sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
a5df2a13f43578f649ae611d415a1f3267abb87fbb25b83129b54ab9deeca720 | JavaScript | 200,000 | 3 | window.pdocSearch = (function(){
/** elasticlunr - http://weixsong.github.io * Copyright (C) 2017 Oliver Nightingale * Copyright (C) 2017 Wei Song * MIT Licensed */!function(){function e(e){if(null===e||"object"!=typeof e)return e;var t=e.constructor();for(var n in e)e.hasOwnProperty(n)&&(t[n]=e[n]);return t}var t=func... |
b16e4ecc3cd24767daf90bc6b0e709f5d9301df067e4c8676bcf705d146847bb | JavaScript | 200,000 | 8 | /*! jQuery v1.8.3 jquery.com | jquery.org/license */
(function(e,t){function _(e){var t=M[e]={};return v.each(e.split(y),function(e,n){t[n]=!0}),t}function H(e,n,r){if(r===t&&e.nodeType===1){var i="data-"+n.replace(P,"-$1").toLowerCase();r=e.getAttribute(i);if(typeof r=="string"){try{r=r==="true"?!0:r==="false"?!1:r===... |
c66c0c76a9e1ef0703bbb1294b006e33526f32cf06d50b0e4de841d054644238 | JavaScript | 200,000 | 10 | /*!
* Chart.js
* http://chartjs.org/
* Version: 2.7.2
*
* Copyright 2018 Chart.js Contributors
* Released under the MIT license
* https://github.com/chartjs/Chart.js/blob/master/LICENSE.md
*/
!function(t){if("object"==typeof exports&&"undefined"!=typeof module)module.exports=t();else if("function"==typeof defin... |
f107809addad950fa854e4977c0f12e5f3883154647927a3fe9d53fbed55d838 | JavaScript | 200,000 | 699 | /*!
* jQuery UI 1.8.13
*
* Copyright 2011, AUTHORS.txt (http://jqueryui.com/about)
* Dual licensed under the MIT or GPL Version 2 licenses.
* http://jquery.org/license
*
* http://docs.jquery.com/UI
*/
(function(c,j){function k(a,b){var d=a.nodeName.toLowerCase();if("area"===d){b=a.parentNode;d=b.name;if(!a.href... |
f24b9f05efdaa09d1a7c5b9e8f982e678a4d473c8a46d69a88ef5a53406e2a3c | JavaScript | 200,000 | 7,573 | /*!
* jQuery JavaScript Library v3.3.1 -ajax,-ajax/jsonp,-ajax/load,-ajax/parseXML,-ajax/script,-ajax/var/location,-ajax/var/nonce,-ajax/var/rquery,-ajax/xhr,-manipulation/_evalUrl,-event/ajax,-effects,-effects/Tween,-effects/animatedSelector
* https://jquery.com/
*
* Includes Sizzle.js
* https://sizzlejs.com/
*
... |
d683935f98daec037f1b5027194aae8044360b72a32fafd62326884e9e7405ff | JavaScript | 200,002 | 6,147 | /*!
* Bootstrap v4.4.1 (https://getbootstrap.com/)
* Copyright 2011-2019 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)
* Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
*/
(function (global, factory) {
typeof exports === 'object' && typeof module !== ... |
46b4cd509ee0a80b927623fadaf59913aa61a6db261edea8211b4064104c9242 | JavaScript | 200,004 | 1 | !function(root,factory){"object"==typeof exports&&"object"==typeof module?module.exports=factory(require("react"),require("prop-types"),require("immutable"),require("react-immutable-proptypes"),require("reselect"),require("classnames"),require("serialize-error"),require("url-parse"),require("deep-extend"),require("js-y... |
59036a222f19065a9f69112b6d22e3fbacea09e3e3a1851efa5769af259a1e8a | JavaScript | 200,004 | 51 | !function(e,t){"object"==typeof exports&&"object"==typeof module?module.exports=t():"function"==typeof define&&define.amd?define([],t):"object"==typeof exports?exports.SwaggerUIBundle=t():e.SwaggerUIBundle=t()}(this,function(){return function(e){var t={};function n(r){if(t[r])return t[r].exports;var i=t[r]={i:r,l:!1,ex... |
6eebd8266f0a833bf8a1044586edadcabf400474c4413f82aa534b80964191f0 | JavaScript | 200,005 | 4,877 | (function(f){if(typeof exports==="object"&&typeof module!=="undefined"){module.exports=f()}else if(typeof define==="function"&&define.amd){define([],f)}else{var g;if(typeof window!=="undefined"){g=window}else if(typeof global!=="undefined"){g=global}else if(typeof self!=="undefined"){g=self}else{g=this}g.deeplearn = f(... |
c746c2918b6ec967a0020803fff715d6a7f46b622c747f0d86ac902eee11c77a | JavaScript | 200,005 | 6,939 | /*! DataTables 1.10.2
* ©2008-2014 SpryMedia Ltd - datatables.net/license
*/
/**
* @summary DataTables
* @description Paginate, search and order HTML tables
* @version 1.10.2
* @file jquery.dataTables.js
* @author SpryMedia Ltd (www.sprymedia.co.uk)
* @contact www.sprymedia.co.uk/contac... |
a59612b94e5c356315c5a920e64c33c9efec278f981a77adc56e7595c89c509b | JavaScript | 200,012 | 1 | var e=Object.defineProperty,t=(t,o)=>{let n={};for(var i in t)e(n,i,{get:t[i],enumerable:!0});return o||e(n,Symbol.toStringTag,{value:"Module"}),n},o="bottom",n="right",a="left",s="auto",r=["top",o,n,a],l="start",c="clippingParents",d="viewport",h="popper",u="reference",p=r.reduce(function(e,t){return e.concat([t+"-"+l... |
d9640dac61faa0306f47a50f0bb9f58417bf94c2ca544654cebec06598fe8b16 | JavaScript | 200,032 | 39 | /*!
