diff --git a/doc/Introduction_to_TF24_IVIM-MRI_CodeCollection_github_and_IVIM_Analysis_using_Python.ipynb b/doc/Introduction_to_TF24_IVIM-MRI_CodeCollection_github_and_IVIM_Analysis_using_Python.ipynb
index 23c52cf..63bd908 100644
--- a/doc/Introduction_to_TF24_IVIM-MRI_CodeCollection_github_and_IVIM_Analysis_using_Python.ipynb
+++ b/doc/Introduction_to_TF24_IVIM-MRI_CodeCollection_github_and_IVIM_Analysis_using_Python.ipynb
@@ -45,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "id": "56273b2a",
    "metadata": {},
    "outputs": [
@@ -54,7 +54,7 @@
      "evalue": "invalid syntax (882689640.py, line 2)",
      "output_type": "error",
      "traceback": [
-      "\u001b[1;36m  Cell \u001b[1;32mIn[1], line 2\u001b[1;36m\u001b[0m\n\u001b[1;33m    pip3 install numpy\u001b[0m\n\u001b[1;37m         ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
+      "\u001b[1;36m  Cell \u001b[1;32mIn[3], line 2\u001b[1;36m\u001b[0m\n\u001b[1;33m    pip3 install numpy\u001b[0m\n\u001b[1;37m         ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
@@ -76,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "id": "9654f12d-6cfc-4639-884e-d7f2b23fa59f",
    "metadata": {},
    "outputs": [],
@@ -105,18 +105,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "d06c9f5a",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Folder 'download' created.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Download the data. \n",
     "download_data()"
@@ -132,7 +124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "id": "6bd0f61a",
    "metadata": {},
    "outputs": [],
@@ -168,17 +160,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "id": "9c977b4e",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x23ba1c0f9d0>"
+       "<matplotlib.colorbar.Colorbar at 0x1e89a7df890>"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -220,7 +212,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 8,
    "id": "120fc47a",
    "metadata": {},
    "outputs": [
@@ -230,7 +222,7 @@
        "Text(0.5, 1.0, 'normalized signal as function of b-value')"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -279,7 +271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
    "id": "8ddef255-88b6-4c79-8f6c-4ee85e931588",
    "metadata": {},
    "outputs": [
@@ -315,7 +307,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
    "id": "b183a0dc",
    "metadata": {},
    "outputs": [
@@ -325,7 +317,7 @@
        "Text(0.5, 1.0, 'direction 6')"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -441,7 +433,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 11,
    "id": "b7e8872a",
    "metadata": {},
    "outputs": [],
@@ -460,7 +452,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 12,
    "id": "12deef83",
    "metadata": {},
    "outputs": [
@@ -484,7 +476,7 @@
        " '__pycache__']"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -504,19 +496,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 13,
    "id": "f695e259",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\ivan5\\Box\\OSIPI\\TF2.4_IVIM-MRI_CodeCollection\\doc\\..\\src\\standardized\\OGC_AmsterdamUMC_Bayesian_biexp.py:54: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n",
-      "  if len(prior_in) is 4:\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from src.standardized.IAR_LU_biexp import IAR_LU_biexp\n",
     "from src.standardized.OGC_AmsterdamUMC_Bayesian_biexp import OGC_AmsterdamUMC_Bayesian_biexp"
@@ -524,7 +507,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 14,
    "id": "6b7ab9cb",
    "metadata": {},
    "outputs": [],
@@ -535,7 +518,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 15,
    "id": "7c9060bb",
    "metadata": {},
    "outputs": [
@@ -543,7 +526,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 1/1.0 [00:00<00:00, 62.98it/s]\n"
+      "100%|██████████| 1/1.0 [00:00<00:00, 124.94it/s]\n"
      ]
     },
     {
@@ -552,7 +535,7 @@
        "{'f': array(0.04609779), 'D*': array(0.01136011), 'D': array(0.00071134)}"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -572,7 +555,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 16,
    "id": "9fee9a3e",
    "metadata": {},
    "outputs": [
@@ -580,7 +563,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 1/1.0 [00:00<00:00, 55.56it/s]\n"
+      "100%|██████████| 1/1.0 [00:00<00:00, 52.90it/s]\n"
      ]
     },
     {
@@ -589,7 +572,7 @@
        "{'f': array(0.04611801), 'D*': array(0.0113541), 'D': array(0.0007113)}"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -611,7 +594,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 18,
    "id": "e35f4f51",
    "metadata": {},
    "outputs": [
@@ -619,8 +602,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 1/1.0 [00:00<00:00, 167.24it/s]\n",
-      "100%|██████████| 1/1.0 [00:00<00:00, 82.84it/s]\n"
+      "100%|██████████| 1/1.0 [00:00<00:00, 175.81it/s]\n",
+      "100%|██████████| 1/1.0 [00:00<00:00, 68.72it/s]\n"
      ]
     },
     {
@@ -629,13 +612,13 @@
