex_size*simplex_size] = 1 # 1 mole-formula per formula unit of a phase
 
         for trial_idx in range(simplex_size):
-            for i in range(simplex_size):
-                for j in range(simplex_size):
-                    f_contig_trial[i + j*simplex_size] = f_trial_matrix[i + j*simplex_size + trial_idx*simplex_size*simplex_size]
+            #print('trial simplex ', np.asarray(<int[:simplex_size]>&trial_simplices[trial_idx*simplex_size]))
             for simplex_idx in range(simplex_size):
-                ici = included_composition_indices[simplex_idx]
-                if ici >= 0:
-                    fractions[trial_idx*simplex_size + simplex_idx] = composition[ici]
-                else:
-                    # ici = -1, refers to N=1 condition
-                    fractions[trial_idx*simplex_size + simplex_idx] = total_moles
-            solve(f_contig_trial, simplex_size, &fractions[trial_idx*simplex_size], int_tmp)
+                fractions[trial_idx*simplex_size + simplex_idx] = 0
+            simplex_fractions(compositions, fixed_chempot_indices, <int[:simplex_size]>&trial_simplices[trial_idx*simplex_size],
+                              fixed_lincomb_molefrac_coefs, fixed_lincomb_molefrac_rhs, &fractions[trial_idx*simplex_size])
             smallest_fractions[trial_idx] = _min(&fractions[trial_idx*simplex_size], simplex_size)
-
+        #print('smallest_fractions ', np.asarray(<double[:simplex_size]>&smallest_fractions[0]))
         # Choose simplex with the largest smallest-fraction
         saved_trial = argmax(smallest_fractions, simplex_size)
+        #print('saved_trial', saved_trial)
         if smallest_fractions[saved_trial] < -simplex_size:
             break
         # Should be exactly one candidate simplex
         for i in range(simplex_size):
             candidate_simplex[i] = trial_simplices[saved_trial*simplex_size + i]
+        #print('candidate_simplex ', np.asarray(<int[:simplex_size]>&candidate_simplex[0]))
         for i in range(simplex_size):
             idx = candidate_simplex[i]
             for ici in range(simplex_size):
@@ -262,7 +336,6 @@ cpdef double hyperplane(double[:,::1] compositions,
     result_simplex[simplex_size:] = 0
 
     # 1-D
-    free(included_composition_indices)
     free(best_guess_simplex)
     free(free_chempot_indices)
     free(candidate_simplex)
diff --git a/pycalphad/core/lower_convex_hull.py b/pycalphad/core/lower_convex_hull.py
index 5266c491e..38d25d926 100644
--- a/pycalphad/core/lower_convex_hull.py
+++ b/pycalphad/core/lower_convex_hull.py
@@ -2,14 +2,13 @@
 The lower_convex_hull module handles geometric calculations associated with
 equilibrium calculation.
 """
-from pycalphad.core.cartesian import cartesian
-from pycalphad.core.constants import MIN_SITE_FRACTION
+from pycalphad.property_framework.computed_property import LinearCombination
 from .hyperplane import hyperplane
+from pycalphad.variables import ChemicalPotential, MassFraction, MoleFraction, IndependentPotential, SiteFraction, SystemMolesType
 import numpy as np
-import itertools
 
 
-def lower_convex_hull(global_grid, state_variables, result_array):
+def lower_convex_hull(global_grid, state_variables, conds_keys, phase_record_factory, result_array):
     """
     Find the simplices on the lower convex hull satisfying the specified
     conditions in the result array.
@@ -20,6 +19,10 @@ def lower_convex_hull(global_grid, state_variables, result_array):
         A sample of the energy surface of the system.
     state_variables : List[v.StateVariable]
         A list of the state variables (e.g., P, T) used in this calculation.
+    conds_keys : List
+        A list of the keys of the conditions used in this calculation.
+    phase_record_factory : PhaseRecordFactory
+        PhaseRecordFactory object corresponding to this calculation.
     result_array : Dataset
         This object will be modified!
         Coordinates correspond to conditions axes.
@@ -38,43 +41,11 @@ def lower_convex_hull(global_grid, state_variables, result_array):
     None yet.
     """
     state_variables = sorted(state_variables, key=str)
-    comp_conds = sorted([x for x in sorted(result_array.coords.keys()) if x.startswith('X_')])
-    comp_conds_indices = sorted([idx for idx, x in enumerate(sorted(result_array.coords['component']))
-                                 if 'X_'+x in comp_conds])
-    comp_conds_indices = np.array(comp_conds_indices, dtype=np.uintp)
-    pot_conds = sorted([x for x in sorted(result_array.coords.keys()) if x.startswith('MU_')])
-    pot_conds_indices = sorted([idx for idx, x in enumerate(sorted(result_array.coords['component']))
-                                if 'MU_'+x in pot_conds])
-    pot_conds_indices = np.array(pot_conds_indices, dtype=np.uintp)
+    local_conds_keys = [c for c in conds_keys if getattr(c, 'phase_name', None) is not None]
+    str_conds_keys = [str(c) for c in conds_keys]
 
-    if len(set(pot_conds_indices) & set(comp_conds_indices)) > 0:
-        raise ValueError('Cannot specify component chemical potential and amount simultaneously')
-
-    if len(comp_conds) > 0:
-        cart_values = cartesian([result_array.coords[cond] for cond in comp_conds])
-    else:
-        cart_values = np.atleast_2d(1.)
-    # TODO: Handle W(comp) as well as X(comp) here
-    comp_values = np.zeros(cart_values.shape[:-1] + (len(result_array.coords['component']),))
-    for idx in range(comp_values.shape[-1]):
-        if idx in comp_conds_indices:
-            comp_values[..., idx] = cart_values[..., np.where(comp_conds_indices == idx)[0][0]]
-        elif idx in pot_conds_indices:
-            # Composition value not used
-            comp_values[..., idx] = 0
-        else:
-            # Dependent component (composition value not used)
-            comp_values[..., idx] = 0
-    # Prevent compositions near an edge from going negative
-    comp_values[np.nonzero(comp_values < MIN_SITE_FRACTION)] = MIN_SITE_FRACTION*10
-
-    if len(pot_conds) > 0:
-        cart_pot_values = cartesian([result_array.coords[cond] for cond in pot_conds])
-
-    #result_array['Phase'] = force_indep_align(result_array.Phase)
     # factored out via profiling
     result_array_GM_values = result_array.GM
-    result_array_GM_dims = result_array.data_vars['GM'][0]
     result_array_points_values = result_array.points
     result_array_MU_values = result_array.MU
     result_array_NP_values = result_array.NP
@@ -88,46 +59,115 @@ def lower_convex_hull(global_grid, state_variables, result_array):
     num_comps = len(result_array.coords['component'])
 
     it = np.nditer(result_array_GM_values, flags=['multi_index'])
-    comp_coord_shape = tuple(len(result_array.coords[cond]) for cond in comp_conds)
-    pot_coord_shape = tuple(len(result_array.coords[cond]) for cond in pot_conds)
+
     while not it.finished:
-        indep_idx = []
+        primary_index = it.multi_index
+        # grid_index is constructed at every iteration, based on state variables (independent potentials)
+        grid_index = []
         # Relies on being ordered
+        for lc in local_conds_keys:
+            coord_idx = conds_keys.index(lc)
+            grid_index.append(primary_index[coord_idx])
         for sv in state_variables:
-            if str(sv) in result_array.coords.keys():
-                coord_idx = list(result_array.coords.keys()).index(str(sv))
-                indep_idx.append(it.multi_index[coord_idx])
+            if sv in conds_keys:
+                coord_idx = conds_keys.index(sv)
+                grid_index.append(primary_index[coord_idx])
             else:
                 # free state variable
-                indep_idx.append(0)
-        indep_idx = tuple(indep_idx)
-        if len(comp_conds) > 0:
-            comp_idx = np.ravel_multi_index(tuple(idx for idx, key in zip(it.multi_index, result_array_GM_dims) if key in comp_conds), comp_coord_shape)
-            idx_comp_values = comp_values[comp_idx, :]
-        else:
-            idx_comp_values = np.atleast_1d(1.)
-        if len(pot_conds) > 0:
-            pot_idx = np.ravel_multi_index(tuple(idx for idx, key in zip(it.multi_index, result_array_GM_dims) if key in pot_conds), pot_coord_shape)
-            idx_pot_values = np.array(cart_pot_values[pot_idx, :])
+                grid_index.append(0)
+        grid_index = tuple(grid_index)
 
-        idx_global_grid_X_values = global_grid_X_values[indep_idx]
-        idx_global_grid_GM_values = global_grid_GM_values[indep_idx]
+        idx_global_grid_X_values = global_grid_X_values[grid_index]
+        idx_global_grid_GM_values = global_grid_GM_values[grid_index]
         idx_result_array_MU_values = result_array_MU_values[it.multi_index]
         idx_result_array_MU_values[:] = 0
-        for idx in range(len(pot_conds_indices)):
-            idx_result_array_MU_values[pot_conds_indices[idx]] = idx_pot_values[idx]
+        idx_fixed_lincomb_molefrac_coefs = []
+        idx_fixed_lincomb_molefrac_rhs = []
+        idx_fixed_chempot_indices = []
+
+        for coord_idx, str_cond_key in enumerate(sorted(result_array.coords.keys())):
+            try:
+                cond_key = conds_keys[str_conds_keys.index(str_cond_key)]
+            except ValueError:
+                continue
+            rhs = result_array.coords[str_cond_key][primary_index[coord_idx]]
+            if isinstance(cond_key, IndependentPotential):
+                # Already handled above in construction of grid_index
+                continue
+            if isinstance(cond_key, SiteFraction):
+                # Already handled above in construction of grid_index
+                continue
+            elif isinstance(cond_key, ChemicalPotential):
+                component_idx = result_array.coords['component'].index(str(cond_key.species))
+                idx_fixed_chempot_indices.append(component_idx)
+                idx_result_array_MU_values[component_idx] = rhs
+            elif isinstance(cond_key, MassFraction):
+                # wA = k -> (1-k)*MWA*xA - k*MWB*xB - k*MWC*xC = 0
+                component_idx = result_array.coords['component'].index(str(cond_key.species))
+                coef_vector = np.zeros(num_comps)
+                coef_vector -= rhs
+                coef_vector[component_idx] += 1
+                # multiply coef_vector times a vector of molecular weights
+                coef_vector = np.multiply(coef_vector, phase_record_factory.molar_masses)
+                idx_fixed_lincomb_molefrac_coefs.append(coef_vector)
+                idx_fixed_lincomb_molefrac_rhs.append(0.)
+            elif isinstance(cond_key, MoleFraction):
+                if cond_key.phase_name is not None:
+                    # Phase-local condition already handled in construction of grid
+                    continue
+                component_idx = result_array.coords['component'].index(str(cond_key.species))
+                coef_vector = np.zeros(num_comps)
+                coef_vector[component_idx] = 1
+                idx_fixed_lincomb_molefrac_coefs.append(coef_vector)
+                idx_fixed_lincomb_molefrac_rhs.append(rhs)
+            elif isinstance(cond_key, SystemMolesType):
+                coef_vector = np.ones(num_comps)
+                idx_fixed_lincomb_molefrac_coefs.append(coef_vector)
+                idx_fixed_lincomb_molefrac_rhs.append(rhs)
+            elif isinstance(cond_key, LinearCombination):
+                coef_vector = np.zeros(num_comps)
+                if cond_key.denominator == 1:
+                    for symbol_idx, symbol in enumerate(cond_key.symbols):
+                        if symbol != 1:
+                            coef_idx = result_array.coords['component'].index(str(symbol.species))
+                            coef_vector[coef_idx] = cond_key.coefs[symbol_idx]
+                        else:
+                            idx_fixed_lincomb_molefrac_rhs.append(rhs-cond_key.coefs[symbol_idx])
+                else:
+                    # This is a molar ratio
+                    denominator_idx = cond_key.symbols.index(cond_key.denominator)
+                    for symbol_idx, symbol in enumerate(cond_key.symbols):
+                        if symbol_idx == denominator_idx:
+                            coef_idx = result_array.coords['component'].index(str(symbol.species))
+                            coef_vector[coef_idx] = cond_key.coefs[symbol_idx] - rhs
+                        elif symbol != 1:
+                            coef_idx = result_array.coords['component'].index(str(symbol.species))
+                            coef_vector[coef_idx] = cond_key.coefs[symbol_idx]
+                        else:
+                            if cond_key.coefs[symbol_idx] != 0:
+                                # Constant term for molar ratio should be zero
+                                raise ValueError(f'Unsupported condition {cond_key}')
+                            idx_fixed_lincomb_molefrac_rhs.append(-cond_key.coefs[symbol_idx])
+                idx_fixed_lincomb_molefrac_coefs.append(coef_vector)
+            else:
+                raise ValueError(f'Unsupported condition {cond_key}')
+
+        idx_fixed_lincomb_molefrac_coefs = np.atleast_2d(idx_fixed_lincomb_molefrac_coefs)
+        idx_fixed_lincomb_molefrac_rhs = np.atleast_1d(idx_fixed_lincomb_molefrac_rhs)
+        idx_fixed_chempot_indices = np.array(idx_fixed_chempot_indices, dtype=np.uintp)
+
         idx_result_array_NP_values = result_array_NP_values[it.multi_index]
         idx_result_array_points_values = result_array_points_values[it.multi_index]
+
         result_array_GM_values[it.multi_index] = \
             hyperplane(idx_global_grid_X_values, idx_global_grid_GM_values,
-                       idx_comp_values, idx_result_array_MU_values, float(global_grid.coords['N'][0]),
-                       pot_conds_indices, comp_conds_indices,
+                       idx_result_array_MU_values, idx_fixed_chempot_indices, idx_fixed_lincomb_molefrac_coefs, idx_fixed_lincomb_molefrac_rhs,
                        idx_result_array_NP_values, idx_result_array_points_values)
         # Copy phase values out
         points = result_array_points_values[it.multi_index]
-        result_array_Phase_values[it.multi_index][:num_comps] = global_grid_Phase_values[indep_idx].take(points, axis=0)[:num_comps]
-        result_array_X_values[it.multi_index][:num_comps] = global_grid_X_values[indep_idx].take(points, axis=0)[:num_comps]
-        result_array_Y_values[it.multi_index][:num_comps] = global_grid_Y_values[indep_idx].take(points, axis=0)[:num_comps]
+        result_array_Phase_values[it.multi_index][:num_comps] = global_grid_Phase_values[grid_index].take(points, axis=0)[:num_comps]
+        result_array_X_values[it.multi_index][:num_comps] = global_grid_X_values[grid_index].take(points, axis=0)[:num_comps]
+        result_array_Y_values[it.multi_index][:num_comps] = global_grid_Y_values[grid_index].take(points, axis=0)[:num_comps]
         # Special case: Sometimes fictitious points slip into the result
         if '_FAKE_' in result_array_Phase_values[it.multi_index]:
             new_energy = 0.
@@ -140,7 +180,7 @@ def lower_convex_hull(global_grid, state_variables, result_array):
                     result_array_Y_values[midx] = np.nan
                     idx_result_array_NP_values[idx] = np.nan
                 else:
-                    new_energy += idx_result_array_NP_values[idx] * global_grid.GM[np.index_exp[indep_idx + (points[idx],)]]
+                    new_energy += idx_result_array_NP_values[idx] * global_grid.GM[np.index_exp[grid_index + (points[idx],)]]
                     molesum += idx_result_array_NP_values[idx]
             result_array_GM_values[it.multi_index] = new_energy / molesum
         it.iternext()
diff --git a/pycalphad/core/minimizer.pxd b/pycalphad/core/minimizer.pxd
index 69e39fc7b..466fffca3 100644
--- a/pycalphad/core/minimizer.pxd
+++ b/pycalphad/core/minimizer.pxd
@@ -1,8 +1,9 @@
 cdef class SystemState:
-    cdef list compsets
-    cdef list cs_states
+    cdef public list compsets
+    cdef public list cs_states
     cdef object dof
-    cdef int iteration, num_statevars, iterations_since_last_phase_change
+    cdef public int iteration
+    cdef int num_statevars, iterations_since_last_phase_change
     cdef int[::1] metastable_phase_iterations
     cdef int[::1] times_compset_removed
     cdef double mass_residual, largest_chemical_potential_difference
@@ -16,7 +17,7 @@ cdef class SystemState:
     cdef double[::1] _driving_forces
     cdef double[:, ::1] _phase_energies_per_mole_atoms
     cdef double[:, :, ::1] _phase_amounts_per_mole_atoms
-    cdef void recompute(self, SystemSpecification spec)
+    cpdef void recompute(self, SystemSpecification spec)
     cdef double[::1] driving_forces(self)
     cdef void increment_phase_metastability_counters(self)
 
@@ -24,8 +25,8 @@ cdef class SystemSpecification:
     cdef int num_statevars, num_components, max_num_free_stable_phases
     cdef double prescribed_system_amount
     cdef double ALLOWED_MASS_RESIDUAL
-    cdef double[::1] initial_chemical_potentials, prescribed_elemental_amounts
-    cdef int[::1] prescribed_element_indices
+    cdef double[::1] initial_chemical_potentials, prescribed_mole_fraction_rhs
+    cdef double[:,::1] prescribed_mole_fraction_coefficients
     cdef int[::1] free_chemical_potential_indices, free_statevar_indices
     cdef int[::1] fixed_chemical_potential_indices, fixed_statevar_indices, fixed_stable_compset_indices
     cpdef bint check_convergence(self, SystemState state)
diff --git a/pycalphad/core/minimizer.pyx b/pycalphad/core/minimizer.pyx
index cddddfe81..31cdcd714 100644
--- a/pycalphad/core/minimizer.pyx
+++ b/pycalphad/core/minimizer.pyx
@@ -42,13 +42,15 @@ cdef void invert_matrix(double *A, int N, int* ipiv) nogil:
 
 @cython.boundscheck(False)
 cdef void compute_phase_matrix(double[:,::1] phase_matrix, double[:,::1] hess,
-                               double[:, ::1] cons_jac_tmp, CompositionSet compset,
-                               int num_statevars, double[::1] chemical_potentials, double[::1] phase_dof,
-                               int[::1] fixed_phase_dof_indices) nogil:
+                               double[:, ::1] cons_jac_tmp, double[:, ::1] phase_local_jac_tmp,
+                               CompositionSet compset, int num_statevars, double[::1] chemical_potentials,
+                               double[::1] phase_dof) nogil:
     "Compute the LHS of Eq. 41, Sundman 2015."
     cdef int comp_idx, i, j, cons_idx, fixed_dof_idx
     cdef int num_components = chemical_potentials.shape[0]
     compset.phase_record.internal_cons_jac(cons_jac_tmp, phase_dof)
+    if compset.num_phase_local_conditions > 0:
+        compset.phase_record.phase_local_cons_jac(phase_local_jac_tmp, phase_dof, compset.phase_local_cons_jac)
 
     for i in range(compset.phase_record.phase_dof):
         for j in range(compset.phase_record.phase_dof):
@@ -59,10 +61,10 @@ cdef void compute_phase_matrix(double[:,::1] phase_matrix, double[:,::1] hess,
             phase_matrix[compset.phase_record.phase_dof+i, j] = cons_jac_tmp[i, num_statevars+j]
             phase_matrix[j, compset.phase_record.phase_dof+i] = cons_jac_tmp[i, num_statevars+j]
 
-    for cons_idx in range(fixed_phase_dof_indices.shape[0]):
-        fixed_dof_idx = fixed_phase_dof_indices[cons_idx]
-        phase_matrix[compset.phase_record.phase_dof + compset.phase_record.num_internal_cons + cons_idx, fixed_dof_idx] = 1
-        phase_matrix[fixed_dof_idx, compset.phase_record.phase_dof + compset.phase_record.num_internal_cons] = 1
+    for i in range(compset.num_phase_local_conditions):
+        for j in range(compset.phase_record.phase_dof):
+            phase_matrix[compset.phase_record.phase_dof+compset.phase_record.num_internal_cons+i, j] = phase_local_jac_tmp[i, num_statevars+j]
+            phase_matrix[j, compset.phase_record.phase_dof+compset.phase_record.num_internal_cons+i] = phase_local_jac_tmp[i, num_statevars+j]
 
 
 cdef void write_row_stable_phase(double[:] out_row, double* out_rhs, int[::1] free_chemical_potential_indices,
@@ -79,7 +81,7 @@ cdef void write_row_stable_phase(double[:] out_row, double* out_rhs, int[::1] fr
     # 1a. This phase row: free stable composition sets = zero contribution
     free_variable_column_offset += free_stable_compset_indices.shape[0]
     # 1a. This phase row: free state variables
-    for i in range(free_statevar_indices.shape[0]):
+    for i in range(free_statevar_indices.shape[0]):   
         statevar_idx = free_statevar_indices[i]
         out_row[free_variable_column_offset + i] = -grad[statevar_idx]
     out_rhs[0] = energy
@@ -96,7 +98,9 @@ cdef void write_row_fixed_mole_fraction(double[:] out_row, double* out_rhs, int
                                         double[:, ::1] mass_jac, double[:, ::1] c_component,
                                         double[:, ::1] c_statevars, double[::1] c_G, double[:, ::1] masses,
                                         double moles_normalization, double[::1] moles_normalization_grad,
-                                        double[::1] phase_amt, int idx):
+                                        double[::1] phase_amt, int idx, double prefactor):
+    if prefactor == 0.0:
+        return
     cdef int free_variable_column_offset = 0
     cdef int num_statevars = c_statevars.shape[1]
     cdef int chempot_idx, compset_idx, statevar_idx, i, j
@@ -104,10 +108,10 @@ cdef void write_row_fixed_mole_fraction(double[:] out_row, double* out_rhs, int
     for i in range(free_chemical_potential_indices.shape[0]):
         chempot_idx = free_chemical_potential_indices[i]
         for j in range(c_component.shape[1]):
-            out_row[free_variable_column_offset + i] += \
+            out_row[free_variable_column_offset + i] += prefactor * \
                 (phase_amt[idx]/current_system_amount) * mass_jac[component_idx, num_statevars+j] * c_component[chempot_idx, j]
         for j in range(c_component.shape[1]):
-            out_row[free_variable_column_offset + i] += \
+            out_row[free_variable_column_offset + i] += prefactor * \
                 (phase_amt[idx]/current_system_amount) * (-system_mole_fractions[component_idx] * moles_normalization_grad[num_statevars+j]) * c_component[chempot_idx, j]
     free_variable_column_offset += free_chemical_potential_indices.shape[0]
     # 2a. This component row: free stable composition sets
@@ -115,34 +119,34 @@ cdef void write_row_fixed_mole_fraction(double[:] out_row, double* out_rhs, int
         compset_idx = free_stable_compset_indices[i]
         # Only fill this out if the current idx is equal to a free composition set
         if compset_idx == idx:
-            out_row[free_variable_column_offset + i] = \
+            out_row[free_variable_column_offset + i] += prefactor * \
                 (1./current_system_amount)*(masses[component_idx, 0] - system_mole_fractions[component_idx] * moles_normalization)
     free_variable_column_offset += free_stable_compset_indices.shape[0]
     # 2a. This component row: free state variables
     for i in range(free_statevar_indices.shape[0]):
         statevar_idx = free_statevar_indices[i]
         for j in range(c_statevars.shape[0]):
-            out_row[free_variable_column_offset + i] += \
+            out_row[free_variable_column_offset + i] += prefactor * \
                 (phase_amt[idx]/current_system_amount) * mass_jac[component_idx, num_statevars+j] * c_statevars[j, statevar_idx]
         for j in range(c_statevars.shape[0]):
-            out_row[free_variable_column_offset + i] += \
+            out_row[free_variable_column_offset + i] += prefactor * \
                 (phase_amt[idx]/current_system_amount) * (-system_mole_fractions[component_idx] * moles_normalization_grad[num_statevars+j]) * c_statevars[j, statevar_idx]
     # 3.
     for j in range(c_G.shape[0]):
-        out_rhs[0] += -(phase_amt[idx]/current_system_amount) * \
+        out_rhs[0] += -prefactor * (phase_amt[idx]/current_system_amount) * \
             mass_jac[component_idx, num_statevars+j] * c_G[j]
     for j in range(c_G.shape[0]):
-        out_rhs[0] += -(phase_amt[idx]/current_system_amount) * \
+        out_rhs[0] += -prefactor * (phase_amt[idx]/current_system_amount) * \
             (-system_mole_fractions[component_idx] * moles_normalization_grad[num_statevars+j]) * c_G[j]
     # 4. Subtract fixed chemical potentials from phase RHS
     for i in range(fixed_chemical_potential_indices.shape[0]):
         chempot_idx = fixed_chemical_potential_indices[i]
         # 5. Subtract fixed chemical potentials from fixed component RHS
         for j in range(c_component.shape[1]):
-            out_rhs[0] -= (phase_amt[idx]/current_system_amount) * chemical_potentials[
+            out_rhs[0] -= prefactor * (phase_amt[idx]/current_system_amount) * chemical_potentials[
                 chempot_idx] * mass_jac[component_idx, num_statevars+j] * c_component[chempot_idx, j]
         for j in range(c_component.shape[1]):
-            out_rhs[0] -= (phase_amt[idx]/current_system_amount) * chemical_potentials[
+            out_rhs[0] -= prefactor * (phase_amt[idx]/current_system_amount) * chemical_potentials[
                 chempot_idx] * (-system_mole_fractions[component_idx] * moles_normalization_grad[num_statevars+j]) * c_component[chempot_idx, j]
 
 cdef void write_row_fixed_mole_amount(double[:] out_row, double* out_rhs, int component_idx,
@@ -190,13 +194,14 @@ cdef void write_row_fixed_mole_amount(double[:] out_row, double* out_rhs, int co
 cdef void fill_equilibrium_system(double[::1,:] equilibrium_matrix, double[::1] equilibrium_rhs,
                                   SystemSpecification spec, SystemState state):
     cdef int stable_idx, idx, component_row_offset, component_idx, fixed_idx, free_idx
-    cdef int fixed_component_idx, comp_idx, system_amount_index
+    cdef int fixed_component_idx, comp_idx, system_amount_index, fixed_molefrac_cond_idx
     cdef CompositionSet compset
     cdef CompsetState csst
     cdef int num_components = state.chemical_potentials.shape[0]
     cdef int num_stable_phases = state.free_stable_compset_indices.shape[0]
     cdef int num_fixed_phases = spec.fixed_stable_compset_indices.shape[0]
-    cdef int num_fixed_components = spec.prescribed_elemental_amounts.shape[0]
+    cdef int num_fixed_mole_fraction_conditions = spec.prescribed_mole_fraction_rhs.shape[0]
+    cdef double prefactor
 
     for stable_idx in range(state.free_stable_compset_indices.shape[0]):
         idx = state.free_stable_compset_indices[stable_idx]
@@ -222,22 +227,23 @@ cdef void fill_equilibrium_system(double[::1,:] equilibrium_matrix, double[::1]
         idx = state.free_stable_compset_indices[stable_idx]
         compset = state.compsets[idx]
         csst = state.cs_states[idx]
-        # 2. Contribute to the row of all fixed components (fixed mole fraction)
+        # 2. Contribute to the row of all fixed mole fraction conditions
         component_row_offset = num_stable_phases + num_fixed_phases
-        for fixed_component_idx in range(num_fixed_components):
-            component_idx = spec.prescribed_element_indices[fixed_component_idx]
-            write_row_fixed_mole_fraction(equilibrium_matrix[component_row_offset + fixed_component_idx, :],
-                                          &equilibrium_rhs[component_row_offset + fixed_component_idx],
-                                          component_idx, spec.free_chemical_potential_indices,
-                                          state.free_stable_compset_indices,
-                                          spec.free_statevar_indices, spec.fixed_chemical_potential_indices,
-                                          state.chemical_potentials,
-                                          state.mole_fractions, state.system_amount, csst.mass_jac,
-                                          csst.c_component, csst.c_statevars,
-                                          csst.c_G, csst.masses, csst.moles_normalization,
-                                          csst.moles_normalization_grad, state.phase_amt, idx)
-
-        system_amount_index = component_row_offset + num_fixed_components
+        for fixed_molefrac_cond_idx in range(num_fixed_mole_fraction_conditions):
+            for component_idx in range(spec.prescribed_mole_fraction_coefficients.shape[1]):
+                prefactor = spec.prescribed_mole_fraction_coefficients[fixed_molefrac_cond_idx, component_idx]
+                write_row_fixed_mole_fraction(equilibrium_matrix[component_row_offset + fixed_molefrac_cond_idx, :],
+                                            &equilibrium_rhs[component_row_offset + fixed_molefrac_cond_idx],
+                                            component_idx, spec.free_chemical_potential_indices,
+                                            state.free_stable_compset_indices,
+                                            spec.free_statevar_indices, spec.fixed_chemical_potential_indices,
+                                            state.chemical_potentials,
+                                            state.mole_fractions, state.system_amount, csst.mass_jac,
+                                            csst.c_component, csst.c_statevars,
+                                            csst.c_G, csst.masses, csst.moles_normalization,
+                                            csst.moles_normalization_grad, state.phase_amt, idx, prefactor)
+
+        system_amount_index = component_row_offset + num_fixed_mole_fraction_conditions
         # 2X. Also handle the N=1 row
         for component_idx in range(num_components):
             write_row_fixed_mole_amount(equilibrium_matrix[system_amount_index, :],
@@ -252,22 +258,23 @@ cdef void fill_equilibrium_system(double[::1,:] equilibrium_matrix, double[::1]
         idx = spec.fixed_stable_compset_indices[fixed_idx]
         compset = state.compsets[idx]
         csst = state.cs_states[idx]
-        # 2. Contribute to the row of all fixed components (fixed mole fraction)
+        # 2. Contribute to the row of all fixed mole fraction conditions
         component_row_offset = num_stable_phases + num_fixed_phases
-        for fixed_component_idx in range(num_fixed_components):
-            component_idx = spec.prescribed_element_indices[fixed_component_idx]
-            write_row_fixed_mole_fraction(equilibrium_matrix[component_row_offset + fixed_component_idx, :],
-                                          &equilibrium_rhs[component_row_offset + fixed_component_idx],
-                                          component_idx, spec.free_chemical_potential_indices,
-                                          state.free_stable_compset_indices,
-                                          spec.free_statevar_indices, spec.fixed_chemical_potential_indices,
-                                          state.chemical_potentials,
-                                          state.mole_fractions, state.system_amount, csst.mass_jac,
-                                          csst.c_component, csst.c_statevars,
-                                          csst.c_G, csst.masses, csst.moles_normalization,
-                                          csst.moles_normalization_grad, state.phase_amt, idx)
-
-        system_amount_index = component_row_offset + num_fixed_components
+        for fixed_molefrac_cond_idx in range(num_fixed_mole_fraction_conditions):
+            for component_idx in range(spec.prescribed_mole_fraction_coefficients.shape[1]):
+                prefactor = spec.prescribed_mole_fraction_coefficients[fixed_molefrac_cond_idx, component_idx]
+                write_row_fixed_mole_fraction(equilibrium_matrix[component_row_offset + fixed_molefrac_cond_idx, :],
+                                            &equilibrium_rhs[component_row_offset + fixed_molefrac_cond_idx],
+                                            component_idx, spec.free_chemical_potential_indices,
+                                            state.free_stable_compset_indices,
+                                            spec.free_statevar_indices, spec.fixed_chemical_potential_indices,
+                                            state.chemical_potentials,
+                                            state.mole_fractions, state.system_amount, csst.mass_jac,
+                                            csst.c_component, csst.c_statevars,
+                                            csst.c_G, csst.masses, csst.moles_normalization,
+                                            csst.moles_normalization_grad, state.phase_amt, idx, prefactor)
+
+        system_amount_index = component_row_offset + num_fixed_mole_fraction_conditions
         # 2X. Also handle the N=1 row
         for component_idx in range(num_components):
             write_row_fixed_mole_amount(equilibrium_matrix[system_amount_index, :],
@@ -281,27 +288,26 @@ cdef void fill_equilibrium_system(double[::1,:] equilibrium_matrix, double[::1]
 
