1048 lines
100 KiB
Plaintext
1048 lines
100 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "initial_id",
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"metadata": {
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"collapsed": true,
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"ExecuteTime": {
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"end_time": "2024-01-24T07:55:37.559571600Z",
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"start_time": "2024-01-24T07:55:35.286921700Z"
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}
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||
},
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"outputs": [],
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"source": [
|
||
"import numpy as np\n",
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"import pandas as pd"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"outputs": [],
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"source": [
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"import os"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-01-24T07:55:43.027207900Z",
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"start_time": "2024-01-24T07:55:43.011508100Z"
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}
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},
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"id": "edeebedebcfbfaea"
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},
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{
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"cell_type": "code",
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||
"execution_count": 3,
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||
"outputs": [],
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"source": [
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"raw_dataset_path = \"./raw_dataset\"\n",
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"dataset_path = \"./dataset\"\n",
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||
"if not os.path.exists(dataset_path):\n",
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" os.makedirs(dataset_path)"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-01-24T07:55:43.994656600Z",
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"start_time": "2024-01-24T07:55:43.980635100Z"
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}
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},
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"id": "97fcd29fcef2f6e2"
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"['Channel_02.xlsx', 'Channel_03.xlsx', 'Channel_04.xlsx', 'Channel_05.xlsx', 'Channel_06.xlsx', 'Channel_07.xlsx', 'Channel_08.xlsx', 'Channel_09.xlsx', 'Channel_10.xlsx', 'Channel_11.xlsx']\n"
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||
]
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||
}
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],
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||
"source": [
|
||
"file_list = []\n",
|
||
"if os.path.exists(raw_dataset_path):\n",
|
||
" file_list = os.listdir(raw_dataset_path)\n",
|
||
" if len(file_list) > 0:\n",
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||
" print(file_list)"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-01-24T07:55:47.321452800Z",
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"start_time": "2024-01-24T07:55:47.281687200Z"
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}
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},
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"id": "34ef1d4940b070ac"
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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||
"outputs": [],
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||
"source": [
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||
"user_dict = {}\n",
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||
"freq_point = {}\n",
|
||
"file_temp = file_list[0]\n",
|
||
"# month_2 = pd.read_excel()"
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||
],
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||
"metadata": {
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"collapsed": false,
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||
"ExecuteTime": {
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||
"end_time": "2024-01-24T08:05:28.328512700Z",
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"start_time": "2024-01-24T08:05:28.304339300Z"
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}
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},
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"id": "8b430d34ef733a81"
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||
},
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{
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||
"cell_type": "code",
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||
"execution_count": 6,
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||
"outputs": [
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||
{
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"data": {
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||
"text/plain": "'Channel_02.xlsx'"
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},
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||
"execution_count": 6,
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||
"metadata": {},
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||
"output_type": "execute_result"
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}
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||
],
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||
"source": [
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||
"file_temp"
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||
],
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||
"metadata": {
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||
"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-01-24T08:05:30.846556100Z",
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"start_time": "2024-01-24T08:05:30.770308100Z"
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}
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||
},
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"id": "df5d8247ba887864"
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},
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{
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||
"cell_type": "code",
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||
"execution_count": 7,
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||
"outputs": [
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||
{
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"data": {
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||
"text/plain": "<module 'ntpath' from 'D:\\\\Apps\\\\Anaconda3\\\\lib\\\\ntpath.py'>"
