diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 009f7f65..315d101c 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -15,7 +15,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8","3.9","3.10"]
+        python-version: ["3.8","3.9","3.10","3.11"]
         os: [ubuntu-latest,macos-latest,windows-latest]
     
     runs-on: ${{matrix.os}}
diff --git a/.readthedocs.yaml b/.readthedocs.yaml
index 5c2617ef..723ca6ca 100644
--- a/.readthedocs.yaml
+++ b/.readthedocs.yaml
@@ -7,4 +7,9 @@ build:
 
 # Build documentation in the docs/ directory with Sphinx
 sphinx:
-   configuration: docs/conf.py
\ No newline at end of file
+   configuration: docs/conf.py
+
+# Explicitly set the version of Python and its requirements
+python:
+  install:
+    - requirements: docs/requirements.txt
\ No newline at end of file
diff --git a/docs/requirements.in b/docs/requirements.in
new file mode 100644
index 00000000..1ee13a2b
--- /dev/null
+++ b/docs/requirements.in
@@ -0,0 +1,4 @@
+# Defining the exact version will make sure things don't break
+sphinx==5.3.0
+sphinx_rtd_theme==1.1.1
+readthedocs-sphinx-search==0.1.1
\ No newline at end of file
diff --git a/docs/requirements.txt b/docs/requirements.txt
new file mode 100644
index 00000000..549fb24c
--- /dev/null
+++ b/docs/requirements.txt
@@ -0,0 +1,62 @@
+#
+# This file is autogenerated by pip-compile with Python 3.8
+# by the following command:
+#
+#    pip-compile
+#
+alabaster==0.7.13
+    # via sphinx
+babel==2.12.1
+    # via sphinx
+certifi==2023.7.22
+    # via requests
+charset-normalizer==3.2.0
+    # via requests
+docutils==0.17.1
+    # via
+    #   sphinx
+    #   sphinx-rtd-theme
+idna==3.4
+    # via requests
+imagesize==1.4.1
+    # via sphinx
+importlib-metadata==6.8.0
+    # via sphinx
+jinja2==3.1.2
+    # via sphinx
+markupsafe==2.1.3
+    # via jinja2
+packaging==23.1
+    # via sphinx
+pygments==2.16.1
+    # via sphinx
+pytz==2023.3
+    # via babel
+readthedocs-sphinx-search==0.1.1
+    # via -r requirements.in
+requests==2.31.0
+    # via sphinx
+snowballstemmer==2.2.0
+    # via sphinx
+sphinx==5.3.0
+    # via
+    #   -r requirements.in
+    #   sphinx-rtd-theme
+sphinx-rtd-theme==1.1.1
+    # via -r requirements.in
+sphinxcontrib-applehelp==1.0.4
+    # via sphinx
+sphinxcontrib-devhelp==1.0.2
+    # via sphinx
+sphinxcontrib-htmlhelp==2.0.1
+    # via sphinx
+sphinxcontrib-jsmath==1.0.1
+    # via sphinx
+sphinxcontrib-qthelp==1.0.3
+    # via sphinx
+sphinxcontrib-serializinghtml==1.1.5
+    # via sphinx
+urllib3==2.0.4
+    # via requests
+zipp==3.16.2
+    # via importlib-metadata
diff --git a/examples/Dopant_Prediction/doper_example.ipynb b/examples/Dopant_Prediction/doper_example.ipynb
index 67a111d5..dde348c8 100644
--- a/examples/Dopant_Prediction/doper_example.ipynb
+++ b/examples/Dopant_Prediction/doper_example.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "dc3ab04f",
    "metadata": {},
@@ -9,6 +10,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "71bfdd77",
    "metadata": {},
@@ -18,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 18,
    "id": "58e48522",
    "metadata": {},
    "outputs": [],
@@ -27,57 +29,110 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "0d722cf1",
    "metadata": {},
    "source": [
-    "The Doper module includes the functions `get_dopants`. These require an input (tuple(str)), which is formed by the ionic species of the material.\n",
+    "The Doper module includes `get_dopants` function. These require an input (tuple(str)), which is formed by the ionic species of the material.\n",
     "\n",
-    "By default, the top five p-type and n-type candidates are reported. "
+    "By default, the top five p-type and n-type candidates are reported. Use `num_dopants` input to modify the number of outputs.\n",
+    "\n",
+    "The output format:\n",
+    "\n",
+    "(dict): Dopant suggestions, given as a dictionary with keys \"n_type_cation\", \"p_type_cation\", \"n_type_anion\", \"p_type_anion\".\n",
+    "\n",
+    "Each key contains a list of possible dopants in the order of probability (Highest --> Lowest).\n",
+    "\n",
+    "Each possible dopant is represented with tuple: ('substituted dopant', 'original specie', 'probability') "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "193682c3",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'n-type cation substitutions': [('Ta5+', 8.790371775858281e-05),\n",
-       "  ('Nb5+', 7.830035204694342e-05),\n",
-       "  ('Sb5+', 6.259166355036722e-05),\n",
-       "  ('Ru5+', 4.904126561555437e-05),\n",
-       "  ('Re5+', 4.546178573532138e-05)],\n",
-       " 'p-type cation substitutions': [('Na1+', 0.00010060400812977031),\n",
-       "  ('Zn2+', 8.56373996146833e-05),\n",
-       "  ('Mn2+', 8.563568688381837e-05),\n",
-       "  ('Mg2+', 6.777016806765154e-05),\n",
-       "  ('Fe3+', 6.259479321178562e-05)],\n",
-       " 'n-type anion substitutions': [('F1-', 0.01508116810515677),\n",
-       "  ('Cl1-', 0.004737202729901607),\n",
-       "  ('H1-', 9.31310255126729e-08),\n",
-       "  ('C1-', 9.31310255126729e-08),\n",
-       "  ('N1-', 9.31310255126729e-08)],\n",
-       " 'p-type anion substitutions': [('N3-', 0.0014663800608945628),\n",
-       "  ('C4-', 9.31310255126729e-08),\n",
-       "  ('C3-', 9.31310255126729e-08),\n",
-       "  ('Si4-', 9.31310255126729e-08),\n",
-       "  ('Si3-', 9.31310255126729e-08)]}"
+       "{'n-type cation substitutions': [('Ta5+', 'Ti4+', 8.790371775858281e-05),\n",
+       "  ('Nb5+', 'Ti4+', 7.830035204694342e-05),\n",
+       "  ('Sb5+', 'Ti4+', 6.259166355036722e-05),\n",
+       "  ('Ru5+', 'Ti4+', 4.904126561555437e-05),\n",
+       "  ('Re5+', 'Ti4+', 4.546178573532138e-05),\n",
+       "  ('Ir5+', 'Ti4+', 3.4652057352167286e-05),\n",
+       "  ('W6+', 'Ti4+', 3.4638026110457894e-05),\n",
+       "  ('Bi5+', 'Ti4+', 1.9582397953056897e-05),\n",
+       "  ('Mo6+', 'Ti4+', 1.6924395455176864e-05),\n",
+       "  ('Te6+', 'Ti4+', 1.4299724897106019e-05),\n",
+       "  ('U5+', 'Ti4+', 1.4299724897106019e-05),\n",
+       "  ('U6+', 'Ti4+', 1.4299724897106019e-05),\n",
+       "  ('As5+', 'Ti4+', 1.1731025674408029e-05),\n",
+       "  ('I5+', 'Ti4+', 9.377039539879602e-06),\n",
+       "  ('P5+', 'Ti4+', 9.37392687948258e-06)],\n",
+       " 'p-type cation substitutions': [('Na1+', 'Ti4+', 0.00010060400812977031),\n",
+       "  ('Zn2+', 'Ti4+', 8.56373996146833e-05),\n",
+       "  ('Mn2+', 'Ti4+', 8.563568688381837e-05),\n",
+       "  ('Mg2+', 'Ti4+', 6.777016806765154e-05),\n",
+       "  ('Fe3+', 'Ti4+', 6.259479321178562e-05),\n",
+       "  ('Ca2+', 'Ti4+', 5.5340892351269824e-05),\n",
+       "  ('Ni2+', 'Ti4+', 5.534033894511336e-05),\n",
+       "  ('V3+', 'Ti4+', 5.312098771970144e-05),\n",
+       "  ('Li1+', 'Ti4+', 4.90559802023167e-05),\n",
+       "  ('Co2+', 'Ti4+', 4.3842431279341393e-05),\n",
+       "  ('K1+', 'Ti4+', 4.3838924025131085e-05),\n",
+       "  ('Cr3+', 'Ti4+', 4.234338028148513e-05),\n",
+       "  ('Mn3+', 'Ti4+', 3.7092622638148224e-05),\n",
+       "  ('Fe2+', 'Ti4+', 3.333914056567426e-05),\n",
+       "  ('Al3+', 'Ti4+', 3.0368749390927986e-05)],\n",
+       " 'n-type anion substitutions': [('F1-', 'O2-', 0.01508116810515677),\n",
+       "  ('Cl1-', 'O2-', 0.004737202729901607),\n",
+       "  ('H1-', 'O2-', 9.31310255126729e-08),\n",
+       "  ('C1-', 'O2-', 9.31310255126729e-08),\n",
+       "  ('N1-', 'O2-', 9.31310255126729e-08),\n",
+       "  ('O1-', 'O2-', 9.31310255126729e-08),\n",
+       "  ('Na1-', 'O2-', 9.31310255126729e-08),\n",
+       "  ('Si1-', 'O2-', 9.31310255126729e-08),\n",
+       "  ('P1-', 'O2-', 9.31310255126729e-08),\n",
+       "  ('S1-', 'O2-', 9.31310255126729e-08),\n",
+       "  ('K1-', 'O2-', 9.31310255126729e-08),\n",
+       "  ('Ti1-', 'O2-', 9.31310255126729e-08),\n",
+       "  ('V1-', 'O2-', 9.31310255126729e-08),\n",
+       "  ('Cr1-', 'O2-', 9.31310255126729e-08),\n",
+       "  ('Mn1-', 'O2-', 9.31310255126729e-08)],\n",
