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Anna Woodard annawoodard

  • University of Chicago
  • Chicago, USA
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import parsl
from parsl.providers import SlurmProvider
from parsl.config import Config
from parsl.executors.ipp import IPyParallelExecutor
from parsl.app.app import python_app
config = Config(
executors=[
IPyParallelExecutor(
# only the first time:
git clone git@github.com:annawoodard/coffeandbacon.git
source coffeandbacon/setup_lcg.sh
pip install https://github.com/Parsl/parsl/zipball/master --user
mkdir coffeandbacon/analysis/runinfo
fs sa coffeandbacon/analysis/runinfo nd_campus rlidwk # this is necessary because condor logs will be written here-- otherwise jobs fail for me with a permission denied error
cd coffeandbacon/analysis/
./compile_corrections.py # this step could be automated, I'm just following what's already implemented
./boostedHbbProcessor.py # this step could be automated, I'm just following what's already implemented
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@annawoodard
annawoodard / python3-vs-numpy.ipynb
Created July 24, 2020 21:40
python-vs-numpy.ipynb
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@annawoodard
annawoodard / rsync.sh
Created March 23, 2021 14:52
rsync radiology -> cri
LOCK_NAME="chimec_sync"
LOCK_DIR='/tmp/'${LOCK_NAME}.lock
PID_FILE=${LOCK_DIR}'/'${LOCK_NAME}'.pid'
if mkdir ${LOCK_DIR} 2>/dev/null; then
# If the ${LOCK_DIR} doesn't exist, then start working & store the ${PID_FILE}
echo $$ > ${PID_FILE}
rsync --remove-source-files -avPz /mnt/hiro/ t.cri.awoodard@gardner.cri.uchicago.edu:/gpfs/data/huo-lab/Image/ChiMEC/ >& /home/annawoodard/rsync.log
rm -rf ${LOCK_DIR}
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from scipy.stats import norm
def detectable_difference(alpha, power, proportion, n_positives):
"""
Calculate the detectable difference in proportions for a given sample size, power, significance level, and proportion.
Parameters:
alpha: Significance level
power: Desired power of the test
@annawoodard
annawoodard / checkpoint.py
Last active September 6, 2023 20:52
pytorch checkpointing
def restart_from_checkpoint(checkpoint_path, restore_objects=None, **kwargs):
"""
Re-start training or inference from a previous checkpoint.
Args:
checkpoint_path (str): Path to checkpoint file
restore_objects (dict): Dict containing objects to reload from checkpoint
**kwargs (dict): Keyword args containing model states to reload
Returns: