From 939a676e82a050dc321854753e1b39da17fb02db Mon Sep 17 00:00:00 2001
From: Fernando Benitez <fbenitez@turing.ac.uk>
Date: Fri, 4 Nov 2022 13:37:39 +0000
Subject: [PATCH] testing scenario, adding some dependencies I need to play
 with Jupiter

---
 aspics/kernels/ramp_ua.cl      |    5 +-
 calibration/abc_notebook.ipynb |  400 +++++------
 config/new_parameters.yml      |    2 +-
 poetry.lock                    | 1208 +++++++++++++++++++++++++++++++-
 pyproject.toml                 |    3 +
 5 files changed, 1391 insertions(+), 227 deletions(-)

diff --git a/aspics/kernels/ramp_ua.cl b/aspics/kernels/ramp_ua.cl
index 5fc3b63..6a5db58 100644
--- a/aspics/kernels/ramp_ua.cl
+++ b/aspics/kernels/ramp_ua.cl
@@ -205,11 +205,10 @@ float get_mortality_prob_for_age(ushort age, ushort sex, int origin, ushort cvd,
   //    oddBMI = params->bmi_multipliers[originNew * 4] + params->bmi_multipliers[originNew * 4 + 1] * 0.99 * new_bmi + params->bmi_multipliers[originNew * 4 + 2] * pown(0.99 * new_bmi,2) + params->bmi_multipliers[originNew * 4 + 3] * pown(0.99 * new_bmi,3);
   //} else if (new_bmi > 27.7){ //Scenario 5
   //   oddBMI = params->bmi_multipliers[originNew * 4] + params->bmi_multipliers[originNew * 4 + 1] * (new_bmi - 1) + params->bmi_multipliers[originNew * 4 + 2] * pown(new_bmi -1,2) + params->bmi_multipliers[originNew * 4 + 3] * pown(new_bmi - 1,3); 
-  } else if (new_bmi > 50){ //oddBMI is calculated using 50 for [!!! BMI VARIABLE !!!]} //Scenario 6
-  //   oddBMI = params->bmi_multipliers[originNew * 4] + params->bmi_multipliers[originNew * 4 + 1] * 50 + params->bmi_multipliers[originNew * 4 + 2] * pown(50,2) + params->bmi_multipliers[originNew * 4 + 3] * pown(50,3);
+  } else if (new_bmi > 50){ //oddBMI is calculated using 50 for [!!! BMI VARIABLE !!!]
+    oddBMI = params->bmi_multipliers[originNew * 4] + params->bmi_multipliers[originNew * 4 + 1] * 50.0 + params->bmi_multipliers[originNew * 4 + 2] * pown(50.0,2) + params->bmi_multipliers[originNew * 4 + 3] * pown(50.0,3);
   }else {
     oddBMI = params->bmi_multipliers[originNew * 4] + params->bmi_multipliers[originNew * 4 + 1] * new_bmi + params->bmi_multipliers[originNew * 4 + 2] * pown(new_bmi,2) + params->bmi_multipliers[originNew * 4 + 3] * pown(new_bmi,3);
-
   }
   //printf("Age_ID: %d, Origin: %d, Final BMI: %f, bmi_multipliers: %f,%f,%f \n", age, originNew, new_bmi, params->bmi_multipliers[originNew * 3], params->bmi_multipliers[originNew * 3 + 1], params->bmi_multipliers[originNew * 3 + 2]);
   
