Initial commit

41fd4304 · Thijs vanEeden · 41fd4304 · 41fd4304 · 41fd4304 · 41fd4304
Commit 41fd4304 authored 3 years ago by Thijs vanEeden
--- a/.ipynb_checkpoints/checks-checkpoint.ipynb
+++ b/.ipynb_checkpoints/checks-checkpoint.ipynb
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9ca0bae6-c86d-4348-b9ad-a76bafb09cb7",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'str' object is not callable",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-2-211c2e002ecf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mkm3dia\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mdb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkm3dia\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDBManager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msession_cookie\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: 'str' object is not callable"
+     ]
+    }
+   ],
+   "source": [
+    "import km3dia\n",
+    "db = km3dia.DBManager()\n",
+    "df.session_cookie()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c2433240-3251-42d5-bcfc-94cdd236de9c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "km3net-env-1.9",
+   "language": "python",
+   "name": "km3net-env-1.9"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
+%% Cell type:code id:9ca0bae6-c86d-4348-b9ad-a76bafb09cb7 tags:
+
+``` python
+import km3dia
+db = km3dia.DBManager()
+df.session_cookie()
+```
+
+%% Output
+
+    ---------------------------------------------------------------------------
+    TypeError                                 Traceback (most recent call last)
+    <ipython-input-2-211c2e002ecf> in <module>
+          1 import km3dia
+          2 db = km3dia.DBManager()
+    ----> 3 db.session_cookie()
+
+    TypeError: 'str' object is not callable
+
+%% Cell type:code id:c2433240-3251-42d5-bcfc-94cdd236de9c tags:
+
+``` python
+```
--- a/.ipynb_checkpoints/pmt_gain-checkpoint.ipynb
+++ b/.ipynb_checkpoints/pmt_gain-checkpoint.ipynb
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6b9d9ddb-8cf4-4c9c-bdc0-0de78f0ec35d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#pip install km3dia"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4d699873-c366-4870-a262-56b4c1312855",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#pip install xmltodict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9034b755-3525-4a06-bbd5-59fe2a1ce3d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import xmltodict\n",
+    "\n",
+    "import km3db\n",
+    "import km3dia"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d9bf120b-39cc-4adc-8c2f-ecb425d7cd18",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dia = km3dia.DBManager(container='pd')\n",
+    "DOMInt = km3dia.DOMIntegrationSummary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "1959e023-8415-465c-90a2-7da4b9c3a93b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>SERIAL</th>\n",
+       "      <th>PBS</th>\n",
+       "      <th>VARIANT</th>\n",
+       "      <th>VERSION</th>\n",
+       "      <th>SiteID</th>\n",
+       "      <th>PhaseID</th>\n",
+       "      <th>Complete</th>\n",
+       "      <th>Registered</th>\n",
+       "      <th>NotConform</th>\n",
+       "      <th>Repair</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Gain 21</th>\n",
+       "      <th>Gain 22</th>\n",
+       "      <th>Gain 23</th>\n",
+       "      <th>Gain 24</th>\n",
+       "      <th>Gain 25</th>\n",
+       "      <th>Gain 26</th>\n",
+       "      <th>Gain 27</th>\n",
+       "      <th>Gain 28</th>\n",
+       "      <th>Gain 29</th>\n",
+       "      <th>Gain 30</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UPI</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3.4/TSFP/1.909</th>\n",
+       "      <td>909</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>TSFP</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>14</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.273</td>\n",
+       "      <td>1.491</td>\n",
+       "      <td>1.364</td>\n",
+       "      <td>1.102</td>\n",
+       "      <td>1.654</td>\n",
+       "      <td>1.623</td>\n",
+       "      <td>1.732</td>\n",
+       "      <td>1.285</td>\n",