     # Add mass residual to fixed component row RHS, plus N=1 row
     component_row_offset = num_stable_phases + num_fixed_phases
-    system_amount_index = component_row_offset + num_fixed_components
-    for fixed_component_idx in range(num_fixed_components):
-        component_idx = spec.prescribed_element_indices[fixed_component_idx]
-        component_residual = state.mole_fractions[component_idx] - spec.prescribed_elemental_amounts[fixed_component_idx]
-        equilibrium_rhs[component_row_offset + fixed_component_idx] -= component_residual
+    system_amount_index = component_row_offset + num_fixed_mole_fraction_conditions
+    for fixed_molefrac_cond_idx in range(num_fixed_mole_fraction_conditions):
+        component_residual = np.dot(spec.prescribed_mole_fraction_coefficients[fixed_molefrac_cond_idx, :], state.mole_fractions) - spec.prescribed_mole_fraction_rhs[fixed_molefrac_cond_idx]
+        equilibrium_rhs[component_row_offset + fixed_molefrac_cond_idx] -= component_residual
     system_residual = state.system_amount - spec.prescribed_system_amount
     equilibrium_rhs[system_amount_index] -= system_residual
 
 
 cdef class SystemSpecification:
     def __init__(self, int num_statevars, int num_components, double prescribed_system_amount,
-                   double[::1] initial_chemical_potentials, double[::1] prescribed_elemental_amounts,
-                   int[::1] prescribed_element_indices, int[::1] free_chemical_potential_indices,
+                   double[::1] initial_chemical_potentials, double[:, ::1] prescribed_mole_fraction_coefficients,
+                   double[::1] prescribed_mole_fraction_rhs, int[::1] free_chemical_potential_indices,
                    int[::1] free_statevar_indices, int[::1] fixed_chemical_potential_indices,
                    int[::1] fixed_statevar_indices, int[::1] fixed_stable_compset_indices):
         self.num_statevars = num_statevars
         self.num_components = num_components
         self.prescribed_system_amount = prescribed_system_amount
         self.initial_chemical_potentials = initial_chemical_potentials
-        self.prescribed_elemental_amounts = prescribed_elemental_amounts
-        self.prescribed_element_indices = prescribed_element_indices
+        self.prescribed_mole_fraction_coefficients = prescribed_mole_fraction_coefficients
+        self.prescribed_mole_fraction_rhs = prescribed_mole_fraction_rhs
         self.free_chemical_potential_indices = free_chemical_potential_indices
         self.free_statevar_indices = free_statevar_indices
         self.fixed_chemical_potential_indices = fixed_chemical_potential_indices
@@ -309,20 +315,22 @@ cdef class SystemSpecification:
         self.fixed_stable_compset_indices = fixed_stable_compset_indices
         self.max_num_free_stable_phases = num_components + len(free_statevar_indices) - len(fixed_stable_compset_indices)
 
-        # Assuming the prescribed_elemental_amounts doesn't change, this is
+        # Assuming the prescribed_mole_fraction_rhs doesn't change, this is
         # constant and we can keep extra computation (especially calls into
         # NumPy out of the run loop)
-        if self.prescribed_elemental_amounts.shape[0] > 0:
-            self.ALLOWED_MASS_RESIDUAL = min(1e-8, np.min(self.prescribed_elemental_amounts)/10.0)
+        if self.prescribed_mole_fraction_rhs.shape[0] > 0:
+            # With linear combinations of conditions, RHS can now be exactly zero
+            # This means the smallest allowed mass residual needs to be limited to prevent instability
+            self.ALLOWED_MASS_RESIDUAL = max(1e-12, min(1e-8, np.min(np.abs(self.prescribed_mole_fraction_rhs))/10.0))
             # Also adjust mass residual if we are near the edge of composition space
-            self.ALLOWED_MASS_RESIDUAL = min(self.ALLOWED_MASS_RESIDUAL, (1-np.sum(self.prescribed_elemental_amounts))/10.0)
+            self.ALLOWED_MASS_RESIDUAL = min(self.ALLOWED_MASS_RESIDUAL, (1-np.sum(np.abs(self.prescribed_mole_fraction_rhs)))/10.0)
         else:
             self.ALLOWED_MASS_RESIDUAL = 1e-8
 
     def __getstate__(self):
         return (self.num_statevars, self.num_components, self.prescribed_system_amount,
-                np.array(self.initial_chemical_potentials), np.array(self.prescribed_elemental_amounts),
-                np.array(self.prescribed_element_indices), np.array(self.free_chemical_potential_indices),
+                np.array(self.initial_chemical_potentials), np.array(self.prescribed_mole_fraction_coefficients),
+                np.array(self.prescribed_mole_fraction_rhs), np.array(self.free_chemical_potential_indices),
                 np.array(self.free_statevar_indices), np.array(self.fixed_chemical_potential_indices),
                 np.array(self.fixed_statevar_indices), np.array(self.fixed_stable_compset_indices))
     def __setstate__(self, state):
@@ -404,6 +412,7 @@ cdef class CompsetState:
     cdef int[::1] fixed_phase_dof_indices
     cdef int[::1] ipiv
     cdef double[:, ::1] cons_jac_tmp
+    cdef double[:, ::1] phase_local_jac_tmp
 
     def __init__(self, SystemSpecification spec, CompositionSet compset):
         self.x = np.zeros(spec.num_statevars + compset.phase_record.phase_dof)
@@ -414,10 +423,10 @@ cdef class CompsetState:
         self.masses = np.zeros((spec.num_components, 1))
         self.mass_jac = np.zeros((spec.num_components,
                                   spec.num_statevars + compset.phase_record.phase_dof))
-        self.phase_matrix = np.zeros((compset.phase_record.phase_dof + compset.phase_record.num_internal_cons,
-                                      compset.phase_record.phase_dof + compset.phase_record.num_internal_cons))
-        self.full_e_matrix = np.zeros((compset.phase_record.phase_dof + compset.phase_record.num_internal_cons,
-                                       compset.phase_record.phase_dof + compset.phase_record.num_internal_cons))
+        self.phase_matrix = np.zeros((compset.phase_record.phase_dof + compset.phase_record.num_internal_cons + compset.num_phase_local_conditions,
+                                      compset.phase_record.phase_dof + compset.phase_record.num_internal_cons + compset.num_phase_local_conditions))
+        self.full_e_matrix = np.zeros((compset.phase_record.phase_dof + compset.phase_record.num_internal_cons + compset.num_phase_local_conditions,
+                                       compset.phase_record.phase_dof + compset.phase_record.num_internal_cons + compset.num_phase_local_conditions))
         self.c_G = np.zeros(compset.phase_record.phase_dof)
         self.c_statevars = np.zeros((compset.phase_record.phase_dof, spec.num_statevars))
         self.c_component = np.zeros((spec.num_components, compset.phase_record.phase_dof))
@@ -428,6 +437,7 @@ cdef class CompsetState:
         self.ipiv = np.empty(self.phase_matrix.shape[0], dtype=np.int32)
         self.delta_y = np.zeros(compset.phase_record.phase_dof)
         self.cons_jac_tmp = np.zeros((compset.phase_record.num_internal_cons, spec.num_statevars + compset.phase_record.phase_dof))
+        self.phase_local_jac_tmp = np.zeros((compset.num_phase_local_conditions, spec.num_statevars + compset.phase_record.phase_dof))
 
     def __getstate__(self):
         return (np.array(self.x), self.energy, np.array(self.grad), np.array(self.hess),
@@ -507,12 +517,12 @@ cdef class SystemState:
          self.largest_phase_amt_change[0], self.largest_y_change[0], self.free_stable_compset_indices, self.system_amount, self.mole_fractions) = state
 
     @cython.boundscheck(False)
-    cdef void recompute(self, SystemSpecification spec):
+    cpdef void recompute(self, SystemSpecification spec):
         cdef int num_components = spec.num_components
         cdef CompositionSet compset
         cdef CompsetState csst
         cdef double[::1] x
-        cdef int idx, comp_idx, cons_idx, i, j, stable_idx, fixed_idx, component_idx, fixed_component_idx, num_phase_dof
+        cdef int idx, comp_idx, cons_idx, i, j, stable_idx, fixed_idx, fixed_molefrac_cond_idx, num_phase_dof
         cdef double mu_c_sum
         cdef double phase_comp_sum
         self.mole_fractions[:] = 0
@@ -534,9 +544,8 @@ cdef class SystemState:
             self.mole_fractions[comp_idx] /= self.system_amount
 
         self.mass_residual = 0.0
-        for fixed_component_idx in range(spec.prescribed_elemental_amounts.shape[0]):
-            component_idx = spec.prescribed_element_indices[fixed_component_idx]
-            self.mass_residual += abs(self.mole_fractions[component_idx] - spec.prescribed_elemental_amounts[fixed_component_idx])
+        for fixed_molefrac_cond_idx in range(spec.prescribed_mole_fraction_rhs.shape[0]):
+            self.mass_residual += abs(np.dot(spec.prescribed_mole_fraction_coefficients[fixed_molefrac_cond_idx,:], self.mole_fractions) - spec.prescribed_mole_fraction_rhs[fixed_molefrac_cond_idx])
 
         for idx in range(len(self.compsets)):
             compset = self.compsets[idx]
@@ -563,8 +572,7 @@ cdef class SystemState:
             compset.phase_record.formulagrad(csst.grad, x)
             compset.phase_record.internal_cons_func(csst.internal_cons, x)
 
-            compute_phase_matrix(csst.phase_matrix, csst.hess, csst.cons_jac_tmp, compset, spec.num_statevars, self.chemical_potentials, x,
-                                 csst.fixed_phase_dof_indices)
+            compute_phase_matrix(csst.phase_matrix, csst.hess, csst.cons_jac_tmp, csst.phase_local_jac_tmp, compset, spec.num_statevars, self.chemical_potentials, x)
             # Copy the phase matrix into the e matrix and invert the e matrix
             for i in range(csst.full_e_matrix.shape[0]):
                 for j in range(csst.full_e_matrix.shape[1]):
@@ -624,18 +632,18 @@ cdef class SystemState:
             else:
                 self.metastable_phase_iterations[idx] += 1
 
-cpdef construct_equilibrium_system(SystemSpecification spec, SystemState state, int num_reserved_rows):
+cpdef construct_equilibrium_system(SystemSpecification spec, SystemState state, int num_reserved_rows) except *:
     cdef double[::1,:] equilibrium_matrix  # Fortran ordering required by call into lapack
     cdef double[::1] equilibrium_soln
-    cdef int num_stable_phases, num_fixed_phases, num_fixed_components, num_free_variables
+    cdef int num_stable_phases, num_fixed_phases, num_fixed_mole_fraction_conditions, num_free_variables
 
     num_stable_phases = state.free_stable_compset_indices.shape[0]
     num_fixed_phases = spec.fixed_stable_compset_indices.shape[0]
-    num_fixed_components = len(spec.prescribed_elemental_amounts)
+    num_fixed_mole_fraction_conditions = spec.prescribed_mole_fraction_rhs.shape[0]
     num_free_variables = spec.free_chemical_potential_indices.shape[0] + num_stable_phases + \
                          spec.free_statevar_indices.shape[0]
 
-    equilibrium_matrix = np.zeros((num_stable_phases + num_fixed_phases + num_fixed_components + num_reserved_rows + 1,
+    equilibrium_matrix = np.zeros((num_stable_phases + num_fixed_phases + num_fixed_mole_fraction_conditions + num_reserved_rows + 1,
                                    num_free_variables), order='F')
     equilibrium_rhs = np.zeros(equilibrium_matrix.shape[0])
     if (equilibrium_matrix.shape[0] != equilibrium_matrix.shape[1]):
@@ -643,6 +651,144 @@ cpdef construct_equilibrium_system(SystemSpecification spec, SystemState state,
     fill_equilibrium_system(equilibrium_matrix, equilibrium_rhs, spec, state)
     return np.asarray(equilibrium_matrix), np.asarray(equilibrium_rhs)
 
+cpdef state_variable_differential(SystemSpecification spec, SystemState state, int target_statevar_index):
+    # Sundman et al 2015, Eq. 74
+    cdef double[::1,:] equilibrium_matrix  # Fortran ordering required by call into lapack
+    cdef double[::1] equilibrium_soln, delta_chemical_potentials, delta_statevars, delta_phase_amounts
+    cdef int[::1] orig_fixed_statevar_indices, orig_free_statevar_indices
+    cdef int chempot_idx, statevar_idx, i
+
+    orig_fixed_statevar_indices = np.array(spec.fixed_statevar_indices)
+    orig_free_statevar_indices = np.array(spec.free_statevar_indices)
+    delta_chemical_potentials = np.zeros(spec.num_components)
+    delta_statevars = np.zeros(spec.num_statevars)
+    delta_phase_amounts = np.zeros(state.free_stable_compset_indices.shape[0])
+    spec.fixed_statevar_indices = np.setdiff1d(spec.fixed_statevar_indices, np.array(target_statevar_index))
+    spec.free_statevar_indices = np.append(spec.free_statevar_indices, target_statevar_index).astype(np.int32)
+
+    try:
+        equilibrium_matrix, equilibrium_soln = construct_equilibrium_system(spec, state, 1)
+        equilibrium_soln[:] = 0
+        # target_statevar_index is the last column of the matrix, by construction
+        equilibrium_matrix[-1, -1] = 1
+        equilibrium_soln[-1] = 1
+        lstsq(&equilibrium_matrix[0,0], equilibrium_matrix.shape[0], equilibrium_matrix.shape[1],
+          &equilibrium_soln[0], 1e-16)
+        for i in range(spec.free_chemical_potential_indices.shape[0]):
+            chempot_idx = spec.free_chemical_potential_indices[i]
+            delta_chemical_potentials[chempot_idx] = equilibrium_soln[i]
+        for i in range(state.free_stable_compset_indices.shape[0]):
+            delta_phase_amounts[i] = equilibrium_soln[spec.free_chemical_potential_indices.shape[0] + i]
+        for i in range(spec.free_statevar_indices.shape[0]):
+            statevar_idx = spec.free_statevar_indices[i]
+            delta_statevars[statevar_idx] = equilibrium_soln[spec.free_chemical_potential_indices.shape[0] + 
+                                                             state.free_stable_compset_indices.shape[0] + i]
+        return np.asarray(delta_chemical_potentials), np.asarray(delta_statevars), np.asarray(delta_phase_amounts)
+    finally:
+        spec.fixed_statevar_indices = orig_fixed_statevar_indices
+        spec.free_statevar_indices = orig_free_statevar_indices
+
+cpdef fixed_component_differential(SystemSpecification spec, SystemState state, int target_component_index):
+    # Based on Sundman et al 2015, Eq. 74, with some modifications
+    cdef double[::1,:] equilibrium_matrix  # Fortran ordering required by call into lapack
+    cdef double[::1] equilibrium_soln, delta_chemical_potentials, delta_statevars, delta_phase_amounts
+    cdef np.ndarray comparison_array = np.zeros(spec.prescribed_mole_fraction_coefficients.shape[1])
+    comparison_array[target_component_index] = 1
+    cdef int num_stable_phases = state.free_stable_compset_indices.shape[0]
+    cdef int num_fixed_phases = spec.fixed_stable_compset_indices.shape[0]
+    cdef int num_fixed_mole_fraction_conditions = spec.prescribed_mole_fraction_rhs.shape[0]
+    cdef int chempot_idx, statevar_idx, i
+    cdef bint component_was_fixed = False
+
+    for i in range(spec.prescribed_mole_fraction_coefficients.shape[0]):
+        if np.all(np.asarray(spec.prescribed_mole_fraction_coefficients[i]) == comparison_array):
+            component_was_fixed = True
+    if not component_was_fixed:
+        raise ValueError('Target component was not fixed in the present calculation')
+
+    delta_chemical_potentials = np.zeros(spec.num_components)
+    delta_statevars = np.zeros(spec.num_statevars)
+    delta_phase_amounts = np.zeros(state.free_stable_compset_indices.shape[0])
+
+    equilibrium_matrix, equilibrium_soln = construct_equilibrium_system(spec, state, 0)
+    equilibrium_soln[:] = 0
+
+    # delta mole fractions must sum to zero; we have degrees of freedom to decide how to distribute
+    # for now, redistribute evenly over all other fixed components
+    for i in range(spec.prescribed_mole_fraction_coefficients.shape[0]):
+        if np.all(np.asarray(spec.prescribed_mole_fraction_coefficients[i]) == comparison_array):
+            equilibrium_soln[num_stable_phases + num_fixed_phases + i] = 1
+        else:
+            equilibrium_soln[num_stable_phases + num_fixed_phases + i] = -1/(num_fixed_mole_fraction_conditions)
+    lstsq(&equilibrium_matrix[0,0], equilibrium_matrix.shape[0], equilibrium_matrix.shape[1],
+        &equilibrium_soln[0], 1e-16)
+    for i in range(spec.free_chemical_potential_indices.shape[0]):
+        chempot_idx = spec.free_chemical_potential_indices[i]
+        delta_chemical_potentials[chempot_idx] = equilibrium_soln[i]
+    for i in range(state.free_stable_compset_indices.shape[0]):
+        delta_phase_amounts[i] = equilibrium_soln[spec.free_chemical_potential_indices.shape[0] + i]
+    for i in range(spec.free_statevar_indices.shape[0]):
+        statevar_idx = spec.free_statevar_indices[i]
+        delta_statevars[statevar_idx] = equilibrium_soln[spec.free_chemical_potential_indices.shape[0] + 
+                                                            state.free_stable_compset_indices.shape[0] + i]
+    return np.asarray(delta_chemical_potentials), np.asarray(delta_statevars), np.asarray(delta_phase_amounts)
+
+cpdef chemical_potential_differential(SystemSpecification spec, SystemState state, int target_component_index):
+    # Sundman et al 2015, Eq. 74
+    cdef double[::1,:] equilibrium_matrix  # Fortran ordering required by call into lapack
+    cdef double[::1] equilibrium_soln, delta_chemical_potentials, delta_statevars, delta_phase_amounts
+    cdef int[::1] orig_fixed_statevar_indices, orig_free_statevar_indices
+    cdef int chempot_idx, statevar_idx, i
+    cdef bint component_was_fixed = False
+
+    for i in range(spec.fixed_chemical_potential_indices.shape[0]):
+        if spec.fixed_chemical_potential_indices[i] == target_component_index:
+            component_was_fixed = True
+    if not component_was_fixed:
+        raise ValueError('Target chemical potential was not fixed in the present calculation')
+
+    # Release chemical potential condition
+    orig_fixed_chemical_potential_indices = np.array(spec.fixed_chemical_potential_indices)
+    orig_free_chemical_potential_indices = np.array(spec.free_chemical_potential_indices)
+    delta_chemical_potentials = np.zeros(spec.num_components)
+    delta_statevars = np.zeros(spec.num_statevars)
+    delta_phase_amounts = np.zeros(state.free_stable_compset_indices.shape[0])
+    spec.fixed_chemical_potential_indices = np.setdiff1d(spec.fixed_chemical_potential_indices, np.array(target_component_index))
+    spec.free_chemical_potential_indices = np.append(spec.free_chemical_potential_indices, target_component_index).astype(np.int32)
+
+    try:
+        equilibrium_matrix, equilibrium_soln = construct_equilibrium_system(spec, state, 1)
+        equilibrium_soln[:] = 0
+        equilibrium_matrix[-1, target_component_index] = 1
+        equilibrium_soln[-1] = 1
+        lstsq(&equilibrium_matrix[0,0], equilibrium_matrix.shape[0], equilibrium_matrix.shape[1],
+          &equilibrium_soln[0], 1e-16)
+        for i in range(spec.free_chemical_potential_indices.shape[0]):
+            chempot_idx = spec.free_chemical_potential_indices[i]
+            delta_chemical_potentials[chempot_idx] = equilibrium_soln[i]
+        for i in range(state.free_stable_compset_indices.shape[0]):
+            delta_phase_amounts[i] = equilibrium_soln[spec.free_chemical_potential_indices.shape[0] + i]
+        for i in range(spec.free_statevar_indices.shape[0]):
+            statevar_idx = spec.free_statevar_indices[i]
+            delta_statevars[statevar_idx] = equilibrium_soln[spec.free_chemical_potential_indices.shape[0] + 
+                                                             state.free_stable_compset_indices.shape[0] + i]
+        return np.asarray(delta_chemical_potentials), np.asarray(delta_statevars), np.asarray(delta_phase_amounts)
+    finally:
+        spec.fixed_chemical_potential_indices = orig_fixed_chemical_potential_indices
+        spec.free_chemical_potential_indices = orig_free_chemical_potential_indices
+
+cpdef site_fraction_differential(CompsetState csst, double[::1] delta_chempots, double[::1] delta_statevars):
+    # Sundman et al 2015, Eq. 78
+    cdef double[::1] delta_y = np.zeros(csst.delta_y.shape[0])
+    cdef int chempot_idx, statevar_idex
+
+    for i in range(delta_y.shape[0]):
+        for statevar_idx in range(delta_statevars.shape[0]):
+            delta_y[i] += csst.c_statevars[i, statevar_idx] * delta_statevars[statevar_idx]
+        for chempot_idx in range(delta_chempots.shape[0]):
+            delta_y[i] += csst.c_component[chempot_idx, i] * delta_chempots[chempot_idx]
+    return np.asarray(delta_y)
+
 cpdef solve_state(SystemSpecification spec, SystemState state):
     cdef double[::1,:] equilibrium_matrix  # Fortran ordering required by call into lapack
     cdef double[::1] equilibrium_soln
@@ -695,7 +841,8 @@ cpdef advance_state(SystemSpecification spec, SystemState state, double[::1] equ
             # 1. NP>0 (the phase would not be a free_stable_compset if not) and
             # 2. delta_NP<0 (must be true if assumption #1 is true and this condition is true)
             # The largest allowable step size satisfies the equation: (NP + step_size * delta_NP = MIN_PHASE_AMOUNT)
-            phase_amt_step_size = min(phase_amt_step_size, (MIN_PHASE_AMOUNT - state.phase_amt[compset_idx]) / equilibrium_soln[soln_index_offset + i])
+            if abs(equilibrium_soln[soln_index_offset + i]) > MIN_PHASE_AMOUNT:
+                phase_amt_step_size = min(phase_amt_step_size, (MIN_PHASE_AMOUNT - state.phase_amt[compset_idx]) / equilibrium_soln[soln_index_offset + i])
     # Update the phase amounts using the largest allowable step size
     state.largest_phase_amt_change[0] = 0
     for i in range(state.free_stable_compset_indices.shape[0]):
@@ -856,7 +1003,7 @@ cdef bint change_phases(SystemSpecification spec, SystemState state):
     for cs_idx in range(state.metastable_phase_iterations.shape[0]):
         should_add_compset = (
             (state.metastable_phase_iterations[cs_idx] >= MIN_REQUIRED_METASTABLE_PHASE_ITERATIONS_TO_ADD)
-            and (driving_forces[cs_idx] > 1e-5)
+            and (driving_forces[cs_idx] > MIN_DRIVING_FORCE_TO_ADD)
             and (state.times_compset_removed[cs_idx] < MAX_ALLOWED_TIMES_COMPSET_REMOVED)
         )
         if should_add_compset:
diff --git a/pycalphad/core/phase_rec.pxd b/pycalphad/core/phase_rec.pxd
index 72eebbbe6..e6679d182 100644
--- a/pycalphad/core/phase_rec.pxd
+++ b/pycalphad/core/phase_rec.pxd
@@ -1,6 +1,9 @@
 # distutils: language = c++
 
 cimport cython
+from libcpp.map cimport map
+from libcpp.utility cimport pair
+from libcpp.string cimport string
 import numpy
 cimport numpy
 
@@ -12,6 +15,27 @@ cdef class FastFunction:
     cdef void *func_data
     cdef void call(self, double *out, double *inp) nogil
 
+cdef class FastFunctionFactory:
+    cdef object phase_record_factory
+    cdef unicode phase_name
+    cdef numpy.ndarray _cache
+    cdef map[pair[string, string], int] _cache_property_map
+    cdef int _cache_cur_idx
+    cdef void** _cache_ptr
+    cdef void* get_func(self, string property_name) except * nogil
+    cdef void* get_grad(self, string property_name) except * nogil
+    cdef void* get_hess(self, string property_name) except * nogil
+    cpdef FastFunction get_cons_func(self)
+    cpdef FastFunction get_cons_jac(self)
+    cpdef FastFunction get_cons_hess(self)
+    cpdef int get_cons_len(self)
+    cpdef FastFunction get_mole_fraction_func(self, unicode element_name)
+    cpdef FastFunction get_mole_fraction_grad(self, unicode element_name)
+    cpdef FastFunction get_mole_fraction_hess(self, unicode element_name)
+    cpdef FastFunction get_mole_formula_func(self, unicode element_name)
+    cpdef FastFunction get_mole_formula_grad(self, unicode element_name)
+    cpdef FastFunction get_mole_formula_hess(self, unicode element_name)
+
 @cython.final
 cdef public class PhaseRecord(object)[type PhaseRecordType, object PhaseRecordObject]:
     cdef FastFunction _obj
@@ -30,15 +54,23 @@ cdef public class PhaseRecord(object)[type PhaseRecordType, object PhaseRecordOb
     cdef numpy.ndarray _formulamolehessians
     cdef void** _formulamolehessians_ptr
     cdef public size_t num_internal_cons
+    cdef public object phase_record_factory
+    cdef public FastFunctionFactory function_factory
     cdef public object variables
     cdef public object state_variables
     cdef public object components
     cdef public object pure_elements
     cdef public object nonvacant_elements
+    cdef public double[::1] molar_masses
     cdef public double[::1] parameters
     cdef public int phase_dof
     cdef public int num_statevars
-    cdef public unicode phase_name
+    cdef public unicode phase_name 
+    cpdef void prop(self, double[::1] out, double[::1] dof, string property_name) except * nogil
+    cpdef void prop_2d(self, double[::1] out, double[:, ::1] dof, string property_name) except * nogil
+    cpdef void prop_parameters_2d(self, double[:, ::1] out, double[:, ::1] dof,
+                                  double[:, ::1] parameters, string property_name) except * nogil
+    cpdef void prop_grad(self, double[::1] out, double[::1] dof, string property_name) except * nogil
     cpdef void obj(self, double[::1] out, double[::1] dof) nogil
     cpdef void formulaobj(self, double[::1] out, double[::1] dof) nogil
     cpdef void obj_2d(self, double[::1] out, double[:, ::1] dof) nogil
@@ -48,20 +80,11 @@ cdef public class PhaseRecord(object)[type PhaseRecordType, object PhaseRecordOb
     cpdef void internal_cons_func(self, double[::1] out, double[::1] dof) nogil
     cpdef void internal_cons_jac(self, double[:,::1] out, double[::1] dof) nogil
     cpdef void internal_cons_hess(self, double[:,:,::1] out, double[::1] dof) nogil
+    cpdef void phase_local_cons_func(self, double[::1] out, double[::1] dof, FastFunction func) nogil
+    cpdef void phase_local_cons_jac(self, double[:, ::1] out, double[::1] dof, FastFunction jac_func) nogil
     cpdef void mass_obj(self, double[::1] out, double[::1] dof, int comp_idx) nogil
     cpdef void mass_obj_2d(self, double[::1] out, double[:, ::1] dof, int comp_idx) nogil
     cpdef void formulamole_obj(self, double[::1] out, double[::1] dof, int comp_idx) nogil
     cpdef void formulamole_grad(self, double[::1] out, double[::1] dof, int comp_idx) nogil
     cpdef void formulamole_hess(self, double[:,::1] out, double[::1] dof, int comp_idx) nogil
-    # Used only to reconstitute if pickled (i.e. via __reduce__)
-    cdef public object ofunc_
-    cdef public object formulaofunc_
-    cdef public object formulagfunc_
-    cdef public object formulahfunc_
-    cdef public object internal_cons_func_
-    cdef public object internal_cons_jac_
-    cdef public object internal_cons_hess_
-    cdef public object massfuncs_
-    cdef public object formulamolefuncs_
-    cdef public object formulamolegradfuncs_
-    cdef public object formulamolehessianfuncs_
+
diff --git a/pycalphad/core/phase_rec.pyx b/pycalphad/core/phase_rec.pyx
index 031b543ac..dea54ea10 100644
--- a/pycalphad/core/phase_rec.pyx
+++ b/pycalphad/core/phase_rec.pyx
@@ -1,6 +1,10 @@
 # distutils: language = c++
 
 cimport cython
+from libcpp.map cimport map
+from libcpp.utility cimport pair
+from libcpp.string cimport string
+from cython.operator cimport dereference as deref
 from libc.stdlib cimport malloc, free
 import numpy as np
 cimport numpy as np
@@ -28,6 +32,93 @@ cdef class FastFunction:
         if self.f_ptr != NULL:
             self.f_ptr(out, inp, self.func_data)
 