diff --git a/calibration/abc_notebook.ipynb b/calibration/abc_notebook.ipynb
index 2f7a513..4f53283 100644
--- a/calibration/abc_notebook.ipynb
+++ b/calibration/abc_notebook.ipynb
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 6,
    "id": "1084c0b9",
    "metadata": {
     "pycharm": {
@@ -168,7 +168,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "58b2847b",
    "metadata": {
     "collapsed": false,
@@ -180,10 +180,10 @@
     {
      "data": {
       "text/plain": [
-       "'/Users/fbenitez/Documents/ResearchATI/uatk-aspics/uatk-aspics/calibration'"
+       "'/home/fernando/Documents/ua-aspics/calibration'"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -209,7 +209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 7,
    "id": "c991bc94",
    "metadata": {},
    "outputs": [
@@ -240,89 +240,83 @@
        "      <th>asymptomatic</th>\n",
        "      <th>symptomatic</th>\n",
        "      <th>recovered</th>\n",
-       "      <th>Day</th>\n",
-       "      <th>Current</th>\n",
-       "      <th>Scenario 1</th>\n",
-       "      <th>Scenario 2</th>\n",
-       "      <th>Scenario 3</th>\n",
-       "      <th>Scenario 4</th>\n",
+       "      <th>baseline</th>\n",
+       "      <th>scenario2</th>\n",
+       "      <th>scenario3</th>\n",
+       "      <th>scenario4</th>\n",
+       "      <th>scenario5</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>177157</td>\n",
-       "      <td>18</td>\n",
+       "      <td>177175</td>\n",
+       "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>4</td>\n",
        "      <td>247</td>\n",
-       "      <td>26</td>\n",
-       "      <td>1</td>\n",
-       "      <td>8</td>\n",
-       "      <td>8</td>\n",
-       "      <td>8</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
+       "      <td>15</td>\n",
+       "      <td>19</td>\n",
+       "      <td>21</td>\n",
+       "      <td>21</td>\n",
+       "      <td>19</td>\n",
+       "      <td>19</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>177120</td>\n",
-       "      <td>50</td>\n",
-       "      <td>1</td>\n",
-       "      <td>8</td>\n",
+       "      <td>177175</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
        "      <td>205</td>\n",
-       "      <td>62</td>\n",
-       "      <td>2</td>\n",
-       "      <td>14</td>\n",
-       "      <td>14</td>\n",
-       "      <td>16</td>\n",
-       "      <td>9</td>\n",
-       "      <td>7</td>\n",
+       "      <td>30</td>\n",
+       "      <td>46</td>\n",
+       "      <td>50</td>\n",
+       "      <td>50</td>\n",
+       "      <td>46</td>\n",
+       "      <td>46</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>177103</td>\n",
-       "      <td>52</td>\n",
-       "      <td>1</td>\n",
-       "      <td>23</td>\n",
+       "      <td>177175</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
        "      <td>157</td>\n",
-       "      <td>106</td>\n",
-       "      <td>3</td>\n",
-       "      <td>18</td>\n",
-       "      <td>18</td>\n",
-       "      <td>21</td>\n",
-       "      <td>12</td>\n",
-       "      <td>10</td>\n",
+       "      <td>49</td>\n",
+       "      <td>75</td>\n",
+       "      <td>83</td>\n",
+       "      <td>83</td>\n",
+       "      <td>75</td>\n",
+       "      <td>75</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>177080</td>\n",
-       "      <td>46</td>\n",
-       "      <td>2</td>\n",
-       "      <td>50</td>\n",
-       "      <td>121</td>\n",
-       "      <td>140</td>\n",
+       "      <td>177175</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
        "      <td>4</td>\n",
-       "      <td>21</td>\n",
-       "      <td>22</td>\n",
-       "      <td>28</td>\n",
-       "      <td>15</td>\n",
-       "      <td>12</td>\n",
+       "      <td>120</td>\n",
+       "      <td>62</td>\n",
+       "      <td>99</td>\n",
+       "      <td>112</td>\n",
+       "      <td>112</td>\n",
+       "      <td>99</td>\n",
+       "      <td>99</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>177070</td>\n",
-       "      <td>34</td>\n",
-       "      <td>5</td>\n",
-       "      <td>67</td>\n",
-       "      <td>92</td>\n",
-       "      <td>168</td>\n",
-       "      <td>5</td>\n",
-       "      <td>24</td>\n",
-       "      <td>24</td>\n",
-       "      <td>35</td>\n",
-       "      <td>17</td>\n",
-       "      <td>13</td>\n",
+       "      <td>177175</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>91</td>\n",
+       "      <td>70</td>\n",
+       "      <td>122</td>\n",
+       "      <td>135</td>\n",
+       "      <td>135</td>\n",
+       "      <td>122</td>\n",
+       "      <td>122</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -337,131 +331,125 @@
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
-       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>295</th>\n",
-       "      <td>164521</td>\n",
+       "      <td>177175</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>12784</td>\n",
-       "      <td>296</td>\n",
-       "      <td>155</td>\n",
-       "      <td>141</td>\n",
-       "      <td>194</td>\n",
-       "      <td>121</td>\n",
-       "      <td>108</td>\n",
+       "      <td>101</td>\n",
+       "      <td>184</td>\n",
+       "      <td>199</td>\n",
+       "      <td>199</td>\n",
+       "      <td>184</td>\n",
+       "      <td>184</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>296</th>\n",
-       "      <td>164521</td>\n",
+       "      <td>177175</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>12784</td>\n",
-       "      <td>297</td>\n",
-       "      <td>155</td>\n",
-       "      <td>141</td>\n",
-       "      <td>194</td>\n",
-       "      <td>121</td>\n",
-       "      <td>108</td>\n",
+       "      <td>101</td>\n",
+       "      <td>184</td>\n",
+       "      <td>199</td>\n",
+       "      <td>199</td>\n",
+       "      <td>184</td>\n",
+       "      <td>184</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>297</th>\n",
-       "      <td>164521</td>\n",
+       "      <td>177175</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>12784</td>\n",
-       "      <td>298</td>\n",
-       "      <td>155</td>\n",
-       "      <td>141</td>\n",
-       "      <td>194</td>\n",
-       "      <td>121</td>\n",
-       "      <td>108</td>\n",
+       "      <td>101</td>\n",
+       "      <td>184</td>\n",
+       "      <td>199</td>\n",
+       "      <td>199</td>\n",
+       "      <td>184</td>\n",
+       "      <td>184</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>298</th>\n",
-       "      <td>164521</td>\n",
+       "      <td>177175</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>12784</td>\n",
-       "      <td>299</td>\n",
-       "      <td>155</td>\n",
-       "      <td>141</td>\n",
-       "      <td>194</td>\n",
-       "      <td>121</td>\n",
-       "      <td>108</td>\n",
+       "      <td>101</td>\n",
+       "      <td>184</td>\n",
+       "      <td>199</td>\n",
+       "      <td>199</td>\n",
+       "      <td>184</td>\n",
+       "      <td>184</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>299</th>\n",
-       "      <td>164521</td>\n",
+       "      <td>177175</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>12784</td>\n",
-       "      <td>300</td>\n",
-       "      <td>155</td>\n",
-       "      <td>141</td>\n",
-       "      <td>194</td>\n",
-       "      <td>121</td>\n",
-       "      <td>108</td>\n",
+       "      <td>101</td>\n",
+       "      <td>184</td>\n",
+       "      <td>199</td>\n",
+       "      <td>199</td>\n",
+       "      <td>184</td>\n",
+       "      <td>184</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>300 rows × 12 columns</p>\n",
+       "<p>300 rows × 11 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
        "     susceptible  exposed  presymptomatic  asymptomatic  symptomatic  \\\n",
-       "0         177157       18               0             4          247   \n",
-       "1         177120       50               1             8          205   \n",
-       "2         177103       52               1            23          157   \n",
-       "3         177080       46               2            50          121   \n",
-       "4         177070       34               5            67           92   \n",
+       "0         177175        0               0             4          247   \n",
+       "1         177175        0               0             4          205   \n",
+       "2         177175        0               0             4          157   \n",
+       "3         177175        0               0             4          120   \n",
+       "4         177175        0               0             2           91   \n",
        "..           ...      ...             ...           ...          ...   \n",
-       "295       164521        0               0             0            0   \n",
-       "296       164521        0               0             0            0   \n",
-       "297       164521        0               0             0            0   \n",
-       "298       164521        0               0             0            0   \n",
-       "299       164521        0               0             0            0   \n",
+       "295       177175        0               0             0            0   \n",
+       "296       177175        0               0             0            0   \n",
+       "297       177175        0               0             0            0   \n",
+       "298       177175        0               0             0            0   \n",
+       "299       177175        0               0             0            0   \n",
        "\n",
-       "     recovered  Day  Current  Scenario 1  Scenario 2  Scenario 3  Scenario 4  \n",
-       "0           26    1        8           8           8           4           2  \n",
-       "1           62    2       14          14          16           9           7  \n",
-       "2          106    3       18          18          21          12          10  \n",
-       "3          140    4       21          22          28          15          12  \n",
-       "4          168    5       24          24          35          17          13  \n",
-       "..         ...  ...      ...         ...         ...         ...         ...  \n",
-       "295      12784  296      155         141         194         121         108  \n",
-       "296      12784  297      155         141         194         121         108  \n",
-       "297      12784  298      155         141         194         121         108  \n",
-       "298      12784  299      155         141         194         121         108  \n",
-       "299      12784  300      155         141         194         121         108  \n",
+       "     recovered  baseline  scenario2  scenario3  scenario4  scenario5  \n",
+       "0           15        19         21         21         19         19  \n",
+       "1           30        46         50         50         46         46  \n",
+       "2           49        75         83         83         75         75  \n",
+       "3           62        99        112        112         99         99  \n",
+       "4           70       122        135        135        122        122  \n",
+       "..         ...       ...        ...        ...        ...        ...  \n",
+       "295        101       184        199        199        184        184  \n",
+       "296        101       184        199        199        184        184  \n",
+       "297        101       184        199        199        184        184  \n",
+       "298        101       184        199        199        184        184  \n",
+       "299        101       184        199        199        184        184  \n",
        "\n",
-       "[300 rows x 12 columns]"
+       "[300 rows x 11 columns]"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df = pd.read_csv(\"data/bmi_scenarios/Current.csv\")\n",
+    "df = pd.read_csv(\"data/scenarios/total_counts.csv\")\n",
     "df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 8,
    "id": "aaa1d8f6",
    "metadata": {},
    "outputs": [
@@ -486,53 +474,53 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Current</th>\n",
-       "      <th>Scenario 1</th>\n",
-       "      <th>Scenario 2</th>\n",
-       "      <th>Scenario 3</th>\n",
-       "      <th>Scenario 4</th>\n",
+       "      <th>baseline</th>\n",
+       "      <th>scenario2</th>\n",
+       "      <th>scenario3</th>\n",
+       "      <th>scenario4</th>\n",
+       "      <th>scenario5</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>8</td>\n",
-       "      <td>8</td>\n",
-       "      <td>8</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
+       "      <td>19</td>\n",
+       "      <td>21</td>\n",
+       "      <td>21</td>\n",
+       "      <td>19</td>\n",
+       "      <td>19</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>14</td>\n",
-       "      <td>14</td>\n",
-       "      <td>16</td>\n",
-       "      <td>9</td>\n",
-       "      <td>7</td>\n",
+       "      <td>46</td>\n",
+       "      <td>50</td>\n",
+       "      <td>50</td>\n",
+       "      <td>46</td>\n",
+       "      <td>46</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>18</td>\n",
-       "      <td>18</td>\n",
-       "      <td>21</td>\n",
-       "      <td>12</td>\n",
-       "      <td>10</td>\n",
+       "      <td>75</td>\n",
+       "      <td>83</td>\n",
+       "      <td>83</td>\n",
+       "      <td>75</td>\n",
+       "      <td>75</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>21</td>\n",
-       "      <td>22</td>\n",
-       "      <td>28</td>\n",
-       "      <td>15</td>\n",
-       "      <td>12</td>\n",
+       "      <td>99</td>\n",
+       "      <td>112</td>\n",
+       "      <td>112</td>\n",
+       "      <td>99</td>\n",
+       "      <td>99</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>24</td>\n",
-       "      <td>24</td>\n",
-       "      <td>35</td>\n",
-       "      <td>17</td>\n",
-       "      <td>13</td>\n",
+       "      <td>122</td>\n",
+       "      <td>135</td>\n",
+       "      <td>135</td>\n",
+       "      <td>122</td>\n",
+       "      <td>122</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -544,43 +532,43 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>295</th>\n",
-       "      <td>155</td>\n",
-       "      <td>141</td>\n",
-       "      <td>194</td>\n",
-       "      <td>121</td>\n",
-       "      <td>108</td>\n",
+       "      <td>184</td>\n",
+       "      <td>199</td>\n",
+       "      <td>199</td>\n",
+       "      <td>184</td>\n",
+       "      <td>184</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>296</th>\n",
-       "      <td>155</td>\n",
-       "      <td>141</td>\n",
-       "      <td>194</td>\n",
-       "      <td>121</td>\n",
-       "      <td>108</td>\n",
+       "      <td>184</td>\n",
+       "      <td>199</td>\n",
+       "      <td>199</td>\n",
+       "      <td>184</td>\n",
+       "      <td>184</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>297</th>\n",
-       "      <td>155</td>\n",
-       "      <td>141</td>\n",
-       "      <td>194</td>\n",
-       "      <td>121</td>\n",
-       "      <td>108</td>\n",
+       "      <td>184</td>\n",
+       "      <td>199</td>\n",
+       "      <td>199</td>\n",
+       "      <td>184</td>\n",
+       "      <td>184</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>298</th>\n",
-       "      <td>155</td>\n",
-       "      <td>141</td>\n",
-       "      <td>194</td>\n",
-       "      <td>121</td>\n",
-       "      <td>108</td>\n",
+       "      <td>184</td>\n",
+       "      <td>199</td>\n",
+       "      <td>199</td>\n",
+       "      <td>184</td>\n",
+       "      <td>184</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>299</th>\n",
-       "      <td>155</td>\n",
-       "      <td>141</td>\n",
-       "      <td>194</td>\n",
-       "      <td>121</td>\n",
-       "      <td>108</td>\n",
+       "      <td>184</td>\n",
+       "      <td>199</td>\n",
+       "      <td>199</td>\n",
+       "      <td>184</td>\n",
+       "      <td>184</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -588,43 +576,43 @@
        "</div>"
       ],
       "text/plain": [
-       "     Current  Scenario 1  Scenario 2  Scenario 3  Scenario 4\n",
-       "0          8           8           8           4           2\n",
-       "1         14          14          16           9           7\n",
-       "2         18          18          21          12          10\n",
-       "3         21          22          28          15          12\n",
-       "4         24          24          35          17          13\n",
-       "..       ...         ...         ...         ...         ...\n",
-       "295      155         141         194         121         108\n",
-       "296      155         141         194         121         108\n",
-       "297      155         141         194         121         108\n",
-       "298      155         141         194         121         108\n",
-       "299      155         141         194         121         108\n",
+       "     baseline  scenario2  scenario3  scenario4  scenario5\n",
+       "0          19         21         21         19         19\n",
+       "1          46         50         50         46         46\n",
+       "2          75         83         83         75         75\n",
+       "3          99        112        112         99         99\n",
+       "4         122        135        135        122        122\n",
+       "..        ...        ...        ...        ...        ...\n",
+       "295       184        199        199        184        184\n",
+       "296       184        199        199        184        184\n",
+       "297       184        199        199        184        184\n",
+       "298       184        199        199        184        184\n",
+       "299       184        199        199        184        184\n",
        "\n",
        "[300 rows x 5 columns]"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "filtered_data = df.loc[:,['Current', 'Scenario 1','Scenario 2', 'Scenario 3', 'Scenario 4']]\n",
+    "filtered_data = df.loc[:,['baseline', 'scenario2','scenario3', 'scenario4', 'scenario5']]\n",
     "filtered_data"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 10,
    "id": "257d843a",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 1080x576 with 1 Axes>"
+       "<Figure size 1500x800 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1614,7 +1602,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.8.10"
   },
   "vscode": {
    "interpreter": {
diff --git a/config/new_parameters.yml b/config/new_parameters.yml
index 12dae40..877b259 100644
--- a/config/new_parameters.yml
+++ b/config/new_parameters.yml
@@ -4,7 +4,7 @@
 ########################################################
 microsim:
   study-area: "Manchester"
-  iterations: 100 # Number of model iterations (days)
+  iterations: 300 # Number of model iterations (days)
   use-lockdown: FALSE # Whether to preload the lockdown scenario
   start-date: 0 # Counted as number of days since 15/02/2020 (currently, max is 435)
   output: TRUE # Whether to generate output or not
diff --git a/poetry.lock b/poetry.lock
index 00a06d9..1b68122 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,3 +1,73 @@
+[[package]]
+name = "anyio"
+version = "3.6.2"
+description = "High level compatibility layer for multiple asynchronous event loop implementations"
+category = "dev"
+optional = false
+python-versions = ">=3.6.2"
+
+[package.dependencies]
+idna = ">=2.8"
+sniffio = ">=1.1"
+
+[package.extras]
+doc = ["packaging", "sphinx-rtd-theme", "sphinx-autodoc-typehints (>=1.2.0)"]
+test = ["coverage[toml] (>=4.5)", "hypothesis (>=4.0)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "contextlib2", "uvloop (<0.15)", "mock (>=4)", "uvloop (>=0.15)"]
+trio = ["trio (>=0.16,<0.22)"]
+
+[[package]]
+name = "appnope"
+version = "0.1.3"
+description = "Disable App Nap on macOS >= 10.9"
+category = "dev"
+optional = false
+python-versions = "*"
+
+[[package]]
+name = "argon2-cffi"
+version = "21.3.0"
+description = "The secure Argon2 password hashing algorithm."
+category = "dev"
+optional = false
+python-versions = ">=3.6"
+
+[package.dependencies]
+argon2-cffi-bindings = "*"
+
+[package.extras]
+dev = ["pre-commit", "cogapp", "tomli", "coverage[toml] (>=5.0.2)", "hypothesis", "pytest", "sphinx", "sphinx-notfound-page", "furo"]
+docs = ["sphinx", "sphinx-notfound-page", "furo"]
+tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pytest"]
+
+[[package]]
+name = "argon2-cffi-bindings"
+version = "21.2.0"
+description = "Low-level CFFI bindings for Argon2"
+category = "dev"
+optional = false
+python-versions = ">=3.6"
+
+[package.dependencies]
+cffi = ">=1.0.1"
+
+[package.extras]
+dev = ["pytest", "cogapp", "pre-commit", "wheel"]
+tests = ["pytest"]
+
+[[package]]
+name = "asttokens"
+version = "2.1.0"
+description = "Annotate AST trees with source code positions"
+category = "dev"
+optional = false
+python-versions = "*"
+
+[package.dependencies]
+six = "*"
+
+[package.extras]
+test = ["astroid (<=2.5.3)", "pytest"]
+
 [[package]]
 name = "atomicwrites"
 version = "1.4.0"
@@ -20,6 +90,29 @@ docs = ["furo", "sphinx", "zope.interface", "sphinx-notfound-page"]
 tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "zope.interface", "cloudpickle"]
 tests_no_zope = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "cloudpickle"]
 
