sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 10.7k | content stringlengths 1 200k |
|---|---|---|---|---|
eb6d7a1e69e0c571d9b04250ee6fba15313219fcff5fedec632ee90b7135ecb0 | Julia | 3,891 | 143 | using Molly
using ParallelTestRunner
using Suppressor
# Suppress warnings for unavailable backends, warn later
@suppress_err using AMDGPU
@suppress_err using CUDA
@suppress_err using Metal
@suppress_err using oneAPI
const n_threads_per_job = 4
const run_visualize_tests = get(ENV, "VISTESTS", "1") != "0"
if !run_visu... |
633b52a05e9302ed0f86d809f6850fea9d21b2ebb3b8413b03f60cc76272343a | Julia | 3,901 | 137 | export Mie
@doc raw"""
Mie(; m, n, cutoff, use_neighbors, shortcut, σ_mixing, ϵ_mixing, weight_special)
The Mie generalized interaction between two atoms.
When `m` equals 6 and `n` equals 12 this is equivalent to the Lennard-Jones interaction.
The potential energy is defined as
```math
V(r_{ij}) = C \varepsilon_... |
8f62741a464fc7c6b5be811dd4e7181af76ab927e34f3e173834fadd56b36d3c | Julia | 4,529 | 131 | # Mixing functions for non-bonded parameters
shortcut_pair(::Nothing, args...) = false
struct LJZeroShortcut end
function shortcut_pair(::LJZeroShortcut, atom_i, atom_j, args...)
return iszero_value(atom_i.ϵ) || iszero_value(atom_j.ϵ) ||
iszero_value(atom_i.σ) || iszero_value(atom_j.σ) ||
i... |
fcaf3b4a23533fd80504088d951f5f6677d9e00875eb24d20f25446f6f52ee5e | Julia | 4,623 | 126 | # -*- coding: utf-8 -*- https://github.com/bkamins/Julia-DataFrames-Tutorial repository).
#[confidence intervals](https://en.wikipedia.org/wiki/Confidence_interval)
#[density estimators](https://en.wikipedia.org/wiki/Density_estimation)
#[probit model](https://en.wikipedia.org/wiki/Probit_model)
#[bootstrapping](https:... |
4f9c760d1aadb720c3c25b8f75f0a59639e1f4ab197e44798f47d83a6e3d07d2 | Julia | 4,655 | 97 | # Machine-learning interatomic potentials (core definitions).
#
# The ANIPotential struct, the scalar AEV helpers (cosine_cutoff, celu01) and the public
# function stubs live here in core Molly. The implementations that need Lux/HDF5/
# KernelAbstractions are in ext/MollyLuxExt.jl (loaded when Lux and HDF5 are availabl... |
c6da9c0a584b25ee22bc3fe4561e896ed00c80e87276a4f1e070790ccbca5dd5 | Julia | 4,715 | 112 | @testset "Differentiable protein" begin
function create_sys(AT, n_threads, nonbonded_method)
ff = MolecularForceField(joinpath.(ff_dir, ["ff99SBildn.xml"])...; units=false)
return System(
joinpath(data_dir, "6mrr_nowater.pdb"),
ff;
units=false,
array_t... |
84f18813f7bab32dd606c80fcd99ba0f072346a38cb8f3b9d7cb967141877b5b | Julia | 4,779 | 127 | export PeriodicTorsion
@doc raw"""
PeriodicTorsion(; periodicities, phases, ks, proper)
A periodic torsion angle between four atoms.
`phases` are in radians.
The potential energy is defined as
```math
V(\phi) = \sum_{n=1}^N k_n (1 + \cos(n \phi - \phi_{s,n}))
```
where `ϕ` is the angle between the planes defined... |
cf240601195cdce0b8306836c22bb0c3342eefc36df4377034eabb12cff83e24 | Julia | 4,941 | 149 | export DPDInteraction
@doc raw"""
DPDInteraction(; a, γ, σ, r_c, dt, use_neighbors, key)
The dissipative particle dynamics (DPD) interaction between two particles.
Combines conservative, dissipative, and random pairwise forces as described in
[Groot and Warren 1997](https://doi.org/10.1063/1.474784).
The total ... |
4c29284494bd68e331a069ead88a8db76fcba3730147b5361598fb6a12b861b7 | Julia | 5,387 | 140 | # Python ASE interface
# This file is only loaded when PythonCall is imported
module MollyPythonCallExt
using Molly
using Molly: from_device, to_device
using PythonCall
import AtomsCalculators
using GPUArrays
using StaticArrays
using Unitful
# See PythonCall precompilation documentation
const ase = Ref{Py}()
functi... |
345d9c388272260fe2e66ec7f9a38b04ec93a7175abf8959a2b8585868fade6a | Julia | 5,486 | 262 | ##
using Molly
using CUDA
using GLMakie
using Random
CUDA.device!(parse(Int, get(ENV, "MOLLY_CUDA_DEVICE", "0")))
##
FT = Float32
AT = CuArray
RNG_SEED = 42
OUTPUT_PREFIX = "awh_dipeptide"
rng = MersenneTwister(RNG_SEED)
DT = FT(4)u"fs"
TIME_EQ = FT(100)u"ps"
STEPS_EQ = Int(floor(TIME_EQ/DT))
TEMP = FT(310)u"K"
T... |
d04a2f88858587241e9b13aca6817c9c97f9f9548899b237f37940a7687454ec | Julia | 5,551 | 116 | @testset "Analysis" begin
# Displacements and distances
coords = [SVector(1.0, 1.0, 1.0), SVector(2.0, 2.0, 2.0)]
boundary = CubicBoundary(10.0)
disps = displacements(coords, boundary)
@test disps[1, 2] == SVector(1.0, 1.0, 1.0)
@test disps[2, 1] == SVector(-1.0, -1.0, -1.0)
dists = distance... |
513af1f49e5a3ecb4022d175d308d57d6dda6547ff4892f6093862fb1e273df9 | Julia | 5,625 | 165 | using CUDA
using BenchmarkTools
using StaticArrays
using Unitful
using LinearAlgebra
using Random
using Printf
include("gpu_profile_utils.jl")
env_int(name::AbstractString, default::Int) = something(tryparse(Int, get(ENV, name, string(default))), default)
function setup_benchmark_system(n_atoms;
... |
eed238603c3402fb54c9d2d1a785dad083d8596ce4f74e9a9085b7a1f405d7fd | Julia | 5,660 | 140 | # Visualize simulations
# This file is only loaded when GLMakie is imported
module MollyGLMakieExt
using Molly
using GLMakie
import AtomsBase
using Colors
using Unitful
using LinearAlgebra
function Molly.visualize(coord_logger,
boundary,
out_filepath::AbstractString... |
828a41eb4703da4bd4de3a80ef942e4e852d40426971a98968601a469bfc2443 | Julia | 6,457 | 175 | # Analysis tools
export
displacements,
distances,
rmsd,
radius_gyration,
hydrodynamic_radius,
visualize,
rdf
"""
displacements(coords, boundary)
Calculate the pairwise vector displacements of a set of coordinates, accounting
for the periodic boundary conditions.