+       " 'p-type anion substitutions': [('N3-', 'O2-', 0.0014663800608945628),\n",
+       "  ('C4-', 'O2-', 9.31310255126729e-08),\n",
+       "  ('C3-', 'O2-', 9.31310255126729e-08),\n",
+       "  ('Si4-', 'O2-', 9.31310255126729e-08),\n",
+       "  ('Si3-', 'O2-', 9.31310255126729e-08),\n",
+       "  ('P3-', 'O2-', 9.31310255126729e-08),\n",
+       "  ('Mn3-', 'O2-', 9.31310255126729e-08),\n",
+       "  ('Ge4-', 'O2-', 9.31310255126729e-08),\n",
+       "  ('Ge3-', 'O2-', 9.31310255126729e-08),\n",
+       "  ('As3-', 'O2-', 9.31310255126729e-08),\n",
+       "  ('Tc3-', 'O2-', 9.31310255126729e-08),\n",
+       "  ('Sn4-', 'O2-', 9.31310255126729e-08),\n",
+       "  ('Sb3-', 'O2-', 9.31310255126729e-08),\n",
+       "  ('Re3-', 'O2-', 9.31310255126729e-08),\n",
+       "  ('Ir3-', 'O2-', 9.31310255126729e-08)]}"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "material = Doper((\"Ti4+\", \"O2-\"))\n",
-    "material.get_dopants()"
+    "material.get_dopants()\n",
+    "\n",
+    "# 15 possible dopants\n",
+    "material.get_dopants(15)"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "11ec5825",
    "metadata": {},
@@ -87,36 +142,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 19,
    "id": "df5a5dbb",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'n-type cation substitutions': [('Ge4+', 0.0015914201920862318),\n",
-       "  ('Ge4+', 0.0015914201920862318),\n",
-       "  ('Zn2+', 0.001162004835910505),\n",
-       "  ('Si4+', 0.0008777853600526021),\n",
-       "  ('Si4+', 0.0008777853600526021)],\n",
-       " 'p-type cation substitutions': [('Cu1+', 0.0036311406230084362),\n",
-       "  ('Cu1+', 0.0036311406230084362),\n",
-       "  ('Zn2+', 0.001162004835910505),\n",
-       "  ('Ag1+', 0.0004187742948950742),\n",
-       "  ('Ag1+', 0.0004187742948950742)],\n",
-       " 'n-type anion substitutions': [('Cl1-', 0.000708721114826238),\n",
-       "  ('F1-', 0.00021217514709258802),\n",
-       "  ('H1-', 9.31310255126729e-08),\n",
-       "  ('C1-', 9.31310255126729e-08),\n",
-       "  ('N1-', 9.31310255126729e-08)],\n",
-       " 'p-type anion substitutions': [('N3-', 0.0007862635952461277),\n",
-       "  ('C4-', 9.31310255126729e-08),\n",
-       "  ('C3-', 9.31310255126729e-08),\n",
-       "  ('Si4-', 9.31310255126729e-08),\n",
-       "  ('Si3-', 9.31310255126729e-08)]}"
+       "{'n-type cation substitutions': [('Si4+', 'Ge4+', 0.0008777853600526021),\n",
+       "  ('Co2+', 'Zn2+', 0.00038676299875561366),\n",
+       "  ('Cd2+', 'Zn2+', 0.00034479571082730946),\n",
+       "  ('Mg2+', 'Zn2+', 0.00034293540270251613),\n",
+       "  ('Mn2+', 'Zn2+', 0.00033249741948425897)],\n",
+       " 'p-type cation substitutions': [('Ag1+', 'Cu1+', 0.0004187742948950742),\n",
+       "  ('Co2+', 'Zn2+', 0.00038676299875561366),\n",
+       "  ('Cd2+', 'Zn2+', 0.00034479571082730946),\n",
+       "  ('Mg2+', 'Zn2+', 0.00034293540270251613),\n",
+       "  ('Mn2+', 'Zn2+', 0.00033249741948425897)],\n",
+       " 'n-type anion substitutions': [('Cl1-', 'S2-', 0.000708721114826238),\n",
+       "  ('F1-', 'S2-', 0.00021217514709258802),\n",
+       "  ('H1-', 'S2-', 9.31310255126729e-08),\n",
+       "  ('C1-', 'S2-', 9.31310255126729e-08),\n",
+       "  ('N1-', 'S2-', 9.31310255126729e-08)],\n",
+       " 'p-type anion substitutions': [('N3-', 'S2-', 0.0007862635952461277),\n",
+       "  ('C4-', 'S2-', 9.31310255126729e-08),\n",
+       "  ('C3-', 'S2-', 9.31310255126729e-08),\n",
+       "  ('Si4-', 'S2-', 9.31310255126729e-08),\n",
+       "  ('Si3-', 'S2-', 9.31310255126729e-08)]}"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -127,119 +182,46 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "f2524a92",
    "metadata": {},
    "source": [
-    "If you want to plot the results in the form of heatmap, set `plot_heatmap` True."
+    "If you want to plot the results in the form of heatmap, use `plot_dopants` method.\n",
+    "\n",
+    "`num_dopants` input can also be used."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 20,
    "id": "dc29d078",
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "{'n-type cation substitutions': [('Ge4+', 0.0015914201920862318),\n",
-       "  ('Ge4+', 0.0015914201920862318),\n",
-       "  ('Zn2+', 0.001162004835910505),\n",
-       "  ('Si4+', 0.0008777853600526021),\n",
-       "  ('Si4+', 0.0008777853600526021),\n",
-       "  ('Co2+', 0.00038676299875561366),\n",
-       "  ('Cd2+', 0.00034479571082730946),\n",
-       "  ('Mg2+', 0.00034293540270251613),\n",
-       "  ('Mn2+', 0.00033249741948425897),\n",
-       "  ('Cu2+', 0.00025278822910808205)],\n",
-       " 'p-type cation substitutions': [('Cu1+', 0.0036311406230084362),\n",
-       "  ('Cu1+', 0.0036311406230084362),\n",
-       "  ('Zn2+', 0.001162004835910505),\n",
-       "  ('Ag1+', 0.0004187742948950742),\n",
-       "  ('Ag1+', 0.0004187742948950742),\n",
-       "  ('Co2+', 0.00038676299875561366),\n",
-       "  ('Cd2+', 0.00034479571082730946),\n",
-       "  ('Mg2+', 0.00034293540270251613),\n",
-       "  ('Mn2+', 0.00033249741948425897),\n",
-       "  ('Na1+', 0.00025743109681607455)],\n",
-       " 'n-type anion substitutions': [('Cl1-', 0.000708721114826238),\n",
-       "  ('F1-', 0.00021217514709258802),\n",
-       "  ('H1-', 9.31310255126729e-08),\n",
-       "  ('C1-', 9.31310255126729e-08),\n",
-       "  ('N1-', 9.31310255126729e-08),\n",
-       "  ('O1-', 9.31310255126729e-08),\n",
-       "  ('Na1-', 9.31310255126729e-08),\n",
-       "  ('Si1-', 9.31310255126729e-08),\n",
-       "  ('P1-', 9.31310255126729e-08),\n",
-       "  ('S1-', 9.31310255126729e-08)],\n",
-       " 'p-type anion substitutions': [('N3-', 0.0007862635952461277),\n",
-       "  ('C4-', 9.31310255126729e-08),\n",
-       "  ('C3-', 9.31310255126729e-08),\n",
-       "  ('Si4-', 9.31310255126729e-08),\n",
-       "  ('Si3-', 9.31310255126729e-08),\n",
-       "  ('P3-', 9.31310255126729e-08),\n",
-       "  ('Mn3-', 9.31310255126729e-08),\n",
-       "  ('Ge4-', 9.31310255126729e-08),\n",
-       "  ('Ge3-', 9.31310255126729e-08),\n",
-       "  ('As3-', 9.31310255126729e-08)]}"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1200x800 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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8+fPT7586daqOHj2qwYMHq1q1agYmzLmUlBS9/PLLOnv2rJ5//vn0IS2SeY9RuXLl1KlTJ+3bt09r1qzJ9t+Z9f3j7e2tdu3a6aefftKVKxm/gPz444+KiopSt27dsvzbuLg4TZ06VW3atFGVKlVUq1YtPffcc9qzZ48zot81u92uJUuWqGfPnqpRo4aqVaumli1batSoUTp37pzR8W4rq/P17eZDGTZsmCpUqKCzZ886JV9O3enzJzk5WdOnT1ezZs1UuXJltWrVSmFhYc6MmGN9+/aVJB04cCD9vnPnzmno0KGqVauWqlevrp49e2rnzp1GRcxSwsb5krunvP/xitwr1lfSrz/IdvVClvsm/rJcMWOaKmpAcV39VwVdnztEtutRin71EUW/atywseSLx3V97RRZfO/TfS99m6noIkkWTx8VbD1Ufu3eTL8v5cpZRc19RhdeK6ML/y6hyx8+oYSjm50Z/a7c/D4PCwtTcHCwqlSpoiZNmujjjz+WzWYzOmK63F633bhxI733b+XKldWuXTutXr3aablvJaftSRMdHa033nhD9erVU9WqVdWlSxetW7fO4Xnzmt1uV3h4uLp3765HH31U1apVU6dOnUxxnZNflSlTRsOGDdOlS5c0ZswYSdLFixc1duxY+fv76913382w/7Vr1zRlyhQ98cQTqlq1qmrUqKFnnnlGv/zyiwHpXZ/dYpXNRDe7xfBSgksxzQDqrOZ5kKSXXnpJf/zxh5588kklJydr1apVGjFihCIjI/XCCy84OWVGAQEBkqRTp06pSpUq2fqbxMRE9enTR9evX1eLFi3k6empIkWKODLmXRs5cqS2bdumDz74QE2aNNHVq1c1Z84cPfLII4Yfg9yYMGGCtm7dqiZNmujVV1/NtN2sx2jo0KH67rvvNHnyZLVq1eqW75mbmfn9061bNy1cuFDLly/P0H140aJFKly4sJo3b57pbxISEvTUU09p3759qlq1qvr166crV65o5cqV2rx5s6ZOnarg4GAntiJn7Ha7hg4dqpUrV6p48eJq3769ChYsqLNnz2rlypVq0qSJS3TPzc5rz5Xcqj1Dhw7V/v371bRpU1mtVq1evVpjxoyRu7u7evTo4eSU2fP3L18XL15USEiIIiIi1LhxY1WtWlXHjx/X008/bZp5rVLO/KaUP/fIo9Y/ZC14nzwb9lDykW1K3PylvP/xSoZ9EzbO1425L8lSIECej/eQxcdfSb+u1bUPOsmekiSLm4dBrZDiti+QbCkq0OhZufkXu+2+Fo/UXqIp0RcUOamlbFfPyatKK7mXflTJF/7QlWkd5fVQE2fEzjPvvfeeduzYoRYtWqhRo0Zau3atPv74YyUlJWX5WWuE3Fy3JScnq1+/foqOjlabNm0UFxenlStX6l//+pfmzp2rxo0bOzLybeWmPUlJSerbt6/i4+PVpUsXxcTE6LvvvtMLL7ygSZMmpQ9fNDu73a5XX31Vy5cvV7ly5dShQwd5eHhoy5YtGjFihI4dO6YRI0YYHTNf6t27t9auXavvvvtObdq00ZIlSxQdHa1JkyapePHi6ftdvXpVPXv21NGjR1WnTh01btxYsbGx+vHHH9WnTx9NmzZNrVu3NrAlgHMZXnhZuHChJKl27dpZbj9z5oxWrVqlggULSpJefPFFdezYUVOnTlW7du1UpkwZp2X9uyeffFLLly/XiBEjdODAATVu3FhVqlRJ/yDMyqVLl1SpUiV988038vb2dmLa3PPz89PEiRPVt29fvf7664qOjpa7u7smTZokd3fDX0I58u2332revHkKCgrSlClTZLVmrtSa9RiVKlVKffr00dy5cxUeHp6tL35mfv889thjqlixohYtWpReeLlw4YI2b96sfv36Zfnamj17tvbt26cOHTpo8uTJ6V8y+/Xrpy5duuiNN97Q448/nt5es1mwYIFWrlyphg0batasWRleX/Hx8YqPjzcw3Z3d6Xztau7UnvPnz2vVqlXy8/OTlPo6a9u2rebOnWvawsvnn38uSekTBk+aNEkRERF65ZVX9OKLL6bvt3DhQo0cOdKQjH+XsDG1N6Vnw9T/U8+6nXVjwXAlbArLUHixXb+qG1/+R/L2k9+Yn+VWrJwkyd5ttK592FX2k/tkKWzcOS3xeOrqUF6Vsr9qTOyy0bJdPaeC7d+S35Ovp99/Y/NcRX/pWsMqfvvtN61cuVLFiqUWnQYPHqxWrVrpiy++0JAhQ+Tp6Wlwwtxdt0VEROiRRx5RWFhYehs6dOigvn37Gl54yU17Ll68qPLly2vRokXy8EgtVD777LPq2LGj3n77bbVs2dK0n6E3+/rrr7V8+XKFhIRo3Lhx6dcMiYmJeumllzRnzhy1b9/e5XpluwKLxaIJEyaobdu2eu2115SYmKg2bdqoU6dOGfYbO3asjh49qvfeey9DL+bIyEh17txZb775ppo0aWLK6QoAR3Bq/6BTp05p6tSpmjp1qt577z316tVLH330kQoXLqzhw7OeZO7FF1/M8AFQtGhRPfvss0pOTtaKFSucFT1LrVu31vDhw2W32zVr1iw99dRTqlWrllq0aKExY8bo5MmTWf7df/7zH1N9oc+OBg0aqG/fvtq7d69OnDih1157Lcvuq2Z24MABjRw5Ur6+vvrkk0/k7+9/y33NeoxefPFF+fn5adq0aYqLi8vW/mZ9/0hS165ddfjwYR08eFCStGTJEqWkpNxymNHixYvl4eGh119/PcMv+w8//LC6du2q6OhoU3eXDgsLk5ubm8aOHZvp9eXt7Z0+VtoMcnO+NrPctOe1115LL7pIqSts1axZUydOnNC1a9ecFf2Wbm7ThAkT9M9//lMzZsyQl5eXXn31VSUmJmrlypUqXLiw+vfvn+Fvu3fvrnLlyhmU/H/syYlK3Pa1LL6F5PFoam81S4EAedRoK1vEcSX9sSV936S9q6T4a/Jq2je96CJJFjd3+XQ2voiUEhMhSXILzF6vNXtyouJ2L5HVr6gKtso4oahPw35yK+ZaS5oPHjw4vegipc4T16pVK12/fl1//vmngcn+J7fXbW+++WaGwlHDhg11//33ZxjSZ4Tctuff//53etFFksqXL69u3bopJibG1J+hNwsLC1OBAgXSeyGm8fT0TO9hZYbrnPyqVKlS6t27txITE+Xp6ZlpVc203sgNGzbMdE1XpEgRDRgwQFeuXNGWLVuE7LPJYrobss+p3RVOnz6tadOmZbivcOHCWrhw4S0vALP6JTLtvt9//z3vQ+bQgAED1LNnT23cuFF79uzRwYMHtX//foWFhSk8PFxTp05Vq1at0vf38vJSpUqVDEyce6+//ro+//xzFS1a1OVmlk8bWpOYmKiPPvpIDz300C33NfMxKlSokAYOHKjJkycrNDQ0wy/YWTH7+6dz586aPHmywsPDVa1aNS1evFiPPvqoKlasmGnf2NhYnTlzRuXLl1fJkiUzba9Xr56++uorHTp0KNOvLmZw48YNHTt2TEFBQSpbtqzRce4oN+drM8tNe7L6pbREiRKSpJiYGMN/Fb65TWnLSXfo0EHPP/+8KlWqpD/++EMJCQlq0KBBpl8UrVaratasafgX4qQ938l+7Yo8mz8ri/v/vth6Pd5TSTuXKHFjmDwqPS5JSjmTWqB1r5B5BSq3B2tJLrb8dHLEUSkpXh4PNZHFI2Mh1mK1yvPBeoq76DpL5VatWjXTfTe/X8wip9dt/v7+WU7AX6JEifQ5VoyU0/Z4eHjosccey/Q4derUUVhYmGk/Q28WFxenP/74Q8WKFdOnn36aaXvaiognTpxwdrR7RmRkpL755htJqb2M1q9fn6HAcuDAAaWkpCghIUFTp07N9PenTp2SlHqM/r46EpBfOfUq5eb16S9fvqxvv/1W77//vgYNGqQlS5bI19c3098ULlw4031p822Y4RdHSSpYsKDatm2rtm3bSkr9gjhp0iQtWLBAI0aMUJMmTdJ/KSlcuHCWE6C5grRf6D09PV2qDUlJSXrppZd04cIFvfTSS2rTps1t9zf7MXrmmWcUFham2bNnq2fPnrfd1+zvn8KFC6tp06b67rvv1LJlS506dUrPPfdclvum5b3VfDtp98fGxjom7F1K++Jx8/hnM8vN+drMctOem3u7pEn7ZdUME4be3KaspL0XsjoP3O5+Z0rYmDpZsVfDjEO33B9pKUtAcSXuWqoCfSbK4uMve1zqe8jil/kcYLFaZSlobHvc/IsrJeKIUq6el3vxWxf309jjoiVJVr+iWW633mGeGLPJ6v2SNn9SSkqKs+PcVk6u27Jql5TaNjOcB6SctadQoUJZDrNOOx+Y9TP0ZtHR0bLb7YqIiMhUUL/ZjRs3nJjq3vLmm28qKipKw4cP18yZM/XOO++oUaNG6cXWq1evSpJ2796t3bt33/JxOEa4lxg2FXHhwoX13HPP6YUXXtDx48f10UcfZbnf5cuXM90XGRkpSYb/2ngrfn5+GjNmjO6//35FRUXpyJEj6dvM/IU+vxo7dqx++eUXNW/ePFtLEZr9GHl7e2vIkCGKjY3VJ598ctt9XeH9061bN0VHR2vEiBHy9vbWP/7xjyz3S8ublv/v0tpqlnb9XdrFe0REhMFJcu525+u0C/i0XxhvZtYL+Ox+/ri6tNdcVueB293vLLbLZ5X823pJUuw7bRTVLyD9dvXZ+2SPjpASbyhxR+qqchaf1OGh9tjM5wC7zSb7NWPb41k+dbLihMMbsrW/xSd1Hg5b7KUst9tiLuZJLtzZ7a7bXNHt2nP16tUsC0Zp54NbFZrMJO1zvlq1ajp27NgtbwsWLDA4af60ZMkS/fjjj2rVqpUGDBigUaNGKTY2Vm+++b/V2tKOUf/+/W97jP71r38Z1QyXZPSwIoYa3R3D14B64YUXVLx4cS1YsCDLJUezWm4s7b7szuBuBIvFYso5Qu41Cxcu1FdffaUHH3xQH374oemLKtkVEhKi8uXLKywsTOfPn7/lfq7w/mnWrJmKFi2qiIgIBQcH3/Kiz8/PT6VLl9bp06d14ULmZWZ37NghSapcubJD8+aWr69v+pKrtxp3b3ZZna/TJnH8e0HJZrPp8OHDTs+YE3f6/HF15cqVk5eXlw4cOKCEhIQM22w2m+FLsCdsXiDZbXJ/qIE8mzyV+fb/vWDSJt91K5069Cv5WOalsFNO7JZSMhf/nMmnfm/J6qa4LaFKuUUxJY09KUHuxStKHt5KOr1X9qSME2vbbTYlntjhyLj4m/x23Xar9iQlJWnfvn2Z7t+1a5ck836G3qxgwYIqX768jh8/bqphbPeC8+fPa9y4cQoMDNT48eMlSR07dlSrVq30888/a9GiRZKk6tWry2KxmGI4HmAWhhdevL29NXDgQCUlJWn69OmZts+YMSPDkIjIyEjNnTtX7u7uat++vTOjZvLVV1/p119/zXLbmjVrdOLECfn7+2c5XwUcb8+ePXr77bdVsGBBffLJJy7xK052ubm5pU+eebtutmZ+/6Rxd3fXzJkz9cknn9xxydEuXbooKSlJkyZNkt1uT7//yJEjWrx4sfz8/Ey9NGGfPn2UkpKi0aNHZ1rBKCEhIb1rrllldb5Omwdl8eLFGfadO3euzpw54/SMOXGnzx9X5+npqbZt2+ry5cuaM2dOhm3ffPONofO72O12JW4KkywWFRjwqXyfnZ75NnCm3IKqK+XEbqWc/V0eNdpK3gWV8PMXSrl08n+PlZKsuG/HG9aWNO7Fysu39VDZrl1W1H+7KjnyZKZ97EnxuvbjNMWufFcWd0/51OwsW+wlXVuX8Twet3WeUlxofhdXkd+u23Lbno8++khJSUnp/z5+/LgWLVokPz+/DPPBmNnTTz+tuLg4vfHGG1kOVzlz5ky+LKgbbcSIEYqNjdWYMWMyDP0eP368AgMD9c477+j8+fMqWrSo2rZtqz179mj27NkZrtnS7Nu3L1sLRQD5hSlmouvRo4dmzZqlpUuX6oUXXlBQUFD6ttKlS6tt27YKDg5WcnKyVq1apcuXL+uVV14xdClcSfr55581atQoBQUFqVatWipWrJhu3LihQ4cOadeuXbJarXr77bdZJs0A165d0+DBg5WUlKSaNWvqu+++u+3+9erVU/369Z2ULm+0adNGNWrUuO2vCWZ+/9ysevXq2dpv4MCB2rBhg5YuXarjx4+rQYMGunLlilatWqXk5GR98MEHph1qJEm9e/fWzp07tWrVKrVs2TJ92c7z589r06ZNmjBhgqkLR1Lm83W3bt00e/Zsffzxxzp06JDKlCmjAwcO6OjRo6pbt6527szcO8FMbvf5kx+8/vrr2rp1qz788EP98ssvqlKlio4fP64NGzaoUaNG2rx5syG5kn/fIFvkablXbiy3omVvuZ9noz6KOzVMCRvnq0CvCSrQ4x3dmPeyYkc3kUfdLrIU8FfSr2tlcfeUpVBJyWpsr0a/9m9JSfG6/tN/dentmvKq1FTuJStLbh5KuXxKCYfXy379igq2fyt1/05jlfDHz7q2YqySjm+Te+nqSr7whxJ++0GelVsq8dCPhrYnv8lv1225aU+xYsUUExOj9u3bq3nz5oqNjdWKFSuUkJCgd955x9SfoTfr2bOn9u3bpyVLlmjPnj1q2LChihUrpsuXL+v48ePav3+/PvroIz3wwANGR803vvzyS23evFlPPPGE2rVrl2FbkSJF9NZbb+nf//63Ro4cqTlz5ujtt9/WiRMnNHHiRC1dulQ1atSQn5+fzp8/r4MHD+rkyZPatm2bfHx8DGqR6zHb8B4zZXEFpii8eHl56fnnn9fYsWM1bdo0TZo0KX3btGnTNGXKFK1YsUJXrlxR2bJl9eqrr6p79+4GJk41bNgw1apVS5s3b9auXbt08WLqeOzixYurS5cu6tu3b5arYsDxrl69qkuXUrt679ixI30Yyu24WuFFSn0N3m6CXTO/f3LDy8tL8+fP16xZs7Ry5UqFhobKx8dHderU0QsvvJDlKk5mYrFYNHXqVDVq1EjffPONli5dKrvdruLFi6tt27ZZrgpiNlmdr+fPn6+JEydq8+bNcnNzU/369RUeHq4ZM2aYvvByu8+f/KBYsWIKDw/XxIkTtWnTJu3atUvVqlXT559/rm3bthlWeEkbPuTZ+Knb7ufZIERxX49U4rav5dP9bXk16yeLbyHFfzdZiZu/lKWAvzwee1I+3d9W9GuPyOpn7IpbFqtV/t3ek3ed7rqx8TMlHtuqhKNbJLtNbgEl5FW5pQo06COvyqmreLgFlFCR19Yp5ttRSvj9RyUe2yKPMo/pviHLlPjHRgoveSy/Xbflpj0eHh76/PPP9f7772vJkiWKjY1VpUqVNHjwYJfp7SKlfp6+//77atq0qb755hutX79eN27c0H333aeyZcvqP//5jxo2bGh0zHzjzJkzmjhxogoXLqyxY8dmuU/79u31/fffa82aNQoPD1dISIjCw8M1f/58rVy5UsuXL5fNZlPRokX18MMPa/DgwQoMDHRySwDjWOxZ9f3KwvXr17OcV8EVBQUFKSEhgfaYFO0xN9pjbrTH3PJje9aet6v3T8Z2F0+JOK6Y4TXlUbezCr44L9ePc6h7Qe2PsKnf6vg77+wC9j1dQL72uHz1estv7x/aY160x9xKlCjhciss5lZMTIwCAgI07mqYvP0LGB0nXXzMDY0q1EfR0dHy9/c3Oo7pmaLHCwAAwJ3YrkfJ4llAFo//DZ2wJ8Yp7ss3JEmeNbNeFQ0AAFeXYrEoxUQLhZgpiyswfHJdAACA7Eg+vEXRQyvp2ifP6sY3b+n6nMGKeaOukvZ/L/fKqfO+AAAA89q1a5fatm2rwMBA+fr6qm7duvryyy9z9Bg2m03Tp09X9erV5ePjo6JFi6p79+46evToXT9vUlKSFi9erH79+qly5cry9fWVn5+f6tWrpxkzZiglJSXHbZbo8QIAAFyE2/2V5V61uZKP7pB9z0pJkrX4g/LuMlLeTwyRxcrvSQAAmNWGDRsUHBwsT09P9ejRQwEBAVqyZIl69+6tkydP6o033sjW4wwaNEizZ89WlSpVNGTIEEVEROjrr7/WDz/8oK1bt6pKlSq5ft7jx4+rW7du8vPzU4sWLdShQwdFR0drxYoVGjx4sL7//nstW7ZMlhz2+KHwAgAAXIJbifIq+GKo0TEAAHA6V1/VKDk5Wc8995wsFos2btyoGjVqSJJGjx6tBg0aaPTo0QoJCVHFihVv+zjr16/X7Nmz1bhxY61duzZ95ba+ffuqdevWeuGFF/Tzzz/n+nn9/Pw0Y8YMPf300ypQ4H9z6kyePFnNmjXTihUrtGjRIoWEhOSo/fw0BAAAAAAAHOann37S8ePH1atXr/Tih5Ra6Bg1apSSk5MVGnrnH1dmz54tSRo/fnx60UWSWrZsqeDgYG3cuFFHjhzJ9fPef//9euGFFzIUXSTJ19dXr7zyiiRlKOxkF4UXAAAAAADgMBs2bJAktWnTJtO2tPuyU9DYsGGDfH199fjjj2faFhwcnOlx8up5JcnDw0OS5O6e84FDDDUCAAAAAMDEbLLKZqJ+E2lZYmJiMtzv5eWVoSdKmrSJb7MaShQYGKgiRYrcdnJcSbp+/brOnz+vatWqyc3NLdP2tMe++XHy4nnTzJ07V1LWRZw7Mc+RAwAAAAAALqN06dIKCAhIv02YMCHL/aKjoyVJAQEBWW739/dP3+dWsvMYN++XV88rSbNmzdLq1avVokULtW3b9o77/x09XgAAAAAAQI6dOXMmveAhKcveLq5u5cqVeumllxQUFKSwsLBcPQaFFwAAAAAATMwui+wmWtUoLYu/v3+GwsutpPU4uVXvkpiYmFv2SsnJY9y8X14875o1a9S1a1cVL15cP/30k0qWLHnbjLfCUCMAAAAAAOAwWc2/kiYqKkqRkZF3XEra19dXJUuW1J9//qmUlJRM27Oaz+Vunvf7779Xp06dVKRIEa1fv14PPvjgbfPdDoUXAAAAAADgME2bNpUk/fDDD5m2pd2Xts+dHuf69evasmVLpm1r1qzJ9Di5fd60oktgYKDWr1+vChUq3DHb7VB4AQAAAADAxGyymO6WEy1bttSDDz6oL7/8Uvv27Uu/PzY2VuPGjZO7u7v69euXfn9kZKQOHz6syMjIDI8zcOBASdLIkSOVmJiYfv+PP/6oNWvWqEmTJnrooYdy/bxS5qLLnXriZAdzvAAAAAAAAIdxd3fXZ599puDgYDVu3Fg9e/aUv7+/lixZoj///FPjx4/PUDCZPn263n77bY0ePVpjxoxJv7958+Z67rnn9Nlnn6lGjRpq166dIiIi9PXXX8vf31+ffPLJXT3v4cOH1alTJyUkJKhZs2b66quvMrWlbNmymYo1d2x/jvYGAAAAAADIoebNm2vz5s0aPXq0vvnmGyUmJqpq1aoaN26cevfune3HmTlzpqpXr66ZM2fq448/VsGCBdW+fXu98847GYoouXneCxcuKCEhQZK0cOHCLJ+/adOmFF4AAAAAAMhP7LkY3uNIuV1hqW7dulq9evUd9xszZkyGni43s1qtGjJkiIYMGZLnz9usWTPZ7fZsP252MccLAAAAAACAg1B4AQAAAAAAcBCGGgEAAAAAYGK5WUnIkcyUxRXQ4wUAAAAAAMBBKLwAAAAAAAA4CEONAAAAAAAwsRRJKSYa3pNidAAXQ48XAAAAAAAAB6HwAgAAAAAA4CAMNQIAAAAAwMTssshuoqFGZsriCujxAgAAAAAA4CAUXgAAAAAAAByEoUYAAAAAAJiYTRbZTDS8x0xZXAE9XgAAAAAAAByEwgsAAAAAAICDMNQIAAAAAAATS7FblGI3z/AeM2VxBfR4AQAAAAAAcBAKLwAAAAAAAA7CUCMAAAAAAEyMVY1cGz1eAAAAAAAAHITCCwAAAAAAgIMw1AgAAAAAABOzyyK7iYb3mCmLK6DHCwAAAAAAgINQeAEAAAAAAHAQhhoBAAAAAGBiNll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nTmjIkCGqU6eO6tatq7Fjx9o+g/v371e/fv1Uq1Yt1a1bV88995yuXr2a6/F6enoqKChI33zzTYbvx9dff634+Hh169bN7t/+888/mjJlih566CHVrFlTwcHBhilFyu45VZLOnTunZ555Ro0aNVK1atXUqFEjPfPMMzp37pyDo81Zc+bMUUBAgPbu3at169apU6dOqlmzZp6c0wr4ryHxApv//e9/OnjwoFq0aKEiRYqoc+fOSk1N1bp16zLs+/rrr2v48OE6dOiQWrVqpQEDBigwMFC7d+/Wt99+64To796hQ4cUGhoqDw8P9ezZUw888IC+/PJL9e3bV4mJic4O75ZMJpPtv2NiYvTBBx+oePHi6tChg/r27aty5cpp5cqV6tatmy5duuTESPOusWPHKn/+/HrzzTeVmpq1caBPPvmkNm/erLZt26pHjx6Ki4vTs88+q/fee8/B0d65tJ81I8tOfyZJCQkJ6tmzp06fPq3g4GA1aNBA27dvV79+/XTs2DGFhIToypUr6tatm2rWrKnPP/9c48ePd3g71q1bp9TUVPXs2fO2IyEKFCiQ7t+vvfaaFi9erIceekhdu3bVX3/9pXHjxikqKkqjRo2y3Q3v2LGjTp06pWHDhun06dOObE62JSUl6bvvvpPJZFL16tVvud+4ceO0dOlSDRkyRK+//rpT5/PJ7nHr37+/unTpIklq0KCBRo8erdGjR6tatWq5EW6O+vnnn9W9e3dFRUWpXr16GjhwoNq0aaN8+fLp448/dnZ4WXL69Gl1795dSUlJ6t69u+6//359+umnGjFihPbt26c+ffrI09NTPXv2VLly5bR69Wq98sorTom1W7duSk5O1saNG9NtX7NmjXx9fdWiRYsMf3Pt2jX17t1bH330kcqVK6d+/fqpQoUKGjNmjD7//PPcCv2WsnNOPXXqlIKDg7VmzRrVqFFDAwcOVI0aNbRmzRoFBwfr1KlTuRN0Dlq0aJGmTp2q8uXLq2/fvqpXr56zQ4IBuKWaDPdA1lFqBBvrnfbOnTtLkoKCgvTyyy9rzZo1Gj58uG2/7du3a+HChapataqWL1+uokWL2p5LSUnRP//8k5th55ht27Zpzpw5tonopH8n5vzyyy/Vvn17J0Z3a0uWLJEkPfDAA3r44Ye1Z8+eDBNRfvLJJ5o4caKWLVumkSNHOiPMPK1MmTLq06ePPvjgA0VGRqpnz563/ZvTp0/rs88+U+HChSVJI0eOVKdOnWyfw3Llyjk67GxL+1kzsqz2Z1ZHjx7VgAED9Pzzz9u2TZ06VStXrlTPnj311FNP2eaJslgsGjJkiLZv367Dhw+rRo0aDmvHvn37JOmOSmxOnDihTz/9VL6+vpKkLl26qFu3bho7dqyqVKmizZs365577pEkNWnSRGPGjNGHH36oKVOm5FwDsuG3336zrS5nsVgUHx+vnTt3KjY2VpMmTVKFChXs/t2VK1c0cuRI7d69W88884wGDx6cm2Hbld3jNmDAANsd7oceesiw5TBpj9HNateurWbNmmn9+vVKSkrS+++/r1atWqXbx9nlcJnF37RpU9WpU8f275iYGE2ePNnud37IkCGaPXu2rWwxOTlZwcHB+uSTTzR+/PhcLxWrXbu2KleurDVr1tjiPX/+vHbt2qX+/fvbTUAuWLBA//vf/9SjRw+9+uqrtu3BwcEaMGBAboV+S9k5p06ZMkVxcXF65ZVX0u23atUqTZ48WVOnTtXSpUtzI+wcEx0drbVr16pq1arODgVADiHxAkn/1gL7+PjY6oS9vLzUqlUrbd68Od0EZ8uXL5ckTZ48OV3SRbox0ZuR69NvpX79+umSLtKNO0nr16/XwYMHDZN4SXvhaJ3wdN++fSpQoIAmTJhg+5F1s86dO+ull17St99+S+LFQUaOHKnIyEiFh4erU6dOKliw4G33tyZdpBuTUw4cOFAzZszQpk2bNGrUKEeHfEu3+6wZVXb6M6tChQpp3Lhx6bZ16NBBK1euVJEiRdSvXz/bdpPJpKCgIG3fvl1Hjx51aOLlwoULkqRSpUpl+29HjhyZrj+oXbu2ypYtq9OnT2v8+PG2pIskPf7448qXL5+OHDly90Hfod9//13h4eEZtrds2TLTFWni4uI0ePBgHTlyRDNnzlRwcLCDo8yauzluRpbZMZKk/v37p1v+2tPTM8M+N18z5LbM4vf29k6XeClbtqytrFJK/52vXr16urmi8uXLp8cff1xvv/22Tpw44ZRroK5du2rGjBk6dOiQatasaRtxlVmZ0fr165UvXz6NHTs23fYmTZrokUce0e7du3Mh6tvLyjn13Llz2rNnjwICAtSjR490z/Xo0UMRERHavXu3zp07pzJlyuRW6HetR48eJF2APIZSI0iSvvzyS8XHxysoKEj58+e3bbdexKad3OzAgQPKnz+/HnrooVyP05Hs/XiyXjQnJCTkdjiZsl44hoeHa+nSpTp79qw6duyodevW2VaZ2bp1q/r376/69euratWqCggIUOXKlXX58uVM5+zB3StSpIiGDh2q2NjYLC0XaW/osHXbzz//nOPxZVdWPmtGlJ3+zMrf3z9dIkL6d5WW+++/P0OJVcmSJSVJsbGxORp7TrJXmmON++YSFnd3dxUrVsyp7WnSpIlOnDhhe3z33XeaPXu2fvjhB3Xv3l2//vpruv3j4uLUs2dPnThxQu+//75hki552c3HKO1j8uTJkm4k8dzc3DRixAhNmjRJGzduNMw8G5nFf/Moj2rVqsnNLf0lcmbfHenfvsJZ35/g4GDly5fPNtJv7dq1qlWrlipXrpxh38uXL+v06dPy9/e3uxKVkUpasnJOtZ4rGzRokKGfNplMtiT70aNHHRtsDqtVq5azQ4ABuaUa74GsI/ECSf/+ELEOy7dq0qSJSpQooS1bttjmBrl06ZJKlCiR4aLE1Xl5eWXYZh2iazYbZ720tBeOR44c0a5du/TWW2/Z7owsWrRIo0aN0s8//6wmTZpo0KBBtvkCvLy8lJSU5OQW2Gf9PN3q/2uLxZJuXyMaMGCA/Pz8tHDhwtsOq7c3Osl6t/Ty5csOiS87bvdZM6rs9GdWaUceWVm///aesy5rnJycnBMhZ8r6ebiTH3S3ijuz/s5Iy876+vqqQ4cOCgsLU0JCgubPn5/u+T///FOnTp1S6dKlDVf6djfHzdXVrVtXy5YtU7169bRp0yaNHz9eTZs2VefOnbVnzx5nh5clt/ru3KqvcNb3x9fXV82aNdOnn36qqKgo/fbbb5mOdrH2fZmNjjXaqOXbnVOt58rM4rZud7X57TI7PgBcl3F/vSDXnDt3zjYhbvfu3W2rKQQEBKhq1ar666+/dO3aNW3evFnSjQv2v/76y1DJCNyQkpKid955R35+ftqyZYveeusthYWFacyYMXrqqacc/iPxblh/CN5qjiDrqg32LnyNwtPTU6NHj9alS5duO0muvVVxrCUKRm6jlTUBZu/HhrMucrPbnxldYGCgJBlm6L8zWO/8Hj58ON32atWqafr06Tp16pSeeOIJQ63a9l8/bg899JAiIiL0ww8/aPny5Ro4cKCOHz+uIUOG6LfffnN2eHlSt27ddPHiRT377LPy9PTMtDzaem7J7PtiPQcZxe3Oqdb2ZBa3tZ2ucE5Ny1UmsgeQdSReoLVr18psNqtevXoKCQnJ8LDeNbYOYa1Vq5ZtpQkYS3x8vC5duqTatWtnuFvy008/6fr1606K7PYqVqyofPny6eDBg5neNfzxxx8l3Sj9MLKQkBBVqlRJy5cv1x9//JHpft9//32m2261gotRWJNl9u7q3/wjObdktz8zui5dusjd3V2rVq26bWLB6Kuv3SlrMtZesr9bt26aMWOGTpw4oT59+hgm+cJxu8HT01MNGzbUc889pxEjRuj69ev/2WSUozVv3lwlSpRQbGys2rRpY3dUm3Sj3y5btqx+++03/fXXXxmet3decrZbnVOt58qYmBjbqFgri8WimJgYSfZLxABX4+yyIkqN7g6T6/7HWSwWrV27ViaTSW+88YbKli1rd79jx47pwIEDOnbsmPr06aPt27frlVde0YoVK1SkSBHbftZVjYw2VPW/wtfXV56enjp8+LCuXbtmm4ju4sWLeumll5wc3a0VKFBA7dq104YNG/TOO+9kWNXjf//7nyIjI1WoUCG1bt3aSVFmjbu7uyZMmKCRI0dmOhGlJL377rt69NFH092x++CDD+Th4aEOHTrkVrh3zMvLSxUqVNC+fft06tQplS9fXtKNod+zZs3K9XjupD+rUqVKLkeZPeXLl9eQIUP0/vvva9CgQQoPD8/QrsTERC1fvlx///23Jk6c6KRIHcNsNttWI8ls7okuXbrIZDJp0qRJ6tOnj5YvX+70Yfr/5eMWExOj+++/P8MPf+uIBHuT7uLueXh4aP78+YqNjb3thN+dOnXSvHnz9Pbbb6db1Wjnzp2GTIzd6pxapkwZNWzYUHv37lVkZKS6d+9uey4yMlLHjx/Xww8/7FIT6wLIm0i8/Mft3r1bZ86cUcOGDTP9kSLduKs4bdo0rV69WpMnT9bgwYO1aNEitWrVSo899ph8fX0VGxur3bt3a/DgwYZZjvC/xs3NTb1799bixYvVvn17Pfroo7p8+bKioqJUpkwZ+fn5OTvEW3ruued04MABhYeHa9u2bWrQoIEKFCigX3/9VV9//bUk6c0335S3t7eTI7291q1bq06dOrZROvaULVtW7dq1U5s2bZSSkqLPPvtMcXFxGj9+vCGXkrZn4MCBmjJlikJCQtS2bVtZLBbt2LHDKXNu3Gl/ZnTjx49XYmKiIiIi1Lp1azVs2FBVqlSRh4eHzpw5o927dys+Pl7jx493dqh35ealfv/++2/t3btXJ0+eVOnSpW+5ypd1Yt1JkyapV69eWrFihdNvAOTF43ar5aQlacyYMVq8eLG+/fZb2/ewQIECOnz4sHbv3i1/f/90KwIhZz344INZ2m/o0KH64osv9PHHH+v48eOqX7++/vjjD23ZskUtWrTQtm3bHBxp9t3qnDpt2jT16NFDzz//vL755hsFBAToxIkT+vrrr1WsWDFNmzbNCRHbd+zYMYWFhdl9zpEr5AFwPhIv/3HW4faZTcJm1aFDB02fPl0bNmxQWFiYnnnmGdWpU0fLli3T559/rsTERJUsWVIPP/ywGjVqlBuhIxNPP/20fHx8tG7dOtuPj6CgII0ZM0bt2rVzdni35Ovrq08++UQffPCBvvrqK61atUrJyckqXry42rZtq0GDBrnUhUlYWJhCQ0MzfT48PFxvv/22Nm3apL///lvly5fXhAkT0t2xM7rQ0FAlJydryZIlioyMVIkSJdSlSxeNGjUq14d232l/ZnRubm56/vnn1bFjR61YsUIxMTGKiYmR2WxWyZIl1bhxY3Xt2lWNGzd2dqh35ealfvPnz6/77rtPAwcO1PDhw1WsWLFb/n1wcLBt5Evv3r21fPlyu6u25Ja8eNxutZy0dCPx0qtXL3l5eenAgQOKjo6WJFvirH///i4310ZedM8992jlypWaNWuWvvjiCx0+fFiVK1fWnDlzdOnSJUMmXqTMz6kVK1bU+vXrFR4erqioKG3fvl3FihVT165dNXr0aN17771OiNa+2NhYrVu3zu5zCQkJlEThltxSJTcDTf9DqVH2mCw3F0TCJV25ckXnz593dhg5wt/fX4mJibTHoGjP3evVq5eio6N14sSJHH9tjo+x0R5joz3GRnuMjfYYW6lSpVSoUCFnh4E7kJCQIB8fH4U8FK98HsYZ9Z2ckqDI74rq4sWLLjEa3dmYXBcAAAAAAMBBKDUCAAAAAMDATAYrNTJRapQtjHgBAAAAAABwEEa8AEAuW7lypbNDAAAAAJBLSLwAAAAAAGBgbmaT3FKNU2vkZjZOLK6AUiMAAAAAAAAHIfECAAAAAADgIJQaAQAAAABgYG6pxho14caqRtlipGMHAAAAAACQp5B4AQAAAAAAcBBKjQAAAAAAMDBKjVybkY4dAAAAAABAnkLiBQAAAAAAwEEoNQIAAAAAwMAoNXJtRjp2AAAAAAAAeQqJFwAAAAAAAAeh1AgAAAAAAAOj1Mi1GenYAQAAAAAA5CkkXgAAAAAAAByEUiMAAAAAAAyMUiPXZqRjBwAAAAAAkKeQeAEAAAAAAHAQSo0AAAAAADAwt1TJzeLsKP7lZnZ2BK6FES8AAAAAAAAOQuIFAAAAAADAQSg1AgAAAADAwNxSTXKzmJwdho2b2TixuAJGvAAAAAAAADgIiRcAAAAAAAAHodQIAAAAAAADMxlsVSMTqxplCyNeAAAAAAAAHITECwAAAAAAgINQagQAAAAAgIG5GazUyI1So2xhxAsAAAAAAICDkHgBAAAAAABwEEqNAAAAAAAwMEqNXBsjXgAAAAAAAByExAsAAAAAAICDUGoEAAAAAICBUWrk2hjxAgAAAAAA4CAkXgAAAAAAAByEUiMAAAAAAAzMzWywUiMDxeIKGPECAAAAAADgICReAAAAAAAAHIRSIwAAAAAADMwtVXIzOTuKf1FqlD2MeAEAAAAAAHAQEi8AAAAAAAAOQqkRAAAAAAAG5pZCqZErY8QLAAAAAACAg5B4AQAAAAAAcBBKjQAAAAAAMDBWNXJtjHgBAAAAAABwEBIvAAAAAAAADkKpEQAAAAAABkapkWtjxAsAAAAAAICDkHgBAAAAAABwEEqNAAAAAAAwMJPZWKMmDFT15BKMdOwAAAAAAADyFJPFYmFanDwgJSVFiYmJzg4jRxQsWFBms5n2GBTtMTbaY2y0x9hoj7HRHmOjPcZWoEABeXhQ7OCKEhIS5OPjo3E6rQLydnY4NolK0GyV1cWLF+XtbZy4jIrECwAAAAAABnT9+nVVqFBB58+fd3YoGZQqVUq//vqrPD09nR2K4ZF4AQAAAADAoK5fv66kpCRnh5FB/vz5SbpkEYkXAAAAAAAAB2FyXQAAAAAAAAch8QIAAAAAAOAgJF4AAAAAAAAchMQLAAAAAACAg5B4AQAAAAAAcBASLwAAAAAAAA5C4gUAAAAAAMBB/g+TkspX7sJrvQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 1200x800 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1200x800 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1200x800 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "ename": "ValueError",
+     "evalue": "too many values to unpack (expected 2)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[20], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m quaternary\u001b[39m.\u001b[39;49mplot_dopants(\u001b[39m10\u001b[39;49m)\n",