* Material Design for Bootstrap 4
* Version: MDB FREE 4.13.0
*
*
* Copyright: Material Design for Bootstrap
* https://mdbootstrap.com/
*
* Read the license: https://mdbootstrap.com/general/license/
*
*
* Documentation: https://mdbootstrap.com/
*
* Getting started: https://mdbootstrap.... |
78fdf60127e3634351452e5d58408da5785540cb6ea597c718be6a7adc937456 | JavaScript | 200,049 | 13 | !function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.SwaggerUIStandalonePreset=e():t.SwaggerUIStandalonePreset=e()}(this,function(){return function(t){var e={};function n(r){if(e[r])return e[r].exports;var... |
6b7214af9d9055dcec42ed27502efe7ee5a9c6cc1381332b73553064d93fe0a4 | JavaScript | 200,162 | 6,009 | /*! markdown-it 8.4.1 https://github.com//markdown-it/markdown-it @license MIT */(function(f){if(typeof exports==="object"&&typeof module!=="undefined"){module.exports=f()}else if(typeof define==="function"&&define.amd){define([],f)}else{var g;if(typeof window!=="undefined"){g=window}else if(typeof global!=="undefined"... |
a3ebb4dcfc761d9df167f3499578d53b32a7a4c6d0530ea527528f8aea426937 | Julia | 128 | 7 | function fn2str(func::Function)
strip(string(func), ['!'])
end
function obj2str(obj)
strip(string(obj), ['(', ')'])
end |
7eeef8a3110b9d81ec158fb96875d06f6988b61a3ba0b412f3608c5015a3d860 | Julia | 157 | 6 | include(srcdir("rasters.jl"))
let fn = datadir("random_raster_150x150.csv")
raster = generate_random_raster((150, 150))
write_raster(fn, raster)
end |
230e47968f7907b07e890abea403abc8a312ddbd98776b65e37e2b4ee2b7e712 | Julia | 162 | 7 | include(srcdir("main.jl"))
let fn = datadir("random_raster.csv")
raster = load_raster(fn)
triple_correlation_network_classifications(raster, 15, 15)
end |
6ea180de3422402e0dc9808110dd9f3df8fd24f2580ea8b22ee5eb5d0691c3bb | Julia | 210 | 11 | @quickactivate "RasterUniqueCharacterizationCode"
n_trials=15;
n_tests=15;
n_signals=15;
MEA_spikes=167;
lag_extents=(10,10);
signal_jitters=(12,12);
include(scriptsdir("detection","trial_detect.jl"))
zscore! |
0782eb8c8853cbd5abab288a9cd0423f517994978df53c084fb0306cfe737c38 | Julia | 215 | 11 | @quickactivate "RasterUniqueCharacterizationCode"
n_trials=15;
n_tests=15;
n_signals=13;
MEA_spikes=167;
lag_extents=(10,10);
signal_jitters=(12,12);
include(scriptsdir("detection","trial_detect_RATE.jl"))
zscore! |
c68dcef8a2eb23ead3798973ba2ae3833b40235d0cf9079b51ad2ffd0a889942 | Julia | 229 | 11 | @quickactivate "RasterUniqueCharacterizationCode"
@warn "TRIVIAL TEST PARAMS"
n_trials=90;
n_tests=3;
n_signals=13;
MEA_spikes=167;
lag_extents=(10,10);
signal_jitters=(12,12);
include(scriptsdir("detection","trial_detect.jl")) |
30b27d156a6ebe4c487baea0287416cdeae22ff931483091884fc9941f13620a | Julia | 337 | 16 | using DrWatson
@quickactivate "RasterUniqueCharacterizationCode"
println(
"""
Currently active project is: $(projectname())
Path of active project: $(projectdir())
Have fun with your new project!
You can help us improve DrWatson by opening
issues on GitHub, submitting feature requests,
or even opening your own Pull... |
aecb710fa4e2daf04a9b8b2e04bda72c77a5724d700d729a6259d91ce8239b95 | Julia | 392 | 18 | using DifferentialEquations, Plots
A = 1
B = 1
C = 4
x0 = 1
function f(du, u, p, t)
du[1] = -0.5 * u[1]^2 + 0.5 * C^2 * u[2] / B
du[2] = -A + u[1] * u[2]
end
function bc1!(residual, u, p, t)
residual[1] = u[1][1] - x0
residual[2] = u[end][2]
end
tspan = (0,1)
bvp1 = BVProblem(f, bc1!, [... |
0b38b635337712121fad5f9c224e2461ffe06ccfd8574da64faa558394fb281a | Julia | 394 | 18 | @quickactivate "RasterUniqueCharacterizationCode"
using TripleCorrelations
using HypothesisTests, Random, Statistics
using ProgressMeter
using CairoMakie; plot_ext = "png"
using Dates
using Pkg
using Base.Threads
using DataFrames, AlgebraOfGraphics, Colors
using JLD2
include(srcdir("roman_encode.jl"))
include(srcdir(... |
a7699364870efb3663442454f1766e551875b11837c07770ecc7e64cf9c404bb | Julia | 418 | 11 | ##
# Activate and set up the project environment
using Pkg
Pkg.activate(".")
Pkg.instantiate() # install
##
# Precompile to accelerate the computation
using PackageCompiler
@time create_sysimage([:DifferentialEquations, :DiffEqFlux, :Plots, :DataFrames, :Optimization, :SciMLSensitivity, :InfiniteOpt], sysimag... |
3548d4bc4b2af55c37f00f4b4ad6fec9815d2e90bb4197063e92d209c7c6de56 | Julia | 435 | 16 | @quickactivate "RasterUniqueCharacterizationCode"
using TripleCorrelations
using UnicodePlots
raster_size = (1500, 2000)
lag_extents = (5,7)
raster = zeros(Bool, raster_size...)
expectations = map(100:100:5000000) do n_spikes
raster[1:n_spikes] .= 1
expectation_conditioned_on_spike_count(raster, Periodic(),... |
4775e85ea19504ab9db9661c8cda8a91cbb2827f3d621e30349ac0048b3fcf16 | Julia | 483 | 19 | using DrWatson; quickactivate(@__DIR__)
using CSV, Tables, IterTools
using DataFrames
using LoopVectorization
using CairoMakie
using OffsetArrays
using Dates
using DataStructures
using TripleCorrelations
include(srcdir("rasters.jl"))
include(srcdir("motif_classes.jl"))
#include(srcdir("triple_correlation.jl"))
includ... |
3f64eae479ea59e3498e0168b4ff3d0be962e05ddca3dd5957f376eb1b43df89 | Julia | 601 | 18 |
include(srcdir("main.jl"))
using BenchmarkTools
let raster_size = (150, 150),
λ_max = (7, 7);
raster = generate_random_raster(raster_size)
plain_array = Array{Float64,4}(undef, length.([-λ_max[1]:λ_max[1], -λ_max[2]:λ_max[2], -λ_max[1]:λ_max[1], -λ_max[2]:λ_max[2]])...)