        "Text(0.5, 1.0, 'algorithm 2')"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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dd2XTTTJLmMjISBMwIyMjb+o8H/120Kz63GLzo98OZvn6WuXT/3x1+eudQ+RGxcfHm/v27TPj4+Pz5Hy1X1hiVn1usVn7hSV5cr5r6dChg9muXTvn65SUFNPb29scMmSIc1tYWJgJmBs2bDBN0zRfeukls3v37hnOc/LkSRMwDxw4kOX7REREmIC5e/du0zRNc9SoUWbnzp1Nh8ORqeyxY8dMwNyxY4dz26VLl0zAXLlypWmaprly5UoTMOfPn+8sExMTY3p6eprr16/PcL6HH37YvP/++03TNM3x48eb9evXz/C+zz33nAmYly5dyrLuv/76q2m1Ws2TJ086t/3yyy8mYM6bNy/LY0zTNN9++22zRYsWztcTJ040mzRpkm35dD/88IMZGBiY7f5r/X3Lq+/g4iYvPpfc5tKVZe6ftiHDf3NzDpEbpWxSNmUlP7Ipr3PJpXegfv/9d/r160eFChVyPCPH6tWradGiBZ6entSoUYPPPvss3+t59TCHq8fVfhBy8LozGl25/z8rDmfaXxC3m0XygivWkmncuLHzz1arlcDAQBo1auTcFhQUBEBERAQA27ZtY+XKlfj4+Dh/6tWrB+AcCnHkyBEeeOABatSogZ+fH9WrVwcgNDQUgOHDh7Nz507q1q3L6NGjWbZs2Q3VvWXLls4/79u3j4SEBLp165ahbl9//bWzXvv376d169YZFv1r06bNNd9j//79VKlShUqVKl3zmB9//JF27doRHByMj48PL730krO917Jy5Uq6detGxYoV8fX1ZejQoVy4cIHY2NjrHlsUFcVsujpD0h+ev95Me+nZtP7IBayGkWEmWeWSFCXKptxRNt0clz4xFhsbS5MmTRgxYgR33XXXdcsfO3aM3r178+ijj/LNN9+wbt06Hn/8ccqWLZuj42/U1Q8opocSkKEjdb0ZjUZ3qe18sNfDauHJzrVy9bCviKtl9Y+0glhLxt3dPcNrwzAybEv/Qk8f4uBwOOjXrx9vvfVWpnOVL18egH79+lG5cmWmT59OhQoVcDgcNGzYkKSkJACaN2/OsWPH+OWXX/jtt9+499576dq1Kz/++CMWS9q1J9P8+3c9fbz21by9vZ1/Tq/fzz//TMWKFTOUs9lsmc6ZU1kdc/Wq6xs3buS+++7jlVdeoUePHvj7+zNr1izee++9a577xIkT9O7dm5EjR/Laa69RunRp1q5dy8MPP5xtm4u6ophN6bmUXcfnWjPtXZlN6Z2oOhN+US5JkaFsUjYVdDa5tAPVq1cvevXqlePyn332GVWqVOHDDz8EoH79+mzdupV33303X0PqypmNDDOFpxrEM+VP3yyvzF3rF/XqqyNAjh/2FXG1rP5RltXUyYVB8+bN+emnn6hWrVqWMwtduHCB/fv38/nnn9O+fXsA1q5dm6mcn58fgwcPZvDgwdx999307NmTixcvUrZsWSBtPHuzZs0AcjS9aoMGDbDZbISGhtKhQ4dsy1x9x2Pjxo3XPW9oaChnzpyhQoUKAGzYsCFDmXXr1lG1alUmTJjg3HbixIkMZTw8PEhNTc2wbevWraSkpPDee+85w/mHH364Zn2KuqKYTS+39+P/2te85u9pdq7OJqthKJekyFA2KZtckU1Fas7CDRs20L179wzbevTowRdffEFycnKmKwEAiYmJJCYmOl9HRUXd0Hun//KZK18ndc0CzNRBPN31WUbl8JfyWldHrrzdXFh+yUWuduUV7isVxrVknnjiCaZPn87999/Pv/71L8qUKcPhw4eZNWsW06dPp1SpUgQGBjJt2jTKly9PaGhopodhP/jgA8qXL0/Tpk2xWCzMmTOH4OBgAgICsFgstG7dmjfffJNq1apx/vx5XnzxxevWy9fXl2eeeYannnoKh8NBu3btiIqKYv369fj4+DBs2DBGjhzJe++9x7hx4/jnP//Jtm3bnDMOZadr167UrVuXoUOH8t577xEVFZUhjABq1apFaGgos2bN4tZbb+Xnn39m3rx5GcpUq1aNY8eOsXPnTipVquScRSolJYWPP/6Yfv36sW7dugIZnlaUuDqbDDOFJr8P4dLmcHYnP8q4bg/kOEuuzqb0BeHTO1HKJSnslE3KJpdkU46flspnXOeBMtM0zdq1a5tvvPFGhm3r1q0zAfPMmTNZHjNx4kQTyPRzQw/qOhzmnJf6m+ZEv7Sf/3YzzYvHr3tYVg/hXj2RhB7UlfyU1w/qFqQOHTqYY8aMybCtatWq5gcffJBh29XfIQcPHjQHDRpkBgQEmHa73axXr545duxY5wOwISEhZv369U2bzWY2btzYXLVqVYZzTJs2zWzatKnp7e1t+vn5mV26dDG3b9/uPP++ffvM1q1bm3a73WzatKm5bNmyLB/UvfrhWofDYX700Udm3bp1TXd3d7Ns2bJmjx49zNWrVzvLLFq0yKxVq5Zps9nM9u3bm19++eU1H9Q1TdM8cOCA2a5dO9PDw8OsU6eOuXTp0kyfyb/+9S8zMDDQ9PHxMQcPHmx+8MEHpr+/v3N/QkKCedddd5kBAQEmYM6YMcM0TdN8//33zfLly5t2u93s0aOH+fXXX1+zPsVpEokikU3RZ83dLzf5O5sWPWWaSXHXPSy7SSeym1BCJK8pm5RNplkw2ZTXuWSY5g0MaswHhmEwb948Bg4cmG2ZOnXqMGLECMaPH+/ctm7dOtq1a0dYWBjBwcGZjsnqKl/lypWJjIzEz88vV3VMv1J3p9t6Jlm/wM+IB5sf9HkfGt0NV43rTHf1KtlXT0KRvkq2HtiV/JKQkMCxY8eoXr06np6erq6OFHPX+vsWFRWFv7//DX0Hu0JRyab/hOzlOfcfeNj6c9rGsvXgzulQvnG2x12ZTdlNQpH+nJVySfKDskkKSl7nUpEawhccHEx4eHiGbREREbi5uREYGJjlMTabzfkA3M3IGC59mPlzNxpveprmiYdh7iPw5+K0jpR35no8ddUq2EXpdrOIiFxb4cimW3i4y3fM//F/3L57AmXP/QnTO0PH5+H2sWDNHPdXZtPVuXR1PimXRET+VqQ6UG3atGHRokUZti1btoyWLVtmOcY8r2R1Z2h4n478x+MbVq96h9Ee87Humw+hG6DfFKjb85rnu7pDdSVd4RMRKVoKUzYNvHsI073rUHn9BHqyBVa8BgeXwsBPoUz2+aJcEhHJOZeuAxUTE8POnTudM4SkPyCWPvf7+PHjGTp0qLP8yJEjOXHiBOPGjWP//v18+eWXfPHFFzzzzDP5Ws/s7hg92a0+m6o+youBH6YNl4g5C98Php8ehdgLWr1dRKQIKurZ9GivVsys9CqfBDyTNsz81Bb49HZY+yGkpiibRERukks7UFu3bqVZs2bOqRbHjRtHs2bNePnll4G0aRivXEirevXqLFmyhFWrVtG0aVNee+01pkyZkq/TxELalbnsrsC1rVmG708F8kmdL6DtaDAssPsH4j5swYHlX2PN+rEoEREppIpFNtUqyzvhzfmyyXdQswukJsJvEzn7QTt++S0Eq0XhJCJyowrNJBIFJT8eYM4wjKJeFBe+fYTAuKNpO2t3h97vQqmqefJeIjdCD+pKQSpOk0gUlHzNpq61GV1mKwmLn8UzJRoHViy3PwkdngMP7+ufSCSfKJukoOR1Lrn0DlRRlBoTS2pMTIZto7vUZly3OrwfcpA6UyNoc3Eim6o8ClYPOLQMPmn119CJ/F8ZWURESp6Uc+cwr1po0plNvx2izpxStI95k8OBnbGQCus+gk9aw8FfXVRjEZGiSx2oXLr03XccvqMDYa+8QsLBv8eQj+5S27kgLlYbrf7vXRi5Dqq2g5R4+G1i2hj0o6tdWHsRESmOTv/rWY506875adNJuXjRuf3KbIq0BlJr1Dy4fxb4V4bIUPjuXvj+frh03HWVFxEpYtSByqW4LVtwxMVx+ftZHOs/gBMPDSFqyRI+/nUfSakOZ1BNWX4IytaB4YthwFTwKgPnD8DX/WHOCIg85eqmiIhIMZB6+TKJf/5J8pkznHv/fQ536MjpZ58lfudOpvx2MHM21e0Fj2+EtqPA4gYHlqSNlFj1JiTFubo5IiKFnjpQuVR52udUmTkT3+7dwWolbutWTo97mubPDueThM3sfbKpczjflOWH0hbXbfYgjNoKtz6aNsnE3rnwcUuFlYiI3DRrQAC1Vq2k/OTJeDZqhJmcTNTCRRy/735qTHiM9+xH+fOFOzJmk80Hur+eNlKiWntISYBVk+E/t8LuH6FkPR4tIpIr6kDlkmEYeLduRaUpH1FrxXKO9LqPizZfSidGU2PpDxzu0pVBP33IGxVj+GDZn2lBBWAvBX3ehX+shipt04b1rZoM/2kJO78HR+q131hE8l3Hjh0ZO3ZsjsvPnDmTgICAa5b5888/ad26NZ6enjRt2pTjx49jGIZzimyRvGDx9CRg0ECqz/mBanN+4HTrLiRa3KgZeYYG30/lUIeO3Lt+Ni819Py7EwVQrh4MWwR3z0gb1hd1Cn56GL7sCaGbXNsoEQGUTYWROlA3wT0oiEO972f7O19R8cMP8LrtNnA4iFmxguafTOLHde9TZdlPGcajU74xjFgC98wE/yoQdRrmj4RP28L+RbrqJ1LMTJw4EW9vbw4cOMDy5cupXLkyYWFhNGzYEIBVq1ZhGAaXL192bUWl2LA3asTuB0ezYfJMyj37LO5VquCIieHSt9/S9vUn+W7PDMpuWY0jKSntAMOAhnfCk1ug0wRw94KTG+HL7vDdfRC+x7UNEpE8p2y6OW6urkBR9/fq7fXx69mTxCNHuDRrNpHz5+N1Ppy6C77i8JLv8O3Rg1L3DcbeogWGYcAtg6BOT9j0Oaz9AM79CbMfggrNocvLULOTS9slInnjyJEj9OnTh6pV/17KIDg42IU1kpLg72xqQenhw4hdv4HLs2cRvWIlpQ7vpdThvRxeOIOAu+4i4N578KhcGdzt0OFZaPogrH4TdnwLB3+Bg0uh0d3QcTwE1nRpu0Qkbyibbo7uQOUxW82aBE94gdq/r6b8G6/j2bBh2nj0xYs58dAQjvbtx8WvvyY1MjItrNqNhTF/QPtnwN0bzmyH/w2Er/rBqa2ubo4UZ6YJSbGu+cnFndaOHTsyatQoxo4dS6lSpQgKCmLatGnExsYyYsQIfH19qVmzJr/88kuG41avXs1tt92GzWajfPnyPP/886SkpDj3x8bGMnToUHx8fChfvjzvvfdepvdOSkri2WefpWLFinh7e9OqVStWrVqV47obhsG2bdt49dVXMQyDSZMmZRgmcfz4cTp1SrtYUqpUKQzDYPjw4Tk+v0hOGBYLPu1up9LHH1Nr+W+UeeIJ3MqVI/XiRS5Mn86Rbt0JffgRokJCMJOTwb8i9P8YntgMt9wJmLB7DnxyGywaC1FnXN0kKc5clU25HAGkbCrZdAcqn1js9rQre3fdRfzuPVz+YTaRi38m6cgRzv57MhHvvY9fz54EDB6MvVlTjC4vQat/wpr3YOuXcOx3+G8XqNc3bUhFUANXN0mKm+Q4+HcF17z3C2dytYDnV199xbPPPsvmzZuZPXs2jz32GPPnz2fQoEG88MILfPDBBwwZMoTQ0FC8vLw4ffo0vXv3Zvjw4Xz99df8+eefPProo3h6ejJp0iQA/vWvf7Fy5UrmzZtHcHAwL7zwAtu2baNp06bO9x0xYgTHjx9n1qxZVKhQgXnz5tGzZ092795N7dq1r1vvsLAwunbtSs+ePXnmmWfw8fHh/Pnzzv2VK1fmp59+4q677uLAgQP4+flht9tz/LmI5JZ7cDBlRz1JmcdGEr1yJZdn/0Ds2rXErltH7Lp1uJUti/9dd1Lqnntwr1gL7pmRdqFv+WtwOAS2zYA/vofbHoV248CrtKubJMWNq7Ipl7kEyqaSTHegCoC9UUPKv/YatX9fTdDLL2GrUwczMZHIBQs48cADHOvfP+2uVIoH9HoLRm2Dpg+lzdj35+K056Pm/hMuHnN1U0RcokmTJrz44ovUrl2b8ePHY7fbKVOmDI8++ii1a9fm5Zdf5sKFC+zatQuAqVOnUrlyZf7zn/9Qr149Bg4cyCuvvMJ7772Hw+EgJiaGL774gnfffZdu3brRqFEjvvrqK1KvWIj0yJEjfP/998yZM4f27dtTs2ZNnnnmGdq1a8eMGTNyVO/g4GDc3Nzw8fEhODgYHx+fDPutViulS6f9A7RcuXIEBwfj7++fR5+aSPYMNzf8unWjyn+nUzNkGYGPPoI1MJCUc+e48NnnHO7ajdBH/0HUsmWYZRrAQz/CiF+gSpu0GfvWfwwfNYHVb0NitKubI+ISyqaSS3egCpDV15fSDzxAqfvvJ+GPP7g0+weifvmFxEOH0+5Kvfsevj16EHDP3XgN+A/G7aNhxeuwfyHsmgV7foIWw+COf4FvwY1T/SDkIFaLweguma9qTFl+iFSHecV4eyky3