xzackli

{
    "python.terminal.activateEnvironment": false,
    "python.defaultInterpreterPath": "/fs/lustre/cita/zack/miniconda3/bin/",
    "terminal.integrated.env.linux": {
        "XDG_CACHE_HOME": "/fs/lustre/cita/zack/cache/",
        "JULIA_DEPOT_PATH": "/fs/lustre/cita/zack/jl"
    },
    "julia.additionalArgs": [
    
    ],

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using Bolt
using Plots
𝕡 = CosmoParams(Ω_c=0.224) # set kwargs like so to change the default values
bg = Background(𝕡)
𝕣 = Bolt.RECFAST(bg; OmegaB=𝕡.Ω_b, Yp=𝕡.Y_p, OmegaG=𝕡.Ω_r, Tnow=2.725)

@time rhist = Bolt.recfastsolve(𝕣);

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using ReverseDiff
using ReverseDiff: GradientTape, compile

import ForwardDiff
using Interpolations


function test_diff(p)
    t = 0.0:0.1:10.0
    u = sin.(4 .* t)

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small_tiles   0.44394799401034324
large_tiles   0.4916475503080907
high_cadence_constant_scan_1_el   0.40603891843921086
high_cadence_constant_scan_3_el   0.5005072385162868
high_cadence_variable_scan   0.4041414803590428

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# using Revise
using Bolt
using Plots
using Printf
using Interpolations, DataInterpolations
using DelimitedFiles
using OrdinaryDiffEq

import Bolt: PerturbationIntegrator, unpack, hierarchy!, initial_conditions, δ_kron
using OffsetArrays

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I'm running this on with Pixell.jl on the alm2map branch. I need to find time to write some unit tests before I merge that branch to master. Benchmark results are on a 16-core AMD EPYC.

using Pixell, BenchmarkTools, Tullio
using Pixell: degree, arcminute  # not exported by default

## let's make up a big patch similar to ACT DR6 / SO view of the southern sky
box = [180   -180;            # RA
       -70     30] * degree   # DEC
shape, wcs = geometry(CarClenshawCurtis, box, 8 * arcminute)  # note: 8x downgraded from native ACT/SO maps

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; Function Attrs: willreturn mustprogress
define nonnull {} addrspace(10)* @preprocess_julia__ODEFunction_15_4335({ [12 x {} addrspace(10)*] } %0, {} addrspace(10)* nocapture nofree nonnull readnone %1) local_unnamed_addr #3 !dbg !9 {
entry:
  %2 = call {}*** @julia.get_pgcstack() #4
  %3 = call cc38 nonnull {} addrspace(10)* bitcast ({} addrspace(10)* ({} addrspace(10)*, {} addrspace(10)**, i32, {} addrspace(10)*)* @jl_invoke to {} addrspace(10)* ({} addrspace(10)*, {} addrspace(10)*, {} addrspace(10)*, {} addrspace(10)*)*)({} addrspace(10)* addrspacecast ({}* inttoptr (i64 139935024279360 to {}*) to {} addrspace(10)*), {} addrspace(10)* addrspacecast ({}* inttoptr (i64 139935249317888 to {}*) to {} addrspace(10)*), {} addrspace(10)* addrspacecast ({}* inttoptr (i64 139934913746984 to {}*) to {} addrspace(10)*), {} addrspace(10)* addrspacecast ({}* inttoptr (i64 139939739935008 to {}*) to {} addrspace(10)*)) #4, !dbg !10
  %4 = call cc37 nonnull {} addrspace(10)* bitcast ({} addrspace(10)* ({} addrspace(10)*

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Step 1

Create a package locally using PkgTemplates

using PkgTemplates
t = Template(;
    user="YOURGITHUBUSERNAME",
    authors="YOUR ACTUAL NAME",
 julia=v"1.5",

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import numpy as np
import healpy as hp
import matplotlib.pyplot as plt
import pymaster as nmt

# This script showcases the ability of namaster to compute Gaussian
# estimates of the covariance matrix.
# A similar example for flat-sky fields can be found in
# test/sample_covariance_flat.py

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#------Experiments to test (separated with commas)-----

data.experiments=['act_s1316_sim','Planck_lensing','Planck_lowl','Planck_highl_TTTEEE_lite']


#------ Parameter list -------

# data.parameters[class name] = [mean, min, max, 1-sigma, scale, role]
# - if min max irrelevant, put to None
# - if fixed, put 1-sigma to 0