I need an open source code or python library to eliminate or decrease Ionosphere and Troposphere effects in positioning. could you please help me?
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I have tried for a fews days to use Tensorflow profiler to measure resource usage. But I can't understand the interface in order to get the data I need. I think it has something to do with "Metrics" option in the trace viewer, but it is not clear to me how to get information about CPU, Memory, Disk usage and etc from the interface in a certain interval. Everything appears to be in "ns". If you happen to know a tutorial that goes deep in the trace viewer, please. Let me know.
If this kind of profiling is not possible, also, let me know.
This image shows the "Metrics" option that I refer to
Please help.
I am solving a mixed-integer linear programming model on Python platform using the solver Gurobi. However, The gap of the model still keeps 100 % during the long-running time.
I am trying to use the codes to limit the running time. The solution is found, but I don't know why the gap in the model doesn't reduce.
I have attached the file below.
Could you help me to fix this problem?
Thank you very much!
The gap is defined as:
gap = |bestfound - bestbound|/|bestfound|
(see the Gurobi documentation).
The best bound in your screenshot stays at 0, so the gap does not change and remains 100%.
You could also add a small objective offset to enforce a non-zero dual bound. This will enable a more expressive gap. You could use the ObjCon model attribute to achieve this.
I am attempting to mesh a complicated design (~80,000 faces) for a microchannel heat sink, as pictured, and I would appreciate some advice. I have tried a range of different mesh controls (especially face sizing and body sizing), mesh settings and element sizes, and all have failed to produce a working mesh. The most common errors are shown in the linked picture, in particular the one regarding "The following surfaces cannot be meshed with acceptable quality. Try using a different element size or virtual topology." However, I have already reduced the element size to 2x10^-6 m, which takes two days to resolve before failure.
Unfortunately I cannot alter the geometry significantly, as it is imported from generation in SolidWORKS as either a STEP or an x.t file. As such, any advice for how I can successfully mesh the geometry for CFD analysis in FLUENT would be greatly appreciated.
I can provide more details or the geometry file itself if required.
Thanks in advance.
Meshing Attempt
Probably your cad design is not clean at all. But it is impossible to notice from this image. If you don't have control over the geometry source it is trouble. Because you might ask somebody else about check and fix something. First check you can do with your model it's trying to reduce the number of elements until the minimum possible value. Then if the mesh runs properly you can relay in the surfaces of your cad model. After that, you can refine the mesh, but the refining process is something that you have to do following some error criteria. If you are also the designer why not try to simplify a bit the geometry if you consider it is really hard to mesh? Meshing properly is a hard task, you should go step-by-step until you reach some solution. Also, you must not allow the preprocessor mesh automatically, without giving some criteria. Probably the first thing you have to answer even before apply any mesh is, what is your Reynolds number? And what is the most valuable result in which you can base the goodness of your discretization?
Thank you for your suggestions. In the end I solved the issue by importing the original mesh generated by COMSOL into SpaceClaim, then employing both the "Smooth" and "Reduce Faces" tools in tandem to simplify the geometry, before finally using SolidWORKS to turn the smoothed mesh into a solid body. This body retained many of the same features as the original, but was much less complex, having two orders of magnitude fewer faces. In turn, this permitted both meshing and heat transfer analysis in FLUENT.
my question refers to gif optimization.
I've used the main known softwares to achieve gif optimization, but each of them allows to optimize one gif at the same time.
Does someone know how to optimize thousands of gifs at the same time?
Thanks in advance
The OpenSource project, ImageMagick, offers a suite of command-line tools for batch processing images.
Check out the article "ImageMagick v6 Examples -- Animation Optimization" for a how-to on performing some GIF opimizations.
I am using Tesseract OCR (via pytesser) and PIL (Python Image Library) for automated test of an application.
I am checking that the displayed text is ok by making a screenshot and getting the text thanks to tesseract.
I had some issues in the beginning and it seems to work better since I have increased the size of the screenshot thanks to the bicubic interpolation of PIL.
Unfortunatelly, I still have some mistakes like confusion between '0' and 'O'. I can imagine that I will have other similar issues in the future.
I would like to know if there are some techniques to prepare an image in order to help the OCR. Any idea is welcomed.
Thanks in advance
Shameless plug and disclaimer: my company packages Tesseract for use in .NET
Tesseract is an OK OCR engine. It can miss a lot and gets readily confused by non-text. The best thing you can do for it is to make sure it gets text only. The next best thing is to give it something sanely binarized (adaptive or dynamic threshold to get there) or grayscale and let it try to do binarization.
Train tesseract to recognize your font
Make image extra clean and with enough free space around characters
Profit :)
Here are few real world examples.
First image is original image (croped power meter numbers)
Second image is slightly cleaned up image in GIMP, around 50% OCR accuracy in tesseract
Third image is completely cleaned image - 100% OCR recognized without any training!
Even under the best conditions OCR variants will sneak up on you. Your best option will be to design your tests to be aware of them.
For distinguishing between 0 and O, one simple solution is to choose a font that distinguishes between both (eg: 0 has a dash or dot in its middle). Would that be acceptable in your application?
Another solution is to apply a dictionary-based step after the character-by-character analysis of the text - feeding the recognized text into some form of spell-checker or validator to differentiate between difficult characters.
For instance, a round symbol followed by other numbers is most likely to be a zero, while the same symbol followed by letters is most likely to be a capital o. It's a trivial example, but it shows how context is necessary to make a more reliable OCR system.