+[[package]]
+name = "backcall"
+version = "0.2.0"
+description = "Specifications for callback functions passed in to an API"
+category = "dev"
+optional = false
+python-versions = "*"
+
+[[package]]
+name = "beautifulsoup4"
+version = "4.11.1"
+description = "Screen-scraping library"
+category = "dev"
+optional = false
+python-versions = ">=3.6.0"
+
+[package.dependencies]
+soupsieve = ">1.2"
+
+[package.extras]
+html5lib = ["html5lib"]
+lxml = ["lxml"]
+
 [[package]]
 name = "black"
 version = "22.3.0"
@@ -42,6 +135,33 @@ d = ["aiohttp (>=3.7.4)"]
 jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
 uvloop = ["uvloop (>=0.15.2)"]
 
+[[package]]
+name = "bleach"
+version = "5.0.1"
+description = "An easy safelist-based HTML-sanitizing tool."
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+six = ">=1.9.0"
+webencodings = "*"
+
+[package.extras]
+css = ["tinycss2 (>=1.1.0,<1.2)"]
+dev = ["build (==0.8.0)", "flake8 (==4.0.1)", "hashin (==0.17.0)", "pip-tools (==6.6.2)", "pytest (==7.1.2)", "Sphinx (==4.3.2)", "tox (==3.25.0)", "twine (==4.0.1)", "wheel (==0.37.1)", "black (==22.3.0)", "mypy (==0.961)"]
+
+[[package]]
+name = "cffi"
+version = "1.15.1"
+description = "Foreign Function Interface for Python calling C code."
+category = "dev"
+optional = false
+python-versions = "*"
+
+[package.dependencies]
+pycparser = "*"
+
 [[package]]
 name = "click"
 version = "8.1.2"
@@ -61,6 +181,108 @@ category = "main"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 
+[[package]]
+name = "contourpy"
+version = "1.0.6"
+description = "Python library for calculating contours of 2D quadrilateral grids"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+numpy = ">=1.16"
+
+[package.extras]
+bokeh = ["bokeh", "selenium"]
+docs = ["docutils (<0.18)", "sphinx (<=5.2.0)", "sphinx-rtd-theme"]
+test = ["pytest", "matplotlib", "pillow", "flake8", "isort"]
+test-minimal = ["pytest"]
+test-no-codebase = ["pytest", "matplotlib", "pillow"]
+
+[[package]]
+name = "cycler"
+version = "0.11.0"
+description = "Composable style cycles"
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[[package]]
+name = "debugpy"
+version = "1.6.3"
+description = "An implementation of the Debug Adapter Protocol for Python"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[[package]]
+name = "decorator"
+version = "5.1.1"
+description = "Decorators for Humans"
+category = "dev"
+optional = false
+python-versions = ">=3.5"
+
+[[package]]
+name = "defusedxml"
+version = "0.7.1"
+description = "XML bomb protection for Python stdlib modules"
+category = "dev"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+
+[[package]]
+name = "entrypoints"
+version = "0.4"
+description = "Discover and load entry points from installed packages."
+category = "dev"
+optional = false
+python-versions = ">=3.6"
+
+[[package]]
+name = "executing"
+version = "1.2.0"
+description = "Get the currently executing AST node of a frame, and other information"
+category = "dev"
+optional = false
+python-versions = "*"
+
+[package.extras]
+tests = ["asttokens", "pytest", "littleutils", "rich"]
+
+[[package]]
+name = "fastjsonschema"
+version = "2.16.2"
+description = "Fastest Python implementation of JSON schema"
+category = "dev"
+optional = false
+python-versions = "*"
+
+[package.extras]
+devel = ["colorama", "jsonschema", "json-spec", "pylint", "pytest", "pytest-benchmark", "pytest-cache", "validictory"]
+
+[[package]]
+name = "fonttools"
+version = "4.38.0"
+description = "Tools to manipulate font files"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.extras]
+all = ["fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "zopfli (>=0.1.4)", "lz4 (>=1.7.4.2)", "matplotlib", "sympy", "skia-pathops (>=0.5.0)", "uharfbuzz (>=0.23.0)", "brotlicffi (>=0.8.0)", "scipy", "brotli (>=1.0.1)", "munkres", "unicodedata2 (>=14.0.0)", "xattr"]
+graphite = ["lz4 (>=1.7.4.2)"]
+interpolatable = ["scipy", "munkres"]
+lxml = ["lxml (>=4.0,<5)"]
+pathops = ["skia-pathops (>=0.5.0)"]
+plot = ["matplotlib"]
+repacker = ["uharfbuzz (>=0.23.0)"]
+symfont = ["sympy"]
+type1 = ["xattr"]
+ufo = ["fs (>=2.2.0,<3)"]
+unicode = ["unicodedata2 (>=14.0.0)"]
+woff = ["zopfli (>=0.1.4)", "brotlicffi (>=0.8.0)", "brotli (>=1.0.1)"]
+
 [[package]]
 name = "glfw"
 version = "2.5.3"
@@ -72,6 +294,14 @@ python-versions = "*"
 [package.extras]
 preview = ["glfw-preview"]
 