+cdef class FastFunctionFactory:
+    def __cinit__(self, phase_record_factory, phase_name):
+        cdef int INITIAL_CACHE_SIZE = 100
+        self.phase_record_factory = phase_record_factory
+        self.phase_name = phase_name
+        self._cache = np.empty(INITIAL_CACHE_SIZE, dtype='object')
+        self._cache_ptr = <void**> self._cache.data
+        self._cache_cur_idx = -1
+
+    cdef void* get_func(self, string property_name) except * nogil:
+        cdef pair[string, string] cache_key = pair[string, string](string(<char*>'func'), property_name)
+        cdef map[pair[string, string], int].iterator it
+        it = self._cache_property_map.find(cache_key)
+        if it == self._cache_property_map.end():
+            with gil:
+                self._cache_cur_idx += 1
+                if self._cache_cur_idx > self._cache.shape[0]:
+                    raise ValueError('Cache error')
+                self._cache[self._cache_cur_idx] = FastFunction(self.phase_record_factory.get_phase_property(self.phase_name, (<bytes>property_name).decode('utf-8'), include_grad=False, include_hess=False).func)
+                self._cache_property_map[cache_key] = self._cache_cur_idx
+            it = self._cache_property_map.find(cache_key)
+        return <void*>self._cache_ptr[deref(it).second]
+
+    cdef void* get_grad(self, string property_name) except * nogil:
+        cdef pair[string, string] cache_key = pair[string, string](string(<char*>'grad'), property_name)
+        cdef map[pair[string, string], int].iterator it
+        it = self._cache_property_map.find(cache_key)
+        if it == self._cache_property_map.end():
+            with gil:
+                self._cache_cur_idx += 1
+                if self._cache_cur_idx > self._cache.shape[0]:
+                    raise ValueError('Cache error')
+                self._cache[self._cache_cur_idx] = FastFunction(self.phase_record_factory.get_phase_property(self.phase_name, (<bytes>property_name).decode('utf-8'), include_grad=True, include_hess=False).grad)
+                self._cache_property_map[cache_key] = self._cache_cur_idx
+            it = self._cache_property_map.find(cache_key)
+        return <void*>self._cache_ptr[deref(it).second]
+
+    cdef void* get_hess(self, string property_name) except * nogil:
+        cdef pair[string, string] cache_key = pair[string, string](string(<char*>'hess'), property_name)
+        cdef map[pair[string, string], int].iterator it
+        it = self._cache_property_map.find(cache_key)
+        if it == self._cache_property_map.end():
+            with gil:
+                self._cache_cur_idx += 1
+                if self._cache_cur_idx > self._cache.shape[0]:
+                    raise ValueError('Cache error')
+                self._cache[self._cache_cur_idx] = FastFunction(self.phase_record_factory.get_phase_property(self.phase_name, (<bytes>property_name).decode('utf-8'), include_grad=False, include_hess=True).hess)
+                self._cache_property_map[cache_key] = self._cache_cur_idx
+            it = self._cache_property_map.find(cache_key)
+        return <void*>self._cache_ptr[deref(it).second]
+
+    cpdef FastFunction get_cons_func(self):
+        return FastFunction(self.phase_record_factory.get_phase_constraints(self.phase_name).internal_cons_func)
+
+    cpdef FastFunction get_cons_jac(self):
+        return FastFunction(self.phase_record_factory.get_phase_constraints(self.phase_name).internal_cons_jac)
+
+    cpdef FastFunction get_cons_hess(self):
+        return FastFunction(self.phase_record_factory.get_phase_constraints(self.phase_name).internal_cons_hess)
+
+    cpdef int get_cons_len(self):
+        return self.phase_record_factory.get_phase_constraints(self.phase_name).num_internal_cons
+
+    cpdef FastFunction get_mole_fraction_func(self, unicode element_name):
+        return FastFunction(self.phase_record_factory.get_phase_formula_moles_element(self.phase_name,
+                                element_name, per_formula_unit=False).func)
+
+    cpdef FastFunction get_mole_fraction_grad(self, unicode element_name):
+        return FastFunction(self.phase_record_factory.get_phase_formula_moles_element(self.phase_name,
+                                element_name, per_formula_unit=False).grad)
+
+    cpdef FastFunction get_mole_fraction_hess(self, unicode element_name):
+        return FastFunction(self.phase_record_factory.get_phase_formula_moles_element(self.phase_name,
+                                element_name, per_formula_unit=False).hess)
+
+    cpdef FastFunction get_mole_formula_func(self, unicode element_name):
+        return FastFunction(self.phase_record_factory.get_phase_formula_moles_element(self.phase_name,
+                                element_name, per_formula_unit=True).func)
+
+    cpdef FastFunction get_mole_formula_grad(self, unicode element_name):
+        return FastFunction(self.phase_record_factory.get_phase_formula_moles_element(self.phase_name,
+                                element_name, per_formula_unit=True).grad)
+
+    cpdef FastFunction get_mole_formula_hess(self, unicode element_name):
+        return FastFunction(self.phase_record_factory.get_phase_formula_moles_element(self.phase_name,
+                                element_name, per_formula_unit=True).hess)
+
 @cython.boundscheck(False)
 @cython.wraparound(False)
 cdef double* alloc_dof_with_parameters(double[::1] dof, double[::1] parameters) nogil:
@@ -72,128 +163,74 @@ cdef public class PhaseRecord(object)[type PhaseRecordType, object PhaseRecordOb
     between Model implementations. PhaseRecords are immutable after initialization.
     """
     def __reduce__(self):
-            return PhaseRecord, (self.components, self.state_variables, self.variables, np.array(self.parameters),
-                                 self.ofunc_,
-                                 self.formulaofunc_, self.formulagfunc_, self.formulahfunc_,
-                                 self.massfuncs_,
-                                 self.formulamolefuncs_, self.formulamolegradfuncs_, self.formulamolehessianfuncs_,
-                                 self.internal_cons_func_, self.internal_cons_jac_, self.internal_cons_hess_,
-                                 self.num_internal_cons)
-
-    def __cinit__(self, object comps, object state_variables, object variables,
-                  double[::1] parameters, object ofunc,
-                  object formulaofunc, object formulagfunc, object formulahfunc,
-                  object massfuncs,
-                  object formulamolefuncs, object formulamolegradfuncs, object formulamolehessianfuncs,
-                  object internal_cons_func, object internal_cons_jac, object internal_cons_hess,
-                  size_t num_internal_cons):
+            return PhaseRecord, (self.phase_record_factory, self.phase_name)
+
+    def __cinit__(self, object phase_record_factory, str phase_name):
         cdef:
-            int var_idx, el_idx
-        self.components = comps
-        desired_active_pure_elements = [list(x.constituents.keys()) for x in self.components]
-        desired_active_pure_elements = [el.upper() for constituents in desired_active_pure_elements for el in constituents]
-        pure_elements = sorted(set(desired_active_pure_elements))
-        nonvacant_elements = sorted([x for x in set(desired_active_pure_elements) if x != 'VA'])
-
-        self.variables = variables
-        self.state_variables = state_variables
-        self.num_statevars = len(state_variables)
-        self.pure_elements = pure_elements
-        self.nonvacant_elements = nonvacant_elements
-        self.phase_dof = 0
-        self.parameters = parameters
-        self.num_internal_cons = num_internal_cons
-
-        for variable in variables:
-            if not isinstance(variable, v.SiteFraction):
-                continue
-            self.phase_name = <unicode>variable.phase_name
-            self.phase_dof += 1
-
-        # Used only to reconstitute if pickled (i.e. via __reduce__)
-        self.ofunc_ = ofunc
-        self.formulaofunc_ = formulaofunc
-        self.formulagfunc_ = formulagfunc
-        self.formulahfunc_ = formulahfunc
-        self.internal_cons_func_ = internal_cons_func
-        self.internal_cons_jac_ = internal_cons_jac
-        self.internal_cons_hess_ = internal_cons_hess
-        self.massfuncs_ = massfuncs
-        self.formulamolefuncs_ = formulamolefuncs
-        self.formulamolegradfuncs_ = formulamolegradfuncs
-        self.formulamolehessianfuncs_ = formulamolehessianfuncs
-
-        if ofunc is not None:
-            self._obj = FastFunction(ofunc)
-        if formulaofunc is not None:
-            self._formulaobj = FastFunction(formulaofunc)
-        if formulagfunc is not None:
-            self._formulagrad = FastFunction(formulagfunc)
-        if formulahfunc is not None:
-            self._formulahess = FastFunction(formulahfunc)
-        if internal_cons_func is not None:
-            self._internal_cons_func = FastFunction(internal_cons_func)
-        if internal_cons_jac is not None:
-            self._internal_cons_jac = FastFunction(internal_cons_jac)
-        if internal_cons_hess is not None:
-            self._internal_cons_hess = FastFunction(internal_cons_hess)
-        if massfuncs is not None:
-            self._masses = np.empty(len(nonvacant_elements), dtype='object')
-            for el_idx in range(len(nonvacant_elements)):
-                self._masses[el_idx] = FastFunction(massfuncs[el_idx])
-            self._masses_ptr = <void**> self._masses.data
-        if formulamolefuncs is not None:
-            self._formulamoles = np.empty(len(nonvacant_elements), dtype='object')
-            for el_idx in range(len(nonvacant_elements)):
-                self._formulamoles[el_idx] = FastFunction(formulamolefuncs[el_idx])
-            self._formulamoles_ptr = <void**> self._formulamoles.data
-        if formulamolegradfuncs is not None:
-            self._formulamolegrads = np.empty(len(nonvacant_elements), dtype='object')
-            for el_idx in range(len(nonvacant_elements)):
-                self._formulamolegrads[el_idx] = FastFunction(formulamolegradfuncs[el_idx])
-            self._formulamolegrads_ptr = <void**> self._formulamolegrads.data
-        if formulamolehessianfuncs is not None:
-            self._formulamolehessians = np.empty(len(nonvacant_elements), dtype='object')
-            for el_idx in range(len(nonvacant_elements)):
-                self._formulamolehessians[el_idx] = FastFunction(formulamolehessianfuncs[el_idx])
-            self._formulamolehessians_ptr = <void**> self._formulamolehessians.data
+            int el_idx
+        self.phase_record_factory = phase_record_factory
+        self.components = phase_record_factory.comps
+        self.phase_name = phase_name
+        self.variables = phase_record_factory.models[phase_name].site_fractions
+        self.state_variables = phase_record_factory.state_variables
+        self.num_statevars = len(phase_record_factory.state_variables)
+        self.pure_elements = phase_record_factory.pure_elements
+        self.nonvacant_elements = phase_record_factory.nonvacant_elements
+        self.molar_masses = phase_record_factory.molar_masses
+        self.parameters = phase_record_factory.param_values
+        
+        self.phase_dof = len(phase_record_factory.models[phase_name].site_fractions)
 
-    @cython.boundscheck(False)
-    @cython.wraparound(False)
-    cpdef void obj(self, double[::1] outp, double[::1] dof) nogil:
-        # dof.shape[0] may be oversized by the caller; do not trust it
-        cdef double* dof_concat = alloc_dof_with_parameters(dof[:self.num_statevars+self.phase_dof], self.parameters)
-        cdef int num_dof = self.num_statevars + self.phase_dof + self.parameters.shape[0]
-        self._obj.call(&outp[0], &dof_concat[0])
-        if self.parameters.shape[0] > 0:
-            free(dof_concat)
+        self.function_factory = FastFunctionFactory(phase_record_factory, phase_name)
+
+        self._internal_cons_func = self.function_factory.get_cons_func()
+        self._internal_cons_jac = self.function_factory.get_cons_jac()
+        self._internal_cons_hess = self.function_factory.get_cons_hess()
+        self.num_internal_cons = self.function_factory.get_cons_len()
+        self._masses = np.empty(len(self.nonvacant_elements), dtype='object')
+        for el_idx, el in enumerate(self.nonvacant_elements):
+            self._masses[el_idx] = self.function_factory.get_mole_fraction_func(el)
+        self._masses_ptr = <void**> self._masses.data
+        self._formulamoles = np.empty(len(self.nonvacant_elements), dtype='object')
+        for el_idx, el in enumerate(self.nonvacant_elements):
+            self._formulamoles[el_idx] = self.function_factory.get_mole_formula_func(el)
+        self._formulamoles_ptr = <void**> self._formulamoles.data
+        self._formulamolegrads = np.empty(len(self.nonvacant_elements), dtype='object')
+        for el_idx, el in enumerate(self.nonvacant_elements):
+            self._formulamolegrads[el_idx] = self.function_factory.get_mole_formula_grad(el)
+        self._formulamolegrads_ptr = <void**> self._formulamolegrads.data
+        self._formulamolehessians = np.empty(len(self.nonvacant_elements), dtype='object')
+        for el_idx, el in enumerate(self.nonvacant_elements):
+            self._formulamolehessians[el_idx] = self.function_factory.get_mole_formula_hess(el)
+        self._formulamolehessians_ptr = <void**> self._formulamolehessians.data
 
     @cython.boundscheck(False)
     @cython.wraparound(False)
-    cpdef void formulaobj(self, double[::1] outp, double[::1] dof) nogil:
+    cpdef void prop(self, double[::1] outp, double[::1] dof, string property_name) except * nogil:
         # dof.shape[0] may be oversized by the caller; do not trust it
         cdef double* dof_concat = alloc_dof_with_parameters(dof[:self.num_statevars+self.phase_dof], self.parameters)
         cdef int num_dof = self.num_statevars + self.phase_dof + self.parameters.shape[0]
-        self._formulaobj.call(&outp[0], &dof_concat[0])
+        (<FastFunction>self.function_factory.get_func(property_name)).call(&outp[0], &dof_concat[0])
         if self.parameters.shape[0] > 0:
             free(dof_concat)
 
     @cython.boundscheck(False)
     @cython.wraparound(False)
-    cpdef void obj_2d(self, double[::1] outp, double[:, ::1] dof) nogil:
+    cpdef void prop_2d(self, double[::1] outp, double[:, ::1] dof, string property_name) except * nogil:
         # dof.shape[1] may be oversized by the caller; do not trust it
         cdef double* dof_concat = alloc_dof_with_parameters_vectorized(dof[:, :self.num_statevars+self.phase_dof], self.parameters)
         cdef int i
         cdef int num_inps = dof.shape[0]
         cdef int num_dof = self.num_statevars + self.phase_dof + self.parameters.shape[0]
         for i in range(num_inps):
-            self._obj.call(&outp[i], &dof_concat[i * num_dof])
+           (<FastFunction>self.function_factory.get_func(property_name)).call(&outp[i], &dof_concat[i * num_dof])
         if self.parameters.shape[0] > 0:
             free(dof_concat)
 
     @cython.boundscheck(False)
     @cython.wraparound(False)
-    cpdef void obj_parameters_2d(self, double[:, ::1] outp, double[:, ::1] dof, double[:, ::1] parameters) nogil:
+    cpdef void prop_parameters_2d(self, double[:, ::1] outp, double[:, ::1] dof,
+                                  double[:, ::1] parameters, string property_name) except * nogil:
         """
         Calculate objective function using custom parameters.
         Note dof and parameters are vectorized separately, i.e., broadcast against each other.
@@ -217,15 +254,36 @@ cdef public class PhaseRecord(object)[type PhaseRecordType, object PhaseRecordOb
                 for param_idx in range(num_params):
                     dof_concat[j * num_dof + dof_offset + param_idx] = parameters[j, param_idx]
             for j in range(num_param_inps):
-                self._obj.call(&outp[i,j], &dof_concat[j * num_dof])
+                (<FastFunction>self.function_factory.get_func(property_name)).call(&outp[i,j], &dof_concat[j * num_dof])
         free(dof_concat)
 
+    @cython.boundscheck(False)
+    @cython.wraparound(False)
+    cpdef void prop_grad(self, double[::1] out, double[::1] dof, string property_name) except * nogil:
+        # dof.shape[0] may be oversized by the caller; do not trust it
+        cdef double* dof_concat = alloc_dof_with_parameters(dof[:self.num_statevars+self.phase_dof], self.parameters)
+        (<FastFunction>self.function_factory.get_grad(property_name)).call(&out[0], &dof_concat[0])
+        if self.parameters.shape[0] > 0:
+            free(dof_concat)
+
+    cpdef void obj(self, double[::1] outp, double[::1] dof) nogil:
+        self.prop(outp, dof, <char*>'GM')
+
+    cpdef void formulaobj(self, double[::1] outp, double[::1] dof) nogil:
+        self.prop(outp, dof, <char*>'G')
+
+    cpdef void obj_2d(self, double[::1] outp, double[:, ::1] dof) nogil:
+        self.prop_2d(outp, dof, <char*>'GM')
+
+    cpdef void obj_parameters_2d(self, double[:, ::1] outp, double[:, ::1] dof, double[:, ::1] parameters) nogil:
+        self.prop_parameters_2d(outp, dof, parameters, <char*>'GM')
+
     @cython.boundscheck(False)
     @cython.wraparound(False)
     cpdef void formulagrad(self, double[::1] out, double[::1] dof) nogil:
         # dof.shape[0] may be oversized by the caller; do not trust it
         cdef double* dof_concat = alloc_dof_with_parameters(dof[:self.num_statevars+self.phase_dof], self.parameters)
-        self._formulagrad.call(&out[0], &dof_concat[0])
+        (<FastFunction>self.function_factory.get_grad(<char*>'G')).call(&out[0], &dof_concat[0])
         if self.parameters.shape[0] > 0:
             free(dof_concat)
 
@@ -235,7 +293,7 @@ cdef public class PhaseRecord(object)[type PhaseRecordType, object PhaseRecordOb
     cpdef void formulahess(self, double[:, ::1] out, double[::1] dof) nogil:
         # dof.shape[0] may be oversized by the caller; do not trust it
         cdef double* dof_concat = alloc_dof_with_parameters(dof[:self.num_statevars+self.phase_dof], self.parameters)
-        self._formulahess.call(&out[0,0], &dof_concat[0])
+        (<FastFunction>self.function_factory.get_hess(<char*>'G')).call(&out[0,0], &dof_concat[0])
         if self.parameters.shape[0] > 0:
             free(dof_concat)
 
@@ -267,6 +325,24 @@ cdef public class PhaseRecord(object)[type PhaseRecordType, object PhaseRecordOb
         if self.parameters.shape[0] > 0:
             free(dof_concat)
 
+    @cython.boundscheck(False)
+    @cython.wraparound(False)
+    cpdef void phase_local_cons_func(self, double[::1] out, double[::1] dof, FastFunction func) nogil:
+        # dof.shape[0] may be oversized by the caller; do not trust it
+        cdef double* dof_concat = alloc_dof_with_parameters(dof[:self.num_statevars+self.phase_dof], self.parameters)
+        func.call(&out[0], &dof_concat[0])
+        if self.parameters.shape[0] > 0:
+            free(dof_concat)
+
+    @cython.boundscheck(False)
+    @cython.wraparound(False)
+    cpdef void phase_local_cons_jac(self, double[:, ::1] out, double[::1] dof, FastFunction jac_func) nogil:
+        # dof.shape[0] may be oversized by the caller; do not trust it
+        cdef double* dof_concat = alloc_dof_with_parameters(dof[:self.num_statevars+self.phase_dof], self.parameters)
+        jac_func.call(&out[0, 0], &dof_concat[0])
+        if self.parameters.shape[0] > 0:
+            free(dof_concat)
+
     @cython.boundscheck(False)
     @cython.wraparound(False)
     cpdef void mass_obj(self, double[::1] out, double[::1] dof, int comp_idx) nogil:
diff --git a/pycalphad/core/polytope.py b/pycalphad/core/polytope.py
new file mode 100644
index 000000000..8d1731f4f
--- /dev/null
+++ b/pycalphad/core/polytope.py
@@ -0,0 +1,208 @@
+"""
+This module provides functions to uniformly sample points subject to a system of linear
+inequality constraints, :math:`Ax <= b` (convex polytope), and linear equality
+constraints, :math:`Ax = b` (affine projection).
+
+A comparison of MCMC algorithms to generate uniform samples over a convex polytope is
+given in [Chen2018]_. Here, we use the Hit & Run algorithm described in [Smith1984]_.
+The R-package `hitandrun`_ provides similar functionality to this module.
+
+Based on https://github.com/DavidWalz/polytope-sampling
+Used under the terms of the MIT license. License information can be found in the pycalphad LICENSE.txt.
+
+References
+----------
+.. [Chen2018] Chen Y., Dwivedi, R., Wainwright, M., Yu B. (2018) Fast MCMC Sampling
+    Algorithms on Polytopes. JMLR, 19(55):1−86
+    https://arxiv.org/abs/1710.08165
+.. [Smith1984] Smith, R. (1984). Efficient Monte Carlo Procedures for Generating
+    Points Uniformly Distributed Over Bounded Regions. Operations Research,
+    32(6), 1296-1308.
+    www.jstor.org/stable/170949
+.. _`hitandrun`: https://cran.r-project.org/web/packages/hitandrun/index.html
+"""
+import numpy as np
+import scipy.linalg
+import scipy.optimize
+
+
+def check_Ab(A, b):
+    """Check if matrix equation Ax=b is well defined.
+
+    Parameters
+    ----------
+    A : 2d-array of shape (n_constraints, dimension)
+        Left-hand-side of Ax <= b.
+    b : 1d-array of shape (n_constraints)
+        Right-hand-side of Ax <= b.
+
+    """
+    assert A.ndim == 2
+    assert b.ndim == 1
+    assert A.shape[0] == b.shape[0]
+
+
+def chebyshev_center(A, b):
+    """Find the center of the polytope Ax <= b.
+
+    Parameters
+    ----------
+    A : 2d-array of shape (n_constraints, dimension)
+        Left-hand-side of Ax <= b.
+    b : 1d-array of shape (n_constraints)
+        Right-hand-side of Ax <= b.
+
+    Returns
+    -------
+    1d-array of shape (dimension)
+        Chebyshev center of the polytope
+    """
+    res = scipy.optimize.linprog(
+        np.r_[np.zeros(A.shape[1]), -1],
+        A_ub=np.hstack([A, np.linalg.norm(A, axis=1, keepdims=True)]),
+        b_ub=b,
+        bounds=(None, None),
+    )
+    if not res.success:
+        raise Exception("Unable to find Chebyshev center")
+    return res.x[:-1]
+
+
+def constraints_from_bounds(lower, upper):
+    """Construct the inequality constraints Ax <= b that correspond to the given
+    lower and upper bounds.
+
+    Parameters
+    ----------
+    lower : array-like
+        lower bound in each dimension
+    upper : array-like
+        upper bound in each dimension
+
+    Returns
+    -------
+    A: 2d-array of shape (2 * dimension, dimension)
+        Left-hand-side of Ax <= b.
+    b: 1d-array of shape (2 * dimension)
+        Right-hand-side of Ax <= b.
+    """
+    n = len(lower)
+    A = np.row_stack([-np.eye(n), np.eye(n)])
+    b = np.r_[-np.array(lower), np.array(upper)]
+    return A, b
+
+
+def affine_subspace(A, b):
+    """Compute a basis of the nullspace of A, and a particular solution to Ax = b.
+    This allows to to construct arbitrary solutions as the sum of any vector in the
+    nullspace, plus the particular solution.
+
+    Parameters
+    ----------
+    A : 2d-array of shape (n_constraints, dimension)
+        Left-hand-side of Ax <= b.
+    b : 1d-array of shape (n_constraints)
+        Right-hand-side of Ax <= b.
+
+    Returns
+    -------
+    N: 2d-array of shape (dimension, dimension)
+        Orthonormal basis of the nullspace of A.
+    xp: 1d-array of shape (dimension)
+        Particular solution to Ax = b.
+    """
+    N = scipy.linalg.null_space(A)
+    xp = np.linalg.pinv(A) @ b
+    return N, xp
+
+def sample(n_points, lower, upper, A1=None, b1=None, A2=None, b2=None):
+    """Sample a number of points from a convex polytope A1 x <= b1 using the Hit & Run
+    algorithm.
+
+    Lower and upper bounds need to be provided to ensure that the polytope is bounded.
+    Equality constraints A2 x = b2 may be optionally provided.
+
+    Parameters
+    ----------
+    n_points : int
+        Number of samples to generate.
+    lower: 1d-array of shape (dimension)
+        Lower bound in each dimension. If not wanted set to -np.inf.
+    upper: 1d-array of shape (dimension)
+        Upper bound in each dimension. If not wanted set to np.inf.
+    A1 : 2d-array of shape (n_constraints, dimension)
+        Left-hand-side of A1 x <= b1.
+    b1 : 1d-array of shape (n_constraints)
+        Right-hand-side of A1 x <= b1.
+    A2 : 2d-array of shape (n_constraints, dimensions), optional
+        Left-hand-side of A2 x = b2.
+    b2 : 1d-array of shape (n_constraints), optional
+        Right-hand-side of A2 x = b2.
+
+    Returns
+    -------
+    2d-array of shape (n_points)
+        Points sampled from the polytope.
+    """
+    A, b = constraints_from_bounds(lower, upper)
+    if (A1 is not None) and (b1 is not None):
+        A1 = np.r_[A, A1]
+        b1 = np.r_[b, b1]
+    else:
+        A1, b1 = A, b
+
+    if (A2 is not None) and (b2 is not None):
+        check_Ab(A2, b2)
+        N, xp = affine_subspace(A2, b2)
+    else:
+        N = np.eye(A1.shape[1])
+        xp = np.zeros(A1.shape[1])
+
+    if N.shape[1] == 0:
+        # zero-dimensional polytope, return unique solution
+        X = np.atleast_2d(np.linalg.solve(A2, b2))
+        return X
+
+    # project to the affine subspace of the equality constraints
+    At = A1 @ N
+    bt = b1 - A1 @ xp
+
+    try:
+        x0 = chebyshev_center(At, bt)
+    except:
+        # Unable to find center
+        return np.empty((0, A1.shape[1]))
+
+    test_point = x0[np.newaxis, :] @ N.T + xp
+    if np.any(test_point < lower-1e-10) or np.any(test_point > upper+1e-10):
+        # Starting point is not feasible
+        return np.empty((0, A1.shape[1]))
+
+    X = np.empty((n_points, At.shape[1]))
+    x = x0
+    rng = np.random.RandomState(1769)
+    with np.errstate(divide='ignore', invalid='ignore'):
+        directions = rng.randn(n_points, At.shape[1])
+        directions /= np.linalg.norm(directions, axis=0)
+        for i in range(n_points):
+            # sample random direction from unit hypersphere
+            direction = directions[i]
+
+            # distances to each face from the current point in the sampled direction
+            D = (bt - x @ At.T) / (direction @ At.T)
+
+            # distance to the closest face in and opposite to direction
+            lo = max(D[D < 1e-10])
+            hi = min(D[D > -1e-10])
+            if hi < lo:
+                # Amount of 'wiggle room' is down in the numerical noise
+                lo = 0.0
+                hi = 0.0
+            # make random step
+            x += rng.uniform(lo, hi) * direction
+            X[i] = x
+
+    # project back
+    X = X @ N.T + xp
+    X = np.clip(X, lower, upper)
+    return X
\ No newline at end of file
diff --git a/pycalphad/core/solver.py b/pycalphad/core/solver.py
index 59b8ba3c0..f1ac51111 100644
--- a/pycalphad/core/solver.py
+++ b/pycalphad/core/solver.py
@@ -40,7 +40,7 @@ def get_system_spec(self, composition_sets, conditions):
         ----------
         composition_sets : List[pycalphad.core.composition_set.CompositionSet]
             List of CompositionSet objects in the starting point. Modified in place.
-        conditions : OrderedDict[str, float]
+        conditions : OrderedDict[StateVariable, float]
             Conditions to satisfy.
 