"""
function displacem... |
18c9621d6a3cedfde610a85d22a845f67509c4621fecce4a4f81b304b17baccd | Julia | 6,489 | 158 | # Benchmark suite for Molly
# Run with something like:
# using Molly, PkgBenchmark
# results = benchmarkpkg(Molly, BenchmarkConfig(env=Dict("JULIA_NUM_THREADS" => 16)))
# export_markdown(out_file, results)
using Molly
using BenchmarkTools
using CUDA
using DelimitedFiles
const run_parallel_tests = Threads.nthre... |
6b761114a372c0dbb726b5a0458b01899da8e20cf00302744ac4fd6d5ad864df | Julia | 7,465 | 322 | ##
using Molly
using CUDA
using GLMakie
using Random
CUDA.device!(parse(Int, get(ENV, "MOLLY_CUDA_DEVICE", "0")))
##
FT = Float32
AT = CuArray
RNG_SEED = 42
OUTPUT_PREFIX = "tss_dipeptide"
rng = MersenneTwister(RNG_SEED)
DT = FT(4)u"fs"
TIME_EQ = FT(100)u"ps"
STEPS_EQ = Int(floor(TIME_EQ/DT))
TEMP = FT(310)u"K"
T... |
f96dc6282c61710bc9b713ea07e3ec9ac5421cb914c9acd0d7169e4ecac44b66 | Julia | 8,635 | 280 | # Cutoff strategies for long-range interactions
export
NoCutoff,
DistanceCutoff,
ShiftedPotentialCutoff,
ShiftedForceCutoff,
CubicSplineCutoff,
PolynomialCutoff
abstract type AbstractCutoff{P} end
Base.:+(c1::T, ::T) where {T <: AbstractCutoff} = c1
cutoff_sqdist(::AbstractCutoff{0}) = nothi... |
939ef8397782fd845838a414dd0ebb05ee72ecdb4b580cc05bd213eb4df84d09 | Julia | 9,015 | 257 | mutable struct TSSPMFEpochAccumulator{A}
index::Int
accumulator::A
end
mutable struct TSSPMFDeconvolutionBackend{N, T, S, F_CV, G, C, A}
state::S
cv_function::F_CV
grid::G
log_coupling_matrix::C
accumulator::A
epoch_accumulators::Vector{TSSPMFEpochAccumulator{A}}
end
function tss_auto_... |
8b30bada32e82928212bde66351bae357442c05868111079e91e5102a65a2521 | Julia | 9,091 | 236 | # Handle units
export ustrip_vec
# Unit types to dispatch on
@derived_dimension MolarMass Unitful.𝐌/Unitful.𝐍
@derived_dimension BoltzmannConstUnits Unitful.𝐌*Unitful.𝐋^2*Unitful.𝐓^-2*Unitful.𝚯^-1
@derived_dimension MolarBoltzmannConstUnits Unitful.𝐌*Unitful.𝐋^2*Unitful.𝐓^-2*Unitful.𝚯^-1*Unitful.𝐍^-1
add_... |
e9d17b7c27c13f2a25cee1bae87ca46939063b861bc6c51f5013b23e37909f5b | Julia | 9,838 | 329 | # Bias potentials
export
LinearBias,
bias_gradient,
SquareBias,
FlatBottomSquareBias,
PeriodicFlatBottomBias,
BiasPotential
@doc raw"""
LinearBias(k, cv_target)
A linear bias on a collective variable (CV) towards a target value.
The potential energy is defined as
```math
V(\boldsymbol{s... |
15b2ee48b3226849a825e813729924327e02f861661e268a10b488cfd512a6d5 | Julia | 9,851 | 225 | @testset "Differentiable simulation" begin
runs = [ # gpu par fwd f32 obc2 gbn2 tol_σ tol_r0
("CPU" , Array, false, false, false, false, false, 1e-8, 1e-5),
("CPU forward" , Array, false, true , false, false, false, 0.5 , 0.1 ),
("CPU f32" ... |
178782df7632b5d79d821c052008f21b292f004ae940081fcefe402ef198aa79 | Julia | 9,956 | 334 | export
DoubleExponential,
DoubleExponentialSoftCore
@doc raw"""
DoubleExponential(; cutoff, use_neighbors, α, β, shortcut, σ_mixing, ϵ_mixing,
weight_special)
The double exponential interaction between two atoms.
The potential energy is defined as
```math
V(r_{ij}) = \varepsilon_{i... |
bbd19a17c950320512b8737af2959b1b7dd3c22974f85ea739a4be8029a5f817 | Julia | 9,985 | 215 | @testset "CHARMM OpenMM protein comparison" begin
pme_mesh_dims = (46, 46, 51)
ff = MolecularForceField(
joinpath.(ff_dir, ["charmm36.xml", "charmm36_water.xml"])...;
strictness=:nowarn,
)
show(devnull, ff)
@test_throws ForceFieldXMLError MolecularForceField(
joinpath.(ff_dir... |
76e285a767659dd8f7c4c76d0923253845e9c9b791a70d509b8cc227d5594c10 | Julia | 10,398 | 303 | @inline function online_pmf_logaddexp(a::T, b::T) where T
if a == -T(Inf)
return b
elseif b == -T(Inf)
return a
end
m = max(a, b)
return m + log(exp(a - m) + exp(b - m))
end
function online_pmf_tuple(values::Tuple, ::Type{T}) where T
return tuple((safe_ustrip(T, value) for value... |
2fe491fc6115cc52f62338e29d1a4bdae1c4aa34151c604a9b1aa759555698d9 | Julia | 10,730 | 319 | """
TSSHistoryForgetting(; alpha=0.19, n_epochs=16, phi=nothing)
Configure geometric history forgetting for TSS free-energy estimates.
