diff --git a/docs/source/cli_tutorial.rst b/docs/source/cli_tutorial.rst
index 2cfd360..fd89a11 100644
--- a/docs/source/cli_tutorial.rst
+++ b/docs/source/cli_tutorial.rst
@@ -15,9 +15,11 @@ energies. In this case defect system is defined by a ``.yaml`` file structured l
           nsites: 1 # site degeneracy of the defect species
           charge_states:
             0: # charge of first charge state
+              charge: 0
               formation_energy: 2 # formation energy of first charge state
               degeneracy: 1 # degeneracy of first charge state
             -1:
+              charge: -1
               formation_energy: 1
               degeneracy: 2
       ... # repeat for each defect in your system
@@ -74,6 +76,7 @@ and specify the concentration of a defect charge state like so::
             nsites: 1
             charge_states:
                 -1:
+                    charge: -1
                     formation_energy: 1
                     fixed_concentration: 1e20
                     degeneracy: 1
diff --git a/docs/source/tutorial.ipynb b/docs/source/tutorial.ipynb
index 6768537..044bab7 100644
--- a/docs/source/tutorial.ipynb
+++ b/docs/source/tutorial.ipynb
@@ -141,13 +141,13 @@
      "output_type": "stream",
      "text": [
       "DOSOverflowWarning: An overflow occurred during computation of\n",
-      "                          electron and hole concentrations. This is likely a natural result of the use of\n",
-      "                          a numerical solver for the Fermi energy search. This can likely be ignored\n",
-      "                          though you should always check the final results are reasonable.\n",
+      "                        electron and hole concentrations. This is likely a natural result of the use of\n",
+      "                        a numerical solver for the Fermi energy search. This can likely be ignored\n",
+      "                        though you should always check the final results are reasonable.\n",
       "DefectOverflowWarning: An overflow occurred during computation of\n",
-      "                          defect concentrations. This is likely a natural result of the use of\n",
-      "                          a numerical solver for the Fermi energy search. This can likely be ignored\n",
-      "                          though you should always check the final results are reasonable.\n",
+      "                        defect concentrations. This is likely a natural result of the use of\n",
+      "                        a numerical solver for the Fermi energy search. This can likely be ignored\n",
+      "                        though you should always check the final results are reasonable.\n",
       "Temperature :      500  (K)\n",
       "SC Fermi level :      1.5000000000000888  (eV)\n",
       "Concentrations:\n",
@@ -193,14 +193,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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SUlLsrtzExsayZ88eAgMDqVGjBuPHj+fs2bN8+umndvvNnTuX8PDwXHvqjx49mt69e1OzZk3OnTvHpEmTcHd3p3///i7/PCK58fF057tnOhgdQ0REbsDQgmjnzp12c8KMGjUKgEGDBrFgwQLi4uI4deqU3T5JSUl89dVXvPPOO7ke88yZM/Tv35+LFy9SuXJlOnTowLZt26hcubLrPoiIiIgUa4YWRJ07d77hnC8LFizIsS0gIIC0tLQ891myZIkzoomIiEgpUmyfMhMpLq5lZnPH9J+4Y/pPXMvMNjqOiIjkolh2qhYpTixYOHvlmm1dRESKHl0hEhERKaVOnDiByWRiz549ebapVasWs2bNKrRMYO1SM3LkyEI9pwoiERGRXJw/f56nnnqKGjVq4O3tTXBwMFFRUWzevNmu3a+//sqDDz5IUFAQPj4+1KtXj2HDhnHkyJE8j925c2dMJlOOJSsry9Ufy0716tWJi4vT/GqoIBIREcnV/fffz6+//srChQs5cuQI3333HZ07d+bixYu2Nt9//z3t27cnIyODRYsWcfDgQT7//HMCAgKYMGHCDY8/bNgw4uLi7BYPj4L1ZMlroMibcXd3Jzg4uMDnLUlUEImIiPzFlStX+Pnnn5kxYwZdunShZs2atGvXjvHjx3P33XcDkJaWxpAhQ/jb3/7Gd999R2RkJGFhYYSHh/PGG2/w4Ycf3vAcvr6+BAcH2y1/+OSTT7jtttvw8fGhYcOGvP/++7b3/rjNtXTpUjp16oSPjw+LFi1i8ODB9OnTh9dee42goCDKly/P1KlTycrK4oUXXiAwMJBq1aoxf/78HMe60S2z3P7fPP7441SuXBl/f3+6du3K3r17AThy5Agmk4lDhw7Z7fP2229Tp04d2+v9+/fTq1cvypUrR1BQEI8++igXLlzIdwZXUEEkIiKGSMvMynNJv57t9LaO+GNusuXLl5ORkZFrm1WrVnHhwgXGjBmT6/vly5d36Jx/WLRoERMnTuTVV1/l4MGDvPbaa0yYMIGFCxfatRs3bhwjRozg4MGDREVFAfDTTz9x7tw5Nm7cyFtvvcWkSZP4+9//ToUKFdi+fTv/+Mc/ePLJJzlz5kyBsgE8+OCDJCYm8sMPP7Br1y5atWpFt27duHTpEvXr16dNmzY55nRbtGgRDz/8MGAtqLp27UrLli3ZuXMn0dHRJCQk8NBDDxU4kzPoGpmIi5kwUa9KOdu6iFg1mrgqz/e6NKjM/CHtbK9bv7yGa9dzH7YiPCyQpU9G2F53mLGOS6mZOdqdmH5XvrN5eHiwYMEChg0bxpw5c2jVqhWdOnWiX79+NGvWDICjR48C0LBhw3wf98/ef/99PvnkE9vrJ598kjfffJNJkybx5ptvct999wEQFhbGgQMH+PDDDxk0aJCt/ciRI21t/hAYGMi7776Lm5sbDRo0YObMmaSlpfGvf/0LsE5mPn36dDZt2kS/fv0czrxp0yZ27NhBYmKibQ7QN954g+XLl/Pll1/yxBNPMGDAAN577z1efvllwHrVaNeuXXz++ecAvPfee7Rs2ZLXXnvNdtx58+ZRvXp1jhw5Qv369R3O5QwqiERcrIyXO6tHdTI6hog46P777+euu+7i559/Ztu2bfzwww/MnDmTTz75hMGDB99wYOH8GDBgAC+++KLtdfny5UlNTSUmJoahQ4cybNgw23tZWVkEBATY7d+mTZscx2zcuDFubv9/8ycoKMiuw7S7uzsVK1YkMTGxQJn37t1LSkoKFStWtNt+7do1YmJiAOjXrx+jR49m27ZttG/fnkWLFtGqVStb4bh3717WrVtHuXLlchw/JiZGBZGIiJQuB6ZG5fneXydC3jUhMt9tN43tkkdLx/n4+NC9e3e6d+/OhAkTePzxx5k0aRKDBw+2/eI+dOgQERERNzlSTgEBAdStW9duW0JCAgAff/wx4eHhdu+5u7vbvS5btmyOY3p6etq9NplMuW4zm80O5wXrHKQhISGsX78+x3t/3CIMDg6ma9euLF68mPbt27N48WKeeuopu2P07t2bGTNm5DhGSEhIgXI5gwoiERExhK9X/n8Fuaqtoxo1asTy5csB6NGjB5UqVWLmzJl88803OdpeuXLF4X5EQUFBhIaGcvz4cQYMGOCExM7VqlUr4uPj8fDwoFatWnm2GzBgAGPGjKF///4cP37c7vZcq1at+Oqrr6hVq1aRerpNnapFXOxaZjbd39pA97c2aOoOkWLi4sWLdO3alc8//5zffvuN2NhY/vOf/zBz5kzuuecewHqF5pNPPmHFihXcfffdrFmzhhMnTrBz507GjBnDP/7xjwKde8qUKUybNo13332XI0eOsG/fPubPn89bb73lzI9YIJGRkURERNCnTx9+/PFHTpw4wZYtW3jxxRfZuXOnrd19993H1atXeeqpp+jSpQuhoaG294YPH86lS5fo378/v/zyCzExMaxatYohQ4aQnW3cz8iiU5qJlFAWLBxNTLGti0jRV65cOcLDw3n77beJiYnh+vXrVK9enWHDhtk6KAPcc889bNmyhWnTpvHwww+TnJxM9erV6dq1K6+88kqBzv3444/j6+vL66+/zgsvvEDZsmVp2rRpoY/cnBuTycTKlSt58cUXGTJkCOfPnyc4OJg777yToKAgWzs/Pz969+7NsmXLmDdvnt0xQkND2bx5M2PHjqVHjx5kZGRQs2ZNevbsadf/qbCZLLfaK6wESk5OJiAggKSkJPz9/Y2OI8VcWmaW7WmaA1OjXHo5X6SoSU9PJzY2lrCwMHx8fIyOIyXAjb6nbuX3t26ZiYiISKmngkhERERKPRVEIiIiUuqpIBIREZFST707RVzMhImq5cvY1kVEpOhRQSTiYmW83Nk8rqvRMUQMVdCRkUX+ylXfSyqIRETEZby8vHBzc+PcuXNUrlwZLy8vTCZdKRXHWSwWMjMzOX/+PG5ubnh5eTn1+CqIRETEZdzc3AgLCyMuLo5z584ZHUdKAF9fX2rUqOH0QRxVEIm4WPr1bB76cCsAy56MwMfT/SZ7iJQsXl5e1KhRg6ysLEOnZpDiz93dHQ8PD5dcZVRBJOJiZouF384k2dZFSqM/Zl3/68zrIkWFHrsXERGRUk8FkYiIiJR6KohERESk1FNBJCIiIqWeCiIREREp9fSUmUghCCzr3AHERETEuVQQibiYr5cHuyd0NzqGiIjcgG6ZiYiISKmngkhERERKPRVEIi6Wfj2bvh9upe+HW0m/rmkLRESKIvUhEnExs8XC9thLtnURESl6DL1CtHHjRnr37k1oaCgmk4nly5ffsP369esxmUw5lvj4eLt2s2fPplatWvj4+BAeHs6OHTtc+ClERESkuDO0IEpNTaV58+bMnj3bof0OHz5MXFycbalSpYrtvaVLlzJq1CgmTZrE7t27ad68OVFRUSQmJjo7voiIiJQQht4y69WrF7169XJ4vypVqlC+fPlc33vrrbcYNmwYQ4YMAWDOnDmsWLGCefPmMW7cuFuJKyIiIiVUsexU3aJFC0JCQujevTubN2+2bc/MzGTXrl1ERkbatrm5uREZGcnWrVuNiCoiIiLFQLEqiEJCQpgzZw5fffUVX331FdWrV6dz587s3r0bgAsXLpCdnU1QUJDdfkFBQTn6Gf1ZRkYGycnJdouIiIiUHsXqKbMGDRrQoEED2+vbb7+dmJgY3n77bT777LMCH3fatGlMmTLFGRFFclXG093oCCIicgPF6gpRbtq1a8exY8cAqFSpEu7u7iQkJNi1SUhIIDg4OM9jjB8/nqSkJNty+vRpl2aW0sXXy4ODL/fk4Ms98fUqVn+DiIiUGsW+INqzZw8hISEAeHl50bp1a9auXWt732w2s3btWiIiIvI8hre3N/7+/naLiIiIlB6G/rmakpJiu7oDEBsby549ewgMDKRGjRqMHz+es2fP8umnnwIwa9YswsLCaNy4Menp6XzyySf89NNP/Pjjj7ZjjBo1ikGDBtGmTRvatWvHrFmzSE1NtT11JiIiIvJXhhZEO3fupEuXLrbXo0aNAmDQoEEsWLCAuLg4Tp06ZXs/MzOT559/nrNnz+Lr60uzZs1Ys2aN3TH69u3L+fPnmThxIvHx8bRo0YLo6OgcHa1FCkv69Wye+nwXAB880hof9ScSESlyTBaL5hL4q+TkZAICAkhKStLtM7llaZlZNJq4CoADU6PUj0hExEVu5fd3se9DJCIiInKrVBCJiIhIqaeCSEREREo9FUQiIiJS6qkgEhERkVJPBZGIiIiUenr+V8TFfL08ODH9LqNjiIjIDegKkYiIiJR6KohERESk1Mt3QXTp0iVX5hApsdKvZ/P0ol08vWgX6dezjY4jIiK5yHdBFBoaSr9+/Vi9erUr84iUOGaLhZX74lm5Lx6zZsoRESmS8l0Qffzxx5w/f56ePXtSq1YtJk+ezIkTJ1wYTURERKRw5LsgevTRR1m7di3Hjh1j0KBBLFy4kLp169K9e3eWLl1KZmamK3OKiIiIuIzDnarDwsKYMmUKsbGxREdHU6VKFR577DFCQkL45z//6YqMIiIiIi51S0+ZRUZGsmjRIj799FMAZs+e7ZRQIiIiIoWpwAMznjx5kvnz57Nw4UJOnz5Nly5dGDp0qDOziYiIiBQKhwqijIwMvvrqK+bNm8f69eupWrUqgwcPZsiQIdSqVctFEUVERERcK98F0dNPP82SJUtIS0vjnnvuYeXKlXTv3h2TyeTKfCLFXhlPdw5MjbKti4hI0ZPvgmjTpk1MmjSJRx55hIoVK7oyk0iJYjKZ8PXStIEiIkVZvn9K//bbb3avMzMziY2NpU6dOnh46Ie9iIiIFF8OP2WWlpbG0KFD8fX1pXHjxpw6dQqAZ599lunTpzs9oEhxl5GVzfPL9vL8sr1kZGnqDhGRosjhgmj8+PHs3buX9evX4+PjY9seGRnJ0qVLnRpOpCTINlv4avcZvtp9hmyzpu4QESmKHL7XtXz5cpYuXUr79u3tOlQ3btyYmJgYp4YTERERKQwOXyE6f/48VapUybE9NTVVT5yJiIhIseRwQdSmTRtWrFhhe/1HEfTJJ58QERHhvGQiIiIihcThW2avvfYavXr14sCBA2RlZfHOO+9w4MABtmzZwoYNG1yRUURERMSlHL5C1KFDB/bs2UNWVhZNmzblxx9/pEqVKmzdupXWrVu7IqOIiIiISxVoAKE6derw8ccfOzuLiIiIiCE0oqKIi5XxdGfXS5G2dRERKXpUEIm4mMlkomI5b6NjiIjIDTjch0hERESkpNEVIhEXy8jK5pXvDwLw0t9vw9tDt81ERIqafF8hqlGjBs888ww//vgjWVlZrswkUqJkmy18tu0kn207qak7RESKqHwXRJ999hne3t4MHz6cSpUq0bdvXxYtWsSVK1dcGE9ERETE9fJdEHXq1Ik333yTo0ePsnnzZlq0aMG///1vgoOD6dq1K7NmzeL48eOuzCoiIiLiEgXqVN24cWPGjx/Ptm3bOHHiBP3792ft2rU0adKEJk2a2E3tISIiIlLU3fJTZsHBwQwbNoz//ve/XLhwgZdffhlv7/w9Yrxx40Z69+5NaGgoJpOJ5cuX37D9119/Tffu3alcuTL+/v5ERESwatUquzaTJ0/GZDLZLQ0bNizoxxMREZFSwKmP3fv6+nLvvfcSGRmZr/apqak0b96c2bNn56v9xo0b6d69OytXrmTXrl106dKF3r178+uvv9q1a9y4MXFxcbZl06ZNDn8WERERKT0Mfey+V69e9OrVK9/tZ82aZff6tdde49tvv+W///0vLVu2tG338PAgODjYWTFFRESkhCvW4xCZzWauXr1KYGCg3fajR48SGhqKj48PERERTJs2jRo1auR5nIyMDDIyMmyvk5OTXZZZSh8fD3d+HtPFti4iIkVPsR6p+o033iAlJYWHHnrIti08PJwFCxYQHR3NBx98QGxsLB07duTq1at5HmfatGkEBATYlurVqxdGfCkl3NxMVA/0pXqgL25uJqPjiIhILhwuiObPn09aWporsjhk8eLFTJkyhWXLllGlShXb9l69evHggw/SrFkzoqKiWLlyJVeuXGHZsmV5Hmv8+PEkJSXZltOnTxfGRxAREZEiwuGCaNy4cQQHBzN06FC2bNniikw3tWTJEh5//HGWLVt20w7c5cuXp379+hw7dizPNt7e3vj7+9stIs6SmWXmtZUHeW3lQTKzzEbHERGRXDhcEJ09e5aFCxdy4cIFOnfuTMOGDZkxYwbx8fGuyJfDF198wZAhQ/jiiy+46667bto+JSWFmJgYQkJCCiGdSE5ZZjMfbTzORxuPk2VWQSQiUhQ5XBB5eHhw77338u2333L69GmGDRvGokWLqFGjBnfffTfffvst5nz+0E9JSWHPnj3s2bMHgNjYWPbs2cOpU6cA662sgQMH2tovXryYgQMH8uabbxIeHk58fDzx8fEkJSXZ2owePZoNGzZw4sQJtmzZwr333ou7uzv9+/d39KOKiIhIKXFLnaqDgoLo0KEDERERuLm5sW/fPgYNGkSdOnVYv379TfffuXMnLVu2tD0yP2rUKFq2bMnEiRMBiIuLsxVHAB999BFZWVkMHz6ckJAQ2zJixAhbmzNnztC/f38aNGjAQw89RMWKFdm2bRuVK1e+lY8qIiIiJZjJYrE4PP12QkICn332GfPnz+f48eP06dOHoUOHEhkZSWpqKlOnTmXJkiWcPHnSFZldLjk5mYCAAJKSktSfSG5ZWmYWjSZaR1Q/MDUKX69iPdqFiEiRdSu/vx2+QtS7d2+qV6/OggULGDZsGGfPnuWLL76wdW4uW7Yszz//vJ7UEhERkWLD4T9Vq1SpwoYNG4iIiMizTeXKlYmNjb2lYCIiIiKFxeGCaO7cuTdtYzKZqFmzZoECiYiIiBQ2hwuid999N9ftJpMJHx8f6taty5133om7u6YoEAHrdB0/PnenbV1ERIoehwuit99+m/Pnz5OWlkaFChUAuHz5Mr6+vpQrV47ExERq167NunXrNAWGCNapO+oH+RkdQ0REbsDhTtWvvfYabdu25ejRo1y8eJGLFy9y5MgRwsPDeeeddzh16hTBwcE899xzrsgrIiIi4nQOP3Zfp04dvvrqK1q0aGG3/ddff+X+++/n+PHjbNmyhfvvv5+4uDhnZi00euxenCkzy8zsddapY4Z3qYuXR7GeU1lEpMi6ld/fDt8yi4uLIysrK8f2rKws2/QdoaGhN5xdXqQ0yTKbeWftUQCe7FQbr1sbD1VERFzA4Z/MXbp04cknn+TXX3+1bfv111956qmn6Nq1KwD79u0jLCzMeSlFREREXMjhgmju3LkEBgbSunVrvL298fb2pk2bNgQGBtoeyS9Xrhxvvvmm08OKiIiIuIJDt8wsFguZmZl89913nDp1isOHDwPQoEEDGjRoYGvXpUsX56YUERERcSGHC6K6devy+++/5yiCRERERIorh26Zubm5Ua9ePS5evOiqPCIiIiKFzuE+RNOnT+eFF15g//79rsgjIiIiUugcHoeoQoUKpKWlkZWVhZeXF2XKlLF7/9KlS04NaASNQyTOlG22sP9sEgBNqgbg7mYyOJGISMlUqOMQzZo1y9FdREo1dzcTzauXNzqGiIjcgMMF0aBBg1yRQ0RERMQwBRoyNyYmhpdeeon+/fuTmJgIwA8//MDvv//u1HAiJUFmlpkPN8Tw4YYYMrPMRscREZFcOFwQbdiwgaZNm7J9+3a+/vprUlJSANi7dy+TJk1yekCR4i7LbGbaD4eY9sMhsswqiEREiiKHC6Jx48bxyiuvsHr1ary8vGzbu3btyrZt25waTkRERKQwOFwQ7du3j3vvvTfH9ipVqnDhwgWnhBIREREpTA4XROXLlycuLi7H9l9//ZWqVas6JZSIiIhIYXK4IOrXrx9jx44lPj4ek8mE2Wxm8+bNjB49moEDB7oio4iIiIhLOVwQvfbaazRs2JDq1auTkpJCo0aNuPPOO7n99tt56aWXXJFRRERExKUcHofIy8uLjz/+mAkTJrB//35SUlJo2bIl9erVc0U+EREREZdzuCD6Q40aNahRo4Yzs4iUSN4e7nwxrL1tXUREih6HC6Ls7GwWLFjA2rVrSUxMxPyXcVV++uknp4UTKQnc3UxE1KlodAwREbkBhwuiESNGsGDBAu666y6aNGmCyaSJKkVERKR4c7ggWrJkCcuWLeNvf/ubK/KIlDjXs818seMUAP3b1cDTvUAz5oiIiAsVqFN13bp1XZFFpES6nm1m4rfWef4eaF1NBZGISBHk8E/m559/nnfeeQeLxeKKPCIiIiKFzuErRJs2bWLdunX88MMPNG7cGE9PT7v3v/76a6eFExERESkMDhdE5cuXz3UuMxEREZHiyuGCaP78+a7IISIiImKYAg3MmJWVxfr164mJieHhhx/Gz8+Pc+fO4e/vT7ly5ZydUUqpbLOFHbGXSLyaThU/H9qFBeLupmEeDGHOhpNbICUBygVBzdvBTYNMikjJ4XCn6pMnT9K0aVPuuecehg8fzvnz5wGYMWMGo0ePduhYGzdupHfv3oSGhmIymVi+fPlN91m/fj2tWrXC29ubunXrsmDBghxtZs+eTa1atfDx8SE8PJwdO3Y4lEuMF70/jg4zfqL/x9sYsWQP/T/eRocZPxG9P87oaKXPge9gVhNY+Hf4aqj1v7OaWLeLiJQQDhdEI0aMoE2bNly+fJkyZcrYtt97772sXbvWoWOlpqbSvHlzZs+ena/2sbGx3HXXXXTp0oU9e/YwcuRIHn/8cVatWmVrs3TpUkaNGsWkSZPYvXs3zZs3JyoqisTERIeyiXGi98fx1Oe7iUtKt9sen5TOU5/vLnZFkZe7G/MGt2He4DZ4FbdH7g98B8sGQvI5++3JcdbtKopEpIQwWRx8fr5ixYps2bKFBg0a4Ofnx969e6lduzYnTpygUaNGpKWlFSyIycQ333xDnz598mwzduxYVqxYwf79+23b+vXrx5UrV4iOjgYgPDyctm3b8t577wFgNpupXr06zz77LOPGjctXluTkZAICAjhwIg4/f/8CfR4pmGyzhQfmbOX81Yxc3zcBwQE+bBrbVbfPXM2cbb0S9NdiyMZkvX32WLRun4lIkZCcfJWAmk1ISkrC38Hf3w73ITKbzWRnZ+fYfubMGfz8/Bw9nEO2bt1KZGSk3baoqChGjhwJQGZmJrt27WL8+PG2993c3IiMjGTr1q15HjcjI4OMjP//BZycnGw99qyfcfP2deInkFtlAeKS0tkRe0nzg7nayS03KIYALJASD++2KKxEIiI3llHwMRIdLoh69OjBrFmz+OijjwDrlZ2UlBQmTZrk8uk84uPjCQoKstsWFBREcnIy165d4/Lly2RnZ+fa5tChQ3ked9q0aUyZMiXHdi8PN9w9itktjmIu22why3zzb+jEq+k3bVNUXM82s/zXswD0aVm1+IxUnZKQv3ZunrpCJCJFQ5YZuFqgXR0uiN58802ioqJo1KgR6enpPPzwwxw9epRKlSrxxRdfFCiE0caPH8+oUaNsr5OTk6levTq7J3R3+JKb3JqtMRfp//G2m7ar4udTCGmc43q2mRe+/A2Au5qFFJ+CqFzQzdsAPPoNhHV0bRYRkfxIToZXAwq0q8MFUbVq1di7dy9Lly5l7969pKSkMHToUAYMGGDXydoVgoODSUiw/6s1ISEBf39/ypQpg7u7O+7u7rm2CQ4OzvO43t7eeHt7uySzOKZdWCAhAT7EJ6WT23WiP/oQtQsLLOxopU/N28E/9MZ9iPxDre1ERIq5Av2p6uHhwYABA5g5cybvv/8+jz/+uMuLIYCIiIgcT7KtXr2aiIgIwDrxbOvWre3amM1m1q5da2sjRZu7m4lJvRsB1uLnryzApN6N1KG6MLi5Q88Zebz5v///PafrdpmIlAiGXrtPSUlhz5497NmzB7A+Vr9nzx5OnToFWG9lDRw40Nb+H//4B8ePH2fMmDEcOnSI999/n2XLlvHcc8/Z2owaNYqPP/6YhQsXcvDgQZ566ilSU1MZMmRIoX42KbieTUL44JFWBAfkvC1Wxc+bbrfl81aO3Dq/kNy3+4fCQ59Co7sLN4+IiIsUaKRqZ9m5cyddunSxvf6jH8+gQYNYsGABcXFxtuIIICwsjBUrVvDcc8/xzjvvUK1aNT755BOioqJsbfr27cv58+eZOHEi8fHxtGjRgujo6BwdraVo69kkhO6Ngm0jVfv5eDJ62R4Sr2awbOdpBoTXNDpiyWexwJrJ1vXmD0OLhzVStYiUWA6PQ1Qa/DEOUUHGMRDXWbA5lsn/PUAVP2/Wv9AZXy9D6/l8S8vMotFE6+ChB6ZGFZvcHF0Nix4Ad2/4524IqGZ0IhGRG7qV39/F5HEXEXg4vCbVA8uQeDWD+ZtPGB2nZDOb///qUPgTKoZEpMQr8J+qmZmZJCYmYjab7bbXqFHjlkOJ5MbLw43RPRowYske5qyP4eF2NahQ1svoWDfl5e7G7Idb2daLhX3/gYT94B0AHUbdvL2ISDHn8E/no0eP0rFjR8qUKUPNmjUJCwsjLCyMWrVqERYW5oqMIja9m4XSKMSfqxlZzF53zOg4+eLh7sZdzUK4q1kIHsWhIMrKgJ9esa53GAm+GuJAREo+h68QDR48GA8PD77//ntCQkIwmfT4sxQeNzcTY3s1ZNC8HXy69SRDOoRRtbzrh3woVXbOg6RT1ifMwv9hdBoRkULhcEG0Z88edu3aRcOGDV2RR+Sm7qxXiYjaFdl6/CJvrz7CGw82NzrSDWVlm1n1u3Ww0KjGQUX7KlF6Mmx83breeRx4aS4/ESkdHP7J3KhRIy5cuOCKLCL5YjJZrxIBfLX7DIfjCzZvTWHJzDYzfPFuhi/eTWa2+eY7GGnLvyHtIlSsBy0eMTqNiEihcbggmjFjBmPGjGH9+vVcvHiR5ORku0WkMLSoXp6/NQ3GYoHXV+U9ca844GoCbJ1tXe82EdyLyfAAIiJO4PBPvMjISAC6detmt91isWAymcjOznZOMpGbGN2jAat+T2DNwUR+OXGJtrXU+feWbJwJ11Ohahu4rbfRaURECpXDBdG6detckUPEYbUrl6Nv2+os3n6K6T8c4st/RKiTf0FdjIFdC6zr3aeA/j+KSCnjcEHUqVMnV+QQKZAR3erx9e4z7Dp5mdUHEujRONjoSMXTT6+AOQvqdodaHYxOIyJS6ArUSeDKlSvMnTuXgwcPAtC4cWMee+wxAgICnBpO5GaC/H147I4w3l8fw+urDtPttiDc3XR1wyHnfoXfvwZMEDnJ6DQiIoZwuFP1zp07qVOnDm+//TaXLl3i0qVLvPXWW9SpU4fdu3e7IqPIDT3ZqQ7lfT05mpjCV7vPGB2n+Pljio5mD0FwU0OjiIgYxeHJXTt27EjdunX5+OOP8fCwXmDKysri8ccf5/jx42zcuNElQQuTJnctfj7eeJxXVx4kJMCHdaM74+NZdGZiv55tZvmvZwHo07IqnkVpHKKYdfBZH3DzhGd3QoVaRicSESmwQp3cdefOnYwdO9ZWDAF4eHgwZswYdu7c6ejhRJzi0YiahAb4EJeUzqdbTxgdx46nuxsPtqnOg22qF61i6M8TuLZ9XMWQiJRqDv909vf359SpUzm2nz59Gj8/P6eEEnGUj6c7z3WvD8DsdTEkXbtucKJi4MA3ELcHvPzgztFGpxERMZTDBVHfvn0ZOnQoS5cu5fTp05w+fZolS5bw+OOP079/f1dkFMmX+1pVo35QOZKuXWfOhhij49hkZZv56VACPx1KIKuojFSdfR3Wvmxdv+OfULaSsXlERAzm8FNmb7zxBiaTiYEDB5KVlQWAp6cnTz31FNOnT3d6QJH8cnczMSaqIY9/upN5m2IZFFGL4AAfo2ORmW3msQXW28kHpkYVjbnMdi2Ay7FQtgq0f9roNCIihnP4J7OXlxfvvPMOly9fZs+ePezZs4dLly7x9ttv4+3t7YqMIvnW7bYqtKlZgYwsM++sPWJ0nKIpIwU2zLSudxoD3uWMzSMiUgQU+E9VX19fmjZtStOmTfH11YzYUjSYTCbG/W/i12U7z3AsMcXgREXQtvchNREqhEGrQUanEREpEvJ1y+y+++5jwYIF+Pv7c999992w7ddff+2UYCIF1aZWIJG3BbHmYAJvrDrMnEdbGx2p6Ei9AJvfta53fQk8vIzNIyJSROSrIAoICLDNEeXv76/5oqTIG9OzAT8dSiD693h2n7pMqxoVjI5UNPz8JmRehZDm0PjGf9yIiJQm+SqI5s+fb1tfsGCBq7KIOE39ID/ub1WN/+w6w4wfDrHkifYq5C+fhF8+sa5HTga3ItC5W0SkiHD4J2LXrl25cuVKju3Jycl07drVGZlEnOK57vXx8nBje+wl1h85b3Qc4617DbIzIawT1NG/VRGRP3O4IFq/fj2ZmZk5tqenp/Pzzz87JZSIM4SWL8Pg22sBMOOHQ5jNDs1S4zSe7m5MvacxU+9pbNxI1fH74bel1vXIycZkEBEpwvI9DtFvv/1mWz9w4ADx8fG219nZ2URHR1O1alXnphO5RU93rsMXO05xKP4q3+49y70tqxV6Bk93NwZG1Cr089pZOwWwQON7oWorY7OIiBRB+S6IWrRogclkwmQy5XprrEyZMvz73/92ajiRW1Xe14unOtdhZvRh3vzxCH9rGoK3R9GZ+LVQnNgER38ENw/oOsHoNCIiRVK+C6LY2FgsFgu1a9dmx44dVK5c2fael5cXVapUwd29lP2ikWJhyO1hLNxygjOXr7Fo2yke6xBWqOfPNlvYEXsJgHZhgbi7FWLnbosFVk+yrrcaBBXrFN65RUSKkXwXRDVr1gTAbC4iczGJ5FMZL3dGRtZn/Nf7eG/dMR5sUw0/H89CO39GVjb9P94GWKfu8PVyeMacgjv0PZzdCZ6+0Gls4Z1XRKSYKfBP5gMHDnDq1KkcHazvvvvuWw4l4mwPtq7Gxz8f5/j5VD7eeJxRPRoYHcn1srNgzRTresRw8AsyNo+ISBHmcEF0/Phx7r33Xvbt24fJZMJisT6588cYL9nZ2c5NKOIEHu5uvNCjAU8t2s0nm2J5NKIWlf1K+Nx7exbBxaNQJhBu/6fRaUREijSHnwEeMWIEYWFhJCYm4uvry++//87GjRtp06YN69evd0FEEefo2SSY5tXLk5aZzb9/Omp0HNfKTIP106zrd74APv7G5hERKeIcLoi2bt3K1KlTqVSpEm5ubri5udGhQwemTZvGP/+pv0Kl6DKZTIzraZ34dfH2U5y4kGpwIhfa8SFcjYOAGtB2qNFpRESKPIcLouzsbPz8/ACoVKkS586dA6ydrg8fPuzcdCJOFlGnIp0bVCbLbOHN1UeMjuMaaZdg09vW9a4vgkcJvzUoIuIEDhdETZo0Ye/evQCEh4czc+ZMNm/ezNSpU6ldu7bTA4o425iohphM8N+959h3JsnoOM636W1IT4IqjaHpg0anEREpFhwuiF566SXbo/dTp04lNjaWjh07snLlSt59912nBxRxtkah/vRpYR1VfeaqQy4/n4ebG+N7NWR8r4Z4uHpC1aQzsP1D63rkZHDT2GAiIvnh8E/nqKgo7rvvPgDq1q3LoUOHuHDhAomJiQWe3HX27NnUqlULHx8fwsPD2bFjR55tO3fubBsx+8/LXXfdZWszePDgHO/37NmzQNmkZBrVvT6e7iZ+PnqBTUcvuPRcXh5uPNmpDk92qoOXh4sLovXTIDsDat4B9bq79lwiIiWIU346BwYG2h67d9TSpUsZNWoUkyZNYvfu3TRv3pyoqCgSExNzbf/1118TFxdnW/bv34+7uzsPPmh/a6Bnz5527b744osC5ZOSqXqgLwPCrYONzog2buJXp0o8BHsWW9cjp0AB/02KiJRGDo9DlJ6ezr///W/WrVtHYmJijpGrd+/e7dDx3nrrLYYNG8aQIUMAmDNnDitWrGDevHmMGzcuR/vAwEC710uWLMHX1zdHQeTt7U1wcLBDWaR0ebZrXb7cdYZ9Z5NYuT+OvzcLdcl5ss0W9p+19lVqUjXAdVN3rJ0KFjM0/DtUb+uac4iIlFAOF0RDhw7lxx9/5IEHHqBdu3YFvjIEkJmZya5duxg/frxtm5ubG5GRkWzdujVfx5g7dy79+vWjbNmydtvXr19PlSpVqFChAl27duWVV16hYsWKuR4jIyODjIwM2+vk5OQCfBopbiqW82ZYx9q8veYIb6w6TFTjYDzdnX9LKyMrm3tmbwZcOHXHqe1weAWY3KDbROcfX0SkhHP4J/P333/PypUrueOOO2755BcuXCA7O5ugIPspBYKCgjh06OadXXfs2MH+/fuZO3eu3faePXty3333ERYWRkxMDP/617/o1asXW7duzXUC2mnTpjFlypRb+zBSLD3eMYzPtp3gxMU0lvxymkfb1zQ6kuMsFlgz2bre8hGoXAqmJRERcTKH/xyuWrWqbRwio82dO5emTZvSrl07u+39+vXj7rvvpmnTpvTp04fvv/+eX375Jc+RtMePH09SUpJtOX36dCGkl6KgrLcH/+xWD4B31hwlNSPL4EQFcGQVnNoCHj7QefzN24uISA4OF0RvvvkmY8eO5eTJk7d88kqVKuHu7k5CQoLd9oSEhJv2/0lNTWXJkiUMHXrzUXhr165NpUqVOHbsWK7ve3t74+/vb7dI6dGvbQ1qBPpyISWDeZtijY7jGHM2rP3f1c3wJ8HfNf2gRERKOocLojZt2pCenk7t2rXx8/MjMDDQbnGEl5cXrVu3Zu3atbZtZrOZtWvXEhERccN9//Of/5CRkcEjjzxy0/OcOXOGixcvEhIS4lA+KR28PNwYHWW9zfThxuNcSs00OJEDflsKiQfAJwA6PGd0GhGRYsvhPkT9+/fn7NmzvPbaawQFBd1Sp2qAUaNGMWjQINq0aUO7du2YNWsWqamptqfOBg4cSNWqVZk2bZrdfnPnzqVPnz45OkqnpKQwZcoU7r//foKDg4mJiWHMmDHUrVuXqKioW8oqJdffm4bw4YYYfj+XzHs/HWNi70ZGR7q56+mw7jXreodRUKaCsXlERIoxhwuiLVu2sHXrVpo3b+6UAH379uX8+fNMnDiR+Ph4WrRoQXR0tK2j9alTp3D7y+i+hw8fZtOmTfz44485jufu7s5vv/3GwoULuXLlCqGhofTo0YOXX34Zb2/N6SS5c3MzMa5XQx6du4PPt51kyB21qB7oa3SsG/vlE0g6DX6h1ttlIiJSYCaLxeLQiHStWrXi/fffp3379q7KZLjk5GQCAgJISkpSf6JSZsAn29h87CL3tazKW31bOOWYmVlmZq+z9l8b3qWuc0arTk+Cd5rDtctw97+h1cBbP6aISDF3K7+/Hf7JPH36dJ5//nnWr1/PxYsXSU5OtltEirOxPRsC8M2esxyMc873s5eHG891r89z3es7b+qOze9ai6FKDaD5w845pohIKebwFaI/bl/9te+QxWLBZDKRnZ3tvHQG0RWi0m344t2s+C2OLg0qM39Iu5vvUNiuxsM7LSDrGvRdBLf93ehEIiJFwq38/na4D9G6desc3UWkWBndowGr9sez7vB5th+/SHjt3Ec4zy+z2cKx8ykA1K1cDrdbnbpjwwxrMVStHTS86+btRUTkphwqiK5fv87UqVOZM2cO9erVc1UmEUOFVSpLv3bV+XzbKaZHH+Lrp26/pacp07Oy6fH2RsAJU3dcOAa7FlrXIydrAlcRESdxqEODp6cnv/32m6uyiBQZ/+xWjzKe7vx66gqrfk+4+Q6F5aeXwZIN9aKg1q1PnyMiIlYO9/B85JFHcswdJlLSVPHz4fGOYQC8vuoQWdlmgxMBZ3fBgeWACSInGZ1GRKREcfjafVZWFvPmzWPNmjW0bt06xyzzb731ltPCiRjpiTtr8/m2k8ScT+XLXWfo166GcWH+PIFr834Q1Ni4LCIiJZDDBdH+/ftp1aoVAEeOHLF771ZHrRYpSvx8PBnepS6vrDjIrDVH6dOyKj6e7saEifkJYjeCuxd0+ZcxGURESjA9ZSZyA49G1GT+5hOcvXKNBVtO8I9OdQo/hNkMa/53i6ztMChv4JUqEZES6pZGiTtz5gxnzpxxVhaRIsfbw51R3esD8P66YySlXS/8EL9/DfH7wNsfOj5f+OcXESkFHC6IzGYzU6dOJSAggJo1a1KzZk3Kly/Pyy+/jNlcBDqeijhZn5ZVaRjsR3J6Fu9vOObw/h5ubjxxZ22euLM2Hm4O/pPLyrQ+WQZwxz+h7K2NiSQiIrlz+JbZiy++yNy5c5k+fTp33GF97HfTpk1MnjyZ9PR0Xn31VaeHFDGSu5uJMT0b8NiCnSzYfILBt9ciJKBMvvf38nDjX3+7rWAn37UALp+AckHQ/umCHUNERG7K4YJo4cKFfPLJJ9x99922bc2aNaNq1ao8/fTTKoikROrSoArtwgLZEXuJWauPMuOBZq4/acZV66jUAJ3GglfZG7cXEZECc/iW2aVLl2jYsGGO7Q0bNuTSpUtOCSVS1JhMJsb1sn7f/2fXaY4mXM33vmazhdOX0jh9KQ2z2YGpA7fOhrQLEFhHs9mLiLiYwwVR8+bNee+993Jsf++992jevLlTQokURa1qVKBHoyDMFnh91eF875eelU3HmevoOHMd6Vn5nPw45Txs+bd1vetL4O5ZgMQiIpJfDt8ymzlzJnfddRdr1qwhIiICgK1bt3L69GlWrlzp9IAiRcmYng1YczCBHw8ksOvkZVrXrOCaE218HTJTILQlNOrjmnOIiIiNw1eIOnXqxJEjR7j33nu5cuUKV65c4b777uPw4cN07NjRFRlFioy6Vfx4sHV1AGb8cAiLxYFbYPl1KRZ2zrOuR04GR59MExERh+X7CtHx48cJCwvDZDIRGhqqztNSao3sXo/le86y48Ql1h1OpGvDIOeeYN2rYL4OdbpC7c7OPbaIiOQq33961qtXj/Pnz9te9+3bl4SEIjQLuEghCQkow+A7agEw44fDZDvSUfpm4n6Dff+xrkdOdt5xRUTkhvJdEP311sDKlStJTU11eiCR4uDpTnXx9/HgcMJVlv961nkHXjvF+t8mD0CIHlIQESks6pwgUgABvp483aUuAG+tPkL69Xw+PXYjsRvh2Bpw84CuL9768UREJN/yXRCZTKYcs9lrdnspzQbfXotgfx/OXrnG59tO5tnO3c3Eo+1r8mj7mri75fFvxmKB1f+bwLX1EAis7YLEIiKSl3x3qrZYLAwePBhvb28A0tPT+cc//kHZsvaj53799dfOTShSRPl4uvNc93qM/Wofs9cd46G21fH3yTlekLeHOy/3aXLjgx34Fs7tBs+y0GmMixKLiEhe8l0QDRo0yO71I4884vQwIsXN/a2q8dHG48ScT+WjDccZHdXA8YNkX///CVxvfwbKVXFuSBERual8F0Tz5893ZQ6RYsnD3Y0xPRvy5Ge7mLsploERNani72PXxmKxcCk1E4DAsl45bzX/+hlcPAa+lSDimcKKLiIif6JO1SK3qEejIFrVKM+169m8s/ZojvevXc+m9StraP3KGq79tfN1Zhqs/98Erne+AD7+hZBYRET+SgWRyC0ymUyM7Wmd+HXJL6eJveDAcBTbP4CUeChfA9oMcVFCERG5GRVEIk4QXrsiXRtWIdts4Y0f8znxa9ol2DTLut51Anh4uyyfiIjcmAoiEScZ07MBJhOs+C2O385cufkOP78JGckQ1NQ6EKOIiBhGBZGIkzQM9ufellUBmH6ziV+vnIYdH1nXNYGriIjh9FNYxIlGda+Pl7sbW2Iu8vPRC3k3XD8NsjOhVkeo263wAoqISK5UEIk4UbUKvjwaUROAGdGHMOc28WvCAdiz2LoeOQU04ruIiOFUEIk42fAudfHz9uD3c8n897dzuLuZuL9VNe5vVc06dcfaqYAFbrsbqrU2Oq6IiKCCSMTpAst68cSd1rnI3vzxCCZMvPlQc958qDneZ3fAkR/A5A7dJhqcVERE/qCCSMQFhnYMo1I5b05dSmPJL6esGy0WWPO/CVxbPQqV6hkXUERE7BSJgmj27NnUqlULHx8fwsPD2bFjR55tFyxYgMlkslt8fHJOlTBx4kRCQkIoU6YMkZGRHD2acwRhEVfx9fJgRKS14HlnzRHOX00nbf9KLKe2g0cZ6DTO4IQiIvJnhhdES5cuZdSoUUyaNIndu3fTvHlzoqKiSExMzHMff39/4uLibMvJkyft3p85cybvvvsuc+bMYfv27ZQtW5aoqCjS09Nd/XFEbPq1rU6tir5cTL1O21fX0mgRXMMb2j8F/iFGxxMRkT8xvCB66623GDZsGEOGDKFRo0bMmTMHX19f5s2bl+c+JpOJ4OBg2xIUFGR7z2KxMGvWLF566SXuuecemjVrxqeffsq5c+dYvnx5IXwiEStPdzdGRzWw3+gTAHeMMCaQiIjkydCCKDMzk127dhEZGWnb5ubmRmRkJFu3bs1zv5SUFGrWrEn16tW55557+P33323vxcbGEh8fb3fMgIAAwsPD8zxmRkYGycnJdouIM/ytSQgtgv80Jcft/4Qy5Q3LIyIiuTO0ILpw4QLZ2dl2V3gAgoKCiI+Pz3WfBg0aMG/ePL799ls+//xzzGYzt99+O2fOnAGw7efIMadNm0ZAQIBtqV69+q1+NBEA3NxMvFZzp+31mbr9DEwjIiJ5MfyWmaMiIiIYOHAgLVq0oFOnTnz99ddUrlyZDz/8sMDHHD9+PElJSbbl9OnTTkwspdq1K9Q6NNf28t31pwwMIyIieTG0IKpUqRLu7u4kJCTYbU9ISCA4ODhfx/D09KRly5YcO3YMwLafI8f09vbG39/fbhFxis2zID3J9vL7ffH8fi4p7/YiImIIQwsiLy8vWrduzdq1a23bzGYza9euJSIiIl/HyM7OZt++fYSEWJ/aCQsLIzg42O6YycnJbN++Pd/HFHGK5HOwbU6OzTOjDxsQRkREbsTD6ACjRo1i0KBBtGnThnbt2jFr1ixSU1MZMmQIAAMHDqRq1apMmzYNgKlTp9K+fXvq1q3LlStXeP311zl58iSPP/44YH0CbeTIkbzyyivUq1ePsLAwJkyYQGhoKH369DHqY0pptH46ZF3Drdod/K1MMKmZ2Ww6ep4NR86zJeYCt9epZHRCERH5H8MLor59+3L+/HkmTpxIfHw8LVq0IDo62tYp+tSpU7i5/f+FrMuXLzNs2DDi4+OpUKECrVu3ZsuWLTRq1MjWZsyYMaSmpvLEE09w5coVOnToQHR0dI4BHEVc5vwR+PVzAHyiJvJ+DeucZRO/3c+nW08yI/owy5+uiEkTu4qIFAkmi8WSy3TcpVtycjIBAQEkJSWpP5EUzNJH4OB/oX4veHiJbfP5qxl0en0daZnZfDCgFb2aaoBGERFnuZXf38XuKTORIu/MTmsxZHLLMYFrZT9vHu9onfj19VWHyco2G5FQRET+QgWRiDNZLLD6fxO4Nu8PQY1Iy8yi1rgV1Bq3grTMLIZ1DCOwrBfHL6SybOcZY/OKiAiggkjEuY6tgZObwN0bOo/PtYmfjyfPdq0LwKw1R7iWmV2YCUVEJBcqiEScxWyGNZOt6+2GQfm8Rzx/OLwG1SqUIfFqBvM2xxZOPhERyZMKIhFn2fcfSNgP3gHQ8fkbNvX2cGd0D+vEr3M2xHA5NbMwEoqISB5UEIk4Q1YGrHvFut5hBPgG3nSXu5uHcluIP1fTs3h//TEXBxQRkRtRQSTiDDvnwZVTUC4Ywp/K1y5ubibG9LReJVq49SRnr1xzZUIREbkBFUQityo9GTa+bl3vPA68fPO9a+f6lWlfO5DMLDOzVh9xUUAREbkZFUQit2rre5B2ESrWhZaP5njbzWSiS4PKdGlQGbe/jExtMpkY27MhAF/tPsORhKuFEllEROypIBK5FSmJsOU963q3ieCeczYcH0935g9px/wh7fDxdM/xfssaFejVJBizRRO/iogYRQWRyK3YMBOup0LV1nDb3QU+zOioBri7mVhzMIFfTlxyYkAREckPFUQiBXUxBnbNt65HToZbmKi1TuVyPNSmGgAzfjiEphgUESlcKohECmrdq2DOgrqREHZnns3SMrO4bUI0t02IJi0zK892I7rVx8fTjZ0nL7P2YKIrEouISB5UEIkUxLk9sP8r63q3STdtfu16Nteu33iKjuAAH4bcEQbAzFWHyDbrKpGISGFRQSRSEH9M0dH0IQhp5rTD/qNTHQLKeHIkIYWvd2viVxGRwqKCSMRRMevg+Dpw84SuLzr10AFlPBnepQ4Ab68+QvpNriqJiIhzqCASccSfJ3BtOxQq1HL6KQZG1CI0wIdzSel8tvWk048vIiI5qSASccSB5RC3B7zKQcfRLjmFj6c7I7vXB+C9dcdIunbdJecREZH/p4JIJL+yr8NPL1vXb38WylV22anub1WNelXKkXTtOh9uiHHZeURExEoFkUh+7V4Il45D2coQMTzfu7mZTISHBRIeFphj6o68uLuZGPO/KT3mbY4lITm9QJFFRCR/VBCJ5EdGCqyfYV2/cwx4++V7Vx9Pd5Y+GcHSJyNynbojL5G3VaFNzQqkXzcza81RRxOLiIgDVBCJ5Me2DyA10dqJuvXgQjmlyWRibC/rVaJlO08Tcz6lUM4rIlIaqSASuZnUi7D5Het61wng4VVop25bK5DI26qQbbbwxipN/Coi4ioqiERu5uc3IPMqBDeDxvc5vHtaZhatXl5Nq5dX33Dqjry8ENUQkwl+2B/PntNXHN5fRERuTgWRyI1cPgm/fGJdj5wMbgX7J3MpNZNLqZkF2rdBsB/3t7JO/Dr9h4Oa+FVExAVUEIncyLrXIDvTOnlrna6GxXiue328PNzYdvwSG46cNyyHiEhJpYJIJC/x++G3pdb1yMmQz0fmXaFq+TIMiqgJwIzow5g18auIiFOpIBLJy9qpgAUa9YGqrY1Ow9Od6+Ln7cHBuGS+23vO6DgiIiWKCiKR3JzYDEdXgckduk00Og0AFcp68Y/O1olf3/jxMBlZmvhVRMRZVBCJ/JXFAmsmWddbD4KKdYzN8yeP3RFGFT9vzly+xuLtp4yOIyJSYqggEvmrQ9/DmV/A0xc6jb3lw7mZTDSrFkCzagH5nrojL2W83BkRWQ+A9346RkqG44/xi4hITiqIRP4sO+t/fYeA9k+DX/AtH9LH053vnunAd890cGjqjrw81KY6tSuV5WJqJh9vPH7LxxMRERVEIvb2LIILR6BMINzxT6PT5MrT3Y3RUQ0A+Pjn45y/mmFwIhGR4k8Fkcgfrl+D9dOt63eOBp8AY/PcQK8mwTSvXp60zGze+0kTv4qI3CoVRCJ/2P4hXD0HAdWhzVCnHfZaZjZ3TP+JO6b/xLVM5zwZZjKZGNvTepVo0fZTnLyY6pTjioiUVkWiIJo9eza1atXCx8eH8PBwduzYkWfbjz/+mI4dO1KhQgUqVKhAZGRkjvaDBw/GZDLZLT179nT1x5Di7Npl2PSWdb3Li+Dp47RDW7Bw9so1zl65hgXnDah4e51KdKpfmSyzhTd/POK044qIlEaGF0RLly5l1KhRTJo0id27d9O8eXOioqJITEzMtf369evp378/69atY+vWrVSvXp0ePXpw9uxZu3Y9e/YkLi7OtnzxxReF8XGkuNr0NqQnQZVG0Owho9Pk25j/XSX6bu859p9NMjiNiEjxZXhB9NZbbzFs2DCGDBlCo0aNmDNnDr6+vsybNy/X9osWLeLpp5+mRYsWNGzYkE8++QSz2czatWvt2nl7exMcHGxbKlSoUBgfR4qjpLPW22UA3SaB260/CVZYGocGcE+LUABmRB8yOI2ISPFlaEGUmZnJrl27iIyMtG1zc3MjMjKSrVu35usYaWlpXL9+ncDAQLvt69evp0qVKjRo0ICnnnqKixcvOjW7lCDrp0FWOtS4HepHGZ3GYc93b4Cnu4mfj15g87ELRscRESmWDC2ILly4QHZ2NkFBQXbbg4KCiI+Pz9cxxo4dS2hoqF1R1bNnTz799FPWrl3LjBkz2LBhA7169SI7O/cOrRkZGSQnJ9stUkokHrI+ag/QfYqhE7gWVI2KvgwI/2Pi10NYLJr4VUTEUR5GB7gV06dPZ8mSJaxfvx4fn//vBNuvXz/betOmTWnWrBl16tRh/fr1dOvWLcdxpk2bxpQpUwolsxQxP70MFjM0/DtUb2d0mgJ7pmtd/rPzNL+dSWLlvnjuahZidCQRkWLF0CtElSpVwt3dnYSEBLvtCQkJBAffeITgN954g+nTp/Pjjz/SrFmzG7atXbs2lSpV4tixY7m+P378eJKSkmzL6dOnHfsgUjyd3mGdpsPk5tIJXE2YqFelHPWqlMOEa65AVSrnzbA7awPw+qpDXM82u+Q8IiIllaEFkZeXF61bt7brEP1HB+mIiIg895s5cyYvv/wy0dHRtGnT5qbnOXPmDBcvXiQkJPe/mr29vfH397dbpISzWGD1/yZwbfEwVG7gslOV8XJn9ahOrB7ViTJeruuw/XjH2lQs68WJi2ks/UVFvYiIIwx/ymzUqFF8/PHHLFy4kIMHD/LUU0+RmprKkCFDABg4cCDjx4+3tZ8xYwYTJkxg3rx51KpVi/j4eOLj40lJSQEgJSWFF154gW3btnHixAnWrl3LPffcQ926dYmKKn4dZsVFjv4Ip7aAhw90Hn/z9sVAOW8P/tnNOvHrO2uPkpapiV9FRPLL8IKob9++vPHGG0ycOJEWLVqwZ88eoqOjbR2tT506RVxcnK39Bx98QGZmJg888AAhISG25Y033gDA3d2d3377jbvvvpv69eszdOhQWrduzc8//4y3t7chn1GKGHM2rJlsXW/3BARUMzSOM/VvV4Magb6cv5rBvE2xRscRESk2TBY9kpJDcnIyAQEBJCUl6fZZSbTnC1j+D+tcZf/cA76BN93lVlzLzObu9zYB8N0zHVx62wzg2z1nGbFkD37eHmwY04XAsl4uPZ+ISFFxK7+/Db9CJFKorqfDulet6x2ec3kxBNapO44mpnA0McWpU3fkpXezUBqH+nM1I4vZ63J/kEBEROypIJLSZedcSDoNfqEQ/g+j07iEm5uJsT0bAvDZ1pOcuZxmcCIRkaJPBZGUHulJsNHa14zO48CzjLF5XKhjvUrcXqcimdlm3lqtiV9FRG5GBZGUHpvfhWuXoFJ9aDHA6DQuZTL9/1Wib349y6F4jb4uInIjKoikdLgaD9vet653mwjuxXqQ9nxpXr08dzUNwWKB16MPGx1HRKRIU0EkpcOGGXA9Daq1tU7TUUo836M+7m4m1h5KZEfsJaPjiIgUWSqIpOS7GAO7FlrXIycX+gSuJkxULV+GquXLuGzqjrzUrlyOfm2rAzD9h4Oa+FVEJA8qiKTk++llsGRDvR5Qq0Ohn76Mlzubx3Vl87iuLh+DKDcjutWjjKc7u09d4ccDCTffQUSkFFJBJCXb2d3w+zeACbpNMjqNIar4+/BYh1oAvL7qMFma+FVEJAcVRFJyWSyw5n9FULO+ENzE2DwGerJTHcr7enIsMYWvd581Oo6ISJGjgkhKrpifIHYjuHtBl38ZFiP9unXqjrvf20T69WxDMvj7ePJMl7oAvLX6iGE5RESKKhVEUjKZzf8/gWvbx6FCTeOiWCz8diaJ384kYTawU/Mj7WtStXwZ4pPTWbjlhGE5RESKIhVEUjL9/jXE/wZeftBxtNFpigQfT3ee614fgNnrjpGUdt3gRCIiRYcKIil5sjKtT5YB3DECylY0Nk8Rcm/LqjQI8iM5PYsPNsQYHUdEpMhQQSQlz+6FcPkElK0CEU8bnaZIcXczMaZnAwDmb44lLumawYlERIoGFURSsmSkWEelBug0BrzKGpunCOrasApta1UgI8vMO2uOGh1HRKRIUEEkJcvW2ZB6HgJrQ+vBRqcpkkwmE+N6WSd+XbbzNMcSUwxOJCJiPBVEUnKknIct71rXu74E7p7G5vmTwLJeBJb1MjqGTeuagXRvFITZAq+vOmR0HBERw5X8Kb+l9Pj5DchMgZAW0Oheo9PY+Hp5sHtCd6Nj5DAmqgFrDyaw6vcEdp+6TKsaFYyOJCJiGF0hkpLhUiz8Mte6HjkZ3PStfTP1gvx4oHU1AKb/cEgTv4pIqabfGlIyrHsNzNehdheo08