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||
},
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||
"execution_count": 7,
|
||
"metadata": {},
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||
"output_type": "execute_result"
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||
}
|
||
],
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||
"source": [
|
||
"os.path"
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||
],
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||
"metadata": {
|
||
"collapsed": false,
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||
"ExecuteTime": {
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||
"end_time": "2024-01-24T08:06:07.946601Z",
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||
"start_time": "2024-01-24T08:06:07.923697900Z"
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||
}
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||
},
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||
"id": "5fbd685fb403d624"
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||
},
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||
{
|
||
"cell_type": "code",
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||
"execution_count": 8,
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||
"outputs": [
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||
{
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||
"data": {
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||
"text/plain": "'./raw_dataset\\\\Channel_02.xlsx'"
|
||
},
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"os.path.join(raw_dataset_path, file_temp)"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
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||
"end_time": "2024-01-24T08:06:37.377941200Z",
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||
"start_time": "2024-01-24T08:06:37.355576600Z"
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||
}
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||
},
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||
"id": "17b36e1bf46376b7"
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||
},
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||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"outputs": [],
|
||
"source": [
|
||
"month_2 = pd.read_excel(os.path.join(raw_dataset_path, file_temp))"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-01-24T08:08:02.675539400Z",
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||
"start_time": "2024-01-24T08:07:13.301903200Z"
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||
}
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||
},
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||
"id": "218a0b12842d39bd"
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||
},
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||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"outputs": [
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||
{
|
||
"data": {
|
||
"text/plain": " DemodeIP VastIP Business BeginTime Duration \\\n0 10.1.2.14 99.100.94.209 数据 2023-02-09 18:44:44 00 00:00:10 \n1 10.1.2.17 99.100.65.129 数据 2023-02-09 18:44:44 00 00:00:10 \n2 10.1.2.17 99.100.37.209 数据 2023-02-09 18:44:44 00 00:00:10 \n3 10.1.2.17 99.100.96.33 数据 2023-02-09 18:44:49 00 00:00:10 \n4 10.1.2.17 99.100.91.1 数据 2023-02-09 18:44:45 00 00:00:15 \n... ... ... ... ... ... \n501906 10.1.2.17 99.100.106.113 数据 2023-02-28 22:24:01 00 00:00:11 \n501907 10.1.2.17 99.100.90.17 数据 2023-02-28 22:23:59 00 00:00:15 \n501908 10.1.2.17 99.100.94.17 数据 2023-02-28 22:23:51 00 00:00:24 \n501909 10.1.2.17 99.100.93.33 数据 2023-02-28 22:24:00 00 00:00:17 \n501910 10.1.2.17 99.100.106.161 数据 2023-02-28 22:24:07 00 00:00:11 \n\n StreamNum SlotType CarrierType Frequency \n0 8 64 K 1 146 220 KHz \n1 1 64 K 1 147 570 KHz \n2 1 64 K 1 147 480 KHz \n3 1 64 K 1 147 570 KHz \n4 1 64 K 1 147 570 KHz \n... ... ... ... ... \n501906 2 64 K 1 148 140 KHz \n501907 2 64 K 1 148 140 KHz \n501908 1 64 K 1 147 480 KHz \n501909 1 64 K 1 147 660 KHz \n501910 1 64 K 1 147 750 KHz \n\n[501911 rows x 9 columns]",
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||
"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>DemodeIP</th>\n <th>VastIP</th>\n <th>Business</th>\n <th>BeginTime</th>\n <th>Duration</th>\n <th>StreamNum</th>\n <th>SlotType</th>\n <th>CarrierType</th>\n <th>Frequency</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>10.1.2.14</td>\n <td>99.100.94.209</td>\n <td>数据</td>\n <td>2023-02-09 18:44:44</td>\n <td>00 00:00:10</td>\n <td></td>\n <td>8</td>\n <td>64 K</td>\n <td>1 146 220 KHz</td>\n </tr>\n <tr>\n <th>1</th>\n <td>10.1.2.17</td>\n <td>99.100.65.129</td>\n <td>数据</td>\n <td>2023-02-09 18:44:44</td>\n <td>00 00:00:10</td>\n <td></td>\n <td>1</td>\n <td>64 K</td>\n <td>1 147 570 KHz</td>\n </tr>\n <tr>\n <th>2</th>\n <td>10.1.2.17</td>\n <td>99.100.37.209</td>\n <td>数据</td>\n <td>2023-02-09 18:44:44</td>\n <td>00 00:00:10</td>\n <td></td>\n <td>1</td>\n <td>64 K</td>\n <td>1 147 480 KHz</td>\n </tr>\n <tr>\n <th>3</th>\n <td>10.1.2.17</td>\n <td>99.100.96.33</td>\n <td>数据</td>\n <td>2023-02-09 18:44:49</td>\n <td>00 00:00:10</td>\n <td></td>\n <td>1</td>\n <td>64 K</td>\n <td>1 147 570 KHz</td>\n </tr>\n <tr>\n <th>4</th>\n <td>10.1.2.17</td>\n <td>99.100.91.1</td>\n <td>数据</td>\n <td>2023-02-09 18:44:45</td>\n <td>00 00:00:15</td>\n <td></td>\n <td>1</td>\n <td>64 K</td>\n <td>1 147 570 KHz</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>501906</th>\n <td>10.1.2.17</td>\n <td>99.100.106.113</td>\n <td>数据</td>\n <td>2023-02-28 22:24:01</td>\n <td>00 00:00:11</td>\n <td></td>\n <td>2</td>\n <td>64 K</td>\n <td>1 148 140 KHz</td>\n </tr>\n <tr>\n <th>501907</th>\n <td>10.1.2.17</td>\n <td>99.100.90.17</td>\n <td>数据</td>\n <td>2023-02-28 22:23:59</td>\n <td>00 00:00:15</td>\n <td></td>\n <td>2</td>\n <td>64 K</td>\n <td>1 148 140 KHz</td>\n </tr>\n <tr>\n <th>501908</th>\n <td>10.1.2.17</td>\n <td>99.100.94.17</td>\n <td>数据</td>\n <td>2023-02-28 22:23:51</td>\n <td>00 00:00:24</td>\n <td></td>\n <td>1</td>\n <td>64 K</td>\n <td>1 147 480 KHz</td>\n </tr>\n <tr>\n <th>501909</th>\n <td>10.1.2.17</td>\n <td>99.100.93.33</td>\n <td>数据</td>\n <td>2023-02-28 22:24:00</td>\n <td>00 00:00:17</td>\n <td></td>\n <td>1</td>\n <td>64 K</td>\n <td>1 147 660 KHz</td>\n </tr>\n <tr>\n <th>501910</th>\n <td>10.1.2.17</td>\n <td>99.100.106.161</td>\n <td>数据</td>\n <td>2023-02-28 22:24:07</td>\n <td>00 00:00:11</td>\n <td></td>\n <td>1</td>\n <td>64 K</td>\n <td>1 147 750 KHz</td>\n </tr>\n </tbody>\n</table>\n<p>501911 rows × 9 columns</p>\n</div>"
|
||
},
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"month_2"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-01-24T08:08:15.679497800Z",
|
||
"start_time": "2024-01-24T08:08:15.625749700Z"
|
||
}
|
||
},
|
||
"id": "cd5e9dc00b3ea405"
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"outputs": [],
|
||
"source": [
|
||