`alpha` controls the retained fraction of the current history time, `n_epochs`
sets the target number of retained epochs, and `phi` optionally overrides the
epoch growth factor in... |
2549c292fefc9fbb0169839262a0bd5f842a57565f8600d0a1249e7e38ff22c1 | Julia | 11,078 | 287 | const TSS_COVDET_GAMMA_EPSILON = 0.01
struct TSSCovDetAdaptiveGamma{T}
epsilon_gamma::T
rung_neighbors::Vector{Vector{NTuple{3, Int}}}
rung_volumes::Vector{T}
dimension::Int
end
function validate_tss_local_adaptive_gamma(adaptive_gamma)
isnothing(adaptive_gamma) && return nothing
adaptive_gamm... |
da77a69c38152c5097a2b329fca5076c9d504b29f760fb3e6e6fead53d9f8bfa | Julia | 11,753 | 346 | export CMAPTorsion
"""
CMAPTorsion(index, size)
Torsional correction map (CMAP) for sets of five atoms, for example protein ϕ and ψ
backbone torsion angles.
The CMAP data is stored in the `data` field of the associated [`InteractionList5Atoms`](@ref).
Only compatible with 3D systems.
"""
struct CMAPTorsion
... |
4c4ccb45464d5946a40b966beeba416a1502809af8da29283ea18b46c3123fe8 | Julia | 11,991 | 252 | @testset "GPU Optimizations" begin
if run_cuda_tests
n_atoms = 100
D = 3
T = Float64
coords = [SVector{D, T}(0.1 * i, 0.1 * i, 0.1 * i) for i in 1:n_atoms]
boundary = CubicBoundary(T(20.0), T(20.0), T(20.0))
atoms = [Atom(index=i, mass=T(1.0), charge=T(0.0), σ=T(0.3),... |
9883682121e3bc2ed91a3009470342936891a7280f779a2a8e87f166f7ac96f1 | Julia | 13,588 | 345 | # Convenience struct that stores the interaction lists that change
# across thermodynamic states (e.g., along a reaction coordinate or replica states).
struct LambdaHamiltonian{PI, SI, GI}
pairwise_inters::PI
specific_inter_lists::SI
general_inters::GI
end
# AlchemicalPartition(thermo_states::AbstractArray... |
747118452ff5d851a47254f71f0aa6659811b29c48d36a483e109c6ae6ce281d | Julia | 13,994 | 482 | ##
using Molly
using CUDA
using Unitful
using GLMakie
using Random
##
# --- Simulation Constants ---
CUDA.device!(parse(Int, get(ENV, "MOLLY_CUDA_DEVICE", "0")))
FT = Float32
AT = CuArray
Δt = FT(4)u"fs"
T0 = FT(310)u"K"
P0 = FT(1)u"bar"
RNG_SEED = 20240520
OUTPUT_PREFIX = "awh_solvation"
N_LAMBDA_STATES = 20
N_MD_ST... |
b9333c665b5f25e6d6a165d3ebc573f4f6094b7f6c67dcf4e6638f656efa929d | Julia | 14,030 | 363 | # Virtual sites
export
OneParticleSite,
TwoParticleAverageSite,
ThreeParticleAverageSite,
OutOfPlaneSite,
place_virtual_sites!
struct VirtualSite{T, IC}
type::Int # 1/2/3/4 for OneParticleSite/TwoParticleAverageSite/ThreeParticleAverageSite/OutOfPlaneSite
atom_ind::Int
atom_1::Int
... |
c3004a704da17ced74efc643f88927f40accac12ead07935da3cf969bd37648b | Julia | 14,386 | 415 | using Molly
using Molly: box_sides, sorted_morton_seq!
using CUDA
using StaticArrays
function gpu_cuda_ext()
ext = Base.get_extension(Molly, :MollyCUDAExt)
isnothing(ext) && error("MollyCUDAExt is not loaded, import CUDA before profiling GPU kernels")
return ext
end
function gpu_stage_time_ms!(f::F) where... |
24a5933bbe442b286cf91c063267b5f6fcb00c933c19c8cc534d913757ae9e97 | Julia | 14,676 | 447 | BigInt(9)^9^9
#setup
using Pkg
]add https://github.com/kskyten/Transpilers.jl
using Transpilers
transpile(Expr, py"1 + 1")
transpile(String, py"lambda x, y: x * y")
open("output.jl", "w") do f
transpile(f,
py"""
import numpy as np
def foo(x, y):
x + y * np.dot(x, y)
""")
end
P... |
43242030a97b8352be7420d2076ab7c6e410594e052e45491906b4abf3a71633 | Julia | 14,707 | 363 | export