+[[package]]
+name = "idna"
+version = "3.4"
+description = "Internationalized Domain Names in Applications (IDNA)"
+category = "dev"
+optional = false
+python-versions = ">=3.5"
+
 [[package]]
 name = "imgui"
 version = "1.4.1"
@@ -91,28 +321,517 @@ pyglet = ["pyopengl", "pyglet", "pyglet (>=1.5.6)"]
 sdl2 = ["pyopengl", "pysdl2"]
 
 [[package]]
-name = "linetimer"
-version = "0.1.5"
-description = "A small Python class to quickly measure the time taken while executing a block of indented lines"
-category = "main"
+name = "importlib-metadata"
+version = "5.0.0"
+description = "Read metadata from Python packages"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+zipp = ">=0.5"
+
+[package.extras]
+docs = ["sphinx (>=3.5)", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "furo", "jaraco.tidelift (>=1.4)"]
+perf = ["ipython"]
+testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "flake8 (<5)", "pytest-cov", "pytest-enabler (>=1.3)", "packaging", "pyfakefs", "flufl.flake8", "pytest-perf (>=0.9.2)", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)", "importlib-resources (>=1.3)"]
+
+[[package]]
+name = "importlib-resources"
+version = "5.10.0"
+description = "Read resources from Python packages"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
+
+[package.extras]
+docs = ["sphinx (>=3.5)", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "furo", "jaraco.tidelift (>=1.4)"]
+testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "flake8 (<5)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)"]
+
+[[package]]
+name = "ipykernel"
+version = "6.17.0"
+description = "IPython Kernel for Jupyter"
+category = "dev"
+optional = false
+python-versions = ">=3.8"
+
+[package.dependencies]
+appnope = {version = "*", markers = "platform_system == \"Darwin\""}
+debugpy = ">=1.0"
+ipython = ">=7.23.1"
+jupyter-client = ">=6.1.12"
+matplotlib-inline = ">=0.1"
+nest-asyncio = "*"
+packaging = "*"
+psutil = "*"
+pyzmq = ">=17"
+tornado = ">=6.1"
+traitlets = ">=5.1.0"
+
+[package.extras]
+docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinxcontrib-github-alt"]
+test = ["flaky", "ipyparallel", "pre-commit", "pytest-cov", "pytest-timeout", "pytest (>=7.0)"]
+
+[[package]]
+name = "ipython"
+version = "8.6.0"
+description = "IPython: Productive Interactive Computing"
+category = "dev"
+optional = false
+python-versions = ">=3.8"
+
+[package.dependencies]
+appnope = {version = "*", markers = "sys_platform == \"darwin\""}
+backcall = "*"
+colorama = {version = "*", markers = "sys_platform == \"win32\""}
+decorator = "*"
+jedi = ">=0.16"
+matplotlib-inline = "*"
+pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""}
+pickleshare = "*"
+prompt-toolkit = ">3.0.1,<3.1.0"
+pygments = ">=2.4.0"
+stack-data = "*"
+traitlets = ">=5"
+
+[package.extras]
+all = ["black", "ipykernel", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "docrepr", "matplotlib", "stack-data", "pytest (<7)", "typing-extensions", "pytest (<7.1)", "pytest-asyncio", "testpath", "nbconvert", "nbformat", "ipywidgets", "notebook", "ipyparallel", "qtconsole", "curio", "matplotlib (!=3.2.0)", "numpy (>=1.20)", "pandas", "trio"]
+black = ["black"]
+doc = ["ipykernel", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "docrepr", "matplotlib", "stack-data", "pytest (<7)", "typing-extensions", "pytest (<7.1)", "pytest-asyncio", "testpath"]
+kernel = ["ipykernel"]
+nbconvert = ["nbconvert"]
+nbformat = ["nbformat"]
+notebook = ["ipywidgets", "notebook"]
+parallel = ["ipyparallel"]
+qtconsole = ["qtconsole"]
+test = ["pytest (<7.1)", "pytest-asyncio", "testpath"]
+test_extra = ["pytest (<7.1)", "pytest-asyncio", "testpath", "curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.20)", "pandas", "trio"]
+
+[[package]]
+name = "ipython-genutils"
+version = "0.2.0"
+description = "Vestigial utilities from IPython"
+category = "dev"
+optional = false
+python-versions = "*"
+
+[[package]]
+name = "ipywidgets"
+version = "8.0.2"
+description = "Jupyter interactive widgets"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+ipykernel = ">=4.5.1"
+ipython = ">=6.1.0"
+jupyterlab-widgets = ">=3.0,<4.0"
+traitlets = ">=4.3.1"
+widgetsnbextension = ">=4.0,<5.0"
+
+[package.extras]
+test = ["jsonschema", "pytest (>=3.6.0)", "pytest-cov", "pytz"]
+
+[[package]]
+name = "jedi"
+version = "0.18.1"
+description = "An autocompletion tool for Python that can be used for text editors."
+category = "dev"
+optional = false
+python-versions = ">=3.6"
+
+[package.dependencies]
+parso = ">=0.8.0,<0.9.0"
+
+[package.extras]
+qa = ["flake8 (==3.8.3)", "mypy (==0.782)"]
+testing = ["Django (<3.1)", "colorama", "docopt", "pytest (<7.0.0)"]
+
+[[package]]
+name = "jinja2"
+version = "3.1.2"
+description = "A very fast and expressive template engine."
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+MarkupSafe = ">=2.0"
+
+[package.extras]
+i18n = ["Babel (>=2.7)"]
+
+[[package]]
+name = "jsonschema"
+version = "4.17.0"
+description = "An implementation of JSON Schema validation for Python"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+attrs = ">=17.4.0"
+importlib-resources = {version = ">=1.4.0", markers = "python_version < \"3.9\""}
+pkgutil-resolve-name = {version = ">=1.3.10", markers = "python_version < \"3.9\""}
+pyrsistent = ">=0.14.0,<0.17.0 || >0.17.0,<0.17.1 || >0.17.1,<0.17.2 || >0.17.2"
+
+[package.extras]
+format = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3987", "uri-template", "webcolors (>=1.11)"]
+format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "uri-template", "webcolors (>=1.11)"]
+
+[[package]]
+name = "jupyter"
+version = "1.0.0"
+description = "Jupyter metapackage. Install all the Jupyter components in one go."
+category = "dev"
+optional = false
+python-versions = "*"
+
+[package.dependencies]
+ipykernel = "*"
+ipywidgets = "*"
+jupyter-console = "*"
+nbconvert = "*"
+notebook = "*"
+qtconsole = "*"
+
+[[package]]
+name = "jupyter-client"
+version = "7.4.4"
+description = "Jupyter protocol implementation and client libraries"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+entrypoints = "*"
+jupyter-core = ">=4.9.2"
+nest-asyncio = ">=1.5.4"
+python-dateutil = ">=2.8.2"
+pyzmq = ">=23.0"
+tornado = ">=6.2"
+traitlets = "*"
+
+[package.extras]
+doc = ["ipykernel", "myst-parser", "sphinx-rtd-theme", "sphinx (>=1.3.6)", "sphinxcontrib-github-alt"]
+test = ["codecov", "coverage", "ipykernel (>=6.12)", "ipython", "mypy", "pre-commit", "pytest", "pytest-asyncio (>=0.18)", "pytest-cov", "pytest-timeout"]
+
+[[package]]
+name = "jupyter-console"
+version = "6.4.4"
+description = "Jupyter terminal console"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+ipykernel = "*"
+ipython = "*"
+jupyter-client = ">=7.0.0"
+prompt-toolkit = ">=2.0.0,<3.0.0 || >3.0.0,<3.0.1 || >3.0.1,<3.1.0"
+pygments = "*"
+
+[package.extras]
+test = ["pexpect"]
+
+[[package]]
+name = "jupyter-core"
+version = "4.11.2"
+description = "Jupyter core package. A base package on which Jupyter projects rely."
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+pywin32 = {version = ">=1.0", markers = "sys_platform == \"win32\" and platform_python_implementation != \"PyPy\""}
+traitlets = "*"
+
+[package.extras]
+test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"]
+
+[[package]]
+name = "jupyter-server"
+version = "1.21.0"
+description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications."
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+anyio = ">=3.1.0,<4"
+argon2-cffi = "*"
+jinja2 = "*"
+jupyter-client = ">=6.1.12"
+jupyter-core = ">=4.7.0"
+nbconvert = ">=6.4.4"
+nbformat = ">=5.2.0"
+packaging = "*"
+prometheus-client = "*"
+pywinpty = {version = "*", markers = "os_name == \"nt\""}
+pyzmq = ">=17"
+Send2Trash = "*"
+terminado = ">=0.8.3"
+tornado = ">=6.1.0"
+traitlets = ">=5.1"
+websocket-client = "*"
+
+[package.extras]
+test = ["coverage", "ipykernel", "pre-commit", "pytest-console-scripts", "pytest-cov", "pytest-mock", "pytest-timeout", "pytest-tornasync", "pytest (>=7.0)", "requests"]
+
+[[package]]
+name = "jupyterlab-pygments"
+version = "0.2.2"
+description = "Pygments theme using JupyterLab CSS variables"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[[package]]
+name = "jupyterlab-widgets"
+version = "3.0.3"
+description = "Jupyter interactive widgets for JupyterLab"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[[package]]
+name = "kiwisolver"
+version = "1.4.4"
+description = "A fast implementation of the Cassowary constraint solver"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[[package]]
+name = "linetimer"
+version = "0.1.5"
+description = "A small Python class to quickly measure the time taken while executing a block of indented lines"
+category = "main"
+optional = false
+python-versions = "*"
+
+[[package]]
+name = "markupsafe"
+version = "2.1.1"
+description = "Safely add untrusted strings to HTML/XML markup."
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[[package]]
+name = "matplotlib"
+version = "3.6.2"
+description = "Python plotting package"
+category = "main"
+optional = false
+python-versions = ">=3.8"
+
+[package.dependencies]
+contourpy = ">=1.0.1"
+cycler = ">=0.10"
+fonttools = ">=4.22.0"
+kiwisolver = ">=1.0.1"
+numpy = ">=1.19"
+packaging = ">=20.0"
+pillow = ">=6.2.0"
+pyparsing = ">=2.2.1"
+python-dateutil = ">=2.7"
+setuptools_scm = ">=7"
+
+[[package]]
+name = "matplotlib-inline"
+version = "0.1.6"
+description = "Inline Matplotlib backend for Jupyter"
+category = "dev"
+optional = false
+python-versions = ">=3.5"
+
+[package.dependencies]
+traitlets = "*"
+
+[[package]]
+name = "mistune"
+version = "2.0.4"
+description = "A sane Markdown parser with useful plugins and renderers"
+category = "dev"
+optional = false
+python-versions = "*"
+
+[[package]]
+name = "more-itertools"
+version = "8.12.0"
+description = "More routines for operating on iterables, beyond itertools"
+category = "dev"
+optional = false
+python-versions = ">=3.5"
+
+[[package]]
+name = "mypy-extensions"
+version = "0.4.3"
+description = "Experimental type system extensions for programs checked with the mypy typechecker."
+category = "dev"
+optional = false
+python-versions = "*"
+
+[[package]]
+name = "nbclassic"
+version = "0.4.8"
+description = "A web-based notebook environment for interactive computing"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+argon2-cffi = "*"
+ipykernel = "*"
+ipython-genutils = "*"
+jinja2 = "*"
+jupyter-client = ">=6.1.1"
+jupyter-core = ">=4.6.1"
+jupyter-server = ">=1.8"
+nbconvert = ">=5"
+nbformat = "*"
+nest-asyncio = ">=1.5"
+notebook-shim = ">=0.1.0"
+prometheus-client = "*"
+pyzmq = ">=17"
+Send2Trash = ">=1.8.0"
+terminado = ">=0.8.3"
+tornado = ">=6.1"
+traitlets = ">=4.2.1"
+
+[package.extras]
+docs = ["sphinx", "nbsphinx", "sphinxcontrib-github-alt", "sphinx-rtd-theme", "myst-parser"]
+json-logging = ["json-logging"]
+test = ["pytest", "coverage", "requests", "testpath", "nbval", "pytest-playwright", "pytest-cov", "pytest-tornasync", "requests-unixsocket"]
+
+[[package]]
+name = "nbclient"
+version = "0.7.0"
+description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor."
+category = "dev"
+optional = false
+python-versions = ">=3.7.0"
+
+[package.dependencies]
+jupyter-client = ">=6.1.5"
+nbformat = ">=5.0"
+nest-asyncio = "*"
+traitlets = ">=5.2.2"
+
+[package.extras]
+sphinx = ["autodoc-traits", "mock", "moto", "myst-parser", "Sphinx (>=1.7)", "sphinx-book-theme"]
+test = ["black", "check-manifest", "flake8", "ipykernel", "ipython", "ipywidgets", "mypy", "nbconvert", "pip (>=18.1)", "pre-commit", "pytest (>=4.1)", "pytest-asyncio", "pytest-cov (>=2.6.1)", "setuptools (>=60.0)", "testpath", "twine (>=1.11.0)", "xmltodict"]
+
+[[package]]
+name = "nbconvert"
+version = "7.2.3"
+description = "Converting Jupyter Notebooks"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+beautifulsoup4 = "*"
+bleach = "*"
+defusedxml = "*"
+importlib-metadata = {version = ">=3.6", markers = "python_version < \"3.10\""}
+jinja2 = ">=3.0"
+jupyter-core = ">=4.7"
+jupyterlab-pygments = "*"
+markupsafe = ">=2.0"
+mistune = ">=2.0.3,<3"
+nbclient = ">=0.5.0"
+nbformat = ">=5.1"
+packaging = "*"
+pandocfilters = ">=1.4.1"
+pygments = ">=2.4.1"
+tinycss2 = "*"
+traitlets = ">=5.0"
+
+[package.extras]
+all = ["ipykernel", "ipython", "ipywidgets (>=7)", "myst-parser", "nbsphinx (>=0.2.12)", "pre-commit", "pyppeteer (>=1,<1.1)", "pyqtwebengine (>=5.15)", "pytest", "pytest-cov", "pytest-dependency", "sphinx-rtd-theme", "sphinx (==5.0.2)", "tornado (>=6.1)"]
+docs = ["ipython", "myst-parser", "nbsphinx (>=0.2.12)", "sphinx-rtd-theme", "sphinx (==5.0.2)"]
+qtpdf = ["pyqtwebengine (>=5.15)"]
+qtpng = ["pyqtwebengine (>=5.15)"]
+serve = ["tornado (>=6.1)"]
+test = ["ipykernel", "ipywidgets (>=7)", "pre-commit", "pyppeteer (>=1,<1.1)", "pytest", "pytest-cov", "pytest-dependency"]
+webpdf = ["pyppeteer (>=1,<1.1)"]
+
+[[package]]
+name = "nbformat"
+version = "5.7.0"
+description = "The Jupyter Notebook format"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+fastjsonschema = "*"
+jsonschema = ">=2.6"
+jupyter-core = "*"
+traitlets = ">=5.1"
+
+[package.extras]
+test = ["check-manifest", "pep440", "pre-commit", "pytest", "testpath"]
+
+[[package]]
+name = "nest-asyncio"
+version = "1.5.6"
+description = "Patch asyncio to allow nested event loops"
+category = "dev"
 optional = false
-python-versions = "*"
+python-versions = ">=3.5"
 
