- cloudreg.scripts.run_colm_pipeline_ec2.run_colm_pipeline(ssh_key_path, instance_id, input_s3_path, output_s3_path, num_channels, autofluorescence_channel, log_s3_path=None, instance_type='r5d.24xlarge')¶
Run COLM pipeline on EC2 instance
ssh_key_path (str) – Local path to ssh key needed for this server
instance_id (str) – ID of the EC2 instance to run pipeline on
input_s3_path (str) – S3 Path to raw data
output_s3_path (str) – S3 path to store precomputed volume. Volume is stored at output_s3_path/channel for each channel.
num_channels (int) – Number of channels in this volume
autofluorescence_channel (int) – Autofluorescence channel number
log_s3_path (str, optional) – S3 path to store intermediates including vignetting correction and Terastitcher files. Defaults to None.
instance_type (str, optional) – AWS EC2 instance type. Defaults to “r5d.24xlarge”.
- cloudreg.scripts.colm_pipeline.colm_pipeline(input_s3_path, output_s3_path, channel_of_interest, autofluorescence_channel, raw_data_path, stitched_data_path, log_s3_path=None)¶
Run COLM pipeline including vignetting correction, stitching, illumination correction, and upload to S3 in Neuroglancer-compatible format
input_s3_path (str) – S3 path to raw COLM data. Should be of the form s3://<bucket>/<experiment>
output_s3_path (str) – S3 path to store precomputed volume. Precomputed volumes for each channel will be stored under this path. Should be of the form s3://<bucket>/<path_to_precomputed>
channel_of_interest (int) – Channel number to operate on. Should be a single integer.
autofluorescence_channel (int) – Autofluorescence channel number. Should be a single integer.
raw_data_path (str) – Local path where corrected raw data will be stored.
stitched_data_path (str) – Local path where stitched slices will be stored.
log_s3_path (str, optional) – S3 path at which pipeline intermediates can be stored including bias correction tile and xml files from Terastitcher. Defaults to None.