offset_array = OffsetArray{Float64}... |
7da6a13a4ee2353057797554e0170c40b38bffc6a4e7fcef5a4b4dbcdc1a3807 | Julia | 625 | 18 | @quickactivate "RasterUniqueCharacterizationCode"
using DataFrames, CSV
include(srcdir("motif_classes.jl"))
include(srcdir("enumerate_lag_motifs_OLD.jl"))
include(srcdir("enumerate_lag_motifs.jl"))
include(srcdir("latex_table.jl"))
classes = generate_all_lag_motif_classes()
# remap_old_filenames(plotsdir("old_motif_... |
fa6d7a9150b70a86513196e09c06faed57f80ef5a1739323dbfa0a82fc76829b | Julia | 645 | 44 | using InfiniteOpt, Ipopt, Plots
# Parmeters
# Time span
t0 = 0
tf = 1
# Initial values
x1 = 1
# Model
model = InfiniteModel(Ipopt.Optimizer)
# infinite_parameter
@infinite_parameter(model, t ∈ [t0, tf], num_supports = 101)
# variable
@variable(model, x, Infinite(t))
@variable(model, u, Infinite... |
886b2c40fa54421c0d75d91422fd97bdebaf7adb1847f2074d158047d412ca9f | Julia | 671 | 45 | using InfiniteOpt, Ipopt, Plots
using KNITRO
# Parmeters
# Time span
t0 = 0
tf = 1
# Initial values
x1 = 5
# Model
model = InfiniteModel(KNITRO.Optimizer)
# infinite_parameter
@infinite_parameter(model, t ∈ [t0, tf], num_supports = 101)
# variable
@variable(model, x , Infinite(t))
@variable(mo... |
5d8ff2cb82873b1367d06dd8d795bf705e15dadf19f9073e660c14fc1bbc5f83 | Julia | 672 | 45 | using InfiniteOpt, Ipopt, Plots
using KNITRO
# Parmeters
# Time span
t0 = 0
tf = 2
# Initial values
x1 = 1
# Model
model = InfiniteModel(Ipopt.Optimizer)
# infinite_parameter
@infinite_parameter(model, t ∈ [t0, tf], num_supports = 101)
# variable
@variable(model, x , Infinite(t))
@variable(mod... |
76c333cccfe647b35fc9e04a3ee73d0ef6c642e724f7562e9bd3521c503ee6a8 | Julia | 691 | 44 | using InfiniteOpt, Ipopt, Plots
using KNITRO
# Parmeters
# Time span
t0 = 0
tf = 2
# Initial values
x1 = 5
# Model
model = InfiniteModel(Ipopt.Optimizer)
# infinite_parameter
@infinite_parameter(model, t ∈ [t0, tf], num_supports = 101)
# variable
@variable(model, x, Infinite(t))
@variable(mode... |
c744cba25b93228bf39638f1d6cfc1fda788403eb3cdd86b94a38006a586b54e | Julia | 720 | 49 | using InfiniteOpt, Ipopt, Plots
using KNITRO
# Parmeters
A = 1
B = 1
C = 4
# Time span
t0 = 0
tf = 1
# Initial values
x1 = 1
# Model
model = InfiniteModel(Ipopt.Optimizer)
# infinite_parameter
@infinite_parameter(model, t ∈ [t0, tf], num_supports = 101)
# variable
@variable(model, x, Infinit... |
84c1d7beeca2644444d4424fe9138b7863ff448949c3b4455682e0a8be62b68a | Julia | 785 | 30 |
function offset_motif_numeral(n::Integer)
roman_encode_zero(n-1)
end
# https://www.rosettacode.org/wiki/Roman_numerals/Encode#Julia
function roman_encode_zero(n::Integer)
if n == 0 return "0" end
if n < 0 || n > 4999 throw(DomainError(n)) end
DR = [["I", "X", "C", "M"] ["V", "L", "D", "MMM"]]
rn... |
61e6e56eff66d639b29a1dd96d0fec78bbf1759305a472274262daad1c16e52e | Julia | 801 | 47 | using InfiniteOpt, Ipopt, Plots
# Parmeters
β = 0.3
γ = 0.1
i_max = 1000
# Time span
t0 = 0
tf = 50
# Initial values
i0 = 1
# Model
model = InfiniteModel(Ipopt.Optimizer)
# infinite_parameter
@infinite_parameter(model, t ∈ [t0, tf], num_supports = 101,
derivative_method = OrthogonalCollocation... |
4022c2db1db83bb58ae222642b2491402c2e3bab2dc6332dd0edd16e2e7199b2 | Julia | 856 | 23 | using DrWatson; quickactivate(@__DIR__)
include(srcdir("main.jl"))
potential_fig = let N_neurons = 150, N_times = 150, max_lag = 7, transform=log10;
neuron_max_lag = time_max_lag = max_lag
raster = BitMatrix(ones(N_neurons, N_times))
fig = @time with_theme(bar_theme) do
fig = Figure()
fig[1,1] = potential_a... |
8528c5945d82972c7726174ecee1235cb11389d2b58834140d00b796c233bd99 | Julia | 865 | 28 |
include(srcdir("main.jl"))
TT_fig, NN_fig = let freq=0.1, θ=-2π, N_neurons=150, N_times=150,
neuron_window=31, time_window=31;
raster_coords = IterTools.product(1:N_neurons, 1:N_times)
raster_signal = map(raster_coords) do (i_neuron, i_time)
i_neuron % 10 == 1 ? (sin(2π*freq*i_time + θ*i_neuron/N_neurons) +... |
b3cffb8dbd9baa87681f3b69b1d8ed240b51c4585276ffe6b2816fe07b8b00db | Julia | 887 | 22 | @quickactivate "RasterUniqueCharacterizationCode"