ActiveThermoState
# ExtendedStateSpace(thermo_states; reuse_neighbors=true)
#
# An expanded ensemble over a collection of thermodynamic states.
#
# `thermo_states` supplies the systems, integrators, temperatures, and optional
# pressures for each state. The resulting state space stores shared alchemical
# p... |
6e0fead50181432a46a82488657c3ff88586cd23388cedbac5c03507eba4759a | Julia | 15,733 | 444 | mutable struct TSSLocalEstimator{T, ES, AS, ST, AG}
state_space::ES # The different hamiltonians
active_state::AS # The hamiltonian that is currently active
state_indices::Vector{Int} # Local index to global state index
local_index_by_state::Vector{Int} # Global index to local index, 0 mean not in lo... |
8d045129ecad2db92c43937897c3727a51d3e6c9df5d870973664b37421f89eb | Julia | 16,260 | 557 | ##
using Molly
using CUDA
using Unitful
using GLMakie
using Random
##
# --- Simulation Constants ---
CUDA.device!(parse(Int, get(ENV, "MOLLY_CUDA_DEVICE", "0")))
FT = Float32
AT = CuArray
Δt = FT(4)u"fs"
T0 = FT(310)u"K"
P0 = FT(1)u"bar"
RNG_SEED = 20240520
OUTPUT_PREFIX = "tss_solvation"
N_LAMBDA_STATES = 20
TSS_WIN... |
59fad8999972528108e691e2c60b9e1db83eb5d0379e9d716f7b4d6c17bd8693 | Julia | 17,929 | 422 | # Taking gradients with respect to force field parameters
export
parameter_prefix,
parameter_fields,
ParameterPlan,
extract_parameters,
inject_gradients
"""
parameter_prefix(inter)
parameter_prefix(inter, inter_type)
The prefix that the parameters of an interaction have in a parameter dic... |
d4e7c5e31d383ba9422f91ab537a748f1a824ebe4f9567b4149defafa8b8dfd7 | Julia | 18,991 | 506 | export
PMFDeconvolution,
pmf
struct PMFGrid{N, T, E, C, W, V}
edges::E
centers::C
widths::W
shape::NTuple{N, Int}
volumes::V
end
function PMFGrid(grid; T::Type = Float64)
edges = online_pmf_edges(grid, T)
centers = online_pmf_centers(edges)
widths = online_pmf_widths(edges)
... |
8868560c32c7c81c843faf559f2a8423540a12c8c09ec91228901148f4dff784 | Julia | 20,611 | 470 | @testset "Energy gradients" begin
inter = LennardJones()
boundary = CubicBoundary(5.0)
a1, a2 = Atom(σ=0.3, ϵ=0.5), Atom(σ=0.3, ϵ=0.5)
function force_direct(dist)
c1 = SVector(1.0, 1.0, 1.0)
c2 = SVector(dist + 1.0, 1.0, 1.0)
vec = vector(c1, c2, boundary)
F = force(inte... |
823ce03dd3dafdb2a791ee29194c9813e5b3873834319121e663c3bf377acf61 | Julia | 20,711 | 664 | struct BiasNaNGradient end
Molly.bias_gradient(::BiasNaNGradient, cv_sim) = NaN * u"kJ * mol^-1 * nm^-1"
@testset "Collective variables" begin
c1 = SVector(1.0, 1.0, 1.0)u"nm"
c2 = SVector(1.3, 1.0, 1.0)u"nm"
c3 = SVector(0.1, 1.0, 1.0)u"nm"
c4 = SVector(1.8, 1.0, 1.0)u"nm"
c5 = SVector(1.0, 1.2, ... |
48c28cbbdf05f74a08fc69824a4207a4d210ab8eb8ec3dc8d7d14f8c1728227e | Julia | 21,798 | 696 | ### A Pluto.jl notebook ###
# v0.17.1
using Markdown
using InteractiveUtils
# ╔═╡ 18e4f150-1eaf-11ec-0dfa-5feeb4c0626b
using Distributed
# ╔═╡ 8073be21-c42d-4616-9c62-32f47115334a
using NetworkInference
# ╔═╡ 33b974c0-ad65-4ef4-ba75-d6a00a184664
using LightGraphs
# ╔═╡ ad2c4547-1e59-4b33-9631-cccaa908df78
using Gr... |
5aa4002046c9f807da491727aa3fc1561b1aa953afd70d05cbc85c042e1f9223 | Julia | 22,324 | 617 | export
AWHState,
AWHSimulation
awh_count(n::Integer, singular::AbstractString, plural::AbstractString=string(singular, "s")) =
string(n, " ", n == 1 ? singular : plural)
# Convenience struct to store relevant things
# when running an AWH simulation.
mutable struct AWHStats{T}
step_indices::Vector... |
e5e99323e75f777d9a3bd4b7f81be107a2586528f3ddd12e05edb9ba7d6ce4af | Julia | 23,464 | 619 | @testset "Immediate thermostat" begin
n_atoms = 100
n_steps = 10_000
temp = 10.0u"K"
boundary = CubicBoundary(4.0u"nm")
for AT in array_list
sys = System(
atoms=to_device([Atom(mass=10.0u"g/mol", σ=0.04u"nm", ϵ=0.1u"kJ * mol^-1")
for _ in 1:n_atoms],... |
dbd6d43d48028c48fa167ab20f048e29a94e6a9b2aeaceca87a523efd42f8457 | Julia | 24,266 | 660 | export
DistanceConstraint,
AngleConstraint,
apply_position_constraints!,
apply_velocity_constraints!,
check_position_constraints,
check_velocity_constraints,
check_constraints
# Split `n_items` constraints or clusters over at most `n_threads` chunks on CPU
# The ranges are contiguous rather... |
2a4e8a4769522d8aa9af5664e7c9347ee3b1163eba01c8c4d4ba983eaac17ef6 | Julia | 25,623 | 578 | # Energy calculation
export
total_energy,
kinetic_energy_tensor,
kinetic_energy,
virial,
scalar_virial,
temperature,
potential_energy,
pairwise_pe
"""
total_energy(system, neighbors=find_neighbors(sys), step_n=0, buffers=nothing;
n_threads=Threads.nthreads(), pairw... |
9aacc3f8538871ee2cbe43cd57bd56966f5da5f92e415224b837b5f91b8b8cf9 | Julia | 26,590 | 655 | tss_step_wrapper(sys, neighbors, step_n, buffers; kwargs...) = step_n
tss_step_logger() = (step=GeneralObservableLogger(tss_step_wrapper, Int, 1),)
function make_tss_thermo_states(; n_atoms=6, n_states=3)
atom_mass = 10.0u"g/mol"
boundary = CubicBoundary(2.0u"nm")
coords = place_atoms(n_atoms, boundary; mi... |
646237c2c950bf97ac8e0ac0c007c55a8b8634175dd8f7c62ba5f3d71f3ac7ca | Julia | 27,062 | 677 | @testset "GPU Consistency" begin
if run_cuda_tests
@testset "33-atom (No Cancellation)" begin
n_atoms = 33
D = 3
T = Float64
coords = [SVector{D, T}(0.5 * i, 0.5 * i, 0.5 * i) for i in 1:n_atoms]
boundary = CubicBoundary(T(20.0), T(20.0), T(20.0))
... |
61bdf63c9e0ee2b7585d56021fb0e1510f1f581cc1df3b4ed14a1e044f04a10e | Julia | 27,466 | 633 | @testset "Amber OpenMM protein comparison" begin
ff = MolecularForceField(joinpath.(ff_dir, ["ff99SBildn.xml", "tip3p_standard.xml"])...)