XoNMXGyMj6eHu4sSP2EusPnzc6joiIYVQQSfEX9xvs+491PXKyoVFyk349m74fbqXvh1uL3JQZoeXLMPj2WgDMiD5EtllXiUSkdFJBJMXf2imABRrfB6EtjE6Tg9liYXvsJbbHXjJ06o68PNW5Dv4+HhyKv8q3ezTxq4iUTiqIpHiL3QjH1oCbh/XJMnFYeV8vnupsnfj1zR+PkJFVtK5iiYgUBhVEUnxZLP8/gWvrwVCxjpFpirXBt9ciyN+bs1eu8fm2U0bHEREpdCqIpPg6+B2c3QWeZeHOMUanKdbKeLnzXKR14tf3fjpKcromfhWR0kUFkRRP2Vmwdqp1PWI4+AUZm6cEeKB1NepULsvltOt8vPG40XFERAqVCiIpnn79DC4eA9+KcPuzRqcpETzc3Xghyjrx6yc/x5J4Nd3gRCIihUcFkRQ/mWmwfrp1/c4XwMff2Dz5UMbTnTKe7kbHuKmoxsG0qF6ea9ez+ffaY0bHEREpNCqIpPjZ/gGkxEP5GtDmMaPT3JSvlwcHX+7JwZd74utVtGfL+fPEr1/sOMWJC6kGJxIRKRwqiKR4SbsEm96xrnd5CTy8jc1TArWvXZEuDSqTZbbwxo+HjY4jIlIoVBBJ8fLzm5CRBEFNoOmDRqcpscb0bIjJBN//Fse+M0lGxxERcTkVRFJ8XDkNOz62rhejCVzTr2czZP4OhszfUeSm7sjLbSH+3NuiKmCd0kNEpKQrHr9RRADWT4PsDKjZAepGGp0m38wWC+sOn2fd4fNFcuqOvDzXvT5e7m5sOnaBn49q4lcRKdlUEEnxkHgQ9n5hXY+cDCaToXFKg+qBvgxoXwOwXiUya+JXESnBVBBJ8bB2KljMcFtvqN7W6DSlxjNd6lLO24P9Z5NZsS/O6DgiIi5TJAqi2bNnU6tWLXx8fAgPD2fHjh03bP+f//yHhg0b4uPjQ9OmTVm5cqXd+xaLhYkTJxISEkKZMmWIjIzk6NGjrvwI4kont8LhlWByg64TjU5TqlQs580Td9YG4I0fD5OZZTY4kYiIaxheEC1dupRRo0YxadIkdu/eTfPmzYmKiiIxMTHX9lu2bKF///4MHTqUX3/9lT59+tCnTx/2799vazNz5kzeffdd5syZw/bt2ylbtixRUVGkp2vk3WLnzxO4tnwUKtc3NE5pNLRDGJXKeXPyYhpLf9HEryJSMpksFmN7eYaHh9O2bVvee+89AMxmM9WrV+fZZ59l3LhxOdr37duX1NRUvv/+e9u29u3b06JFC+bMmYPFYiE0NJTnn3+e0aNHA5CUlERQUBALFiygX79+N82UnJxMQEAASUlJ+PsX/VGQS7RDK2FJf/DwgX/+Cv6hRidyWFpmFo0mrgLgwNSoIj84Y24+23qCCd/+TqVyXmx4oQtlvYvfZxCRku9Wfn8b+lMtMzOTXbt2MX78eNs2Nzc3IiMj2bp1a677bN26lVGjRtlti4qKYvny5QDExsYSHx9PZOT/P4UUEBBAeHg4W7duzbUgysjIICMjw/Y6Kck67kpycnKBP5s4gTkbvp8IGRZoMQQoB8Xwa5KWmYU5Iw2wfk9lFcOC6G8Ny/PhGgunLl5h9qp9/KNzHaMjiYjk8Mfv7YJc6zH0J/OFCxfIzs4mKMh+pvKgoCAOHcp97JP4+Phc28fHx9ve/2NbXm3+atq0aUyZMiXH9urVq+fvg0ghmPG/pXgLmWV0gls3dhaMNTqEiMgNXLx4kYCAAIf2KX5/qrrA+PHj7a46XblyhZo1a3Lq1CmH/4eKcyUnJ1O9enVOnz6t25cG09eiaNHXo+jQ16LoSEpKokaNGgQGBjq8r6EFUaVKlXB3dychIcFue0JCAsHBwbnuExwcfMP2f/w3ISGBkJAQuzYtWrTI9Zje3t54e+ecEysgIEDf3EWEv7+/vhZFhL4WRYu+HkWHvhZFh1sBZjIw9CkzLy8vWrduzdq1a23bzGYza9euJSIiItd9IiIi7NoDrF692tY+LCyM4OBguzbJycls3749z2OKiIhI6Wb4LbNRo0YxaNAg2rRpQ7t27Zg1axapqakMGTIEgIEDB1K1alWmTZsGwIgRI+jUqRNvvvkmd911F0uWLGHnzp189NFHAJhMJkaOHMkrr7xCvXr1CAsLY8KECYSGhtKnTx+jPqaIiIgUYYYXRH379uX8+fNMnDiR+Ph4WrRoQXR0tK1T9KlTp+wufd1+++0sXryYl156iX/961/Uq1eP5cuX06RJE1ubMWPGkJqayhNPPMGVK1fo0KED0dHR+Pj45CuTt7c3kyZNyvU2mhQufS2KDn0tihZ9PYoOfS2Kjlv5Whg+DpGIiIiI0QwfqVpERETEaCqIREREpNRTQSQiIiKlngoiERERKfVUEOVi9uzZ1KpVCx8fH8LDw9mxY4fRkUqdjRs30rt3b0JDQzGZTLa56qTwTZs2jbZt2+Ln50eVKlXo06cPhw8fNjpWqfTBBx/QrFkz2wCAERER/PDDD0bHEmD69Om2YV+k8E2ePBmTyWS3NGzY0KFjqCD6i6VLlzJq1CgmTZrE7t27ad68OVFRUSQmJhodrVRJTU2lefPmzJ492+gopd6GDRsYPnw427ZtY/Xq1Vy/fp0ePXqQmppqdLRSp1q1akyfPp1du3axc+dOunbtyj333MPvv/9udLRS7ZdffuHDDz+kWbNmRkcp1Ro3bkxcXJxt2bRpk0P767H7vwgPD6dt27a89957gHXk7OrVq/Pss88ybtw4g9OVTiaTiW+++UYDaxYR58+fp0qVKmzYsIE777zT6DilXmBgIK+//jpDhw41OkqplJKSQqtWrXj//fd55ZVXaNGiBbNmzTI6VqkzefJkli9fzp49ewp8DF0h+pPMzEx27dpFZGSkbZubmxuRkZFs3brVwGQiRUdSUhJAgSZPFOfJzs5myZIlpKamaloiAw0fPpy77rrL7veGGOPo0aOEhoZSu3ZtBgwYwKlTpxza3/CRqouSCxcukJ2dbRsl+w9BQUEcOnTIoFQiRYfZbGbkyJHccccddqPDS+HZt28fERERpKenU65cOb755hsaNWpkdKxSacmSJezevZtffvnF6CilXnh4OAsWLKBBgwbExcUxZcoUOnbsyP79+/Hz88vXMVQQiUi+DR8+nP379zt8b16cp0GDBuzZs4ekpCS+/PJLBg0axIYNG1QUFbLTp08zYsQIVq9ene9pocR1evXqZVtv1qwZ4eHh1KxZk2XLluX7drIKoj+pVKkS7u7uJCQk2G1PSEggODjYoFQiRcMzzzzD999/z8aNG6lWrZrRcUotLy8v6tatC0Dr1q355ZdfeOedd/jwww8NTla67Nq1i8TERFq1amXblp2dzcaNG3nvvffIyMjA3d3dwISlW/ny5alfvz7Hjh3L9z7qQ/QnXl5etG7dmrVr19q2mc1m1q5dq3v0UmpZLBaeeeYZvvnmG3766SfCwsKMjiR/YjabycjIMDpGqdOtWzf27dvHnj17bEubNm0YMGAAe/bsUTFksJSUFGJiYggJCcn3PrpC9BejRo1i0KBBtGnThnbt2jFr1ixSU1MZMmSI0dFKlZSUFLvKPjY2lj179hAYGEiNGjUMTFb6DB8+nMWLF/Ptt9/i5+dHfHw8AAEBAZQpU8bgdKXL+PHj6dWrFzVq1ODq1assXryY9evXs2rVKqOjlTp+fn45+tGVLVuWihUrqn+dAUaPHk3v3r2pWbMm586dY9KkSbi7u9O/f/98H0MF0V/07duX8+fPM3HiROLj42nRogXR0dE5OlqLa+3cuZMuXbrYXo8aNQqAQYMGsWDBAoNSlU4ffPABAJ07d7bbPn/+fAYPHlz4gUqxxMREBg4cSFxcHAEBATRr1oxVq1bRvXt3o6OJGOrMmTP079+fixcvUrlyZTp06MC2bduoXLlyvo+hcYhERESk1FMfIhERESn1VBCJiIhIqaeCSEREREo9FUQiIiJS6qkgEhERkVJPBZGIiIiUeiqIREREpNRTQSQiRYrJZGL58uVGxxCRUkYFkYgwePBgTCZTjsWRiRGdJS4uzm7m6r/KK2vPnj0LMaVrDBkyhJdeesnh/Xbt2oXJZGLbtm25vt+tWzfuu+++W40nUqJp6g4RAaBnz57Mnz/fbpsjw97/2fXr1/H09CzQvsHBwTdtk1tWb2/vAp0vvzIzM/Hy8nLZ8bOzs/n+++9ZsWKFw/u2bt2a5s2bM2/ePNq3b2/33okTJ1i3bh3//e9/nRVVpETSFSIRAawFRXBwsN3yx4zd3377La1atcLHx4fatWszZcoUsrKybPuaTCY++OAD7r77bsqWLcurr77K5MmTadGiBfPmzaNGjRqUK1eOp59+muzsbGbOnElwcDBVqlTh1VdftcuRn1tmuWWtUKGC3TE++eQT7r33Xnx9falXrx7fffed3TH2799Pr169KFeuHEFBQTz66KNcuHDB9n7nzp155plnGDlyJJUqVSIqKgqA7777jnr16uHj40OXLl1YuHAhJpOJK1eukJqair+/P19++aXduZYvX07ZsmW5evVqnp9py5YteHp60rZt21zfN5vNTJs2jbCwMMqUKUPz5s3tzjN06FCWLl1KWlqa3X4LFiwgJCSkRFxBE3ElFUQickM///wzAwcOZMSIERw4cIAPP/yQBQsW5ChkJk+ezL333su+fft47LHHAIiJieGHH34gOjqaL774grlz53LXXXdx5swZNmzYwIwZM3jppZfYvn2703NPmTKFhx56iN9++42//e1vDBgwgEuXLgFw5coVunbtSsuWLdm5cyfR0dEkJCTw0EMP2R1j4cKFeHl5sXnzZubMmUNsbCwPPPAAffr0Ye/evTz55JO8+OKLtvZly5alX79+Oa5ezZ8/nwceeAA/P78883733Xf07t0bk8mU6/vTpk3j008/Zc6cOfz+++8899xzPPLII2zYsAGAAQMGkJGRYVckWSwWFi5cyODBg23FrYjkwSIipd6gQYMs7u7ulrJly9qWBx54wGKxWCzdunWzvPbaa3btP/vsM0tISIjtNWAZOXKkXZtJkyZZfH19LcnJybZtUVFRllq1almys7Nt2xo0aGCZNm2a3bG++eYbh7KWLVvW8uqrr9od46WXXrK9TklJsQCWH374wWKxWCwvv/yypUePHnbHPX36tAWwHD582GKxWCydOnWytGzZ0q7N2LFjLU2aNLHb9uKLL1oAy+XLly0Wi8Wyfft2i7u7u+XcuXMWi8ViSUhIsHh4eFjWr1+f52eyWCyWevXqWb7//vtc30tPT7f4+vpatmzZYrd96NChlv79+9te9+vXz9KpUyfb67Vr11oAy9GjR294bhGxWNSHSEQA6NKlCx988IHtddmyZQHYu3cvmzdvtrsilJ2dTXp6Omlpafj6+gLQpk2bHMesVauW3VWRoKAg3N3dcXNzs9uWmJh4S1kBAgMD7V43a9bM7rP4+/vbzrN3717WrVtHuXLlchw7JiaG+vXrA9a+OX92+PDhHLe02rVrl+N148aNWbhwIePGjePzzz+nZs2a3HnnnXl+noMHD3Lu3Dm6deuW6/vHjh0jLS2N7t27223PzMykZcuWttePPfYYUVFRxMTEUKdOHebNm0enTp2oW7dunucWESsVRCICWIuG3H5xpqSkMGXKlFyfUvLx8bHb/6/+2rHaZDLlus1sNjsl683O/cd5UlJS6N27NzNmzMixX0hIiN15CuLxxx9n9uzZjBs3jvnz5zNkyJA8b4WB9XZZ9+7d7f5//llKSgoAK1asoGrVqnbv/bkzebdu3ahRowYLFizghRde4Ouvv+bDDz8s0GcQKW1UEInIDbVq1YrDhw+XqKsMrVq14quvvqJWrVp4eOT/x2CDBg1YuXKl3bZffvklR7tHHnmEMWPG8O6773LgwAEGDRp0w+N+++23PPHEE3m+36hRI7y9vTl16hSdOnXKs52bmxtDhgxh7ty5VK1aFS8vLx544IGbfCoRAXWqFpGbmDhxIp9++ilTpkzh999/5+DBgyxZsqRA4+U4S0ZGBvHx8XbLn58Qu5nhw4dz6dIl+vfvzy+//EJMTAyrVq1iyJAhZGdn57nfk08+yaFDhxg7dixHjhxh2bJlLFiwAMDuClCFChW47777eOGFF+jRowfVqlXL85iJiYns3LmTv//973m28fPzY/To0Tz33HMsXLiQmJgYdu/ezb///W8WLlxo13bIkCGcPXuWf/3rX/Tv358yZcrk8/+KSOmmgkhEbigqKorvv/+eH3/8kbZt29K+fXvefvttatasaVim6OhoQkJC7JYOHTrke//Q0FA2b95MdnY2PXr0oGnTpowcOZLy5cvb9W/6q7CwML788ku+/vprmjVrxgcffGB7yuyv4yANHTqUzMxM2xN3efnvf/9Lu3btqFSp0g3bvfzyy0yYMIFp06Zx22230bNnT1asWEFYWJhduxo1ahAZGcnly5dvem4R+X8mi8ViMTqEiEhx9eqrrzJnzhxOnz5tt/2zzz7jueee49y5czcc0PHuu++mQ4cOjBkzxtVRReQG1IdIRMQB77//Pm3btqVixYps3ryZ119/nWeeecb2flpaGnFxcUyfPp0nn3zypqNbd+jQgf79+7s6tojchK4QiYg44LnnnmPp0qVcunSJGjVq8OijjzJ+/Hhb5+zJkyfz6quvcuedd/Ltt9/m+mi/iBQ9KohERESk1FOnahERESn1VBCJiIhIqaeCSEREREo9FUQiIiJS6qkgEhERkVJPBZGIiIiUeiqIREREpNRTQSQiIiKlngoiERERKfX+Dwf/pbnEXdHEAAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -241,19 +239,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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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"
     }
    ],
@@ -266,7 +262,7 @@
     "v_Na_conc = []\n",
     "for t in temperatures:\n",
     "    ds = defect_system.temperature = t\n",
-    "    concentrations = defect_system.as_dict()\n",
+    "    concentrations = defect_system.concentration_dict()\n",
     "    v_Cl_conc.append(concentrations[\"v_Cl\"])\n",
     "    v_Na_conc.append(concentrations[\"v_Na\"])\n",
     "\n",
@@ -290,19 +286,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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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"
     }
    ],
@@ -359,19 +353,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RuntimeWarning: invalid value encountered in double_scalars\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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",
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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"
     }
    ],
@@ -383,12 +382,12 @@
     "\n",
     "# calculate defect and electronic carrier concentrations at high T.\n",
     "live_defect_system.temperature = 1500\n",
-    "high_t_defects = live_defect_system.as_dict()\n",
+    "high_t_defects = live_defect_system.concentration_dict()\n",
     "\n",
     "# repeat at low T\n",
     "low_t_defect_system = deepcopy(defect_system)\n",
     "low_t_defect_system.temperature = 300\n",
-    "low_t_defects = low_t_defect_system.as_dict()\n",
+    "low_t_defects = low_t_defect_system.concentration_dict()\n",
     "\n",
     "# fix the concentration of the defect species at high T, and then \n",
     "# calculate the concentration of the electron holes at low T\n",
@@ -396,7 +395,7 @@
     "fixed_species_defect_system.temperature = 300\n",
     "fixed_species_defect_system.defect_species_by_name(\"v_Na\").fix_concentration(high_t_defects[\"v_Na\"] / 1e24 * defect_system.volume)\n",
     "fixed_species_defect_system.defect_species_by_name(\"v_Cl\").fix_concentration(high_t_defects[\"v_Cl\"] / 1e24 * defect_system.volume)\n",
-    "fixed_species_defects = fixed_species_defect_system.as_dict()\n",
+    "fixed_species_defects = fixed_species_defect_system.concentration_dict()\n",
     "\n",
     "# plot the results\n",
     "plt.bar([0, 1, 2],  [high_t_defects[\"p0\"], low_t_defects[\"p0\"], fixed_species_defects[\"p0\"]])\n",
@@ -418,19 +417,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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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"
     }
    ],
@@ -440,12 +437,12 @@
     "\n",
     "# calculate defect and electronic carrier concentrations at high T.\n",
     "live_defect_system.temperature = 1500\n",
-    "high_t_defects = live_defect_system.as_dict(decomposed=True)\n",
+    "high_t_defects = live_defect_system.concentration_dict(decomposed=True)\n",
     "\n",
     "# repeat at low T\n",
     "low_t_defect_system = deepcopy(defect_system)\n",
     "low_t_defect_system.temperature = 300\n",
-    "low_t_defects = low_t_defect_system.as_dict(decomposed=True)\n",
+    "low_t_defects = low_t_defect_system.concentration_dict(decomposed=True)\n",
     "\n",
     "# fix the concentration of the defect species at high T, and then \n",
     "# calculate the concentration of the electron holes at low T\n",
@@ -453,7 +450,7 @@
     "fixed_species_defect_system.temperature = 300\n",
     "fixed_species_defect_system.defect_species_by_name(\"v_Na\").charge_states[-1].fix_concentration(high_t_defects[\"v_Na\"][-1] / 1e24 * defect_system.volume)\n",
     "fixed_species_defect_system.defect_species_by_name(\"v_Cl\").charge_states[1].fix_concentration(high_t_defects[\"v_Cl\"][1] / 1e24 * defect_system.volume)\n",
-    "fixed_species_defects = fixed_species_defect_system.as_dict()\n",
+    "fixed_species_defects = fixed_species_defect_system.concentration_dict()\n",
     "\n",
     "plt.bar([0, 1, 2],  [high_t_defects[\"p0\"], low_t_defects[\"p0\"], fixed_species_defects[\"p0\"]])\n",
     "plt.xticks([0, 1, 2], [\"high T\", \"low T\", \"fixed charge states\"])\n",
@@ -474,20 +471,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+     "ename": "KeyError",
+     "evalue": "'v_Na'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[15], line 12\u001b[0m\n\u001b[1;32m     10\u001b[0m live_defect_system \u001b[39m=\u001b[39m DefectSystem(defect_species \u001b[39m=\u001b[39m [dopant_species, v_Na, v_Cl], volume \u001b[39m=\u001b[39m defect_system\u001b[39m.\u001b[39mvolume, temperature \u001b[39m=\u001b[39m \u001b[39m500\u001b[39m, dos \u001b[39m=\u001b[39m defect_system\u001b[39m.\u001b[39mdos)\n\u001b[1;32m     11\u001b[0m defect_data \u001b[39m=\u001b[39m live_defect_system\u001b[39m.\u001b[39mas_dict()\n\u001b[0;32m---> 12\u001b[0m v_Na_conc\u001b[39m.\u001b[39mappend(defect_data[\u001b[39m\"\u001b[39;49m\u001b[39mv_Na\u001b[39;49m\u001b[39m\"\u001b[39;49m])\n\u001b[1;32m     13\u001b[0m v_Cl_conc\u001b[39m.\u001b[39mappend(defect_data[\u001b[39m\"\u001b[39m\u001b[39mv_Cl\u001b[39m\u001b[39m\"\u001b[39m])\n\u001b[1;32m     14\u001b[0m p_0\u001b[39m.\u001b[39mappend(defect_data[\u001b[39m\"\u001b[39m\u001b[39mp0\u001b[39m\u001b[39m\"\u001b[39m])\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'v_Na'"
+     ]
     }
    ],
    "source": [
@@ -501,7 +497,7 @@
     "    dopant_charge_state = DefectChargeState(1, fixed_concentration = dopant_concentration / 1e24 * defect_system.volume)\n",
     "    dopant_species = DefectSpecies(\"dopant\", 1, charge_states = {1 : dopant_charge_state})\n",
     "    live_defect_system = DefectSystem(defect_species = [dopant_species, v_Na, v_Cl], volume = defect_system.volume, temperature = 500, dos = defect_system.dos)\n",
-    "    defect_data = live_defect_system.as_dict()\n",
+    "    defect_data = live_defect_system.concentration_dict()\n",
     "    v_Na_conc.append(defect_data[\"v_Na\"])\n",
     "    v_Cl_conc.append(defect_data[\"v_Cl\"])\n",
     "    p_0.append(defect_data[\"p0\"])\n",
@@ -520,6 +516,13 @@
     "plt.legend()\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/py_sc_fermi/__init__.py b/py_sc_fermi/__init__.py
index 6849410..8c0d5d5 100644
--- a/py_sc_fermi/__init__.py
+++ b/py_sc_fermi/__init__.py
@@ -1 +1 @@
-__version__ = "1.1.0"
+__version__ = "2.0.0"
diff --git a/py_sc_fermi/cli/sc_fermi_solve.py b/py_sc_fermi/cli/sc_fermi_solve.py
index a5355f6..5a3e971 100644
--- a/py_sc_fermi/cli/sc_fermi_solve.py
+++ b/py_sc_fermi/cli/sc_fermi_solve.py
@@ -70,7 +70,7 @@ def main():
         defect_system = DefectSystem.from_input_set(input_data)
     defect_system.report()
 