         Returns
@@ -48,28 +48,73 @@ def get_system_spec(self, composition_sets, conditions):
         SystemSpecification
 
         """
+        # Prevent circular import
+        from pycalphad.variables import ChemicalPotential, MoleFraction, SiteFraction
         compsets = composition_sets
         state_variables = compsets[0].phase_record.state_variables
         nonvacant_elements = compsets[0].phase_record.nonvacant_elements
         num_statevars = len(state_variables)
         num_components = len(nonvacant_elements)
         chemical_potentials = np.zeros(num_components)
-        prescribed_elemental_amounts = []
-        prescribed_element_indices = []
+        prescribed_mole_fraction_coefficients = []
+        prescribed_mole_fraction_rhs = []
+        local_conditions = {key: value for key, value in conditions.items()
+                            if getattr(key, 'phase_name', None) is not None}
+        for compset in compsets:
+            phase_local_conditions = {key: value for key, value in local_conditions.items()
+                                      if compset.phase_record.phase_name == key.phase_name}
+            compset.set_local_conditions(phase_local_conditions)
         for cond, value in conditions.items():
-            if str(cond).startswith('X_'):
+            if isinstance(cond, MoleFraction) and cond.phase_name is None:
+                el = str(cond)[2:]
+                el_idx = list(nonvacant_elements).index(el)
+                prescribed_mole_fraction_rhs.append(float(value))
+                coefs = np.zeros(num_components)
+                coefs[el_idx] = 1.0
+                prescribed_mole_fraction_coefficients.append(coefs)
+            elif isinstance(cond, MoleFraction) and cond.phase_name is not None:
+                # phase-local condition; already handled
+                continue
+            elif isinstance(cond, SiteFraction):
+                # phase-local condition; already handled
+                continue
+            elif str(cond).startswith('W_'):
+                # wA = k -> (1-k)*MWA*xA - k*MWB*xB - k*MWC*xC = 0
                 el = str(cond)[2:]
                 el_idx = list(nonvacant_elements).index(el)
-                prescribed_elemental_amounts.append(float(value))
-                prescribed_element_indices.append(el_idx)
-        prescribed_element_indices = np.array(prescribed_element_indices, dtype=np.int32)
-        prescribed_elemental_amounts = np.array(prescribed_elemental_amounts)
+                coef_vector = np.zeros(num_components)
+                coef_vector -= value
+                coef_vector[el_idx] += 1
+                # multiply coef_vector times a vector of molecular weights
+                coef_vector = np.multiply(coef_vector, compsets[0].phase_record.molar_masses)
+                prescribed_mole_fraction_rhs.append(0.)
+                prescribed_mole_fraction_coefficients.append(coef_vector)
+            elif str(cond).startswith('LinComb_'):
+                coefs = np.zeros(num_components)
+                constant = 0.0
+                for symbol, coef in zip(cond.symbols, cond.coefs):
+                    if symbol == 1:
+                        constant = coef
+                        continue
+                    el = str(symbol)[2:]
+                    el_idx = list(nonvacant_elements).index(el)
+                    coefs[el_idx] = coef
+                if cond.denominator == 1:
+                    prescribed_mole_fraction_rhs.append(float(value) - float(constant))
+                else:
+                    # Adjust coefficients to account for molar ratio
+                    prescribed_mole_fraction_rhs.append(-float(constant))
+                    denominator_idx = cond.symbols.index(cond.denominator)
+                    coefs[denominator_idx] -= float(value)
+                prescribed_mole_fraction_coefficients.append(coefs)
+        prescribed_mole_fraction_coefficients = np.atleast_2d(prescribed_mole_fraction_coefficients)
+        prescribed_mole_fraction_rhs = np.array(prescribed_mole_fraction_rhs)
         prescribed_system_amount = conditions.get('N', 1.0)
-        fixed_chemical_potential_indices = np.array([nonvacant_elements.index(key[3:]) for key in conditions.keys() if key.startswith('MU_')], dtype=np.int32)
+        fixed_chemical_potential_indices = np.array([nonvacant_elements.index(str(key)[3:]) for key in conditions.keys() if str(key).startswith('MU_')], dtype=np.int32)
         free_chemical_potential_indices = np.array(sorted(set(range(num_components)) - set(fixed_chemical_potential_indices)), dtype=np.int32)
         for fixed_chempot_index in fixed_chemical_potential_indices:
             el = nonvacant_elements[fixed_chempot_index]
-            chemical_potentials[fixed_chempot_index] = conditions.get('MU_' + str(el))
+            chemical_potentials[fixed_chempot_index] = conditions.get(ChemicalPotential(el))
         fixed_statevar_indices = []
         for statevar_idx, statevar in enumerate(state_variables):
             if str(statevar) in [str(k) for k in conditions.keys()]:
@@ -78,13 +123,23 @@ def get_system_spec(self, composition_sets, conditions):
         fixed_statevar_indices = np.array(fixed_statevar_indices, dtype=np.int32)
         fixed_stable_compset_indices = np.array([i for i, compset in enumerate(compsets) if compset.fixed], dtype=np.int32)
         spec = SystemSpecification(num_statevars, num_components, prescribed_system_amount,
-                                   chemical_potentials, prescribed_elemental_amounts,
-                                   prescribed_element_indices,
+                                   chemical_potentials, prescribed_mole_fraction_coefficients,
+                                   prescribed_mole_fraction_rhs,
                                    free_chemical_potential_indices, free_statevar_indices,
                                    fixed_chemical_potential_indices, fixed_statevar_indices,
                                    fixed_stable_compset_indices)
         return spec
 
+    @staticmethod
+    def _fix_state_variables_in_compsets(composition_sets, conditions):
+        "Ensure state variables in each CompositionSet are set to the fixed value."
+        str_state_variables = [str(k) for k in composition_sets[0].phase_record.state_variables]
+        for compset in composition_sets:
+            for k,v in conditions.items():
+                if str(k) in str_state_variables:
+                    statevar_idx = str_state_variables.index(str(k))
+                    compset.dof[statevar_idx] = v
+
     def solve(self, composition_sets, conditions):
         """
         Minimize the energy under the specified conditions using the given candidate composition sets.
@@ -102,6 +157,7 @@ def solve(self, composition_sets, conditions):
 
         """
         spec = self.get_system_spec(composition_sets, conditions)
+        self._fix_state_variables_in_compsets(composition_sets, conditions)
         state = spec.get_new_state(composition_sets)
         converged = spec.run_loop(state, 1000)
 
diff --git a/pycalphad/core/starting_point.py b/pycalphad/core/starting_point.py
index fe7b0ed3b..2208b9145 100644
--- a/pycalphad/core/starting_point.py
+++ b/pycalphad/core/starting_point.py
@@ -1,6 +1,7 @@
 from pycalphad import variables as v
 from pycalphad.core.lower_convex_hull import lower_convex_hull
 from pycalphad.core.light_dataset import LightDataset
+from pycalphad.property_framework.computed_property import LinearCombination
 from xarray import Dataset
 import numpy as np
 from collections import OrderedDict
@@ -28,6 +29,9 @@ def global_min_is_possible(conditions, state_variables):
     for cond in conditions.keys():
         if cond in state_variables or \
            isinstance(cond, v.MoleFraction) or \
+           isinstance(cond, v.MassFraction) or \
+           isinstance(cond, v.SiteFraction) or \
+           isinstance(cond, LinearCombination) or \
            isinstance(cond, v.ChemicalPotential) or \
            cond == v.N:
             continue
@@ -68,15 +72,16 @@ def starting_point(conditions, state_variables, phase_records, grid):
         len(nonvacant_elements) + 1)  # +1 is to accommodate the degenerate degree of freedom at the invariant reactions
     coord_dict['component'] = nonvacant_elements
     conds_as_strings = [str(k) for k in conditions.keys()]
-    specified_elements = set()
+    number_dof = len(nonvacant_elements) - 1
     for i in conditions.keys():
-        # Assume that a condition specifying a species contributes to constraining it
-        if not hasattr(i, 'species'):
+        if not (hasattr(i, 'species') or isinstance(i, LinearCombination)):
             continue
-        specified_elements |= set(i.species.constituents.keys()) - {'VA'}
-    dependent_comp = set(nonvacant_elements) - specified_elements
-    if len(dependent_comp) != 1:
-        raise ValueError('Number of dependent components different from one')
+        if hasattr(i, 'species') and hasattr(i, 'phase_name') and i.phase_name is not None:
+            # Phase-local conditions do not reduce the total degrees of freedom
+            continue
+        number_dof -= 1
+    if number_dof != 0:
+        raise ValueError('Number of degrees of freedom is not zero')
 
     ds_vars = {'NP':     (conds_as_strings + ['vertex'], np.empty(grid_shape + (len(nonvacant_elements)+1,))),
                'GM':     (conds_as_strings, np.empty(grid_shape)),
@@ -97,8 +102,9 @@ def starting_point(conditions, state_variables, phase_records, grid):
         ds_vars.update({str(f_sv): (conds_as_strings, np.empty(grid_shape))})
 
     result = LightDataset(ds_vars, coords=coord_dict, attrs={'engine': 'pycalphad %s' % pycalphad_version})
+
     if global_min_enabled:
-        result = lower_convex_hull(grid, state_variables, result)
+        result = lower_convex_hull(grid, state_variables, sorted(conditions.keys(), key=str), phase_records, result)
     else:
         raise NotImplementedError('Conditions not yet supported')
 
diff --git a/pycalphad/core/utils.py b/pycalphad/core/utils.py
index 35787f9bc..cab50ad35 100644
--- a/pycalphad/core/utils.py
+++ b/pycalphad/core/utils.py
@@ -6,13 +6,14 @@
 import pycalphad.variables as v
 from pycalphad.core.halton import halton
 from pycalphad.core.constants import MIN_SITE_FRACTION
-from symengine import lambdify, Symbol
+from pycalphad.property_framework.units import Q_
+from symengine import Symbol
 import numpy as np
 import operator
 import functools
 import itertools
 import collections
-from collections.abc import Iterable, Mapping
+from collections.abc import Iterable, Mapping, KeysView
 
 
 def point_sample(comp_count, pdof=10):
@@ -93,7 +94,9 @@ def unpack_condition(tup):
             return np.arange(tup[0], tup[1], tup[2], dtype=np.float_)
         else:
             raise ValueError('Condition tuple is length {}'.format(len(tup)))
-    elif isinstance(tup, Iterable):
+    elif isinstance(tup, Q_):
+        return tup
+    elif isinstance(tup, Iterable) and np.ndim(tup) != 0:
         return [float(x) for x in tup]
     else:
         return [float(tup)]
@@ -101,12 +104,14 @@ def unpack_condition(tup):
 def unpack_phases(phases):
     "Convert a phases list/dict into a sorted list."
     active_phases = None
-    if isinstance(phases, (list, tuple, set)):
+    if isinstance(phases, (list, tuple, set, KeysView)):
         active_phases = sorted(phases)
     elif isinstance(phases, dict):
         active_phases = sorted(phases.keys())
     elif type(phases) is str:
         active_phases = [phases]
+    else:
+        raise ValueError('Cannot unpack phases into recognizable input')
     return active_phases
 
 def generate_dof(phase, active_comps):
@@ -417,7 +422,7 @@ def get_state_variables(models=None, conds=None):
         for c in conds:
             # StateVariable instances are ok (e.g. P, T, N, V, S),
             # however, subclasses (X, Y, MU, NP) are not ok.
-            if type(c) is v.StateVariable:
+            if isinstance(c, (v.IndependentPotential, v.SystemMolesType)):
                 state_vars.add(c)
     return state_vars
 
diff --git a/pycalphad/core/workspace.py b/pycalphad/core/workspace.py
new file mode 100644
index 000000000..3ba20a9c4
--- /dev/null
+++ b/pycalphad/core/workspace.py
@@ -0,0 +1,444 @@
+import warnings
+from collections import OrderedDict, Counter, defaultdict
+from collections.abc import Mapping
+from copy import copy
+from pycalphad.property_framework.computed_property import DotDerivativeComputedProperty
+import pycalphad.variables as v
+from pycalphad.core.utils import unpack_components, unpack_condition, unpack_phases, filter_phases, instantiate_models
+from pycalphad import calculate
+from pycalphad.core.starting_point import starting_point
+from pycalphad.codegen.phase_record_factory import PhaseRecordFactory
+from pycalphad.core.eqsolver import _solve_eq_at_conditions
+from pycalphad.core.composition_set import CompositionSet
+from pycalphad.core.solver import Solver, SolverBase
+from pycalphad.core.light_dataset import LightDataset
+from pycalphad.plot.renderers import Renderer, DEFAULT_PLOT_RENDERER
+from pycalphad.model import Model
+import numpy as np
+import numpy.typing as npt
+from typing import Optional, Tuple, Type
+from pycalphad.io.database import Database
+from pycalphad.variables import Species, StateVariable
+from pycalphad.core.conditions import Conditions
+from pycalphad.property_framework import ComputableProperty, as_property
+from pycalphad.property_framework.units import unit_conversion_context, ureg, as_quantity, Q_
+from runtype import isa
+from runtype.pytypes import Dict, List, Sequence, SumType, Mapping, NoneType
+from typing import TypeVar
+
+
+
+def _adjust_conditions(conds) -> 'OrderedDict[StateVariable, List[float]]':
+    "Adjust conditions values to be in the implementation units of the quantity, and within the numerical limit of the solver."
+    new_conds = OrderedDict()
+    minimum_composition = 1e-10
+    for key, value in sorted(conds.items(), key=str):
+        key = as_property(key)
+        # If conditions have units, convert to impl units and strip them
+        if isinstance(value, Q_):
+            value = value.to(key.implementation_units).magnitude
+        if isinstance(key, v.MoleFraction):
+            vals = unpack_condition(value)
+            # "Zero" composition is a common pattern. Do not warn for that case.
+            if np.any(np.logical_and(np.asarray(vals) < minimum_composition, np.asarray(vals) > 0)):
+                warnings.warn(
+                    f"Some specified compositions are below the minimum allowed composition of {minimum_composition}.")
+            new_conds[key] = [min(max(val, minimum_composition), 1-minimum_composition) for val in vals]
+        else:
+            new_conds[key] = unpack_condition(value)
+        if getattr(key, 'display_units', '') != '':
+            new_conds[key] = Q_(new_conds[key], units=key.display_units).to(key.implementation_units)
+    return new_conds
+
+class SpeciesList:
+    @classmethod
+    def cast_from(cls, s: Sequence) -> "SpeciesList":
+        return sorted(Species.cast_from(x) for x in s)
+
+class PhaseList:
+    @classmethod
+    def cast_from(cls, s: SumType([str, Sequence[str]])) -> "PhaseList":
+        if isinstance(s, str):
+            s = [s]
+        return sorted(PhaseName.cast_from(x) for x in s)
+
+class PhaseName:
+    @classmethod
+    def cast_from(cls, s: str) -> "PhaseName":
+        return s.upper()
+
+class ConditionValue:
+    @classmethod
+    def cast_from(cls, value: SumType([float, Sequence[float]])) -> "ConditionValue":
+        return unpack_condition(value)
+
+class ConditionKey:
+    @classmethod
+    def cast_from(cls, key: SumType([str, StateVariable])) -> "ConditionKey":
+        return as_property(key)
+
+class TypedField:
+    def __init__(self, default_factory=None, dependsOn=None):
+        self.default_factory = default_factory
+        self.dependsOn = dependsOn
+
+    def __set_name__(self, owner, name):
+        self.type = owner.__annotations__.get(name, None)
+        self.public_name = name
+        self.private_name = '_' + name
+        if self.dependsOn is not None:
+            for dependency in self.dependsOn:
+                owner._callbacks[dependency].append(self.on_dependency_update)
+
+    def __set__(self, obj, value):
+        if (self.type != NoneType) and not isa(value, self.type) and value is not None:
+            try:
+                value = self.type.cast_from(value)
+            except TypeError as e:
+                raise e
+        elif value is None and self.default_factory is not None:
+            value = self.default_factory(obj)
+        oldval = getattr(obj, self.private_name, None)
+        setattr(obj, self.private_name, value)
+        for cb in obj._callbacks[self.public_name]:
+            cb(obj, self.public_name, oldval, value)
+
+    def __get__(self, obj, objtype=None):
+        if not hasattr(obj, self.private_name):
+            if self.default_factory is not None:
+                default_value = self.default_factory(obj)
+                setattr(obj, self.private_name, default_value)
+        return getattr(obj, self.private_name)
+
+    def on_dependency_update(self, obj, updated_attribute, old_val, new_val):
+        pass
+
+class ComponentsField(TypedField):
+    def __init__(self, dependsOn=None):
+        super().__init__(default_factory=lambda obj: unpack_components(obj.database, sorted(x.name for x in obj.database.species if x.name != '/-')),
+                         dependsOn=dependsOn)
+    def __set__(self, obj, value):
+        comps = sorted(unpack_components(obj.database, value))
+        super().__set__(obj, comps)
+
+    def __get__(self, obj, objtype=None):
+        getobj = super().__get__(obj, objtype=objtype)
+        return sorted(unpack_components(obj.database, getobj))
+
+class PhasesField(TypedField):
+    def __init__(self, dependsOn=None):
+        super().__init__(default_factory=lambda obj: filter_phases(obj.database, obj.components),
+                         dependsOn=dependsOn)
+    def __set__(self, obj, value):
+        phases = sorted(unpack_phases(value))
+        super().__set__(obj, phases)
+
+    def __get__(self, obj, objtype=None):
+        getobj = super().__get__(obj, objtype=objtype)
+        return filter_phases(obj.database, obj.components, getobj)
+
+class DictField(TypedField):
+    def get_proxy(self, obj):
+        class DictProxy:
+            @staticmethod
+            def unwrap():
+                return TypedField.__get__(self, obj)
+            def __getattr__(pxy, name):
+                getobj = TypedField.__get__(self, obj)
+                if getobj == pxy:
+                    raise ValueError('Proxy object points to itself')
+                return getattr(getobj, name)
+            def __getitem__(pxy, item):
+                return TypedField.__get__(self, obj).get(item)
+            def __iter__(pxy):
+                return TypedField.__get__(self, obj).__iter__()
+            def __setitem__(pxy, item, value):
+                conds = TypedField.__get__(self, obj)
+                conds[item] = value
+                self.__set__(obj, conds)
+            def __delitem__(pxy, item):
+                conds = TypedField.__get__(self, obj)
+                del conds[item]
+                self.__set__(obj, conds)
+            def __len__(pxy):
+                return len(TypedField.__get__(self, obj))
+            def __str__(pxy):
+                return str(TypedField.__get__(self, obj))
+            def __repr__(pxy):
+                return repr(TypedField.__get__(self, obj))
+        return DictProxy()
+
+    def __get__(self, obj, objtype=None):
+        return self.get_proxy(obj)
+
+class ConditionsField(DictField):
+    def __set__(self, obj, value):
+        conds = Conditions(obj)
+        for k, v in value.items():
+            conds[k] = v
+        super().__set__(obj, conds)
+
+class ModelsField(DictField):
+    def __init__(self, dependsOn=None):
+        super().__init__(default_factory=lambda obj: instantiate_models(obj.database, obj.components, obj.phases,
+                                                                        model=None, parameters=obj.parameters),
+                         dependsOn=dependsOn)
+    def __set__(self, obj, value):
+        # Unwrap proxy objects before being stored
+        if hasattr(value, 'unwrap'):
+            value = value.unwrap()
+        try:
+            # Expand specified Model type into a dict of instances
+            value = instantiate_models(obj.database, obj.components, obj.phases, model=value, parameters=obj.parameters)
+            super().__set__(obj, value)
+        except AttributeError:
+            super().__set__(obj, None)
+
+    def on_dependency_update(self, obj, updated_attribute, old_val, new_val):
+        self.__set__(obj, self.default_factory(obj))
+
+class PRFField(TypedField):
+    def __init__(self, dependsOn=None):
+        def make_prf(obj):
+            try:
+                prf = PhaseRecordFactory(obj.database, obj.components, obj.conditions,
+                                         obj.models.unwrap() if hasattr(obj.models, 'unwrap') else obj.models,
+                                         parameters=obj.parameters)
+                return prf
+            except AttributeError:
+                return None
+        super().__init__(default_factory=make_prf, dependsOn=dependsOn)
+
+    def on_dependency_update(self, obj, updated_attribute, old_val, new_val):
+        self.__set__(obj, self.default_factory(obj))
+
+class SolverField(TypedField):
+    def on_dependency_update(self, obj, updated_attribute, old_val, new_val):
+        self.__set__(obj, self.default_factory(obj))
+
+class EquilibriumCalculationField(TypedField):
+    def __get__(self, obj, objtype=None):
+        if (not hasattr(obj, self.private_name)) or (getattr(obj, self.private_name) is None):
+            try:
+                default_value = obj.recompute()
+            except AttributeError:
+                default_value = None
+            setattr(obj, self.private_name, default_value)
+        return getattr(obj, self.private_name)
+
+    def on_dependency_update(self, obj, updated_attribute, old_val, new_val):
+        self.__set__(obj, None)
+
+def _as_quantity(prop: ComputableProperty, qt: npt.ArrayLike):
+    if not isinstance(qt, Q_):
+        return Q_(qt, prop.implementation_units)
+    else:
+        return qt
+
+# Defined to allow type checking for Model or its subclasses
+ModelType = TypeVar('ModelType', bound=Model)
+
+class Workspace:
+    _callbacks = defaultdict(lambda: [])
+    database: Database = TypedField(lambda _: None)
+    components: SpeciesList = ComponentsField(dependsOn=['database'])
+    phases: PhaseList = PhasesField(dependsOn=['database', 'components'])
+    conditions: Conditions = ConditionsField(lambda wks: Conditions(wks))
+    verbose: bool = TypedField(lambda _: False)
+    models: Mapping[PhaseName, ModelType] = ModelsField(dependsOn=['phases', 'parameters'])
+    parameters: SumType([NoneType, Dict]) = DictField(lambda _: OrderedDict())
+    renderer: Renderer = TypedField(lambda wks: DEFAULT_PLOT_RENDERER(wks))
+    phase_record_factory: Optional[PhaseRecordFactory] = PRFField(dependsOn=['phases', 'conditions', 'models', 'parameters'])
+    calc_opts: SumType([NoneType, Dict]) = DictField(lambda _: OrderedDict())
+    solver: SolverBase = SolverField(lambda obj: Solver(verbose=obj.verbose), dependsOn=['verbose'])
+    eq: Optional[LightDataset] = EquilibriumCalculationField(dependsOn=['phase_record_factory', 'calc_opts', 'solver'])
+
+    def __init__(self, *args, **kwargs):
+        # Assume positional arguments are specified in class typed-attribute definition order
+        for arg, attrname in zip(args, ['database', 'components', 'phases', 'conditions']):
+            setattr(self, attrname, arg)
+        attributes = list(self.__annotations__.keys())
+        for kwarg_name, kwarg_val in kwargs.items():
+            if kwarg_name not in attributes:
+                raise ValueError(f'{kwarg_name} is not a Workspace attribute')
+            setattr(self, kwarg_name, kwarg_val)
+
+    def recompute(self):
+        # Assumes implementation units from this point
+        unitless_conds = OrderedDict((key, as_quantity(key, value).to(key.implementation_units).magnitude) for key, value in self.conditions.items())
+        str_conds = OrderedDict((str(key), value) for key, value in unitless_conds.items())
+        local_conds = {key: as_quantity(key, value).to(key.implementation_units).magnitude
+                       for key, value in self.conditions.items()
+                       if getattr(key, 'phase_name', None) is not None}
+        state_variables = self.phase_record_factory.state_variables
+        self.phase_record_factory.update_parameters(self.parameters.unwrap())
+
+        # 'calculate' accepts conditions through its keyword arguments
+        grid_opts = self.calc_opts.copy()
+        statevar_strings = [str(x) for x in state_variables]
+        grid_opts.update({key: value for key, value in str_conds.items() if key in statevar_strings})
+
+        if 'pdens' not in grid_opts:
+            grid_opts['pdens'] = 60
+
+        grid = calculate(self.database, self.components, self.phases, model=self.models.unwrap(), fake_points=True,
+                        phase_records=self.phase_record_factory, output='GM', parameters=self.parameters.unwrap(),
+                        to_xarray=False, conditions=local_conds, **grid_opts)
+        properties = starting_point(unitless_conds, state_variables, self.phase_record_factory, grid)
+        return _solve_eq_at_conditions(properties, self.phase_record_factory, grid,
+                                       list(unitless_conds.keys()), state_variables,
+                                       self.verbose, solver=self.solver)
+
+    def calculate_equilibrium(self):
+        self.eq = self.recompute()
+
+    def _detect_phase_multiplicity(self):
+        multiplicity = {k: 0 for k in sorted(self.phase_record_factory.keys())}
+        prop_GM_values = self.eq.GM
+        prop_Phase_values = self.eq.Phase
+        for index in np.ndindex(prop_GM_values.shape):
+            cur_multiplicity = Counter()
+            for phase_name in prop_Phase_values[index]:
+                if phase_name == '' or phase_name == '_FAKE_':
+                    continue
+                cur_multiplicity[phase_name] += 1
+            for key, value in cur_multiplicity.items():
+                multiplicity[key] = max(multiplicity[key], value)
+        return multiplicity
+
+    def _expand_property_arguments(self, args: Sequence[ComputableProperty]):
+        "Mutates args"
+        multiplicity = self._detect_phase_multiplicity()
+        indices_to_delete = []
+        i = 0
+        while i < len(args):
+            if hasattr(args[i], 'phase_name') and args[i].phase_name == '*':
+                indices_to_delete.append(i)
+                phase_names = sorted(self.phase_record_factory.keys())
+                additional_args = args[i].expand_wildcard(phase_names=phase_names)
+                args.extend(additional_args)
+            elif hasattr(args[i], 'species') and args[i].species == v.Species('*'):
+                indices_to_delete.append(i)
+                internal_to_phase = hasattr(args[i], 'sublattice_index')
+                if internal_to_phase:
+                    components = [x for x in self.phase_record_factory[args[i].phase_name].variables
+                                  if x.sublattice_index == args[i].sublattice_index]
+                else:
+                    components = self.phase_record_factory[args[i].phase_name].nonvacant_elements
+                additional_args = args[i].expand_wildcard(components=components)
+                args.extend(additional_args)
+            elif hasattr(args[i], 'sublattice_index') and args[i].sublattice_index == v.Species('*'):
+                raise ValueError('Wildcard not yet supported in sublattice index')
+            elif isinstance(args[i], DotDerivativeComputedProperty):
+                numerator_args = [args[i].numerator]
+                self._expand_property_arguments(numerator_args)
+                denominator_args = [args[i].denominator]
+                self._expand_property_arguments(denominator_args)
+                if (len(numerator_args) > 1) or (len(denominator_args) > 1):
+                    for n_arg in numerator_args:
+                        for d_arg in denominator_args:
+                            args.append(DotDerivativeComputedProperty(n_arg, d_arg))
+                    indices_to_delete.append(i)
+            else:
+                # This is a concrete ComputableProperty
+                if hasattr(args[i], 'phase_name') and (args[i].phase_name is not None) \
+                    and not ('#' in args[i].phase_name) and multiplicity[args[i].phase_name] > 1:
+                    # Miscibility gap detected; expand property into multiple composition sets
+                    additional_phase_names = [args[i].phase_name+'#'+str(multi_idx+1)
+                                              for multi_idx in range(multiplicity[args[i].phase_name])]
+                    indices_to_delete.append(i)
+                    additional_args = args[i].expand_wildcard(phase_names=additional_phase_names)
+                    args.extend(additional_args)
+            i += 1
+        
+        # Watch deletion order! Indices will change as items are deleted
+        for deletion_index in reversed(indices_to_delete):
+            del args[deletion_index]
+
+    @property
+    def ndim(self) -> int:
+        _ndim = 0
+        for cond_val in self.conditions.values():
+            if len(cond_val) > 1:
+                _ndim += 1
+        return _ndim
+
+    def enumerate_composition_sets(self):
+        if self.eq is None:
+            return
+        prop_GM_values = self.eq.GM
+        prop_Y_values = self.eq.Y
+        prop_NP_values = self.eq.NP
+        prop_Phase_values = self.eq.Phase
+        conds_keys = [str(k) for k in self.eq.coords.keys() if k not in ('vertex', 'component', 'internal_dof')]
+        state_variables = list(self.phase_record_factory.values())[0].state_variables
+        str_state_variables = [str(k) for k in state_variables]
+
+        for index in np.ndindex(prop_GM_values.shape):
+            cur_conds = OrderedDict(zip(conds_keys,
+                                        [np.asarray(self.eq.coords[b][a], dtype=np.float_)
+                                        for a, b in zip(index, conds_keys)]))
+            state_variable_values = [cur_conds[key] for key in str_state_variables]
+            state_variable_values = np.array(state_variable_values)
+            composition_sets = []
+            for phase_idx, phase_name in enumerate(prop_Phase_values[index]):
+                if phase_name == '' or phase_name == '_FAKE_':
+                    continue
+                # phase_name can be a numpy.str_, which is different from the builtin str
+                phase_record = self.phase_record_factory[str(phase_name)]
+                sfx = prop_Y_values[index + np.index_exp[phase_idx, :phase_record.phase_dof]]
+                phase_amt = prop_NP_values[index + np.index_exp[phase_idx]]
+                compset = CompositionSet(phase_record)
+                compset.update(sfx, phase_amt, state_variable_values)
+                composition_sets.append(compset)
+            yield index, composition_sets
+
+    def get(self, *args: Tuple[ComputableProperty], values_only=True):
+        args = list(map(as_property, args))
+        self._expand_property_arguments(args)
+        arg_units = {arg: (ureg.Unit(getattr(arg, 'implementation_units', '')),
+                           ureg.Unit(getattr(arg, 'display_units', '')))
+                     for arg in args}
+
+        arr_size = self.eq.GM.size
+        results = dict()
+
+        prop_MU_values = self.eq.MU
+        str_conds_keys = [str(k) for k in self.eq.coords.keys() if k not in ('vertex', 'component', 'internal_dof')]
+        conds_keys = [None] * len(str_conds_keys)
+        for k in self.conditions.keys():
+            cond_idx = str_conds_keys.index(str(k))
+            conds_keys[cond_idx] = k
+        local_index = 0
+
+        for index, composition_sets in self.enumerate_composition_sets():
+            cur_conds = OrderedDict(zip(conds_keys,
+                                        [np.asarray(self.eq.coords[b][a], dtype=np.float_)
+                                        for a, b in zip(index, str_conds_keys)]))
+            chemical_potentials = prop_MU_values[index]
+            
+            for arg in args:
+                prop_implementation_units, prop_display_units = arg_units[arg]
+                context = unit_conversion_context(composition_sets, arg)
+                if results.get(arg, None) is None:
+                    results[arg] = np.zeros((arr_size,) + arg.shape)
+                results[arg][local_index, ...] = Q_(arg.compute_property(composition_sets, cur_conds, chemical_potentials),
+                                                    prop_implementation_units).to(prop_display_units, context).magnitude
+            local_index += 1
+        
+        for arg in args:
+            _, prop_display_units = arg_units[arg]
+            results[arg] = Q_(results[arg], prop_display_units)
+
+        if values_only:
+            return list(results.values())
+        else:
+            return results
+
+    def copy(self):
+        return copy(self)
+
+    @property
+    def plot(self):
+        return self.renderer
+
diff --git a/pycalphad/io/database.py b/pycalphad/io/database.py
index e51984485..db72aff2d 100644
--- a/pycalphad/io/database.py
+++ b/pycalphad/io/database.py
@@ -119,6 +119,10 @@ def __new__(cls, *args):
         else:
             raise ValueError('Invalid number of parameters: '+len(args))
 
