added code to read validation data txts

Validation Data/read_validation_data_long.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -15,66 +15,187 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
+    "%matplotlib qt5\n",
+    "\n",
     "def unpack_line(str):\n",
+    "    time_format = \"%d.%m.%Y %H:%M:%S.%f\"\n",
     "    index_1 = str.find(';')\n",
-    "    index_2 = str.find(';',index_1)\n",
+    "    index_2 = str.find(';',index_1+1)\n",
-    "    index_3 = str.find(';',index_2)\n",
+    "    index_3 = str.find(';',index_2+1)\n",
-    "    index_4 = str.find(';',index_3)\n",
+    "    index_4 = str.find(';',index_3+1)\n",
-    "    index_5 = str.find(';',index_4)\n",
+    "    index_5 = str.find(';',index_4+1)\n",
     "    parameter   = str[0:index_1]\n",
-    "    value       = str[index_2:index_3]\n",
+    "    value       = float(str[index_2+1:index_3])\n",
-    "    timestamp   = str[index_5:] \n",
+    "    timestamp   = time.mktime(datetime.strptime(str[index_5+1:-2],time_format).timetuple())\n",
     "    return parameter,value,timestamp\n",
     "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [
+   "outputs": [],
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[1;32mv:\\georg\\Documents\\Persönliche Dokumente\\Arbeit\\Kelag\\Coding\\Python\\DT_Slot_3\\Kelag_DT_Slot_3\\Validation Data\\read_validation_data_long.ipynb Cell 3\u001b[0m in \u001b[0;36m<cell line: 7>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/v%3A/georg/Documents/Pers%C3%B6nliche%20Dokumente/Arbeit/Kelag/Coding/Python/DT_Slot_3/Kelag_DT_Slot_3/Validation%20Data/read_validation_data_long.ipynb#W1sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m timestamp_old    \u001b[39m=\u001b[39m \u001b[39m0.\u001b[39m\n\u001b[0;32m      <a href='vscode-notebook-cell:/v%3A/georg/Documents/Pers%C3%B6nliche%20Dokumente/Arbeit/Kelag/Coding/Python/DT_Slot_3/Kelag_DT_Slot_3/Validation%20Data/read_validation_data_long.ipynb#W1sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mopen\u001b[39m(\u001b[39m'\u001b[39m\u001b[39mAugust_1_22.txt\u001b[39m\u001b[39m'\u001b[39m) \u001b[39mas\u001b[39;00m txt_file:\n\u001b[1;32m----> <a href='vscode-notebook-cell:/v%3A/georg/Documents/Pers%C3%B6nliche%20Dokumente/Arbeit/Kelag/Coding/Python/DT_Slot_3/Kelag_DT_Slot_3/Validation%20Data/read_validation_data_long.ipynb#W1sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m     \u001b[39mfor\u001b[39;00m line \u001b[39min\u001b[39;00m txt_file:\n\u001b[0;32m      <a href='vscode-notebook-cell:/v%3A/georg/Documents/Pers%C3%B6nliche%20Dokumente/Arbeit/Kelag/Coding/Python/DT_Slot_3/Kelag_DT_Slot_3/Validation%20Data/read_validation_data_long.ipynb#W1sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m         parameter_new, value_new, timestamp_new \u001b[39m=\u001b[39m unpack_line(line)\n",
-      "File \u001b[1;32mc:\\Users\\georg\\anaconda3\\envs\\DT_Slot_3\\lib\\encodings\\cp1252.py:22\u001b[0m, in \u001b[0;36mIncrementalDecoder.decode\u001b[1;34m(self, input, final)\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[39mclass\u001b[39;00m \u001b[39mIncrementalDecoder\u001b[39;00m(codecs\u001b[39m.\u001b[39mIncrementalDecoder):\n\u001b[1;32m---> 22\u001b[0m     \u001b[39mdef\u001b[39;00m \u001b[39mdecode\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39minput\u001b[39m, final\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m):\n\u001b[0;32m     23\u001b[0m         \u001b[39mreturn\u001b[39;00m codecs\u001b[39m.\u001b[39mcharmap_decode(\u001b[39minput\u001b[39m,\u001b[39mself\u001b[39m\u001b[39m.\u001b[39merrors,decoding_table)[\u001b[39m0\u001b[39m]\n",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
