Hi all,
I am coding in the Jupyter Lab on the Red Pitaya and was wondering if there was any way to have the ADC run continuously even after a trigger happens. What I have coded currently is a loop that collects data and then the program is triggered. Then, I have to apply a reset and then a start command. The reset and start potion of the code is slowing the program down by 8 microseconds and I need it to be faster. Is there any way to code it so that the ADC continues to run after the trigger so that I don't need that section of code? By the way I am coding in python.
Thanks for any help that can be offered!
How to get the ADC to run continuosly
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Re: How to get the ADC to run continuosly
Hi,
you can check supported acquisition-related API functions in https://redpitaya.readthedocs.io/en/lat ... cquisition
There, you will find the following Python function which does what you need:
You should call this function just before setting the trigger source.
you can check supported acquisition-related API functions in https://redpitaya.readthedocs.io/en/lat ... cquisition
There, you will find the following Python function which does what you need:
Code: Select all
rp_AcqSetArmKeep(<enable>)
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Re: How to get the ADC to run continuosly
Our code is currently written using the mercury library instead of the rp where the rp_AcqSetArmKeep(<enable>) code is. Is there an equivalent for mercury? I just thought I would ask before I set out to completely rewrite our code using rp.
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Re: How to get the ADC to run continuosly
Hello whatup.itswhit,
No, there is no such setting on the Mercury image. The problem with the Mercury image is that the settings and functions user are not documented well and do not encompass the full Red Pitaya functionality, which is why we moved away from it and completely remade the whole Jupyter Notebook
No, there is no such setting on the Mercury image. The problem with the Mercury image is that the settings and functions user are not documented well and do not encompass the full Red Pitaya functionality, which is why we moved away from it and completely remade the whole Jupyter Notebook
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