This repository contains the error correction code and preprocessing code for Nucleic Acid Memory(NAM)
The codes are tested with python 3.7
Use the package manager pip to install the required packages
pip install numpy scipy numba matplotlib lmfit tqdm yaml h5py
Or use the requirements.txt file:
pip install -r requirements.txt(optional) To import PAINT/STORM movies (nd2, spe, tif, dax) directly into script, 3d_daostorm must be installed from https://github.com/ZhuangLab/storm-analysis Script can be run on localization data (csv, txt, hdf5) without 3d_daostorm installed. Acceptable formats include ThunderStorm, Picasso, and 3d_daostorm.
python localization_preprocessing/dnam_mixed_origami_process.py [-h] [-f FILE] [-v] [-N NUMBER_CLUSTERS]
[-s SKIP_CLUSTERS]
[-d DRIFT_CORRECT_SIZE] [-ps PIXEL_SIZE]
[-x XML] [-ft FILTER_FILE]
[-gf GRID_FILE] [-gr GRID_SHAPE_ROWS]
[-gc GRID_SHAPE_COLS]
[-md MIN_DRIFT_CLUSTERS]
[-gdx GLOBAL_DELTA_X]
[-st SCALED_THRESHOLD] [-rf]
dNAM origami process script
optional arguments:
-h, --help show this help message and exit
-f FILE, --file FILE File name
-v, --verbose Print details of execution on console
-N NUMBER_CLUSTERS, --number-clusters NUMBER_CLUSTERS
Number of clusters to analyze
-s SKIP_CLUSTERS, --skip-clusters SKIP_CLUSTERS
Number of clusters to skip
-d DRIFT_CORRECT_SIZE, --drift-correct-size DRIFT_CORRECT_SIZE
Final size of drift correct slice (0 is no drift
correction))
-ps PIXEL_SIZE, --pixel-size PIXEL_SIZE
Pixel size. Needed if reading Thunderstorm csv
-x XML, --xml XML XML config file for 3d-daostorm analyis (default =
default_config.xml)
-ft FILTER_FILE, --filter-file FILTER_FILE
XML filter file for post-drift correct filtering
(default is no filters)
-gf GRID_FILE, --grid-file GRID_FILE
CSV file containing x,y coordinates of grid points
(default is DNAM average grid)
-gr GRID_SHAPE_ROWS, --grid-shape-rows GRID_SHAPE_ROWS
Rows in the grid
-gc GRID_SHAPE_COLS, --grid-shape-cols GRID_SHAPE_COLS
Columns in the grid
-md MIN_DRIFT_CLUSTERS, --min-drift-clusters MIN_DRIFT_CLUSTERS
Min number of cluster to attempt fine drift correction
(default 5000)
-gdx GLOBAL_DELTA_X, --global-delta-x GLOBAL_DELTA_X
Starting guess for global localization precision due
to drift correct, etc
-st SCALED_THRESHOLD, --scaled-threshold SCALED_THRESHOLD
Threshold for binary counts, as a fraction of the
average of the 10 brightest points
-rf, --redo-fitting Redo grid fitting, even if fitted grid data exists
(has no effect on data without fits)
Example filter xml file:
<?xml version="1.0" encoding="iso-8859-1"?>
<filters>
<!-- Valid filter names are frame, x, y, photons, sx, bg, and lpx. Every implemented filter must have
"type" attribute as "absolute" or "percentile". "low" and "high" attributes must be set. Percentile
filters must have values between 0.0 and 1.0. Low must be lower than high
All units are pixels and photons
Filters will be applied in listed order.-->
<sx type="absolute" low="0.9" high="1.4"></sx>
<photons type="percentile" low=".01" high ="0.95"></photons>
<lpx type="percentile" low=".01" high="0.90"></lpx>
</filters>User the following command to encode a given file to a list of origami matrices
python error_correction/encode.py
-h , --help, show this help message and exit
-f , --file_in, file to encode
-o , --file_out, File to write the output
-r , --redundancy, Percentage of redundant origami
-fo, --formatted_output, Print the origami as matrix instead of single line
-v , --verbose, Print details on the console. 0 -> error. 1->debug, 2->info, 3->warning
-d , --degree, Degree old/new
Use the following command to decode any encoded file:
python error_correction/decode.py
-h, --help, show this help message and exit
-f , --file_in File to decode
-o , --file_out File to write output
-fz , --file_size, File size that will be decoded
-tp , --threshold_parity, Minimum weight for a parity bit cell to be consider that as an error
-td , --threshold_data, Minimum weight for a data bit cell to be consider as an error
-v , --verbose, Print details on the console. 0 -> error, 1 -> debug, 2 -> info, 3 -> warning
-r , --redundancy, How much redundancy was used during encoding
-ior, --individual_origami_info, Store individual origami information
-e , --error, Maximum number of error that the algorithm will try to fix
-fp , --false_positive, 0 can also be 1.
-d , --degree, Degree old/new
-cf , --correct_file, Original encoded file. Helps to check the status automatically.
You can use docker to run the algorithm
To build the docker image use the following command
sudo docker build -t dnam .Run the docker image as a container:
sudo docker run -it dnam {path/to/the/script/to_run} [options]For example to encode a file
sudo docker run -it dnam error_correction/encode.py -f test.txt -o test_output.outTo copy the output file from docker container to host use:
sudo docker cp [container_name]:/[output_file_name] [path/to/copy/the/file]For example:
sudo docker cp 98be599794ac:/test_output.out ./To get the container_name use:
sudo docker ps -a
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