+      "File \u001b[0;32m~/Documents/smactClone/SMACT/smact/dopant_prediction/doper.py:191\u001b[0m, in \u001b[0;36mDoper.plot_dopants\u001b[0;34m(self, num_dopants)\u001b[0m\n\u001b[1;32m    184\u001b[0m results \u001b[39m=\u001b[39m {\n\u001b[1;32m    185\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39mn-type cation substitutions\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_type_cat[:num_dopants],\n\u001b[1;32m    186\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39mp-type cation substitutions\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mp_type_cat[:num_dopants],\n\u001b[1;32m    187\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39mn-type anion substitutions\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_type_an[:num_dopants],\n\u001b[1;32m    188\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39mp-type anion substitutions\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mp_type_an[:num_dopants],\n\u001b[1;32m    189\u001b[0m }\n\u001b[1;32m    190\u001b[0m \u001b[39mfor\u001b[39;00m key, val \u001b[39min\u001b[39;00m results\u001b[39m.\u001b[39mitems():\n\u001b[0;32m--> 191\u001b[0m     dict_results \u001b[39m=\u001b[39m {utilities\u001b[39m.\u001b[39mparse_spec(x)[\u001b[39m0\u001b[39m]: y \u001b[39mfor\u001b[39;00m x, y \u001b[39min\u001b[39;00m val}\n\u001b[1;32m    192\u001b[0m     plotting\u001b[39m.\u001b[39mperiodic_table_heatmap(\n\u001b[1;32m    193\u001b[0m         elemental_data\u001b[39m=\u001b[39mdict_results,\n\u001b[1;32m    194\u001b[0m         cmap\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mrainbow\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    197\u001b[0m         show_plot\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m,\n\u001b[1;32m    198\u001b[0m     )\n",
+      "File \u001b[0;32m~/Documents/smactClone/SMACT/smact/dopant_prediction/doper.py:191\u001b[0m, in \u001b[0;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    184\u001b[0m results \u001b[39m=\u001b[39m {\n\u001b[1;32m    185\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39mn-type cation substitutions\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_type_cat[:num_dopants],\n\u001b[1;32m    186\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39mp-type cation substitutions\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mp_type_cat[:num_dopants],\n\u001b[1;32m    187\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39mn-type anion substitutions\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_type_an[:num_dopants],\n\u001b[1;32m    188\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39mp-type anion substitutions\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mp_type_an[:num_dopants],\n\u001b[1;32m    189\u001b[0m }\n\u001b[1;32m    190\u001b[0m \u001b[39mfor\u001b[39;00m key, val \u001b[39min\u001b[39;00m results\u001b[39m.\u001b[39mitems():\n\u001b[0;32m--> 191\u001b[0m     dict_results \u001b[39m=\u001b[39m {utilities\u001b[39m.\u001b[39mparse_spec(x)[\u001b[39m0\u001b[39m]: y \u001b[39mfor\u001b[39;00m x, y \u001b[39min\u001b[39;00m val}\n\u001b[1;32m    192\u001b[0m     plotting\u001b[39m.\u001b[39mperiodic_table_heatmap(\n\u001b[1;32m    193\u001b[0m         elemental_data\u001b[39m=\u001b[39mdict_results,\n\u001b[1;32m    194\u001b[0m         cmap\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mrainbow\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    197\u001b[0m         show_plot\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m,\n\u001b[1;32m    198\u001b[0m     )\n",
+      "\u001b[0;31mValueError\u001b[0m: too many values to unpack (expected 2)"
+     ]
     }
    ],
    "source": [
-    "quaternary.get_dopants(num_dopants=10, plot_heatmap=True)"
+    "quaternary.plot_dopants(10)"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "pymatgen",
+   "display_name": "venv",
    "language": "python",
-   "name": "pymatgen"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -251,12 +233,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
-   }
+   "version": "3.9.7"
   }
  },
  "nbformat": 4,
diff --git a/requirements-dev.txt b/requirements-dev.txt
index e78271de..b9274086 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -1,7 +1,7 @@
 pre-commit ==3.3.3
 black ==23.3.0
 isort ==5.12.0
-pytest ==7.3.1
+pytest ==7.4.0
 nbqa ==1.7.0
 pyupgrade ==3.8.0
 pytest-cov ==4.1.0
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index a470ab69..a810b3ed 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,11 +1,154 @@
-numpy == 1.24.3
-scipy ==1.11.1
-spglib ==2.0.2
-future ==0.18.3
-ase ==3.22.1
-pymatgen ==2023.6.28
-pandas == 2.0.2
-pathos ==0.3.0
-pytest ==7.3.1
-pytest-subtests ==0.11.0
-pydantic ==1.*
\ No newline at end of file
+# This file is autogenerated by pip-compile with Python 3.9
+# by the following command:
+#
+#    pip-compile
+#
+ase==3.22.1
+    # via SMACT (setup.py)
+certifi==2023.7.22
+    # via requests
+charset-normalizer==3.2.0
+    # via requests
+contourpy==1.1.0
+    # via matplotlib
+cycler==0.11.0
+    # via matplotlib
+dill==0.3.7
+    # via
+    #   multiprocess
+    #   pathos
+emmet-core==0.66.0
+    # via mp-api
+fonttools==4.42.1
+    # via matplotlib
+future==0.18.3
+    # via uncertainties
+idna==3.4
+    # via requests
+importlib-resources==6.0.1
+    # via matplotlib
+joblib==1.3.2
+    # via pymatgen
+kiwisolver==1.4.5
+    # via matplotlib
+latexcodec==2.0.1
+    # via pybtex
+matplotlib==3.7.2
+    # via
+    #   ase
+    #   pymatgen
+monty==2023.8.8
+    # via
+    #   emmet-core
+    #   mp-api
+    #   pymatgen
+mp-api==0.34.3
+    # via pymatgen
+mpmath==1.3.0
+    # via sympy
+msgpack==1.0.5
+    # via mp-api
+multiprocess==0.70.15
+    # via pathos
+networkx==3.1
+    # via pymatgen
+numpy==1.24.3
+    # via
+    #   SMACT (setup.py)
+    #   ase
+    #   contourpy
+    #   matplotlib
+    #   pandas
+    #   pymatgen
+    #   scipy
+    #   spglib
+packaging==23.1
+    # via
+    #   matplotlib
+    #   plotly
+palettable==3.3.3
+    # via pymatgen
+pandas==2.0.3
+    # via
+    #   SMACT (setup.py)
+    #   pymatgen
+pathos==0.3.0
+    # via SMACT (setup.py)
+pillow==10.0.0
+    # via matplotlib
+plotly==5.16.1
+    # via pymatgen
+pox==0.3.3
+    # via pathos
+ppft==1.7.6.7
+    # via pathos
+pybtex==0.24.0
+    # via
+    #   emmet-core
+    #   pymatgen
+pydantic==1.10.12
+    # via
+    #   emmet-core
+    #   pymatgen
+pymatgen==2023.7.20
+    # via
+    #   SMACT (setup.py)
+    #   emmet-core
+    #   mp-api
+pyparsing==3.0.9
+    # via matplotlib
+python-dateutil==2.8.2
+    # via
+    #   matplotlib
+    #   pandas
+pytz==2023.3
+    # via pandas
+pyyaml==6.0.1
+    # via pybtex
+requests==2.31.0
+    # via
+    #   mp-api
+    #   pymatgen
+ruamel-yaml==0.17.32
+    # via pymatgen
+ruamel-yaml-clib==0.2.7
+    # via ruamel-yaml
+scipy==1.10.1
+    # via
+    #   SMACT (setup.py)
+    #   ase
+    #   pymatgen
+six==1.16.0
+    # via
+    #   latexcodec
+    #   pybtex
+    #   python-dateutil
+spglib==2.0.2
+    # via
+    #   SMACT (setup.py)
+    #   emmet-core
+    #   pymatgen
+sympy==1.12
+    # via pymatgen
+tabulate==0.9.0
+    # via pymatgen
+tenacity==8.2.3
+    # via plotly
+tqdm==4.66.1
+    # via pymatgen
+typing-extensions==4.7.1
+    # via
+    #   emmet-core
+    #   mp-api
+    #   pydantic
+tzdata==2023.3
+    # via pandas
+uncertainties==3.1.7
+    # via pymatgen
+urllib3==2.0.4
+    # via requests
+zipp==3.16.2
+    # via importlib-resources
+
+# The following packages are considered to be unsafe in a requirements file:
+# setuptools
\ No newline at end of file
diff --git a/setup.py b/setup.py
index 858a026b..2cc88833 100644
--- a/setup.py
+++ b/setup.py
@@ -5,10 +5,10 @@
 __copyright__ = (
     "Copyright Daniel W. Davies, Adam J. Jackson, Keith T. Butler (2019)"
 )
-__version__ = "2.5.1"
+__version__ = "2.5.3"
 __maintainer__ = "Anthony O. Onwuli"
 __maintaier_email__ = "anthony.onwuli16@imperial.ac.uk"
-__date__ = "May 2 2023"
+__date__ = "August 23 2023"
 
