diff --git a/rfid_project.ipynb b/rfid_project.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "5c97bc34",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pyodbc\n",
+ "import pandas as pd\n",
+ "from sqlalchemy import create_engine\n",
+ "import urllib"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "7d08db1a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "params = urllib.parse.quote_plus(\"DRIVER={ODBC Driver 17 for SQL Server};\"\n",
+ " \"SERVER=dbserver.mif.pg.gda.pl,1433;\"\n",
+ " \"DATABASE=silkycoders;\"\n",
+ " \"UID=LOGIN;\"\n",
+ " \"PWD=HASLO\")\n",
+ "\n",
+ "engine = create_engine(\"mssql+pyodbc:///?odbc_connect={}\".format(params))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "55a5bbdb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "query = 'SELECT [EPC],[PROXIMITY],[TIMESTAMP],[MEASUREMENT]FROM [silkycoders].[rfid].[Logs] WHERE MEASUREMENT = 1;'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "b072f454",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.read_sql_query(query, engine)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "83ef096e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = df.sort_values(by=\"TIMESTAMP\").reset_index(drop=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "7172cc5a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df[\"HOUR\"] = df.TIMESTAMP.astype('datetime64[ns]').dt.hour.astype(int)\n",
+ "df[\"MIN\"] = df.TIMESTAMP.astype('datetime64[ns]').dt.minute.astype(int)\n",
+ "df[\"SEC\"] = df.TIMESTAMP.astype('datetime64[ns]').dt.second.astype(int)\n",
+ "df[\"MICROSEC\"] = df.TIMESTAMP.astype('datetime64[ns]').dt.microsecond.astype(int)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "2572ee69",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df[\"MILISEC\"] = df.MICROSEC/1000 + df.SEC*1000 + df.MIN*60000 + df.HOUR*3600000"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "b273fdb2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df[\"DELTA_MS\"] = df.MILISEC - df.MILISEC[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "f090bb86",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ "<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>EPC</th>\n",
+ " <th>PROXIMITY</th>\n",
+ " <th>TIMESTAMP</th>\n",
+ " <th>MEASUREMENT</th>\n",
+ " <th>HOUR</th>\n",
+ " <th>MIN</th>\n",
+ " <th>SEC</th>\n",
+ " <th>MICROSEC</th>\n",
+ " <th>MILISEC</th>\n",
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+ ],
+ "text/plain": [
+ " EPC PROXIMITY TIMESTAMP \\\n",
+ "0 3035684754340E0000B594FD -70.4 2021-10-26T09:46:33.735 \n",
+ "1 303568480C2B874000B59A39 -61.8 2021-10-26T09:46:33.747 \n",
+ "2 303568480C357A0000B59999 -74.4 2021-10-26T09:46:33.751 \n",
+ "3 30356847541DA78000B5BA4D -78.0 2021-10-26T09:46:33.754 \n",
+ "4 30356847542A2B0000B5B215 -71.1 2021-10-26T09:46:33.758 \n",
+ "... ... ... ... \n",
+ "10917 30356847542B6DC000B5B6BD -78.0 2021-10-26T09:48:17.687 \n",
+ "10918 30356847542B6CC000B5B642 -78.9 2021-10-26T09:48:17.688 \n",
+ "10919 3035684754501E0000B59704 -88.4 2021-10-26T09:48:17.689 \n",
+ "10920 303568475450208000B5B622 -80.5 2021-10-26T09:48:17.690 \n",
+ "10921 30356847542A2B0000B5B21C -78.0 2021-10-26T09:48:17.691 \n",
+ "\n",
+ " MEASUREMENT HOUR MIN SEC MICROSEC MILISEC DELTA_MS \n",
+ "0 1 9 46 33 735000 35193735.0 0.0 \n",
+ "1 1 9 46 33 747000 35193747.0 12.0 \n",
+ "2 1 9 46 33 751000 35193751.0 16.0 \n",
+ "3 1 9 46 33 754000 35193754.0 19.0 \n",
+ "4 1 9 46 33 758000 35193758.0 23.0 \n",
+ "... ... ... ... ... ... ... ... \n",
+ "10917 1 9 48 17 687000 35297687.0 103952.0 \n",
+ "10918 1 9 48 17 688000 35297688.0 103953.0 \n",
+ "10919 1 9 48 17 689000 35297689.0 103954.0 \n",
+ "10920 1 9 48 17 690000 35297690.0 103955.0 \n",
+ "10921 1 9 48 17 691000 35297691.0 103956.0 \n",
+ "\n",
+ "[10922 rows x 10 columns]"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6a4d3973",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
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+ "name": "python3"
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+}