L3Srri56r1zoXHjxs4/W61WAgMDadSokXNbUFAQABEREQDs37+fNm3apD1v+Jfbb7+dmJgYTp06xaVLl0hKSqJNmzbO/aVLl6Zu3brO19u3b8c0TerUyfh3OzExkcDAwFzVX6Qw86hcmXJPP03ZUaOIXrGCS7NnE7dhI7Fr1hC7Zg3WMmUIGDSQgLvvxmPEL3AoBJa/Cmd3w8o30p7lbf80tPw/cPcssHorm4opV2VTLnMJlE0lmTpQLmAYBvamTbE3bUrQ+OeJXLSIy3N+JPHPP4latIioRYvwqFaNgHvuxn/gh7i1ewpWvAZHVsCW/6Y92NvsQWg1Espc/1btzbJaDN4POQiQIaimLD/E+yEHGaeAKpoMI9fDFVzF3d09w2vDMDJsSw8jh8MBgGmaGQIqfVt6WTMHY90dDgdWq5Vt27ZhtVoz7Lv6ap1IcWB4eODXsyd+PXuSdOIEl3/8icvz5pF6/jwXpv+XC9P/i1erVgTccw++I37DcvhnWPEGXDwCv45PuyvV6h/QfFiBDO1TNhVTyqZrUjYVDupA5ZOcXhmz+vlR+sEHKfXAAyTs2cPlH+YQ9fPPJB0/TsQ77xLx4Uf4du5MwD3P4N12LMbK1+DUlrSO1Jb/Qu3u0PoxqNEp7UsnH6S34cqgujKgsmqjiCs1aNCAn376KUNYrV+/Hl9fXypWrEipUqVwd3dn48aNVKlSBYBLly5x8OBBOnToAECzZs1ITU0lIiKC9u3b51tdPTw8ADIM0RDJLznNJo+qVSn39DjKjh6V9qzUnB+JXbuWuE2biNu0CWtAAP4DBhBw1/fYojfAqrcg+gz8Nintz03vh1aPQdn868Qom6SoUTYVH3oGKp+kXxlzLlb4l/Qvd6slY2fHMAzsjRpR/rVXqb3md4JfexXPxo0hOZnoX3/l5COPcOQfr3E++S6S+/4P6vYGDDi0DP43CKa2hm0zITk+X9ozuktt5yr2dSb8ooCSQu3xxx/n5MmTjBo1ij///JMFCxYwceJExo0bh8ViwcfHh4cffph//etfLF++nD179jB8+HAslr+/EuvUqcODDz7I0KFDmTt3LseOHWPLli289dZbLFmyJM/qWrVqVQzDYPHixZw7d46YmJg8O7fI1XKdTe7u+HXvTpXp06j1WwhlHn8ct+BgUi9f5uJXX3G0/0COvxfC5Wqv4uj5EQQ1SlsofuuX8Mmt8M1dcPi3fFvjUNkkRYmyqfhQByqfXPmlnh5UOb0yZvH2ptQ991D9h9lUXzCfUg8+iMXPj+TTpzn30RQOD3uBk6v8iWr4EY4Wj4KHT9o6UovGwPsN0sam58M0s6O71MbDaiEp1YGH1aKAkkKrYsWKLFmyhM2bN9OkSRNGjhzJww8/zIsvvugs884773DHHXfQv39/unbtSrt27WjRokWG88yYMYOhQ4fy9NNPU7duXfr378+mTZuoXLlyntb1lVde4fnnnycoKIgnn3wyz84tcrWbySb3ihUpO3oUtZb/RqXPPsWnSxewWonfvp2wCS9z6MlPCDvTmbjbPsKs89dFvsO/pXWiPmkFW2dAUly+tEnZJEWBsqn4MMycDLgsRqKiovD39ycyMhI/P798f7/0YEr/cr/RK2OOhASif/2VS3PmEL91m3O7xd8f/57d8K9r4Hl2Lkbkyb92uKUt1tv6MajYIpuzuqYtUvASEhI4duwY1atXx9Oz4B7ylpLpWn/fCvo7uKgoqtmUHBFB5Lz5XP7xR5JPnnRu96hWDf+eHfAPCsP92I+Q9NfVa3spaDECbn0kbb2pQtQWKXjKJikoeZ1L6kAVgDoTfnFeGTv4Rq+bPl/isWNEzl9A5IIFpISHO7fbatfG//Y6+Hvvwu3Cpr8PqNwqrSNVrx9Yb+yxt6uvUGqcedGikJKCpA5U7hX1bDIdDuK2bCVy3jyifv0VM/6v4eQWC96tb8O/sT++KcuxxIT+td0NGgyE1o9DpRu/yKdsKtqUTVJQ8jqXNIlEPpuy/JAzoJJSHUxZfuimv9Rt1atT7qmxlB09itiNG4mcN5/okBASDx0i4tAhIqxWfG7ti3/NBHxSVmM5uQlObgK/Sn/NkDQ07SpgLtpwdSBl9fCuiIgUDXmdTYbFgner2/BudRtBL75I9LJlRM6bR9yWLcSu30jserD4+uLX5k4CyoXimbwVY8+PsOdHqHRb2kW++v1zdZFP2SQirqIOVD7K7soY5O5LPbtZkwyrlS8TypHa41FGT3yZqF9+IXLuPOJ37iRm43ZiNoLVvyZ+zcrj77cHT/MURsjLsOpNaPpAjqdBT3WYWV7NS3+d6ihRNzFFRIq0/M4mq483X/s3JHXoLTzxhmfaiIn580k+c4bLyzZyGfCo3BL/Bjb8vbbhfmoz/Lg57SLfbY/keBp0ZZOIuIo6UPkkL6+M5WStC6uvL6XuvZdS995L4tFjRM6fnzbE7+xZLq2K4hLu2Co3xb9aNP6Bx3DLxTTo11qIUFf3RESKjoLOJo8qVSg7ehRlnnyCuM1b0ob4LVtG0skznDsJ5yxl8K4XTEBQKD6pp7DkYhp0ZZOIuIo6UPkkL6+M5XatC1uN6pQb9xRlx4wmdsNGIufOJfq330g8GUHESYiwVMCnlg/+5Y7jm7oM49AyKFsvrSPVeDC422+m6SIiUki5KpsMiwXv1q3wbt2KoJdeIvrXpVyeN4/4rduI3XeG2H1uWLxr4FfDJCD4FJ5bvsTY+iXU6pqWTTW75NtahyIiuaVJJIqQm5lpKDUqiqglvxA5bx7xf/zh3G719sCvcjQBVSOxBaRgeJWGln/NkORXIb+aIgVMD+pKQdIkErlXlD+Xm8mmpBMniFywgMvz55NyJsy53aOMDf+K5/CvFoe73QFl6v59kc/DK7+aIgVM2SQFRbPw3aSiHFKQN7MmJR49SuS8eUTOX0DKuXPO7bZA8K8SiX/VeNy8LHk+Dbq4jkJKCpI6ULlX1D+Xm80m0+EgbtMmLs+bR/SyEMyEhLQdBnhXSCagajQ+FROw+OT9NOjiOsomKSh5nUtaSLcIyWrWpBthq1GDck8/Ta2VK6g8fRp+vXtheHiQeAEidvhzaGEwJ1f7Ev3LIszPOsMX3WHvPEhNyeMWiYhIUZcX2WRYLHi3aUPFt9+m9to1lH/9NewtWoAJsafdOb2+NIcWVCBsjYP4BR9jftgYfnwYTm27/slFRPKYnoEqIvJq1qQrGW5u+LRvj0/79qRGRhL1yy9cnjuPhF27iDltJ+a0HavNgV/V/QTsegTPqsE3NA26iIgUT/mRTVYfHwLuvpuAu+8m6fhxLs+fnzZiIjycy4e9uXzYGw+/ZAL2LcVvy1zca7e8oWnQRURulL5pioCCWOvC6u9Pqfvuo9R995F4+DCR8+dzecECUs+d59JBHy4d9MEWkEjAlrfwq/UWbs37Q5P7oVp7sOhGpohISVMQ2eRRrRrlxo6l7KhRfw3xm0/0smUkRUHEH+5E7PLFO/gwAesfw6fuBCwtBkOTB645e5+IyM3Sv3yLgGvNmjSuW508X+vCVqsW5Z55htorV1J52uf49uyJ4e5O4mV3zu7w59CPfpz8+FciXx2M452GsPxVOH9jwwlFrmf48OEYhoFhGLi7uxMUFES3bt348ssvcTgcGcoeP36c4cOHu6aiIiVMQWaTYbXi3bYtFd9JG+IX/Oor2Js1A9MgNswzbYjfNw7C3v+S2Jdvx/y8E2yeDnEX86wOIldSNpVsugNVBLhqrQvDzQ2fO+7A5447SL18mcglS4icN5+E3buJOeNJzBlPjC2p+C7/Ar+q/8GnRUOMFg/ALXfmaBFEkZzq2bMnM2bMIDU1lbNnz7J06VLGjBnDjz/+yMKFC5k9ezatW7fGarUCYJomU6dO5f7776d0af1dFMkPrsqmDOseHjvmXKg35exZLh/15vJRb9w2nMav6qv4VXsJz9adMZo9CLW6gZtHvtVLSh5lU8mlDpTkiDUggNIPPEDpBx4g8ehRohb/TOTiRSSHniQq1IuoUC8sm07jV+lV/Kq/jFf7LhjNHkhbw8Pq7urqSxZM0yQ+Jd4l7213s2PkYk0Xm81GcHAwABUrVqR58+a0bt2aLl26MHPmTBo0aMCwYcNo164dp06domfPntx2223Y7VrTTKQ4s1WvTrmnxlJ29Cjitmwl6ufFRC1dSkp0DBcP+HDxALiv24J/1d/xq2vD1v5uaHIflG+qdaUKKVdlU25zCZRNJZnLpzGfOnUq77zzDmFhYdxyyy18+OGHtG/fPtvy3377LW+//TaHDh3C39+fnj178u677xIYGJij9yvqU8UWJqZpkrBnD1GLfybq58WknL/g3OdmT8WvSjx+dT3x7HQXRtP7IbhxkQ2sD0IOYrUYWV5VnbL8EKkO85pXY10tq+k745LjaPVdK5fUZ9MDm/Byz9laLsOHD+fy5cvMnz8/076mTZtSoUIFlixZQnJyMl27dmXdunUsWrSIXr1ubJp/uXnFYRpzZVPR5UhKInbtWqIWLyZ6+XLMxCTnPlupJPyrxOPXrBLu7R5IW1fKr7wLa3vjinouQeHKptzkEiibippiNY357NmzGTt2LBMmTGDHjh20b9+eXr16ERoammX5tWvXMnToUB5++GH27t3LnDlz2LJlC4888kgB11wADMPA3qgRQeOfp9bq1VSZOZOAe+7G4uNNSryViwd8OL7QjSMv/cS5J/uQ+EZrWDcFosNdXfVcs1oM3g85mGl63vSHqK2WotkxLOrq1avH8ePH2bx5M126dKFNmzZ07NiRDz/8kJdffpmE9LVkRHJB2VS0WTw88O3cmYrvv0+d9eup8M7b+HS4A6wWEi95EPGHP4dnRnN8/BQuPd6ClM/7w645kBTn6qrninKp8FI2FX8uHcL3/vvv8/DDDztD5sMPP+TXX3/l008/ZfLkyZnKb9y4kWrVqjF69GgAqlevzj//+U/efvvtbN8jMTGRxMRE5+uoqKg8boXAXw/4tm6Fd+tWBL30UtrVv0WLiF6+nOQYOL/Xl/N7o7D9/BH+Vd/Cr31L3O8YAvX6gHvhv5Wd1cxSWc1AVZTY3exsemCTy947L5imiWEYHDx4kBkzZmC1Wpk0aRIzZsxg6tSpxMXFaXFGyTVlU/Fh8fbGv18//Pv1I+XSJaJ/XUbUwvnEbd9J/Dkb8edshG87iPesZ/Gv+S98u/fEctsQqNKm0M8wWxxzCVyXTXmVS6BsKglc1oFKSkpi27ZtPP/88xm2d+/enfXr12d5TNu2bZkwYQJLliyhV69eRERE8OOPP9KnT59s32fy5Mm88soreVp3ubb0q3++nTvjiI0lesUKohbOJ2b9xrSrf5c8iNh5EK9Zz+JX81l8e3THre0wqNK6UA/xuzKs/rPiMEmpjiIdUoZh5Gq4QmG0f/9+qlevzkMPPQSkzXQEaW174oknXFgzKaqUTcWXW6lSlLpvMKXuG0xyWBhRS34hcsFcEg8eITbMk9gwMDasxLfiUvzq++Dd5z4sLR+A0jVcXfVsFbdcAmWTFA0uu7xy/vx5UlNTCQoKyrA9KCiI8PCsh3i1bduWb7/9lsGDB+Ph4UFwcDABAQF8/PHH2b7P+PHjiYyMdP6cPHkyT9sh15Z+9a/y9C+ovXYtwZMm4dW0IQBx52yEb7Rx6NVVhA5/gMgnG+NY+jpcOu7aSl/D6C618bBaSEp14GG1FOmQKupWrFjB7t27ueuuu5zbqlWrxsyZM11XKSnylE0lg3v58gQ+/H/UWLiYGkt+psxjj+FRsRxmqoWoUDunfk3l0NP/I+yhjsRO7IC5ZQYkRLq62llSLhUuyqaSweX3p6+e8ST9tmdW9u3bx+jRo3n55ZfZtm0bS5cu5dixY4wcOTLb89tsNvz8/DL8iGukX/2rOmsOtVauoNwzz+BZq4pzHY8zy1M4+PT/OD24I9EvdMCxaQYkFK5hLVOWH3KGVFKqI9PYc8kfiYmJhIeHc/r0abZv386///1vBgwYQN++fRk6dKirqyfFkLKp5LDVqEHZMaOp8dsqqs2ZQ+mhD+JWyhdHsoXLR70JnR3B4Ucnc/a+psR/eC/mwRBITXF1tZ2US66jbCq5XDaEr0yZMlit1kxX9CIiIjJd+Us3efJkbr/9dv71r38B0LhxY7y9vWnfvj2vv/465csXzZl0SiL38uUJfORhAh95OG1a9AXziVr4E0lhF4kKtRMVGoFl0Zv4VX0Nvzua4zXgnxi1O4PF6rI6Xz22PP015O+aJwJLly6lfPnyuLm5UapUKZo0acKUKVMYNmwYlkL+nIIULcqmkittYqSG2Bs1pNxz44nbuo2oeT8Qtew3UuISufinlYt/7sb9m8fxr23Br1cPbD3+CUG3uKzOyiXXUjaVXC7rQHl4eNCiRQtCQkIYNGiQc3tISAgDBgzI8pi4uDjc3DJW+crFyaRostWoQdmnxlFm7FMk7NlL1LxZRC35hZTLcVw+bOPy4b24ffcEfrUs+PXsime/xzGCGhRoHbN6MDerB3gl782cOVNDH6TAKJsE/poYqdVteLe6jeBXk4hZu5aoOd8QvW5z2sRIO+D8jhBsU5fg39Afv0GDce8wAnzKFlgdlUuupWwq2Vw6C9+4ceMYMmQILVu2pE2bNkybNo3Q0FDnsIfx48dz+vRpvv76awD69evHo48+yqeffkqPHj0ICwtj7Nix3HbbbVSoUMGVTZE88PfVv9cpN+EV4rZuJeqHr4hasZaUeLi4By7uWY77Z7/iV88H3+7d8Oz5MEa5uvlet1SHmeWDuemvUx36R5JIcaFskisZV0+M9FsIUXO+Jmb7fhIvuxOxNo6ItTOwl/0cvyYV8e13F+7tHgTvMvlaL+WSiOu4tAM1ePBgLly4wKuvvkpYWBgNGzZkyZIlVK1aFYCwsLAM624MHz6c6Oho/vOf//D0008TEBBA586deeutt1zVBMknaVf/WuHdqhXBSUnE/L6KqFlfEr1pN8kxblzYmsCFrYtw/2gevnW88e3WBXufRzCC6udLfa61GKGu8IkUL8omyY7F2xv/AQPxHzAwbVr0RXOJmjuLuD9PEX/Og/jfznH2t8/wDJyCX5MK+PYdhEeHofnSmVIuibiOYZaw8QVa7b1oc8TGEr3sZ6LnzyJm+5+YyX//9XWzp+Jb245ft07Y+z6KUd5149ILm2utwC2S1/J6xfeSQJ9L0ZYcHk70vO+I+nkh8YfPZthnK5WMX+MgfPsMxNZ5eIEO8yvslE1SUPI6l9SBkiLLER9PzPKlRM/7lpgt+3EkOZz7rLZUfGt74te5A14D/4lRoaELa+p6CikpSOpA5Z4+l+IjOSKC6AWziP55AXEHzsAV/8qy+Sfj26gcvr37Y+v2fxi+5VxX0UJA2SQFRR2om6SQKp4ciYnErgoh+qf/Eb15D46EKzpTHg58atrw69we70H/wKjYuFAv2JsfFFJSkNSByj19LsVTysWLRC+cTfTiecTuOwl/RxMevin4NiyDb+++ePZ8BMM361keizNlkxSUvM4llz4DJZJXLDYbvj364tujL2ZSErFrVqR1pjb+QWocRO5PJnL/CizTfsOnhgd+HW/H+65/YqnctMR1pkREpGC4lS5NqeGPUWr4Y6RGRhK9eA7Ri34kdvcJkqLduLDhMhc2fIP7mzPxvSUQv5698OzzDwx/TX0vUpipAyXFjuHhgU+Xnvh06UlwSgpxG34n+seZRK/bQUpMClEHUog6sBrji5X4VPPAr2MbfO76B5ZqLdSZEhGRfGH19yfgwUcIePARUmNiiVkyh+gFc4j54yjJsW5c3BzJxc2zcHvnW3wblMKvR0/s/f+JEaCZHEUKG3WgpND7IOQgVouR5axCU5YfItVhZjsbkeHmhnf7zni370yQw0H8prVEz/mSqLXbSYlKJvpwCtGH12DM+B2fqm743tEKn7v/gbXmbepMiYhItm4mm6w+3vjfOxz/e4fjiIsjZuk8ohfMImbHEVLirVzaFsWlbT9gff97fOsF4Ne9O16DHsMoVTG/myUiOaBlkqXQs1oM3g85yJTlhzJsT19E0GrJWUfHsFjwanMHQe/PpNamP6j2vy8I7H877qU8MFMNoo+mcmbmeg71H8bJng25/PpwUg9ugJL1mKCIiORAXmWTxcsLvzsfpOJXi6i9ZQeV3p6Ef9u6WGwGqQlWLu+MJvTtnzjUoTNn7rmVmGnjMS+ezI8miUgO6Q6UFHpZraye1QrsuWEYBvZb22K/tS1lTZPEP7YR9cN0on/fRNL5RGJOOIg5sYmwbzfiXdGKb/sW+N7zKG712+nOlIiI5Es2WWw2fPsPxrf/4LTneVcsJnruN0RvOUBqPETujiFy93wsH8/Fp7Yvft06433X41jKVcvLponIdWgWPiky0oPJw2ohKdVxwwF1PYl7dxI1exrRqzeSeDb+7x2GiVd5K763N8X3zmG4N+kKlqJxE7coz3Q0fPhwvvrqKwDc3NwoXbo0jRs35v7772f48OFYisj/g5JEs/Dlnj6XoqsgsslMSSFu9S9E//g1UZv3kRr793R+hpsDn5o++HXpgPedj2Ct1CBP3zs/KZukoOR1Lun/rhQZo7vUdgaUh9WSbyut225pStlXp1Jj9XZqLvyRsg90w7OCF5gGcWccnJ2zncP3j+Fo+wace7IP8Uv+i5kUf/0Tyw3r2bMnYWFhHD9+nF9++YVOnToxZswY+vbtS0pKiqurJyIlWEFkk+HmhneXfgR/OofaW3ZTddoHlO7eBDdfK2aKhegDcZye+gsHu99JaPdGXHx5KMnbfwWH4/onlxumbCq5NIRPiowpyw85Ayop1cGU5YfyrROVzqPOLZR5eQplXoakoweInvUZ0avWEh8aTeIFg8TfjnL+t/dws7+DT4Ny+HbpitfAR7CULvwP+pqmiRnvmo6fYbdj5GIopM1mIzg4GICKFSvSvHlzWrduTZcuXZg5cyaPPPJIflVVROSaCjqbDIsFrzt64nVHT8qZJgkblhM9ZwbRG3eRdCmF2NAUYkO3cPaHLdhKm/g0q4lv74F4dn0Qw+aVb/XKK67KptzmEiibSjJ1oKRIuHpcefprIN87Uek8atQl8IUPCHwBUiLCiJn7JTHLQ4jZf5aUeAuXt53n8rZZGO99h3d1b3zbt8Lnzv/DrfatBVK/3DLj4znQvIVL3rvu9m0YXjcX5J07d6ZJkybMnTtXISUiLuHqbDIMA3vbrtjbdqUckLhvBzFzZxC9ZkPahb6LBonLj3Jh+fu42d/Fp35ZfLp0xXvgP7AEFs7p0V2VTXmRS6BsKinUgZJCL6uHcrN6eLcguZUrT8DICQSMnIAjIYG4pbOIXjKPmO2HSYmBmMPxxBxeBTNWYg+24nNrA3z7Dsaj3UAMq37t8kq9evXYtWuXq6shIiVQYcwmW4Nm2Bo0IxBIORdO7NwviV4eQuz+8LQLfdsvcHn7bIz3Z+Fd3Qvfdrfhc+fDuNUpnBf6iiplU/Gnf8lJoZfqMLN8KDf9darDtfOgWDw98Rk4HJ+BwzFNk8TNq4ie/z9iNmwnITyR+HAH8Yv2cG7RHtx9J+DbuAo+3Xvh1ef/MHwCcvQeN7PeSHYMu52627fl6pi8YtjteXIe0zRzPeRCRCQvFPZscisbjP8/X8D/ny/gSEwkbuksYpbMI3r7IVKiHX9d6FsNM1fhGWTF99b6+PQbjK3doBxd6MuPXALXZVNe5RIom0oCdaCk0LvWF3BBX927HsMw8GzVCc9WnSgLJJ84RMyP/yV69RriDl8kOdrCxXWnuLhuOpbXP8enTil8OrbH585/YK2YfVvS1xuBjG2+8grojdQ1L4YruNL+/fupXr26q6shIiVQUcomi82Gz4Bh+AwYRpBpkrhl9d8X+sISSDjrIGHxXs4tfhl3n5fwaVwJ3+698eqb/YW+/MglUDZJ0aAOlEg+cq9am1JPv0Wpp8ERFUnMwq+IWfYzMbtCSU2wELU3kqi9i2HqIrwq2fBt0wyfgUPwaNY5w3pT+bHeSFG3YsUKdu/ezVNPPeXqqoiIFBmGYeB5W0c8b+uYdqEv9DAxP/2XmFW/E3v4IskxBpfWn+bS+ulY3vgcn9ql8OnYLu1CX6W/O0XKpawpm0oGdaBECojFzx+/h0bj99BozJQU4lcvImbRLKK37CPpQgpxJ5OIO7mJsz9swlYKfJrXwqfXQOzdH8Tw8MwQVv9ZcThf18IqbBITEwkPDyc1NZWzZ8+ydOlSJk+eTN++fRk6dKirqyciUmS5V6lFqafepNRT4IiOJHbR10T/+jMxu06QGm8hal8kUft+hk8X41XRA582zfAd8BAeLbqW6FwCZVNJpg6UiAsYbm54dRmEV5dBlAOS9m0jeu4MYtZuIu5ENImXDBKXH+bC8nexev49RfqTgx7lPyvyfy2swmbp0qWUL18eNzc3SpUqRZMmTZgyZQrDhg3TYoUiInnE4uuP7wOj8H1gFGZqKgm/LyJ64SxiNu8l8UIKcaeSiZuzmYg5m/EIAN/mtXik1wCmW8oSnepWonIJlE0lmWGapmufcixgWu1dCrvU8+FpwylW/EbMvnAcyX8P5TMsJonl3Nlfvibzyrbl9v4DGd31+uPMi/Jq71L05PWK7yWBPhcp7JL27yBm7pdEr9lI3IloMP/OJovNQWR5X7aUq4dX/+GMuLtrjs6pbJKCkte5pA6USCFmJiQQ9+v3RP8yj8ith3HEZPx1NXxNUmsGU7VvX7z6DsUSUC7L8yikpCCpA5V7+lykKEk9f5aYef/l9KIlWI5dwLziQh+GiVtZNwJua4BP94F4drobw90jy/Mom6Sg5HUuaQifSCFmeHriPWAEX/i0433/A7xd6zKdT6wgdvNO4k7GYUYbWHae5eTOLzAm/xevKl743NYU7z6D8WjZDUNDCEREJI9ZywTxVY17eb9BU555oiojErYQs2whsTsPkXghlZSIVM4v3s35xbux2l7Fu24gPu1ux7v/MNyq3eLq6ovcNHWgRIqAVIfJuO51ubdLbeAhygCpF84Su/hrQhcvwf1oOI5Yg9hj8cQe2wCzN+DuY+LdsBI+HTtj7fGQq5sgIiLFSPo6WE92qQ00xHvACACSD+9m82cfU3rfH1hORpKaaBC16yJRuxbB1IV4lnPDu1kdfLr3w2g30KVtELlRGsInUgyYpknStlXELJ5F7KbtxJ2IxnT8PaTCUTEYx0svUb16FbxKl8Hw8dcif5JvNIQv9/S5SHFkJiYQv/xHYn5dQMz2P0k8l5KxQJVgUl58merVq+FVNgiLZ9Fe/0kKLw3hE5FMDMPA1rITtpadCAQckZeIXfI1scuXEbPrGImpYKY4SI6MIzH6FIblFBZPd6y+vlgCAjHcba5ughQjJey6nIhkw7B54tX7Ibx6P0Q5IPn4AWIXfkXsmrXEHIggJQnMpFSSL0WTGBmLxQ0sdhsWP38sfoEYVqurmyDFRF7nkjpQIsWQxb8UvvePwff+MZimScKuDRyJSSbRauBpgumA1LhkUuMuwtmLWNwNLN52rH4BGL4BGEbGZ6fORiUAEOSX+SHfa+2TkikuLg4Ad