show(devnull, ff)
pme_mesh_dims = (46, 46, 51)
sys = System(
joinpath(data_dir, "6mrr_equil.pdb"),
ff;
nonbonded_method=SetupCoulombReactionFi... |
dbb5db460342b14b8a5227b1b9035e109ea8e7adedf71beaf7bec6a892e8d19d | Julia | 28,501 | 509 | # Static analysis of the main code paths with JET
# Error analysis (test_call) is run on every entry point here. Optimization analysis
# (test_opt) is only run where it can be clean, which is the analysis, spatial and
# thermostat code: forces! and potential_energy deliberately use function barriers, since
# Val... |
ef30161e8d9afb23623c07e2984b26e214ff13101b88bbafed52658fa3a3cde3 | Julia | 29,589 | 861 | # Calculate collective variables
export
CalcMinDist,
CalcMaxDist,
CalcCMDist,
CalcSingleDist,
CalcDist,
calculate_cv,
cv_gradient,
CalcRg,
CalcRMSD,
CalcTorsion
# Does not account for periodic boundary conditions, assumes appropriate unwrapping
function center_of_mass(coords, a... |
548e4fd7f94d591018dc4999a9d8fbb6bcfefb0fe15d8c667d58f080b4eaa30b | Julia | 30,982 | 957 | export
LennardJones,
LJDispersionCorrection,
LennardJonesSoftCoreBeutler,
LennardJonesSoftCoreGapsys,
AshbaughHatch
@doc raw"""
LennardJones(; cutoff, use_neighbors, shortcut, σ_mixing, ϵ_mixing, weight_special)
The Lennard-Jones 6-12 interaction between two atoms.
The potential energy is def... |
87a0a19178f59037eae19749b4c465e8c20ebda7e3e10d67cab0454196281007 | Julia | 33,830 | 966 | struct TSSWindow
index::Int
state_indices::Vector{Int}
evaluation_state_indices::Vector{Int}
function TSSWindow(index::Integer, state_indices;
evaluation_state_indices = state_indices,
check_contiguous::Bool = true)
if !(index > 0)
throw(... |
ff24ec202f41b4c86b33031abbe85bdd48b27613eb72031f69ad1ec6a8249b8a | Julia | 34,384 | 923 | simulation_step_wrapper(sys, neighbors, step_n, buffers; kwargs...) = step_n
mutable struct StepTrackingCoupler
n_steps::Int
history::Vector{Int}
end
StepTrackingCoupler(n_steps::Integer) = StepTrackingCoupler(Int(n_steps), Int[])
function Molly.apply_coupling!(sys, buffers, coupler::StepTrackingCoupler, sim... |
21b101f83aa09d7bfb44fb1cc3c4c894a6dc3c0bbbbdc5371a8dc14973be7bee | Julia | 35,267 | 740 | # ML potential tests, reference data is generated by test/reference/torchani_reference.py
# Small reference data (committed); the model weights ani2x.h5 and the large 6mrr_ani2x.json
# come from the lazily-downloaded ANI-2x artifact.
const REF_DIR = joinpath(@__DIR__, "..", "data", "ani_reference")
const ANI_DIR = Mol... |
7725b3116439f8e8e50a61d3087d88c2714ba2182309b935c81110700ee91572 | Julia | 35,676 | 1,022 | export
assemble_mbar_inputs,
iterate_mbar,
mbar_weights,
mbar_pmf,
pmf_with_uncertainty
const LOG_PREVFLOAT0 = log(nextfloat(0.0)) # ≈ -744
const LOG_FLOATMAX = log(floatmax(Float64)) # ≈ 709
# Evaluate potential energy for a state i on a frame (coords, boundary)
@inline function calc_energy!... |
222320d2fb02ec4ccd2b842f1097dd41184f8a9e043faa21d7ec601f5fa5acee | Julia | 36,689 | 964 | function windowed_tss_coupling(state::TSSState)
if isnothing(state.coupling)
throw(ArgumentError("global TSS visit control is not enabled for this TSSState."))
end
return state.coupling
end
function validate_windowed_tss_coupling_params(::Type{FT};
to... |
4845a498f6fb4262d3c1cac83a3a05e8d5a96d2b2b922d1db8cc4003abb762be | Julia | 36,928 | 958 | # Neighbor finders
export
use_neighbors,
NoNeighborFinder,
find_neighbors,
GPUNeighborFinder,
DistanceNeighborFinder,
TreeNeighborFinder,
CellListMapNeighborFinder
"""
use_neighbors(inter)
Whether a pairwise interaction uses the neighbor list, default `false`.
Custom pairwise interac... |
2bf7802a2eabe6f17978c4a6283f42f3adeaf5358d4c446569ced97eee7ef666 | Julia | 39,053 | 826 | # KernelAbstractions.jl kernels, CUDA kernels are in an extension
kernel_maybe_velocity(velocities, i) = velocities[i]
kernel_maybe_velocity(::Nothing, i) = nothing
@inline function sum_pairwise_forces_gpu(inters::Tuple{T}, dr, atom_i, atom_j, ::Val{F},
special, coord_i, coord... |
8fd3e849cda75fd484128e1551af8545ebe37bb335c928cb0ce958e19798f15e | Julia | 40,566 | 1,065 | function log_windowed_tss_stats!(
stats::WindowedTSSStats{FT},
state::TSSState{FT},
update_window::Int,
visited_state::Int,
next_state::Int,
max_delta_f::FT;
replica_indices = [1],
replica_update_windows = [update_window],
replica_visited_states = [visited_state],
replica_sampled... |
86e0e3809bc5f3bdbaebe9de9da88d7487823c909acf86e0497db403170324a6 | Julia | 42,946 | 1,014 | const temp_fp_dcd = tempname(cleanup=true) * ".dcd"
const temp_fp_trr = tempname(cleanup=true) * ".trr"
const temp_fp_pdb = tempname(cleanup=true) * ".pdb"
const temp_fp_xyz = tempname(cleanup=true) * ".xyz"
const temp_fp_mol2 = tempname(cleanup=true) * ".mol2"
const temp_fp_mp4 = tempname(cleanup=true) * ".mp4"
... |
1b00b241202c45f83adce3eb5e0596226ce90f5f00c4aa9cbbb7d8747844c570 | Julia | 43,344 | 1,094 | # Temperature and pressure coupling methods
export
apply_coupling!,
ImmediateThermostat,
VelocityRescaleThermostat,
AndersenThermostat,
BerendsenThermostat,
BerendsenBarostat,
CRescaleBarostat,
MonteCarloBarostat
"""
apply_coupling!(system, buffers, coupling, simulator, neighbors=n... |
513541fc30b26f40bdf22ee7ebf863c34ecb60ef59c5c9b713131d03e8ddf5a2 | Julia | 44,858 | 1,159 | # Deal with residues