include(srcdir("load_results.jl"))
using CairoMakie; plot_ext = "png"
using AlgebraOfGraphics, Colors
using AlgebraOfGraphics: categorical
results_dir = joinpath(projectdir(), "remote_plots", "rate_divide_35trials_3sigs_MEA_100x101_167spk_periodic_2022_06_01-104707")... |
037e43a032829befc030fdcab856b2c79388040638b697cec5610649c705ba36 | Julia | 933 | 51 | using InfiniteOpt, Ipopt, Plots
using KNITRO
# Parmeters
# Time span
t0 = 0
tf = 5
# Initial values
x10 = 0.7
x20 = 0.4
# Model
model = InfiniteModel(KNITRO.Optimizer)
# infinite_parameter
@infinite_parameter(model, t ∈ [t0, tf], num_supports = 101)
# variable
@variable(model, x1 ≥ 0, Infinite(t... |
0c522ea85e000e38dc065993caa34402bc4f2b244682e068e70a46c4120432fb | Julia | 1,049 | 28 | @quickactivate "RasterUniqueCharacterizationCode"
using TripleCorrelations
using DataFrames
using JLD2
function load_results_df(filename)
loaded = JLD2.load(filename)
prior_results_dict = loaded["prior_results_dict"]
DataFrame(
mapreduce(vcat, keys(prior_results_dict)) do key
_, _, per... |
abcbdfb94af556c25e9d48148436246e7f43dcf594a6d50ee2cd94b19bae2b10 | Julia | 1,086 | 39 | using RobotDynamics
using ForwardDiff
using FiniteDiff
using StaticArrays
# Define the model struct with parameters
RobotDynamics.@autodiff struct SEIR <: RobotDynamics.ContinuousDynamics
β::Float64
ξ::Float64
γ::Float64
end
γ0 = 0.303
β0 = 0.727
N = 1e4
ξ0 = 0.2
SEIR() = SEIR(β0, ξ0, γ0)
RobotDynamics.st... |
a3b87b6447f1b27ca4de1c81d8e0040f06ac4949500d852e96e0ba3fe5513139 | Julia | 1,523 | 47 | using NeuralPDE
using Flux
using DifferentialEquations
using LinearAlgebra
d = 100 # number of dimensions
X0 = fill(0.0f0, d) # initial value of stochastic control process
tspan = (0.0f0, 1.0f0)
λ = 1.0f0
g(X) = log(0.5f0 + 0.5f0 * sum(X .^ 2))
f(X, u, σᵀ∇u, p, t) = -λ * sum(σᵀ∇u .^ 2)
μ_f(X, p, t) = zero(X... |
1bc257cd10f68a89dbfcedaaf47aa5cbcc1ed0a98331da612b485877c18f7d4e | Julia | 1,655 | 42 | @quickactivate "RasterUniqueCharacterizationCode"
using TripleCorrelations
using HypothesisTests, Random, Statistics
using ProgressMeter
using CairoMakie; plot_ext = "png"
using Dates
using Pkg
using Base.Threads
using DataFrames, AlgebraOfGraphics, Colors
using JLD2
include(srcdir("roman_encode.jl"))
include(srcdir(... |
c981baa969a0eb33fd32090b2b0fb5d73734e20a8b2061a6a68e90fb22f40f29 | Julia | 1,673 | 55 | @quickactivate "RasterUniqueCharacterizationCode"
using TripleCorrelations
using HypothesisTests, Random, Statistics
using ProgressMeter
using CairoMakie; plot_ext = "png"
using Dates
using Pkg
using Base.Threads
using DataFrames, AlgebraOfGraphics, Colors
using JLD2
include(srcdir("main.jl"))
let conditioned_on = R... |
8c60204b50ea9415a0ee80ab448160c0f827863d83f5fcdf42421d190c59a430 | Julia | 1,922 | 64 | using Flux, DiffEqFlux, DifferentialEquations, Optimization, OptimizationOptimJL, Random, Plots, Ipopt
using OptimizationMOI
#rng = Random.default_rng()
tspan = (0.0f0, 50.0f0)
ann = FastChain(FastDense(1, 32, relu), FastDense(32, 1), (x, p) -> 0.6 * sigmoid.(x) .+ 0.2)
# Set the SEIR parameters
γ = 0.303
β = 0.... |
c641f09be09b539607f8922dc0c3de1af9caa0f43de487bbfb77a24432566361 | Julia | 1,982 | 59 |
abstract type AbstractLag{N,T} end
function Base.Tuple(lag::AbstractLag)
lag.λ
end
struct Lag{N,T} <: AbstractLag{N,T}
λ::NTuple{N,T}
end
struct TernarizedLag{N,T} <: AbstractLag{N,T}# come up with better name
λ::NTuple{N,T}
end
function ternarize_triplet_1d_lags((l1, l2))
if l1 > l2 > 0
return... |
02bd0441ff8e4d9546d2361ad25c442be2e5b6df0c80d8f794c5e28425fcf8a7 | Julia | 2,003 | 81 | using InfiniteOpt, Ipopt, Plots
using KNITRO
exponential_relaxation(z, δ) = exp(one(typeof(z)) - z / δ) - one(typeof(z)) - log(δ)
function relaxed_log_barrier(z; δ = 0.3f0)
return max(z > δ ? -log(z) : exponential_relaxation(z, δ), zero(typeof(z)))
end
# Set the SEIR parameters
γ = 0.303
β = 0.727
N = 10... |
d85071c9739c9c4c546fa4f82819fa2271d40572000337e9527bccf827f88e6a | Julia | 2,021 | 61 | function make_neuron_raster(dims...)
arr = Array{Bool}(undef, dims...)