+    @classmethod
+    def cast_from(cls, val):
+        return cls(val)
+
     def __hash__(self):
         return fhash(self.__dict__)
 
diff --git a/pycalphad/io/tdb.py b/pycalphad/io/tdb.py
index e58dc721f..c15ebba14 100644
--- a/pycalphad/io/tdb.py
+++ b/pycalphad/io/tdb.py
@@ -10,7 +10,7 @@
 import re
 from symengine.lib.symengine_wrapper import UniversalSet, Union, Complement
 from symengine import sympify, And, Or, Not, EmptySet, Interval, Piecewise, Add, Mul, Pow
-from symengine import Symbol, LessThan, StrictLessThan, S, E
+from symengine import Float, Symbol, LessThan, StrictLessThan, S, E
 from tinydb import where
 from pycalphad import Database
 from pycalphad.io.database import DatabaseExportError
@@ -59,7 +59,7 @@ def _sympify_string(math_string):
         if type(node) not in _AST_WHITELIST: #pylint: disable=W1504
             raise ValueError('Expression from TDB file not in whitelist: '
                              '{}'.format(expr_string))
-    return sympify(expr_string).xreplace(v.supported_variables_in_databases).n()
+    return sympify(expr_string).xreplace(get_supported_variables()).n()
 
 def _parse_action(func):
     """
@@ -378,10 +378,29 @@ def _process_species(db, sp_name, sp_comp, charge=0, *args):
     constituents = {sp_comp[i]: sp_comp[i+1] for i in range(0, len(sp_comp), 2)}
     db.species.add(Species(sp_name, constituents, charge=charge))
 
+# g/mol
+_molmass = \
+    {"H": 1.008, "HE": 4.003, "LI": 6.941, "BE": 9.012, "B": 10.811, "C": 12.011, "N": 14.007, "O": 15.999,
+     "F": 18.998, "NE": 20.18, "NA": 22.99, "MG": 24.305, "AL": 26.982, "SI": 28.086, "P": 30.974, "S": 32.065,
+     "CL": 35.453, "AR": 39.948, "K": 39.098, "CA": 40.078, "SC": 44.956, "TI": 47.867, "V": 50.942, "CR": 51.996,
+     "MN": 54.938, "FE": 55.845, "CO": 58.933, "NI": 58.693, "CU": 63.546, "ZN": 65.39, "GA": 69.723, "GE": 72.64,
+     "AS": 74.922, "SE": 78.96, "BR": 79.904, "KR": 83.8, "RB": 85.468, "SR": 87.62, "Y": 88.906, "ZR": 91.224,
+     "NB": 92.906, "MO": 95.94, "TC": 98, "RU": 101.07, "RH": 102.906, "PD": 106.42, "AG": 107.868, "CD": 112.411,
+     "IN": 114.818, "SN": 118.71, "SB": 121.76, "TE": 127.6, "I": 126.905, "XE": 131.293, "CS": 132.906,
+     "BA": 137.327, "LA": 138.906, "CE": 140.116, "PR": 140.908, "ND": 144.24, "PM": 145, "SM": 150.36,
+     "EU": 151.964, "GD": 157.25, "TB": 158.925, "DY": 162.5, "HO": 164.93, "ER": 167.259, "TM": 168.934,
+     "YB": 173.04, "LU": 174.967, "HF": 178.49, "TA": 180.948, "W": 183.84, "RE": 186.207, "OS": 190.23,
+     "IR": 192.217, "PT": 195.078, "AU": 196.967, "HG": 200.59, "TL": 204.383, "PB": 207.2, "BI": 208.98,
+     "PO": 209, "AT": 210, "RN": 222, "FR": 223, "RA": 226, "AC": 227, "TH": 232.038, "PA": 231.036, "U": 238.029,
+     "NP": 237, "PU": 244, "AM": 243, "CM": 247, "BK": 247, "CF": 251, "ES": 252, "FM": 257, "MD": 258, "NO": 259,
+     "LR": 262, "RF": 261, "DB": 262, "SG": 266, "BH": 264, "HS": 277, "MT": 268
+     }
+
 def _process_reference_state(db, el, refphase, mass, H298, S298):
+    # If user database doesn't specify mass, use periodic table values (if the element exists)
     db.refstates[el] = {
         'phase': refphase,
-        'mass': mass,
+        'mass': mass if mass != 0. else _molmass.get(el, 0.),
         'H298': H298,
         'S298': S298,
     }
@@ -614,6 +633,10 @@ def _symmetry_added_parameter(dbf, param):
                 return True
     return False
 
+def get_supported_variables():
+    "When loading databases, these symbols should be replaced by their IndependentPotential counter-parts"
+    return {Symbol(x): getattr(v, x) for x in v.__dict__ if isinstance(getattr(v, x), (v.IndependentPotential, Float))}
+
 
 def write_tdb(dbf, fd, groupby='subsystem', if_incompatible='warn'):
     """
diff --git a/pycalphad/model.py b/pycalphad/model.py
index 75b71421d..6aba32ac3 100644
--- a/pycalphad/model.py
+++ b/pycalphad/model.py
@@ -10,6 +10,7 @@
 from pycalphad.core.errors import DofError
 from pycalphad.core.constants import MIN_SITE_FRACTION
 from pycalphad.core.utils import unpack_components, get_pure_elements, wrap_symbol
+from pycalphad.io.tdb import get_supported_variables
 import numpy as np
 from collections import OrderedDict
 
@@ -269,7 +270,7 @@ def __init__(self, dbe, comps, phase_name, parameters=None):
 
         for name, value in self.models.items():
             # XXX: xreplace hack because SymEngine seems to let Symbols slip in somehow
-            self.models[name] = self.symbol_replace(value, symbols).xreplace(v.supported_variables_in_databases)
+            self.models[name] = self.symbol_replace(value, symbols).xreplace(get_supported_variables())
 
         self.site_fractions = sorted([x for x in self.variables if isinstance(x, v.SiteFraction)], key=str)
         self.state_variables = sorted([x for x in self.variables if not isinstance(x, v.SiteFraction)], key=str)
@@ -430,9 +431,12 @@ def degree_of_ordering(self):
     #pylint: disable=C0103
     # These are standard abbreviations from Thermo-Calc for these quantities
     energy = GM = property(lambda self: self.ast)
+    energy_implementation_units = GM_implementation_units = 'J / mol'
+    energy_display_units = GM_display_units = 'kJ / mol'
     formulaenergy = G = property(lambda self: self.ast * self._site_ratio_normalization)
     entropy = SM = property(lambda self: -self.GM.diff(v.T))
     enthalpy = HM = property(lambda self: self.GM - v.T*self.GM.diff(v.T))
+    formulaenthalpy = H = property(lambda self: self.G - v.T*self.G.diff(v.T))
     heat_capacity = CPM = property(lambda self: -v.T*self.GM.diff(v.T, v.T))
     #pylint: enable=C0103
     mixing_energy = GM_MIX = property(lambda self: self.GM - self.endmember_reference_model.GM)
diff --git a/pycalphad/plot/binary/map.py b/pycalphad/plot/binary/map.py
index ca4f99ad0..245355de3 100644
--- a/pycalphad/plot/binary/map.py
+++ b/pycalphad/plot/binary/map.py
@@ -1,14 +1,17 @@
 
 import time
 from copy import deepcopy
+from collections import OrderedDict
 import numpy as np
 from pycalphad import calculate, variables as v
 from pycalphad.codegen.callables import build_phase_records
 from pycalphad.core.eqsolver import _solve_eq_at_conditions
-from pycalphad.core.equilibrium import _adjust_conditions
+from pycalphad.core.workspace import _adjust_conditions
 from pycalphad.core.starting_point import starting_point
 from pycalphad.core.utils import instantiate_models, get_state_variables, \
     unpack_components, unpack_condition, filter_phases, get_pure_elements
+from pycalphad.property_framework.units import Q_
+from pycalphad.property_framework import as_quantity
 from .compsets import get_compsets, find_two_phase_region_compsets
 from .zpf_boundary_sets import ZPFBoundarySets
 
@@ -55,7 +58,7 @@ def map_binary(dbf, comps, phases, conds, eq_kwargs=None, calc_kwargs=None,
     calc_kwargs = calc_kwargs or {}
     # implicitly add v.N to conditions
     if v.N not in conds:
-        conds[v.N] = [1.0]
+        conds[v.N] = Q_([1.0], 'mol')
     if 'pdens' not in calc_kwargs:
         calc_kwargs['pdens'] = 50
 
@@ -71,7 +74,7 @@ def map_binary(dbf, comps, phases, conds, eq_kwargs=None, calc_kwargs=None,
                                parameters=parameters, build_gradients=True, build_hessians=True)
 
     indep_comp = [key for key, value in conds.items() if isinstance(key, v.MoleFraction) and len(np.atleast_1d(value)) > 1]
-    indep_pot = [key for key, value in conds.items() if (type(key) is v.StateVariable) and len(np.atleast_1d(value)) > 1]
+    indep_pot = [key for key, value in conds.items() if isinstance(key, v.IndependentPotential) and len(np.atleast_1d(value)) > 1]
     if (len(indep_comp) != 1) or (len(indep_pot) != 1):
         raise ValueError('Binary map requires exactly one composition and one potential coordinate')
     if indep_pot[0] != v.T:
@@ -93,9 +96,13 @@ def map_binary(dbf, comps, phases, conds, eq_kwargs=None, calc_kwargs=None,
     equilibrium_time = 0
     convex_hulls_calculated = 0
     convex_hull_time = 0
-    curr_conds = {key: unpack_condition(val) for key, val in conds.items()}
-    str_conds = sorted([str(k) for k in curr_conds.keys()])
+    curr_conds = {key: unpack_condition(val) for key, val in sorted(conds.items(), key=str)}
+    # XXX: Small hack to fix unit conversion issue. This whole module should be replaced with mapping
+    curr_conds[v.N] = [1.0]
     grid_conds = _adjust_conditions(curr_conds)
+    # Assumes implementation units from this point
+    unitless_conds = OrderedDict((key, as_quantity(key, value).to(key.implementation_units).magnitude)
+                                 for key, value in grid_conds.items())
     for T_idx in range(temperature_grid.size):
         T = temperature_grid[T_idx]
         iter_equilibria = 0
@@ -106,7 +113,7 @@ def map_binary(dbf, comps, phases, conds, eq_kwargs=None, calc_kwargs=None,
         Xmax_visited = 0.0
         hull_time = time.time()
         grid = calculate(dbf, comps, phases, fake_points=True, output='GM',
-                         T=T, P=grid_conds[v.P], N=1, model=models,
+                         T=T, P=unitless_conds[v.P], N=1, model=models,
                          parameters=parameters, to_xarray=False, **calc_kwargs)
         hull = starting_point(eq_conds, statevars, prxs, grid)
         convex_hull_time += time.time() - hull_time
@@ -121,7 +128,7 @@ def map_binary(dbf, comps, phases, conds, eq_kwargs=None, calc_kwargs=None,
             eq_conds[comp_cond] = [float(Xeq)]
             eq_time = time.time()
             start_point = starting_point(eq_conds, statevars, prxs, grid)
-            eq_ds = _solve_eq_at_conditions(start_point, prxs, grid, str_conds, statevars, False)
+            eq_ds = _solve_eq_at_conditions(start_point, prxs, grid, list(unitless_conds.keys()), statevars, False)
             equilibrium_time += time.time() - eq_time
             equilibria_calculated += 1
             iter_equilibria += 1
@@ -146,7 +153,7 @@ def map_binary(dbf, comps, phases, conds, eq_kwargs=None, calc_kwargs=None,
                 eq_time = time.time()
                 # TODO: starting point could be improved by basing it off the previous calculation
                 start_point = starting_point(eq_conds, statevars, prxs, grid)
-                eq_ds = _solve_eq_at_conditions(start_point, prxs, grid, str_conds, statevars, False)
+                eq_ds = _solve_eq_at_conditions(start_point, prxs, grid, list(unitless_conds.keys()), statevars, False)
                 equilibrium_time += time.time() - eq_time
                 equilibria_calculated += 1
                 compsets = get_compsets(eq_ds, indep_comp, indep_comp_idx)
diff --git a/pycalphad/plot/eqplot.py b/pycalphad/plot/eqplot.py
index 0cee7c6f0..b3c45acc3 100644
--- a/pycalphad/plot/eqplot.py
+++ b/pycalphad/plot/eqplot.py
@@ -86,7 +86,7 @@ def eqplot(eq, ax=None, x=None, y=None, z=None, tielines=True, tieline_color=(0,
                          for key, value in sorted(eq.coords.items(), key=str)
                          if (key in ('T', 'P', 'N')) or (key.startswith('X_'))])
     indep_comps = sorted([key for key, value in conds.items() if isinstance(key, v.MoleFraction) and len(value) > 1], key=str)
-    indep_pots = [key for key, value in conds.items() if (type(key) is v.StateVariable) and len(value) > 1]
+    indep_pots = [key for key, value in conds.items() if isinstance(key, v.IndependentPotential) and len(value) > 1]
 
     # determine what the type of plot will be
     if len(indep_comps) == 1 and len(indep_pots) == 1:
diff --git a/pycalphad/plot/renderers.py b/pycalphad/plot/renderers.py
new file mode 100644
index 000000000..d6c68ce27
--- /dev/null
+++ b/pycalphad/plot/renderers.py
@@ -0,0 +1,94 @@
+from pycalphad.property_framework import ComputableProperty, as_property
+from pycalphad.property_framework.units import ureg
+from typing import Tuple
+from abc import ABCMeta
+import weakref
+from collections import defaultdict
+import numpy as np
+
+class Renderer(metaclass=ABCMeta):
+    def __init__(self, wks: "pycalphad.core.workspace.Workspace"):
+        self.workspace = wks
+    def __enter__(self):
+        # Object will be deleted when __exit__ is called
+        return weakref.proxy(self)
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.workspace = None
+
+def _property_axis_label(prop: ComputableProperty) -> str:
+    propname = getattr(prop, 'display_name', None)
+    if propname is not None:
+        result = str(propname)
+        display_units = ureg.Unit(getattr(prop, 'display_units', ''))
+        if len(f'{display_units:~P}') > 0:
+            result += f' [{display_units:~P}]'
+        return result
+    else:
+        return str(prop)
+
+class MatplotlibRenderer(Renderer):
+    def __call__(self, x: ComputableProperty, *ys: Tuple[ComputableProperty], ax=None):
+        if self.workspace.ndim > 1:
+            raise ValueError('Dimension of calculation is greater than one')
+        import matplotlib.pyplot as plt
+        ax = ax if ax is not None else plt.gca()
+        x = as_property(x)
+        data = self.workspace.get(x, *ys, values_only=False)
+
+        num_y = 0
+        for y in data.keys():
+            if y == x:
+                continue
+            num_y += 1
+
+        if num_y > 1:
+            display_groupings = defaultdict(lambda: [])
+            for y in data.keys():
+                if y == x:
+                    continue
+                display_units = ureg.Unit(getattr(y, 'display_units', None))
+                display_groupings[display_units].append(y)
+            if len(display_groupings) > 1:
+                # TODO: Add more axes for each distinct grouping
+                raise ValueError('Cannot plot distinct quantities on same plot')
+            else:
+                unit_to_display = list(display_groupings.keys())[0]
+                ylabel = f'{unit_to_display:~P}'
+        else:
+            ylabel = None
+            for y in data.keys():
+                if y == x:
+                    continue
+                ylabel = _property_axis_label(y)
+        
+        for y in data.keys():
+            if y == x:
+                continue
+            if np.all(np.isnan(data[y].magnitude)):
+                continue
+            ax.plot(data[x].magnitude, data[y].magnitude, label=getattr(y, 'display_name', str(y)))
+        ax.set_ylabel(ylabel)
+        ax.set_xlabel(_property_axis_label(x))
+        # Suppress legend if there is only one line
+        if num_y > 1:
+            ax.legend(fontsize='small')
+
+class PandasRenderer(Renderer):
+    def __call__(self, *ys: Tuple[ComputableProperty]):
+        import pandas as pd
+        data = self.workspace.get(*ys, values_only=False)
+        stripped_data = {}
+        for key, value in data.items():
+            stripped_data[_property_axis_label(key)] = value.magnitude
+        return pd.DataFrame.from_dict(stripped_data)
+
+def set_plot_renderer(klass):
+    global DEFAULT_PLOT_RENDERER
+    if not isinstance(klass, type):
+        klass = getattr(globals(), klass, None)
+    if klass is None:
+        raise AttributeError(f"Unknown plot renderer: {klass}")
+    DEFAULT_PLOT_RENDERER = klass
+
+
+DEFAULT_PLOT_RENDERER = MatplotlibRenderer
diff --git a/pycalphad/plot/ternary.py b/pycalphad/plot/ternary.py
index 36cc9237c..1a3ac0713 100644
--- a/pycalphad/plot/ternary.py
+++ b/pycalphad/plot/ternary.py
@@ -45,7 +45,7 @@ def ternplot(dbf, comps, phases, conds, x=None, y=None, eq_kwargs=None, **plot_k
     """
     eq_kwargs = eq_kwargs if eq_kwargs is not None else dict()
     indep_comps = [key for key, value in conds.items() if isinstance(key, v.MoleFraction) and len(np.atleast_1d(value)) > 1]
-    indep_pots = [key for key, value in conds.items() if (type(key) is v.StateVariable) and len(np.atleast_1d(value)) > 1]
+    indep_pots = [key for key, value in conds.items() if isinstance(key, v.IndependentPotential) and len(np.atleast_1d(value)) > 1]
     if (len(indep_comps) != 2) or (len(indep_pots) != 0):
         raise ValueError('ternplot() requires exactly two composition coordinates')
     full_eq = equilibrium(dbf, comps, phases, conds, **eq_kwargs)
diff --git a/pycalphad/plot/triangular.py b/pycalphad/plot/triangular.py
index 6f3fe6375..69b30b681 100644
--- a/pycalphad/plot/triangular.py
+++ b/pycalphad/plot/triangular.py
@@ -34,21 +34,21 @@ class TriangularAxes(Axes):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self.set_aspect(1, adjustable='box', anchor='SW')
-        self.cla()
+        self.clear()
 
     def _init_axis(self):
         self.xaxis = maxis.XAxis(self)
         self.yaxis = maxis.YAxis(self)
         self._update_transScale()
 
-    def cla(self):
+    def clear(self):
         """
         Hard-code axes limits to be on [0, 1] for both axes.
 
         Warning: Limits not on [0, 1] may lead to clipping issues!
         """
         # Don't forget to call the base class
-        super().cla()
+        super().clear()
 
