new flat mockups#90
Conversation
… per conversations with cal block Stefano and Tim and Rachael
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I still want to add a few more details here but this is a crude and not polished to pseudo code flat workflow. Still need to:
@BradleySappington to give you an idea here. There are two different flat components that will be derived. One is the Large scale flat from all 18 detectors at the same time looking at a flattened sky. And then there is the Pixel scale flat which is what we originally had. There is only a data model and a flattened correction for Flat or FLAT or ref_type = FLAT that is the combination of these two components. Imagining we could make each component separately and store the Large flats on the repo, or deliver p-flats at a high cadence, I decided to mock up meta data for us to treat the components like their own files. This would allow us to populate meta data for different products and trace delivery and monitor with RDMT let's say. Feedback welcome. |
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The code looks good. What I am not sure is if we will end up having two different files or a single file with different data blocks for the different components. Any thoughts? |
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What I think we should do now is a workflow like this
What do you think @fjaviersanchez |
BradleySappington
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theory here is good, so each instance of this is a single pflat and lflat combined?
| # 2. Apply L-flat correction assuming multiplicative | ||
| # 3. Normalize | ||
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| wfi_fov_p_flat = self._assemble_detector_grid(self.pflat_list) |
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where is this getting instantiated?
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What do you mean? wfi_fov_p_flat? or the method _assemble_detector()? A lot of this is TBD so internal methods like this could all be changed or not needed as drafted.
This is a mock-up for how we could do both pixel level and large scale flats in the RFP
Provided separate classes for each type of flat with a base class flat that would combine the different products to deliver a combined product to CRDS.