"user_dict = month_2['VastIP'].unique()"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-01-24T08:08:53.695050100Z",
|
||
"start_time": "2024-01-24T08:08:53.658025800Z"
|
||
}
|
||
},
|
||
"id": "98c5135e5a79dfb9"
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": "array(['99.100.94.209', '99.100.65.129', '99.100.37.209', '99.100.96.33',\n '99.100.91.1', '99.100.103.161', '99.100.104.193', '99.100.104.65',\n '99.100.34.129', '99.100.38.225', '99.100.106.33',\n '99.100.106.145', '99.100.99.241', '99.100.97.225', '99.100.37.33',\n '99.100.102.161', '99.100.88.225', '99.100.93.129',\n '99.100.39.129', '99.100.37.161', '99.100.100.225',\n '99.100.101.241', '99.100.93.161', '99.100.97.129', '99.100.2.81',\n '99.100.104.49', '99.100.103.145', '99.100.104.161',\n '99.100.98.97', '99.100.94.17', '99.100.90.225', '99.100.101.81',\n '99.100.99.17', '99.100.38.49', '99.100.105.193', '99.100.103.225',\n '99.100.91.33', '99.100.93.33', '99.100.106.209', '99.100.105.177',\n '99.100.102.177', '99.100.39.81', '99.100.67.161', '99.100.92.65',\n '99.100.92.1', '99.100.106.161', '99.100.105.145',\n '99.100.105.225', '99.100.88.65', '99.100.90.1', '99.100.70.241',\n '99.100.66.145', '99.100.66.97', '99.100.69.161', '99.100.97.81',\n '99.100.106.1', 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'99.100.109.49', '99.100.93.225', '99.100.34.145',\n '99.100.101.33', '99.100.68.33', '99.100.37.129', '99.100.104.225',\n '99.100.108.97', '99.100.114.225', '99.100.38.241',\n '99.100.98.113', '99.100.111.145', '99.100.110.1', '99.100.110.17',\n '99.100.91.17', '99.100.132.209', '99.100.69.17', '99.100.101.97',\n '99.100.92.81', '99.100.93.209', '99.100.36.241', '99.100.92.49',\n '99.100.67.225', '99.100.93.17', '99.100.70.1', '99.100.70.193',\n '99.100.38.97'], dtype=object)"
|
||
},
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"user_dict"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-01-24T08:08:57.350387600Z",
|
||
"start_time": "2024-01-24T08:08:57.296165300Z"
|
||
}
|
||
},
|
||
"id": "f1408bccf6d0da33"
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"outputs": [],
|
||
"source": [
|
||
"file_temp = file_list[1]"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-01-24T08:14:22.900688600Z",
|
||
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|
||
}
|
||
},
|
||
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|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"outputs": [],
|
||
"source": [
|
||
"month_3 = pd.read_excel(os.path.join(raw_dataset_path, file_temp))"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-01-24T08:16:55.615824600Z",
|
||
"start_time": "2024-01-24T08:15:12.920108100Z"
|
||
}
|
||
},
|
||
"id": "e51dcae677c978ab"
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"outputs": [],
|
||
"source": [
|
||
"user_dict_month3 = month_3.VastIP.unique()"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-01-24T08:17:48.624764100Z",
|
||
"start_time": "2024-01-24T08:17:48.591291200Z"
|
||
}
|
||
},
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||
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||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"outputs": [
|
||
{
|
||
"data": {
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||
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|
||
},
|
||
"execution_count": 16,
|
||
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|
||
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|
||
}
|
||
],
|
||
"source": [
|
||
"user_dict_month3"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-01-24T08:17:51.929728100Z",
|
||
"start_time": "2024-01-24T08:17:51.902868300Z"
|
||
}
|
||
},
|
||
"id": "22bf9bede244b747"
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"outputs": [
|
||
{
|
||
"ename": "ValueError",
|
||
"evalue": "operands could not be broadcast together with shapes (312,) (430,) ",
|
||
"output_type": "error",
|
||
"traceback": [
|
||
"\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
|
||
"\u001B[1;31mValueError\u001B[0m Traceback (most recent call last)",
|
||
"\u001B[1;32m<ipython-input-17-af7e0816ee30>\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[1;32m----> 1\u001B[1;33m \u001B[0muser_dict\u001B[0m \u001B[1;33m+\u001B[0m \u001B[0muser_dict_month3\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m",
|
||
"\u001B[1;31mValueError\u001B[0m: operands could not be broadcast together with shapes (312,) (430,) "
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"user_dict + user_dict_month3"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-01-24T08:18:12.658480100Z",
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||
"start_time": "2024-01-24T08:18:12.533357Z"
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||
}
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},
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||
"id": "e86bd06c06712b5a"
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||
},
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||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"outputs": [],
|
||
"source": [
|
||
"user_dict_2_3 = np.union1d(user_dict_month3, user_dict)"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-01-24T08:20:12.078282800Z",
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||
"start_time": "2024-01-24T08:20:12.048371700Z"
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||
}
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||
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"cell_type": "markdown",
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|
||
"collapsed": false
|
||
},
|
||
"id": "25db5d7f0b4f8733"
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"outputs": [],
|
||
"source": [
|
||
"user_dict_2_3\n",
|
||
"from tqdm import tqdm"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-01-24T08:46:30.722495600Z",
|
||
"start_time": "2024-01-24T08:46:30.594081600Z"
|
||
}
|
||
},
|
||
"id": "f208635994d4914"
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" 0%| | 0/10 [00:49<?, ?it/s]\n"
|
||
]
|
||
},
|
||
{
|
||
"ename": "TypeError",
|
||
"evalue": "'<' not supported between instances of 'str' and 'float'",
|
||
"output_type": "error",
|
||
"traceback": [
|
||
"\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
|
||
"\u001B[1;31mTypeError\u001B[0m Traceback (most recent call last)",
|
||