3d3F9dERAoT92p1CRj9bwJGg5mcTMyqeZzwsJBoAU/AkQKO6ESIjsA4E4HFZsXi7Y0loDQWu0+GcymXJDeSkpIAsOZRp1wdKJFizjAM7E3aEhgWxqXLl3ErUwbP1ETM2BgcCUmYqUAikBgDF2MwLKcwPNyweHtj9Q3A8LCRnJTIhZhEkpMSKePz992q8zFp2wN9bCQkuKyJUkiYpklcXBwREREEBATkWVCJSPFjuLvj2+3ev7OpdClsyQmY8fGYCcmYDiDOAXGX4dxlDCtYPD2wePtg8Q0gOSlVuSQ54nA4OHfuHF5eXri55U3XRx0okRIiODgYgHPnz/+90eKOmZqEGReDIzEJM8WR6TjDAoaHO6luNv4844av3R0/T3eiEpKJik/Bz+5GdLw70ecKqiVS2AUEBDj/vomIXIszmy5e+nuj1Q0zMR5HfBxmUjJmaubhV4abgenmzlHcOe3lpVySa7JYLFSpUiXPnv9WB0qkhDAMg/Lly1OuXDmSk5OzLGMmxBO/bgnxG1YRt+84SZdSnftsQEV3k+gyXuwIrMwq/8bc2qkTQ5pXL6AWSFHg7u6uO08ikmM5yabUC+HEr5hP3JZNxB0+S2p82nYLEAgYXibnywSwp1R1/Dv1p32P1gVWfykaPDw8sOTh0i6ahU9EspV8ZA+xC78mZu06Yg9dwJGU8cqNmw941ymH12234d3jHtzr3+aimkpRoe/grOlzEckZ0+EgcdMyYpf8QMxfayLiuDKbTGxlrHg3qIbX7Xfg1et+rOWquKy+UvjdyPevy1fZnDp1qnNKwRYtWrBmzZprlk9MTGTChAlUrVoVm81GzZo1+fLLLwuotiIli3vNhgQ89TaVflpHna1/cOSf/+BIvYqklLGCYZISA5HbIwj7bDGHBw3j8G31OfNQZyL/8wLJh3e6uvoiN0zZJFI4GRYLnm16Evjal1T9dTtr3pzJvNZ3cLZmIPgBGCSed3Dx96OcmjyTgx26c+yORpx9vD8x331A6oUzrm6CFAMuHcI3e/Zsxo4dy9SpU7n99tv5/PPP6dWrF/v27aNKlayvFtx7772cPXuWL774glq1ahEREUFKSkqWZUUk73y8JpT3z9Zh3Ki+9O1Sm08XbeH0/P9xd8ph/ENPkxCeSHKUQeTWMCK3zoP/zMMjwMCrbgW829yOV8/7cKtW39XNELkuZZNI0TBl+SHeX3+BcUOeoWOX2kxZfoh5i1YxyXsfdU7vJW7/KZIiISEihYQVh7i44hC89jn2YA+8GtbEq30XvLrfhyWgjKubIkWMS4fwtWrViubNm/Ppp586t9WvX5+BAwcyefLkTOWXLl3Kfffdx9GjRylduvQNvaeGSYjk3pTlh3g/5CDjutVhdJfaWW5/oqkv8ctmE7t2BXF7jpJwNhnIOOTPVtqCV/3KeLW9Ha+eD+BWsWYBt0RcrSh8ByubRAq/nOTS6C61ST6ym7ils4nduIG4A2EkR131z16Lib28De/GdfC6oyv2boOx+AQUbGPEpW7k+9dlHaikpCS8vLyYM2cOgwYNcm4fM2YMO3fuZPXq1ZmOefzxxzl48CAtW7bkf//7H97e3vTv35/XXnsNu92e5fskJiaSmJjofB0VFUXlypUVUiK58EHIQawWI0NIpZuy/BCpDpOnutXJsD014iRxS2cRu241cXuPk3g+9aojTWxl3PBuUFXj1EuQwt5RUDaJFA03kksAyX9uI3bpD8Rt3kzswXBSYjLuNywm9kp2vBrXxbtjd+xd7sW4ag0qKV5uJJdcNoTv/PnzpKamEhQUlGF7UFAQ4eHhWR5z9OhR1q5di6enJ/PmzeP8+fM8/vjjXLx4Mdux5pMnT+aVV17J8/qLlCRZhVC6rMILwFquMr5D/4Xv0H8BkHL6KHG/ziJu/Rpi94WSdNFB4vlUEn8/ysXfj8KbM/As645Xw+p4394Re8/7sQaWz5f2iGRH2SRSNNxILgG412tBQL0WBJC2dl3yng3ELvuJuM1biDt0jpQ4g7jQBOJC/+D84j8wrG/jVdkLr2YN8O7YC88OgzA8vfK+QVKk5PoO1JYtW/jwww9Zv3494eHhGIZBUFAQbdu25amnnqJly5Y5Os+ZM2eoWLEi69evp02bNs7tb7zxBv/73//4888/Mx3TvXt31qxZQ3h4OP7+/gDMnTuXu+++m9jY2Cyv9Okqn0jhlBJ6kLil3xO7fh1xf54i6fJVX0WGiWewB94Na+J1Rxe8ut+PxT/QNZWVPJNfd6CUTSJys0yHg6Sda4gL+YnYLduJO3ye1ISMQ9Etbib2qj54N2uIV6c+eLbvj+Fhy+aMUhTk+x2o+fPnc++999KlSxfGjBlDUFAQpmkSERHBsmXLuP322/nhhx8YMGDAdc9VpkwZrFZrpit6ERERma78pStfvjwVK1Z0BhSkjUs3TZNTp05Ru3bmKw42mw2bTX+xRQobtyp18PvHRPz+kfY6+chu4n79gdgN64k7cIbkKIOEsGQSwv7kQsifMPE/GqcuWVI2iUheMCwWbM07YGvegVL8NWX65hDifltA3LYdxB29RGqiQeyRWGKPbIIfN2Fxfwmvan54tWiMd+e+2Nr2wXBzd3VTJJ/l6g5Uw4YNeeihh3j++eez3P/WW2/x9ddfs3fv3hydr1WrVrRo0YKpU6c6tzVo0IABAwZk+aDutGnTGDt2LBEREfj4pI1HXbBgAXfeeScxMTHZjjW/UmEffy8iaXI8Tr1JPbw7dMfe5R6NUy8C8uM7WNkkIgXBTEkhccMvxK5YRNy2P4g7FokjOeMdKqvNxKt6AF4tmuLVdQC227pjaHHxQi3fJ5Hw9PRk165d1KmT9bjTAwcO0KRJExISEnJ0vtmzZzNkyBA+++wz2rRpw7Rp05g+fTp79+6latWqjB8/ntOnT/P1118DEBMTQ/369WndujWvvPIK58+f55FHHqFDhw5Mnz49R++pkBIpejKPU48gJS5jaBlWM22cetMGeHXshb1j3o1Tv9GHlSWz/PgOVjaJiCuYyUkkrF1E3IrFxO7YQ/zxaBwpV3WoPE28apbGu2VzvLoNwqN5JwzLzS/DqlzKO/k+hK9mzZrMnz+fZ599Nsv9CxYsoEaNGjk+3+DBg7lw4QKvvvoqYWFhNGzYkCVLllC1alUAwsLCCA0NdZb38fEhJCSEUaNG0bJlSwIDA7n33nt5/fXXc9MMESliDMPAo1FbPBq1dQ6rSNr5O3Ehc4ndso24wxdITTCIPR5P7PFtMH8bFrfXsFf1xbvZLXh17oNnuxsfp261GLwfchAg2+lyxXWUTSLiCoa7B/ZOd2HvdBeBgJkQT8LvC4hduYS4nXuJC40lNcEgeu8lovcuh6+W4+Zl4lWrLF63tcS72124N2p7Qx0q5ZJr5eoO1E8//cR9991H9+7d6d69O0FBQRiGQXh4OCEhISxbtoxZs2Zx55135medb4qu8okUP6bDQdKW34gNmZ9hnPqVLO4mXtX98GreGO8u/bC16YPhlvNrSFevLZLdGiRybfnxHaxsEpHCyIyPJX7lT8SuXErcH/uJPxWP6ciYTW7e4F2nHF6tbsO7+z24N7gtx+dXLuWNAlkHasOGDXz00Uds2LDB+ZBtcHAwbdq0YcyYMRlmLSqMFFIixZ9znPryhcRt33Xtceotm+HVZQC2Vt2vexUwPZw8rBaSUh0KqRuQX9/ByiYRKewcMZHE//YDcb+HELvrIPFnEuCqDpW7L3jVLY9369Z49RyMe60m1zyncunmFamFdF1FISVS8uR8nHog3rf+NU69WccsO1R1JvxCUqoDD6uFg2/0KqAWFB/6Ds6aPheRksdx+QJxv80m7vflxO0+THx4IpgZs8nD38CrXkW82rTBu9cDuFWtl+k8yqWb47IO1OrVq4mNjaVNmzaUKlXqZk+XrxRSImImxJGwegGxq5YQt2MfcSdjMVOvGlbhHKd+K97d78K9YRs+XnlEV/puUkF+ByubRKQoSb0YTvyvs4ldu4K4PUdJiEjO3KEqbcG7fmW82rTDq+f9TD3gUC7dpHzvQL3zzjvExMQ4V083TZNevXqxbNkyAMqVK8fy5cu55ZZbbqD6BUMhJSJXc8TFkLDiJ2JX/5rtOHWrt8mFMr44mrTg1gcf5LOIcry//LDCKpfy4ztY2SQixVFqxEnifp1N3NpVxO49TuL51MyF/CGlRkWq9ejKT14tmLQ9SbmUS/negWrevDnPPfccgwcPBmDOnDkMGzaMkJAQ6tevz9ChQ/Hy8uKHH364sRYUAIWUiFyPc5z66hBid2c9Tt3qaWIGebPPpyz2Vu3p9sjDWEsHu6jGRUd+fAcrm0SkJEg5c4y4pd8Tt34NUXtOkHo58z/hrb5wqZQ31K1H40ED8WzbO8+W9Ciu8r0DVapUKdavX0/9+vUBGDFiBCkpKfzvf/8DYOPGjdxzzz2cPHnyBqpfMBRSIpJbnyzYRK39v9LszE7i9hwhITwx0x0qDBNbaTfstcpjb9oUe/teeDS9I1cz/ZUE+fEdrGwSkZLmg5CDlLp0gjsvryd2wzriDpwm6WIqkHmNRM/yntjrVMHesjX2Dv1wr9nINZUupPJ9Hajk5GRstr/XUdmwYQNjxoxxvq5QoQLnz5/PzSlFRAq9Jwa0ggGtnK8dcdEkrvuZ+A0rid+1l/jjF0iOMUi8kErihVNc3nQKPl+MxcPEXskHe4Na2G+9HXungVjLVXZhS4onZZOIlDRpi+TWAbrh94+0bannThO/ej7xm9YSv+8w8SejcSQZxJ9KJP7UIVhxCN7+H24+YK9WGnvDBni16YytfV8sXr6ubE6Rk6sOVK1atfj999+pUaMGoaGhHDx4kA4dOjj3nzp1isDAwDyvpIhIYWLx8sXe7T7s3e5zbks+tpf4VQuJ37qR+AMnSAhLwJFkEHs0ltijf8DiP2DiVDxKWbDXDMLepDH2dj2xteyM4e7hwtYUfcomERGwlq2Iz91P4HP3E0Dakh5Ju9YQv2Yp8Tu2E384jMQLKaTEGETvuUj0nrUway2G5RU8gzzS7lK1uBX7HX1xq9Pshhb4LSly1YF67LHHePLJJ1mzZg0bN26kTZs2NGjQwLl/xYoVNGvWLM8rKSJS2LlXvwX36rfgNyLttZkQR8LGX4hfv4L4P3YTf+wcyVGQdMlB0tYwIreGwRe/YnE38azohb1+Tewt22DvNAi3CtVd25giRtkkIpKZ4eaGrXknbM07EfDXttSL4ST8voj4jauJ33eI+NBIUhMM4sOSiQ87AquPwPuzcPMysVcthb1hfeytO+B5R38svoV7NtOClOtpzL/44gsWL15McHAwEydOJDj474emH3/8cbp168agQYPyvKJ5RePMRcRVUkIPEr96IfFbNxC//yjxZ+Ixr1qPCtLW/bDXKIe9cUPst3fF1qonhs3TBTXOe/n1HaxsEhHJPdPhIHnPBuJ//zntLtWh0yScyzx9OoaJZzl37LUrYW/eEnv7Xrjf0rpY3KXK90kkli1bRqdOnXB3d7/hSrqaQkpECgszOYnETb8Svy6E+D92EX80gqQsZlUyrCb2Cnbs9apjb9kKe8cBWS6mWBTkx3ewsklEJO84Ii+QsGYR8RtWEb/3T+JOXCY1PvPFPqunib2qP/Zb6mJvdQf2O/pjKVXOBTW+OfnegapRowYXL16kR48eDBw4kF69ehEQEHCj9XUJhZSIFGapYceJX7WA+C3rid9/mPhTsTiSMweXuy/Ya5TB3qgh9jad8GzbB8Pu7YIa505+fAcrm0RE8o