package org.niit.mock
import java.io.{File, PrintWriter}
import java.sql.Timestamp
import java.text.SimpleDateFormat
import java.util.Date
import com.google.gson.Gson
import org.niit.bean.Answer
import scala.collection.mutable.ArrayBuffer
import scala.util.Random
/**
* 在线教育学生学习数据模拟程序
*/
object Simulator {
//模拟数据
//学生ID
val arr1 = ArrayBuffer[String]()
for (i <- 1 to 50) {
arr1 += "学生ID_" + i
}
//教材ID
val arr2 = Array("教材ID_1", "教材ID_2")
//年级ID
val arr3 = Array("年级ID_1", "年级ID_2", "年级ID_3", "年级ID_4", "年级ID_5", "年级ID_6")
//科目ID
val arr4 = Array("科目ID_1_数学", "科目ID_2_语文", "科目ID_3_英语")
//章节ID
val arr5 = Array("章节ID_chapter_1", "章节ID_chapter_2", "章节ID_chapter_3")
//题目ID与教材、年级、科目、章节的对应关系,
val questionMap = collection.mutable.HashMap[String, ArrayBuffer[String]]()
var questionID = 1
for (textbookID <- arr2; gradeID <- arr3; subjectID <- arr4; chapterID <- arr5) {
val key = new StringBuilder()
.append(textbookID).append("^")
.append(gradeID).append("^")
.append(subjectID).append("^")
.append(chapterID)
val questionArr = ArrayBuffer[String]()
for (i <- 1 to 20) {
questionArr += "题目ID_" + questionID
questionID += 1
}
questionMap.put(key.toString(), questionArr)
}
val sdf = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss")
def genQuestion() = {
//随机教材ID
val textbookIDRandom = arr2(Random.nextInt(arr2.length))
//随机年级ID
val gradeIDRandom = arr3(Random.nextInt(arr3.length))
//随机科目ID
val subjectIDRandom = arr4(Random.nextInt(arr4.length))
//随机章节ID
val chapterIDRandom = arr5(Random.nextInt(arr5.length))
val key = new StringBuilder()
.append(textbookIDRandom).append("^")
.append(gradeIDRandom).append("^")
.append(subjectIDRandom).append("^")
.append(chapterIDRandom)
//取出题目
val questionArr = questionMap(key.toString())
//随机题目ID
val questionIDRandom = questionArr(Random.nextInt(questionArr.length))
//随机题目扣分
val deductScoreRandom = Random.nextInt(11)+1
//随机学生ID
val studentID = arr1(Random.nextInt(arr1.length))
//答题时间
val ts = System.currentTimeMillis()
val timestamp = new Timestamp(ts)
val answerTime = sdf.format(new Date(ts))
Answer(studentID, textbookIDRandom, gradeIDRandom, subjectIDRandom, chapterIDRandom, questionIDRandom, deductScoreRandom, answerTime, timestamp)
}
//测试模拟数据
def main(args: Array[String]): Unit = {
val printWriter = new PrintWriter(new File("output/question_info.json"))
val gson = new Gson()
for (i <- 1 to 2000) {
println(s"第{$i}条")
val jsonString = gson.toJson(genQuestion())
println(jsonString)
//{"student_id":"学生ID_44","textbook_id":"教材ID_1","grade_id":"年级ID_4","subject_id":"科目ID_3_英语","chapter_id":"章节ID_chapter_3","question_id":"题目ID_701","score":10,"answer_time":"2020-01-11 17:20:42","ts":"Jan 11, 2020 5:20:42 PM"}
printWriter.write(jsonString + "\n")
//Thread.sleep(200)
}
printWriter.flush()
printWriter.close()
}
}
这段代码创建了一个气象模拟数据生成器，为10个不同的位置生成天气数据。数据包括天气类型、温度、湿度和时间戳。
```scala
package org.example.mock
import java.io.{File, PrintWriter}
import java.sql.Timestamp
import java.text.SimpleDateFormat
import java.util.Date
import com.google.gson.Gson
import org.example.bean.Weather
import scala.collection.mutable.ArrayBuffer
import scala.util.Random
/**
* Weather simulation data generator
*/
object WeatherSimulator {
// Simulate data
// Weather types
val weatherTypes = Array("Sunny", "Cloudy", "Rainy", "Snowy", "Windy", "Foggy")
// Locations
val locations = ArrayBuffer[String]()
for (i <- 1 to 10) {
locations += "Location_" + i
}
val sdf = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss")
def genWeather(): Weather = {
// Random location
val location = locations(Random.nextInt(locations.length))
// Random weather type
val weatherType = weatherTypes(Random.nextInt(weatherTypes.length))
// Random temperature
val temperature = Random.nextInt(101) - 50
// Random humidity
val humidity = Random.nextInt(101)
// Random timestamp
val ts = System.currentTimeMillis()
val timestamp = new Timestamp(ts)
val weatherTime = sdf.format(new Date(ts))
Weather(location, weatherType, temperature, humidity, weatherTime, timestamp)
}
// Test simulated data
def main(args: Array[String]): Unit = {
val printWriter = new PrintWriter(new File("output/weather_data.json"))
val gson = new Gson()
for (i <- 1 to 2000) {
println(s"Record {$i}")
val jsonString = gson.toJson(genWeather())
println(jsonString)
printWriter.write(jsonString + "\n")
}
printWriter.flush()
printWriter.close()
}
}
```
可以借鉴下
import io
import sys
import requests
import os
import bs4
from bs4 import BeautifulSoup
import numpy as np
import pandas as pd
sys.stdout = io.TextIOWrapper(sys.stdout.buffer,encoding='gb18030') #改变标准输出的默认编码, 防止控制台打印乱码
target_year_list = ["2011", "2012", "2013", "2014", "2015", "2016", "2017", "2018","2019"]
target_month_list = ["01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12"]
#得到一个以城市名拼音为键，城市名为名的数据字典，{"ZHENGZHOU":"郑州","KAIFENG":"开封",...}
def get_city_dict(file_path):
city_dict = {}
with open(file_path, 'r',encoding='UTF-8') as file:
#line_list = f.readline()
for line in file:
line = line.replace("\r\n", "")
city_name = (line.split(" ")[0]).strip()
city_pinyin = ((line.split(" ")[1]).strip()).lower()
#赋值到字典中...