"\u001B[1;32m<ipython-input-21-c59a103666a6>\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[0;32m 4\u001B[0m \u001B[0mtp\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mpd\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mread_excel\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mos\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mpath\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mjoin\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mraw_dataset_path\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mf\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 5\u001B[0m \u001B[0muser_dict\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mnp\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0munion1d\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0muser_dict\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mtp\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mVastIP\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0munique\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m----> 6\u001B[1;33m \u001B[0mfreq_point\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mnp\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0munion1d\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mfreq_point\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mtp\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mFrequency\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0munique\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m",
|
||
"\u001B[1;32m<__array_function__ internals>\u001B[0m in \u001B[0;36munion1d\u001B[1;34m(*args, **kwargs)\u001B[0m\n",
|
||
"\u001B[1;32mD:\\Apps\\Anaconda3\\lib\\site-packages\\numpy\\lib\\arraysetops.py\u001B[0m in \u001B[0;36munion1d\u001B[1;34m(ar1, ar2)\u001B[0m\n\u001B[0;32m 747\u001B[0m \u001B[0marray\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;36m1\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;36m2\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;36m3\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;36m4\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;36m6\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 748\u001B[0m \"\"\"\n\u001B[1;32m--> 749\u001B[1;33m \u001B[1;32mreturn\u001B[0m \u001B[0munique\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mnp\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mconcatenate\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mar1\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mar2\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0maxis\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;32mNone\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 750\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 751\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
|
||
"\u001B[1;32m<__array_function__ internals>\u001B[0m in \u001B[0;36munique\u001B[1;34m(*args, **kwargs)\u001B[0m\n",
|
||
"\u001B[1;32mD:\\Apps\\Anaconda3\\lib\\site-packages\\numpy\\lib\\arraysetops.py\u001B[0m in \u001B[0;36munique\u001B[1;34m(ar, return_index, return_inverse, return_counts, axis)\u001B[0m\n\u001B[0;32m 259\u001B[0m \u001B[0mar\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mnp\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0masanyarray\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mar\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 260\u001B[0m \u001B[1;32mif\u001B[0m \u001B[0maxis\u001B[0m \u001B[1;32mis\u001B[0m \u001B[1;32mNone\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 261\u001B[1;33m \u001B[0mret\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0m_unique1d\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mar\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mreturn_index\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mreturn_inverse\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mreturn_counts\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 262\u001B[0m \u001B[1;32mreturn\u001B[0m \u001B[0m_unpack_tuple\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mret\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 263\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
|
||
"\u001B[1;32mD:\\Apps\\Anaconda3\\lib\\site-packages\\numpy\\lib\\arraysetops.py\u001B[0m in \u001B[0;36m_unique1d\u001B[1;34m(ar, return_index, return_inverse, return_counts)\u001B[0m\n\u001B[0;32m 320\u001B[0m \u001B[0maux\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mar\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0mperm\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 321\u001B[0m \u001B[1;32melse\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 322\u001B[1;33m \u001B[0mar\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0msort\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 323\u001B[0m \u001B[0maux\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mar\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 324\u001B[0m \u001B[0mmask\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mnp\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mempty\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0maux\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mshape\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mdtype\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mnp\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mbool_\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
|
||
"\u001B[1;31mTypeError\u001B[0m: '<' not supported between instances of 'str' and 'float'"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"user_dict = []\n",
|
||
"freq_point\n",
|
||
"for f in tqdm(file_list):\n",
|
||
" tp = pd.read_excel(os.path.join(raw_dataset_path, f))\n",
|
||
" user_dict = np.union1d(user_dict, tp.VastIP.unique())\n",
|
||
" freq_point = np.union1d(freq_point, tp.Frequency.unique())"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-01-24T08:48:49.422148500Z",
|
||
"start_time": "2024-01-24T08:47:59.874651800Z"
|
||
}
|
||
},
|
||
"id": "5a75925d095fd2a7"
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": "array(['1 146 220 KHz', '1 147 570 KHz', '1 147 480 KHz', '1 147 660 KHz',\n '1 146 130 KHz', '1 148 140 KHz', '1 147 750 KHz', '1 146 400 KHz',\n '1 147 840 KHz', '1 149 310 KHz', '1 146 040 KHz', '1 146 310 KHz',\n '1 147 930 KHz', '1 149 400 KHz', '1 148 770 KHz', '1 146 490 KHz',\n '1 146 670 KHz', '1 148 860 KHz', '1 146 580 KHz', '1 147 120 KHz',\n '1 147 300 KHz', '1 148 230 KHz', '1 148 950 KHz', '1 146 760 KHz',\n '1 079 720 KHz', '1 146 850 KHz', '1 146 940 KHz', '1 147 030 KHz',\n '1 147 390 KHz', '1 078 930 KHz', '1 149 040 KHz', '1 147 210 KHz',\n nan, '1 079 540 KHz', '1 078 580 KHz', '1 079 360 KHz',\n '1 079 180 KHz', '1 143 560 KHz', '1 144 930 KHz', '1 077 040 KHz'],\n dtype=object)"
|
||
},
|
||
"execution_count": 22,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"month_2.Frequency.unique()"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-01-24T08:49:58.297198300Z",
|
||
"start_time": "2024-01-24T08:49:58.208071300Z"
|
||
}
|
||
},
|
||
"id": "7468058e6858efaf"
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"outputs": [
|
||
{
|
||
"ename": "TypeError",
|
||
"evalue": "'<' not supported between instances of 'str' and 'float'",
|
||
"output_type": "error",
|
||
"traceback": [
|
||
"\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
|
||
"\u001B[1;31mTypeError\u001B[0m Traceback (most recent call last)",
|
||