 [[package]]
-name = "more-itertools"
-version = "8.12.0"
-description = "More routines for operating on iterables, beyond itertools"
+name = "notebook"
+version = "6.5.2"
+description = "A web-based notebook environment for interactive computing"
 category = "dev"
 optional = false
-python-versions = ">=3.5"
+python-versions = ">=3.7"
+
+[package.dependencies]
+argon2-cffi = "*"
+ipykernel = "*"
+ipython-genutils = "*"
+jinja2 = "*"
+jupyter-client = ">=5.3.4"
+jupyter-core = ">=4.6.1"
+nbclassic = ">=0.4.7"
+nbconvert = ">=5"
+nbformat = "*"
+nest-asyncio = ">=1.5"
+prometheus-client = "*"
+pyzmq = ">=17"
+Send2Trash = ">=1.8.0"
+terminado = ">=0.8.3"
+tornado = ">=6.1"
+traitlets = ">=4.2.1"
+
+[package.extras]
+docs = ["sphinx", "nbsphinx", "sphinxcontrib-github-alt", "sphinx-rtd-theme", "myst-parser"]
+json-logging = ["json-logging"]
+test = ["pytest", "coverage", "requests", "testpath", "nbval", "selenium (==4.1.5)", "pytest-cov", "requests-unixsocket"]
 
 [[package]]
-name = "mypy-extensions"
-version = "0.4.3"
-description = "Experimental type system extensions for programs checked with the mypy typechecker."
+name = "notebook-shim"
+version = "0.2.2"
+description = "A shim layer for notebook traits and config"
 category = "dev"
 optional = false
-python-versions = "*"
+python-versions = ">=3.7"
+
+[package.dependencies]
+jupyter-server = ">=1.8,<3"
+
+[package.extras]
+test = ["pytest", "pytest-console-scripts", "pytest-tornasync"]
 
 [[package]]
 name = "numpy"
@@ -126,7 +845,7 @@ python-versions = ">=3.8"
 name = "packaging"
 version = "21.3"
 description = "Core utilities for Python packages"
-category = "dev"
+category = "main"
 optional = false
 python-versions = ">=3.6"
 