arr .= 0
make_neuron_raster(arr)
end
function make_neuron_raster(arr::BitMatrix)
dims = size(arr)
OffsetArray(arr, [1:dim for dim in dims[begin:end-1]]..., 0:(dims[end]-1))
end
function load_raster(fn)
arr = CSV.read(fn, Arr... |
57831c1820cf6572ec7c12f0c03a472ec14fd8116da6d8f25309ee0ad1fbecb9 | Julia | 2,038 | 45 | @quickactivate "RasterUniqueCharacterizationCode"
using TripleCorrelations
using HypothesisTests, Random, Statistics
using ProgressMeter
using CairoMakie; plot_ext = "png"
using Dates
using Pkg
using Base.Threads
using DataFrames, AlgebraOfGraphics, Colors
using JLD2
include(srcdir("roman_encode.jl"))
include(srcdir(... |
39d972afed6befc6b7a1322e0a5aa86ed178ad945c030a8627adc71ee872bad8 | Julia | 2,065 | 64 | using Flux, OrdinaryDiffEq, SciMLSensitivity, Optimization, OptimizationOptimisers, OptimizationOptimJL, Plots
u0 = Float32[2.; 0.]
datasize = 30
tspan = (0.0f0,1.5f0)
function trueODEfunc(du,u,p,t)
true_A = [-0.1 2.0; -2.0 -0.1]
du .= ((u.^3)'true_A)'
end
t = range(tspan[1],tspan[2],length=datasize... |
23391f390bf5543cb4bb0b70c6595d3a958c773fc7a9b7e5fb65b2f42e87a7fd | Julia | 2,195 | 62 | @quickactivate "RasterUniqueCharacterizationCode"
using TripleCorrelations
using HypothesisTests, Random, Statistics
using ProgressMeter
using CairoMakie; plot_ext = "png"
using Dates
using Pkg
using Base.Threads
using DataFrames, AlgebraOfGraphics, Colors
using JLD2
include(srcdir("roman_encode.jl"))
include(srcdir(... |
f803d06c32caceb64958a6d026ed9cb661bee2a66fe3a621c96dea40244e1281 | Julia | 2,281 | 90 | using InfiniteOpt, Ipopt, Plots
using Ipopt
# Parmeters
# Time span
t0 = 0
tf = 1
# Initial values
x1 = 1
# Model
model = InfiniteModel(KNITRO.Optimizer)
# infinite_parameter
@infinite_parameter(model, t ∈ [t0, tf], num_supports = 101)
# variable
@variable(model, x ≥ 0, Infinite(t))
@variable(... |
a7f4fe4811b547664e58c3d1d81930e351646c8fffd3c1d4bfb286406acac657 | Julia | 2,571 | 59 |
include(srcdir("main.jl"))
const InfoFlowRow = @NamedTuple{spike_motif::String, n1_num::Int, n2_num::Int, t1_num::Int, t2_num::Int, motif_class_num::Int}
lag_1(ifr::InfoFlowRow) = TernarizedLag((ifr.n1_num, ifr.t1_num))
lag_2(ifr::InfoFlowRow) = TernarizedLag((ifr.n2_num, ifr.t2_num))
#lag_triplet(ifr::InfoFlowRow) ... |
ac9a0dacd5552cbbdfa658f79cadb75dcd69e1c070209cb277b982f54f6b82ec | Julia | 2,777 | 89 |
function _to_plusminus(tup, i_nz)
if tup[i_nz] == false
[(tup[begin:i_nz-1]..., 0, tup[i_nz+1:end]...)]
else
[(tup[begin:i_nz-1]..., 1, tup[i_nz+1:end]...),
(tup[begin:i_nz-1]..., -1, tup[i_nz+1:end]...)]
end
end
function expand_plusminus(nz_tup)
# Expand nonzero indicator stu... |
f2927c5f515cb5e7f6dde9ec1427703c95a5befb55ed0947b6db4aa2310f8697 | Julia | 2,793 | 60 | include(srcdir("main.jl"))
using Parameters
@inline tuple_join(x) = x
@inline tuple_join(x,y::AbstractString) = (x..., y)
@inline tuple_join(x::AbstractString,y) = (x, y...)
@inline tuple_join(x::Tuple,y::Tuple) = (x..., y...)
@inline tuple_join(x,y,z...) = (x..., tuple_join(y,z...)...)
function calc_counts_coord(n1... |
86733a8f95bba35f57fa358947d4ac9330b44ef3358f8994b5c6f6fe2e7a4fb8 | Julia | 2,814 | 97 | function name_info_flow_row(row)
(spike_motif=row[1], n1_num=row[2], t1_num=row[3], n2_num=row[4], t2_num=row[5], motif_class_num=row[6])
end
sign_str(x) = if x > 0
"+"
elseif x < 0
"-"
else
"0"
end
abs_rel_sym(x1, x2) = if abs(x1) < abs(x2)
"<"
elseif abs(x1) > abs(x2)
">"
else
"="
end
... |
7827c0136f209f1c3958ce90566a1c5da72d903a93b0a12486b30ec333da2546 | Julia | 3,127 | 96 |
function _to_plusminus_OLD(tup, i_nz)
if tup[i_nz] == false
[(tup[begin:i_nz-1]..., 0, tup[i_nz+1:end]...)]
else
[(tup[begin:i_nz-1]..., 1, tup[i_nz+1:end]...),
(tup[begin:i_nz-1]..., -1, tup[i_nz+1:end]...)]
end
end
function expand_plusminus_OLD(nz_tup)
# Expand nonzero indic... |
dac26488cd5a9225a5f8dec275ce069572204336c4c72fca467c06fdec011076 | Julia | 3,246 | 100 | using InfiniteOpt, Ipopt, Plots
# Parmeters
# Time span
t0 = 0
tf = 4
# Initial values
x1 = 0
# Model
model = InfiniteModel(Ipopt.Optimizer)
# infinite_parameter
@infinite_parameter(model, t ∈ [t0, tf], num_supports = 401)
# variable
@variable(model, x, Infinite(t))
@variable(model, u ≤ 5, Infinite(t))
# ... |
78a013694e0ed598ddb99e62c919e06ee044c101914b76023f74d6fe4aab34ca | Julia | 3,256 | 99 | using InfiniteOpt, Ipopt, Plots
# Parmeters
# Time span
t0 = 0
tf = 4
# Initial values
x1 = 0
# Model
model = InfiniteModel(Ipopt.Optimizer)
# infinite_parameter
@infinite_parameter(model, t ∈ [t0, tf], num_supports = 101)
# variable
@variable(model, x, Infinite(t))
@variable(model, u ≤ 5, Infinite(t))
# ... |
ffab33cb80d4f813de0f4a931623942d7ec07e79f98f8b231c4472efd86dfb05 | Julia | 3,350 | 76 | let neuron_max_lag=2,
time_max_lag=2
raster = load_raster(datadir("flow_illustration","manual_raster_7x7.csv"))
neuron_lag_range = -(neuron_max_lag):(neuron_max_lag)
time_lag_range = -(time_max_lag):(time_max_lag)
(N_neurons, N_times) = size(raster)
time_range = (0-minimum(time_lag_ran... |
2607579102072de9e5569a90d1f119c5f310a5086d770051eef10c11a13fbbfa | Julia | 3,376 | 112 | noto_sans_bold = assetpath("fonts", "NotoSans-Bold.ttf")
abbrev_count_label = x -> begin
if x >= 1000000000
try
"$(Int(x / 1000000000))B"
catch
"$(x / 1000000000)B"
end
elseif x >= 1000000
try
"$(Int(x / 100... |
2236aa86a4f5b5fcc48ed50652456a4a123ced160fccc2a8411eacb8cb7476d0 | Julia | 3,401 | 122 | using InfiniteOpt, Ipopt, Plots
# Parmeters
# Time span
t0 = 0
tf = 1
# Initial values
x1 = 1
# Model
model = InfiniteModel(Ipopt.Optimizer)
# infinite_parameter
@infinite_parameter(model, t ∈ [t0, tf], num_supports = 101)
# variable
@variable(model, x ≥ 0, Infinite(t))
@variable(model, u, Infi... |
ca086028bcb360a30deadb9581dc6a62dff1e6772a6d41f6b6a9ad31c3117223 | Julia | 3,860 | 88 | @quickactivate "RasterUniqueCharacterizationCode"
using TripleCorrelations
using HypothesisTests, Random, Statistics
using ProgressMeter
using CairoMakie; plot_ext = "png"
using Dates
using Pkg
using Base.Threads
include(srcdir("roman_encode.jl"))
include(srcdir("peristimulus_testing.jl"))
# if !@isdefined(an_timese... |
460cb7a6cb43bca3178b3388388d2a2d6cfaf100bb900fb28436184efd589223 | Julia | 3,962 | 87 | @quickactivate "RasterUniqueCharacterizationCode"
using TripleCorrelations
using HypothesisTests, Random, Statistics
using ProgressMeter
using CairoMakie; plot_ext = "png"
using Dates
using Pkg
using Base.Threads
using DataFrames, AlgebraOfGraphics
using JLD2
using Colors
include(srcdir("roman_encode.jl"))
include(sr... |
f09ba486c9e149727ab666a089956c032b34e6a2d41b6a342ca7b6dcb9b41ff4 | Julia | 3,966 | 128 | # Example 3.2 in Book: Lenhart S, Workman J T. Optimal control applied to biological models[M]. Chapman and Hall/CRC, 2007.
using InfiniteOpt, Ipopt, Plots
# Parmeters
# Time span
t0 = 0
tf = 1
# Initial values
x1 = 1
# Model
model = InfiniteModel(Ipopt.Optimizer)
# infinite_parameter
@infinite_pa... |
408ebeac2463d98f6b0f1fa2a90a23cb2f4d18ec3b735c2b098b8189454c3554 | Julia | 3,970 | 138 | # Part One: using direct collection method
using InfiniteOpt, Ipopt, Distributions, Plots
# Set the SEIR parameters
b = 0.525
d = 0.5
c = 0.0001
e = 0.5
g = 0.1
a = 0.2
A = 1.0
# Set the domain information
t0 = 0
tf = 20
# Set the intial condition values
s0 = 1000
e0 = 100
i0 = 50
n0 = 1165
model = InfiniteModel(I... |
6b729f95e1d2d6dd0dbe3381576fa56b6f6bf8ee6091d55710fcc75bd744804b | Julia | 3,973 | 90 | @quickactivate "RasterUniqueCharacterizationCode"
using TripleCorrelations
using HypothesisTests, Random, Statistics
Random.seed!(1234)
using ProgressMeter
using CairoMakie; plot_ext = "png"
using Dates
using Pkg
include(srcdir("roman_encode.jl"))
include(srcdir("peristimulus_testing.jl"))
if !@isdefined(an_top_time... |
2d23514997f196b2b588d45b9c98b040a27453ac89219b6f41f45ae31792738e | Julia | 4,088 | 138 | # Part One: using direct collection method
using InfiniteOpt, Ipopt, Distributions, Plots
# Set the SEIR parameters
b = 0.525
d = 0.5
c = 0.0001
e = 0.5
g = 0.1
a = 0.2
A = 0.1
# Set the domain information
t0 = 0
tf = 20
# Set the intial condition values
s0 = 1000
e0 = 100
i0 = 50
n0 = 1165
... |
baa8e5ce8e38978ad7baf74774c204d29f4fd4b010070961b2c50be8a7c0ef2e | Julia | 4,158 | 130 | include(srcdir("main.jl"))
using BenchmarkTools
using Test
@testset "Network Motif I" begin
fn = datadir("I.csv")
raster_I = load_raster(fn)
contributions_I = triple_correlation_network_classifications(raster_I, 15, 15)
@show contributions_I
@test contributions_I[1] == 289 && all(contributions_I[... |
e88f54c1405aed60ee75cd5ca523a1cdfa9e81360c085327a5d5e6bf63f14e62 | Julia | 4,174 | 81 | # This script contains functions that are predefined to be used in Artificial Neural Network training script.