"\u001B[1;32m<ipython-input-23-262ec1c7bed4>\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[1;32m----> 1\u001B[1;33m \u001B[0mfreq_point\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mnp\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0munion1d\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mfreq_point\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mmonth_2\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mFrequency\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0munique\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m",
|
||
"\u001B[1;32m<__array_function__ internals>\u001B[0m in \u001B[0;36munion1d\u001B[1;34m(*args, **kwargs)\u001B[0m\n",
|
||
"\u001B[1;32mD:\\Apps\\Anaconda3\\lib\\site-packages\\numpy\\lib\\arraysetops.py\u001B[0m in \u001B[0;36munion1d\u001B[1;34m(ar1, ar2)\u001B[0m\n\u001B[0;32m 747\u001B[0m \u001B[0marray\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;36m1\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;36m2\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;36m3\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;36m4\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;36m6\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 748\u001B[0m \"\"\"\n\u001B[1;32m--> 749\u001B[1;33m \u001B[1;32mreturn\u001B[0m \u001B[0munique\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mnp\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mconcatenate\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mar1\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mar2\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0maxis\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;32mNone\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 750\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 751\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
|
||
"\u001B[1;32m<__array_function__ internals>\u001B[0m in \u001B[0;36munique\u001B[1;34m(*args, **kwargs)\u001B[0m\n",
|
||
"\u001B[1;32mD:\\Apps\\Anaconda3\\lib\\site-packages\\numpy\\lib\\arraysetops.py\u001B[0m in \u001B[0;36munique\u001B[1;34m(ar, return_index, return_inverse, return_counts, axis)\u001B[0m\n\u001B[0;32m 259\u001B[0m \u001B[0mar\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mnp\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0masanyarray\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mar\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 260\u001B[0m \u001B[1;32mif\u001B[0m \u001B[0maxis\u001B[0m \u001B[1;32mis\u001B[0m \u001B[1;32mNone\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 261\u001B[1;33m \u001B[0mret\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0m_unique1d\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mar\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mreturn_index\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mreturn_inverse\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mreturn_counts\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 262\u001B[0m \u001B[1;32mreturn\u001B[0m \u001B[0m_unpack_tuple\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mret\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 263\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
|
||
"\u001B[1;32mD:\\Apps\\Anaconda3\\lib\\site-packages\\numpy\\lib\\arraysetops.py\u001B[0m in \u001B[0;36m_unique1d\u001B[1;34m(ar, return_index, return_inverse, return_counts)\u001B[0m\n\u001B[0;32m 320\u001B[0m \u001B[0maux\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mar\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0mperm\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 321\u001B[0m \u001B[1;32melse\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 322\u001B[1;33m \u001B[0mar\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0msort\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 323\u001B[0m \u001B[0maux\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mar\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 324\u001B[0m \u001B[0mmask\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mnp\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mempty\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0maux\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mshape\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mdtype\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mnp\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mbool_\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
|
||
"\u001B[1;31mTypeError\u001B[0m: '<' not supported between instances of 'str' and 'float'"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"freq_point = np.union1d(freq_point, month_2.Frequency.unique())"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-01-24T08:50:31.422526800Z",
|
||
"start_time": "2024-01-24T08:50:31.249467100Z"
|
||
}
|
||
},
|
||
"id": "1ef93050f810cbf7"
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [],
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"id": "3c2d8d1392126a87"
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 29,
|
||
"outputs": [],
|
||
"source": [
|
||
"tt = month_2['Frequency']"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-01-24T08:54:41.090942500Z",
|
||
"start_time": "2024-01-24T08:54:40.985068400Z"
|
||
}
|
||
},
|
||
"id": "cf856df5660045c9"
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 30,
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": "0 1 146 220 KHz\n1 1 147 570 KHz\n2 1 147 480 KHz\n3 1 147 570 KHz\n4 1 147 570 KHz\n ... \n501906 1 148 140 KHz\n501907 1 148 140 KHz\n501908 1 147 480 KHz\n501909 1 147 660 KHz\n501910 1 147 750 KHz\nName: Frequency, Length: 501911, dtype: object"
|
||
},
|
||
"execution_count": 30,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"tt"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-01-24T08:54:42.335759900Z",
|
||
"start_time": "2024-01-24T08:54:42.236689100Z"
|
||
}
|
||
},
|
||
"id": "d8cf6daf86319783"
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 31,
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": "array(['1 146 220 KHz', '1 147 570 KHz', '1 147 480 KHz', '1 147 660 KHz',\n '1 146 130 KHz', '1 148 140 KHz', '1 147 750 KHz', '1 146 400 KHz',\n '1 147 840 KHz', '1 149 310 KHz', '1 146 040 KHz', '1 146 310 KHz',\n '1 147 930 KHz', '1 149 400 KHz', '1 148 770 KHz', '1 146 490 KHz',\n '1 146 670 KHz', '1 148 860 KHz', '1 146 580 KHz', '1 147 120 KHz',\n '1 147 300 KHz', '1 148 230 KHz', '1 148 950 KHz', '1 146 760 KHz',\n '1 079 720 KHz', '1 146 850 KHz', '1 146 940 KHz', '1 147 030 KHz',\n '1 147 390 KHz', '1 078 930 KHz', '1 149 040 KHz', '1 147 210 KHz',\n 'nan', '1 079 540 KHz', '1 078 580 KHz', '1 079 360 KHz',\n '1 079 180 KHz', '1 143 560 KHz', '1 144 930 KHz', '1 077 040 KHz'],\n dtype='<U13')"
|
||
},
|
||
"execution_count": 31,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"tt.unique().astype(str)"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-01-24T08:55:03.715707100Z",
|
||
"start_time": "2024-01-24T08:55:03.635708400Z"
|
||
}
|
||
},
|
||
"id": "2e8c1fcfd8c0f967"
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 32,