@@ -154,6 +873,26 @@ pytz = ">=2020.1"
 [package.extras]
 test = ["hypothesis (>=5.5.3)", "pytest (>=6.0)", "pytest-xdist (>=1.31)"]
 
+[[package]]
+name = "pandocfilters"
+version = "1.5.0"
+description = "Utilities for writing pandoc filters in python"
+category = "dev"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+
+[[package]]
+name = "parso"
+version = "0.8.3"
+description = "A Python Parser"
+category = "dev"
+optional = false
+python-versions = ">=3.6"
+
+[package.extras]
+qa = ["flake8 (==3.8.3)", "mypy (==0.782)"]
+testing = ["docopt", "pytest (<6.0.0)"]
+
 [[package]]
 name = "pathspec"
 version = "0.9.0"
@@ -162,6 +901,45 @@ category = "dev"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
 
+[[package]]
+name = "pexpect"
+version = "4.8.0"
+description = "Pexpect allows easy control of interactive console applications."
+category = "dev"
+optional = false
+python-versions = "*"
+
+[package.dependencies]
+ptyprocess = ">=0.5"
+
+[[package]]
+name = "pickleshare"
+version = "0.7.5"
+description = "Tiny 'shelve'-like database with concurrency support"
+category = "dev"
+optional = false
+python-versions = "*"
+
+[[package]]
+name = "pillow"
+version = "9.3.0"
+description = "Python Imaging Library (Fork)"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.extras]
+docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-issues (>=3.0.1)", "sphinx-removed-in", "sphinxext-opengraph"]
+tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
+
+[[package]]
+name = "pkgutil-resolve-name"
+version = "1.3.10"
+description = "Resolve a name to an object."
+category = "dev"
+optional = false
+python-versions = ">=3.6"
+
 [[package]]
 name = "platformdirs"
 version = "2.5.1"
@@ -185,6 +963,28 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 [package.extras]
 dev = ["pre-commit", "tox"]
 
+[[package]]
+name = "prometheus-client"
+version = "0.15.0"
+description = "Python client for the Prometheus monitoring system."
+category = "dev"
+optional = false
+python-versions = ">=3.6"
+
+[package.extras]
+twisted = ["twisted"]
+
+[[package]]
+name = "prompt-toolkit"
+version = "3.0.32"
+description = "Library for building powerful interactive command lines in Python"
+category = "dev"
+optional = false
+python-versions = ">=3.6.2"
+
+[package.dependencies]
+wcwidth = "*"
+
 [[package]]
 name = "protobuf"
 version = "3.20.0"
@@ -193,6 +993,36 @@ category = "main"
 optional = false
 python-versions = ">=3.7"
 
+[[package]]
+name = "psutil"
+version = "5.9.3"
+description = "Cross-platform lib for process and system monitoring in Python."
+category = "dev"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+
+[package.extras]
+test = ["ipaddress", "mock", "enum34", "pywin32", "wmi"]
+
+[[package]]
+name = "ptyprocess"
+version = "0.7.0"
+description = "Run a subprocess in a pseudo terminal"
+category = "dev"
+optional = false
+python-versions = "*"
+
+[[package]]
+name = "pure-eval"
+version = "0.2.2"
+description = "Safely evaluate AST nodes without side effects"
+category = "dev"
+optional = false
+python-versions = "*"
+
+[package.extras]
+tests = ["pytest"]
+
 [[package]]
 name = "py"
 version = "1.11.0"
@@ -201,6 +1031,25 @@ category = "dev"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 
+[[package]]
+name = "pycparser"
+version = "2.21"
+description = "C parser in Python"
+category = "dev"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+
+[[package]]
+name = "pygments"
+version = "2.13.0"
+description = "Pygments is a syntax highlighting package written in Python."
+category = "dev"
+optional = false
+python-versions = ">=3.6"
+
+[package.extras]
+plugins = ["importlib-metadata"]
+
 [[package]]
 name = "pyopencl"
 version = "2022.1"
@@ -230,13 +1079,21 @@ python-versions = "*"
 name = "pyparsing"
 version = "3.0.8"
 description = "pyparsing module - Classes and methods to define and execute parsing grammars"
-category = "dev"
+category = "main"
 optional = false
 python-versions = ">=3.6.8"
 
 [package.extras]
 diagrams = ["railroad-diagrams", "jinja2"]
 
+[[package]]
+name = "pyrsistent"
+version = "0.19.2"
+description = "Persistent/Functional/Immutable data structures"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
 [[package]]
 name = "pytest"
 version = "5.4.3"
@@ -291,6 +1148,22 @@ category = "main"
 optional = false
 python-versions = "*"
 
+[[package]]
+name = "pywin32"
+version = "304"
+description = "Python for Window Extensions"
+category = "dev"
+optional = false
+python-versions = "*"
+
+[[package]]
+name = "pywinpty"
+version = "2.0.9"
+description = "Pseudo terminal support for Windows from Python."
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
 [[package]]
 name = "pyyaml"
 version = "6.0"
@@ -299,6 +1172,102 @@ category = "main"
 optional = false
 python-versions = ">=3.6"
 
+[[package]]
+name = "pyzmq"
+version = "24.0.1"
+description = "Python bindings for 0MQ"
+category = "dev"
+optional = false
+python-versions = ">=3.6"
+
+[package.dependencies]
+cffi = {version = "*", markers = "implementation_name == \"pypy\""}
+py = {version = "*", markers = "implementation_name == \"pypy\""}
+
+[[package]]
+name = "qtconsole"
+version = "5.4.0"
+description = "Jupyter Qt console"
+category = "dev"
+optional = false
+python-versions = ">= 3.7"
+
+[package.dependencies]
+ipykernel = ">=4.1"
+ipython-genutils = "*"
+jupyter-client = ">=4.1"
+jupyter-core = "*"
+pygments = "*"
+pyzmq = ">=17.1"
+qtpy = ">=2.0.1"
+traitlets = "<5.2.1 || >5.2.1,<5.2.2 || >5.2.2"
+
+[package.extras]
+doc = ["Sphinx (>=1.3)"]
+test = ["flaky", "pytest", "pytest-qt"]
+
+[[package]]
+name = "qtpy"
+version = "2.2.1"
+description = "Provides an abstraction layer on top of the various Qt bindings (PyQt5/6 and PySide2/6)."
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+packaging = "*"
+
+[package.extras]
+test = ["pytest (>=6,!=7.0.0,!=7.0.1)", "pytest-cov (>=3.0.0)", "pytest-qt"]
+
+[[package]]
+name = "seaborn"
+version = "0.12.1"
+description = "Statistical data visualization"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+matplotlib = ">=3.1,<3.6.1 || >3.6.1"
+numpy = ">=1.17"
+pandas = ">=0.25"
+
+[package.extras]
+dev = ["pytest", "pytest-cov", "pytest-xdist", "flake8", "mypy", "pandas-stubs", "pre-commit"]
+docs = ["numpydoc", "nbconvert", "ipykernel", "sphinx-copybutton", "sphinx-issues", "sphinx-design", "pyyaml", "pydata_sphinx_theme (==0.10.0rc2)"]
+stats = ["scipy (>=1.3)", "statsmodels (>=0.10)"]
+
+[[package]]
+name = "send2trash"
+version = "1.8.0"
+description = "Send file to trash natively under Mac OS X, Windows and Linux."
+category = "dev"
+optional = false
+python-versions = "*"
+
+[package.extras]
+nativelib = ["pyobjc-framework-cocoa", "pywin32"]
+objc = ["pyobjc-framework-cocoa"]
+win32 = ["pywin32"]
+
+[[package]]
+name = "setuptools-scm"
+version = "7.0.5"
+description = "the blessed package to manage your versions by scm tags"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+packaging = ">=20.0"
+tomli = ">=1.0.0"
+typing-extensions = "*"
+
+[package.extras]
+test = ["pytest (>=6.2)", "virtualenv (>20)"]
+toml = ["setuptools (>=42)"]
+
 [[package]]
 name = "six"
 version = "1.16.0"
@@ -307,14 +1276,86 @@ category = "main"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
 
+[[package]]
+name = "sniffio"
+version = "1.3.0"
+description = "Sniff out which async library your code is running under"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[[package]]
+name = "soupsieve"
+version = "2.3.2.post1"
+description = "A modern CSS selector implementation for Beautiful Soup."
+category = "dev"
+optional = false
+python-versions = ">=3.6"
+
+[[package]]
+name = "stack-data"
+version = "0.6.0"
+description = "Extract data from python stack frames and tracebacks for informative displays"
+category = "dev"
+optional = false
+python-versions = "*"
+
+[package.dependencies]
+asttokens = ">=2.1.0"
+executing = ">=1.2.0"
+pure-eval = "*"
+
+[package.extras]
+tests = ["pytest", "typeguard", "pygments", "littleutils", "cython"]
+
+[[package]]
+name = "terminado"
+version = "0.17.0"
+description = "Tornado websocket backend for the Xterm.js Javascript terminal emulator library."
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+ptyprocess = {version = "*", markers = "os_name != \"nt\""}
+pywinpty = {version = ">=1.1.0", markers = "os_name == \"nt\""}
+tornado = ">=6.1.0"
+
+[package.extras]
+docs = ["pydata-sphinx-theme", "sphinx"]
+test = ["pre-commit", "pytest-timeout", "pytest (>=7.0)"]
+
+[[package]]
+name = "tinycss2"
+version = "1.2.1"
+description = "A tiny CSS parser"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+webencodings = ">=0.4"
+
+[package.extras]
+doc = ["sphinx", "sphinx-rtd-theme"]
+test = ["pytest", "isort", "flake8"]
+
 [[package]]
 name = "tomli"
 version = "2.0.1"
 description = "A lil' TOML parser"
-category = "dev"
+category = "main"
 optional = false
 python-versions = ">=3.7"
 