# Struct to carry the information necessary to represent the residue templates
# defined in the force field XML files
struct ResidueTemplate{T, IC}
name::String
atoms::Vector{String}
elements::Vector{Symbol}
types::Vector{String}
virtual_sites::Vector{VirtualSiteTemplate{T, I... |
87e121041c223a26535a7db087da73bf605d638dc18aab2a867b2d452947c8ed | Julia | 44,971 | 1,123 | @testset "Virial correctness" begin
FT = Float64
AT = Array
function potential_deformation(sys, neighbors, q)
T = eltype(q)
z, o = zero(T), one(T)
F = @SMatrix [
o + q[1] q[4] q[5];
z o + q[2] q[6];
z z o + q[3]
... |
e2dea87e23165622a8b1a1426bb6a7f85a6278af01409fda78424a263cc8863e | Julia | 48,057 | 1,355 | # Spatial calculations
export
CubicBoundary,
RectangularBoundary,
TriclinicBoundary,
volume,
density,
box_center,
scale_boundary,
random_coord,
vector_1D,
vector,
wrap_coord_1D,
wrap_coords,
random_velocity,
maxwell_boltzmann,
random_velocities,
random_ve... |
7c118d3b2f5f8349132200c00695cba885a2cd52441189ca20b8e6bb8193c6bc | Julia | 49,162 | 1,274 | # Loggers to record properties throughout a simulation
export
apply_loggers!,
GeneralObservableLogger,
values,
log_property!,
logger_virial_interval,
logger_pressure_interval,
TemperatureLogger,
CoordinatesLogger,
BoxLogger,
VelocitiesLogger,
TotalEnergyLogger,
KineticEn... |
75ecb040963801722acdd6a25e0200708d510267c3f8d20aea1faf586f5ede91 | Julia | 52,869 | 1,420 | export
LINCS,
SetupLINCS
# Internal types for LINCS algorithm
struct LincsCouplingMatrix{R, N, C}
range::R # length K+1, row pointers (CSR format)
neighbors::N # coupled constraint indices
coef::C # mass-weighted coupling coefficients
end
struct LincsCouplingDense{CI, CC, NC}
c... |
b543133282d22538db0f4cfae2b4ee4b79e1acca0ffc5928e448797e5f56cda1 | Julia | 57,506 | 1,401 | export
SHAKE_RATTLE,
SetupSHAKE_RATTLE
"""
SHAKE_RATTLE(; n_atoms, dist_tolerance=1e-8u"nm", vel_tolerance=1e-8u"nm^2 * ps^-1",
dist_constraints=nothing, angle_constraints=nothing,
gpu_block_size=128, max_iters=25, strictness=:warn)
Constrain distances during a simulation... |
15598e0c4139ff08511d4b446de0cc8f47535dfc161087c6be13648b455c974e | Julia | 58,518 | 1,331 | # See https://arxiv.org/pdf/1401.1181.pdf for applying forces to atoms
# See OpenMM documentation and Gromacs manual for other aspects of forces
export
accelerations,
force,
pairwise_force,
SpecificForce1Atoms,
SpecificForce2Atoms,
SpecificForce3Atoms,
SpecificForce4Atoms,
SpecificForce... |
4cba9a45fa366d912a8249c1e0cee85aeecbc87c52f306e166972985d3a18ff6 | Julia | 58,722 | 1,410 | # Long range electrostatic summation methods
# Based on the OpenMM source code
import Base: ==, hash
export
Ewald,
SetupEwald,
PME,
SetupPME,
EwaldExclusion
abstract type AbstractEwald end
const default_ewald_error_tol = 0.0005
AtomsCalculators.@generate_interface function AtomsCalculators.poten... |
493146a37b36d65e515115def3b05745b2517883d33ec43ac743a5e0a9248ca9 | Julia | 59,897 | 1,324 | # Read in a force field
export
MolecularForceField
struct ForceFieldXMLError <: Exception
msg::String
end
Base.showerror(io::IO, e::ForceFieldXMLError) = print(io, "ForceFieldXMLError: ", e.msg)
@enum SpecKind::UInt8 WILD=0 TYPE=1 CLASS=2
struct AtomPattern
kind::SpecKind
val::String
end
function ... |
60d65a6befd7aab781a240e4c80735ab7d17ed428866c7bc54be917e0d0a3b0f | Julia | 63,028 | 1,483 | @testset "Spatial" begin
@test vector_1D(4.0, 6.0, 10.0) == 2.0
@test vector_1D(1.0, 9.0, 10.0) == -2.0
@test vector_1D(6.0, 4.0, 10.0) == -2.0
@test vector_1D(9.0, 1.0, 10.0) == 2.0
@test vector_1D(4.0u"nm", 6.0u"nm", 10.0u"nm") == 2.0u"nm"
@test vector_1D(1.0u"m" , 9.0u"m" , 10.0u"m" ) == ... |
3c30832fde703c186a03294a613316cf92d345574d7b0e7b2272a3fce648a815 | Julia | 70,352 | 1,339 | # Code for taking gradients with Enzyme
# This file is only loaded when Enzyme is imported
module MollyEnzymeExt
using Molly
using Enzyme
using Enzyme.EnzymeCore.EnzymeRules: EnzymeRules, RevConfig, Annotation
using FFTW
using GPUArrays
using KernelAbstractions
const GPUArraysCore = GPUArrays.GPUArraysCore
function... |
eb43097f7e948404d465fb5f7eb5d2afec2859534e21a93a9873497be3cba2b7 | Julia | 70,397 | 2,105 | export
Coulomb,
CoulombScaled,
CoulombSoftCoreBeutler,
CoulombSoftCoreGapsys,
CoulombReactionField,
SetupCoulombReactionField,
CoulombReactionFieldScaled,
CoulombSoftCoreBeutlerReactionField,
CoulombSoftCoreGapsysReactionField,
CoulombEwald,
CoulombEwaldScaled,
CoulombSof... |
e10f3cb1ce509da356af4877f4b4579f99e2fba81712e70d740d8c4c55a688cf | Julia | 72,821 | 1,891 | # Allocation measurements are made through this so that the call is cleanly inferred,
# rather than from the `@testset` scope where the arguments are captured locals
function apply_constraints_n_threads!(sys, coords_ref, n_threads)
apply_position_constraints!(sys, coords_ref; n_threads=n_threads)
apply_velocity... |
ed9f571b1735cc553a95a509ae864f6694df64c59b5312f4971edefe1caef331 | Julia | 76,777 | 1,800 | # Read files to set up a system
# See OpenMM source code
export
place_atoms,
place_diatomics,
is_any_atom,
is_heavy_atom,
add_position_restraints
"""
place_atoms(n_atoms, boundary; min_dist=nothing, max_attempts=100, rng=Random.default_rng())
Generate random coordinates.
