idl调用python_学习用于遥感的Python或从IDL切换的资源

idl调用python

I’m supervising an MSc student for her thesis this summer, and the work she’s doing with me is going to involve a fair amount of programming, in the context of remote sensing & GIS processing. She’s got experience programming in IDL from a programming course during the taught part of her Masters, but has no experience of Python.

今年夏天，我正在指导硕士研究生的论文，在遥感和GIS处理的背景下，她与我一起进行的工作将涉及大量编程工作。 在其硕士课程的教学期间，她从编程课程中获得了IDL编程经验，但是没有Python经验。

I’ve just sent her an email with links to some useful resources, but in the spirit of Matt Might’s Blog tips for busy academics, I thought it would be worth doing a ‘reply to public’, and putting the list of resources here. So, here goes…

我刚刚给她发送了一封电子邮件，其中包含一些有用资源的链接，但是本着Matt Might的博客为忙碌的学者提供的技巧的精神，我认为值得“回复公众”，并将资源清单放在此处。 所以，这里...

1. Software Carpentry Python course:- this is designed for people new to programming, so some of it will be very easy for you. However, it takes you through a good example of scientific programming with Python, including plotting graphs and dealing with arrays. I suggest that you use the Jupyter Notebook to run through this – there are instructions on how to do that in the course, and there is a brief intro to the notebook in this YouTube video (start at approx 3 minutes – the stuff before that is irrelevant for you)
2. Lewis’s Scientific Programming for RS course (from UCL): – The most useful bits will probably be Python 101, Plotting and Numerical Python and Geospatial Data. Click the ‘Course Notes’ under each section to see the detailed notes.
3. Python Scripting with Spatial Data is also good – it gives a good intro to Python, and then covers spatial analysis using GDAL and RIOS (we won’t be using RIOS, but the GDAL stuff is good).

4. Geoprocessing with Python using Open Source GIS: This is a very good set of slides and tutorials – along with assignments, homework tasks and solutions – from a course run at Utah State University. Unfortunately it is now very old – it was written in 2008/9 – and so it refers to a number of out of date things (such as the ‘numeric’ library for Python, which has been replaced by numpy). A lot of the GDAL content is still useful though – for example, this set of slides on reading raster data with GDAL is pretty good.

5. There are various good tutorials from conferences such as SciPy (Scientific Python) and FOSS4G (Free and Open Source Software for Geospatial). For example, you can watch a video of a three-hour tutorial from SciPy 2015 called Geospatial Data with Open Source tools in Python, and you can find the slides and other resources here. This goes into quite a lot of depth, and new Python programmers may find it all quite daunting – but it demonstrates the nice modern ways of doing things (using libraries like Fiona and rasterio) rather than the less-nice and lower-level GDAL library.

1. Software Carpentry Python课程 ：-此课程是为编程新手设计的，因此对您来说有些简单。 但是，它将带您通过Python进行科学编程的一个很好的例子，包括绘制图形和处理数组。 我建议您使用Jupyter Notebook来完成此操作-本课程中有关于如何执行此操作的说明，并且此YouTube视频中对该笔记本做了一个简短的介绍（大约3分钟开始-之前的内容是与您无关
2. Lewis的RS科学编程课程 （来自UCL）：–最有用的位可能是Python 101，绘图和数值Python和地理空间数据。 单击每个部分下面的“课程注释”以查看详细的注释。
3. 使用空间数据进行Python脚本编写也很不错–它为Python提供了很好的介绍，然后介绍了使用GDAL和RIOS进行空间分析（我们不会使用RIOS，但是GDAL的东西很好）。

4. 使用开源GIS使用Python进行地理处理 ：这是一组非常好的幻灯片和教程-以及作业，作业任务和解决方案-来自犹他州立大学的一门课程。 不幸的是，它现在很旧-写于2008/9-因此它引用了许多过时的东西（例如Python的“数字”库，已被numpy取代）。 但是，许多GDAL内容仍然有用-例如，这组有关使用GDAL读取栅格数据的幻灯片非常好。

5. 会议上有许多不错的教程，例如SciPy（科学Python）和FOSS4G（地理空间的免费和开源软件）。 例如，您可以观看来自SciPy 2015的三个小时教程的视频，该视频名为Python中的带有开放源代码工具的地理空间数据 ，您还可以在此处找到幻灯片和其他资源。 这涉及了很多深度，新的Python程序员可能会发现这一切令人望而生畏–但它展示了不错的现代做事方式（使用Fiona和rasterio之类的库），而不是精巧而底层的GDAL库。

There are also a few useful resources for switching from IDL to Python. Specifically:

从IDL切换到Python还有一些有用的资源。 特别：

1. A Numpy reference for IDL users:  (Numpy is the Python library that provides functions to manipulate arrays – unlike IDL, this isn’t included by default in Python – but it does come with the Anaconda distribution I mentioned above).

2. I wrote a blog post comparing a set of ‘Ten Little Programs’ in IDL with equivalents in Python, which should give you an idea of the similarities and differences, and how you can translate some of your code.

1. 针对IDL用户的Numpy参考 ：（Numpy是提供操作数组的函数的Python库-与IDL不同，Python默认不包含该函数-但它确实带有我上面提到的Anaconda发行版）。

2. 我写了一篇博客文章，将IDL中的“十个小程序”与Python中的等效物进行了比较，这应该使您了解相同点和不同点，以及如何翻译某些代码。

This is a very short list of a few resources – I’m sure there are some better ones out there, and so please let me know if you’ve got any recommendations!

这是一小部分资源的简短清单-我敢肯定还有一些更好的资源，因此，如果您有任何建议，请告诉我！

翻译自: https://www.pybloggers.com/2016/05/resources-for-learning-python-for-remote-sensing-or-switching-from-idl/

idl调用python