如何生成随机时间序列数据，以使风速和风角的给定概率分布？
我已将船舶运营的 Excel 数据导入到 Pandas 数据框中。数据帧具有经纬度和时间戳，间隔为20分钟。该数据为一个月航程的数据。我还有真实的风速和风角概率分布。
是否可以生成风速和风角的随机时间序列以适合我已有的 Pandas 数据框？条件是：
- 时间序列应该与我给定的发生概率大致相同。
- 尽管风速和角度是随机的，但风速和角度不应发生太大变化。在一个时间戳上，我们的风速为 10 节，而另一时间戳的风速为 50 节。那是不现实的。
风速的分布是这样的。
最后，我们的想法是能够进行 n 次执行上述相同随机生成的模拟，以计算轴功率。这有点像蒙特卡罗模拟。对此也需要一些指导。
您能给我一些使用 Pandas 的 Python 想法和模块或函数吗？我可以做到这一点吗？然后我可以更深入地研究这些内容来研究并完成这个任务。我添加了带有时间戳和船速的 .csv 文件。
I have imported an excel data of ship operations into a Pandas data frame. The data frame has latitude and longitude and time stamps with an interval of 20 min. The data is for a voyage of one month. I also have the true wind speed and wind angle probability distribution.
Is it possible to generate a random time series of wind speed and wind angles to fit into my already existing Pandas data frame? The condition is that:
- The time series should be approximately be same as my given probability of occurance.
- Even though its random the wind speeds and angles should not change drastically. At one time stamp we have 10 knots wind speed and another one its 50 knots. That is not realistic.
The distribution of wind speeds is like this.
Finally, the idea is to able to carry out a simulation performing the same random generation mentioned above for n number of times to calculate the shaft power. This would be something like a monte carlo simulation. Need some guidance for this too.
Can you please give me some ideas and modules or functions in Python using Pandas that I would be able to do this? I can then dig deeper into those to study and complete this. I have added the .csv file with the time stamps and ship speeds.
.csv file with time stamps, latitude and longitude and ship speed
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