city_dict[city_pinyin] = city_name
return city_dict
file_path = "D:/PP/weather/city.txt"
city_dict = get_city_dict(file_path) #从指定文件city.txt读取城市信息，调用get_city_dict
#得到全部url，格式：url = "http://www.tianqihoubao.com/lishi/beijing（城市名）/month/201812（年月).html"
def get_urls(city_pinyin):
urls = []
for year in target_year_list:
for month in target_month_list:
date = year + month
urls.append("http://www.tianqihoubao.com/lishi/{}/month/{}.html".format(city_pinyin, date))#每年每月每个地市
return urls
#用BeautifulSoup解析每个url返回的网页，以得到有用的数据
def get_soup(url):
try:
r = requests.get(url, timeout=30)
r.raise_for_status() # 若请求不成功,抛出HTTPError 异常
# r.encoding = 'gbk'
soup = BeautifulSoup(r.text, "html.parser")
return soup
# except HTTPError:
# return "Request Error"
except Exception as e:
print(e)
pass
#保存解析后的网页数据
def get_data(url):
print(url)
try:
soup = get_soup(url)
all_weather = soup.find('div', class_="wdetail").find('table').find_all("tr")
data = list()
for tr in all_weather[1:]:
td_li = tr.find_all("td")
for td in td_li:
s = td.get_text()
# print(s.split())
data.append("".join(s.split()))
res = np.array(data).reshape(-1, 4)
return res
except Exception as e:
print(e)
pass
#数据保存到本地csv文件
def saveTocsv(data, city):
'''
将天气数据保存至csv文件
'''
fileName = 'D:/PP/weather/' + city_dict[city] + '_weather.csv'
result_weather = pd.DataFrame(data, columns=['date', 'tq', 'temp', 'wind'])
# print(result_weather)
result_weather.to_csv(fileName,index=False, header=(not os.path.exists(fileName)), encoding='gb18030') #mode='a'追加
print('Save all weather success!')
#主函数
if __name__ == '__main__':
for city in city_dict.keys(): #读城市字典的键
print(city, city_dict[city])
data_ = list()
urls = get_urls(city) #urls保存了所有城市的所有年月的url
for url in urls:
try:
data_.extend(get_data(url)) # 列表合并，将某个城市所有月份的天气信息写到data_
except Exception as e:
print(e)
pass
saveTocsv(data_, city) # 保存为csv
以下内容由CHATGPT及阿里嘎多学长共同生成、有用望采纳：
我可以为您提供一些思路：
首先需要确定生成的气象数据的具体格式和内容，例如温度、湿度、风速等指标，以及时间、地点等元信息。然后可以使用scala编写代码来生成这些数据，并将其保存到文件中。
为了模拟真实的气象数据，可以使用随机数生成器来生成随机的指标值。例如，可以使用scala.util.Random类来生成随机的温度值，然后将其保存到文件中。
为了模拟不同地点的气象数据，可以使用scala中的case类来表示不同的气象站。然后可以使用spark来分布式生成这些数据，并将其保存到分布式文件系统中。
以下是一些伪代码来帮助您入手：
// 定义一个气象站的case类
case class WeatherStation(name: String, latitude: Double, longitude: Double)
// 定义一些气象站的信息
val stations = Seq(
WeatherStation("Station A", 40.0, -120.0),
WeatherStation("Station B", 42.0, -122.0),
WeatherStation("Station C", 38.0, -118.0)
)
// 定义一个生成气象数据的函数
def generateWeatherData(station: WeatherStation, startTime: Long, endTime: Long): Seq[(Long, Double, Double, Double)] = {
val rand = new Random()
val timestamp = startTime
val data = Seq.newBuilder[(Long, Double, Double, Double)]
while (timestamp < endTime) {
val temperature = rand.nextDouble() * 10 + 20 // 生成20-30之间的随机温度
val humidity = rand.nextDouble() * 50 + 50 // 生成50-100之间的随机湿度
val windSpeed = rand.nextDouble() * 10 // 生成0-10之间的随机风速
data += ((timestamp, station.latitude, station.longitude, temperature, humidity, windSpeed))
timestamp += 60 * 60 * 1000 // 每小时生成一条记录
}
data.result()