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": "0 1 146 220 KHz\n1 1 147 570 KHz\n2 1 147 480 KHz\n3 1 147 570 KHz\n4 1 147 570 KHz\n ... \n501906 1 148 140 KHz\n501907 1 148 140 KHz\n501908 1 147 480 KHz\n501909 1 147 660 KHz\n501910 1 147 750 KHz\nName: Frequency, Length: 501911, dtype: object"
|
||
},
|
||
"execution_count": 32,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"tt"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-01-24T08:55:06.818453Z",
|
||
"start_time": "2024-01-24T08:55:06.735628100Z"
|
||
}
|
||
},
|
||
"id": "bb4c682e8be0861a"
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 33,
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"100%|██████████| 10/10 [15:04<00:00, 90.49s/it]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"user_dict = []\n",
|
||
"freq_point\n",
|
||
"for f in tqdm(file_list):\n",
|
||
" tp = pd.read_excel(os.path.join(raw_dataset_path, f))\n",
|
||
" user_dict = np.union1d(user_dict, tp.VastIP.unique())\n",
|
||
" freq_point = np.union1d(freq_point, tp.Frequency.unique().astype(str))"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-01-24T09:10:40.887162400Z",
|
||
"start_time": "2024-01-24T08:55:35.907861Z"
|
||
}
|
||
},
|
||
"id": "83e141a1e9d384e6"
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 34,
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": "array(['99.100.10.97', '99.100.100.1', '99.100.100.113', '99.100.100.129',\n '99.100.100.145', '99.100.100.161', '99.100.100.17',\n '99.100.100.177', '99.100.100.193', '99.100.100.209',\n '99.100.100.225', '99.100.100.241', '99.100.100.33',\n '99.100.100.49', '99.100.100.65', '99.100.100.81', '99.100.100.97',\n '99.100.101.1', '99.100.101.113', '99.100.101.129',\n '99.100.101.145', '99.100.101.161', '99.100.101.17',\n '99.100.101.177', '99.100.101.193', '99.100.101.209',\n '99.100.101.225', '99.100.101.241', '99.100.101.33',\n '99.100.101.49', '99.100.101.65', '99.100.101.81', '99.100.101.97',\n '99.100.102.1', '99.100.102.113', '99.100.102.129',\n '99.100.102.145', '99.100.102.161', '99.100.102.17',\n '99.100.102.177', '99.100.102.193', '99.100.102.209',\n '99.100.102.225', '99.100.102.241', '99.100.102.33',\n '99.100.102.49', '99.100.102.65', '99.100.102.81', '99.100.102.97',\n '99.100.103.1', '99.100.103.11', '99.100.103.113',\n '99.100.103.129', '99.100.103.145', '99.100.103.161',\n '99.100.103.17', '99.100.103.177', '99.100.103.193',\n '99.100.103.209', '99.100.103.225', '99.100.103.241',\n '99.100.103.33', '99.100.103.49', '99.100.103.65', '99.100.103.81',\n '99.100.103.97', '99.100.104.1', '99.100.104.113',\n '99.100.104.129', '99.100.104.145', '99.100.104.161',\n '99.100.104.177', '99.100.104.193', '99.100.104.209',\n '99.100.104.225', '99.100.104.241', '99.100.104.33',\n '99.100.104.49', '99.100.104.65', '99.100.104.81', '99.100.104.97',\n '99.100.105.1', '99.100.105.113', '99.100.105.129',\n '99.100.105.145', '99.100.105.161', '99.100.105.17',\n '99.100.105.177', '99.100.105.193', '99.100.105.209',\n '99.100.105.225', '99.100.105.241', '99.100.105.33',\n '99.100.105.49', '99.100.105.65', '99.100.105.81', '99.100.105.97',\n '99.100.106.1', '99.100.106.113', '99.100.106.129',\n '99.100.106.145', '99.100.106.161', '99.100.106.17',\n '99.100.106.177', '99.100.106.193', '99.100.106.209',\n '99.100.106.225', '99.100.106.241', '99.100.106.33',\n '99.100.106.49', '99.100.106.65', '99.100.106.81', '99.100.106.97',\n '99.100.107.1', '99.100.107.113', '99.100.107.129',\n '99.100.107.145', '99.100.107.17', '99.100.107.177',\n '99.100.107.193', '99.100.107.209', '99.100.107.241',\n '99.100.107.33', '99.100.107.49', '99.100.107.65', '99.100.108.1',\n '99.100.108.17', '99.100.108.177', '99.100.108.193',\n '99.100.108.33', '99.100.108.49', '99.100.108.65', '99.100.108.81',\n '99.100.108.97', '99.100.109.113', '99.100.109.129',\n '99.100.109.145', '99.100.109.161', '99.100.109.17',\n '99.100.109.177', '99.100.109.193', '99.100.109.209',\n '99.100.109.225', '99.100.109.241', '99.100.109.49',\n '99.100.109.65', '99.100.109.81', '99.100.109.97', '99.100.11.1',\n '99.100.11.113', '99.100.11.129', '99.100.11.65', '99.100.11.97',\n '99.100.110.1', '99.100.110.129', '99.100.110.145',\n '99.100.110.17', '99.100.110.209', '99.100.110.225',\n '99.100.110.241', '99.100.110.33', '99.100.110.49',\n '99.100.110.81', '99.100.110.97', '99.100.111.129',\n '99.100.111.145', '99.100.111.161', '99.100.111.177',\n '99.100.111.193', '99.100.111.209', '99.100.111.225',\n '99.100.111.33', '99.100.111.49', '99.100.111.81', '99.100.111.97',\n '99.100.112.113', '99.100.112.129', '99.100.112.145',\n '99.100.112.161', '99.100.112.177', '99.100.112.209',\n '99.100.112.241', '99.100.112.33', '99.100.112.49',\n '99.100.112.81', '99.100.112.97', '99.100.113.1', '99.100.113.113',\n '99.100.113.161', '99.100.113.17', '99.100.113.177',\n '99.100.113.193', '99.100.113.209', '99.100.113.225',\n '99.100.113.241', '99.100.113.33', '99.100.113.49',\n '99.100.113.97', '99.100.114.1', '99.100.114.113',\n '99.100.114.129', '99.100.114.145', '99.100.114.161',\n '99.100.114.17', '99.100.114.177', '99.100.114.209',\n '99.100.114.225', '99.100.114.241', '99.100.114.33',\n '99.100.114.49', '99.100.114.65', '99.100.114.81', '99.100.114.97',\n '99.100.115.1', '99.100.115.113', '99.100.115.129',\n '99.100.115.145', '99.100.115.161', '99.100.115.17',\n '99.100.115.177', '99.100.115.193', '99.100.115.209',\n '99.100.115.225', '99.100.115.241', '99.100.115.33',\n '99.100.115.49', '99.100.115.65', '99.100.115.81', '99.100.115.97',\n '99.100.116.1', '99.100.116.113', '99.100.116.129',\n '99.100.116.145', '99.100.116.161', '99.100.116.177',\n '99.100.116.193', '99.100.116.209', '99.100.116.225',\n '99.100.116.241', '99.100.116.33', '99.100.116.49',\n '99.100.116.65', '99.100.116.81', '99.100.116.97', '99.100.117.1',\n '99.100.117.17', '99.100.119.1', '99.100.119.113',\n '99.100.119.129', '99.100.119.145', '99.100.119.161',\n '99.100.119.177', '99.100.119.193', '99.100.119.49',\n '99.100.119.81', '99.100.120.113', '99.100.120.129',\n '99.100.120.145', '99.100.120.161', '99.100.120.17',\n '99.100.120.177', '99.100.120.193', '99.100.120.225',\n '99.100.120.241', '99.100.120.33', '99.100.120.49',\n '99.100.120.65', '99.100.120.81', '99.100.121.113',\n '99.100.121.129', '99.100.121.161', '99.100.121.17',\n '99.100.121.177', '99.100.121.193', 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"text/plain": "array(['1 076 040 KHz', '1 076 130 KHz', '1 076 220 KHz', '1 076 310 KHz',\n '1 076 340 KHz', '1 076 400 KHz', '1 076 490 KHz', '1 076 580 KHz',\n '1 076 670 KHz', '1 076 850 KHz', '1 076 860 KHz', '1 076 940 KHz',\n '1 077 040 KHz', '1 077 210 KHz', '1 077 560 KHz', '1 077 660 KHz',\n '1 077 885 KHz', '1 078 000 KHz', '1 078 020 KHz', '1 078 140 KHz',\n '1 078 580 KHz', '1 078 860 KHz', '1 078 930 KHz', '1 079 180 KHz',\n '1 079 360 KHz', '1 079 540 KHz', '1 079 720 KHz', '1 143 560 KHz',\n '1 144 930 KHz', '1 146 040 KHz', '1 146 130 KHz', '1 146 220 KHz',\n '1 146 310 KHz', '1 146 400 KHz', '1 146 490 KHz', '1 146 580 KHz',\n '1 146 670 KHz', '1 146 760 KHz', '1 146 850 KHz', '1 146 940 KHz',\n '1 147 030 KHz', '1 147 120 KHz', '1 147 210 KHz', '1 147 300 KHz',\n '1 147 390 KHz', '1 147 480 KHz', '1 147 570 KHz', '1 147 660 KHz',\n '1 147 750 KHz', '1 147 840 KHz', '1 147 930 KHz', '1 148 140 KHz',\n '1 148 230 KHz', '1 148 320 KHz', '1 148 410 KHz', '1 148 500 KHz',\n '1 148 590 KHz', '1 148 680 KHz', '1 148 770 KHz', '1 148 860 KHz',\n '1 148 950 KHz', '1 149 040 KHz', '1 149 130 KHz', '1 149 220 KHz',\n '1 149 310 KHz', '1 149 400 KHz', '1 149 500 KHz', '1 149 590 KHz',\n '1 149 680 KHz', '1 149 770 KHz', 'nan'], dtype='<U32')"