         x_min = 0
         y_min = 0
diff --git a/pycalphad/property_framework/__init__.py b/pycalphad/property_framework/__init__.py
new file mode 100644
index 000000000..a9f380003
--- /dev/null
+++ b/pycalphad/property_framework/__init__.py
@@ -0,0 +1,5 @@
+from .computed_property import as_property, ModelComputedProperty, DotDerivativeComputedProperty
+from .types import ComputableProperty, ConditionableComputableProperty, DifferentiableComputableProperty
+from .metaproperties import DormantPhase, DrivingForce, IsolatedPhase, ReferenceState
+from .tzero import T0
+from .units import DimensionalityError, as_quantity
\ No newline at end of file
diff --git a/pycalphad/property_framework/computed_property.py b/pycalphad/property_framework/computed_property.py
new file mode 100644
index 000000000..41d7ec22e
--- /dev/null
+++ b/pycalphad/property_framework/computed_property.py
@@ -0,0 +1,307 @@
+import numpy.typing as npt
+import numpy as np
+from typing import Dict, Union, List, Optional
+from symengine import Basic, Mul, Pow, S
+import pycalphad.variables as v
+from pycalphad.core.composition_set import CompositionSet
+from pycalphad.core.solver import Solver
+from pycalphad.property_framework.types import ComputableProperty, DotDerivativeDeltas, \
+    DifferentiableComputableProperty, ConditionableComputableProperty
+from pycalphad.property_framework import units
+from copy import copy
+
+class ModelComputedProperty(object):
+    def __init__(self, model_attr_name: str, phase_name: Optional[str] = None):
+        self.implementation_units = getattr(units, model_attr_name + '_implementation_units', '')
+        self.display_units = getattr(units, model_attr_name + '_display_units', '')
+        self.display_name = getattr(units, model_attr_name + '_display_name', model_attr_name)
+        self.model_attr_name = model_attr_name
+        self.phase_name = phase_name
+
+    def __getitem__(self, new_units: str) -> "ModelComputedProperty":
+        "Get ModelComputedProperty with different display units"
+        newobj = copy(self)
+        newobj.display_units = new_units
+        return newobj
+
+    def expand_wildcard(self, phase_names):
+        return [self.__class__(self.model_attr_name, phase_name) for phase_name in phase_names]
+
+    def __str__(self):
+        result = self.model_attr_name
+        if self.phase_name is not None:
+            result += f'({self.phase_name})'
+        return result
+
+    def __eq__(self, other):
+        if self is other:
+            return True
+        if self.__class__ != other.__class__:
+            return False
+        if self.__dict__ == other.__dict__:
+            return True
+        return False
+
+    def __hash__(self):
+        return hash(str(self))
+
+    @property
+    def shape(self):
+        return tuple()
+
+    @property
+    def multiplicity(self):
+        if self.phase_name is not None:
+            tokens = self.phase_name.split('#')
+            if len(tokens) > 1:
+                return int(tokens[1])
+            else:
+                return 1
+        else:
+            return None
+
+    def compute_property(self, compsets: List[CompositionSet], cur_conds: Dict[str, float], chemical_potentials: npt.ArrayLike) -> npt.ArrayLike:
+        if self.phase_name is None:
+            return np.nansum([compset.NP*self._compute_per_phase_property(compset, cur_conds) for compset in compsets])
+        else:
+            tokens = self.phase_name.split('#')
+            phase_name = tokens[0]
+            if len(tokens) > 1:
+                multiplicity = int(tokens[1])
+            else:
+                multiplicity = 1
+            multiplicity_seen = 0
+            for compset in compsets:
+                if compset.phase_record.phase_name != phase_name:
+                    continue
+                multiplicity_seen += 1
+                if multiplicity == multiplicity_seen:
+                    return self._compute_per_phase_property(compset, cur_conds)
+            return np.nan
+
+
+    def dot_derivative(self, compsets, cur_conds, chemical_potentials, deltas: DotDerivativeDeltas) -> npt.ArrayLike:
+        "Compute dot derivative with self as numerator, with the given deltas"
+        state_variables = compsets[0].phase_record.state_variables
+        grad_values = self._compute_property_gradient(compsets, cur_conds, chemical_potentials)
+
+        # Sundman et al, 2015, Eq. 73
+        dot_derivative = np.nan
+        for idx, compset in enumerate(compsets):
+            if compset.NP == 0 and not (compset.fixed):
+                continue
+            func_value = self._compute_per_phase_property(compset, cur_conds)
+            if np.isnan(func_value):
+                continue
+            if np.isnan(dot_derivative):
+                dot_derivative = 0.0
+            grad_value = grad_values[idx]
+            delta_sitefracs = deltas.delta_sitefracs[idx]
+
+            if self.phase_name is None:
+                dot_derivative += deltas.delta_phase_amounts[idx] * func_value
+                dot_derivative += compset.NP * np.dot(deltas.delta_statevars, grad_value[:len(state_variables)])
+                dot_derivative += compset.NP * np.dot(delta_sitefracs, grad_value[len(state_variables):])
+            else:
+                dot_derivative += np.dot(deltas.delta_statevars, grad_value[:len(state_variables)])
+                dot_derivative += np.dot(delta_sitefracs, grad_value[len(state_variables):])
+        return dot_derivative
+
+    def _compute_per_phase_property(self, compset: CompositionSet, cur_conds: Dict[str, float]) -> float:
+        out = np.atleast_1d(np.zeros(1))
+        compset.phase_record.prop(out, compset.dof, self.model_attr_name.encode('utf-8'))
+        return out[0]
+
+    def _compute_property_gradient(self, compsets, cur_conds, chemical_potentials):
+        "Compute partial derivatives of property with respect to degrees of freedom of given CompositionSets"
+        if self.phase_name is not None:
+            tokens = self.phase_name.split('#')
+            phase_name = tokens[0]
+        result = [np.zeros(compset.dof.shape[0]) for compset in compsets]
+        multiplicity_seen = 0
+        for cs_idx, compset in enumerate(compsets):
+            if (self.phase_name is not None) and (compset.phase_record.phase_name != phase_name):
+                continue
+            if self.multiplicity is not None:
+                multiplicity_seen += 1
+                if self.multiplicity != multiplicity_seen:
+                    continue
+            grad = np.zeros((compset.dof.shape[0]))
+            compset.phase_record.prop_grad(grad, compset.dof, self.model_attr_name.encode('utf-8'))
+            result[cs_idx][:] = grad
+        return result
+
+class LinearCombination:
+    display_units = ''
+    implementation_units = ''
+    def __init__(self, expr: Basic):
+        symbols = sorted(expr.free_symbols, key=str)
+        symbol_classes = {s.__class__ for s in symbols}
+        if len(symbol_classes) > 1:
+            raise ValueError(f'Property types in a linear combination must match. Got: {expr}')
+        if list(symbol_classes)[0] != v.MoleFraction:
+            raise ValueError('Only mole fractions are supported in linear combination conditions')
+        # Detect case of molar ratio (x/y = c); convert to (x - c*y = 0)
+        denominator = S.One
+        for mul_atom in expr.atoms(Mul):
+            # Division is stored as a Mul where one argument is a reciprocal
+            for mul_arg in mul_atom.args:
+                if isinstance(mul_arg, Pow) and isinstance(mul_arg.args[0], v.StateVariable):
+                    denominator = mul_arg.args[0]
+        expr = (expr*denominator).expand()
+        coefs = []
+        for s in symbols:
+            coef = expr.diff(s)
+            if float(coef) == int(coef):
+                coef = int(coef)
+            else:
+                coef = float(coef)
+            coefs.append(coef)
+        constant_term = expr + 0
+        for symbol, coef in zip(symbols, coefs):
+            constant_term -= symbol*coef
+        constant_term = float(constant_term)
+        symbols.append(S.One)
+        coefs.append(constant_term)
+        self.coefs = coefs
+        self.symbols = symbols
+        self.denominator = denominator
+
+    def __str__(self):
+        return f"LinComb_{'-'.join([str(s) for s in self.symbols])},{'-'.join([str(s) for s in self.coefs])}"
+
+    def __repr__(self):
+        result = ""
+        for idx, (sym, coef) in enumerate(zip(self.symbols[:-1], self.coefs[:-1])):
+            result += str(coef) + '*' + repr(sym)
+            if idx + 1 < len(self.symbols):
+                # if not the last entry
+                result += '+'
+        result += '=' + str(self.coefs[-1])
+
+    @property
+    def shape(self):
+        return tuple()
+
+    def compute_property(self, compsets: List[CompositionSet], cur_conds: Dict[str, float], chemical_potentials: npt.ArrayLike) -> npt.ArrayLike:
+        result = 0.0
+        for symbol, coef in zip(self.symbols, self.coefs):
+            if symbol == S.One:
+                result += coef
+            else:
+                sym_val = symbol.compute_property(compsets, cur_conds, chemical_potentials)
+                result += coef*sym_val
+        return result
+
+def as_property(inp: Union[str, Basic, ComputableProperty]) -> ComputableProperty:
+    if isinstance(inp, ComputableProperty):
+        return inp
+    elif isinstance(inp, Basic):
+        # Try to convert mathematical expression to a LinComb condition
+        inp = LinearCombination(inp)
+        return inp
+    elif not isinstance(inp, str):
+        raise TypeError(f'{inp} is not a ComputableProperty')
+    dot_tokens = inp.split('.')
+    if len(dot_tokens) == 2:
+        numerator, denominator = dot_tokens
+        numerator = as_property(numerator)
+        denominator = as_property(denominator)
+        return DotDerivativeComputedProperty(numerator, denominator)
+    try:
+        begin_parens = inp.index('(')
+        end_parens = inp.index(')')
+    except ValueError:
+        begin_parens = len(inp)
+        end_parens = len(inp)
+
+    specified_prop = inp[:begin_parens].strip()
+
+    prop = getattr(v, specified_prop, None)
+    if prop is None:
+        prop = ModelComputedProperty
+    if begin_parens != end_parens:
+        specified_args = tuple(x.strip() for x in inp[begin_parens+1:end_parens].split(','))
+        if not isinstance(prop, type):
+            prop_instance = type(prop)(*((specified_prop,)+specified_args))
+        else:
+            if issubclass(prop, v.StateVariable):
+                prop_instance = prop(*(specified_args))
+            else:
+                prop_instance = prop(*((specified_prop,)+specified_args))
+    else:
+        if isinstance(prop, type):
+            prop = prop(specified_prop)
+        prop_instance = prop
+    return prop_instance
+
+class DotDerivativeComputedProperty:
+    def __init__(self, numerator: DifferentiableComputableProperty, denominator: ConditionableComputableProperty):
+        self.numerator = as_property(numerator)
+        if not isinstance(self.numerator, DifferentiableComputableProperty):
+            raise TypeError(f'{self.numerator} is not a differentiable property')
+        self.denominator = as_property(denominator)
+        if not isinstance(self.denominator, ConditionableComputableProperty):
+            raise TypeError(f'{self.denominator} cannot be used in the denominator of a dot derivative')
+
+    @property
+    def shape(self):
+        return tuple()
+
+    @property
+    def implementation_units(self):
+        return str(units.ureg.Unit(self.numerator.implementation_units) / units.ureg.Unit(self.denominator.implementation_units))
+
+    _display_units = None
+    @property
+    def display_units(self):
+        if self._display_units is not None:
+            return self._display_units
+        else:
+            return str(units.ureg.Unit(self.numerator.display_units) / units.ureg.Unit(self.denominator.display_units))
+
+    @display_units.setter
+    def display_units(self, val):
+        self._display_units = val
+
+    def __getitem__(self, new_units: str) -> "DotDerivativeComputedProperty":
+        "Get DotDerivativeComputedProperty with different display units"
+        newobj = copy(self)
+        newobj.display_units = new_units
+        return newobj
+
+    _display_name = None
+    @property
+    def display_name(self):
+        if self._display_name is not None:
+            return self._display_name
+        else:
+            return str(self)
+
+    @display_name.setter
+    def display_name(self, val):
+        self._display_name = val
+
+    def compute_property(self, compsets, cur_conds, chemical_potentials):
+        solver = Solver()
+        spec = solver.get_system_spec(compsets, cur_conds)
+        state = spec.get_new_state(compsets)
+        state.chemical_potentials[:] = chemical_potentials
+        state.recompute(spec)
+        deltas = self.denominator.dot_deltas(spec, state)
+        return self.numerator.dot_derivative(compsets, cur_conds, chemical_potentials, deltas)
+
+    def __str__(self):
+        return str(self.numerator)+'.'+str(self.denominator)
+
+    def __eq__(self, other):
+        if self is other:
+            return True
+        if self.__class__ != other.__class__:
+            return False
+        if self.__dict__ == other.__dict__:
+            return True
+        return False
+
+    def __hash__(self):
+        return hash(str(self))
\ No newline at end of file
diff --git a/pycalphad/property_framework/metaproperties.py b/pycalphad/property_framework/metaproperties.py
new file mode 100644
index 000000000..62a274ebf
--- /dev/null
+++ b/pycalphad/property_framework/metaproperties.py
@@ -0,0 +1,320 @@
+from typing import Dict, List, Optional, Tuple, Union, TYPE_CHECKING
+import numpy.typing as npt
+from pycalphad.core.minimizer import advance_state
+from pycalphad.core.composition_set import CompositionSet
+from pycalphad.core.solver import Solver
+if TYPE_CHECKING:
+    from pycalphad.core.workspace import Workspace
+from pycalphad.property_framework.computed_property import as_property, ComputableProperty
+from pycalphad.property_framework import units
+import numpy as np
+from copy import copy
+
+def find_first_compset(phase_name: str, wks: "Workspace"):
+    for _, compsets in wks.enumerate_composition_sets():
+        for compset in compsets:
+            if compset.phase_record.phase_name == phase_name:
+                return compset
+    return None
+
+class DrivingForce:
+    phase_name: str
+    implementation_units = units.energy_implementation_units
+    display_units = units.energy_display_units
+    display_name = 'Driving Force'
+
+    def __init__(self, phase_name):
+        self.phase_name = phase_name
+
+    def __str__(self):
+        return f'{self.__class__.__name__}({self.phase_name})'
+
+    def expand_wildcard(self, phase_names):
+        return [self.__class__(phase_name) for phase_name in phase_names]
+
+    @property
+    def shape(self):
+        return tuple()
+
+    @property
+    def multiplicity(self):
+        if self.phase_name is not None:
+            tokens = self.phase_name.split('#')
+            if len(tokens) > 1:
+                return int(tokens[1])
+            else:
+                return 1
+        else:
+            return None
+
+    @property
+    def phase_name_without_suffix(self):
+        if self.phase_name is not None:
+            tokens = self.phase_name.split('#')
+            return tokens[0]
+        else:
+            return None
+
+    def filtered(self, input_compsets):
+        "Return a generator of CompositionSets applicable to the current property"
+        multiplicity_seen = 0
+
+        for cs_idx, compset in enumerate(input_compsets):
+            if (self.phase_name is not None) and compset.phase_record.phase_name != self.phase_name_without_suffix:
+                continue
+            if (compset.NP == 0) and (not compset.fixed):
+                continue
+            if self.phase_name is not None:
+                multiplicity_seen += 1
+                if self.multiplicity != multiplicity_seen:
+                    continue
+            yield cs_idx, compset
+
+    def compute_property(self, compsets: List[CompositionSet], cur_conds: Dict[str, float],
+                         chemical_potentials: npt.ArrayLike) -> float:
+        driving_force = float('nan')
+        seen_phases = 0
+        for _, compset in self.filtered(compsets):
+            driving_force = np.dot(chemical_potentials, compset.X) - compset.energy
+            seen_phases += 1
+        if seen_phases > 1:
+            raise ValueError('DrivingForce was passed multiple stable valid CompositionSets')
+        return driving_force
+
+    def dot_derivative(self, compsets, cur_conds, chemical_potentials, deltas: "DotDerivativeDeltas") -> npt.ArrayLike:
+        "Compute dot derivative with self as numerator, with the given deltas"
+        seen_phases = 0
+        dot_derivative = np.nan
+        for cs_idx, compset in self.filtered(compsets):
+            if np.isnan(dot_derivative):
+                dot_derivative = 0.0
+            dot_derivative += np.dot(deltas.delta_chemical_potentials, compset.X)
+            deltas_singlephase = copy(deltas)
+            deltas_singlephase.delta_sitefracs = [deltas.delta_sitefracs[cs_idx]]
+            for el_idx, el in enumerate(compsets[0].phase_record.pure_elements):
+                dot_derivative += chemical_potentials[el_idx] * \
+                    as_property('X({0},{1})'.format(self.phase_name, el)).dot_derivative(compsets, cur_conds, chemical_potentials, deltas)
+            dot_derivative -= as_property('GM({0})'.format(self.phase_name)).dot_derivative(compsets, cur_conds, chemical_potentials, deltas)
+        if seen_phases > 1:
+            raise ValueError('DrivingForce was passed multiple stable valid CompositionSets')
+        return dot_derivative
+
+class DormantPhase:
+    """
+    Meta-property for accessing properties of dormant phases.
+    The configuration of a dormant phase is minimized subject to the potentials of the target calculation.
+    """
+    _compset: CompositionSet
+    max_iterations: int = 50
+
+    def __init__(self, phase: Union[CompositionSet, str],
+                 wks: Optional["Workspace"]):
+        if wks is None:
+            if not isinstance(phase, CompositionSet):
+                raise ValueError('Dormant phase calculation requires a starting point for the phase;'
+                                  ' either a CompositionSet object should be specified, or pass in a Workspace'
+                                  ' of a previous calculation including the phase.'
+                                )
+        if not isinstance(phase, CompositionSet):
+            phase_orig = phase
+            phase = find_first_compset(phase, wks)
+            if phase is None:
+                raise ValueError(f'{phase_orig} is never stable in the specified Workspace')
+        self._compset = phase
+        self.solver = Solver()
+
+    def __str__(self):
+        return f'{self.__class__.__name__}({self._compset.phase_record.phase_name})'
+
+    def __call__(self, prop: ComputableProperty) -> ComputableProperty:
+        prop = as_property(prop)
+        class _autoproperty:
+            shape = prop.shape
+            implementation_units = prop.implementation_units
+            display_units = prop.display_units
+            display_name = prop.display_name
+            @staticmethod
+            def compute_property(equilibrium_compsets: List[CompositionSet], cur_conds: Dict[str, float],
+                            chemical_potentials: npt.ArrayLike) -> float:
+                state_variables = equilibrium_compsets[0].phase_record.state_variables
+                components = equilibrium_compsets[0].phase_record.nonvacant_elements
+                # Fix all state variables and chemical potentials
+                conditions = {}
+                for sv_idx, statevar in enumerate(state_variables):
+                    conditions[str(statevar)] = equilibrium_compsets[0].dof[sv_idx]
+                for comp_idx, comp in enumerate(components):
+                    conditions['MU_'+comp] = chemical_potentials[comp_idx]
+                self.solver._fix_state_variables_in_compsets([self._compset], conditions)
+                spec = self.solver.get_system_spec([self._compset], conditions)
+                state = spec.get_new_state([self._compset])
+                state.chemical_potentials[:] = chemical_potentials
+                state.recompute(spec)
+                converged = False
+                for iteration in range(self.max_iterations):
+                    state.iteration = iteration
+                    converged = spec.check_convergence(state)
+                    if converged:
+                        break
+                    advance_state(spec, state, np.atleast_1d(0.0), 1.0)
+                    state.recompute(spec)
+                self._compset = state.compsets[0]
+                return prop.compute_property([self._compset], cur_conds, chemical_potentials)
+            @staticmethod
+            def dot_derivative(compsets, cur_conds, chemical_potentials, deltas):
+                return prop.dot_derivative(compsets, cur_conds, chemical_potentials, deltas)
+            __str__ = lambda _: f'{prop.__str__()} [Dormant({self._compset.phase_record.phase_name})]'
+        return _autoproperty()
+
+    @property
+    def driving_force(self):
+        return self.__call__(DrivingForce(self._compset.phase_record.phase_name))
+
+class IsolatedPhase:
+    """
+    Meta-property for accessing properties of isolated phases.
+    The configuration of an isolated phase is minimized, by itself, subject to the same conditions as the target calculation.
+    """
+    _compset: CompositionSet
+
+    def __init__(self, phase: Union[CompositionSet, str],
+                 wks: Optional["Workspace"]):
+        if wks is None:
+            if not isinstance(phase, CompositionSet):
+                raise ValueError('Isolated phase calculation requires a starting point for the phase;'
+                                  ' either a CompositionSet object should be specified, or pass in a Workspace'
+                                  ' of a previous calculation including the phase.'
+                                )
+        if not isinstance(phase, CompositionSet):
+            phase_orig = phase
+            phase = find_first_compset(phase, wks)
+            if phase is None:
+                raise ValueError(f'{phase_orig} is never stable in the specified Workspace')
+        self._compset = phase
+        self.solver = Solver()
+
+    def __str__(self):
+        return f'{self.__class__.__name__}({self._compset.phase_record.phase_name})'
+
+    def __call__(self, prop: ComputableProperty) -> ComputableProperty:
+        prop = as_property(prop)
+        class _autoproperty:
+            shape = prop.shape
+            implementation_units = prop.implementation_units
+            display_units = prop.display_units
+            display_name = prop.display_name
+            @staticmethod
+            def compute_property(equilibrium_compsets: List[CompositionSet], cur_conds: Dict[str, float],
+                            chemical_potentials: npt.ArrayLike) -> float:
+                self.solver.solve([self._compset], cur_conds)
+                return prop.compute_property([self._compset], cur_conds, chemical_potentials)
+            @staticmethod
+            def dot_derivative(compsets, cur_conds, chemical_potentials, deltas):
+                return prop.dot_derivative([self._compset], cur_conds, chemical_potentials, deltas)
+            __str__ = lambda _: f'{prop.__str__()} [Isolated({self._compset.phase_record.phase_name})]'
+        return _autoproperty()
+
+    @property
+    def driving_force(self):
+        return self.__call__(DrivingForce(self._compset.phase_record.phase_name))
+
+
+class ReferenceState:
+    """
+    Meta-property for calculations involving reference states.
+    """
+    _reference_wks: List["Workspace"]
+    _fixed_conds: List
+    _floating_conds: List
+
+    def __init__(self,
+                 reference_conditions: List[Tuple[str, Dict]],
+                 wks: "Workspace"
+                ):
+        self._reference_wks = []
+        for phase_name, ref_conds in reference_conditions:
+            new_wks = wks.copy()
+            new_wks.phases = [phase_name]
+            self._floating_conds = sorted(set(wks.conditions.keys()) - set(ref_conds.keys()), key=str)
+            self._fixed_conds = sorted(set(wks.conditions.keys()).intersection(set(ref_conds.keys())), key=str)
+            new_wks.conditions = ref_conds
+            self._reference_wks.append(new_wks)
+        filtered_fixed_conds = []
+        for fic in self._fixed_conds:
+            if len(set([tuple(rwks.conditions[fic]) for rwks in self._reference_wks])) != 1:
+                filtered_fixed_conds.append(fic)
+        self._fixed_conds = filtered_fixed_conds
+        if len(self._fixed_conds)+1 != len(self._reference_wks):
+            raise ValueError('Specified conditions do not define a reference plane')
+
+    def __call__(self, prop: ComputableProperty) -> ComputableProperty:
+        prop = as_property(prop)
+        class _autoproperty:
+            shape = prop.shape
+            implementation_units = prop.implementation_units
+            display_units = prop.display_units
+            display_name = prop.display_name
+            @staticmethod
+            def compute_property(equilibrium_compsets: List[CompositionSet], cur_conds: Dict[str, float],
+                            chemical_potentials: npt.ArrayLike) -> float:
+                # Property contribution prior to reference state change
+                result = prop.compute_property(equilibrium_compsets, cur_conds, chemical_potentials)
+                if not isinstance(result, units.Q_):
+                    result = units.Q_(result, prop.implementation_units)
+
+                # Calculate reference contribution
+
+                # First, compute the plane of reference
+                plane_matrix = np.zeros((len(self._reference_wks), len(self._fixed_conds)+1))
+                # Rightmost column represents the constant term
+                plane_matrix[:, -1] = 1
+                plane_rhs = np.zeros(len(self._fixed_conds)+1)
+                for row_idx, ref_wks in enumerate(self._reference_wks):
+                    for col_idx, fic in enumerate(self._fixed_conds):
+                        plane_matrix[row_idx, col_idx] = ref_wks.conditions[fic]
+                    for floc in self._floating_conds:
+                        ref_wks.conditions[floc] = cur_conds[floc]
+                    if ref_wks.ndim != 0:
+                        raise ValueError('Reference state must be point calculation')
+                    eq_idx, ref_compsets = list(ref_wks.enumerate_composition_sets())[0]
+                    ref_chempots = ref_wks.eq.MU[eq_idx]
+                    plane_rhs[row_idx] = prop.compute_property(ref_compsets, {c: val for c, val in ref_wks.conditions.items()}, ref_chempots)
+                plane_coefs = np.linalg.solve(plane_matrix, plane_rhs)
+
+                # Next, plug fixed conditions of current point into equation of reference plane
+                current_vector = [cur_conds[floc] for floc in self._fixed_conds]
+                reference_offset = units.Q_(np.dot(plane_coefs[:-1], current_vector) + plane_coefs[-1],
+                                            prop.implementation_units)
+                return result - reference_offset
+            @staticmethod
+            def dot_derivative(equilibrium_compsets, cur_conds, chemical_potentials, deltas):
+                # Property contribution prior to reference state change
+                result = prop.dot_derivative(equilibrium_compsets, cur_conds, chemical_potentials, deltas)
+                if not isinstance(result, units.Q_):
+                    result = units.Q_(result, prop.implementation_units)
+
+                # Calculate reference contribution
+
+                # First, compute the plane of reference
+                plane_matrix = np.zeros((len(self._reference_wks), len(self._fixed_conds)+1))
+                # Rightmost column represents the constant term
+                plane_matrix[:, -1] = 1
+                plane_rhs = np.zeros(len(self._fixed_conds)+1)
+                for row_idx, ref_wks in enumerate(self._reference_wks):
+                    for col_idx, fic in enumerate(self._fixed_conds):
+                        plane_matrix[row_idx, col_idx] = ref_wks.conditions[fic]
+                    for floc in self._floating_conds:
+                        ref_wks.conditions[floc] = cur_conds[floc]
+                    if ref_wks.ndim != 0:
+                        raise ValueError('Reference state must be point calculation')
+                    eq_idx, ref_compsets = list(ref_wks.enumerate_composition_sets())[0]
+                    ref_chempots = ref_wks.eq.MU[eq_idx]
+                    plane_rhs[row_idx] = prop.dot_derivative(ref_compsets, {c: val for c, val in ref_wks.conditions.items()}, ref_chempots, deltas)
+                plane_coefs = np.linalg.solve(plane_matrix, plane_rhs)
+
+                # Next, plug fixed conditions of current point into equation of reference plane
+                current_vector = [cur_conds[floc] for floc in self._fixed_conds]
+                reference_offset = units.Q_(np.dot(plane_coefs[:-1], current_vector) + plane_coefs[-1],
+                                            prop.implementation_units)
+                return result - reference_offset
+            __str__ = lambda _: f'{prop.__str__()} [ReferenceState]'
+        return _autoproperty()
diff --git a/pycalphad/property_framework/types.py b/pycalphad/property_framework/types.py
new file mode 100644
index 000000000..35f5bf7c6
--- /dev/null
+++ b/pycalphad/property_framework/types.py
@@ -0,0 +1,41 @@
+from dataclasses import dataclass
+from pint._typing import UnitLike
+import numpy.typing as npt
+from typing import Any, Dict, List, Optional, Tuple
+try:
+    from typing import Protocol, runtime_checkable
+except ImportError:
+    from typing_extensions import Protocol, runtime_checkable
+from pycalphad.core.composition_set import CompositionSet
+
+@dataclass
+class DotDerivativeDeltas:
+    delta_chemical_potentials: Optional[Any]
+    delta_statevars: Optional[Any]
+    delta_parameters: Optional[Any]
+    delta_phase_amounts: Optional[Any]
+    delta_sitefracs: Optional[Any]
+
+@runtime_checkable
+class ComputableProperty(Protocol):
+    implementation_units: UnitLike
+    display_units: UnitLike
+
+    def compute_property(self, compsets: List[CompositionSet], cur_conds: Dict[str, float], chemical_potentials: npt.ArrayLike) -> npt.ArrayLike:
+        ...
+    @property
+    def shape(self) -> Tuple[int]:
+        ...
+
+@runtime_checkable
+class DifferentiableComputableProperty(ComputableProperty, Protocol):
+    "Can be in the numerator of a dot derivative"
+    def dot_derivative(self, compsets: List[CompositionSet], cur_conds: Dict[str, float], chemical_potentials: npt.ArrayLike,
+                       deltas: DotDerivativeDeltas) -> npt.ArrayLike:
+                       ...
+
+@runtime_checkable
+class ConditionableComputableProperty(ComputableProperty, Protocol):
+    "Can be in the denominator of a dot derivative"
+    def dot_deltas(self, spec, state) -> DotDerivativeDeltas:
+        ...
\ No newline at end of file
diff --git a/pycalphad/property_framework/tzero.py b/pycalphad/property_framework/tzero.py
new file mode 100644
index 000000000..ffd3fcc5a
--- /dev/null
+++ b/pycalphad/property_framework/tzero.py
@@ -0,0 +1,100 @@
+from typing import Dict, List, Optional, Tuple, Union, TYPE_CHECKING
+import numpy.typing as npt
+from pycalphad.core.composition_set import CompositionSet
+if TYPE_CHECKING:
+    from pycalphad.core.workspace import Workspace
+from pycalphad.core.solver import Solver
+from pycalphad.property_framework import as_property, DotDerivativeComputedProperty, ConditionableComputableProperty, \
+    ModelComputedProperty
+
+def find_first_compset(phase_name: str, wks: "Workspace"):
+    for _, compsets in wks.enumerate_composition_sets():
+        for compset in compsets:
+            if compset.phase_record.phase_name == phase_name:
+                return compset
+    return None
+
+class T0(object):
+    "T0: (GM(ONE) - GM(TWO))**2 = 0"
+    _phase_one: CompositionSet
+    _phase_two: CompositionSet
+    solver: Solver
+    property_to_optimize: ConditionableComputableProperty
+    minimum_value: float = 298.15
+    maximum_value: float = 6000
+    residual_tol: float = 0.01
+    maximum_iterations: int = 50
+
+    implementation_units = property(lambda self: self.property_to_optimize.implementation_units)
+    display_units = property(lambda self: self.property_to_optimize.display_units)
+
+    def __init__(self, phase_one: Union[CompositionSet, str], phase_two: Union[CompositionSet, str],
+                 wks: Optional["Workspace"]):
+        if wks is None:
+            if not isinstance(phase_one, CompositionSet) and not isinstance(phase_two, CompositionSet):
+                raise ValueError('T0 calculation requires a starting point for both phases;'
+                                  ' either CompositionSet objects should be specified, or pass in a Workspace'
+                                  ' of a previous calculation including the phases.'
+                                )
+        if not isinstance(phase_one, CompositionSet):
+            phase_one_orig = phase_one
+            phase_one = find_first_compset(phase_one, wks)
+            if phase_one is None:
+                raise ValueError(f'{phase_one_orig} is never stable in the specified Workspace')
+        if not isinstance(phase_two, CompositionSet):
+            phase_two_orig = phase_two
+            phase_two = find_first_compset(phase_two, wks)
+            if phase_two is None:
+                raise ValueError(f'{phase_two_orig} is never stable in the specified Workspace')
+        self._phase_one = phase_one
+        self._phase_two = phase_two
+        self.solver = Solver()
+        # This cannot be a class-level attribute because we cannot assume pycalphad.variables is initialized
+        # if it isn't, we will get back a ModelComputedProperty instead of the TemperatureType we want
+        # We cannot just import pycalphad.variables because of a circular import
+        self.property_to_optimize = as_property('T')
+
+    def __str__(self):
+        return f'{self.__class__.__name__}({self._phase_one.phase_record.phase_name},{self._phase_two.phase_record.phase_name})'
+
+    @property
+    def shape(self) -> Tuple[int]:
+        return tuple()
+
+    def compute_property(self, equilibrium_compsets: List[CompositionSet], cur_conds: Dict[str, float],
+                         chemical_potentials: npt.ArrayLike) -> float:
+        s = self.solver
+        initial_conditions = cur_conds
+
+        # T0: (G(BCC) - G(HCP))**2 = 0
+        # G(BCC)**2 - 2*G(BCC)*G(HCP) + G(BCP)**2
+        # grad = 2*G(BCC)*G'(FCC) - 2*(G'(BCC)*G(HCP) + G'(HCP)*G(BCC)) + 2*G(HCP)*G'(HCP)
+        gm_one = ModelComputedProperty('GM', self._phase_one.phase_record.phase_name)
+        gm_one_grad = DotDerivativeComputedProperty(gm_one, self.property_to_optimize)
+        gm_two = ModelComputedProperty('GM', self._phase_two.phase_record.phase_name)
+        gm_two_grad = DotDerivativeComputedProperty(gm_two, self.property_to_optimize)
+        conditions = initial_conditions.copy()
+        for _ in range(self.maximum_iterations):
+            one_result = s.solve([self._phase_one], conditions)
+            two_result = s.solve([self._phase_two], conditions)
+            one_gm = gm_one.compute_property([self._phase_one], conditions, one_result.chemical_potentials)
+            one_grad = gm_one_grad.compute_property([self._phase_one], conditions, one_result.chemical_potentials)
+
+            two_gm = gm_two.compute_property([self._phase_two], conditions, two_result.chemical_potentials)
+            two_grad = gm_two_grad.compute_property([self._phase_two], conditions, two_result.chemical_potentials)
+            t0_grad = 2*one_gm*one_grad - 2*(one_grad*two_gm + two_grad*one_gm) + 2*two_gm*two_grad
+
+            residual = (one_gm-two_gm)**2
+            if abs(t0_grad) < 1e-10:
+                t0_step = 0
+            else:
+                t0_step = -residual/t0_grad
+
+            conditions[self.property_to_optimize] = max(min(conditions[self.property_to_optimize] + t0_step,
+                                                            self.maximum_value),
+                                                        self.minimum_value)
+            if residual < self.residual_tol:
+                break
+        if residual > self.residual_tol:
+            return float('nan')
+        return conditions[self.property_to_optimize]
diff --git a/pycalphad/property_framework/units.py b/pycalphad/property_framework/units.py
new file mode 100644
index 000000000..eb286b281
--- /dev/null
+++ b/pycalphad/property_framework/units.py
@@ -0,0 +1,79 @@
+import pint
+import numpy as np
+import numpy.typing as npt
+from typing import TYPE_CHECKING
+if TYPE_CHECKING:
+    from pycalphad.property_framework import ComputableProperty
+
+ureg = pint.UnitRegistry(preprocessors=[lambda s: s.replace('%', ' percent ')])
+ureg.define('atom = 1/avogadro_number * mol')
+ureg.define('fraction = []')
+ureg.define('percent = 1e-2 fraction = %')
+ureg.define('ppm = 1e-6 fraction')
+pint.set_application_registry(ureg)
+Q_ = ureg.Quantity
+DimensionalityError = pint.DimensionalityError
+
+def as_quantity(prop: "ComputableProperty", qt: npt.ArrayLike):
+    if not isinstance(qt, Q_):
+        return Q_(qt, prop.display_units)
+    else:
+        return qt
+
+energy_implementation_units = GM_implementation_units = 'J / mol'
+energy_display_units = GM_display_units = 'J / mol'
+energy_display_name = GM_display_name = 'Gibbs Energy'
+G_implementation_units = 'J'
+G_display_units = 'J'
+G_display_name = 'Gibbs Energy'
+enthalpy_implementation_units = HM_implementation_units = GM_implementation_units
+enthalpy_display_units = HM_display_units = GM_display_units
+enthalpy_display_name = HM_display_name = 'Enthalpy'
+H_implementation_units = 'J'
+H_display_units = 'J'
+H_display_name = 'Enthalpy'
+entropy_implementation_units = SM_implementation_units = 'J / mol / K'
+entropy_display_units = SM_display_units = 'J / mol / K'
+entropy_display_name = SM_display_name = 'Entropy'
+
+def _conversions_per_formula_unit(compset):
+    components = compset.phase_record.nonvacant_elements
+    num_components = len(components)
+    moles_per_fu = np.zeros((num_components,1))
+    for comp_idx in range(num_components):
+        compset.phase_record.formulamole_obj(moles_per_fu[comp_idx, :], compset.dof, comp_idx)
+    # now we have 'moles per formula unit'
+    # need to convert by adding molecular weight of each element
+    grams_per_mol = np.array(compset.phase_record.molar_masses, dtype='float')
+    grams_per_fu = np.dot(grams_per_mol, moles_per_fu)
+    return moles_per_fu.sum(), grams_per_fu
+
+def unit_conversion_context(compsets, prop):
+    context = pint.Context()
+    # these will be something/mol by convention
+    # XXX: This is a very rough check
+    if not ('/ mol' in str(prop.implementation_units)):
+        return context
+    implementation_units = (ureg.Unit(prop.implementation_units) * ureg.Unit('mol'))
+    molar_weight = 0.0 # g/mol-atom
+    for compset in compsets:
+        if compset.NP > 0:
+            moles_per_fu, grams_per_fu = _conversions_per_formula_unit(compset)
+            grams_per_mol_atoms = (compset.NP / moles_per_fu) * grams_per_fu
+            molar_weight += grams_per_mol_atoms
+    molar_weight = Q_(molar_weight, 'g/mol')
+    per_moles = ureg.get_dimensionality(ureg.Unit('{} / mol'.format(implementation_units)))
+    per_mass = ureg.get_dimensionality(ureg.Unit('{} / g'.format(implementation_units)))
+
+    context.add_transformation(
+        per_moles,
+        per_mass,
+        lambda ureg, x: (x / molar_weight).to_reduced_units()
+    )
+    context.add_transformation(
+        per_mass,
+        per_moles,
+        lambda ureg, x: (x * molar_weight).to_reduced_units()
+    )
+
+    return context
\ No newline at end of file
diff --git a/pycalphad/tests/databases/alzn_mey.tdb b/pycalphad/tests/databases/alzn_mey.tdb
new file mode 100644
index 000000000..31683b1c7
--- /dev/null
+++ b/pycalphad/tests/databases/alzn_mey.tdb
@@ -0,0 +1,94 @@
+$ ALZN
+$
+$ TDB-file for the thermodynamic assessment of the Al-ZN system
+$
+$-------------------------------------------------------------------------------
+$ 2011.11.9
+$ 
+$ TDB file created by T.Abe, K.Hashimoto and Y.sawada
+$
+$ Particle Simulation and Thermodynamics Group, National Institute for 
+$ Materials Science. 1-2-1 Sengen, Tsukuba, Ibaraki 305-0047, Japan
+$ e-mail: abe.taichi@nims.go.jp
+$ Copyright (C) NIMS 2009
+$
+$ ------------------------------------------------------------------------------
+$ PARAMETERS ARE TAKEN FROM 
+$ Reevaluation of the Al-Zn System,
+$ Sabine an Mey, Z.Metallkd., 84 (1993) 451-455.
+$
+$ ------------------------------------------------------------------------------
+
+ ELEMENT /-   ELECTRON_GAS              0.0000E+00  0.0000E+00  0.0000E+00!
+ ELEMENT VA   VACUUM                    0.0000E+00  0.0000E+00  0.0000E+00!
+ ELEMENT AL   FCC_A1                    2.6982E+01  4.5773E+03  2.8322E+01!
+ ELEMENT ZN   HCP_ZN                    6.5390E+01  5.6568E+03  4.1631E+01!
+
+$-------------------------------------------------------------------------------
+$ FUNCTIONS FOR PURE AND OTHERS
+$-------------------------------------------------------------------------------
+ FUNCTION  GHSERAL  298.0
+    -7976.15+137.0715*T-24.36720*T*LN(T)-1.884662E-3*T**2-0.877664E-6*T**3
+    +74092*T**(-1);                                                   700.00 Y
+    -11276.24+223.0269*T-38.58443*T*LN(T)+18.531982E-3*T**2-5.764227E-6*T**3
+    +74092*T**(-1);                                                    933.6 Y
+    -11277.68+188.6620*T-31.74819*T*LN(T)-1234.26E25*T**(-9);        2900.00 N ! 
+ FUNCTION  GALLIQ   298.0
+    +3029.403+125.2307*T-24.36720*T*LN(T)-1.884662E-3*T**2-0.877664E-6*T**3
+    +74092*T**(-1)+79.401E-21*T**7;                                   700.00 Y
+    -270.6860+211.1861*T-38.58443*T*LN(T)+18.53198E-3*T**2-5.764227E-6*T**3
+    +74092*T**(-1)+79.401E-21*T**7;                                    933.6 Y
+    -795.7090+177.4100*T-31.74819*T*LN(T);                           2900.00 N !
+ FUNCTION  GALHCP   298.0  +5481-1.8*T+GHSERAL#;                      6000  N !
+
+
+ FUNCTION GHSERZN    298.0  -7285.787+118.4693*T-23.70131*T*LN(T)
+     -.001712034*T**2-1.264963E-06*T**3;                              692.7 Y
+     -11070.60+172.3449*T-31.38*T*LN(T)+4.70657E+26*T**(-9);           1700 N !
+ $FUNCTION GZNLIQ     298.0  -1.285170+108.1769*T-23.70131*T*LN(T)
+ $    -.001712034*T**2-1.264963E-06*T**3-3.585652E-19*T**7;            692.7 Y
+ $    -11070.60+172.3449*T-31.38*T*LN(T)+4.70657E+26*T**(-9);           1700 N !
+ FUNCTION GZNLIQ     298.14  +7157.213-10.29299*T-3.5896E-19*T**7+GHSERZN#;
+                                                                      692.7 Y
+     +7450.168-10.737066*T-4.7051E+26*T**(-9)+GHSERZN#;                 1700 N !
+ FUNCTION GZNFCC     298.15  +2969.82-1.56968*T+GHSERZN#;               1700 N !
+
+$-------------------------------------------------------------------------------
+ TYPE_DEFINITION % SEQ *!
+ DEFINE_SYSTEM_DEFAULT ELEMENT 2 !
+ DEFAULT_COMMAND DEF_SYS_ELEMENT VA /- !
+
+$-------------------------------------------------------------------------------
+$ PARAMETERS FOR LIQUID PHASE
+$-------------------------------------------------------------------------------
+ PHASE LIQUID %  1  1.0  !
+ CONSTITUENT LIQUID :AL,ZN :  !
+   PARAMETER G(LIQUID,AL;0)      298.15  +GALLIQ#;                      2900 N !
+   PARAMETER G(LIQUID,ZN;0)      298.15  +GZNLIQ#;                      1700 N !
+   PARAMETER G(LIQUID,AL,ZN;0)   298.15  +10465.5-3.39259*T;            6000 N !
+
+$-------------------------------------------------------------------------------
+$ FUNCTIONS FOR FCC_A1
+$-------------------------------------------------------------------------------
+ PHASE FCC_A1  %  1  1.0  !
+ CONSTITUENT FCC_A1  :AL,ZN :  !
+   PARAMETER G(FCC_A1,AL;0)      298.15  +GHSERAL#;                     2900 N !
+   PARAMETER G(FCC_A1,ZN;0)      298.15  +GZNFCC#;                      1700 N !
+   PARAMETER G(FCC_A1,AL,ZN;0)   298.15  +7297.5+0.47512*T;             6000 N !
+   PARAMETER G(FCC_A1,AL,ZN;1)   298.15  +6612.9-4.5911*T;              6000 N !
+   PARAMETER G(FCC_A1,AL,ZN;2)   298.15  -3097.2+3.30635*T;             6000 N !
+
+$-------------------------------------------------------------------------------
+$ FUNCTIONS FOR HCP_A3
+$-------------------------------------------------------------------------------
+ PHASE HCP_A3  %  1  1.0  !
+ CONSTITUENT HCP_A3  :AL,ZN :  !
+  PARAMETER G(HCP_A3,AL;0)       298.15  +GALHCP#;                      2900 N !
+  PARAMETER G(HCP_A3,ZN;0)       298.15  +GHSERZN#;                     1700 N !
+  PARAMETER G(HCP_A3,AL,ZN;0)    298.15  +18821.0-8.95255*T;            6000 N !
+  PARAMETER G(HCP_A3,AL,ZN;3)    298.15  -702.8;                        6000 N !
+
+$
+$------------------------------------------------------------------- END OF LINE
+
+ 
diff --git a/pycalphad/tests/databases/nbre_liu.tdb b/pycalphad/tests/databases/nbre_liu.tdb
new file mode 100644
index 000000000..b3fc057cc
--- /dev/null
+++ b/pycalphad/tests/databases/nbre_liu.tdb
@@ -0,0 +1,130 @@
+$ RENB
+$
+$ TDB-file for the thermodynamic assessment of the RE-NB system
+$
+$-----------------------------------------------------------------------------
+$ 2013.05.013
+$ 
+$ TDB file created by T.Abe, T.Bolotova, Y.Sawada, K.Hashimoto
+$
+$ Particle Simulation and Thermodynamics Group, National Institute for 
+$ Materials Science. 1-2-1 Sengen, Tsukuba, Ibaraki 305-0047, Japan
+$ e-mail: abe.taichi @ nims.go.jp
+$ Copyright (C) NIMS 2014
+$
+$ ------------------------------------------------------------------------------
+$ PARAMETERS ARE TAKEN FROM
+$ First-principles aided thermodynamic modeling o fthe NB-RE system,
+$ X.L.Liu, C.Z.Hargather,Z.-K.Liu, CALPHAD, 41 (2013) 119-127.
+$
+$ ------------------------------------------------------------------------------
+ ELEMENT /-   ELECTRON_GAS              0.0000E+00  0.0000E+00  0.0000E+00!
+ ELEMENT VA   VACUUM                    0.0000E+00  0.0000E+00  0.0000E+00!
+ ELEMENT RE   HCP_A3                    1.8621E+02  5.3555E+03  3.6526E+01 !
+ ELEMENT NB   BCC_A2                    9.2906E+01  5.2200E+03  3.6270E+01 !
+$ ------------------------------------------------------------------------------
+$ Lattice stability 
+$ ------------------------------------------------------------------------------
+ FUNCTION GHSERRE 298.15 -7695.279+128.421589*T-24.348*T*LN(T)
+    -2.53505E-3*T**2+0.192818E-6*T**3+32915*T**(-1); 1200 Y
+    -15775.998+194.667426*T-33.586*T*LN(T)+2.24565E-3*T**2-0.281835E-6*T**3
+    +1376270*T**(-1); 2400 Y
+    -70882.739+462.110749*T-67.956*T*LN(T)+11.84945E-3*T**2-0.788955E-6*T**3
+    +18075200*T**(-1); 3458 Y
+    346325.888-1211.371859*T+140.8316548*T*LN(T)-33.764567E-3*T**2
+    +1.053726E-6*T**3-134548866*T**(-1); 5000 Y
+    -78564.296+346.997842*T-49.519*T*LN(T); 6000 N !
+ FUNCTION GLIQRE 298.15 16125.604+122.076209*T-24.348*T*LN(T)
+    -2.53505E-3*T**2+0.192818E-6*T**3+32915*T**(-1); 1200 Y
+    8044.885+188.322047*T-33.586*T*LN(T)+2.24565E-3*T**2-0.281835E-6*T**3
+    +1376270*T**(-1); 2000 Y
+    568842.665-2527.838455*T+314.1788975*T*LN(T)-89.39817E-3*T**2
+    +3.92854E-6*T**3-163100987*T**(-1); 3458 Y
+    -39044.888+335.723691*T-49.519*T*LN(T); 6000 N !
+ FUNCTION GBCCRE 298.15 17000-3.7*T+GHSERRE; 6000 N !
+  FUNCTION GFCCRE 298.15 11000-1.5*T+GHSERRE; 6000 N !
+$ 
+ FUNCTION GHSERNB  298.15
+    -8519.353+142.045475*T-26.4711*T*LN(T)+0.203475E-3*T**2-0.35012E-6*T**3
+    +93399*T**(-1);  2750.00 Y
+    -37669.3+271.720843*T-41.77*T*LN(T)+1528.238E29*T**(-9);  6000.00 N !
+  FUNCTION GLIQNB 298.15 29781.555-10.816418*T-306.098E-25*T**7+GHSERNB; 2750 Y
+    -7499.398+260.756148*T-41.77*T*LN(T);  6000.00 N !
+ FUNCTION GFCCNB 298.15 13500+1.7*T+GHSERNB; 6000 N !
+ FUNCTION GHCPNB 298.15 10000+2.4*T+GHSERNB; 6000 N !
+
+$------------------------------------------------------------------------------
+ TYPE_DEFINITION % SEQ *!
+ DEFINE_SYSTEM_DEFAULT ELEMENT 2 !
+ DEFAULT_COMMAND DEF_SYS_ELEMENT VA /- !
+$
+$------------------------------------------------------------------------------- 
+$ PARAMETERS FOR  BCC_RENB
+$-------------------------------------------------------------------------------
+PHASE  BCC_RENB  % 1 1 !
+CONSTITUENT   BCC_RENB : RE, NB : !
+PARAMETER G( BCC_RENB,RE;0) 298.5 GBCCRE; 6000 N !
+PARAMETER G( BCC_RENB,NB;0) 298.5 GHSERNB; 6000 N !
+PARAMETER G( BCC_RENB,RE,NB;0) 298.5 -101060+10.568*T ; 6000 N !
+PARAMETER G( BCC_RENB,RE,NB;1) 298.5 -2300 ; 6000 N !
+
+$------------------------------------------------------------------------------- 
+$ PARAMETERS FOR HCPRENB
+$-------------------------------------------------------------------------------
+PHASE HCP_RENB  % 1 1 !
+CONSTITUENT  HCP_RENB : RE, NB : !
+PARAMETER G(HCP_RENB,RE;0) 298.5 GHSERRE; 6000 N !
+PARAMETER G(HCP_RENB,NB;0) 298.5 GHCPNB; 6000 N !
+PARAMETER G(HCP_RENB,RE,NB;0) 298.5 64957-33.387*T ; 6000 N !
+
+$------------------------------------------------------------------------------- 
+$ PARAMETERS FOR LIQUID_RENB
+$-------------------------------------------------------------------------------
+PHASE LIQUID_RENB  % 1 1 !
+CONSTITUENT  LIQUID_RENB : RE, NB : !
+PARAMETER G(LIQUID_RENB,RE;0) 298.5 GLIQRE; 6000 N !
+PARAMETER G(LIQUID_RENB,NB;0) 298.5 GLIQNB; 6000 N !
+PARAMETER G(LIQUID_RENB,RE,NB;0) 298.5 -8017 -23.406*T; 6000 N !
+PARAMETER G(LIQUID_RENB,RE,NB;1) 298.5 -2001 ; 6000 N !
+$------------------------------------------------------------------------------- 
+$ PARAMETERS FOR FCC_RENB
+$-------------------------------------------------------------------------------
+PHASE FCC_RENB  % 1 1 !
+CONSTITUENT  FCC_RENB : RE, NB : !
+PARAMETER G( FCC_RENB,RE;0) 298.5 GFCCRE; 6000 N !
+PARAMETER G( FCC_RENB,NB;0) 298.5 GFCCNB; 6000 N !
+PARAMETER G(FCC_RENB,RE,NB;0) 298.5 27628 ; 6000 N !
+
+$------------------------------------------------------------------------------- 
+$ PARAMETERS FOR SIGMARENB
+$-------------------------------------------------------------------------------
+PHASE SIGMARENB  %  3 10 4 16 !
+CONSTITUENT SIGMARENB :RE:NB:RE,NB: !
+PARAMETER G(SIGMARENB,RE:NB:RE;0) 298.5 
+                      -67437+2.033*T+10*GHSERRE+4*GHSERNB+16*GHSERRE ; 6000 N !
+PARAMETER G(SIGMARENB,RE:NB:NB;0) 298.5 
+   -407760 +0.692*T+8.769*0.001*T**2+10*GHSERRE+4*GHSERNB+16*GHSERNB ; 6000 N !
+PARAMETER G(SIGMARENB,RE:NB:RE,NB;0) 298.5 -479251 -498.110*T ; 6000 N !
+
+$------------------------------------------------------------------------------- 
+$ PARAMETERS FOR CHI_RENB
+$-------------------------------------------------------------------------------
+ 
+PHASE CHI_RENB  %  3 24 10 24 !
+CONSTITUENT CHI_RENB :RE:RE,NB:NB,RE: !
+$PARAMETER G(CHI_RENB,RE:RE:RE;0) 298.5  256567 -25.456*T  ; 6000 N !
+PARAMETER G(CHI_RENB,RE:RE:RE;0) 298.5  +256567
+-25.456*T+24*GHSERRE+10*GHSERRE+24*GHSERRE; 6000 N !
+PARAMETER G(CHI_RENB,RE:NB:RE;0)  298.5  
+                 -758795 -28.528*T+24*GHSERRE+10*GHSERNB+24*GHSERRE  ; 6000 N !
+PARAMETER G(CHI_RENB,RE:RE:NB;0)  298.5  
+                 +252854+24*GHSERRE+10*GHSERRE+24*GHSERNB   ; 6000 N !
+PARAMETER G(CHI_RENB,RE:NB:NB;0)  298.5  
+                 -610088 -13.751*T+24*GHSERRE+10*GHSERNB+24*GHSERNB  ; 6000 N !
+PARAMETER G(CHI_RENB,RE:NB:RE,NB;0)   298.5  -1648141 -289.844*T  ; 6000 N !
+PARAMETER G(CHI_RENB,RE:RE,NB:RE ;0)  298.5  -749989 +280.032*T   ; 6000 N !
+PARAMETER G(CHI_RENB,RE:RE:RE,NB ;0)  298.5  -1200286   ; 6000 N !
+PARAMETER G(CHI_RENB,re:RE,NB:NB ;0)  298.5  -352931    ; 6000 N !
+
+$-------------------------------------------------------------------------------
+$RE-NB End
diff --git a/pycalphad/tests/test_equilibrium.py b/pycalphad/tests/test_equilibrium.py
index a1315e8cd..3915ebd55 100644
--- a/pycalphad/tests/test_equilibrium.py
+++ b/pycalphad/tests/test_equilibrium.py
@@ -10,7 +10,7 @@
 from numpy.testing import assert_allclose
 import numpy as np
 from pycalphad import Database, Model, calculate, equilibrium, EquilibriumError, ConditionError
-from pycalphad.codegen.callables import build_callables, build_phase_records
+from pycalphad.codegen.callables import build_phase_records, PhaseRecordFactory
 from pycalphad.core.solver import SolverBase, Solver
 from pycalphad.core.utils import get_state_variables, instantiate_models
 import pycalphad.variables as v
@@ -62,46 +62,6 @@ def test_phase_records_passed_to_equilibrium(load_database):
     assert_allclose(eqx.GM.values.flat[0], -9.608807e4)
 
