Adam Wheeler ajwheeler

## minimal.yaml
modules:
    - galsim_extra
    - galsim.des

psf:
    type: PSFEx

gal:
    type: Gaussian
    sigma: 2

## clapping.hs
import Euterpea

rythm = [1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0]

r1 = concat $ take 13 (repeat rythm)
r2 = concat [drop i rythm  ++ take i rythm | i <- [0..12]]

clap 1 = c 4 sn
clap 0 = rest sn

## Autodiff.ipynb

      
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                ajwheeler
                / Autodiff.ipynb
            
            
              Created
              June 28, 2021 17:29
            
              
                autodiff with Korg
              
          
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## NLSolver.jl
"""
This module provides a wrapper for `nlsolve`, from `NLsolve.jl`, which is a package for solving
nonlinear systems of equations.

The wrapper extends `nlsolve` to allow the `residuals!` function to take additional fixed parameters
as a tuple of vectors.  This convieniently handles
[julia's closure inneficiency problems](https://docs.julialang.org/en/v1/manual/performance-tips/#man-performance-captured),
but more importantly facilitates the fast differentiation (with `ForwardDiff`) of the minimizer with
respect to the parameters.
"""

## parallelism_with_juliacall.py
from juliacall import Main as jl
import time
jl.include("utils.jl")

def wait_for_spectrum(s):
    while True:
        time.sleep(0.1)
        if jl.isready(s):
            print("Worker is ready!")
            print(jl.fetch(s))
	modules:
	- galsim_extra
	- galsim.des

	psf:
	type: PSFEx

	gal:
	type: Gaussian
	sigma: 2
	import Euterpea

	rythm = [1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0]

	r1 = concat $ take 13 (repeat rythm)
	r2 = concat [drop i rythm ++ take i rythm \| i <- [0..12]]

	clap 1 = c 4 sn
	clap 0 = rest sn
	"""
	This module provides a wrapper for `nlsolve`, from `NLsolve.jl`, which is a package for solving
	nonlinear systems of equations.

	The wrapper extends `nlsolve` to allow the `residuals!` function to take additional fixed parameters
	as a tuple of vectors. This convieniently handles
	[julia's closure inneficiency problems](https://docs.julialang.org/en/v1/manual/performance-tips/#man-performance-captured),
	but more importantly facilitates the fast differentiation (with `ForwardDiff`) of the minimizer with
	respect to the parameters.
	"""
	from juliacall import Main as jl
	import time
	jl.include("utils.jl")

	def wait_for_spectrum(s):
	while True:
	time.sleep(0.1)
	if jl.isready(s):
	print("Worker is ready!")
	print(jl.fetch(s))