Austin based Enthought offers their flagship scientific Python distribution, Canopy. The Canopy Geoscience (or Canopy Geo) variant of the product is a data analysis, exploration and visualization package optimized for geologists & geophysicists, and researchers in petroleum science.
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GitLab
Score 8.9 out of 10
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Enthought Canopy is best suites for scripting data analytical concepts. It has a wide range of data analytical libraries and also is good for data visualization. I would not recommend using Enthought Canopy only as an IDE, there may be better options available. If you're looking for a good data simulation & visualization package, Canopy it is.
We started to use GitLab for hosting git source code repositories of our projects only but slowly we started to use it to store container images, packages, dependency proxy as well infrastucture registry and it is now well suited for Continuous Integration in our projects, It wasn't that good in Continuous Deployment before 12.0 version but after 12.0 it is amazingly good for Continuous Deployment as well since it keeps deployment information in a well organized manner which can be configure in ci yaml configuration.
Providing scientific libraries, both open source and Enthought's own libraries which are excellent.
Training. They provide several courses in python for general use and for data analysis.
Debugging tools. Several IDEs provides tools for debugging, but I think they are insufficient or too general. Canopy has a special debugging tool, specially design for python.
Gitlab is the best in its segment. They have a free version, they have open-source software, they provide a good service with their SaaS product, they are a fully-remote company since the beginning (which means they are fully distributed and have forward-thinking IMO). I would certainly recommend them to everyone.
I find it easy to use, I haven't had to do the integration work, so that's why it is a 9/10, cause I can't speak to how easy that part was or the initial set up, but day to day use is great!
At this point, I do not have much experience with Gitlab support as I have never had to engage them. They have documentation that is helpful, not quite as extensive as other documentation, but helpful nonetheless. They also seem to be relatively responsive on social media platforms (twitter) and really thrived when GitHub was acquired by Microsoft
Before Canopy with its python we were working with Matlab. We decided for Canopy against Matlab for two reasons: First, we believe that python together with NumPy or SciPy can achieve the objectives with less code and therefore less training, and second the prizes are much lower than matlab which is most robust, expensive and less intuitive. It's clear we are making the comparison with python and it has nothing to with canopy. But with Canopy you feel you have all those tools close together without the problem of configuration, besides a lot of personalized libraries that complements a typical python environment.
GitLab is easily the preferred tool when it comes to versioning and source control. With other tools the UI often feels outdated and clunky leading to inefficiency and confusion. With some of the sleeker tools such as GitKraken, while the aesthetic is pleasing, the experience is plagued by a lack of support, lack of optional plugins, and a plethora of bugs that cause unnecessary legwork to resolve. GitLab is the best of both aesthetic and functionality
