dangom

#!/usr/bin/env bash

fixedfile=$1
movingfile=$2
output=$3

antsRegistration --dimensionality 3 --float 0 --verbose 1 \
                 --output [$output,${output}.nii.gz] \
                 --interpolation LanczosWindowedSinc \
                 --winsorize-image-intensities [0.005,0.995] \

dangom

;;; to download all abstracts: 
;;; parallel wget --no-clobber --no-parent -r "https://submissions.mirasmart.com/ISMRM2022/Itinerary/Files/PDFFiles/{}.html" ::: $(seq -f "%04g" 00 10000)
;;; Make a simple index so I can search through abstracts in emacs
;;; So that other people can use it, I have put this into an index.html and use a js search framework that mimics completing-read.
;;; See for example https://cheatcode.co/tutorials/how-to-implement-client-side-search-with-fuse-js
;;;
;; List and open any file on eww
(defvar ismrm-abstract-dir "/Users/dg/ismrm2022/PROGRAM/")

(defun ismrm--find-abstract-file ()

dangom

# use ImageMagick convert
# the order is important. the density argument applies to input.pdf and resize and rotate to output.pdf
convert -density 90 input.pdf -rotate 0.5 -attenuate 0.2 +noise Multiplicative -colorspace Gray output.pdf

dangom

;;; org-roam-weeklies.el --- Weekly-notes for Org-roam -*- coding: utf-8; lexical-binding: t; -*-
;;;
;; Copyright © 2020 Jethro Kuan <jethrokuan95@gmail.com>
;; Copyright © 2020 Leo Vivier <leo.vivier+dev@gmail.com>

;; Author: Jethro Kuan <jethrokuan95@gmail.com>
;; 	Leo Vivier <leo.vivier+dev@gmail.com>
;; URL: https://github.com/org-roam/org-roam
;; Keywords: org-mode, roam, convenience
;; Version: 1.2.3

dangom

""" Echo combination.
1. Motion correction
2. Compute weights on motion corrected data
3. Apply inverse transform on weights
4. Apply weights on original data to minimize interpolation.
Methods:
For the echo combination, the first echo was motion corrected using ANTs for
rigid transformation. The transforms were subsequently applied to the later
echoes. The mean images of the motion corrected echoes were subsequently used
for estimating a T2star map. Weights for each echo were computed from the T2star

dangom

0.016043  0.002221  -0.035477  0.002108  0.021356  0.007767  -0.000762  -0.007581  0.001191  0.021943
0.009563  0.020156  -0.035227  -0.003960  0.010329  -0.036748  0.010542  0.000222  0.001448  0.021009
0.009083  -0.004882  -0.043019  -0.008569  0.005001  0.007674  -0.006975  -0.001861  0.008550  0.019023
0.004607  -0.002042  -0.044749  -0.005607  0.008006  0.018350  -0.005890  -0.004179  0.001811  0.019777
0.000673  -0.005753  -0.045365  -0.001488  0.016821  0.000359  -0.005611  -0.013906  -0.000032  0.027898
-0.006095  -0.003364  -0.043093  -0.007743  0.022372  0.007518  -0.005686  -0.004445  -0.004046  0.024409
-0.001820  0.015352  -0.037703  0.004821  0.020568  -0.028915  0.006992  -0.009169  -0.020281  0.016134
0.003075  -0.002990  -0.048372  0.012588  0.003329  0.007372  -0.015720  -0.007468  0.001225  0.016118
0.008469  0.001528  -0.051274  0.008134  0.033441  0.006119  -0.000766  -0.009467  0.003871  0.014437
0.005616  -0.000346  -0.047836  -0.002901  0.021321  -0.002379  -0.000779  -0.022400  -0.007

dangom

{
 "links": [
  {
   "source": 0,
   "target": 3,
   "value": 13
  },
  {
   "source": 0,
   "target": 6,

dangom

# By Jake VanderPlas
# License: BSD-style

import matplotlib.pyplot as plt
import numpy as np


def discrete_cmap(N, base_cmap=None):
    """Create an N-bin discrete colormap from the specified input map"""

dangom

import subprocess
import io
import pandas as pd

# The fslcc binary to use
FSLCC = '/opt/fsl/5.0.11/bin/fslcc'

# Parameters for the command line. No thresholding, report 4 digits.
FSLCC_PARAMS = ['-t', '0', '-p', '4']

dangom

# Add field
echo '{"hello": "world"}' | jq --arg foo bar '. + {foo: $foo}'
# {
#   "hello": "world",
#   "foo": "bar"
# }

# Override field value
echo '{"hello": "world"}' | jq --arg foo bar '. + {hello: $foo}'
{