 import os
 import unittest
@@ -61,6 +61,7 @@
             "Programming Language :: Python :: 3.8",
             "Programming Language :: Python :: 3.9",
             "Programming Language :: Python :: 3.10",
+            "Programming Language :: Python :: 3.11",
             "Development Status :: 5 - Production/Stable",
             "Intended Audience :: Science/Research",
             "Operating System :: OS Independent",
diff --git a/smact/dopant_prediction/doper.py b/smact/dopant_prediction/doper.py
index dacb68b3..f3dba6ff 100644
--- a/smact/dopant_prediction/doper.py
+++ b/smact/dopant_prediction/doper.py
@@ -1,15 +1,3 @@
-### This Jupyter notebook creates ntype ptype possiblie dopants for input species
-### using SMACT structure prediction
-### Working with Kieth from SCIML and Anthony
-
-###Doper ver 2
-
-# Now 'Doper' can generate possible n-type p-type dopants for multicomponent materials (i.e. Ternary, Quaternary etc).
-# Can plot the result of doping search within a single step
-# """ex) test= Doper(('Cu1+','Zn2+','Ge4+','S2-'))
-#         test.get_dopants(num_dopants = 10, plot_heatmap = True)"""
-
-
 from typing import List, Tuple
 
 from pymatgen.util import plotting
@@ -26,13 +14,15 @@ class Doper:
     Attributes:
         original_species: A tuple which describes the constituent species of a material. For example:
 