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'99.100.98.129': 875,\n '99.100.98.145': 876,\n '99.100.98.161': 877,\n '99.100.98.17': 878,\n '99.100.98.177': 879,\n '99.100.98.193': 880,\n '99.100.98.209': 881,\n '99.100.98.225': 882,\n '99.100.98.241': 883,\n '99.100.98.33': 884,\n '99.100.98.49': 885,\n '99.100.98.65': 886,\n '99.100.98.81': 887,\n '99.100.98.97': 888,\n '99.100.99.1': 889,\n '99.100.99.113': 890,\n '99.100.99.129': 891,\n '99.100.99.145': 892,\n '99.100.99.161': 893,\n '99.100.99.17': 894,\n '99.100.99.177': 895,\n '99.100.99.193': 896,\n '99.100.99.209': 897,\n '99.100.99.225': 898,\n '99.100.99.241': 899,\n '99.100.99.33': 900,\n '99.100.99.49': 901,\n '99.100.99.65': 902,\n '99.100.99.81': 903,\n '99.100.99.97': 904}"
|
||
},
|
||
"execution_count": 37,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"user_dict_code"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-01-24T09:13:12.781101100Z",
|
||
"start_time": "2024-01-24T09:13:12.595603600Z"
|
||
}
|
||
},
|
||
"id": "153149363d5ce162"
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 38,
|
||
"outputs": [],
|
||
"source": [
|
||
"freq_point_code = {freq:i for i, freq in enumerate(freq_point)}"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-01-24T09:14:06.310350200Z",
|
||
"start_time": "2024-01-24T09:14:06.220057700Z"
|
||
}
|
||
},
|
||
"id": "9e75906a71a35f8"
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 39,
|
||
"outputs": [],
|
||
"source": [
|
||
"month_2['user'] = month_2['VastIP'].map(user_dict_code)"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-01-24T09:16:19.955922200Z",
|
||
"start_time": "2024-01-24T09:16:19.737309800Z"
|
||
}
|
||
},
|
||
"id": "bc82193048b91056"
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 51,
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": "'D:\\\\Users\\\\jun\\\\Documents\\\\Code\\\\TEX\\\\Thesis\\\\thesis-code\\\\dataset'"
|
||
},
|
||
"execution_count": 51,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"os.path.abspath(dataset_path)"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-01-24T09:49:12.603433200Z",
|
||
"start_time": "2024-01-24T09:49:12.465395800Z"
|
||
}
|
||
},
|
||
"id": "d4f114090b419f3b"
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 41,
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": "array(['数据', '话音', '数话', '拒绝'], dtype=object)"
|
||
},
|
||
"execution_count": 41,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"month_2.Business.unique()"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-01-24T09:17:12.463629800Z",
|
||
"start_time": "2024-01-24T09:17:12.366662100Z"
|
||
}
|
||
},
|
||
"id": "5971db02e3d4a8be"
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 43,
|
||
"outputs": [],
|
||
"source": [
|
||
"type_dict = {t:i for i, t in enumerate(month_2.Business.unique())}"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-01-24T09:18:31.549105500Z",
|
||
"start_time": "2024-01-24T09:18:31.479563600Z"
|
||
}
|
||
},
|
||
"id": "b63318a80c397d84"
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 48,
|
||
"outputs": [],
|
||
"source": [
|
||
"# month_2.to_json(orient='records', columns = ['BeginTime', ])\n",
|
||
"from datetime import datetime\n",
|
||
"def to_timestamp(s:str):\n",
|
||
" date_time_obj = datetime.strptime(s, '%Y-%m-%d %H:%M:%S')\n",
|
||
"\n",
|
||
"# 将datetime对象转换为整数形式的时间戳\n",
|
||
" timestamp = int(date_time_obj.timestamp())\n",
|
||
" return timestamp\n",
|
||
"month_2['start'] = month_2.BeginTime.map(lambda s:to_timestamp(s))"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-01-24T09:36:12.253212800Z",
|
||
"start_time": "2024-01-24T09:36:07.191276400Z"
|
||
}
|
||
},
|
||
"id": "2f27cfb48f6233c9"
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 56,
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" 0%| | 0/10 [00:00<?, ?it/s]\n"
|
||
]
|
||
},
|
||
{
|
||
"ename": "TypeError",
|
||
"evalue": "read_excel() got an unexpected keyword argument 'read_only'",
|
||
"output_type": "error",
|
||
"traceback": [
|
||
"\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
|
||
"\u001B[1;31mTypeError\u001B[0m Traceback (most recent call last)",
|
||
"\u001B[1;32m<ipython-input-56-3118855aed40>\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[0;32m 1\u001B[0m \u001B[1;32mfor\u001B[0m \u001B[0mf\u001B[0m \u001B[1;32min\u001B[0m \u001B[0mtqdm\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mfile_list\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m----> 2\u001B[1;33m \u001B[0mtp\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mpd\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mread_excel\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mos\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mpath\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mjoin\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mraw_dataset_path\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mf\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mread_only\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;32mTrue\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 3\u001B[0m \u001B[0mtp\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;34m'user'\u001B[0m\u001B[1;33m]\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mtp\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;34m'VastIP'\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mmap\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0muser_dict_code\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 4\u001B[0m \u001B[0mtp\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;34m'type'\u001B[0m\u001B[1;33m]\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mtp\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;34m'Business'\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mmap\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mtype_dict\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 5\u001B[0m \u001B[0mtp\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;34m'freq'\u001B[0m\u001B[1;33m]\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mtp\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;34m'Frequency'\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mmap\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mfreq_point_code\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
|
||