+       "      <td>1.403</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 182 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               SERIAL  PBS VARIANT VERSION SiteID PhaseID Complete Registered  \\\n",
+       "UPI                                                                             \n",
+       "3.4/TSFP/1.909    909  3.4    TSFP       1      1      14        1          0   \n",
+       "\n",
+       "               NotConform Repair  ... Gain 21 Gain 22 Gain 23 Gain 24 Gain 25  \\\n",
+       "UPI                               ...                                           \n",
+       "3.4/TSFP/1.909          0      0  ...       2   1.273   1.491   1.364   1.102   \n",
+       "\n",
+       "               Gain 26 Gain 27 Gain 28 Gain 29 Gain 30  \n",
+       "UPI                                                     \n",
+       "3.4/TSFP/1.909   1.654   1.623   1.732   1.285   1.403  \n",
+       "\n",
+       "[1 rows x 182 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Get measurement results\n",
+    "df_tests = DOMInt.test_results\n",
+    "df_tests = df_tests.reset_index().set_index('UPI')\n",
+    "\n",
+    "# display( df_tests[ df_tests[\"SiteID\"] == \"1\" ] )\n",
+    "# display( df_tests[\"SiteID\"] )\n",
+    "# display( df_tests )\n",
+    "display( df_tests[ df_tests[\"SERIAL\"] == \"909\" ] )\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "8ba41596-7e7a-4925-80f9-98c697e705f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Generate the list of relevant keys\n",
+    "gain_keys = [key for key in df_tests.columns if key.lower().find('gain') != -1]\n",
+    "dc_keys = [key for key in df_tests.columns if key.lower().find('dark') != -1]\n",
+    "DOM_key = ['SERIAL', 'SiteID']\n",
+    "pmt_keys = gain_keys + dc_keys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "7f681bc2-bfb5-4232-8983-de877bf2f1d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Cut the dataframe to no NaN\n",
+    "df_pmt   = df_tests.dropna(subset=pmt_keys)\n",
+    "df_pmt   = df_pmt[pmt_keys + DOM_key]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "5d602ade-f9d6-4303-ab4c-94938eb0660a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert pmt results result branches to float\n",
+    "df_pmt = df_pmt.astype(dict(zip(pmt_keys, [float]*len(pmt_keys))))\n",
+    "\n",
+    "# display( df_pmt )\n",
+    "# display( df_pmt[ df_pmt[\"SiteID\"] == \"1\" ] )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "357eef49-fcb1-4db6-abab-133bbbf7daf1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# df_bad_channels = df_pmt[ df_pmt[\"Gain 22\"] > 1.6 ]\n",
+    "# # df_bad_channels[ df_bad_channels[\"SiteName\"] ==\"Amsterdam\"]\n",
+    "# display(df_bad_channels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "7c303c75-fa66-48f2-a09f-a5bf146538bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def count_chan_above_threshold( threshold = 1.7):\n",
+    "    \n",
+    "    doms = []\n",
+    "    bad_channels = []\n",
+    "    \n",
+    "    for index, row in df_pmt.iterrows():\n",
+    "        bad = 0\n",
+    "        \n",
+    "#         if row[\"SiteID\"] != \"1\": continue # only amsterdam\n",
+    "    \n",
+    "        for gain_key in gain_keys:\n",
+    "            if row[gain_key] > threshold: bad +=1\n",
+    "            \n",
+    "#         if bad == 31: continue # why?    \n",
+    "        \n",
+    "        doms.append( row[\"SERIAL\"] )\n",
+    "        bad_channels.append( bad )\n",
+    "\n",
+    "    return doms, bad_channels\n",
+    "        \n",
+    "doms, bad_channels = count_chan_above_threshold()\n",
+    "bad_doms = [i for i,v in enumerate(bad_channels) if v > 3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "4a9a5b4b-4d41-450d-bc2e-fdb10a870ed8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "failed doms 20\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.hist(bad_channels, [-0.5, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5])\n",
+    "plt.plot(3, 0, 3, 40, color='red')\n",
+    "plt.show()\n",
+    "print(\"failed doms\", len(bad_doms))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4eaed37c-3cae-44ef-88db-fac0ce64acbf",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ca1f27e5-fecb-42d5-8e17-dd58da923f08",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "km3net-env-1.9",
+   "language": "python",