-            >>> test= Doper(('Cu1+','Zn2+','Ge4+','S2-'))
+            >>> test= Doper(("Zn2+","S2-"))
             >>> test.original_species
-                ('Cu1+','Zn2+','Ge4+','S2-')
+                ('Zn2+','S2-')
 
     """
 
-    def __init__(self, original_species: Tuple[str, ...]):
+    def __init__(
+        self, original_species: Tuple[str, ...], filepath: str = None
+    ):
         """
         Intialise the `Doper` class with a tuple of species
 
@@ -41,6 +31,7 @@ def __init__(self, original_species: Tuple[str, ...]):
 
         """
         self.original_species = original_species
+        self._get_dopants(filepath)
 
     def _get_cation_dopants(
         self, element_objects: List[smact.Element], cations: List[str]
@@ -54,15 +45,14 @@ def _get_cation_dopants(
             el_symbol = element.symbol
             for state in oxi_state:
                 for cation in cations:
+                    ele = utilities.unparse_spec((el_symbol, state))
                     _, charge = utilities.parse_spec(cation)
                     if state > charge:
-                        poss_n_type_cat.append(
-                            utilities.unparse_spec((el_symbol, state))
-                        )
+                        if ele not in poss_n_type_cat:
+                            poss_n_type_cat.append(ele)
                     elif state < charge and state > 0:
-                        poss_p_type_cat.append(
-                            utilities.unparse_spec((el_symbol, state))
-                        )
+                        if ele not in poss_p_type_cat:
+                            poss_p_type_cat.append(ele)
 
         return poss_n_type_cat, poss_p_type_cat
 
@@ -77,57 +67,17 @@ def _get_anion_dopants(
             el_symbol = element.symbol
             for state in oxi_state:
                 for anion in anions:
+                    ele = utilities.unparse_spec((el_symbol, state))
                     _, charge = utilities.parse_spec(anion)
                     if state > charge and state < 0:
-                        poss_n_type_an.append(
-                            utilities.unparse_spec((el_symbol, state))
-                        )
+                        if ele not in poss_n_type_an:
+                            poss_n_type_an.append(ele)
                     elif state < charge:
-                        poss_p_type_an.append(
-                            utilities.unparse_spec((el_symbol, state))
-                        )
+                        if ele not in poss_p_type_an:
+                            poss_p_type_an.append(ele)
         return poss_n_type_an, poss_p_type_an
 
-    def _plot_dopants(self, results: dict):
-        """
-        Uses pymatgen plotting utilities to plot the results of doping search
-        """
-        for key, val in results.items():
-            dict_results = {utilities.parse_spec(x)[0]: y for x, y in val}
-            plotting.periodic_table_heatmap(
-                elemental_data=dict_results,
-                cmap="rainbow",
-                blank_color="gainsboro",
-                edge_color="white",
-            )
-
-    def get_dopants(
-        self,
-        num_dopants: int = 5,
-        plot_heatmap: bool = False,
-    ) -> dict:
-        """
-        Args:
-            num_dopants (int): The number of suggestions to return for n- and p-type dopants.
-            plot_heatmap (bool): If True, the results of the doping search are plotted as heatmaps
-
-        Returns:
-            (dict): Dopant suggestions, given as a dictionary with keys
-            "n_type_cation", "p_type_cation", "n_type_anion", "p_type_anion".
-
-        Examples:
-            >>> test = Doper(('Ti4+','O2-'))
-            >>> print(test.get_dopants(num_dopants=2))
-                {'n-type cation substitutions': [('Ta5+', 8.790371775858281e-05),
-                ('Nb5+', 7.830035204694342e-05)],
-                'p-type cation substitutions': [('Na1+', 0.00010060400812977031),
-                ('Zn2+', 8.56373996146833e-05)],
-                'n-type anion substitutions': [('F1-', 0.01508116810515677),
-                ('Cl1-', 0.004737202729901607)],
-                'p-type anion substitutions': [('N3-', 0.0014663800608945628),
-                ('C4-', 9.31310255126729e-08)]}
-        """
-
+    def _get_dopants(self, filepath: str):
         cations = []
         anions = []
         try:
@@ -140,7 +90,7 @@ def get_dopants(
         except Exception as e:
             print(e, "charge is not defined")
 
-        CM = mutation.CationMutator.from_json()
+        CM = mutation.CationMutator.from_json(filepath)
 
         # call all elements
         element_objects = list(smact.element_dictionary().values())
@@ -155,32 +105,94 @@ def get_dopants(
         n_type_cat, p_type_cat, n_type_an, p_type_an = [], [], [], []
         for cation in cations:
             for n_specie, p_specie in zip(poss_n_type_cat, poss_p_type_cat):
-                n_type_cat.append((n_specie, CM.sub_prob(cation, n_specie)))
-                p_type_cat.append((p_specie, CM.sub_prob(cation, p_specie)))
+                if cation == n_specie or cation == p_specie:
+                    continue
+                n_type_cat.append(
+                    (n_specie, cation, CM.sub_prob(cation, n_specie))
+                )
+                p_type_cat.append(
+                    (p_specie, cation, CM.sub_prob(cation, p_specie))
+                )
 
         for anion in anions:
             for n_specie, p_specie in zip(poss_n_type_an, poss_p_type_an):
-                n_type_an.append((n_specie, CM.sub_prob(anion, n_specie)))
-                p_type_an.append((p_specie, CM.sub_prob(anion, p_specie)))
+                if anion == n_specie or cation == p_specie:
+                    continue
+                n_type_an.append(
+                    (n_specie, anion, CM.sub_prob(anion, n_specie))
+                )
+                p_type_an.append(
+                    (p_specie, anion, CM.sub_prob(anion, p_specie))
+                )
 