+[[package]]
+name = "tornado"
+version = "6.2"
+description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
+category = "dev"
+optional = false
+python-versions = ">= 3.7"
+
 [[package]]
 name = "tqdm"
 version = "4.64.0"
@@ -332,6 +1373,18 @@ notebook = ["ipywidgets (>=6)"]
 slack = ["slack-sdk"]
 telegram = ["requests"]
 
+[[package]]
+name = "traitlets"
+version = "5.5.0"
+description = ""
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[package.extras]
+docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
+test = ["pre-commit", "pytest"]
+
 [[package]]
 name = "typing-extensions"
 version = "4.1.1"
@@ -348,12 +1401,58 @@ category = "dev"
 optional = false
 python-versions = "*"
 
+[[package]]
+name = "webencodings"
+version = "0.5.1"
+description = "Character encoding aliases for legacy web content"
+category = "dev"
+optional = false
+python-versions = "*"
+
+[[package]]
+name = "websocket-client"
+version = "1.4.2"
+description = "WebSocket client for Python with low level API options"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[package.extras]
+docs = ["Sphinx (>=3.4)", "sphinx-rtd-theme (>=0.5)"]
+optional = ["python-socks", "wsaccel"]
+test = ["websockets"]
+
+[[package]]
+name = "widgetsnbextension"
+version = "4.0.3"
+description = "Jupyter interactive widgets for Jupyter Notebook"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[[package]]
+name = "zipp"
+version = "3.10.0"
+description = "Backport of pathlib-compatible object wrapper for zip files"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[package.extras]
+docs = ["sphinx (>=3.5)", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "furo", "jaraco.tidelift (>=1.4)"]
+testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "flake8 (<5)", "pytest-cov", "pytest-enabler (>=1.3)", "jaraco.itertools", "func-timeout", "jaraco.functools", "more-itertools", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)"]
+
 [metadata]
 lock-version = "1.1"
 python-versions = "^3.8"
-content-hash = "eb564316a5b89a2a8f4df1498bd8b1ed4ff509e3b1f5ea189802165660de1d64"
+content-hash = "82ac311ee409dc7f96dab11c0246afd83fe7a98ef91e4acb8ccf8e45466ae804"
 