Obtain `n_atoms` coo... |
53ab8905840b87f52af05a370190afe633c92d2e84d1dc32c37f5f6f949aac80 | Julia | 83,117 | 1,835 | # Implicit solvent models
# Based on the OpenMM source code
export
ImplicitSolventOBC,
SetupImplicitSolventOBC,
ImplicitSolventGBN2,
SetupImplicitSolventGBN2
# Generalized Born (GB) implicit solvent models augmented with the
# hydrophobic solvent accessible surface area (SA) term
# Custom GBSA metho... |
d0d19abc8313158804875318fbb038bce0bf704421b2e1261ee711a20e68e0ce | Julia | 86,008 | 1,817 | # ANI (and future ML potential) support via Lux.jl + HDF5.jl
# Loaded when both Lux and HDF5 are in the user environment.
#
# ===========================================================================
# How ANI works (and where each equation lives in this file)
# =======================================================... |
b6c9468fc4ccd17225e5fc803b60c8970ba44caf30d48034d1f0b6b188339f99 | Julia | 86,846 | 2,212 | # Types
import Base: ==, hash
export
PairwiseInteraction,
InteractionList1Atoms,
InteractionList2Atoms,
InteractionList3Atoms,
InteractionList4Atoms,
InteractionList5Atoms,
Atom,
mass,
charge,
AtomData,
MolecularTopology,
NeighborList,
System,
ThermoState,
Re... |
a4e964e71d448839ca658896eef2ae11529cc669ac2916c6e85aaa34c528e0c6 | Julia | 86,875 | 2,059 | @testset "Interactions" begin
c1 = SVector(1.0, 1.0, 1.0)u"nm"
c2 = SVector(1.3, 1.0, 1.0)u"nm"
c3 = SVector(1.4, 1.0, 1.0)u"nm"
c4 = SVector(1.1, 1.0, 1.0)u"nm"
a1 = Atom(atom_type=1, charge=1.0, σ=0.3u"nm", ϵ=0.2u"kJ * mol^-1", λ=1.0)
a2 = Atom(atom_type=2, charge=1.0, σ=0.2u"nm", ϵ=0.1u"kJ * ... |
387c716fda3f111367db0f973cead45d1ca640491d2a245cca667a37981fe9be | Julia | 112,824 | 2,429 | # Different ways to simulate molecules
export
SteepestDescentMinimizer,
simulate!,
VelocityVerlet,
DPDVelocityVerlet,
Verlet,
StormerVerlet,
Langevin,
LangevinSplitting,
OverdampedLangevin,
NoseHoover,
MTSIntegrator,
MTSLangevinIntegrator,
ReplicaExchangeMD,
simu... |
bc1d2ef1dab99a94b7564f741eab95b26e8dff128178bf6ac579c75f0a536cc8 | Julia | 114,120 | 2,839 | """
MollyCUDAExt
CUDA extension for Molly.jl. This module provides highly optimized CUDA kernels
for pairwise force and energy calculations, utilizing warp-level primitives,
Morton ordering for spatial locality, and a tiled preprocessing pipeline.
The pipeline generally follows these steps:
1. **Reordering**: At... |
9472f3f323363ba61a08e4c2ed602b36ac6ec2675f9a79fff1ff118608b1b925 | Julia | 198,348 | 4,081 | # Train Garnet model to assign force field parameters
# Runs multithreaded on CPU, spawns training simulations after a few epochs to run on GPU
# See https://github.com/greener-group/garnet/tree/main/training for how to run this script
# Change submit_training_sims to launch an appropriate job on your system
# License ... |
11ef874a170ebab0532bc7f9f6854ab59ba3c89d2ecb2ef9f88ea146643e289e | Jupyter | 239 | 10 | # %%
#select branch from dataset and go inside
dataSet=STEDdata['5wk']
name='dendrite38-1'
#plot_dsc(dataSet)
file_path = dataSet[name]['path']
dataSet[name] = branch_dsc(file_path, dataSet[name]['res'], dataSet[name]['ex_factor'])
|
d58abe487c7124913632170d565a05fccae0e2936d242987e54723abd3a39363 | Jupyter | 556 | 28 | # %%
%load_ext autoreload
%autoreload 2
# %%
from data import dataloaders
trainloader,valloader,testloader = dataloaders(
modalities=['lab','cxr','med','ecg']
)
# %%
sample = trainloader.dataset[0]
# %%
import numpy as np
import matplotlib.pyplot as plt
fig, axs = plt.subplots(1,4)
axs[0].imshow(np.einsum('chw-... |
9f45cb43c05769420dae717fa1c39ad0c0d81725454ef79ee57538bdb06c0971 | Jupyter | 777 | 46 | # %%
from iehm import *
import glob
# %%
# Get project root
import os
from pathlib import Path
# Go up from notebook to project root
notebook_dir = Path().resolve()
project_root = notebook_dir.parent
# Define test data path
data_dir = project_root / "data" / "test"
# get all .lsm files in directory
files = glob.gl... |
77a3b1205176c63afac09b1db5729dbf6118bd555bb3be84257fba6930a5f5a5 | Jupyter | 934 | 36 | # %% [markdown]
# ## Activation
# %%
from chemprop.nn.utils import Activation
# %% [markdown]
# ### Activation functions
#
# The following activation functions can be specified by name (e.g., `activation = "relu"`):
# %%
for activation in Activation:
print(activation)
# %%
from chemprop.nn import AtomMessagePa... |
f6fa5cefde053dd900a872ae1891e0e55bff8913e21163437c6b2a2b0790a280 | Jupyter | 990 | 53 | # %% [markdown]
# # Clustering
# %%
import scanpy as sc
sc.settings.verbosity = 0
sc.settings.set_figure_params(dpi=80, facecolor="white", frameon=False)
# %%
#Change working Directory
import os
os.chdir('/Users/veronica/Desktop/CSTB_Analysis/')
# %%
#Load the data
adata = sc.read_h5ad('02_Results/CSTB_dimension... |
ce12d933336dade5dd3a5e01c952db2b2f1c452cb149279e30e0558642247643 | Jupyter | 1,012 | 34 | # %%
import pickle
import pandas as pd
import sys
from pathlib import Path
NOTEBOOK_DIR = Path().resolve()
PROJECT_ROOT = NOTEBOOK_DIR.parent # Mapping_Recurrent_Inhibition/
if str(PROJECT_ROOT) not in sys.path:
sys.path.append(str(PROJECT_ROOT))
# Import from other files in the project
from simulator import (
... |
b58d15c00bfc9c6befc2c298f69b149465ac8f114a4d5dbd3bb527d0184ca428 | Jupyter | 1,228 | 59 | # %%
#default_exp stats.coverage
# %% [markdown]
# # stats.coverage
#
# > A submodule for computing statistics and making estimates related to read coverage
# %%
#export
import numpy
import scipy
from matplotlib import pyplot
import seaborn
import pandas as pd
import pyfastx
import pyfaidx
from tqdm import tqdm
impo... |
a1f23162c7d7b5b0225e7e976ec383b037bf66675671279c8fa275b735d37131 | Jupyter | 1,281 | 46 | # %% [markdown]