         # [('B3+', 0.003), ('C4+', 0.001), (), (), ...] : list(tuple(str, float))
         # sort by probability
-        n_type_cat.sort(key=lambda x: x[1], reverse=True)
-        p_type_cat.sort(key=lambda x: x[1], reverse=True)
-        n_type_an.sort(key=lambda x: x[1], reverse=True)
-        p_type_an.sort(key=lambda x: x[1], reverse=True)
+        n_type_cat.sort(key=lambda x: x[-1], reverse=True)
+        p_type_cat.sort(key=lambda x: x[-1], reverse=True)
+        n_type_an.sort(key=lambda x: x[-1], reverse=True)
+        p_type_an.sort(key=lambda x: x[-1], reverse=True)
 
-        results = {
-            "n-type cation substitutions": n_type_cat[:num_dopants],
-            "p-type cation substitutions": p_type_cat[:num_dopants],
-            "n-type anion substitutions": n_type_an[:num_dopants],
-            "p-type anion substitutions": p_type_an[:num_dopants],
-        }
+        self.n_type_cat = n_type_cat
+        self.p_type_cat = p_type_cat
+        self.n_type_an = n_type_an
+        self.p_type_an = p_type_an
 
-        # plot heatmap
+    def get_dopants(
+        self,
+        num_dopants: int = 5,
+    ) -> dict:
+        """
+        Args:
+            num_dopants (int): The number of suggestions to return for n- and p-type dopants.
+        Returns:
+            (dict): Dopant suggestions, given as a dictionary with keys
+            "n_type_cation", "p_type_cation", "n_type_anion", "p_type_anion".
 
-        if plot_heatmap:
-            self._plot_dopants(results)
+        Examples:
+            >>> test = Doper(('Ti4+','O2-'))
+            >>> print(test.get_dopants(num_dopants=2))
+                {'n-type cation substitutions': [('Ta5+', 8.790371775858281e-05),
+                ('Nb5+', 7.830035204694342e-05)],
+                'p-type cation substitutions': [('Na1+', 0.00010060400812977031),
+                ('Zn2+', 8.56373996146833e-05)],
+                'n-type anion substitutions': [('F1-', 0.01508116810515677),
+                ('Cl1-', 0.004737202729901607)],
+                'p-type anion substitutions': [('N3-', 0.0014663800608945628),
+                ('C4-', 9.31310255126729e-08)]}
+        """
 
+        results = {
+            "n-type cation substitutions": self.n_type_cat[:num_dopants],
+            "p-type cation substitutions": self.p_type_cat[:num_dopants],
+            "n-type anion substitutions": self.n_type_an[:num_dopants],
+            "p-type anion substitutions": self.p_type_an[:num_dopants],
+        }
         # return the top (num_dopants) results for each case
         return results
+
+    def plot_dopants(
+        self,
+        num_dopants: int = 5,
+    ) -> None:
+        """
+        Uses pymatgen plotting utilities to plot the results of doping search
+        Args:
+            num_dopants (int): The number of suggestions to return for n- and p-type dopants.
+        Returns:
+            None
+        """
+        results = {
+            "n-type cation substitutions": self.n_type_cat[:num_dopants],
+            "p-type cation substitutions": self.p_type_cat[:num_dopants],
+            "n-type anion substitutions": self.n_type_an[:num_dopants],
+            "p-type anion substitutions": self.p_type_an[:num_dopants],
+        }
+        for key, val in results.items():
+            dict_results = {utilities.parse_spec(x)[0]: y for x, _, y in val}
+            plotting.periodic_table_heatmap(
+                elemental_data=dict_results,
+                cmap="rainbow",
+                blank_color="gainsboro",
+                edge_color="white",
+                show_plot=True,
+            )
diff --git a/smact/screening.py b/smact/screening.py
index faa28d0f..13db4930 100644
--- a/smact/screening.py
+++ b/smact/screening.py
@@ -313,6 +313,7 @@ def smact_filter(
     stoichs: Optional[List[List[int]]] = None,
     species_unique: bool = True,
     oxidation_states_set: str = "default",
+    comp_tuple: bool = False,
 ) -> Union[List[Tuple[str, int, int]], List[Tuple[str, int]]]:
     """Function that applies the charge neutrality and electronegativity
     tests in one go for simple application in external scripts that
@@ -324,6 +325,7 @@ def smact_filter(
         stoichs (list[int]): A selection of valid stoichiometric ratios for each site.
         species_unique (bool): Whether or not to consider elements in different oxidation states as unique in the results.
         oxidation_states_set (string): A string to choose which set of oxidation states should be chosen. Options are 'default', 'icsd', 'pymatgen' and 'wiki' for the default, icsd, pymatgen structure predictor and Wikipedia (https://en.wikipedia.org/wiki/Template:List_of_oxidation_states_of_the_elements) oxidation states respectively.
+        comp_tuple (bool): Whether or not to return the results as a named tuple of elements and stoichiometries (True) or as a normal tuple of elements and stoichiometries (False).
     Returns:
         allowed_comps (list): Allowed compositions for that chemical system
         in the form [(elements), (oxidation states), (ratios)] if species_unique=True
@@ -336,17 +338,17 @@ def smact_filter(
         >>> comps = smact_filter(els, threshold =5 )
         >>> for comp in comps:
         >>>     print(comp)
-        Composition(element_symbols=('Cs', 'Pb', 'I'), oxidation_states=(1, -4, -1), stoichiometries=(5, 1, 1))
-        Composition(element_symbols=('Cs', 'Pb', 'I'), oxidation_states=(1, 2, -1), stoichiometries=(1, 1, 3))
-        Composition(element_symbols=('Cs', 'Pb', 'I'), oxidation_states=(1, 2, -1), stoichiometries=(1, 2, 5))
-        Composition(element_symbols=('Cs', 'Pb', 'I'), oxidation_states=(1, 2, -1), stoichiometries=(2, 1, 4))
-        Composition(element_symbols=('Cs', 'Pb', 'I'), oxidation_states=(1, 2, -1), stoichiometries=(3, 1, 5))
-        Composition(element_symbols=('Cs', 'Pb', 'I'), oxidation_states=(1, 4, -1), stoichiometries=(1, 1, 5))
+        [('Cs', 'Pb', 'I'), (1, -4, -1), (5, 1, 1)]
+        [('Cs', 'Pb', 'I'), (1, 2, -1), (1, 1, 3)]
+        [('Cs', 'Pb', 'I'), (1, 2, -1), (1, 2, 5)]
+        [('Cs', 'Pb', 'I'), (1, 2, -1), (2, 1, 4)]
+        [('Cs', 'Pb', 'I'), (1, 2, -1), (3, 1, 5)]
+        [('Cs', 'Pb', 'I'), (1, 4, -1), (1, 1, 5)]
 
     Example (using stoichs):
         >>> from smact.screening import smact_filter
         >>> from smact import Element
-        >>> comps = smact_filter(els, stoichs = [[1],[1],[3]] )
+        >>> comps = smact_filter(els, stoichs = [[1],[1],[3]], comp_tuple=True )
         >>> for comp in comps:
         >>>     print(comp)
         Composition(element_symbols=('Cs', 'Pb', 'I'), oxidation_states=(1, 2, -1), stoichiometries=(1, 1, 3))
@@ -393,6 +395,8 @@ def smact_filter(
                 for ratio in cn_r:
                     compositions.append(
                         _allowed_compositions(symbols, ox_states, ratio)
+                        if comp_tuple
+                        else (symbols, ox_states, ratio)
                     )
 
     # Return list depending on whether we are interested in unique species combinations
@@ -400,8 +404,12 @@ def smact_filter(
     if species_unique:
         return compositions
     else:
-        compositions = [
-            _allowed_compositions_nonunique(i[0], i[2]) for i in compositions
-        ]
+        if comp_tuple:
+            compositions = [
+                _allowed_compositions_nonunique(i[0], i[2])
+                for i in compositions
+            ]
+        else:
+            compositions = [(i[0], i[2]) for i in compositions]
         compositions = list(set(compositions))
         return compositions
diff --git a/smact/structure_prediction/structure.py b/smact/structure_prediction/structure.py
index 1cf5a56c..e9993774 100644
--- a/smact/structure_prediction/structure.py
+++ b/smact/structure_prediction/structure.py
@@ -274,9 +274,12 @@ def from_py_struct(
                 )
                 struct = oxi_transform.apply_transformation(structure)
                 print("Oxidation states assigned based on ICSD statistics")
+        elif determine_oxi == "predecorated":
+            struct = structure
+
         else:
             raise ValueError(
-                f"Argument for 'determine_oxi', <{determine_oxi}> is not valid. Choose either 'BV','comp_ICSD' or 'both'."
+                f"Argument for 'determine_oxi', <{determine_oxi}> is not valid. Choose either 'BV','comp_ICSD','both' or 'predecorated'."
             )
 
         sites, species = SmactStructure.__parse_py_sites(struct)
diff --git a/smact/tests/test_core.py b/smact/tests/test_core.py
index 09a74973..ccfb2768 100755
--- a/smact/tests/test_core.py
+++ b/smact/tests/test_core.py
@@ -346,6 +346,28 @@ def test_smact_filter(self):
                 (("Na", "Fe", "Cl"), (1, 1, -1), (1, 1, 2)),
             ],
         )
+        result_comp_tuple = smact.screening.smact_filter(
+            [Na, Fe, Cl], threshold=2, comp_tuple=True
+        )
+        self.assertTupleEqual(
+            result_comp_tuple[0].element_symbols, ("Na", "Fe", "Cl")
+        )
+        self.assertTupleEqual(result_comp_tuple[0].stoichiometries, (2, 1, 1))
+        self.assertTupleEqual(
+            result_comp_tuple[0].oxidation_states, (1, -1, -1)
+        )
+        self.assertEqual(
+            set(
+                smact.screening.smact_filter(
+                    [Na, Fe, Cl], threshold=2, species_unique=False
+                )
+            ),
+            {
+                (("Na", "Fe", "Cl"), (2, 1, 1)),
+                (("Na", "Fe", "Cl"), (1, 1, 2)),
+            },
+        )
+
         self.assertEqual(
             len(smact.screening.smact_filter([Na, Fe, Cl], threshold=8)), 77
         )