}
// 生成所有气象站的数据
val allData = stations.flatMap(station => generateWeatherData(station, startTime, endTime))
// 将数据保存到文件中
val writer = new PrintWriter(new File("weather_data.txt"))
allData.foreach(data => writer.write(data.toString() + "\n"))
writer.close()
// 将数据保存到分布式文件系统中
val rdd = spark.parallelize(allData)
rdd.saveAsTextFile("hdfs://path/to/weather_data/")
以下答案由GPT-3.5大模型与博主波罗歌共同编写：
题目要求用Spark生成气象模拟数据，而给出的代码并非与此有关，因此我将解释如何使用Spark生成气象模拟数据。
首先，为了生成气象模拟数据，我们需要一个可靠的气象模型，最常用的是全局气象模型（Global Circulation Model, GCM），例如美国国家大气研究中心的NCAR-CESM（National Center for Atmospheric Research - Community Earth System Model）。这些模型包括复杂的数值算法，需要海量的计算资源。因此，我们需要使用Spark进行并行化计算，使用Spark的强大功能。
在Spark中，我们可以使用Scala，Java或Python，本文采用Scala。
首先，我们需要生成气象网格，通常使用具有规则间距的网格，如经纬度网格。网格每个单元格之间的距离称为分辨率（resolution），决定了模拟的精度。我们可以提供一个经纬度的范围（longitude range, latitude range），以及分辨率，然后生成一个矩形网格，并为每个单元格分配一个ID。
接下来，我们需要确定模拟时间范围和时间步长。我们可以提供一个开始时间和结束时间，以及时间步长，例如每小时模拟一次，然后生成时间网格，为每个时间点分配一个ID。
然后，我们需要为每个网格单元和时间点生成气象数据。这可以通过在每个Spark任务中生成随机数来模拟。例如，我们可以生成温度、湿度、风向和风速等随机数，并将它们存储在类似于JSON的数据格式中。
最后，我们将所有数据写入文件。由于我们使用了Spark，因此可以轻松地扩展并行计算。我们可以采用多种方式将数据写入文件，例如使用Apache Spark的saveAsTextFile方法将数据保存在HDFS中，或者使用Apache Spark SQL将数据保存在Parquet格式中。
下面给出Scala代码示例，仅供参考：
import scala.util.Random
case class GridCell(id: Int, longitude: Double, latitude: Double)
case class TimeStep(id: Int, timestamp: Long)
case class MeteorologyData(gridCellID: Int, timeStepID: Int, temperature: Double, humidity: Double, windDirection: Double, windSpeed: Double)
object MeteorologySimulator {
def main(args: Array[String]): Unit = {
val random = new Random()
val longitudeRange = (0.0, 10.0)
val latitudeRange = (0.0, 10.0)
val gridResolution = 1.0
val timeStart = 1577836800 // 2020-01-01 00:00:00
val timeEnd = 1577923199 // 2020-01-02 00:00:00
val timeStep = 3600 // 1 hour
val grid = generateGrid(longitudeRange, latitudeRange, gridResolution)
val timeGrid = generateTimeGrid(timeStart, timeEnd, timeStep)
val meteorologyData = generateMeteorologyData(grid, timeGrid, random)
val outputFilePath = "/path/to/output/file"
saveData(outputFilePath, meteorologyData)
}
def generateGrid(longitudeRange: (Double, Double), latitudeRange: (Double, Double), gridResolution: Double): Seq[GridCell] = {
// TODO: implement grid generation logic
???
}
def generateTimeGrid(timeStart: Long, timeEnd: Long, timeStep: Int): Seq[TimeStep] = {
// TODO: implement time grid generation logic
???
}
def generateMeteorologyData(grid: Seq[GridCell], timeGrid: Seq[TimeStep], random: Random): Seq[MeteorologyData] = {
grid.flatMap { gridCell =>
timeGrid.map { timeStep =>
val temperature = random.nextDouble() * 30.0 - 10.0
val humidity = random.nextDouble() * 50.0 + 50.0
val windDirection = random.nextDouble() * 360.0
val windSpeed = random.nextDouble() * 10.0
MeteorologyData(gridCell.id, timeStep.id, temperature, humidity, windDirection, windSpeed)
}
}
}
def saveData(outputFilePath: String, data: Seq[MeteorologyData]): Unit = {
// TODO: implement data saving logic
???
}
}
如果我的回答解决了您的问题，请采纳！