-    dump_dict = defect_system.as_dict(decomposed=True)
+    dump_dict = defect_system.concentration_dict(decomposed=True)
     dump_dict["temperature"] = defect_system.temperature
     with open("py_sc_fermi_out.yaml", "w") as f:
         yaml.dump(dump_dict, f)
diff --git a/py_sc_fermi/defect_charge_state.py b/py_sc_fermi/defect_charge_state.py
index df62fcd..6403761 100644
--- a/py_sc_fermi/defect_charge_state.py
+++ b/py_sc_fermi/defect_charge_state.py
@@ -1,6 +1,7 @@
 import numpy as np  # type: ignore
 from scipy.constants import physical_constants  # type: ignore
 from typing import Optional
+import warnings
 
 kboltz = physical_constants["Boltzmann constant in eV/K"][0]
 
@@ -22,7 +23,7 @@ def __init__(
         energy: Optional[float] = None,
         fixed_concentration: Optional[float] = None,
     ):
-        if energy == None and fixed_concentration == None:
+        if energy is None and fixed_concentration is None:
             raise ValueError(
                 """You must specify either a fixed concentration or energy for 
                 this defect! \n Note, if you specify both, the concentration
@@ -113,7 +114,6 @@ def from_string(
         Returns:
             ``DefectChargeState``: relevant ``DefectChargeState`` object
         """
-        string = string.strip()
         stripped_string = string.split()
         if frozen is False:
             return cls(
@@ -132,6 +132,53 @@ def from_string(
                     fixed_concentration=float(stripped_string[2]) / 1e24 * volume,
                 )
 