# This function returns test data, and model performance for Flux neural network models when train test data are given
function flux_model_train(flux_model,
x :: DataFrame,
y :: Vector,
train_i... |
1ec59955b59fd8ebcd7044798c78c0822403db85b5f9d6776b2bc90f85ea164f | Julia | 4,195 | 95 | @quickactivate "RasterUniqueCharacterizationCode"
using TripleCorrelations
using HypothesisTests, Random, Statistics
using ProgressMeter
using CairoMakie; plot_ext = "png"
using Dates
using Pkg
include(srcdir("roman_encode.jl"))
include(srcdir("peristimulus_testing.jl"))
if !@isdefined(an_timeseries_dict) || force_r... |
b8d38aca4a73f8b43c56cafdbd4e11145f16db4e1388d2ffe4c4529d7a804129 | Julia | 4,242 | 152 |
include("roman_encode.jl")
function count_distinct(args...)
return length(unique(args))
end
function _1_neuron_motif_classification(n1, n2, t1, t2)
# All neurons are the same
# Assume t1 <= t2
n_distinct_times = count_distinct(0, t1, t2)
if n_distinct_times == 1
# All neurons and times a... |
89ffac4266fafaf81ba2ee3afb8b4334d37b0012f1561ca41b00fc4bf095c6fb | Julia | 4,333 | 100 | # loading the Julia packages needed.
using DifferentialEquations
using DiffEqFlux, Flux
using Plots
using CSV
using DataFrames
source_data = DataFrame(CSV.File("./Source_Data/Provincial_Daily_Totals.csv"))
data_on = source_data[source_data.Province.=="ONTARIO", :]
n = 30
nday = 59
m = nday
data_acc = data_o... |
dfde132f5a8ab47a09b0e26a1b4be05db8f65c8dc287cbdad23eb6d4aec41ae1 | Julia | 4,410 | 100 | @quickactivate "RasterUniqueCharacterizationCode"
using TripleCorrelations
using HypothesisTests, Random, Statistics
using ProgressMeter
using CairoMakie; plot_ext = "png"
using Dates
using Pkg
include(srcdir("roman_encode.jl"))
include(srcdir("peristimulus_testing.jl"))
if !@isdefined(an_timeseries_dict) || force_r... |
c9c3802bc1ed134e54058ffb439bd412ea1d26809da5880d4763a754a3bcd19b | Julia | 4,561 | 94 | # FIXME should encode stepsize
struct TripleCorrelation{N_dims,T_lag,N_dims2,T_val,ARR<:AbstractArray} <: AbstractArray{T_val,N_dims2}
arr::ARR
extent::NTuple{N_dims,T_lag}
function TripleCorrelation(arr::ARR, extent::NTuple{N_dims,T_lag}) where {N_dims,N_dims2,T_val,T_lag,ARR<:AbstractArray{T_val,N_dims2}}... |
1b6a59f974957702c45e598fb78d9e04234dd573b021b797f560fcdfe176f94f | Julia | 4,611 | 120 | @quickactivate "RasterUniqueCharacterizationCode"
using TripleCorrelations
using HypothesisTests, Random, Statistics
using ProgressMeter
using GLMakie
using Dates
#defines motif_examples dict
include(joinpath(homedir(),"git", "TripleCorrelations", "test", "data", "motif_examples.jl"))
include(joinpath(homedir(), "git... |
6697749a9808131bf1d42b0fc0b949001428854a046327d3a3205f4a961ef13e | Julia | 4,942 | 167 | ##
using InfiniteOpt, Ipopt, Plots
#using KNITRO
exponential_relaxation(z, δ) = exp(one(typeof(z)) - z / δ) - one(typeof(z)) - log(δ)
function relaxed_log_barrier(z; δ=0.3f0)
return max(z > δ ? -log(z) : exponential_relaxation(z, δ), zero(typeof(z)))
end
# Set the SEIR parameters
γ = 0.303
β = 0.727
N =... |
7c1932c5bfca142c48cf3691b36f911a08c9fa7485ff4d4c43ad9bd6bc677aaf | Julia | 5,134 | 163 |
# # Step 0: Setup the environment
# If you haven't finished with this step, please do it.
# 1. Download and Install VScode and Julia
# (Windows) A suggestion is that Do Not Install in path C:
# Download and Install Julia and configure the environment variable
# Download and Install VScode
# After insta... |
93c3d48fdc29d8581e2b08b593a701d1567d67b8824f8ade6d4248227245f73a | Julia | 5,253 | 116 | @quickactivate "RasterUniqueCharacterizationCode"
using TripleCorrelations
using HypothesisTests, Random, Statistics
using ProgressMeter
using CairoMakie; plot_ext = "png"
using Dates
using Pkg
using Base.Threads
using DataFrames, AlgebraOfGraphics
using JLD2
include(srcdir("roman_encode.jl"))
include(srcdir("peristi... |
b0f9527c92712b321952ca41f1c741e827242c8360f5ad5d661238a37b4d6cf7 | Julia | 5,329 | 138 |
include(srcdir("main.jl"))
let N_neurons = 150, N_times = 150, window_size = 15, transform=log10;
# pattern_strings_by_property = Dict(
# "Level of Activity" => "I, II, IV",
# "Local Dynamics" => "II, III, VII, VIII, IX, X, XI",
# "Synchrony" => "V, VII, VIII, XII, XIII",
# "Network Propagation" => "... |
e39b6294d639f45f525e6b1c0ff5b37cf0251cadafe04bf45ef7ca200140b9ef | Julia | 5,763 | 152 | module FitODE_plots
using Plots, FitODE, Printf, Measures
export plot_target_pred, plot_phase, plot_data_orig_smooth
num_rows(matr) = size(matr,1)
array_to_tuple(arr) = (arr...,)
function plot_target_pred(dt; show_lines = false, use_all = true,
num_dim = num_rows(dt.L.ode_data), target_labels = ("hare", "lynx"))
... |
342dfb71b7b62ea15331e6d9ef7951c664297ab995cd9a79d80fbbc7d246b1b4 | Julia | 5,805 | 159 | @quickactivate "RasterUniqueCharacterizationCode"
using TripleCorrelations
using HypothesisTests, Random, Statistics
using ProgressMeter
using CairoMakie; plot_ext = "png"
using Dates
using Pkg
using Base.Threads
using DataFrames, AlgebraOfGraphics, Colors
using JLD2
include(srcdir("roman_encode.jl"))
include(srcdir(... |
4f43f38437ff39d57187a67f8f162cd00d2e15b5edce6ce34749e00afc7e4852 | Julia | 6,342 | 139 | # loading the Julia packages needed.