 
-@select_database("alfe.tdb")
-def test_missing_models_with_phase_records_passed_to_equilibrium_raises(load_database):
-    dbf = load_database()
-    "equilibrium should raise an error if all the active phases are not included in the phase_records"
-    my_phases = ['LIQUID', 'FCC_A1', 'HCP_A3', 'AL5FE2', 'AL2FE', 'AL13FE4', 'AL5FE4']
-    comps = ['AL', 'FE', 'VA']
-    conds = {v.T: 1400, v.P: 101325, v.N: 1.0, v.X('AL'): 0.55}
-
-    models = instantiate_models(dbf, comps, my_phases)
-    phase_records = build_phase_records(dbf, comps, my_phases, conds, models)
-
-    with pytest.raises(ValueError):
-        # model=models NOT passed
-        equilibrium(dbf, comps, my_phases, conds, verbose=True, phase_records=phase_records)
-
-
-@select_database("alfe.tdb")
-def test_missing_phase_records_passed_to_equilibrium_raises(load_database):
-    "equilibrium should raise an error if all the active phases are not included in the phase_records"
-    dbf = load_database()
-    my_phases = ['LIQUID', 'FCC_A1']
-    subset_phases = ['FCC_A1']
-    comps = ['AL', 'FE', 'VA']
-    conds = {v.T: 1400, v.P: 101325, v.N: 1.0, v.X('AL'): 0.55}
-
-    models = instantiate_models(dbf, comps, my_phases)
-    phase_records = build_phase_records(dbf, comps, my_phases, conds, models)
-
-    models_subset = instantiate_models(dbf, comps, subset_phases)
-    phase_records_subset = build_phase_records(dbf, comps, subset_phases, conds, models_subset)
-
-    # Under-specified models
-    with pytest.raises(ValueError):
-        equilibrium(dbf, comps, my_phases, conds, verbose=True, model=models_subset, phase_records=phase_records)
-
-    # Under-specified phase_records
-    with pytest.raises(ValueError):
-        equilibrium(dbf, comps, my_phases, conds, verbose=True, model=models, phase_records=phase_records_subset)
-
-
 @pytest.mark.solver
 @select_database("alfe.tdb")
 def test_eq_single_phase(load_database):
@@ -502,28 +462,24 @@ def test_eq_parameter_override(load_database):
 
 
 @select_database("al_parameter.tdb")
-def test_eq_build_callables_with_parameters(load_database):
+def test_eq_phase_record_factory_with_parameters(load_database):
     """
-    Check build_callables() compatibility with the parameters kwarg.
+    Check PhaseRecordFactory compatibility with the parameters kwarg.
     """
     comps = ["AL"]
     dbf = load_database()
     phases = ['FCC_A1']
     conds = {v.P: 101325, v.T: 500, v.N: 1}
-    conds_statevars = get_state_variables(conds=conds)
     models = {'FCC_A1': Model(dbf, comps, 'FCC_A1', parameters=['VV0000'])}
-    # build callables with a parameter of 20000.0
-    callables = build_callables(dbf, comps, phases,
-                                models=models, parameter_symbols=['VV0000'], additional_statevars=conds_statevars,
-                                build_gradients=True, build_hessians=True)
+    # build PhaseRecordFactory with a parameter of 20000.0
+    prf = PhaseRecordFactory(dbf, comps, conds, models, parameters={'VV0000': 20000})
 
-    # Check that passing callables should skip the build phase, but use the values from 'VV0000' as passed in parameters
-    eq_res = equilibrium(dbf, comps, phases, conds, callables=callables, parameters={'VV0000': 10000})
+    # use the values from 'VV0000' as passed in parameters
+    eq_res = equilibrium(dbf, comps, phases, conds, phase_records=prf, model=models, parameters={'VV0000': 10000})
     np.testing.assert_allclose(eq_res.GM.values.squeeze(), 10000.0)
 
-    # Check that passing callables should skip the build phase,
-    # but use the values from Symbol('VV0000') as passed in parameters
-    eq_res = equilibrium(dbf, comps, phases, conds, callables=callables, parameters={Symbol('VV0000'): 10000})
+    # use the values from Symbol('VV0000') as passed in parameters
+    eq_res = equilibrium(dbf, comps, phases, conds, phase_records=prf, model=models, parameters={Symbol('VV0000'): 10000})
     np.testing.assert_allclose(eq_res.GM.values.squeeze(), 10000.0)
 
 
@@ -1057,6 +1013,33 @@ def test_eq_charge_ndzro(load_database):
     assert np.allclose(Y_PYRO, [9.99970071e-01, 2.99288042e-05, 3.83395063e-02, 9.61660494e-01, 9.93381787e-01,
                                 6.61821340e-03, 1.00000000e+00, 1.39970285e-03, 9.98600297e-01], rtol=5e-4)
 
+@pytest.mark.solver
+def test_issue_503_charged_infeasible_subsystem():
+    "equilibrium suspends a phase with zero feasible points due to internal constraints"
+    tdb = """
+ ELEMENT /-   ELECTRON_GAS              0.0000E+00  0.0000E+00  0.0000E+00!
+ ELEMENT VA   VACUUM                    0.0000E+00  0.0000E+00  0.0000E+00!
+ ELEMENT O    1/2_MOLE_O2(G)            1.5999E+01  4.3410E+03  1.0252E+02!
+ ELEMENT V    BCC_A2                    5.0941E+01  4.5070E+03  3.0890E+01!
+
+ SPECIES O-2                         O1/-2!
+ SPECIES O2                          O2!
+ SPECIES O3                          O3!
+ SPECIES V+2                         V1/+2!
+ SPECIES V+3                         V1/+3!
+
+ PHASE GAS:G %  1  1.0  !
+    CONSTITUENT GAS:G :O,O2,O3 :  !
+
+ PHASE HALITE %  2 1   1 !
+    CONSTITUENT HALITE :V,V+2,V+3,VA : O-2,VA :  !
+"""
+    dbf = Database(tdb)
+    result = equilibrium(dbf, ['O', 'VA'], ['GAS', 'HALITE'], {v.T: 1000, v.P: 1e5})
+    print(result)
+    assert np.all(np.isclose(result.NP.squeeze(), [1.0, np.nan], equal_nan=True))
+    assert np.all(result.Phase.squeeze() == ["GAS", ""])
+
 @pytest.mark.solver
 @select_database("crtiv_ghosh.tdb")
 def test_ternary_three_phase_dilute(load_database):
diff --git a/pycalphad/tests/test_model.py b/pycalphad/tests/test_model.py
index 005d64a04..99dbcdb34 100644
--- a/pycalphad/tests/test_model.py
+++ b/pycalphad/tests/test_model.py
@@ -81,8 +81,8 @@ def test_degree_of_ordering(load_database):
     comps = ['AL', 'FE', 'VA']
     conds = {v.T: 300, v.P: 101325, v.X('AL'): 0.25}
     eqx = equilibrium(dbf, comps, my_phases, conds, output='degree_of_ordering', verbose=True)
-    print('Degree of ordering: {}'.format(eqx.degree_of_ordering.sel(vertex=0).values.flatten()))
-    assert np.isclose(eqx.degree_of_ordering.sel(vertex=0).values.flatten(), np.array([0.6666]), atol=1e-4)
+    print('Degree of ordering: {}'.format(eqx.degree_of_ordering.values.flatten()))
+    assert np.isclose(eqx.degree_of_ordering.values.flatten(), np.array([0.6666]), atol=1e-4)
 