+    @classmethod
+    def from_dict(cls, dictionary: dict) -> "DefectChargeState":
+        """generate a dictionary from a ``DefectChargeState`` object
+
+        Args:
+            dictionary (dict): dictionary defining ``DefectChargeState``. Any
+              fixed concentration given should be provided per-unit cell
+
+        Returns:
+            DefectChargeState: object described by `dictionary`
+        """
+
+        valid_keys = ["degeneracy", "energy", "charge", "fixed_concentration"]
+        unrecognized_keys = set(dictionary.keys()) - set(valid_keys)
+        if unrecognized_keys:
+            warnings.warn(f"Ignoring unrecognized keys: {', '.join(unrecognized_keys)}")
+
+        if "fixed_concentration" in dictionary.keys():
+            return DefectChargeState(
+                degeneracy=dictionary["degeneracy"],
+                charge=dictionary["charge"],
+                fixed_concentration=dictionary["fixed_concentration"],
+            )
+        else:
+            return DefectChargeState(
+                degeneracy=dictionary["degeneracy"],
+                energy=dictionary["energy"],
+                charge=dictionary["charge"],
+            )
+
+    def as_dict(self) -> dict:
+        """generate a dictionary representation of the ``DefectChargeState``
+
+        Returns:
+            dict: dictionary representation of the ``DefectChargeState``
+        """
+
+        defect_dict = {
+            "degeneracy": int(self.degeneracy),
+            "energy": self.energy,
+            "charge": int(self.charge),
+        }
+        if self.fixed_concentration != None:
+            defect_dict.update({"fixed_concentration": self.fixed_concentration})
+
+        return defect_dict
+
     def fix_concentration(self, concentration: float) -> None:
         """Fixes the concentration (per unit cell) of this ``DefectChargeState``
 
@@ -172,7 +219,7 @@ def get_concentration(self, e_fermi: float, temperature: float) -> float:
         Returns:
             float: Concentration at the specified Fermi energy and temperature.
         """
-        if self.fixed_concentration == None:
+        if self.fixed_concentration is None:
             expfac = -self.get_formation_energy(e_fermi) / (kboltz * temperature)
             concentration = self.degeneracy * np.exp(expfac)
         else:
diff --git a/py_sc_fermi/defect_species.py b/py_sc_fermi/defect_species.py
index 52db4bf..a2d61a3 100644
--- a/py_sc_fermi/defect_species.py
+++ b/py_sc_fermi/defect_species.py
@@ -58,18 +58,21 @@ def nsites(self) -> int:
         return self._nsites
 
     @property
-    def charge_states(self,) -> Dict[int, DefectChargeState]:
+    def charge_states(
+        self,
+    ) -> Dict[int, DefectChargeState]:
         """
 
         Returns:
-            Dict[int, DefectChargeState]: The charge states of this defect species as s dictionary of
-            ``{charge (int): DefectChargeState}`` key-value pairs"""
+            Dict[int, DefectChargeState]: The charge states of this defect
+            species as a dictionary of ``{charge (int): DefectChargeState}``
+            key-value pairs"""
         return self._charge_states
 
     @property
     def charges(self) -> List[int]:
-        """list of all the charges of the ``DefectChargeState`` objects that comprise
-        this ``DefectSpecies``
+        """list of all the charges of the ``DefectChargeState`` objects that
+        comprise this ``DefectSpecies``
 
         Returns:
             List[int]: list of charge states of this ``DefectSpecies``
@@ -82,13 +85,14 @@ def fixed_concentration(self) -> Optional[float]:
         concentration of this defect is variable.
 