# ## Ensembling
# %%
from lightning import pytorch as pl
import numpy as np
import torch
from chemprop import data, models, nn
# %% [markdown]
# This is an example [dataloader](./data/dataloaders.ipynb).
# %%
smis = ["C" * i for i in range(1, 4)]
ys = np.random.rand(len(smis), 1)
dset = data.Molecule... |
7a6daaf1cef075bc6a94e753b3e5db62cbfa2bb4b983305684893979877951cb | Jupyter | 1,307 | 41 | # %% [markdown]
# This example implements the first model from "Modeling civil violence: An agent-based computational approach," by Joshua Epstein. The paper (pdf) can be found [here](http://www.uvm.edu/~pdodds/files/papers/others/2002/epstein2002a.pdf).
#
# The model consists of two types of agents: "Citizens" (call... |
485df5206815e4b34cf092d094fa44c414e133cb6fd578cb8e2edf9f2c42bfca | Jupyter | 1,331 | 47 | # %%
import os
import numpy as np
import pandas as pd
import anndata as ad
import numpy as np
import scanpy as sc
import time
import glob
import matplotlib.pyplot as plt
import scvelo as scv
from TSvelo.TSvelo_utils import project_t, get_colors_step_subs, analyze_g_subs, analyze_g
dataset_name = 'dentategyrus'
save_f... |
95f0c452ef6d7456258b456c64291a95763460d3a4ccde4be9ffd0a0d2958dbb | Jupyter | 1,398 | 44 | # %% [markdown]
# # Running Pangolin on Colab
# %% [markdown]
# 0. By default, Colab will not use a GPU. To use a GPU, choose: Runtime -> Change runtime type -> GPU.
# %% [markdown]
# 1. Install Pangolin and dependencies
# %%
!pip install pyvcf gffutils biopython pandas pyfastx
!git clone https://github.com/tkzeng... |
b8c61ceb1b3559a40c5f7a6462b11a9f63741fa4454d3b1c774971f35ea91436 | Jupyter | 1,520 | 78 | # %% [markdown]
# # Feature selection
# %%
import scanpy as sc
import anndata2ri
import logging
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import rpy2.rinterface_lib.callbacks as rcb
import rpy2.robjects as ro
sc.settings.verbosity = 0
sc.settings.set_figure_params(
dpi=80,
face... |
8c3c225ecb5ce644f4dcc6ba8cc9daa6063b8f290deadae1c6c50309acd4c4eb | Jupyter | 1,577 | 57 | # %%
import sys
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from tqdm import tqdm
###changes default of autoreloader to continually reload (2) rather than only on restart
####good for debuggin but does slow code as it is continully reloading modules
%load_ext autoreload
%autoreload 2
#... |
6b24758325d61cc16d1360e3d32a98dc0054a4dcd326dbdd889e528372c11ce3 | Jupyter | 1,682 | 68 | # %% [markdown]
# ## Bond featurizers
# %%
from chemprop.featurizers.bond import MultiHotBondFeaturizer
# %% [markdown]
# This is an example bond to featurize.
# %%
from rdkit import Chem
bond_to_featurize = Chem.MolFromSmiles("CC").GetBondBetweenAtoms(0, 1)
# %% [markdown]
# ### Bond features
# %% [markdown]
# T... |
fea844c076d1ead245b883af0238f83c0e120ecc6bbc2d30994ec76546d21158 | Jupyter | 1,695 | 51 | # %%
import numpy as np
import skimage.io
import h5py
import matplotlib.pyplot as plt
from tqdm.auto import tqdm
# %%
# Load image and set kernel size and stride to match from simulations
image = skimage.io.imread("CL.bmp")/255
image = image.astype(int)
H_full, W_full = image.shape
H_kernel, W_kernel = 8, 8
H_stride,... |
177307145b81bd6124cec486241a7ce3a88786ff3a03d590e3d745e40aa18215 | Jupyter | 1,718 | 91 | # %% [markdown]
# # Dimensionality Reduction
# %%
import scanpy as sc
sc.settings.verbosity = 0
sc.settings.set_figure_params(
dpi=80,
facecolor="white",
frameon=False,
)
# %%
#Change working Directory
import os
os.chdir('/Users/veronica/Desktop/CSTB_Analysis/')
# %%
#Load the data
adata = sc.read_h5... |
90f4214c2ddc49b6f1891da7ac528052a246df8401872e73d7b6a8ad704d4287 | Jupyter | 1,789 | 50 | # %%
import pymaid
import navis as nv
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar
import matplotlib.axes as axx
import matplotlib.font_manager as fm
fontprops = fm.FontProperties(size=18)
import time
from navis.interfaces import neuprint as nvneu
from neuroboom i... |
436365c7f7bb072dc952cbb46acb9e98bbe3a7016351096a07c4fb49041519c4 | Jupyter | 1,791 | 66 | # %%
# Compute global stats for intensity normalization
import os
import numpy as np
import nibabel as nib
import matplotlib.pyplot as plt
from tqdm.notebook import tqdm
PATH = "/path/to/AbdomenCT-1K/"
ID_FILE = "/path/to/train_ids.txt"
OUT_FILE = "/path/to/intensity_stats.csv"
# %%
# Foreground voxels
i_vals_fg = [... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.