+   "name": "km3net-env-1.9"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
+%% Cell type:code id:6b9d9ddb-8cf4-4c9c-bdc0-0de78f0ec35d tags:
+
+``` python
+#pip install km3dia
+```
+
+%% Cell type:code id:4d699873-c366-4870-a262-56b4c1312855 tags:
+
+``` python
+#pip install xmltodict
+```
+
+%% Cell type:code id:9034b755-3525-4a06-bbd5-59fe2a1ce3d3 tags:
+
+``` python
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import xmltodict
+
+import km3db
+import km3dia
+```
+
+%% Cell type:code id:d9bf120b-39cc-4adc-8c2f-ecb425d7cd18 tags:
+
+``` python
+dia = km3dia.DBManager(container='pd')
+DOMInt = km3dia.DOMIntegrationSummary()
+```
+
+%% Cell type:code id:1959e023-8415-465c-90a2-7da4b9c3a93b tags:
+
+``` python
+#Get measurement results
+df_tests = DOMInt.test_results
+df_tests = df_tests.reset_index().set_index('UPI')
+
+# display( df_tests[ df_tests["SiteID"] == "1" ] )
+# display( df_tests["SiteID"] )
+# display( df_tests )
+display( df_tests[ df_tests["SERIAL"] == "909" ] )
+```
+
+%% Output
+
+
+%% Cell type:code id:8ba41596-7e7a-4925-80f9-98c697e705f4 tags:
+
+``` python
+# Generate the list of relevant keys
+gain_keys = [key for key in df_tests.columns if key.lower().find('gain') != -1]
+dc_keys = [key for key in df_tests.columns if key.lower().find('dark') != -1]
+DOM_key = ['SERIAL', 'SiteID']
+pmt_keys = gain_keys + dc_keys
+```
+
+%% Cell type:code id:7f681bc2-bfb5-4232-8983-de877bf2f1d4 tags:
+
+``` python
+# Cut the dataframe to no NaN
+df_pmt   = df_tests.dropna(subset=pmt_keys)
+df_pmt   = df_pmt[pmt_keys + DOM_key]
+```
+
+%% Cell type:code id:5d602ade-f9d6-4303-ab4c-94938eb0660a tags:
+
+``` python
+# Convert pmt results result branches to float
+df_pmt = df_pmt.astype(dict(zip(pmt_keys, [float]*len(pmt_keys))))
+
+# display( df_pmt )
+# display( df_pmt[ df_pmt["SiteID"] == "1" ] )
+```
+
+%% Cell type:code id:357eef49-fcb1-4db6-abab-133bbbf7daf1 tags:
+
+``` python
+# df_bad_channels = df_pmt[ df_pmt["Gain 22"] > 1.6 ]
+# # df_bad_channels[ df_bad_channels["SiteName"] =="Amsterdam"]
+# display(df_bad_channels)
+```
+
+%% Cell type:code id:7c303c75-fa66-48f2-a09f-a5bf146538bc tags:
+
+``` python
+def count_chan_above_threshold( threshold = 1.7):
+
+    doms = []
+    bad_channels = []
+
+    for index, row in df_pmt.iterrows():
+        bad = 0
+
+#         if row["SiteID"] != "1": continue # only amsterdam
+
+        for gain_key in gain_keys:
+            if row[gain_key] > threshold: bad +=1
+
+#         if bad == 31: continue # why?
+
+        doms.append( row["SERIAL"] )
+        bad_channels.append( bad )
+
+    return doms, bad_channels
+
+doms, bad_channels = count_chan_above_threshold()
+bad_doms = [i for i,v in enumerate(bad_channels) if v > 3]
+```
+
+%% Cell type:code id:4a9a5b4b-4d41-450d-bc2e-fdb10a870ed8 tags:
+
+``` python
+import matplotlib.pyplot as plt
+
+plt.hist(bad_channels, [-0.5, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5])
+plt.plot(3, 0, 3, 40, color='red')
+plt.show()
+print("failed doms", len(bad_doms))
+```
+
+%% Output
+
+
+
+    failed doms 20
+
+%% Cell type:code id:4eaed37c-3cae-44ef-88db-fac0ce64acbf tags:
+
+``` python
+```
+
+%% Cell type:code id:ca1f27e5-fecb-42d5-8e17-dd58da923f08 tags:
+
+``` python
+```
--- a/checks.ipynb
+++ b/checks.ipynb
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9ca0bae6-c86d-4348-b9ad-a76bafb09cb7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'xxx.d26b5781'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import km3dia\n",
+    "db = km3dia.DBManager()\n",
+    "db.session_cookie"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c2433240-3251-42d5-bcfc-94cdd236de9c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7edde1b5-733d-4e31-a539-0a9247551b9f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "km3net-env-1.9",
+   "language": "python",
+   "name": "km3net-env-1.9"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
+%% Cell type:code id:9ca0bae6-c86d-4348-b9ad-a76bafb09cb7 tags:
+
+``` python
+import km3dia
+db = km3dia.DBManager()
+db.session_cookie
+```
+
+%% Output
+
+    'xxx.d26b5781'
+
+%% Cell type:code id:c2433240-3251-42d5-bcfc-94cdd236de9c tags:
+
+``` python
+```
+
+%% Cell type:code id:7edde1b5-733d-4e31-a539-0a9247551b9f tags:
+
+``` python
+```
--- a/pmt_gain.ipynb
+++ b/pmt_gain.ipynb