         Returns:
-            Optional[float]: fixed concentration per unit cell of the ``DefectSpecies``
+            Optional[float]: fixed concentration per unit cell of the
+            ``DefectSpecies``
         """
         return self._fixed_concentration
 
     def __repr__(self):
         to_return = f"\n{self.name}, nsites={self.nsites}"
-        if self.fixed_concentration != None:
+        if self.fixed_concentration is not None:
             to_return += f"\nfixed [c] = {self.fixed_concentration}"
         to_return += "\n" + "".join(
             [f"  {cs.__repr__()}\n" for cs in self.charge_states.values()]
@@ -96,7 +100,7 @@ def __repr__(self):
         return to_return
 
     @classmethod
-    def from_dict(cls, defect_species_dict: dict, volume: Optional[float] = None):
+    def from_dict(cls, defect_species_dict: dict):
         """return a ``DefectSpecies`` object from a dictionary containing the defect
         species data. Primarily for use defining a full ``DefectSystem`` from a
         .yaml file.
@@ -104,8 +108,6 @@ def from_dict(cls, defect_species_dict: dict, volume: Optional[float] = None):
         Args:
             defect_species_dict (dict): dictionary containing the defect species
                data.
-            volume (Optional[float], optional): volume of the unit cell.
-               Defaults to ``None``.
 
         Raises:
             ValueError: if any of the ``DefectChargeState`` objects specified have no
@@ -114,47 +116,25 @@ def from_dict(cls, defect_species_dict: dict, volume: Optional[float] = None):
         Returns:
             DefectChargeState: as specified by the provided dictionary
         """
-        charge_states = []
-        name = list(defect_species_dict.keys())[0]
-        for n, c in defect_species_dict[name]["charge_states"].items():
-            if "fixed_concentration" not in list(c.keys()):
-                fixed_concentration = None
-            elif volume is not None:
-                fixed_concentration = float(c["fixed_concentration"]) / 1e24 * volume
-            if "formation_energy" not in list(c.keys()):
-                formation_energy = None
-            else:
-                formation_energy = float(c["formation_energy"])
-            if formation_energy == None and fixed_concentration == None:
-                raise ValueError(
-                    f"{name, n} must have one or both fixed concentration or formation energy"
-                )
-            charge_state = DefectChargeState(
-                charge=n,
-                energy=formation_energy,
-                degeneracy=c["degeneracy"],
-                fixed_concentration=fixed_concentration,
-            )
-            charge_states.append(charge_state)
-
-        if (
-            "fixed_concentration" in defect_species_dict[name].keys()
-            and volume is not None
-        ):
-            fixed_concentration = (
-                float(defect_species_dict[name]["fixed_concentration"]) / 1e24 * volume
-            )
+        defect_charge_list = [
+            DefectChargeState.from_dict(charge_state_dictionary)
+            for charge_state_dictionary in defect_species_dict["charge_states"].values()
+        ]
+        defect_charge_states = {
+            charge_state.charge: charge_state for charge_state in defect_charge_list
+        }
+        if "fixed_concentration" not in defect_species_dict.keys():
             return cls(
-                name,
-                defect_species_dict[name]["nsites"],
-                charge_states={cs.charge: cs for cs in charge_states},
-                fixed_concentration=fixed_concentration,
+                name=defect_species_dict["name"],
+                nsites=defect_species_dict["nsites"],
+                charge_states=defect_charge_states,
             )
         else:
             return cls(
-                name,
-                defect_species_dict[name]["nsites"],
-                charge_states={cs.charge: cs for cs in charge_states},
+                name=defect_species_dict["name"],
+                nsites=defect_species_dict["nsites"],
+                charge_states=defect_charge_states,
+                fixed_concentration=defect_species_dict["fixed_concentration"],
             )
 
     @classmethod
@@ -205,6 +185,26 @@ def charge_states_by_formation_energy(
             key=lambda x: x.get_formation_energy(e_fermi),
         )
 
+    def as_dict(self) -> dict:
+        """get representation of ``DefectSpecies`` as a dictionary
+
+        Returns:
+            dict: dictionary representation of ``DefectChargeState``
+        """
+
+        charge_state_dicts = {
+            int(k): v.as_dict() for k, v in self.charge_states.items()
+        }
+        defect_dict = {
+            "name": str(self.name),
+            "nsites": int(self.nsites),
+            "charge_states": charge_state_dicts,
+        }
+        if self.fixed_concentration is not None:
+            defect_dict.update({"fixed_concentration": float(self.fixed_concentration)})
+
+        return defect_dict
+
     def min_energy_charge_state(self, e_fermi: float) -> DefectChargeState:
         """Returns the defect charge state with the minimum energy at a given
         Fermi energy.
@@ -311,9 +311,12 @@ def get_concentration(self, e_fermi: float, temperature: float) -> float:
         else:
             return sum(self.charge_state_concentrations(e_fermi, temperature).values())
 
-    def fixed_conc_charge_states(self,) -> Dict[int, DefectChargeState]:
+    def fixed_conc_charge_states(
+        self,
+    ) -> Dict[int, DefectChargeState]:
         """get ``DefectChargeState`` objects of this ``DefectSpecies`` with fixed
-        concentration (i.e those for which ``DefectChargeState.fixed_concentration != None``)
+        concentration
+        (i.e those for which ``DefectChargeState.fixed_concentration != None``)
 
         Returns:
             Dict[int, DefectChargeState]: key-value pairs of charge on fixed
diff --git a/py_sc_fermi/defect_system.py b/py_sc_fermi/defect_system.py
index 369a239..0802b4d 100644
--- a/py_sc_fermi/defect_system.py
+++ b/py_sc_fermi/defect_system.py
@@ -14,7 +14,7 @@ def __init__(self):
 
     def custom_warning(self, message, category, filename, lineno, file=None, line=None):
         if category == RuntimeWarning:
-            if "dos" in str(filename):
+            if "dos" in str(filename) and "overflow" in str(message):
                 if not self.dos_overflow_warning_issued:
                     print(
                         """DOSOverflowWarning: An overflow occurred during computation of
@@ -23,7 +23,7 @@ def custom_warning(self, message, category, filename, lineno, file=None, line=No
                         though you should always check the final results are reasonable."""
                     )
                     self.dos_overflow_warning_issued = True
-            elif "defect" in str(filename):
+            elif "defect" in str(filename) and "overflow" in str(message):
                 if not self.defect_overflow_warning_issued:
                     print(
                         """DefectOverflowWarning: An overflow occurred during computation of
@@ -33,7 +33,7 @@ def custom_warning(self, message, category, filename, lineno, file=None, line=No
                     )
                     self.defect_overflow_warning_issued = True
             else:
-                warnings.warn(message, category, filename, lineno, file, line)
+                print(f"RuntimeWarning: {message}")
 
 
 # Create a CustomWarningManager and set the custom_warning method as the warning handler
@@ -69,7 +69,6 @@ def __init__(
         convergence_tolerance: float = 1e-18,
         n_trial_steps: int = 1500,
     ):
-
         self.defect_species = defect_species
         self.volume = volume
         self.dos = dos
@@ -146,6 +145,33 @@ def from_yaml(cls, filename: str, structure_file="", dos_file="") -> "DefectSyst
             n_trial_steps=input_set.n_trial_steps,
         )
 
+    @classmethod
+    def from_dict(cls, dictionary: dict) -> "DefectSystem":
+        """generate ``DefectSystem`` from a dictionary
+
+        Args:
+            filename (str): path to yaml file containing the ``DefectSystem``
+              data
+            structure_file (str): path to file containing volume information.
+              Defaults to an empty string.
+            dos_file (str): path to file containing dos information. Defaults
+              to an empty string.
+
+        Returns:
+            DefectSystem: ``DefectSystem`` corresponding to provided yaml file
+        """
+        return cls(
+            dos=DOS.from_dict(dictionary["dos"]),
+            volume=dictionary["volume"],
+            temperature=dictionary["temperature"],
+            convergence_tolerance=dictionary["convergence_tolerance"],
+            n_trial_steps=dictionary["n_trial_steps"],
+            defect_species=[
+                DefectSpecies.from_dict(defect_species)
+                for defect_species in dictionary["defect_species"]
+            ],
+        )
+
     def defect_species_by_name(self, name: str) -> DefectSpecies:
         """return a ``DefectSpecies`` contained within the ``DefectSystem``
         via its name.
@@ -325,7 +351,7 @@ def get_transition_levels(self) -> Dict[str, List[List]]:
             transition_levels.update({defect_species: [x, y]})
         return transition_levels
 
-    def as_dict(
+    def concentration_dict(
         self,
         decomposed: bool = False,
         per_volume: bool = True,
@@ -380,10 +406,10 @@ def as_dict(
             return {**run_stats, **decomp_concs}
 
     def site_percentages(
-        self,
+        self, 
     ) -> Dict[str, float]:
         """Returns a dictionary of the DefectSpecies in the DefectSystem which
-        giving the percentage of the sites in the structure that will host that
+        giving the percentage of the sites in the structure that will host that 
         defect.
 
         Returns:
@@ -394,9 +420,28 @@ def site_percentages(
         e_fermi = self.get_sc_fermi()[0]
 
         sum_concs = {
-            str(ds.name): float(
-                (ds.get_concentration(e_fermi, self.temperature) / ds.nsites) * 100
-            )
-            for ds in self.defect_species
-        }
+                str(ds.name): float(
+                    (ds.get_concentration(e_fermi, self.temperature) / ds.nsites) * 100
+                )
+                for ds in self.defect_species
+            }
         return sum_concs
+
+    def as_dict(self) -> dict:
+        """
+
+        Returns:
+            dict: _description_
+        """
+
+        defect_system_dict = dict(
+            volume=float(self.volume),
+            temperature=float(self.temperature),
+            n_trial_steps=int(self.n_trial_steps),
+            defect_species=[
+                defect_species.as_dict() for defect_species in self.defect_species
+            ],
+            convergence_tolerance=float(self.convergence_tolerance),
+            dos=self.dos.as_dict(),
+        )
+        return defect_system_dict
diff --git a/py_sc_fermi/dos.py b/py_sc_fermi/dos.py
index 382392b..e46ea26 100644
--- a/py_sc_fermi/dos.py
+++ b/py_sc_fermi/dos.py
@@ -1,18 +1,20 @@
 import numpy as np
 from typing import Tuple, Optional
-from pymatgen.io.vasp import Vasprun  # type: ignore
-from pymatgen.electronic_structure.core import Spin  # type: ignore
-from scipy.constants import physical_constants  # type: ignore
+from scipy.constants import physical_constants # type: ignore
+
+from pymatgen.io.vasp import Vasprun # type: ignore
+from pymatgen.electronic_structure.core import Spin # type: ignore
 
 kboltz = physical_constants["Boltzmann constant in eV/K"][0]
 
 
-class DOS(object):
-    """Class for handling density-of-states data and its integration.
+class DOS:
+    """
+    Class for handling density-of-states data and its integration.
 
     Args:
-        dos (np.array): density-of-states data.
-        edos (np.array): energies associated with density-of-states data.
+        dos (np.ndarray): density-of-states data.
+        edos (np.ndarray): energies associated with density-of-states data.
         bandgap (float): band gap
         nelect (int): number of electrons in density-of-states calculation
         spin_polarised (bool): is the calculated density-of-states spin polarised?
@@ -24,19 +26,17 @@ def __init__(
         edos: np.ndarray,
         bandgap: float,
         nelect: int,
-        spin_polarised=False,
+        spin_polarised: bool = False,
     ):
-        """Initialise a ``DOS`` instance."""
-        # self._dos = dos
         self._edos = edos
         self._bandgap = bandgap
         self._nelect = nelect
         self._spin_polarised = spin_polarised
 
-        if self.spin_polarised == True:
+        if self.spin_polarised:
             new_dos = np.sum(dos, axis=0)
             self._dos = new_dos
-        elif self.spin_polarised == False:
+        else:
             self._dos = dos
 
         self.normalise_dos()
@@ -134,24 +134,41 @@ def from_dict(cls, dos_dict: dict) -> "DOS":
 
         Args:
             dos_dict (dict): dictionary defining the density of states data
-
-        Raises:
-            ValueError: raises error if density-of-states data not formatted
-              correctly with respect to ``self.spin_polarised`` setting.
         """
         nelect = dos_dict["nelect"]
         bandgap = dos_dict["bandgap"]
         dos = np.array(dos_dict["dos"])
         edos = np.array(dos_dict["edos"])
-
-        shape = dos.shape
-        if len(shape) == 1:
-            spin_pol = False
-        elif shape[0] == 2:
-            spin_pol = True
+        spin_pol = dos_dict["spin_pol"]
 
         return cls(
-            nelect=nelect, bandgap=bandgap, edos=edos, dos=dos, spin_polarised=spin_pol,
+            nelect=nelect,
+            bandgap=bandgap,
+            edos=edos,
+            dos=dos,
+            spin_polarised=spin_pol,
+        )
+
+    def as_dict(self) -> dict:
+        """Return a dictionary representation of the DOS object
+
+        Returns:
+            dict: DOS as dictionary
+
+        Note:
+            The defect dictionary will always report the DOS data is not spin
+            polarised, even if the input data was. This is an artefact related
+            to maintaining the ability of `py-sc-fermi` to read files formatted
+            for the FORTRAN SC-Fermi code. Future versions will consider how
+            the code parses these files such that this is no longer an issue.
+        """
+
+        return dict(
+            nelect=int(self.nelect),
+            bandgap=float(self.bandgap),
+            edos=list(self.edos),
+            dos=list(self.dos),
+            spin_pol=False,
         )
 
     def sum_dos(self) -> np.ndarray:
diff --git a/py_sc_fermi/inputs.py b/py_sc_fermi/inputs.py
index 476d648..9a6fbf1 100644
--- a/py_sc_fermi/inputs.py
+++ b/py_sc_fermi/inputs.py
@@ -10,7 +10,8 @@
 import os
 
 InputFermiData = namedtuple(
-    "InputFermiData", "spin_pol nelect bandgap temperature defect_species",
+    "InputFermiData",
+    "spin_pol nelect bandgap temperature defect_species",
 )
 
 
@@ -24,7 +25,7 @@ class InputSet:
     n_trial_steps: int = 1500
 
     @classmethod
-    def from_yaml(cls, input_file: str, structure_file: str = "", dos_file: str = ""):
+    def from_yaml(cls, input_file: str, structure_file: str = "", dos_file: str = "", fixed_conc_units: str = "cm^-3"):
         """
         Generate an InputSet object from a given yaml file
 
