kaczmarj

#!/usr/bin/env bash

source ${FSLDIR}/etc/fslconf/fsl.sh
jupyter-lab --ip=*

kaczmarj

#!/usr/bin/env bash

set -e
set +x

# This bash script:
#   1. adds the CRAN repository
#   2. installs R
#   3. installs R packages for AFNI
#

kaczmarj

#!/usr/bin/env bash

# This script merges multiple ReproZip version 2 pack files.
#
# Example:
#
#   bash merge_packfiles.sh -o merged.rpz packA.rpz packB.rpz packC.rpz
#
#
# Requires reprozip and rsync.

kaczmarj

"""Example of how to get volume of tumor from mask NIfTI."""

import nibabel as nb

# The mask nifti image has contains a 1 in voxels that contain
# tumor and a 0 in voxels that do not contain tumor.
mask_filepath = "/path/to/tumor_mask.nii.gz"
img = nb.load(mask_filepath)
data = img.get_fdata()

kaczmarj

import neurodocker
specs = {'pkg_manager': 'apt', 'instructions': [['base', 'debian'], ['install', ['git', 'vim']]]}
dockerfile_string = neurodocker.Dockerfile(specs).render()

print(dockerfile_string)

kaczmarj

FROM ubuntu:trusty

RUN apt-get -qq update \
    && apt-get install -yq --no-install-recommends \
      curl \
      git \
      graphviz \
      libgraphviz-dev \
      pkg-config \
      software-properties-common \

kaczmarj

"""Filter slides with red titles from powerpoints, and save the filtered slides as one PDF file.

This script can only be run on Windows because it requires some Windows-specific libraries.

How to use
----------
- Install comtypes, pypdf2, and python-pptx using pip. Powerpoint must also be installed.
- Put all of the powerpoints to be filtered in one directory.
- Navigate to that directory in a terminal.
- Run this script. After some time, a file will be created named "output.pdf" with the filtered slides.

kaczmarj

# Training a bayesian neural network with mirrored strategy.
# When using `model.fit`, the keras symbolic tensor error arises, and the common fix of
# experimental_run_tf_function=False does not fix it. Creating our own training loop seems to fix it.
# With lots of help from https://www.tensorflow.org/tutorials/distribute/custom_training#training_loop

from nobrainer.models import bayesian
import numpy as np
import tensorflow as tf

# Set up multi-gpu things.

kaczmarj

kaczmarj

package main

import (
	"fmt"
	"io/ioutil"
	"log"
	"math"
	"os"
	"path"
	"sort"