so where is the problem?
So I can create a gaussian filtered array (4000, 1024, 1024) and I have my original array (4000, 1024, 1024)
But if I want to do original-gaussian, in a frame by frame manner, it doesn't work
I can do as you suggest, and take a single composite frame, to subtract from my original array, and that works ok.
2D spatial gaussian filter
you need to gaussian filter each image independently
by using something like
images.map(lambda v: v - gaussian_filter(v))
Maybe I'll give that a try again.
I'm taking the processing pipeline we've been using in matlab in our lab and trying to convert it over to a Python mode, hence why I've been trying to use Thunder. The frame-by-frame portion was getting me stuck in Spark, so I shifted to try working only in local mode again, but this might still work in Spark mode.
Thanks. I'll try it out and report back for the benefit of others.
@d-v-b I'll let you know if I have issues. When you talk about a few tricks, what do you mean? I was doing the registration about 6 months ago by taking a mean image as the reference to send to all the executors which seemed to work ok. Are you able to save the motion correction models as .jsons now? I was previously doing it with pickled files.
in my case, I want to use a registration method that isn't built in to thunder so I don't have a choice
@d-v-b So I went back to running it on Spark again and it seems to still be slightly faster to load the multi-page tiffs than the individual tiffs. I'm still loading the files from a standard file system since Thunder can't load tiffs from an hdfs. I imagine reading from the hdfs it might be quicker, but it seems like the multipage tiffs are still quicker.
Hello, so I was wondering if anyone has any idea how to produce these kind of plots : http://research.janelia.org/zebrafish/trajectories.html
As far as I see from the paper, that's supposed to be the online methods, but it's a bit sparse in details
As far as I see from the paper, that's supposed to be the online methods, but it's a bit sparse in details
Hello all, I am new to thunder, I have a quick question I was wondering if thunder is an appropriate tool for applying large scale multi variable clustering with a mixture of spatial and temporal data... here is how my data looks like ['lable', 'Origin [lat, long]', 'Destination' [lat,long], 'distance', 'departure_time', 'total_travel_time', 'arrival_time',
]
]
Hello everyone. My organization is trying to get Thunder going on a Yarn cluster using virtual environments. Has anyone tried this? It seems to be a little outside of the norm from what I have found, and it is certainly different from how things were done at Janelia when I was there. I am wondering whether this is an okay idea that we should continue to pursue
@mellertd As far as I understand, yarn mode is different for how you deploy your
spark cluster, in Janelia it is in standalone mode. As far as thunder is concerned, they are both the same. You would need a Jupyter notebook and a way to get a spark context in it, which might be different then how it is done in Janelia. Is this the part you need help with?
I guess the question had more to do with the virtual environment issue. I recall at Janelia, Thunder and dependencies were installed on all the nodes, so you could launch standalone clusters and everything worked. For various reasons, we don’t want to maintain installs on all the nodes and would rather let users manage their own environments. This is possibwith yarn mode, but it is rather clunky and is not officially supported in interactive mode (i.e. in Jupyter)
I am just wondering if anyone had any experience with this, because there seem to be many degrees of freedom to get things working well
I have used a virtual environment with thunder, you should change the environmental variable SPRAK_PYTHON and both the driver and workers would see the same python virtual environment.
Complicated than that
This is whatwe are currently tring:
Hm this chat is unusuable on iOS safari, I’ll paste a link when I can get to my laptop
Okay, this is what we are trying: https://community.hortonworks.com/articles/104947/using-virtualenv-with-pyspark.html
Our tests seem to work with virtualenv, but it is very slow. Haven't gotten it to work with Conda yet, but it should not work any differently. I am currently trying to figure out how we might speed things up
I am definitely not an expert here, and have not used any of these using spark submit. But for interactive mode I have used this code to make sure I am in the right enviorenmt for the driver and workers:
import numpy
print(numpy.__file__)
def test1(x):
import numpy
return numpy.__file__
data = sc.range(10).map(test1).collect()
print(data[0])
and PYSPARK_PYTHON worked by pointing it to the virtual environment from conda:
export PYSPARK_PYTHON=/groups/svoboda/home/moharb/anaconda2/envs/py35/bin/python