 [metadata.files]
+anyio = []
+appnope = []
+argon2-cffi = []
+argon2-cffi-bindings = []
+asttokens = []
 atomicwrites = [
     {file = "atomicwrites-1.4.0-py2.py3-none-any.whl", hash = "sha256:6d1784dea7c0c8d4a5172b6c620f40b6e4cbfdf96d783691f2e1302a7b88e197"},
     {file = "atomicwrites-1.4.0.tar.gz", hash = "sha256:ae70396ad1a434f9c7046fd2dd196fc04b12f9e91ffb859164193be8b6168a7a"},
@@ -362,6 +1461,8 @@ attrs = [
     {file = "attrs-21.4.0-py2.py3-none-any.whl", hash = "sha256:2d27e3784d7a565d36ab851fe94887c5eccd6a463168875832a1be79c82828b4"},
     {file = "attrs-21.4.0.tar.gz", hash = "sha256:626ba8234211db98e869df76230a137c4c40a12d72445c45d5f5b716f076e2fd"},
 ]
+backcall = []
+beautifulsoup4 = []
 black = [
     {file = "black-22.3.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:2497f9c2386572e28921fa8bec7be3e51de6801f7459dffd6e62492531c47e09"},
     {file = "black-22.3.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:5795a0375eb87bfe902e80e0c8cfaedf8af4d49694d69161e5bd3206c18618bb"},
@@ -387,6 +1488,8 @@ black = [
     {file = "black-22.3.0-py3-none-any.whl", hash = "sha256:bc58025940a896d7e5356952228b68f793cf5fcb342be703c3a2669a1488cb72"},
     {file = "black-22.3.0.tar.gz", hash = "sha256:35020b8886c022ced9282b51b5a875b6d1ab0c387b31a065b84db7c33085ca79"},
 ]
+bleach = []
+cffi = []
 click = [
     {file = "click-8.1.2-py3-none-any.whl", hash = "sha256:24e1a4a9ec5bf6299411369b208c1df2188d9eb8d916302fe6bf03faed227f1e"},
     {file = "click-8.1.2.tar.gz", hash = "sha256:479707fe14d9ec9a0757618b7a100a0ae4c4e236fac5b7f80ca68028141a1a72"},
@@ -395,6 +1498,15 @@ colorama = [
     {file = "colorama-0.4.4-py2.py3-none-any.whl", hash = "sha256:9f47eda37229f68eee03b24b9748937c7dc3868f906e8ba69fbcbdd3bc5dc3e2"},
     {file = "colorama-0.4.4.tar.gz", hash = "sha256:5941b2b48a20143d2267e95b1c2a7603ce057ee39fd88e7329b0c292aa16869b"},
 ]
+contourpy = []
+cycler = []
+debugpy = []
+decorator = []
+defusedxml = []
+entrypoints = []
+executing = []
+fastjsonschema = []
+fonttools = []
 glfw = [
     {file = "glfw-2.5.3-py2.py27.py3.py30.py31.py32.py33.py34.py35.py36.py37.py38-none-macosx_10_6_intel.whl", hash = "sha256:797528a7e433c1684cad2807838824e73c0c1fd8e12b01ee0b22ad203f46bf87"},
     {file = "glfw-2.5.3-py2.py27.py3.py30.py31.py32.py33.py34.py35.py36.py37.py38-none-macosx_11_0_arm64.whl", hash = "sha256:1763af8d00e1b1517fccceb768e5470ce754a993bd8020878dca444f9523726b"},
@@ -406,6 +1518,7 @@ glfw = [
     {file = "glfw-2.5.3-py2.py27.py3.py30.py31.py32.py33.py34.py35.py36.py37.py38-none-win_amd64.whl", hash = "sha256:fe9500b9e31847987a77e686569ad1470435688eaa5ebb87ea80cff2ff3300f3"},
     {file = "glfw-2.5.3.tar.gz", hash = "sha256:02cf5e7523e867f5253d2df065cb4590250ce744ca2f8bfdcbad4a1727f1e0b5"},
 ]
+idna = []
 imgui = [
     {file = "imgui-1.4.1-cp27-cp27m-macosx_10_9_x86_64.whl", hash = "sha256:a07aa19cd61baaa854cdf241484c88f5a07f6765fb175129bc72d820c608b524"},
     {file = "imgui-1.4.1-cp27-cp27m-manylinux1_i686.whl", hash = "sha256:4aff47bbc35adf56c3bca6a5aabe584d671c972bbbb85056cfc789afc41bf901"},
@@ -449,10 +1562,31 @@ imgui = [
     {file = "imgui-1.4.1-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:9b90c89f420eed37c09a618e9fe93066d1c4cbbd27da4da9095d1d3c98860d1f"},
     {file = "imgui-1.4.1.tar.gz", hash = "sha256:012fd8d581171bd04ebfa649ca92c7f382a2806c7027bc7c31c8b39899899e7d"},
 ]
+importlib-metadata = []
+importlib-resources = []
+ipykernel = []
+ipython = []
+ipython-genutils = []
+ipywidgets = []
+jedi = []
+jinja2 = []
+jsonschema = []
+jupyter = []
+jupyter-client = []
+jupyter-console = []
+jupyter-core = []
+jupyter-server = []
+jupyterlab-pygments = []
+jupyterlab-widgets = []
+kiwisolver = []
 linetimer = [
     {file = "linetimer-0.1.5-py3-none-any.whl", hash = "sha256:18f917bf9c3afe2ba2375dbf97fd01189c1967fe4c49382036b03201e03c2889"},
     {file = "linetimer-0.1.5.tar.gz", hash = "sha256:f9723860bbbeae05e4e1383b237e5b73f98c71b73e50dc8c2856a0580269752b"},
 ]
+markupsafe = []
+matplotlib = []
+matplotlib-inline = []
+mistune = []
 more-itertools = [
     {file = "more-itertools-8.12.0.tar.gz", hash = "sha256:7dc6ad46f05f545f900dd59e8dfb4e84a4827b97b3cfecb175ea0c7d247f6064"},
     {file = "more_itertools-8.12.0-py3-none-any.whl", hash = "sha256:43e6dd9942dffd72661a2c4ef383ad7da1e6a3e968a927ad7a6083ab410a688b"},
@@ -461,6 +1595,13 @@ mypy-extensions = [
     {file = "mypy_extensions-0.4.3-py2.py3-none-any.whl", hash = "sha256:090fedd75945a69ae91ce1303b5824f428daf5a028d2f6ab8a299250a846f15d"},
     {file = "mypy_extensions-0.4.3.tar.gz", hash = "sha256:2d82818f5bb3e369420cb3c4060a7970edba416647068eb4c5343488a6c604a8"},
 ]
+nbclassic = []
+nbclient = []
+nbconvert = []
+nbformat = []
+nest-asyncio = []
+notebook = []
+notebook-shim = []
 numpy = [
     {file = "numpy-1.22.3-cp310-cp310-macosx_10_14_x86_64.whl", hash = "sha256:92bfa69cfbdf7dfc3040978ad09a48091143cffb778ec3b03fa170c494118d75"},
     {file = "numpy-1.22.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:8251ed96f38b47b4295b1ae51631de7ffa8260b5b087808ef09a39a9d66c97ab"},
@@ -510,10 +1651,16 @@ pandas = [
     {file = "pandas-1.4.2-cp39-cp39-win_amd64.whl", hash = "sha256:09d8be7dd9e1c4c98224c4dfe8abd60d145d934e9fc1f5f411266308ae683e6a"},
     {file = "pandas-1.4.2.tar.gz", hash = "sha256:92bc1fc585f1463ca827b45535957815b7deb218c549b7c18402c322c7549a12"},
 ]
+pandocfilters = []
+parso = []
 pathspec = [
     {file = "pathspec-0.9.0-py2.py3-none-any.whl", hash = "sha256:7d15c4ddb0b5c802d161efc417ec1a2558ea2653c2e8ad9c19098201dc1c993a"},
     {file = "pathspec-0.9.0.tar.gz", hash = "sha256:e564499435a2673d586f6b2130bb5b95f04a3ba06f81b8f895b651a3c76aabb1"},
 ]
+pexpect = []
+pickleshare = []
+pillow = []
+pkgutil-resolve-name = []
 platformdirs = [
     {file = "platformdirs-2.5.1-py3-none-any.whl", hash = "sha256:bcae7cab893c2d310a711b70b24efb93334febe65f8de776ee320b517471e227"},
     {file = "platformdirs-2.5.1.tar.gz", hash = "sha256:7535e70dfa32e84d4b34996ea99c5e432fa29a708d0f4e394bbcb2a8faa4f16d"},
@@ -522,6 +1669,8 @@ pluggy = [
     {file = "pluggy-0.13.1-py2.py3-none-any.whl", hash = "sha256:966c145cd83c96502c3c3868f50408687b38434af77734af1e9ca461a4081d2d"},
     {file = "pluggy-0.13.1.tar.gz", hash = "sha256:15b2acde666561e1298d71b523007ed7364de07029219b604cf808bfa1c765b0"},
 ]
+prometheus-client = []
+prompt-toolkit = []
 protobuf = [
     {file = "protobuf-3.20.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:9d0f3aca8ca51c8b5e204ab92bd8afdb2a8e3df46bd0ce0bd39065d79aabcaa4"},
     {file = "protobuf-3.20.0-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:001c2160c03b6349c04de39cf1a58e342750da3632f6978a1634a3dcca1ec10e"},
@@ -548,10 +1697,15 @@ protobuf = [
     {file = "protobuf-3.20.0-py2.py3-none-any.whl", hash = "sha256:4eda68bd9e2a4879385e6b1ea528c976f59cd9728382005cc54c28bcce8db983"},
     {file = "protobuf-3.20.0.tar.gz", hash = "sha256:71b2c3d1cd26ed1ec7c8196834143258b2ad7f444efff26fdc366c6f5e752702"},
 ]
+psutil = []
+ptyprocess = []
+pure-eval = []
 py = [
     {file = "py-1.11.0-py2.py3-none-any.whl", hash = "sha256:607c53218732647dff4acdfcd50cb62615cedf612e72d1724fb1a0cc6405b378"},
     {file = "py-1.11.0.tar.gz", hash = "sha256:51c75c4126074b472f746a24399ad32f6053d1b34b68d2fa41e558e6f4a98719"},
 ]
+pycparser = []
+pygments = []
 pyopencl = [
     {file = "pyopencl-2022.1-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4bea0e965252f477204a87cc73eca492de5c30252198bc6679ef4968880a2443"},
     {file = "pyopencl-2022.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1ab792ff11a7ff271b1ba39686bd7aae271168e813a7e771bff6aa611ada383b"},
@@ -570,6 +1724,7 @@ pyparsing = [
     {file = "pyparsing-3.0.8-py3-none-any.whl", hash = "sha256:ef7b523f6356f763771559412c0d7134753f037822dad1b16945b7b846f7ad06"},
     {file = "pyparsing-3.0.8.tar.gz", hash = "sha256:7bf433498c016c4314268d95df76c81b842a4cb2b276fa3312cfb1e1d85f6954"},
 ]
+pyrsistent = []
 pytest = [
     {file = "pytest-5.4.3-py3-none-any.whl", hash = "sha256:5c0db86b698e8f170ba4582a492248919255fcd4c79b1ee64ace34301fb589a1"},
     {file = "pytest-5.4.3.tar.gz", hash = "sha256:7979331bfcba207414f5e1263b5a0f8f521d0f457318836a7355531ed1a4c7d8"},
@@ -585,6 +1740,8 @@ pytz = [
     {file = "pytz-2022.1-py2.py3-none-any.whl", hash = "sha256:e68985985296d9a66a881eb3193b0906246245294a881e7c8afe623866ac6a5c"},
     {file = "pytz-2022.1.tar.gz", hash = "sha256:1e760e2fe6a8163bc0b3d9a19c4f84342afa0a2affebfaa84b01b978a02ecaa7"},
 ]
+pywin32 = []
+pywinpty = []
 pyyaml = [
     {file = "PyYAML-6.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:d4db7c7aef085872ef65a8fd7d6d09a14ae91f691dec3e87ee5ee0539d516f53"},
     {file = "PyYAML-6.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9df7ed3b3d2e0ecfe09e14741b857df43adb5a3ddadc919a2d94fbdf78fea53c"},
@@ -620,18 +1777,31 @@ pyyaml = [
     {file = "PyYAML-6.0-cp39-cp39-win_amd64.whl", hash = "sha256:b3d267842bf12586ba6c734f89d1f5b871df0273157918b0ccefa29deb05c21c"},
     {file = "PyYAML-6.0.tar.gz", hash = "sha256:68fb519c14306fec9720a2a5b45bc9f0c8d1b9c72adf45c37baedfcd949c35a2"},
 ]
+pyzmq = []
+qtconsole = []
+qtpy = []
+seaborn = []
+send2trash = []
+setuptools-scm = []
 six = [
     {file = "six-1.16.0-py2.py3-none-any.whl", hash = "sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254"},
     {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
 ]
+sniffio = []
+soupsieve = []
+stack-data = []
+terminado = []
+tinycss2 = []
 tomli = [
     {file = "tomli-2.0.1-py3-none-any.whl", hash = "sha256:939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc"},
     {file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"},
 ]
+tornado = []
 tqdm = [
     {file = "tqdm-4.64.0-py2.py3-none-any.whl", hash = "sha256:74a2cdefe14d11442cedf3ba4e21a3b84ff9a2dbdc6cfae2c34addb2a14a5ea6"},
     {file = "tqdm-4.64.0.tar.gz", hash = "sha256:40be55d30e200777a307a7585aee69e4eabb46b4ec6a4b4a5f2d9f11e7d5408d"},
 ]
+traitlets = []
 typing-extensions = [
     {file = "typing_extensions-4.1.1-py3-none-any.whl", hash = "sha256:21c85e0fe4b9a155d0799430b0ad741cdce7e359660ccbd8b530613e8df88ce2"},
     {file = "typing_extensions-4.1.1.tar.gz", hash = "sha256:1a9462dcc3347a79b1f1c0271fbe79e844580bb598bafa1ed208b94da3cdcd42"},
@@ -640,3 +1810,7 @@ wcwidth = [
     {file = "wcwidth-0.2.5-py2.py3-none-any.whl", hash = "sha256:beb4802a9cebb9144e99086eff703a642a13d6a0052920003a230f3294bbe784"},
     {file = "wcwidth-0.2.5.tar.gz", hash = "sha256:c4d647b99872929fdb7bdcaa4fbe7f01413ed3d98077df798530e5b04f116c83"},
 ]
+webencodings = []
+websocket-client = []
+widgetsnbextension = []
+zipp = []
diff --git a/pyproject.toml b/pyproject.toml
index b5ee225..54f7cdd 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -19,10 +19,13 @@ pyopencl = "^2022.1"
 numpy = "^1.22.3"
 protobuf = "^3.20.0"
 linetimer = "^0.1.5"
+matplotlib = "^3.6.2"
+seaborn = "^0.12.1"
 
 [tool.poetry.dev-dependencies]
 pytest = "^5.2"
 black = "^22.1.0"
+jupyter = "^1.0.0"
 
 [build-system]
 requires = [