"\u001B[1;32mD:\\Apps\\Anaconda3\\lib\\site-packages\\pandas\\util\\_decorators.py\u001B[0m in \u001B[0;36mwrapper\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m 297\u001B[0m )\n\u001B[0;32m 298\u001B[0m \u001B[0mwarnings\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mwarn\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mmsg\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mFutureWarning\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mstacklevel\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mstacklevel\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 299\u001B[1;33m \u001B[1;32mreturn\u001B[0m \u001B[0mfunc\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m*\u001B[0m\u001B[0margs\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 300\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 301\u001B[0m \u001B[1;32mreturn\u001B[0m \u001B[0mwrapper\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
|
||
"\u001B[1;31mTypeError\u001B[0m: read_excel() got an unexpected keyword argument 'read_only'"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"for f in tqdm(file_list):\n",
|
||
" tp = read_excel(os.path.join(raw_dataset_path, f))\n",
|
||
" tp['user'] = tp['VastIP'].map(user_dict_code)\n",
|
||
" tp['type'] = tp['Business'].map(type_dict)\n",
|
||
" tp['freq'] = tp['Frequency'].map(freq_point_code)\n",
|
||
" output = os.path.join(os.path.abspath(dataset_path), f)\n",
|
||
" tp[['BeginTime', 'StreamNum', 'SlotType', 'CarrierType', 'user', 'type', 'freq', 'Duration']].to_csv(output)"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-01-24T10:10:19.189405800Z",
|
||
"start_time": "2024-01-24T10:10:19.078568900Z"
|
||
}
|
||
},
|
||
"id": "535d838c3c5a6789"
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 58,
|
||
"outputs": [
|
||
{
|
||
"ename": "TypeError",
|
||
"evalue": "read_excel() got an unexpected keyword argument 'read_only'",
|
||
"output_type": "error",
|
||
"traceback": [
|
||
"\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
|
||
"\u001B[1;31mTypeError\u001B[0m Traceback (most recent call last)",
|
||
"\u001B[1;32m<ipython-input-58-2f850c593daa>\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[1;32m----> 1\u001B[1;33m \u001B[0mmonth_3\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mpd\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mread_excel\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mos\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mpath\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mjoin\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mraw_dataset_path\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mfile_temp\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mengine\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;34m\"openpyxl\"\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mread_only\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;32mTrue\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m",
|
||
"\u001B[1;32mD:\\Apps\\Anaconda3\\lib\\site-packages\\pandas\\util\\_decorators.py\u001B[0m in \u001B[0;36mwrapper\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m 297\u001B[0m )\n\u001B[0;32m 298\u001B[0m \u001B[0mwarnings\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mwarn\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mmsg\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mFutureWarning\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mstacklevel\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mstacklevel\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 299\u001B[1;33m \u001B[1;32mreturn\u001B[0m \u001B[0mfunc\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m*\u001B[0m\u001B[0margs\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 300\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 301\u001B[0m \u001B[1;32mreturn\u001B[0m \u001B[0mwrapper\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
|
||
"\u001B[1;31mTypeError\u001B[0m: read_excel() got an unexpected keyword argument 'read_only'"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"month_3 = pd.read_excel(os.path.join(raw_dataset_path, file_temp), engine=\"openpyxl\", read_only=True)"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-01-24T10:22:45.160496500Z",
|
||
"start_time": "2024-01-24T10:22:45.061818900Z"
|
||
}
|
||
},
|
||
"id": "e37b0ee51c81f6b8"
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 60,
|
||
"outputs": [],
|
||
"source": [
|
||
"from openpyxl import load_workbook\n",
|
||
"\n",
|
||
"work_book = load_workbook(filename=\"raw_dataset\\\\Channel_03.xlsx\", read_only=True)"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-01-24T10:28:30.248658100Z",
|
||
"start_time": "2024-01-24T10:28:15.850166200Z"
|
||
}
|
||
},
|
||
"id": "679d082e4bb85b62"
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 61,
|
||
"outputs": [],
|
||
"source": [
|
||
"sheet = work_book.active"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-01-24T10:29:22.430465300Z",
|
||
"start_time": "2024-01-24T10:29:22.250192700Z"
|
||
}
|
||
},
|
||
"id": "c4e50e8c88388da4"
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 62,
|
||
"outputs": [],
|
||
"source": [
|
||
"df = pd.DataFrame(sheet.values)"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-01-24T10:30:48.647266600Z",
|
||
"start_time": "2024-01-24T10:29:38.751286Z"
|
||
}
|
||
},
|
||
"id": "cd8b23dfd5ee7488"
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 65,
|
||
"outputs": [],
|
||
"source": [
|
||
"def read_excel(s):\n",
|
||
" ff = load_workbook(filename=s, read_only=True)\n",
|
||
" return pd.DataFrame(ff.active.values)"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-01-24T10:32:17.491830700Z",
|
||
"start_time": "2024-01-24T10:32:17.390355600Z"
|
||
}
|
||
},
|
||
"id": "6ca92cf5801829b8"
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 67,
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"100%|██████████| 10/10 [16:21<00:00, 98.12s/it]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"for f in tqdm(file_list):\n",
|
||
" tp = pd.read_excel(os.path.join(raw_dataset_path, f))\n",
|
||
" tp['user'] = tp['VastIP'].map(user_dict_code)\n",
|
||
" tp['type'] = tp['Business'].map(type_dict)\n",
|
||
" tp['freq'] = tp['Frequency'].map(freq_point_code)\n",
|
||
" output = os.path.join(os.path.abspath(dataset_path), f)\n",
|
||
" tp[['BeginTime', 'StreamNum', 'SlotType', 'CarrierType', 'user', 'type', 'freq', 'Duration']].to_csv(output)"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-01-24T10:51:30.947246100Z",
|
||
"start_time": "2024-01-24T10:35:09.672634400Z"
|
||
}
|
||
},
|
||
"id": "5aeb1b778e7548c9"
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"outputs": [],
|
||
"source": [],
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"id": "7ad1bc8eb29ddfab"
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 2
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython2",
|
||
"version": "2.7.6"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|