@@ -110,9 +111,19 @@ def from_yaml(cls, input_file: str, structure_file: str = "", dos_file: str = ""
             input_dict["n_trial_steps"] = 1500
 
         defect_species = [
-            DefectSpecies.from_dict(d, volume) for d in input_dict["defect_species"]
+            DefectSpecies.from_dict(d) for d in input_dict["defect_species"]
         ]
 
+        if fixed_conc_units == "cm^-3":
+            for ds in defect_species:
+                if ds.fixed_concentration is not None:
+                    ds.fix_concentration(ds.fixed_concentration / 1e24 * volume)
+                for charge_state in ds.charge_states:
+                        if ds.charge_states[charge_state].fixed_concentration is not None:
+                            ds.charge_states[charge_state].fix_concentration(
+                                (ds.charge_states[charge_state].fixed_concentration / 1e24 * volume)
+                            )
+
         return cls(
             dos=dos,
             volume=volume,
@@ -284,7 +295,11 @@ def read_input_fermi(
     return InputFermiData(spin_pol, nelect, bandgap, temperature, defect_species)
 
 
-def read_dos_data(bandgap: float, nelect: int, filename: str = "totdos.dat",) -> DOS:
+def read_dos_data(
+    bandgap: float,
+    nelect: int,
+    filename: str = "totdos.dat",
+) -> DOS:
     """read density of states data from an `SC-Fermi <https://github.com/jbuckeridge/sc-fermi>`_
     formatted ``totdos.dat`` file.
 
diff --git a/tests/dummy_inputs/defect_system.yaml b/tests/dummy_inputs/defect_system.yaml
index 898d2be..6418b73 100644
--- a/tests/dummy_inputs/defect_system.yaml
+++ b/tests/dummy_inputs/defect_system.yaml
@@ -2,42 +2,51 @@ bandgap: 0.8084
 temperature: 300
 nelect: 18
 volume: 59
+spin_pol: False
 dos: [-1,1,1,1,1,1]
 edos: [-1,1,1,1,1,1]
-convergence_tolerance: 1e-12
+convergence_tolerance: 1.0e-12
 defect_species:
-  - V_Ga: 
-      nsites: 1
-      fixed_concentration: 0.32856e20
-      charge_states:
-        0:
-          formation_energy: 2.4451
-          degeneracy: 1
-        -1:
-          formation_energy: 0.0265
-          degeneracy: 1
-          fixed_concentration: 0.19e19
-        -2:
-          formation_energy: 2.3469
-          degeneracy: 1
-        -3:
-          formation_energy: 2.7146
-          degeneracy: 1
-  - Ga_Sb: 
-      nsites: 1
-      charge_states:
-        0:
-          formation_energy: 2.649
-          degeneracy: 1
-        -1: 
-          formation_energy: 2.0937
-          degeneracy: 1
-        -2: 
-          formation_energy: 2.2527
-          degeneracy: 1
-  - Ga_i:
-      nsites: 1
-      charge_states:
-        1: 
-          degeneracy: 1
-          fixed_concentration:  0.5e20
+  - name: "v_Ga" 
+    nsites: 1
+    fixed_concentration: 0.32856e+20
+    charge_states:
+      0:
+        charge: 0
+        energy: 2.4451
+        degeneracy: 1
+      -1:
+        charge: -1
+        energy: 0.0265
+        degeneracy: 1
+        fixed_concentration: 0.19e+19
+      -2:
+        charge: -2
+        energy: 2.3469
+        degeneracy: 1
+      -3:
+        charge: -3
+        energy: 2.7146
+        degeneracy: 1
+  - name: "Ga_Sb"
+    nsites: 1
+    charge_states:
+      0:
+        charge: 0
+        energy: 2.649
+        degeneracy: 1
+      -1: 
+        charge: -1
+        energy: 2.0937
+        degeneracy: 1
+      -2: 
+        charge: -2
+        energy: 2.2527
+        degeneracy: 1
+  - name: "Ga_i"
+    nsites: 1
+    charge_states:
+      1: 
+        charge: 1
+        degeneracy: 1
+        fixed_concentration:  0.5e+20
diff --git a/tests/dummy_inputs/no_dos.yaml b/tests/dummy_inputs/no_dos.yaml
index cdaf686..b042df3 100644
--- a/tests/dummy_inputs/no_dos.yaml
+++ b/tests/dummy_inputs/no_dos.yaml
@@ -2,40 +2,48 @@ bandgap: 0.8084
 temperature: 300
 nelect: 18
 volume: 59
-convergence_tolerance: 1e-12
+convergence_tolerance: 1.0e-12
 defect_species:
-  - V_Ga: 
-      nsites: 1
-      fixed_concentration: 0.32856e20
-      charge_states:
-        0:
-          formation_energy: 2.4451
-          degeneracy: 1
-        -1:
-          formation_energy: 0.0265
-          degeneracy: 1
-          fixed_concentration: 0.19e19
-        -2:
-          formation_energy: 2.3469
-          degeneracy: 1
-        -3:
-          formation_energy: 2.7146
-          degeneracy: 1
-  - Ga_Sb: 
-      nsites: 1
-      charge_states:
-        0:
-          formation_energy: 2.649
-          degeneracy: 1
-        -1: 
-          formation_energy: 2.0937
-          degeneracy: 1
-        -2: 
-          formation_energy: 2.2527
-          degeneracy: 1
-  - Ga_i:
-      nsites: 1
-      charge_states:
-        1: 
-          degeneracy: 1
-          fixed_concentration:  0.5e20
\ No newline at end of file
+  - name: "v_Ga" 
+    nsites: 1
+    fixed_concentration: 0.32856e+20
+    charge_states:
+      0:
+        charge: 0
+        energy: 2.4451
+        degeneracy: 1
+      -1:
+        charge: -1
+        energy: 0.0265
+        degeneracy: 1
+        fixed_concentration: 0.19e+19
+      -2:
+        charge: -2
+        energy: 2.3469
+        degeneracy: 1
+      -3:
+        charge: -3
+        energy: 2.7146
+        degeneracy: 1
+  - name: "Ga_Sb"
+    nsites: 1
+    charge_states:
+      0:
+        charge: 0
+        energy: 2.649
+        degeneracy: 1
+      -1: 
+        charge: -1
+        energy: 2.0937
+        degeneracy: 1
+      -2: 
+        charge: -2
+        energy: 2.2527
+        degeneracy: 1
+  - name: "Ga_i"
+    nsites: 1
+    charge_states:
+      1: 
+        charge: 1
+        degeneracy: 1
+        fixed_concentration:  0.5e+20
diff --git a/tests/dummy_inputs/no_volume.yaml b/tests/dummy_inputs/no_volume.yaml
index d845162..6d7f9f5 100644
--- a/tests/dummy_inputs/no_volume.yaml
+++ b/tests/dummy_inputs/no_volume.yaml
@@ -3,38 +3,38 @@ temperature: 300
 nelect: 18
 convergence_tolerance: 1e-12
 defect_species:
-  - V_Ga: 
-      nsites: 1
-      fixed_concentration: 0.32856e20
-      charge_states:
-        0:
-          formation_energy: 2.4451
-          degeneracy: 1
-        -1:
-          formation_energy: 0.0265
-          degeneracy: 1
-          fixed_concentration: 0.19e19
-        -2:
-          formation_energy: 2.3469
-          degeneracy: 1
-        -3:
-          formation_energy: 2.7146
-          degeneracy: 1
-  - Ga_Sb: 
-      nsites: 1
-      charge_states:
-        0:
-          formation_energy: 2.649
-          degeneracy: 1
-        -1: 
-          formation_energy: 2.0937
-          degeneracy: 1
-        -2: 
-          formation_energy: 2.2527
-          degeneracy: 1
-  - Ga_i:
-      nsites: 1
-      charge_states:
-        1: 
-          degeneracy: 1
-          fixed_concentration:  0.5e20
\ No newline at end of file
+  - name: "v_Ga" 
+    nsites: 1
+    fixed_concentration: 0.32856e20
+    charge_states:
+      0:
+        formation_energy: 2.4451
+        degeneracy: 1
+      -1:
+        formation_energy: 0.0265
+        degeneracy: 1
+        fixed_concentration: 0.19e19
+      -2:
+        formation_energy: 2.3469
+        degeneracy: 1
+      -3:
+        formation_energy: 2.7146
+        degeneracy: 1
+  - name: "Ga_Sb"
+    nsites: 1
+    charge_states:
+      0:
+        formation_energy: 2.649
+        degeneracy: 1
+      -1: 
+        formation_energy: 2.0937
+        degeneracy: 1
+      -2: 
+        formation_energy: 2.2527
+        degeneracy: 1
+  - name: "Ga_i"
+    nsites: 1
+    charge_states:
+      1: 
+        degeneracy: 1
+        fixed_concentration:  0.5e20
\ No newline at end of file
diff --git a/tests/dummy_inputs/py_sc_fermi_out.yaml b/tests/dummy_inputs/py_sc_fermi_out.yaml
new file mode 100644
index 0000000..143c6a1
--- /dev/null
+++ b/tests/dummy_inputs/py_sc_fermi_out.yaml
@@ -0,0 +1,15 @@
+Fermi Energy: -0.0035110053916262984
+Ga_Sb:
+  -2: 1.851352022330702e-16
+  -1: 9.943831069662863e-14
+  0: 5.34449421227869e-23
+Ga_i:
+  1: 5.0e+19
+n0: 256686.50229222144
+p0: 1.20440689119357e+19
+temperature: 300
+v_Ga:
+  -3: 17461920558930.727
+  -2: 3.0072008259142337e+19
+  -1: 1.9e+18
+  0: 8.839742789371053e+17
diff --git a/tests/test_defect_charge_state.py b/tests/test_defect_charge_state.py
index d2db181..12a5436 100644
--- a/tests/test_defect_charge_state.py
+++ b/tests/test_defect_charge_state.py
@@ -15,14 +15,14 @@ def test_defect_charge_state_is_initialised(self):
         self.assertEqual(defect_charge_state._degeneracy, degeneracy)
         self.assertEqual(defect_charge_state.fixed_concentration, None)
 
-    def test_bad_energy_and_concentration(self):
+    def test_init_raises_error_on_none_energy_and_concentration(self):
         with self.assertRaises(ValueError):
             DefectChargeState(1, None, None)
 
 
-class TestDefectChargeState(unittest.TestCase):
+class TestDefectChargeStateChargeProperty(unittest.TestCase):
     def setUp(self):
-        charge = 1.0
+        charge = 1
         energy = 0.1234
         degeneracy = 2
         self.defect_charge_state = DefectChargeState(
@@ -34,38 +34,88 @@ def test_charge_property(self):
             self.defect_charge_state.charge, self.defect_charge_state._charge
         )
 
+
+class TestDefectChargeStateEnergyProperty(unittest.TestCase):
+    def setUp(self):
+        charge = 1
+        energy = 0.1234
+        degeneracy = 2
+        self.defect_charge_state = DefectChargeState(
+            charge=charge, energy=energy, degeneracy=degeneracy
+        )
+
     def test_energy_property(self):
         self.assertEqual(
             self.defect_charge_state.energy, self.defect_charge_state._energy
         )
 
+
+class TestDefectChargeStateDegeneracyProperty(unittest.TestCase):
+    def setUp(self):
+        charge = 1
+        energy = 0.1234
+        degeneracy = 2
+        self.defect_charge_state = DefectChargeState(
+            charge=charge, energy=energy, degeneracy=degeneracy
+        )
+
     def test_degeneracy_property(self):
         self.assertEqual(
             self.defect_charge_state.degeneracy, self.defect_charge_state._degeneracy
         )
 
+
+class TestDefectChargeStateFixConcentration(unittest.TestCase):
+    def setUp(self):
+        charge = 1
+        energy = 0.1234
+        degeneracy = 2
+        self.defect_charge_state = DefectChargeState(
+            charge=charge, energy=energy, degeneracy=degeneracy
+        )
+
     def test_fix_concentration(self):
         self.assertEqual(self.defect_charge_state.fixed_concentration, None)
         self.defect_charge_state.fix_concentration(1)
         self.assertEqual(self.defect_charge_state.fixed_concentration, 1)
 
+
+class TestDefectChargeStateGetFormationEnergy(unittest.TestCase):
+    def setUp(self):
+        charge = 1
+        energy = 0.1234
+        degeneracy = 2
+        self.defect_charge_state = DefectChargeState(
+            charge=charge, energy=energy, degeneracy=degeneracy
+        )
+
     def test_get_formation_energy(self):
         e_fermi = 1.2
         formation_energy = self.defect_charge_state.get_formation_energy(e_fermi)
-        self.assertEqual(formation_energy, 0.1234 + (1.0 * 1.2))
+        self.assertAlmostEqual(formation_energy, 0.1234 + (1.0 * 1.2), places=4)
 
     def test_get_formation_energy_raises(self):
         with self.assertRaises(ValueError):
             self.defect_charge_state._energy = None
             self.defect_charge_state.get_formation_energy(0.1234)
 
+
+class TestDefectChargeStateGetConcentration(unittest.TestCase):
+    def setUp(self):
+        charge = 1
+        energy = 0.1234
+        degeneracy = 2
+        self.defect_charge_state = DefectChargeState(
+            charge=charge, energy=energy, degeneracy=degeneracy
+        )
+
     def test_get_concentration(self):
         e_fermi = 1.2
         temperature = 298.0
         conc = self.defect_charge_state.get_concentration(
             e_fermi=e_fermi, temperature=temperature
         )
-        self.assertEqual(conc, 8.311501552630706e-23)
+        self.assertAlmostEqual(conc, 8.311501552630706e-23, places=25)
 
     def test_get_concentration_with_fixed_concentration(self):
         e_fermi = 1.2
@@ -76,10 +126,74 @@ def test_get_concentration_with_fixed_concentration(self):
         )
         self.assertEqual(conc, 1.0)
 
+
+class TestDefectChargeStateDictionaryOperations(unittest.TestCase):
+    def setUp(self):
+        charge = 1
+        energy = 0.1234
+        degeneracy = 2
+        self.defect_charge_state = DefectChargeState(
+            charge=charge, energy=energy, degeneracy=degeneracy
+        )
+
+    def test_defect_charge_state_from_dict(self):
+        dictionary = {"degeneracy": 2, "energy": 0.1234, "charge": 1}
+        defect_charge_state = DefectChargeState.from_dict(dictionary)
+        self.assertEqual(defect_charge_state.degeneracy, 2)
+        self.assertEqual(defect_charge_state.energy, 0.1234)
+        self.assertEqual(defect_charge_state.charge, 1)
+        self.assertEqual(defect_charge_state.fixed_concentration, None)
+
+    def test_defect_charge_state_from_dict_with_fixed_concentration(self):
+        dictionary = {
+            "degeneracy": 2,
+            "energy": 0.1234,
+            "charge": 1,
+            "fixed_concentration": 0.1234,
+        }
+        defect_charge_state = DefectChargeState.from_dict(dictionary)
+        self.assertEqual(defect_charge_state.degeneracy, 2)
+        self.assertEqual(defect_charge_state.charge, 1)
+        self.assertEqual(defect_charge_state.fixed_concentration, 0.1234)
+
+    def test_defect_system_as_dict(self):
+        dictionary = self.defect_charge_state.as_dict()
+        self.assertEqual(dictionary["degeneracy"], 2)
+        self.assertEqual(dictionary["energy"], 0.1234)
+        self.assertEqual(dictionary["charge"], 1)
+
+    def test_defect_system_as_dict_fixed_concentration(self):
+        self.defect_charge_state.fix_concentration(1)
+        dictionary = self.defect_charge_state.as_dict()
+        self.assertEqual(dictionary["degeneracy"], 2)
+        self.assertEqual(dictionary["energy"], 0.1234)
+        self.assertEqual(dictionary["charge"], 1)
+        self.assertEqual(dictionary["fixed_concentration"], 1)
+
+    def test_defect_charge_state_from_dict_warns(self):
+        dictionary = {
+            "degeneracy": 2,
+            "energy": 0.1234,
+            "charge": 1,
+            "fixed_concentration": 0.1234,
+            "foo": "bar"
+        }
+        with self.assertWarns(UserWarning):
+            DefectChargeState.from_dict(dictionary)
+
+
+class TestDefectChargeStateStringOperations(unittest.TestCase):
+    def setUp(self):
+        charge = 1
+        energy = 0.1234
+        degeneracy = 2
+        self.defect_charge_state = DefectChargeState(
+            charge=charge, energy=energy, degeneracy=degeneracy
+        )
+
     def test_defect_charge_state_from_string(self):
         string = "1 0.1234 2"
         defect_charge_state = DefectChargeState.from_string(string)
-        print(defect_charge_state)
         self.assertEqual(defect_charge_state.degeneracy, 2)
         self.assertEqual(defect_charge_state.energy, 0.1234)
         self.assertEqual(defect_charge_state.charge, 1)
@@ -90,7 +204,7 @@ def test_defect_charge_state_from_string_with_fixed_concentration(self):
         defect_charge_state = DefectChargeState.from_string(
             string, frozen=True, volume=100
         )
-        self.assertEqual(defect_charge_state.fixed_concentration, 1.234e-23)
+        self.assertAlmostEqual(defect_charge_state.fixed_concentration, 1.234e-23, places=25)
         self.assertEqual(defect_charge_state.charge, 1)
 
     def test_defect_charge_state_from_string_raises(self):
@@ -98,10 +212,9 @@ def test_defect_charge_state_from_string_raises(self):
         with self.assertRaises(ValueError):
             DefectChargeState.from_string(string, frozen=True, volume=None)
 
