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Raw image before image-shifting Image after image-shifting After pre-processing, I expanded the dimensions of the image in compliance with the format required for convolution(using Conv2d( ) from hexagdly) Sample output after convolution for different stride values Please have a look at my notebook here Is this what you expected or have I misunderstood it? It's also good that you are familiar with the functionality of the tool and try to solve this problem using CTLearn. @h3li05369 The event that you are showing in your first two plots look good to me! Awesome that you went one step further and get familiar with the usage of hexagdly.Unfortunately, our data format, which is created through DL1 Data-handler, differs from the MAGIC file. I haven't study this package in detail, so could please explain me your four plots in more detail? @Tjark Miener After pre-processing the image with image_shifting addressing scheme(which was recently integrated into ctlearn from hexagldy by @aribrill), it is fed into the Conv2d layer (which is also imported from hexagdly) for 4 different stride sizes.Spent more time in your application than in the PR!
Using Hexagonal Writing Can Help You
You can either transform the hexagonal camera pixels to square image pixels #56 or you can modify your convolution and pooling methods.I am currently studying a MS in astrophysics and I have started working on hexagonal convolution.I want to contribute to this CTLearn issue under the GSo C project.It happened the other day that a team I was working with felt the right answer to a question about which tech stack to use for a microservice was ‘Kotlin with Ktor using a ports and adapters architecture, building with as a multi-module Gradle project with the Kotlin Gradle DSL. Ktor: Because if you need a framework on top of a framework in order to simplify your framework, something has probably gone wrong somewhere.Plus Guice for dependency injection.’Only no-one on the internet seems to have implemented a microservice with that particular combination of structure and technology before. Each individual thing I mentioned, yes, there is Why these things? Ports and Adapters: P&A, also known as ‘hexagonal architecture’, is an incredibly powerful mental tool for producing testable, clean architecture. I have no doubt with sufficient discipline you can accomplish the same thing with other architectural styles. I find the reaction to adding modules to a project, especially a relatively trivial microservice, quite mixed.While reading the pixel positions into CTLearn, we already make sure to perform the right rotation and therefore the pixel positions have the required form above.So you don't need to add this check in your script!Create the final images using the function "map_image()" and plot them, so that you can compare them (and your script) with test_image_mapper.ipynb. We are reading the pixel positions of the IACTs from the fits file in "ctlearn/ctlearn/pixel_pos_files/", which originate from ctapipe-extra.Hello @Tjark Miener, I noticed that in the 'image shifting' section in the 'image_mapping.py', we have shifted the alternate columns by 1 without checking if they are in the required form as shown here: https://github.com/ai4iacts/hexagdly/blob/master/notebooks/how_to_apply_adressing_scheme.ipynb Should our script to test the code contain images which are not aligned in this particular way, or is the input to our CNN always in the correct form? These fits files also contain rotation information.However, in the beginning, I was trying to use the HDF5Data Loader function from ctlearn.data_loading and that threw an error while accessing the for row in MAGIC.iterrows(): test_data_magic['MAGICCam'] = np.concatenate(([0.0], np.array(row['image_charge']))) test_data_magic['MAGICCam'] = np.expand_dims(test_data_magic['MAGICCam'], axis=1) def _process_array_info(self, filename): # get file handle f = self.files[filename] telescopes = for row in