    "source": [
-    "df = pd.DataFrame(columns=['Timestamp','M1-LA','M1-Druck','M2-LA','M2-Druck'])\n",
+    "M1_LA_df    = pd.DataFrame(columns=['Timestamp','M1-LA'])\n",
+    "M2_LA_df    = pd.DataFrame(columns=['Timestamp','M2-LA'])\n",
+    "M1_Druck_df = pd.DataFrame(columns=['Timestamp','M1-Druck'])\n",
+    "M2_Druck_df = pd.DataFrame(columns=['Timestamp','M2-Druck'])\n",
     "\n",
     "\n",
     "parameter_old    = ''\n",
-    "value_old        = 0.\n",
-    "timestamp_old    = 0.\n",
     "value_list      = []\n",
     "timestamp_list  = []\n",
-    "with open('August_1_22.txt') as txt_file:\n",
+    "with open('Juni_1_22.txt') as txt_file:\n",
     "    for line in txt_file:\n",
     "        parameter_new, value_new, timestamp_new = unpack_line(line)\n",
     "        if parameter_new != parameter_old:\n",
-    "            if parameter_old islike \"\"\n",
+    "            if 'M1' in parameter_old and 'Stell_Leitapparat' in parameter_old:\n",
+    "                M1_LA_df['Timestamp']       = timestamp_list[:]\n",
+    "                M1_LA_df['M1-LA']           = value_list[:]\n",
+    "            if 'M1' in parameter_old and 'Spiraldruck' in parameter_old:\n",
+    "                M1_Druck_df['Timestamp']    = timestamp_list[:]\n",
+    "                M1_Druck_df['M1-Druck']     = value_list[:]\n",
+    "            if 'M2' in parameter_old and 'Stell_Leitapparat' in parameter_old:\n",
+    "                M2_LA_df['Timestamp']       = timestamp_list[:]\n",
+    "                M2_LA_df['M2-LA']           = value_list[:]\n",
+    "            if 'M2' in parameter_old and 'Spiraldruck' in parameter_old:\n",
+    "                M2_Druck_df['Timestamp']    = timestamp_list[:]\n",
+    "                M2_Druck_df['M2-Druck']     = value_list[:]\n",
     "            \n",
     "            value_list      = []\n",
     "            timestamp_list  = []\n",
+    "            value_list.append(value_new)\n",
+    "            timestamp_list.append(timestamp_new)\n",
     "\n",
-    "\n"
+    "            parameter_old   = parameter_new\n",
+    "        else:\n",
+    "            if value_new != value_list[-1]:\n",
+    "                value_list.append(value_new)\n",
+    "                timestamp_list.append(timestamp_new) \n",
+    "\n",
+    "    if 'M1' in parameter_old and 'Stell_Leitapparat' in parameter_old:\n",
+    "        M1_LA_df['Timestamp']       = timestamp_list[:]\n",
+    "        M1_LA_df['M1-LA']           = value_list[:]\n",
+    "    if 'M1' in parameter_old and 'Spiraldruck' in parameter_old:\n",
+    "        M1_Druck_df['Timestamp']    = timestamp_list[:]\n",
+    "        M1_Druck_df['M1-Druck']     = value_list[:]\n",
+    "    if 'M2' in parameter_old and 'Stell_Leitapparat' in parameter_old:\n",
+    "        M2_LA_df['Timestamp']       = timestamp_list[:]\n",
+    "        M2_LA_df['M2-LA']           = value_list[:]\n",
+    "    if 'M2' in parameter_old and 'Spiraldruck' in parameter_old:\n",
+    "        M2_Druck_df['Timestamp']    = timestamp_list[:]\n",
+    "        M2_Druck_df['M2-Druck']     = value_list[:]\n",
+    "\n",
+    "M1_LA_df.set_index(['Timestamp'],inplace=True)\n",
+    "M1_Druck_df.set_index(['Timestamp'],inplace=True)\n",
+    "M2_LA_df.set_index(['Timestamp'],inplace=True)\n",
+    "M2_Druck_df.set_index(['Timestamp'],inplace=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2e6c96b7430>]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig1=plt.figure()\n",
+    "plt.plot(M1_LA_df['M1-LA'])\n",
+    "fig2=plt.figure()\n",
+    "plt.plot(M1_Druck_df['M1-Druck'])\n",
+    "fig3=plt.figure()\n",
+    "plt.plot(M2_LA_df['M2-LA'])\n",
+    "fig4=plt.figure()\n",
+    "plt.plot(M2_Druck_df['M2-Druck'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = M1_LA_df.join([M2_LA_df,M1_Druck_df,M2_Druck_df],how='outer')\n",
+    "df.sort_index(axis=0,inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2e6c8733c10>]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig1=plt.figure()\n",
+    "plt.plot(df['M1-LA'])\n",
+    "fig2=plt.figure()\n",
+    "plt.plot(df['M1-Druck'])\n",
+    "fig3=plt.figure()\n",
+    "plt.plot(df['M2-LA'])\n",
+    "fig4=plt.figure()\n",
+    "plt.plot(df['M2-Druck'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_vec = df.index.to_numpy()\n",
+    "M1_LA = df['M1-LA'].to_numpy() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2e6c8563340>]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig5=plt.figure()\n",
+    "plt.plot(t_vec,M1_LA)"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.8.13 ('DT_Slot_3')",
+   "display_name": "Python 3.8.13 ('Georg_DT_Slot3')",
    "language": "python",
    "name": "python3"
   },
@@ -93,7 +214,7 @@
   "orig_nbformat": 4,
   "vscode": {
    "interpreter": {
-    "hash": "4a28055eb8a3160fa4c7e4fca69770c4e0a1add985300856aa3fcf4ce32a2c48"
+    "hash": "84fb123bdc47ab647d3782661abcbe80fbb79236dd2f8adf4cef30e8755eb2cd"
    }
   }
  },

Validation Data/sighting_validation_data.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -15,14 +15,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\georg\\AppData\\Local\\Temp\\ipykernel_34540\\1340824978.py:1: ParserWarning: Length of header or names does not match length of data. This leads to a loss of data with index_col=False.\n",
+      "C:\\Users\\BRANT\\AppData\\Local\\Temp\\7\\ipykernel_7624\\1340824978.py:1: ParserWarning: Length of header or names does not match length of data. This leads to a loss of data with index_col=False.\n",
       "  raw_data = pd.read_csv(\"2015_08_24 18.00 M1 SS100%.csv\",sep=\";\",header=7,index_col=False)\n"
      ]
     }
@@ -43,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -55,23 +55,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1f774d49760>]"
+       "[<matplotlib.lines.Line2D at 0x21d60ce24c0>]"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9c2016ba1ceb4d07a17a483bbf50f9a6",
+       "model_id": "7cd05b652d4a40ca973b5895a8777a64",
        "version_major": 2,
        "version_minor": 0
       },
@@ -104,7 +104,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.8.13 ('DT_Slot_3')",
+   "display_name": "Python 3.8.13 ('Georg_DT_Slot3')",
    "language": "python",
    "name": "python3"
   },
@@ -123,7 +123,7 @@
   "orig_nbformat": 4,
   "vscode": {
    "interpreter": {
-    "hash": "4a28055eb8a3160fa4c7e4fca69770c4e0a1add985300856aa3fcf4ce32a2c48"
+    "hash": "84fb123bdc47ab647d3782661abcbe80fbb79236dd2f8adf4cef30e8755eb2cd"
    }
   }
  },