/pcJByYBtxqxcRv30b8QdPkhCRlGl5DwwTWxk37LUqYG/WHHu7Xng0bV/o71LlewcKYNeuXSxcuJCFCxeya9cubr/9dgYMGED//v2pVq3ajdS7QCmkRKQoMVNSSNq2nPi1y4jbuZOEI+EkZjdVbbAn9rpVsLe4LW2q2lpNXFPpa8iv72Blk4hIwXHERJKw7ue053z37Cf++EVSYjOXs9pMPCv74tWgDvbb2uHZcQDWMhUKvsLXUCAdqCudOXPGGVgrV66kTp06zsBq2bLljZ42XymkRKSoc05Vu3kd8XsPOaeqvZqb919T1TZqkHaXqn0/l09VWxDfwcomEZGCl3xwJ/G/LyJ+25a02WizWjMRE1ugFXvNYOxNm2Fv1wOP5p1cumZigXegrhQbG8vSpUtZsGABS5YsYdy4cbzwwgt5ceo8pZASkeLGdDhI2rk6baranTuIP3SGxAspmR4CNiwmtiAPvOpUSXsI+I4+uNVtcd3hFR+EHMRqMRjdpXamfVOWHyLVYf61Jsn1FfR3sLJJRMQ1zPjYtLtUG1YQv3sv8cfOkxyduZzF3cRe2SdtNtpbb8feaQDWoKrXPLercylX3b1NmzZx8eJFevXq5dz29ddfM3HiRGJjYxk4cCDTp0/H3d2dCxcu5ObUIiJygwyLJdNUtY5LEcSvXkD8pt+J33vQOVVtQlgyCelT1X4wO22q2ioB2BvVx96qQ9pdKv+MayZZLQbvhxwEyBBWU5Yf4v2Qg4zLYUjlF2WTiEjhY9i9sXe9F3vXe53bUo7t+2s22k3EHzhO/Jl4HMnpaybugp93waRP8ShlYK8RjL1JI+ztemC7tWuGNRNdnUu5ugPVq1cvOnbsyHPPPQfA7t27ad68OcOHD6d+/fq88847/POf/2TSpEn5Vd+bpqt8IlISOaeqXfML8Tu2EX/wVPZT1ZZ1x1674t9T1TZsw8crjzhDaXSX2hlCKqsrgNnJj+9gZZOISNGUtmbir8Sv/42E3XuIO3qO5MjMXROLW/qaiTXS1kzsOJCpBxwuy6VcdaDKly/PokWLnGPIJ0yYwOrVq1m7di0Ac+bMYeLEiezbty/HlS5oCikRkTTOqWo3rSZ+z5/En7hESlw2U9VW8edc+UrMcVRhRelWnDV8cx1SkD/fwcomEZHiI+XUEeJXzXeumZhwOg5HFmsmuvsbJJcPYLNnMBsDm/Cbd2NG9WhYILmUqyF8ly5dIigoyPl69erV9OzZ0/n61ltv5eTJk7k5pYiIuIjFPxCvvsPx6jsc+Guq2oM7iF+9iPjtW4k7GErC2SRSEwxiDkZhP7iPoexjSKlfGdj5vVyHVH5RNomIFB9ulWri+9DT+D6U9tpMTiJxcwjx65YR/8du4o+Gk3TJTLtTFXmJ27jEbexnYL1V9Hh7ZcHUMTeFg4KCOHbsGJUrVyYpKYnt27fzyiuvOPdHR0cX6YUMRURKMsNiwb1eC9zrtSD9GpxzqtoNKzm3dRecieRc6VIkpTqYsvxQoehEKZtERIovw90Dz9v74Hl7H0r9tS317AniVy7g8LLfMI4cx3YxiZ0BNTlQQLmUqw5Uz549ef7553nrrbeYP38+Xl5etG/f3rl/165d1KxZM88rKSIirmHx8cerxwP81+1W3k84yLin6jCqbQXGrT+T5QO8rqBsEhEpWaxBVfmybC/eL1eTcQ/WYVSH6ngv/5N3CyiXctWBev3117nzzjvp0KEDPj4+fPXVV3h4/D0jxpdffkn37t3zvJIiIuI6WT2Ym/7fwtCJUjaJiJQsWeXSkz0a4nCzFUgu5aoDVbZsWdasWUNkZCQ+Pj5YrdYM++fMmYOPj0+eVlBERFwr1WFmOWFE+utUR54sJ3jDlE0iIiWLq3MpzxbSLSo005GIiOvoOzhr+lxERFzjRr5/r738vIiIiIiIiDipAyUiIiIiIpJD6kCJiIiIiIjkkDpQIiIiIiIiOeTyDtTUqVOpXr06np6etGjRgjVr1uTouHXr1uHm5kbTpk3zt4IiIlLiKJtERCQ7Lu1AzZ49m7FjxzJhwgR27NhB+/bt6dWrF6Ghodc8LjIykqFDh9KlS5cCqqmIiJQUyiYREbkWl05j3qpVK5o3b86nn37q3Fa/fn0GDhzI5MmTsz3uvvvuo3bt2litVubPn8/OnTtz/J6aKlZExHWKwnewsklEpOQoUtOYJyUlsW3btkyrw3fv3p3169dne9yMGTM4cuQIEydOzNH7JCYmEhUVleFHREQkK8omERG5Hpd1oM6fP09qaipBQUEZtgcFBREeHp7lMYcOHeL555/n22+/xc3NLUfvM3nyZPz9/Z0/lStXvum6i4hI8aRsEhGR63H5JBKGYWR4bZpmpm0AqampPPDAA7zyyivUqVMnx+cfP348kZGRzp+TJ0/edJ1FRKR4UzaJiEh2cnapLB+UKVMGq9Wa6YpeREREpit/ANHR0WzdupUdO3bw5JNPAuBwODBNEzc3N5YtW0bnzp0zHWez2bDZbPnTCBERKVaUTSIicj0uuwPl4eFBixYtCAkJybA9JCSEtm3bZirv5+fH7t272blzp/Nn5MiR1K1bl507d9KqVauCqrqIiBRTyiYREbkel92BAhg3bhxDhgyhZcuWtGnThmnTphEaGsrIkSOBtCEOp0+f5uuvv8ZisdCwYcMMx5crVw5PT89M20VERG6UsklERK7FpR2owYMHc+HCBV599VXCwsJo2LAhS5YsoWrVqgCEhYVdd90NERGRvKRsEhGRa3HpOlCuoLU2RERcR9/BWdPnIiLiGkVqHSgREREREZGiRh0oERERERGRHFIHSkREREREJIfUgRIREREREckhdaBERERERERySB0oERERERGRHFIHSkREREREJIfUgRIREREREckhdaBERERERERySB0oERERERGRHFIHSkREREREJIfUgRIREREREckhdaBERERERERySB0oERERERGRHFIHSkREREREJIfUgRIREREREckhdaBERERERERySB0oERERERGRHFIHSkREREREJIfUgRIREREREckhdaBERERERERySB0oERERERGRHFIHSkREREREJIfUgRIREREREckhdaBERERERERySB0oERERERGRHFIHSkREREREJIfUgRIREREREckhdaBERERERERyyOUdqKlTp1K9enU8PT1p0aIFa9asybbs3Llz6datG2XLlsXPz482bdrw66+/FmBtRUSkJFA2iYhIdlzagZo9ezZjx45lwoQJ7Nixg/bt29OrVy9CQ0OzLP/777/TrVs3lixZwrZt2+jUqRP9+vVjx44dBVxzEREprpRNIiJyLYZpmqar3rxVq1Y0b96cTz/91Lmtfv36DBw4kMmTJ+foHLfccguDBw/m5ZdfzlH5qKgo/P39iYyMxM/P74bqLSIiN6YofAcrm0RESo4b+f512R2opKQktm3bRvfu3TNs7969O+vXr8/RORwOB9HR0ZQuXTrbMomJiURFRWX4ERERyYqySURErsdlHajz58+TmppKUFBQhu1BQUGEh4fn6BzvvfcesbGx3HvvvdmWmTx5Mv7+/s6fypUr31S9RUSk+FI2iYjI9bh8EgnDMDK8Nk0z07asfP/990yaNInZs2dTrly5bMuNHz+eyMhI58/Jkydvus4iIlK8KZtERCQ7bq564zJlymC1WjNd0YuIiMh05e9qs2fP5uGHH2bOnDl07dr1mmVtNhs2m+2m6ysiIsWfsklERK7HZXegPDw8aNGiBSEhIRm2h4SE0LZt22yP+/777xk+fDjfffcdffr0ye9qiohICaJsEhGR63HZHSiAcePGMWTIEFq2bEmbNm2YNm0aoaGhjBw5Ekgb4nD69Gm+/vprIC2ghg4dykcffUTr1q2dVwjtdjv+/v4ua4eIiBQfyiYREbkWl3agBg8ezIULF3j11VcJCwujYcOGLFmyhKpVqwIQFhaWYd2Nzz//nJSUFJ544gmeeOIJ5/Zhw4Yxc+bMgq6+iIgUQ8omERG5FpeuA+UKWmtDRMR19B2cNX0uIiKuUaTWgRIRERERESlq1IESERERERHJIXWgREREREREckgdKBERERERkRxSB0pERERERCSH1IESERERERHJIXWgREREREREckgdKBERERERkRxSB0pERERERCSH1IESERERERHJIXWgREREREREckgdKBERERERkRxSB0pERERERCSH1IESERERERHJIXWgREREREREckgdKBERERERkRxSB0pERERERCSH1IESERERERHJIXWgREREREREckgdKBERERERkRxSB0pERERERCSH1IHKpbjkOFIcKa6uhoiIiFNMUgymabq6GiIiJYKbqytQ1Pxv3//4Zv833FHpDjpW7kjbCm3xdvd2dbVERKQEe2L5E1xIuECHSh3oWLkjzco1w82iiBcRyQ/6ds2lLWe3cDnxMguPLGThkYW4W9xpHtScNuXb0LZCW+qWrovF0I09EREpGHHJcey7sI+E1AS+3vc1X+/7Gj8PP1qXb02bCmnZVMGngqurKSJSbBhmCbvnHxUVhb+/P5GRkfj5+eX6+BRHCjsidrDq5CpWnlzJyeiTGfb72/xpXq45LYJa0CKoBXVL1cXd6p5HtRcRKdpu9ju4uLrZzyU2OZb1Z9az6uQqVp9aTWRiZIb9FX0q0iKoBS2DWtK0XFOq+lXVxT4REW7s+1cdqJtgmibHoo6x4cwGNpzZwObwzcSnxGco42HxoH5gfRqVaUT9wPrUL12f6v7VNbRCREokdaCylpefS4ojhT3n97DhzAbWn1nP7vO7STVTM5Tx9fClcZnG3FLmFhqUbkC9wHpU8K6AYRg39d4iIkWNOlA5kJ/hnexIZv+F/Ww7u42tZ7fyx7k/Ml0FBLBZbdTwr0HtUrWpFVCLKr5VqORbiYo+FfHx8MnTOomIFCbqQGUtPz+X2ORYdkbsZNvZbWw7u429F/aSmJqYqZyvhy+1A2pTu1RtavjXoLJvZSr5VqKCTwVsVlue1klEpLBQByoHCjK8TdMkNDqUXed2sffCXvZf2M+BSweITY7N9pgAWwCVfCpR0bdihv9W8qlEsE8w7hYNBxSRoksdqKwV5OeS7Ejm4KWD7D63m30X9rH/4n4OXz6c7QyzBgZlvcqmZZFvpYwZ5VORsl5lNRxQRIqsItmBmjp1Ku+88w5hYWHccsstfPjhh7Rv3z7b8qtXr2bcuHHs3buXChUq8OyzzzJy5Mgcv5+rw9thOjgVfYrDlw9z6NIhjlw+wqmYU5yKPsWlxEvXPNZiWAj2CqaCTwUCbAH4ePjg6+GLr4cvfh5+aX9298XHw+fv1x6+eLt7K9xEpFBw9XdwTpW0bEpKTeJY5DFnNh2NPMrpmNOcij5FXErcNY/1sHhQ0bciQV5BzuxJ/296Tl2ZUenZZHeza8igiLjcjXz/uvRBnNmzZzN27FimTp3K7bffzueff06vXr3Yt28fVapUyVT+2LFj9O7dm0cffZRvvvmGdevW8fjjj1O2bFnuuusuF7Qg9yyGhSp+VajiV4XOVTpn2BebHMup6FOcijnF6ejTaf/9K8BOx5wmMTWRM7FnOBN7JlfvaWCkhdgVwXVlx+vKfVd2vNI7Yj7uPlgt1rz8GERECq2SmE0eVg/qlq5L3dJ1M2w3TZNLiZcyZVL6hb/w2HCSHGmdr2ORx3L1nm6GW4YLgVldDLw6l5y55e6Dt7u3OmAi4hIuvQPVqlUrmjdvzqeffurcVr9+fQYOHMjkyZMzlX/uuedYuHAh+/fvd24bOXIkf/zxBxs2bMjRe7r6Kt+NcpgOLsRf4FTMKcJiwohOiiY6OZqopChikmLSXv/1E5UURXRSNDHJMVmOc78R3u7eaZ0q97RAc7e442ZxS/sx0v7rbnV3/tnN4paxzJXlrtieXZkr92cqY3HDggXDMDAwsBgZ/5x+ty39zwYGhmFk+LPzuL9ei0jBKArfwcqmnEtxpBAeG86pmFOcizuXIYuik6/KpCuyKsW8+QXpLYbFmUl+Hn7Y3expeWF1w93InBtZ5VBW+ZLbclfuu1beZJdNWeWYskmk4BSpO1BJSUls27aN559/PsP27t27s379+iyP2bBhA927d8+wrUePHnzxxRckJyfj7p75+aDExEQSE//uRERFReVB7QuexbBQ1qssZb3K0qxcsxwfl5iamDHQrgi1qztdV3fEopOjnbMKxibHXvPZraLOYliwYAEDLFiyDbPsgs0g7XX6dufrv/7793+uXS7H580iWJ3vlQO5OT6r7SU92Et6+1uUa8GE1hNcXY18oWzKHTeLW9pzUb6VcnyMaZrEp8RfM5Ou7HilXxBM3xaVFEWKIwWH6XC+Ps3pfGyl62TIn78uHF69DeOKcllkxM3kU2HNpmzzqoR/N5f09o+4ZQT9avYrkPdyWQfq/PnzpKamEhQUlGF7UFAQ4eHhWR4THh6eZfmUlBTOnz9P+fLlMx0zefJkXnnllbyreBFjs9qw2W2UsZe5oeOTHckZOlZRSVHEJMeQ4kgh2ZFMiiMl059THCmkmCkkpyY7/5xdOefrK8pke+4rypimiQMHpmliYuIwHTf1OTlMBw4cUKKmVBG5MeW9M3/XFhfKpvxnGAZe7l54uXsR5B10/QOuYppmxouDf3XAYpNjM+bQFbmRniOZsiqLMtllUbZZdlU2mZiZMupGObNN2SRyXVnNfJ1fXL4Y0dW9ZdM0r9mDzqp8VtvTjR8/nnHjxjlfR0VFUbly5RutbonjbnGnlGcpSnmWcnVVruvKzpQzwK74c/q+9EBK//PV+7P98xXnBJyh6PzvFaNhr952ddm//5N9uatH12b1Plntz+6zye0xuT1XQbmZf4wUh/cvDAJsAa6uQr5TNhVehmHg6eaJp5snZb3Kuro615Vdp+rqjMkumzL8Of34K4/76+JfVtl0vRy5VjZlV0bZlM37K5tcrqpv1QJ7L5d1oMqUKYPVas10RS8iIiLTlbx0wcHBWZZ3c3MjMDAwy2NsNhs2m9avKAmuHHInInIjlE2S19KzCQOsaEImkeLAZf/S9PDwoEWLFoSEhGTYHhISQtu2bbM8pk2bNpnKL1u2jJYtW2Y5xlxERCQ3lE0iInI9Lr1UP27cOP773//y5Zdfsn//fp566ilCQ0Oda2eMHz+eoUOHOsuPHDmSEydOMG7cOPbv38+XX37JF198wTPPPOOqJoiISDGjbBIRkWtx6TNQgwcP5sKFC7z66quEhYXRsGFDlixZQtWqaWMYw8LCCA0NdZavXr06S5Ys4amnnuKTTz6hQoUKTJkypcissyEiIoWfsklERK7FpetAuUJRXWtDRKQ40Hdw1vS5iIi4xo18/+ppexERERERkRxSB0pERERERCSH1IESERERERHJIZcvpFvQ0h/5ioqKcnFNRERKnvTv3hL2+O11KZtERFzjRnKpxHWgoqOjAbTiu4iIC0VHR+Pv7+/qahQayiYREdfKTS6VuFn4HA4HZ86cwdfXF8Mwcn18VFQUlStX5uTJkyVypqSS3n7QZ6D2q/03037TNImOjqZChQpYLBpFnk7ZdHPUfrVf7Vf7CzKXStwdKIvFQqVKlW76PH5+fiXyL2m6kt5+0Geg9qv9N9p+3XnKTNmUN9R+tV/tV/tvRG5zSZf/REREREREckgdKBERERERkRxSByqXbDYbEydOxGazuboqLlHS2w/6DNR+tb8kt7+wKun/X9R+tV/tV/sLsv0lbhIJERERERGRG6U7UCIiIiIiIjmkDpSIiIiIiEgOqQMlIiIiIiKSQ+pAiYiIiIiI5JA6ULk0depUqlevjqenJy1atGDNmjWurtJNmzx5Mrfeeiu+vr6UK1eOgQMHcuDAgQxlTNNk0qRJVKhQAbvdTseOHdm7d2+GMomJiYwaNYoyZcrg7e1N//79OXXqVEE2JU9MnjwZwzAYO3asc1txb//p06d56KGHCAwMxMvLi6ZNm7Jt2zbn/uLc/pSUFF588UWqV6+O3W6nRo0avPrqqzgcDmeZ4tT+33//nX79+lGhQgUMw2D+/PkZ9udVWy9dusSQIUPw9/fH39+fIUOGcPny5XxuXcmlbCr6v5vXo2xSNimbClE2mZJjs2bNMt3d3c3p06eb+/btM8eMGWN6e3ubJ06ccHXVbkqPHj3MGTNmmHv27DF37txp9unTx6xSpYoZExPjLPPmm2+avr6+5k8//WTu3r3bHDx4sFm+fHkzKirKWWbkyJFmxYoVzZCQEHP79u1mp06dzCZNmpgpKSmuaNYN2bx5s1mtWjWzcePG5pgxY5zbi3P7L168aFatWtUcPny4uWnTJvPYsWPmb7/9Zh4+fNhZpji3//XXXzcDAwPNxYsXm8eOHTPnzJlj+vj4mB9++KGzTHFq/5IlS8wJEyaYP/30kwmY8+bNy7A/r9ras2dPs2HDhub69evN9evXmw0bNjT79u1bUM0sUZRNxeN381qUTcomZVPhyiZ1oHLhtttuM0eOHJlhW7169cznn3/eRTXKHxERESZgrl692jRN03Q4HGZwcLD55ptvOsskJCSY/v7+5meffWaapmlevnzZdHd3N2fNmuUsc/r0adNisZhLly4t2AbcoOjoaLN27dpmSEiI2aFDB2dIFff2P/fcc2a7du2y3V/c29+nTx/z//7v/zJsu/POO82HHnrINM3i3f6rQyqv2rpv3z4TMDdu3Ogss2HDBhMw//zzz3xuVcmjbCp+v5tXUjZlrbi3X9k0z/m6MGaThvDlUFJSEtu2baN79+4Ztnfv3p3169e7qFb5IzIyEoDSpUsDcOzYMcLDwzO03Waz0aFDB2fbt23bRnJycoYyFSpUoGHDhkXm83niiSfo06cPXbt2zbC9uLd/4cKFtGzZknvuuYdy5crRrFkzpk+f7txf3Nvfrl07li9fzsGDBwH4448/WLt2Lb179waKf/uvlFdt3bBhA/7+/rRq1cpZpnXr1vj7+xepz6MoUDYV/99NZZOyCZRNhS2b3G6mQSXJ+fPnSU1NJSgoKMP2oKAgwsPDXVSrvGeaJuPGjaNdu3Y0bNgQwNm+rNp+4sQJZxkPDw9KlSqVqUxR+HxmzZrF9u3b2bJlS6Z9xb39R48e5dNPP2XcuHG88MILbN68mdGjR2Oz2Rg6dGixb/9zzz1HZGQk9erVw2q1kpqayhtvvMH9998PFP///1fKq7aGh4dTrly5TOcvV65ckfo8igJlU/H+3VQ2KZuUTYUzm9SByiXDMDK8Nk0z07ai7Mknn2TXrl2sXbs2074baXtR+HxOnjzJmDFjWLZsGZ6entmWK67tdzgctGzZkn//+98ANGvWjL179/Lpp58ydOhQZ7ni2v7Zs2fzzTff8N1333HLLbewc+dOxo4dS4UKFRg2bJizXHFtf1byoq1ZlS+qn0dRoGz6W3H53VQ2KZuUTRkVpmzSEL4cKlOmDFarNVMPNSIiIlOPuKgaNWoUCxcuZOXKlVSqVMm5PTg4GOCabQ8ODiYpKYlLly5lW6aw2rZtGxEREbRo0QI3Nzfc3NxYvXo1U6ZMwc3NzVn/4tr+8uXL06BBgwzb6tevT2hoKFD8////61//4vnnn+e+++6jUaNGDBkyhKeeeorJkycDxb/9V8qrtgYHB3P27NlM5z937lyR+jyKAmVT8f3dVDYpm5RNaQpjNqkDlUMeHh60aNGCkJCQDNtDQkJo27ati2qVN0zT5Mknn2Tu3LmsWLGC6tWrZ9hfvXp1goODM7Q9KSmJ1atXO9veokUL3N3dM5QJCwtjz549hf7z6dKlC7t372bnzp3On5YtW/Lggw+yc+dOatSoUazbf/vtt2eaGvjgwYNUrVoVKP7//+Pi4rBYMn4VWq1W51Sxxb39V8qrtrZp04bIyEg2b97sLLNp0yYiIyOL1OdRFCibiu/vprJJ2aRsSlMosylXU06UcOlTxX7xxRfmvn37zLFjx5re3t7m8ePHXV21m/LYY4+Z/v7+5qpVq8ywsDDnT1xcnLPMm2++afr7+5tz5841d+/ebd5///1ZTh9ZqVIl87fffjO3b99udu7cuVBOlZkTV850ZJrFu/2bN2823dzczDfeeMM8dOiQ+e2335peXl7mN9984yxTnNs/bNgws2LFis6pYufOnWuWKVPGfPbZZ51lilP7o6OjzR07dpg7duwwAfP99983d+zY4ZzyOq/a2rNnT7Nx48bmhg0bzA0bNpiNGjXSNOb5RNlUPH43c0LZpGxSNhWObFIHKpc++eQTs2rVqqaHh4fZvHlz53SqRRmQ5c+MGTOcZRwOhzlx4kQzODjYtNls5h133GHu3r07w3ni4+PNJ5980ixdurRpt9vNvn37mqGhoQXcmrxxdUgV9/YvWrTIbNiwoWmz2cx69eqZ06ZNy7C/OLc/KirKHDNmjFmlShXT09PTrFGjhjlhwgQzMTHRWaY4tX/lypVZ/r4PGzbMNM28a+uFCxfMBx980PT19TV9fX3NBx980Lx06VIBtbLkUTYV/d/NnFA2KZuUTYUjmwzTNM3c3bMSEREREREpmfQMlIiIiIiISA6pAyUiIiIiIpJD6kCJiIiIiIjkkDpQIiIiIiIiOaQOlIiIiIiISA6pAyUiIiIiIpJD6kCJiIiIiIjkkDpQIiIiIiIiOaQOlBRpHTt2ZOzYsQX+vpMmTaJp06b5/j6GYWAYBgEBAfn+Xnlt3rx5uLm5UadOHSIiIm7oHMePH3d+BgXxeYuI3CzlUuGlXJK8og6USCE3Y8YMDh48mKtjVq1aRfny5TFNM59qdW0rV67kgQceYOLEiZQrV46ePXsSFRWVoczx48d5+OGHqV69Ona7nZo1azJx4kSSkpKcZSpXrkxYWBhPP/10QTdBRESyoVxSLpV06kCJFHIBAQGUK1cuV8csXLiQ/v37YxhGPtUqe9u2bWPQoEG8//77vPTSS/z666+ULl2a/v37k5CQ4Cz3559/4nA4+Pzzz9m7dy8ffPABn332GS+88IKzjNVqJTg4GB8fnwJvh4iIZE25pFwq6dSBkiIvJSWFJ598koCAAAIDA3nxxRezvcIVGRmJ3W5n6dKlGbbPnTsXb29vYmJiAHjuueeoU6cOXl5e1KhRg5deeonk5ORs65DVkI2BAwcyfPhw5+ukpCSeffZZKlasiLe3N61atWLVqlW5bu8ff/xBp06d8PX1xc/PjxYtWrB169YMZdKDKr1uo0aNYuzYsZQqVYqgoCCmTZtGbGwsI0aMwNfXl5o1a/LLL784j1+1ahWGYfDrr7/SrFkz7HY7nTt3JiIigl9++YX69evj5+fH/fffT1xcnPO4AwcO0LdvX6ZMmcJjjz0GgLe3Nz///DN+fn4MHjyYlJQUAHr27MmMGTPo3r07NWrUoH///jzzzDPMnTs315+JiEhholxSLknxpg6UFHlfffUVbm5ubNq0iSlTpvDBBx/w3//+N8uy/v7+9OnTh2+//TbD9u+++44BAwY4ryj5+voyc+ZM9u3bx0cffcT06dP54IMPbqqeI0aMYN26dcyaNYtdu3Zxzz330LNnTw4dOpSr8zz44INUqlSJLVu2sG3bNp5//nnc3d2d+/fu3Ut4eDhdunRxbvvqq68oU6YMmzdvZtSoUTz22GPcc889tG3blu3bt9OjRw+GDBmSIXQgbUz9f/7zH9avX8/Jkye59957+fDDD/nuu+/4+eefCQkJ4eOPP3aWr1u3LmFhYQwdOjTDeWw2GwsXLmTBggW4ubll27bIyEhKly6dq89DRKSwUS4pl6SYM0WKsA4dOpj169c3HQ6Hc9tzzz1n1q9fP9tj5s6da/r4+JixsbGmaZpmZGSk6enpaf7888/ZHvP222+bLVq0cL6eOHGi2aRJkwz1GDNmTIZjBgwYYA4bNsw0TdM8fPiwaRiGefr06QxlunTpYo4fPz7b9wXMefPmZdjm6+trzpw5M9tj3njjDfPOO+/MULd27do5X6ekpJje3t7mkCFDnNvCwsJMwNywYYNpmqa5cuVKEzB/++03Z5nJkyebgHnkyBHntn/+859mjx49sq1Lbhw+fNj08/Mzp0+fnmnf1Z+3iEhhpVzKTLkkxY3uQEmR17p16wxjqtu0acOhQ4dITU3l3//+Nz4+Ps6f0NBQ+vTpg5ubGwsXLgTgp59+wtfXl+7duzvP8eOPP9KuXTvnOOeXXnqJ0NDQG67j9u3bMU2TOnXqZKjP6tWrOXLkSK7ONW7cOB555BG6du3Km2++men4BQsWOIdJpGvcuLHzz1arlcDAQBo1auTcFhQUBJBpVqIrjwsKCnIOHbly243OZHSlM2fO0LNnT+655x4eeeSRmz6fiIgrKZeUS1K8qQMlxdrIkSPZuXOn86dChQp4eHhw991389133wFpwyQGDx7svIW/ceNG7rvvPnr16sXixYvZsWMHEyZMyDALz9UsFkum8e1Xjk13OBxYrVa2bduWoT779+/no48+ylWbJk2axN69e+nTpw8rVqygQYMGzJs3D4Dw8HC2b99Onz59Mhxz5VAKSJuG9spt6UHvcDiyPe7qY9K3XX1Mbp05c4ZOnTrRpk0bpk2bdlPnEhEp7JRLaZRLUpRlP+hTpIjYuHFjpte1a9fGarVSunTpLMcuP/jgg3Tv3p29e/eycuVKXnvtNee+devWUbVqVSZMmODcduLEiWvWoWzZsoSFhTlfp6amsmfPHjp16gRAs2bNSE1NJSIigvbt299QO69Up04d6tSpw1NPPcX999/PjBkzGDRoEAsXLqRNmzaUKVPmpt+jIJw+fZpOnTrRokULZsyYgcWiazoiUvQpl5RLUrzpb4UUeSdPnmTcuHEcOHCA77//no8//pgxY8Zc85gOHToQFBTEgw8+SLVq1WjdurVzX61atQgNDWXWrFkcOXKEKVOmOK+kZadz5878/PPP/Pzzz/z55588/vjjXL582bm/Tp06PPjggwwdOpS5c+dy7NgxtmzZwltvvcWSJUty3Nb4+HiefPJJVq1axYkTJ1i3bh1btmyhfv36QNosRwMGDMjx+VzpzJkzdOzYkcqVK/Puu+9y7tw5wsPDCQ8Pd3XVRERuinJJuSTFm+5ASZE3dOhQ4uPjue2227BarYwaNYp//OMf1zzGMAzuv/9+3nnnHV5++eUM+wYMGMBTTz3Fk08+SWJiIn369OGll15i0qRJ2Z7v//7v//jjjz8YOnQobm5uPPXUU86rfOlmzJjB66+/ztNPP83p06cJDAykTZs29O7dO8dttVqtXLhwgaFDh3L27FnKlCnDnXfeySuvvEJsbCzLly+/6VmZCsqyZcs4fPgwhw8fplKlShn2XT3sRESkKFEuKZekeDNM/Y0QKbQMw2DevHkMHDjwumXnzp3Liy++yL59+/K/YgVs0qRJzJ8/n507d7q6KiIiJZpyKY1yqWRTB0qkEDMMA09PTwIDAzl16tQ1yy5btozExET69etXQLXLf6GhoTRo0ICkpCQaNGigoBIRcTHlknJJ1IESKdQOHz4MpA2RqF69uotrU/BSUlI4fvw4kLboYeXKlV1bIRGREk65pFwSdaBERERERERyTLPwiYiIiIiI5JA6UCIiIiIiIjmkDpSIiIiIiEgOqQMlIiIiIiKSQ+pAiYiIiIiI5JA6UCIiIiIiIjmkDpSIiIiIiEgOqQMlIiIiIiKSQ/8PnQnH7t92t9sAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 1000x300 with 2 Axes>"
       ]
@@ -656,7 +639,7 @@
     "plt.plot(np.unique(bval),fit['f']*np.exp(-np.unique(bval)*fit['D*'])+(1-fit['f'])*np.exp(-np.unique(bval)*fit['D']))\n",
     "plt.plot(np.unique(bval),fit['f']*np.exp(-np.unique(bval)*fit['D*']))\n",
     "plt.plot(np.unique(bval),(1-fit['f'])*np.exp(-np.unique(bval)*fit['D']))\n",
-    "plt.legend(['measured data','model fit','D','D*'])\n",
+    "plt.legend(['measured data','model fit','D*','D'])\n",
     "plt.ylabel('S/S0')\n",
     "plt.xlabel('b-value [s/mm^2]')\n",
     "plt.title('algorithm 1')\n",
@@ -670,7 +653,7 @@
     "plt.plot(np.unique(bval),fit['f']*np.exp(-np.unique(bval)*fit['D*'])+(1-fit['f'])*np.exp(-np.unique(bval)*fit['D']))\n",
     "plt.plot(np.unique(bval),fit['f']*np.exp(-np.unique(bval)*fit['D*']))\n",
     "plt.plot(np.unique(bval),(1-fit['f'])*np.exp(-np.unique(bval)*fit['D']))\n",
-    "plt.legend(['measured data','model fit','D','D*'])\n",
+    "plt.legend(['measured data','model fit','D*','D'])\n",
     "plt.ylabel('S/S0')\n",
     "plt.xlabel('b-value [s/mm^2]')\n",
     "plt.title('algorithm 2')\n"
@@ -695,7 +678,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 19,
    "id": "fd1d23a4",
    "metadata": {},
    "outputs": [
@@ -706,7 +689,7 @@
        "        300.,  400.,  500.,  600.,  800., 1000.])"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -728,7 +711,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 42,
    "id": "71970925",
    "metadata": {},
    "outputs": [],
@@ -774,17 +757,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 21,
    "id": "d6307930",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x23ba76af190>"
+       "<matplotlib.colorbar.Colorbar at 0x1e8a0305f50>"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -818,7 +801,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 23,
    "id": "2855a999",
    "metadata": {
     "scrolled": true
@@ -828,7 +811,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 4274/4274 [00:00<00:00, 4809.15it/s]\n"
+      "100%|██████████| 4274/4274 [00:01<00:00, 2720.79it/s]\n"
      ]
     },
     {
@@ -839,7 +822,7 @@
        " 'D': array([0., 0., 0., ..., 0., 0., 0.])}"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -853,7 +836,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 43,
    "id": "f895cf1e",
    "metadata": {},
    "outputs": [
@@ -861,7 +844,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 4274/4274 [00:11<00:00, 374.05it/s]\n"
+      "100%|██████████| 4274/4274 [00:21<00:00, 195.14it/s]\n"
      ]
     }
    ],
@@ -872,17 +855,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 44,
    "id": "fe189fb1",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x23ba8a3d8d0>"
+       "<matplotlib.colorbar.Colorbar at 0x1e8c72c6cd0>"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -961,7 +944,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 26,
    "id": "2a216274",
    "metadata": {},
    "outputs": [
@@ -971,7 +954,7 @@
        "(229120, 15)"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1004,7 +987,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 27,
    "id": "cee14e22",
    "metadata": {},
    "outputs": [
@@ -1012,7 +995,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 229120/229120 [13:34<00:00, 281.35it/s]\n"
+      "100%|██████████| 229120/229120 [27:40<00:00, 137.94it/s] \n"
      ]
     }
    ],
@@ -1047,7 +1030,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 46,
    "id": "397d86c9",
    "metadata": {},
    "outputs": [],
@@ -1077,7 +1060,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },