hatTrick 0.2.1

Hadamard Transform for Time-Resolved Crystallography

hatTrick (HATRX) - Hadamard encoding/decoding for Time-Resolved Crystallography

hatTrick (HATRX) is a lightweight package aimed at making the work of encoding and decoding the hadamard transform simple. This package is primarily aimed at time resolved serial crystallography (TRSC) and seeks to improve signal to noise ratio and indexing rates within the data provided.

Installation

This package is available through pypi and downloadable with the pip package manager

pip install hatTrick

Dependencies

• numpy >= 1.20.0
• pandas >= 1.3.0
• scipy >= 1.7.0
• h5py >= 3.0.0
• bitshuffle >= 0.3.0
• click >= 8.0.0

Example Usage (With Crystfel)

While this package does support access through the python api, it is more simple to build a pipeline along the command line. This makes interacting with crystfel simpler as you can immediately place everything into a job script without the need for an intermediate python script.

For our example, let's assume we have 45 frames, each of 5ms, and want to do a rank 3 encoding of our data.

(1) Encoding Data

This step intakes a list file containing information of where to find your .h5 data as well as information about your data (how many frames you would like to encode, how many frames exist in your data).

LIST_FILE={DATA.LST}
N_MERGED_FRAMES=3           # Must be prime and ≡ 3 (mod 4): 3, 7, 11, 19, 23, 31...
N_FRAMES=45
DATA_LOCATION="entry/data"
DATA_NAME="data"
OUTDIR="hadamard_outputs"
N_FILES=8                   # Process 8 files in parallel
WORKERS_PER_MERGE=4         # Workers per single merge

export LIST_FILE N_MERGED_FRAMES DATA_LOCATION DATA_NAME OUTDIR WORKERS_PER_MERGE

mkdir -p "$OUTDIR" logs

run_hadamard_merge() {
    local INPUT_FILE="$1"
    local BASENAME=$(basename "$INPUT_FILE" .h5)
    local OUTPUT_FILE="${OUTDIR}/${BASENAME}_hadamard.h5"
    
    hatrx encode \
        -f "$INPUT_FILE" \
        -o "$OUTPUT_FILE" \
        --n-frames "$N_FRAMES" \
        --n-merged-frames "$N_MERGED_FRAMES" \
        --type hadamard \
        --data-location "$DATA_LOCATION" \
        --data-name "$DATA_NAME" \
        --n-workers "$WORKERS_PER_MERGE"
}

export -f run_hadamard_merge

cat "$LIST_FILE" | parallel -j "$N_FILES" run_hadamard_merge {}

(2) Indexing, Resolving Ambiguities, Scaling with Crystfel

As normal, you will index, resolve any ambiguities, and scale. Here, we provide this process with Crystfel, noting that you would need to change indexing parameters to those which best fits your data. Anecdotally, hadamard encoded files preform best with a higher SNR compared to your standard data processing hyperparameters.

For our example, let's assume a threshold of 8, SNR of 4.0, min-peaks of 8, and min-pix of 2 were the optimal hyperparameters found. In some of our data, we've found that each of the encoded files arrives at the same optimal hyperparameters; however, it's probably wisest to test each of the sets.

# 110 Encoded Files
indexamajig -g {.GEOM} -i {LIST_110} -o {110.STREAM} --indexing=xgandalf,asdf,mosflm,taketwo --peaks=peakfinder8 --int-radius=3,4,7 --multi --no-check-peaks --threshold=8 --min-snr=4.0 --min-peaks=8 --min-pix-count=2 -p {.CELL}

# 101 Encoded Files
indexamajig -g {.GEOM} -i {LIST_101} -o {101.STREAM} --indexing=xgandalf,asdf,mosflm,taketwo --peaks=peakfinder8 --int-radius=3,4,7 --multi --no-check-peaks --threshold=8 --min-snr=4.0 --min-peaks=8 --min-pix-count=2 -p {.CELL}

# 011 Encoded Files
indexamajig -g {.GEOM} -i {LIST_011} -o {011.STREAM} --indexing=xgandalf,asdf,mosflm,taketwo --peaks=peakfinder8 --int-radius=3,4,7 --multi --no-check-peaks --threshold=8 --min-snr=4.0 --min-peaks=8 --min-pix-count=2 -p {.CELL}

# Combine Streams
cat 110.STREAM 101.STREAM 011.STREAM > data.stream

# Resolve ambiguities together
ambigator -o {AMBI.STREAM} -w 6/mmm --lowres=10.0 --highres=3.0 --ncorr=1000 -j 32 --symmetry=6/m data.stream

# Scale (Custom output split with lists created before x n_frames)
partialator -o data_110 -y 6/m -i {AMBI.STREAM} --model=xsphere --iterations=1 --push-res=1.5 -j 32 --custom-split={LIST_110}

partialator -o data_101 -y 6/m -i {AMBI.STREAM} --model=xsphere --iterations=1 --push-res=1.5 -j 32 --custom-split={LIST_101}

partialator -o data_011 -y 6/m -i {AMBI.STREAM} --model=xsphere --iterations=1 --push-res=1.5 -j 32 --custom-split={LIST_011}

(3) Decoding

Now that we have our encoded data in hkl format, with ambiguities resolved and appropriately scaled together, we can decode the data to retrieve our normal hkls.

hatrx decode -n 3 -p "data_110-*.hkl" -p "data_101-*.hkl" -p "data_011-*.hkl" -o .