 def test_detect_pure_vacancy_phases():
     "Detecting a pure vacancy phase"
diff --git a/pycalphad/tests/test_property_framework.py b/pycalphad/tests/test_property_framework.py
new file mode 100644
index 000000000..f1956df54
--- /dev/null
+++ b/pycalphad/tests/test_property_framework.py
@@ -0,0 +1,105 @@
+from pycalphad.core.workspace import Workspace
+from pycalphad.property_framework import as_property, DotDerivativeComputedProperty, \
+    ModelComputedProperty, T0, IsolatedPhase, DormantPhase, ReferenceState
+import pycalphad.variables as v
+from pycalphad.tests.fixtures import select_database, load_database
+import numpy as np
+import numpy.testing
+
+
+def test_as_property_creation():
+    assert as_property('T') == v.T
+    assert as_property('X(ZN)') == v.X('ZN')
+    assert as_property('X(FCC_A1#1,ZN)') == v.X('FCC_A1#1', 'ZN')
+
+def test_as_property_dot_derivative_creation():
+    assert as_property('HM.T') == DotDerivativeComputedProperty(ModelComputedProperty('HM'), v.T)
+    assert as_property('HM.T') == DotDerivativeComputedProperty('HM', 'T')
+    assert as_property('MU(AL).X(ZN)') == DotDerivativeComputedProperty(v.MU('AL'), v.X('ZN'))
+    assert as_property('NP(LIQUID).T') == DotDerivativeComputedProperty(v.NP('LIQUID'), v.T)
+    assert ModelComputedProperty('SM') != ModelComputedProperty('HM')
+
+def test_property_units():
+    model_prop = ModelComputedProperty('test')
+    model_prop.implementation_units = 'J/mol'
+    model_prop.display_units = 'kJ/mol'
+    assert model_prop['J/mol'].display_units == 'J/mol'
+    assert v.T['degC'].display_units == 'degC'
+    assert v.T.display_units != 'degC'
+    assert v.T['degC'] == v.T
+
+@select_database("alzn_mey.tdb")
+def test_cpf_phase_energy_curves(load_database):
+    wks2 = Workspace(load_database(), ['AL', 'ZN'],
+                    ['FCC_A1', 'HCP_A3', 'LIQUID'],
+                    {v.X('ZN'):(0,1,0.02), v.T: 600, v.P:101325, v.N: 1})
+
+    props = []
+    for phase_name in wks2.phases:
+        # Workaround for poor starting point selection in IsolatedPhase
+        metastable_wks = wks2.copy()
+        metastable_wks.phases = [phase_name]
+        prop = IsolatedPhase(phase_name, metastable_wks)(f'GM({phase_name})')
+        prop.display_name = phase_name
+        props.append(prop)
+    result = {}
+    for prop, value in wks2.get(*props, values_only=False).items():
+        result[prop.display_name] = value
+    np.testing.assert_almost_equal(np.nanmax(result['FCC_A1'].magnitude), -20002.975665, decimal=5)
+    np.testing.assert_almost_equal(np.nanmin(result['FCC_A1'].magnitude), -26718.58552, decimal=5)
+    np.testing.assert_almost_equal(np.nanmax(result['HCP_A3'].magnitude), -15601.975666, decimal=5)
+    np.testing.assert_almost_equal(np.nanmin(result['HCP_A3'].magnitude), -28027.206646, decimal=5)
+    np.testing.assert_almost_equal(np.nanmax(result['LIQUID'].magnitude), -16099.679946, decimal=5)
+    np.testing.assert_almost_equal(np.nanmin(result['LIQUID'].magnitude), -27195.787525, decimal=5)
+
+@select_database("alzn_mey.tdb")
+def test_cpf_driving_force(load_database):
+    wks3 = Workspace(load_database(), ['AL', 'ZN'],
+                ['FCC_A1', 'HCP_A3', 'LIQUID'],
+                {v.X('ZN'):(0,1,0.02), v.T: 600, v.P:101325, v.N: 1})
+    metastable_liq_wks = wks3.copy()
+    metastable_liq_wks.phases = ['LIQUID']
+    liq_driving_force = DormantPhase('LIQUID', metastable_liq_wks).driving_force
+    liq_driving_force.display_name = 'Liquid Driving Force'
+    result, = wks3.get(liq_driving_force)
+    np.testing.assert_almost_equal(np.nanmax(result.magnitude), -610.932599, decimal=5)
+    np.testing.assert_almost_equal(np.nanmin(result.magnitude), -3903.295718, decimal=5)
+
+@select_database("alzn_mey.tdb")
+def test_cpf_tzero(load_database):
+    wks4 = Workspace(load_database(), ['AL', 'ZN'],
+                    ['FCC_A1', 'HCP_A3', 'LIQUID'],
+                    {v.X('ZN'):(0,1,0.02), v.T: 300, v.P:101325, v.N: 1})
+    tzero = T0('FCC_A1', 'HCP_A3', wks4)
+    tzero.maximum_value = 1700 # ZN reference state in this database is not valid beyond this temperature
+    result, = wks4.get(tzero)
+    np.testing.assert_almost_equal(np.nanmax(result.magnitude), 1673.2643290, decimal=5)
+    np.testing.assert_almost_equal(np.nanmin(result.magnitude), 621.72616, decimal=5)
+
+@select_database("nbre_liu.tdb")
+def test_cpf_reference_state(load_database):
+    wks = Workspace(load_database(), ["NB", "RE", "VA"], ["LIQUID_RENB"],
+                    {v.P: 101325, v.T: 2800, v.X("RE"): (0, 1, 0.005)})
+
+    ref = ReferenceState([("LIQUID_RENB", {v.X("RE"): 0}),
+                        ("LIQUID_RENB", {v.X("RE"): 1})
+                        ], wks)
+
+    result, = wks.get(ref('HM'))
+    np.testing.assert_almost_equal(np.nanmax(result.magnitude), 0, decimal=5)
+    np.testing.assert_almost_equal(np.nanmin(result.magnitude), -2034.554367, decimal=5)
+
+@select_database("alzn_mey.tdb")
+def test_cpf_calculation(load_database):
+    wks4 = Workspace(load_database(), ['AL', 'ZN'],
+                    ['LIQUID'],
+                    {v.X('ZN'): 0.3, v.T: 700, v.P:101325, v.N: 1})
+
+    results = wks4.get('HM.T', 'MU(AL).X(ZN)')
+    np.testing.assert_array_almost_equal(np.squeeze(results), [29.63807, -3460.0878], decimal=5)
+    wks4.phases = ['FCC_A1', 'LIQUID', 'HCP_A3']
+    wks4.conditions[v.X('ZN')] = 0.7
+    results = wks4.get('X(LIQUID, AL).T')
+    np.testing.assert_array_almost_equal(np.squeeze(results), [0.00249], decimal=5)
+    results = wks4.get('NP(*).T')
+    np.testing.assert_array_almost_equal(np.squeeze(results), [-0.01147, float('nan'), 0.01147], decimal=5)
diff --git a/pycalphad/tests/test_workspace.py b/pycalphad/tests/test_workspace.py
new file mode 100644
index 000000000..8f79cf678
--- /dev/null
+++ b/pycalphad/tests/test_workspace.py
@@ -0,0 +1,133 @@
+from numpy.testing import assert_allclose
+import numpy as np
+from pycalphad import Workspace, variables as v
+from pycalphad.property_framework import as_property, ComputableProperty, T0, IsolatedPhase, DormantPhase
+from pycalphad.property_framework.units import Q_
+from pycalphad.tests.fixtures import load_database, select_database
+import pytest
+
+@pytest.mark.solver
+@select_database("alzn_mey.tdb")
+def test_workspace_creation(load_database):
+    dbf = load_database()
+    wks = Workspace(database=dbf, components=['AL', 'ZN', 'VA'], phases=['FCC_A1', 'HCP_A3', 'LIQUID'],
+                    conditions={v.N: 1, v.P: 1e5, v.T: (300, 1000, 10), v.X('ZN'): 0.3})
+    wks2 = Workspace(dbf, ['AL', 'ZN', 'VA'], ['FCC_A1', 'HCP_A3', 'LIQUID'],
+                     {v.N: 1, v.P: 1e5, v.T: (300, 1000, 10), v.X('ZN'): 0.3})
+    assert_allclose(wks.eq.GM, wks2.eq.GM)
+
+@select_database("alzn_mey.tdb")
+def test_workspace_conditions_change_clear_result(load_database):
+    dbf = load_database()
+    wks = Workspace(dbf, ['AL', 'ZN', 'VA'], ['FCC_A1', 'HCP_A3', 'LIQUID'],
+                    {v.N: 1, v.P: 1e5, v.T: (300, 1000, 100), v.X('ZN'): 0.3})
+    assert wks._eq is None
+    # Attribute access will trigger calculation
+    assert wks.eq is not None
+    assert wks._eq is wks.eq
+    assert len(wks.eq.coords['T']) == 7
+    # Conditions change should clear previous result
+    wks.conditions[v.T] = 600
+    # Check private member so that calculation is not triggered again
+    assert wks._eq is None
+    # New calculation result will have different shape
+    assert len(wks.eq.coords['T']) == 1
+
+@select_database("alzn_mey.tdb")
+def test_workspace_conditions_specify_units(load_database):
+    dbf = load_database()
+    wks = Workspace(dbf, ['AL', 'ZN', 'VA'], ['FCC_A1', 'HCP_A3', 'LIQUID'],
+                    {v.N: 1, v.P: 1e5, v.T['degC']: (0, 100, 1), v.X('ZN'): 0.3})
+    assert_allclose(wks.conditions[v.T], np.arange(0., 100., 1.) + 273.15)
+    wks.conditions[v.T['degC']] = (10, 300, 5)
+    assert_allclose(wks.conditions[v.T], np.arange(10., 300., 5.) + 273.15)
+    assert_allclose(wks.get(v.T)[0].magnitude, np.arange(10., 300., 5.) + 273.15)
+    assert_allclose(wks.get(v.T['degC'])[0].magnitude, np.arange(10., 300., 5.))
+
+@select_database("alzn_mey.tdb")
+def test_meta_property_creation(load_database):
+    dbf = load_database()
+    wks = Workspace(database=dbf, components=['AL', 'ZN', 'VA'], phases=['FCC_A1', 'HCP_A3', 'LIQUID'],
+                    conditions={v.N: 1, v.P: 1e5, v.T: (300, 1000, 10), v.X('ZN'): 0.3})
+    my_tzero = T0('FCC_A1', 'HCP_A3', wks=wks)
+    assert isinstance(my_tzero, ComputableProperty)
+
+@select_database("alzn_mey.tdb")
+def test_tzero_property(load_database):
+    dbf = load_database()
+    wks = Workspace(database=dbf, components=['AL', 'ZN', 'VA'], phases=['FCC_A1', 'HCP_A3', 'LIQUID'],
+                    conditions={v.N: 1, v.P: 1e5, v.T: 600, v.X('ZN'): (0.01,1-0.01,0.01)})
+    my_tzero = T0('FCC_A1', 'HCP_A3', wks=wks)
+    my_tzero.maximum_value = 1700 # ZN reference state in this database is not valid beyond this temperature
+    assert isinstance(my_tzero, ComputableProperty)
+    assert my_tzero.property_to_optimize == v.T
+    t0_values, = wks.get(my_tzero)
+    assert_allclose(np.nanmax(t0_values.magnitude), 1686.814152)
+    wks.conditions[v.X('ZN')] = 0.3
+    my_tzero.property_to_optimize = v.X('ZN')
+    my_tzero.minimum_value = 0.0
+    my_tzero.maximum_value = 1.0
+    t0_composition, = wks.get(my_tzero)
+    assert_allclose(t0_composition[0].magnitude, Q_(0.86119, 'fraction').magnitude, atol=my_tzero.residual_tol)
+
+@select_database("alzn_mey.tdb")
+def test_dot_derivative_binary_temperature(load_database):
+    dbf = load_database()
+    wks = Workspace(database=dbf, components=['AL', 'ZN', 'VA'], phases=['FCC_A1', 'HCP_A3', 'LIQUID'],
+                    conditions={v.N: 1, v.P: 1e5, v.T: 300, v.X('ZN'): 0.3})
+    x, y_dot = wks.get('T', 'MU(AL).T')
+    # Checked by finite difference
+    assert_allclose(y_dot.magnitude, -28.775364)
+
+@select_database("alzn_mey.tdb")
+def test_dot_derivative_binary_composition(load_database):
+    dbf = load_database()
+    wks = Workspace(database=dbf, components=['AL', 'ZN', 'VA'], phases=['FCC_A1', 'HCP_A3', 'LIQUID'],
+                    conditions={v.N: 1, v.P: 1e5, v.T: 600, v.X('ZN'): 0.1})
+    x, y_dot = wks.get('X(ZN)', 'MU(AL).X(ZN)')
+    # Checked by finite difference
+    assert_allclose(y_dot.magnitude, -2806.93592)
+
+@select_database("alzn_mey.tdb")
+def test_mass_fraction_binary_condition(load_database):
+    dbf = load_database()
+    wks = Workspace(database=dbf, components=['AL', 'ZN', 'VA'], phases=['FCC_A1', 'HCP_A3', 'LIQUID'],
+                    conditions={v.N: 1, v.P: 1e5, v.T: 300, v.W('AL'): 0.1})
+    results = wks.get('W(AL)', 'W(ZN)', 'W(FCC_A1,AL)', 'W(HCP_A3,AL)', 'W(LIQUID,AL)',
+                      'W(FCC_A1,ZN)', 'W(HCP_A3,ZN)', 'W(LIQUID,ZN)')
+    truth = [0.1, 0.9, 0.98650697, 6.64406221e-05, np.nan, 0.01349303, 0.99993356, np.nan]
+    np.testing.assert_almost_equal([x[0].magnitude for x in results], truth, decimal=5)
+
+@select_database("alzn_mey.tdb")
+def test_lincomb_binary_condition(load_database):
+    dbf = load_database()
+    wks = Workspace(database=dbf, components=['AL', 'ZN', 'VA'], phases=['FCC_A1', 'HCP_A3', 'LIQUID'],
+                    conditions={v.T: 300, v.P: 1e5, 0.5*v.X('ZN') - 7*v.X('AL'): 0.1})
+    result = 0.5 * wks.get('X(ZN)')[0].magnitude - 7 * wks.get('X(AL)')[0].magnitude
+    np.testing.assert_almost_equal(result, 0.1, decimal=8)
+
+@select_database("alzn_mey.tdb")
+def test_lincomb_ratio_binary_condition(load_database):
+    dbf = load_database()
+    wks = Workspace(database=dbf, components=['AL', 'ZN', 'VA'], phases=['FCC_A1', 'HCP_A3', 'LIQUID'],
+                    conditions={v.T: 300, v.P: 1e5, v.X('AL')/v.X('ZN'): [0.25, 1, 1.5]})
+    result = wks.get('X(AL)')[0].magnitude / wks.get('X(ZN)')[0].magnitude
+    np.testing.assert_almost_equal(result, [0.25, 1, 1.5], decimal=8)
+
+@select_database("alzn_mey.tdb")
+def test_phaselocal_binary_sitefrac_condition(load_database):
+    dbf = load_database()
+    wks = Workspace(database=dbf, components=['AL', 'ZN', 'VA'], phases=['FCC_A1', 'LIQUID'],
+                    conditions={v.X('ZN'): 0.1, v.T: (890, 1000, 20), v.P: 1e5,
+                                v.Y('LIQUID', 0, 'ZN'): 0.3, v.N: 1})
+    result = wks.get('Y(LIQUID,0,ZN)')[0].magnitude
+    np.testing.assert_almost_equal(result, np.full_like(result, 0.3), decimal=8)
+
+@select_database("alzn_mey.tdb")
+def test_phaselocal_binary_molefrac_condition(load_database):
+    dbf = load_database()
+    wks = Workspace(database=dbf, components=['AL', 'ZN', 'VA'], phases=['FCC_A1', 'LIQUID'],
+                    conditions={v.X('ZN'): 0.1, v.T: (890, 1000, 20), v.P: 1e5,
+                                v.X('LIQUID', 'ZN'): 0.3, v.N: 1})
+    result = wks.get('X(LIQUID,ZN)')[0].magnitude
+    np.testing.assert_almost_equal(result, np.full_like(result, 0.3), decimal=8)
\ No newline at end of file
diff --git a/pycalphad/variables.py b/pycalphad/variables.py
index bce229785..ec50392d5 100644
--- a/pycalphad/variables.py
+++ b/pycalphad/variables.py
@@ -3,10 +3,13 @@
 Classes and constants for representing thermodynamic variables.
 """
 
-import sys
 from symengine import Float, Symbol
 from pycalphad.io.grammar import parse_chemical_formula
-
+from pycalphad.property_framework.types import DotDerivativeDeltas
+from pycalphad.core.minimizer import site_fraction_differential, state_variable_differential, \
+    fixed_component_differential, chemical_potential_differential
+import numpy as np
+from copy import copy
 
 class Species(object):
     """
@@ -76,6 +79,10 @@ def __lt__(self, other):
     def __str__(self):
         return self.name
 
+    @classmethod
+    def cast_from(cls, s: str) -> "Species":
+        return cls(s)
+
     @property
     def escaped_name(self):
         "Name safe to embed in the variable name of complex arithmetic expressions."
@@ -107,14 +114,91 @@ def __repr__(self):
     def __hash__(self):
         return hash(self.name)
 
-
 class StateVariable(Symbol):
     """
     State variables are symbols with built-in assumptions of being real.
     """
+    implementation_units = ''
+    display_units = ''
+
+    @property
+    def display_name(self):
+        return self.name
+
     def __init__(self, name):
         super().__init__(name.upper())
 
+    @property
+    def shape(self):
+        return tuple()
+
+    @property
+    def is_global_property(self):
+        return (not hasattr(self, 'phase_name')) or (self.phase_name is None)
+
+    @property
+    def multiplicity(self):
+        if self.phase_name is not None:
+            tokens = self.phase_name.split('#')
+            if len(tokens) > 1:
+                return int(tokens[1])
+            else:
+                return 1
+        else:
+            return None
+
+    @property
+    def phase_name_without_suffix(self):
+        if self.phase_name is not None:
+            tokens = self.phase_name.split('#')
+            return tokens[0]
+        else:
+            return None
+
+    def filtered(self, input_compsets):
+        "Return a generator of CompositionSets applicable to the current property"
+        multiplicity_seen = 0
+
+        for cs_idx, compset in enumerate(input_compsets):
+            if (self.phase_name is not None) and compset.phase_record.phase_name != self.phase_name_without_suffix:
+                continue
+            if (compset.NP == 0) and (not compset.fixed):
+                continue
+            if self.phase_name is not None:
+                multiplicity_seen += 1
+                if self.multiplicity != multiplicity_seen:
+                    continue
+            yield cs_idx, compset
+
+    def compute_property(self, compsets, cur_conds, chemical_potentials):
+        if len(compsets) == 0:
+            return np.nan
+        state_variables = compsets[0].phase_record.state_variables
+        statevar_idx = state_variables.index(self)
+        return compsets[0].dof[statevar_idx]
+
+    def dot_derivative(self, compsets, cur_conds, chemical_potentials, deltas: DotDerivativeDeltas):
+        "Compute dot derivative with self as numerator, with the given deltas"
+        state_variables = compsets[0].phase_record.state_variables
+        statevar_idx = state_variables.index(self)
+        return deltas.delta_statevars[statevar_idx]
+
+    def dot_deltas(self, spec, state) -> DotDerivativeDeltas:
+        state_variables = state.compsets[0].phase_record.state_variables
+        statevar_idx = sorted(state_variables, key=str).index(self)
+        delta_chemical_potentials, delta_statevars, delta_phase_amounts = \
+        state_variable_differential(spec, state, statevar_idx)
+
+        # Sundman et al, 2015, Eq. 73
+        compsets_delta_sitefracs = []
+        for idx, compset in enumerate(state.compsets):
+            delta_sitefracs = site_fraction_differential(state.cs_states[idx], delta_chemical_potentials,
+                                                         delta_statevars)
+            compsets_delta_sitefracs.append(delta_sitefracs)
+        return DotDerivativeDeltas(delta_chemical_potentials=delta_chemical_potentials, delta_statevars=delta_statevars,
+                                   delta_phase_amounts=delta_phase_amounts, delta_sitefracs=compsets_delta_sitefracs,
+                                   delta_parameters=None)
+
     def __reduce__(self):
         return self.__class__, (self.name,)
 
@@ -131,18 +215,39 @@ def __ne__(self, other):
     def __hash__(self):
         return hash((self.__class__, self.name))
 
+    def __getitem__(self, new_units: str) -> "StateVariable":
+        "Get StateVariable with different display units"
+        newobj = copy(self)
+        newobj.display_units = new_units
+        return newobj
+
 class SiteFraction(StateVariable):
     """
     Site fractions are symbols with built-in assumptions of being real
     and nonnegative. The constructor handles formatting of the name.
     """
+    implementation_units = 'fraction'
+    display_units = 'fraction'
     def __init__(self, phase_name, subl_index, species): #pylint: disable=W0221
         varname = phase_name + str(subl_index) + Species(species).escaped_name
         #pylint: disable=E1121
         super().__init__(varname)
         self.phase_name = phase_name.upper()
-        self.sublattice_index = subl_index
+        self.sublattice_index = int(subl_index)
         self.species = Species(species)
+        if '#' in phase_name:
+            self._self_without_suffix = self.__class__(self.phase_name_without_suffix, subl_index, species)
+        else:
+            self._self_without_suffix = self
+
+    def compute_property(self, compsets, cur_conds, chemical_potentials):
+        state_variables = compsets[0].phase_record.state_variables
+        result = np.atleast_1d(np.zeros(self.shape))
+        for _, compset in self.filtered(compsets):
+            site_fractions = compset.phase_record.variables
+            sitefrac_idx = site_fractions.index(self._self_without_suffix)
+            result[0] += compset.dof[len(state_variables)+sitefrac_idx]
+        return result
 
     def __reduce__(self):
         return self.__class__, (self.phase_name, self.sublattice_index, self.species)
@@ -160,6 +265,14 @@ def __ne__(self, other):
     def __hash__(self):
         return hash((self.phase_name, self.sublattice_index, self.species))
 
+    def expand_wildcard(self, phase_names=None, components=None, sublattice_indices=None):
+        if phase_names is not None:
+            return [self.__class__(phase_name, self.sublattice_index, self.species) for phase_name in phase_names]
+        elif components is not None:
+            return [self.__class__(self.phase_name, self.sublattice_index, comp) for comp in components]
+        else:
+            raise ValueError('All arguments are None')
+
     def _latex(self, printer=None):
         "LaTeX representation."
         #pylint: disable=E1101
@@ -177,11 +290,33 @@ class PhaseFraction(StateVariable):
     Phase fractions are symbols with built-in assumptions of being real
     and nonnegative. The constructor handles formatting of the name.
     """
+    implementation_units = 'fraction'
+    display_units = 'fraction'
     def __init__(self, phase_name): #pylint: disable=W0221
         varname = 'NP_' + str(phase_name)
         super().__init__(varname)
         self.phase_name = phase_name.upper()
 
+    def compute_property(self, compsets, cur_conds, chemical_potentials):
+        result = np.atleast_1d(np.full(self.shape, fill_value=np.nan))
+        for _, compset in self.filtered(compsets):
+            if np.all(np.isnan(result[0])):
+                result[0] = 0.0
+            result[0] += compset.NP
+        return result
+
+    def dot_derivative(self, compsets, cur_conds, chemical_potentials, deltas: DotDerivativeDeltas):
+        "Compute dot derivative with self as numerator, with the given deltas"
+        dot_derivative = np.nan
+        for idx, _ in self.filtered(compsets):
+            if np.isnan(dot_derivative):
+                dot_derivative = 0.0
+            dot_derivative += deltas.delta_phase_amounts[idx]
+        return dot_derivative
+
+    def expand_wildcard(self, phase_names):
+        return [self.__class__(phase_name) for phase_name in phase_names]
+
     def _latex(self, printer=None):
         "LaTeX representation."
         #pylint: disable=E1101
@@ -192,6 +327,8 @@ class MoleFraction(StateVariable):
     MoleFractions are symbols with built-in assumptions of being real
     and nonnegative.
     """
+    implementation_units = 'fraction'
+    display_units = 'fraction'
     def __init__(self, *args): #pylint: disable=W0221
         varname = None
         phase_name = None
@@ -213,6 +350,96 @@ def __init__(self, *args): #pylint: disable=W0221
         super().__init__(varname)
         self.phase_name = phase_name
         self.species = species
+    
+    def expand_wildcard(self, phase_names=None, components=None):
+        if phase_names is not None:
+            return [self.__class__(phase_name, self.species) for phase_name in phase_names]
+        elif components is not None:
+            if self.phase_name is None:
+                return [self.__class__(comp) for comp in components]
+            else:
+                return [self.__class__(self.phase_name, comp) for comp in components]
+        else:
+            raise ValueError('Both phase_names and components are None')
+    
+    def compute_property(self, compsets, cur_conds, chemical_potentials):
+        result = np.atleast_1d(np.zeros(self.shape))
+        result[:] = np.nan
+        for _, compset in self.filtered(compsets):
+            el_idx = compset.phase_record.nonvacant_elements.index(str(self.species))
+            if np.isnan(result[0]):
+                result[0] = 0
+            if self.phase_name is None:
+                result[0] += compset.NP * compset.X[el_idx]
+            else:
+                result[0] += compset.X[el_idx]
+        return result
+
+    def compute_per_phase_property(self, compset, cur_conds):
+        if self.phase_name is not None:
+            tokens = self.phase_name.split('#')
+            phase_name = tokens[0]
+            if (compset.phase_record.phase_name != phase_name):
+                return np.nan
+        el_idx = compset.phase_record.nonvacant_elements.index(str(self.species))
+        return compset.X[el_idx]
+
+    def compute_property_gradient(self, compsets, cur_conds, chemical_potentials):
+        "Compute partial derivatives of property with respect to degrees of freedom of given CompositionSets"
+        result = [np.zeros(compset.dof.shape[0]) for compset in compsets]
+        num_components = len(compsets[0].phase_record.nonvacant_elements)
+        for cs_idx, compset in self.filtered(compsets):
+            masses = np.zeros((num_components, 1))
+            mass_jac = np.zeros((num_components, compset.dof.shape[0]))
+            for comp_idx in range(num_components):
+                compset.phase_record.formulamole_obj(masses[comp_idx, :], compset.dof, comp_idx)
+                compset.phase_record.formulamole_grad(mass_jac[comp_idx, :], compset.dof, comp_idx)
+            el_idx = compset.phase_record.nonvacant_elements.index(str(self.species))
+            result[cs_idx][:] = (mass_jac[el_idx] * masses.sum() - masses[el_idx,0] * mass_jac.sum(axis=0)) \
+                / (masses.sum(axis=0)**2)
+        return result
+
+    def dot_derivative(self, compsets, cur_conds, chemical_potentials, deltas: DotDerivativeDeltas):
+        "Compute dot derivative with self as numerator, with the given deltas"
+        state_variables = compsets[0].phase_record.state_variables
+        grad_values = self.compute_property_gradient(compsets, cur_conds, chemical_potentials)
+
+        # Sundman et al, 2015, Eq. 73
+        dot_derivative = np.nan
+        for idx, compset in enumerate(compsets):
+            if compset.NP == 0 and not (compset.fixed):
+                continue
+            func_value = self.compute_per_phase_property(compset, cur_conds)
+            if np.isnan(func_value):
+                continue
+            if np.isnan(dot_derivative):
+                dot_derivative = 0.0
+            grad_value = grad_values[idx]
+            delta_sitefracs = deltas.delta_sitefracs[idx]
+
+            if self.phase_name is None:
+                dot_derivative += deltas.delta_phase_amounts[idx] * func_value
+                dot_derivative += compset.NP * np.dot(deltas.delta_statevars, grad_value[:len(state_variables)])
+                dot_derivative += compset.NP * np.dot(delta_sitefracs, grad_value[len(state_variables):])
+            else:
+                dot_derivative += np.dot(deltas.delta_statevars, grad_value[:len(state_variables)])
+                dot_derivative += np.dot(delta_sitefracs, grad_value[len(state_variables):])
+        return dot_derivative
+
+    def dot_deltas(self, spec, state) -> DotDerivativeDeltas:
+        component_idx = state.compsets[0].phase_record.nonvacant_elements.index(str(self.species))
+        delta_chemical_potentials, delta_statevars, delta_phase_amounts = \
+        fixed_component_differential(spec, state, component_idx)
+
+        # Sundman et al, 2015, Eq. 73
+        compsets_delta_sitefracs = []
+        for idx, compset in enumerate(state.compsets):
+            delta_sitefracs = site_fraction_differential(state.cs_states[idx], delta_chemical_potentials,
+                                                         delta_statevars)
+            compsets_delta_sitefracs.append(delta_sitefracs)
+        return DotDerivativeDeltas(delta_chemical_potentials=delta_chemical_potentials, delta_statevars=delta_statevars,
+                                   delta_phase_amounts=delta_phase_amounts, delta_sitefracs=compsets_delta_sitefracs,
+                                   delta_parameters=None)
 
     def __reduce__(self):
         if self.phase_name is None:
@@ -234,6 +461,8 @@ class MassFraction(StateVariable):
     """
     Weight fractions are symbols with built-in assumptions of being real and nonnegative.
     """
+    implementation_units = 'fraction'
+    display_units = 'fraction'
     def __init__(self, *args):  # pylint: disable=W0221
         varname = None
         phase_name = None
@@ -256,6 +485,24 @@ def __init__(self, *args):  # pylint: disable=W0221
         self.phase_name = phase_name
         self.species = species
 
+    def compute_property(self, compsets, cur_conds, chemical_potentials):
+        result = np.atleast_1d(np.zeros(self.shape))
+        result[:] = np.nan
+        normalizer = 0.
+        for _, compset in self.filtered(compsets):
+            el_idx = compset.phase_record.nonvacant_elements.index(str(self.species))
+            if np.isnan(result[0]):
+                result[0] = 0
+            if self.phase_name is None:
+                result[0] += compset.NP * compset.phase_record.molar_masses[el_idx] * compset.X[el_idx]
+                for n_idx in range(len(compset.phase_record.nonvacant_elements)):
+                    normalizer += compset.NP * compset.phase_record.molar_masses[n_idx] * compset.X[n_idx]
+            else:
+                result[0] += compset.phase_record.molar_masses[el_idx] * compset.X[el_idx]
+                for n_idx in range(len(compset.phase_record.nonvacant_elements)):
+                    normalizer += compset.phase_record.molar_masses[n_idx] * compset.X[n_idx]
+        return result / normalizer
+
     def __reduce__(self):
         if self.phase_name is None:
             return self.__class__, (self.species,)
@@ -370,12 +617,50 @@ class ChemicalPotential(StateVariable):
     """
     Chemical potentials are symbols with built-in assumptions of being real.
     """
+    implementation_units = 'J / mol'
+    display_units = 'J / mol'
+    display_name = property(lambda self: f'Chemical Potential {self.species}')
+
     def __init__(self, species):
         species = Species(species)
         varname = 'MU_' + species.escaped_name.upper()
         super().__init__(varname)
         self.species = species
 
+    def compute_property(self, compsets, cur_conds, chemical_potentials):
+        phase_record = compsets[0].phase_record
+        el_indices = [(phase_record.nonvacant_elements.index(k), v)
+                       for k, v in self.species.constituents.items()]
+        result = np.atleast_1d(np.zeros(self.shape))
+        for el_idx, multiplicity in el_indices:
+            result[0] += multiplicity * chemical_potentials[el_idx]
+        return result
+
+    def dot_derivative(self, compsets, cur_conds, chemical_potentials, deltas: DotDerivativeDeltas):
+        "Compute dot derivative with self as numerator, with the given deltas"
+        phase_record = compsets[0].phase_record
+        el_indices = [(phase_record.nonvacant_elements.index(k), v)
+                       for k, v in self.species.constituents.items()]
+        result = np.zeros(self.shape)
+        for el_idx, multiplicity in el_indices:
+            result += multiplicity * deltas.delta_chemical_potentials[el_idx]
+        return result
+
+    def dot_deltas(self, spec, state) -> DotDerivativeDeltas:
+        component_idx = state.compsets[0].phase_record.nonvacant_elements.index(str(self.species))
+        delta_chemical_potentials, delta_statevars, delta_phase_amounts = \
+        chemical_potential_differential(spec, state, component_idx)
+
+        # Sundman et al, 2015, Eq. 73
+        compsets_delta_sitefracs = []
+        for idx, compset in enumerate(state.compsets):
+            delta_sitefracs = site_fraction_differential(state.cs_states[idx], delta_chemical_potentials,
+                                                         delta_statevars)
+            compsets_delta_sitefracs.append(delta_sitefracs)
+        return DotDerivativeDeltas(delta_chemical_potentials=delta_chemical_potentials, delta_statevars=delta_statevars,
+                                   delta_phase_amounts=delta_phase_amounts, delta_sitefracs=compsets_delta_sitefracs,
+                                   delta_parameters=None)
+
     def _latex(self, printer=None):
         "LaTeX representation."
         return '\mu_{'+self.species.escaped_name+'}'
@@ -384,11 +669,44 @@ def __str__(self):
         "String representation."
         return 'MU_%s' % self.species.name
 
-temperature = T = StateVariable('T')
-entropy = S = StateVariable('S')
-pressure = P = StateVariable('P')
-volume = V = StateVariable('V')
-moles = N = StateVariable('N')
+
+class IndependentPotential(StateVariable):
+    pass
+
+
+class TemperatureType(IndependentPotential):
+    implementation_units = 'kelvin'
+    display_units = 'kelvin'
+    display_name = 'Temperature'
+
+    def __init__(self):
+        super().__init__('T')
+    def __reduce__(self):
+        return self.__class__, ()
+
+
+class PressureType(IndependentPotential):
+    implementation_units = 'pascal'
+    display_units = 'pascal'
+    display_name = 'Pressure'
+
+    def __init__(self):
+        super().__init__('P')
+    def __reduce__(self):
+        return self.__class__, ()
+
+class SystemMolesType(StateVariable):
+    implementation_units = 'mol'
+    display_units = 'mol'
+    display_name = 'No. Moles'
+    def __init__(self):
+        super().__init__('N')
+    def __reduce__(self):
+        return self.__class__, ()
+
+temperature = T = TemperatureType()
+pressure = P = PressureType()
+moles = N = SystemMolesType()
 site_fraction = Y = SiteFraction
 X = MoleFraction
 W = MassFraction
@@ -397,6 +715,3 @@ def __str__(self):
 si_gas_constant = R = Float(8.3145) # ideal gas constant
 
 CONDITIONS_REQUIRING_HESSIANS = {ChemicalPotential, PhaseFraction}
-
-# When loading databases, these symbols should be replaced by their StateVariable counter-parts defined above
-supported_variables_in_databases = {Symbol('T'): T, Symbol('P'): P, Symbol('R'): R}
diff --git a/setup.py b/setup.py
index b3fc29f89..c0d188f72 100644
--- a/setup.py
+++ b/setup.py
@@ -34,7 +34,7 @@ def read(fname):
     author_email='richard.otis@outlook.com',
     description='CALPHAD tools for designing thermodynamic models, calculating phase diagrams and investigating phase equilibria.',
     # Do NOT include pycalphad._dev here. It is for local development and should not be distributed.
-    packages=['pycalphad', 'pycalphad.codegen', 'pycalphad.core', 'pycalphad.io', 'pycalphad.plot', 'pycalphad.plot.binary', 'pycalphad.tests', 'pycalphad.tests.databases', 'pycalphad.models'],
+    packages=['pycalphad', 'pycalphad.codegen', 'pycalphad.core', 'pycalphad.io', 'pycalphad.plot', 'pycalphad.plot.binary', 'pycalphad.property_framework', 'pycalphad.tests', 'pycalphad.tests.databases', 'pycalphad.models'],
     ext_modules=cythonize(
         CYTHON_EXTENSION_MODULES,
         include_path=CYTHON_EXTENSION_INCLUDES,
@@ -62,13 +62,16 @@ def read(fname):
         'importlib_resources',  # drop when pycalphad drops support for Python<3.9
         'matplotlib>=3.3',
         'numpy>=1.13',
+        'pint',
         'pyparsing>=2.4',
         'pytest',
         'pytest-cov',
+        'runtype',
         'scipy',
         'setuptools_scm[toml]>=6.0',
         'symengine>=0.9.2',  # python-symengine on conda-forge
         'tinydb>=3.8',
+        'typing_extensions', # drop when pycalphad drops support for Python<3.8
         'xarray>=0.11.2',
     ],
     classifiers=[