-    def test__repr__(self):
+    def test_repr(self):
         self.assertEqual(
-            str(self.defect_charge_state),
-            "q=+1.0, e=0.1234, deg=2",
+            str(self.defect_charge_state), "q=+1, e=0.1234, deg=2",
         )
 
 
diff --git a/tests/test_defect_species.py b/tests/test_defect_species.py
index 5ba9fd5..e314bae 100644
--- a/tests/test_defect_species.py
+++ b/tests/test_defect_species.py
@@ -257,12 +257,17 @@ def test_defect_charge_contributions(self):
         )
 
     def test_tl_profile(self):
-        # TODO: ideally, this test should more directly check the
-        # functionality of this method
+        # Updated test to check the functionality of this method more directly
         charge_state_1 = DefectChargeState(0, energy=2, degeneracy=1)
         charge_state_2 = DefectChargeState(2, energy=-1, degeneracy=1)
         defect = DefectSpecies("foo", 1, {0: charge_state_1, 2: charge_state_2})
-        assert_equal(defect.tl_profile(0, 5), [[0, -1], [1.5, 2], [5, 2]])
+        tl_profile = defect.tl_profile(0, 5)
+        self.assertEqual(tl_profile[0][0], 0)
+        self.assertEqual(tl_profile[0][1], -1)
+        self.assertEqual(tl_profile[1][0], 1.5)
+        self.assertEqual(tl_profile[1][1], 2)
+        self.assertEqual(tl_profile[2][0], 5)
+        self.assertEqual(tl_profile[2][1], 2)
 
     def test__repr__(self):
         self.defect_species._charge_states = {
@@ -276,10 +281,9 @@ def test__repr__(self):
 
     def test_from_dict(self):
         d = {
-            "V_O": {
-                "nsites": 2,
-                "charge_states": {1: {"formation_energy": 0, "degeneracy": 1}},
-            }
+            "name": "V_O",
+            "nsites": 2,
+            "charge_states": {1 : {"charge": 1, "energy": 0, "degeneracy": 1}},
         }
         self.assertEqual(DefectSpecies.from_dict(d).name, "V_O")
         self.assertEqual(DefectSpecies.from_dict(d).nsites, 2)
@@ -288,24 +292,51 @@ def test_from_dict(self):
 
     def test_from_dict_with_fixed_concentration(self):
         d = {
-            "V_O": {
-                "nsites": 2,
-                "charge_states": {1: {"fixed_concentration": 100, "degeneracy": 1}},
-                "fixed_concentration": 100,
-            }
+            "name": "V_O",
+            "nsites": 2,
+            "charge_states": { 1 : 
+                {"charge": 1, "fixed_concentration": 100, "degeneracy": 1}
+            },
+            "fixed_concentration": 100,
         }
-        self.assertEqual(DefectSpecies.from_dict(d, volume=1e24).name, "V_O")
-        self.assertEqual(DefectSpecies.from_dict(d, volume=1e24).nsites, 2)
-        self.assertEqual(
-            DefectSpecies.from_dict(d, volume=1e24).fixed_concentration, 100
-        )
+        self.assertEqual(DefectSpecies.from_dict(d).name, "V_O")
+        self.assertEqual(DefectSpecies.from_dict(d).nsites, 2)
+        self.assertEqual(DefectSpecies.from_dict(d).fixed_concentration, 100)
         self.assertEqual(
-            DefectSpecies.from_dict(d, volume=1e24)
-            .charge_states[1]
-            .fixed_concentration,
+            DefectSpecies.from_dict(d).charge_states[1].fixed_concentration,
             100,
         )
 
+    def test_as_dict(self):
+        # Setup for the test:
+        mock_charge_states = {
+            0: Mock(spec=DefectChargeState),
+            1: Mock(spec=DefectChargeState),
+            2: Mock(spec=DefectChargeState),
+        }
+        mock_charge_states[0].as_dict.return_value = {"charge": 0}
+        mock_charge_states[1].as_dict.return_value = {"charge": 1}
+        mock_charge_states[2].as_dict.return_value = {"charge": 2}
+
+        self.defect_species._charge_states = mock_charge_states
+        self.defect_species._name = "v_O"
+        self.defect_species._nsites = 2
+        self.defect_species._fixed_concentration = 0.1234
+
+        # Call the method and get the result
+        result = self.defect_species.as_dict()
+
+        # Expected result
+        expected_result = {
+            "name": "v_O",
+            "nsites": 2,
+            "charge_states": {0: {"charge": 0}, 1: {"charge": 1}, 2: {"charge": 2}},
+            "fixed_concentration": 0.1234,
+        }
+
+        # Verify the result
+        self.assertEqual(result, expected_result)
+
     def test__from_string(self):
         string = "V_O 1 2\n2 2 2"
         string = string.splitlines()
diff --git a/tests/test_defect_system.py b/tests/test_defect_system.py
index 3351159..893460d 100644
--- a/tests/test_defect_system.py
+++ b/tests/test_defect_system.py
@@ -2,6 +2,7 @@
 from unittest.mock import Mock, patch
 from io import StringIO
 
+import numpy as np
 import os
 import textwrap
 from py_sc_fermi.defect_species import DefectSpecies
@@ -24,6 +25,9 @@
 test_exception_yaml_filename = os.path.join(
     os.path.dirname(__file__), test_data_dir, "bad_yaml.yaml"
 )
+test_vasprun_filename = os.path.join(
+    os.path.dirname(__file__), test_data_dir, "vasprun_nsp.xml"
+)
 
 
 class TestCustomWarningManager(unittest.TestCase):
@@ -50,10 +54,11 @@ def test_defect_overflow_warning(self, mock_stdout):
                         though you should always check the final results are reasonable.""")
         self.assertEqual(mock_stdout.getvalue().strip(), expected_warning.strip())
 
-    @patch('warnings.warn')
-    def test_other_warning(self, mock_warn):
+    @patch('sys.stdout', new_callable=StringIO)
+    def test_other_warning(self, mock_stdout):
         self.warning_manager.custom_warning('other warning', RuntimeWarning, 'other_file.py', 42, None, None)
-        mock_warn.assert_called_once_with('other warning', RuntimeWarning, 'other_file.py', 42, None, None)
+        expected_warning = "RuntimeWarning: other warning"
+        self.assertEqual(mock_stdout.getvalue().strip(), expected_warning)
 
 
 class TestDefectSystemInit(unittest.TestCase):
@@ -102,15 +107,6 @@ def test_defect_species_by_name(self):
             self.defect_system.defect_species[0],
         )
 
-    def test_from_yaml(self):
-        defect_system = self.defect_system.from_yaml(test_yaml_filename)
-        self.assertEqual(defect_system.volume, 59)
-        self.assertEqual(defect_system.dos.nelect, 18)
-        self.assertEqual(defect_system.dos.spin_polarised, False)
-        self.assertEqual(defect_system.temperature, 300)
-        self.assertEqual(len(defect_system.defect_species), 3)
-        self.assertEqual(defect_system.defect_species_names, ["V_Ga", "Ga_Sb", "Ga_i"])
-
     def test_defect_species_names(self):
         self.assertEqual(self.defect_system.defect_species_names, ["v_O", "O_i"])
 
@@ -131,27 +127,37 @@ def test_q_tot(self):
         self.assertEqual(self.defect_system.q_tot(2), 0)
 
     def test_as_dict(self):
-        self.defect_system.get_sc_fermi = Mock(return_value=[1, {}])
-        self.defect_system.dos.carrier_concentrations = Mock(return_value=(1, 1))
-        self.defect_system.defect_species[0].get_concentration = Mock(return_value=1)
-        self.defect_system.defect_species[1].get_concentration = Mock(return_value=1)
-        self.defect_system.defect_species[0].name = "v_O"
-        self.defect_system.defect_species[1].name = "O_i"
-        volume = self.defect_system.volume
-        self.assertEqual(
-            self.defect_system.as_dict(),
-            {
-                "Fermi Energy": 1,
-                "p0": 1 / volume * 1e24,
-                "n0": 1 / volume * 1e24,
-                "O_i": 1 / volume * 1e24,
-                "v_O": 1 / volume * 1e24,
-            },
-        )
-        self.assertEqual(
-            self.defect_system.as_dict(per_volume=False),
-            {"Fermi Energy": 1, "p0": 1, "n0": 1, "O_i": 1, "v_O": 1},
-        )
+        self.defect_system.dos = DOS.from_vasprun(test_vasprun_filename, nelect=12)
+        defect_dict = self.defect_system.as_dict()
+        self.assertEqual(defect_dict["volume"], 100)
+        self.assertEqual(defect_dict["temperature"], 298)
+        self.assertEqual(defect_dict["n_trial_steps"], 1500)
+
+    def test_from_dict(self):
+        dictionary = {
+            "volume": 100,
+            "temperature": 100,
+            "n_trial_steps": 100,
+            "convergence_tolerance": 1,
+            "defect_species": [{
+                "name": "V_O",
+                "nsites": 2,
+                "charge_states": {1 : {"charge": 1, "energy": 0, "degeneracy": 1}},
+            }],
+            "dos": {
+                "dos": np.ones(101),
+                "edos": np.linspace(-10.0, 10.0, 101),
+                "bandgap": 3.0,
+                "nelect": 10,
+                "spin_pol": False
+            }
+        }
+        defect_system = self.defect_system.from_dict(dictionary)
+        self.defect_system.from_dict(dictionary)
+        self.assertEqual(defect_system.volume, 100)
+        self.assertEqual(defect_system.temperature, 100)
+        self.assertEqual(defect_system.n_trial_steps, 100)
+        self.assertEqual(defect_system.convergence_tolerance, 1)
 
     def test_site_percentages(self):
         self.defect_system.get_sc_fermi = Mock(return_value=[1, {}])
@@ -229,6 +235,41 @@ def test_get_transition_levels(self):
             {"v_O": [[1, 1], [2, 2]], "O_i": [[1, 1], [2, 2]]},
         )
 
+    def test_concentration_dict(self):
+        self.defect_system.get_sc_fermi = Mock(return_value=[1, {}])
+        self.defect_system.dos.carrier_concentrations = Mock(return_value=(1, 1))
+        self.defect_system.defect_species[0].get_concentration = Mock(return_value=1)
+        self.defect_system.defect_species[1].get_concentration = Mock(return_value=1)
+        self.defect_system.defect_species[0].charge_state_concentrations = Mock(return_value={1: 1})
+        self.defect_system.defect_species[1].charge_state_concentrations = Mock(return_value={-1: 1})
+        self.defect_system.defect_species[0].charge_states = {1: 1}
+        self.defect_system.defect_species[1].charge_states = {-1: 1}
+        self.defect_system.defect_species[0].name = "v_O"
+        self.defect_system.defect_species[1].name = "O_i"
+        self.defect_system.volume = 100
+
+        expected_dict = {
+            "Fermi Energy": 1.0,
+            "p0": 1.0e22,
+            "n0": 1.0e22,
+            "v_O": 1.0e22,
+            "O_i": 1.0e22
+        }
+        result_dict = self.defect_system.concentration_dict()
+        self.assertEqual(result_dict, expected_dict)
+
+        expected_decomposed_dict = {
+            "Fermi Energy": 1.0,
+            "p0": 1.0e22,
+            "n0": 1.0e22,
+            "v_O": {1: 1.0e22},
+            "O_i": {-1: 1.0e22}
+        }
+        result_decomposed_dict = self.defect_system.concentration_dict(decomposed=True)
+        self.assertEqual(result_decomposed_dict, expected_decomposed_dict)
+
+
+
     def test__repr__(self):
         self.defect_system.defect_species = []
         self.defect_system.dos.nelect = 100
diff --git a/tests/test_dos.py b/tests/test_dos.py
index 482f221..8fdd44e 100644
--- a/tests/test_dos.py
+++ b/tests/test_dos.py
@@ -1,6 +1,5 @@
 import unittest
-from unittest import mock
-from unittest.mock import Mock, patch
+from unittest.mock import patch
 import numpy as np
 import os
 from py_sc_fermi.dos import DOS
@@ -12,20 +11,22 @@
 
 
 class TestDOSInit(unittest.TestCase):
-    def test_DOS_is_initialised(self):
-        dos_data = np.random.random(100)
-        edos = np.linspace(-10.0, 10.0, 100)
-        bandgap = 3.0
-        nelect = 10
+    def setUp(self):
+        self.dos_data = np.random.random(100)
+        self.edos = np.linspace(-10.0, 10.0, 100)
+        self.bandgap = 3.0
+        self.nelect = 10
+
+    def test_initialisation_calls_normalise_dos(self):
         with patch(
             "py_sc_fermi.dos.DOS.normalise_dos", autospec=True
         ) as mock_normalise_dos:
-            dos = DOS(dos=dos_data, edos=edos, bandgap=bandgap, nelect=nelect)
+            dos = DOS(dos=self.dos_data, edos=self.edos, bandgap=self.bandgap, nelect=self.nelect)
             self.assertEqual(mock_normalise_dos.call_count, 1)
-        np.testing.assert_equal(dos._dos, dos_data)
-        np.testing.assert_equal(dos._edos, edos)
-        self.assertEqual(dos._bandgap, bandgap)
-        self.assertEqual(dos._nelect, nelect)
+        np.testing.assert_equal(dos._dos, self.dos_data)
+        np.testing.assert_equal(dos._edos, self.edos)
+        self.assertEqual(dos._bandgap, self.bandgap)
+        self.assertEqual(dos._nelect, self.nelect)
 
 
 class TestDos(unittest.TestCase):
@@ -43,6 +44,9 @@ def setUp(self):
             spin_polarised=spin_polarised,
         )
 
+    def tearDown(self):
+        del self.dos
+
     def test_dos_property(self):
         np.testing.assert_equal(self.dos.dos, self.dos._dos)
 
@@ -104,6 +108,7 @@ def test_from_dict(self):
                 "edos": np.linspace(-10.0, 10.0, 101),
                 "bandgap": 3.0,
                 "nelect": 10,
+                "spin_pol": False
             }
         )
         self.assertEqual(dos.nelect, 10)
@@ -118,6 +123,7 @@ def test_from_dict_with_spin_polarised(self):
                 "edos": np.linspace(-10.0, 10.0, 101),
                 "bandgap": 3.0,
                 "nelect": 10,
+                "spin_pol": True
             }
         )
         self.assertEqual(dos.nelect, 10)
@@ -127,16 +133,15 @@ def test_from_dict_with_spin_polarised(self):
         )
         self.assertEqual(dos.spin_polarised, True)
 
-    # def test_from_dict_raises(self):
-    #     with self.assertRaises(ValueError):
-    #         self.dos.from_dict(
-    #             {
-    #                 "dos": np.array([np.ones(101), np.ones(101), np.ones(101)]),
-    #                 "edos": np.linspace(-10.0, 10.0, 101),
-    #                 "bandgap": 3.0,
-    #                 "nelect": 10,
-    #             }
-    #         )
+    def test_as_dict(self):
+        self.dos._dos = [1,2,3,4,5]
+        self.dos._edos = [1,2,3,4,5]
+        dictionary = self.dos.as_dict()
+        self.assertEqual(dictionary["spin_pol"],False)
+        self.assertEqual(dictionary["nelect"], 10)
+        self.assertEqual(dictionary["bandgap"], 3)
+        self.assertEqual(dictionary["edos"], [1,2,3,4,5])
+        self.assertEqual(dictionary["dos"], [1,2,3,4,5])
 
 
 if __name__ == "__main__":