using DifferentialEquations
using DiffEqFlux, Flux
using Plots
using CSV
using DataFrames
using Random
Random.seed!(14);
source_data = DataFrame(CSV.File("./Source_Data/Provincial_Daily_Totals.csv"))
data_on = source_data[source_data.Province.=="ONTARIO", :]
data_on
n = ... |
ab848f7bd0859aac85a73dd2df0a7a64dc454ebc71717c3c2d64069af59f6c4e | Julia | 6,486 | 138 | module FitODE_settings
using Parameters, DifferentialEquations, Dates, Random
export Settings, default_ode, default_node, reset_rseed, recalc_settings
default_ode() = Settings()
default_node() = Settings(use_node=true, rtol=1e-3, atol=1e-4, rtolR=1e-6, atolR=1e-8,
max_it=500, solver = TRBDF2())
reset_rseed(S, rs... |
51318fed22a9d8a79b357e44f390881ef8b6017faef6e6e7dd3ab74fa12d6cc6 | Julia | 7,109 | 140 |
let neuron_max_lag=2,
time_max_lag=2,
N_neurons = 30, N_times = 30,
firing_threshold = 0.7;
all_ones_raster = make_neuron_raster(N_neurons, N_times)
all_ones_raster .= 1
all_ones_contributions = triple_correlation_network_classifications(all_ones_raster, neuron_max_lag, time_max_la... |
fb0e48d155b081444ea713f07d2490bfae8727a92ebc206729621a3171cb3e44 | Julia | 7,207 | 181 | @quickactivate "RasterUniqueCharacterizationCode"
using TripleCorrelations
using HypothesisTests, Random, Statistics
using ProgressMeter
using CairoMakie; plot_ext = "png"
using Dates
using Pkg
using Base.Threads
using DataFrames, AlgebraOfGraphics, Colors
using JLD2
include(srcdir("roman_encode.jl"))
include(srcdir(... |
594189e35562986439762843f70e30c83bafc94d9d67cfff5724a267d1e9948d | Julia | 7,894 | 225 | module FitODE_bayes
using FitODE, StatsPlots, StatsBase, HypothesisTests, Printf, JLD2, Parameters
export psgld_sample, save_bayes, load_bayes, plot_loss_bayes, plot_sgld_epsilon,
plot_autocorr, plot_moving_ave, p_matrix, p_ts, auto_matrix, plot_traj_bayes,
plot_autocorr_hist, pSGLD
@with_kw struct pSGLD
war... |
0b5c83c46904c921d89d7c8acba916ea840dc0bf0bb1c1d960a43b2964f2fd5f | Julia | 8,300 | 200 |
using Memoize, ThreadsX
get_boundary_width(boundary::PeriodicExtended) = boundary.boundary
get_boundary_width(boundary::Periodic) = 0
function run_peristimulus_tests(raster_size,
boundary::AbstractBoundaryCondition,
lag_extents,
n_signals, signal_jitters, noise_rate,
n_trials, n_t... |
0456ed6294ec2054e9829dc5b219c9130de6f2dd1312b37f7bf8a4d79c3335b2 | Julia | 9,046 | 194 | @quickactivate "RasterUniqueCharacterizationCode"
using TripleCorrelations
using HypothesisTests, Random, Statistics
using ProgressMeter
using CairoMakie; plot_ext = "png"
using Dates
using Pkg
using Base.Threads
using DataFrames, AlgebraOfGraphics, Colors
using JLD2
include(srcdir("roman_encode.jl"))
include(srcdir(... |
7ef7ffae7783e912073580a19743b746a3c0201944ef1c4bd6110f0f72f01dbb | Julia | 9,097 | 195 |
include(srcdir("main.jl"))
fig3 = with_theme(bar_theme) do
fig3 = Figure(resolution=(1600, 2400))
contributions_fn = Dict(
"constituent" => constituent_normed_sequence_classes,
"spike_rate" => rate_normed_sequence_classes
)
let N_neurons = 150, N_times = 150,
lag_extents = (14, 14),
boundar... |
e6148582c2495d7819fbde7f43a4ba8faa2fb801b18b37a7c3bb7a0647b920e3 | Julia | 9,481 | 200 | @quickactivate "RasterUniqueCharacterizationCode"
using TripleCorrelations
using HypothesisTests, Random, Statistics
using ProgressMeter
using CairoMakie; plot_ext = "png"
using Dates
using Pkg
using Base.Threads
using DataFrames, AlgebraOfGraphics, Colors
using JLD2
include(srcdir("roman_encode.jl"))
include(srcdir(... |
6f4f0cd72038d1cd3a9a5b52198abf8a1aef2fbb82cf3d3bc10e0c299700bce0 | Julia | 9,521 | 201 | @quickactivate "RasterUniqueCharacterizationCode"
using TripleCorrelations
using HypothesisTests, Random, Statistics
using ProgressMeter
using CairoMakie; plot_ext = "png"
using Dates
using Pkg
using Base.Threads
using DataFrames, AlgebraOfGraphics, Colors
using JLD2
include(srcdir("roman_encode.jl"))
include(srcdir(... |
22978c98612b56e1d242dc6d4d9ade6ce11fe82cd3da3a47b74928e193e98e3d | Julia | 9,675 | 247 | using FitODE, FitODE_settings, FitODE_plots
# Loading imported modules can take a couple of minutes
# First call to a function causes compiling and optimization, which causes a delay
# Subsequent call to a function with different argument types triggers new compile
# Read comments and code in FitODE.jl and FitODE_se... |
da45c51b1870b5e3f3cd169da66b1ba10dec4552d503d94c1d4dc18e33347e6f | Julia | 10,749 | 177 | # Install required packages
using Pkg;
Pkg.add(["DataFrames", "DataFramesMeta", "CSV", "Statistics", "MLJ", "Flux"]);
# Use required packages
using DataFrames, DataFramesMeta, CSV, Statistics, MLJ, Flux, Flux.Zygote;
# Call predefined functions
include("predefined_functions_ML.jl");
# Import all the datasets and... |
be85ae3eaf6ed006073a19ff8edfd0e5f2fad5ee8ababde33b86d46d1daf5de5 | Julia | 12,748 | 323 | module FitODE
using CSV, DataFrames, Statistics, Distributions, Interpolations, QuadGK,
DiffEqFlux, DifferentialEquations, Printf, Plots, JLD2, Parameters
export callback, loss, weights, fit_diffeq, refine_fit, refine_fit_bfgs,
calc_gradient, save_data, load_data, read_data, make_loss_args_all
# re-export from Dif... |
9c53ab79208e5155d448cc5ab2cc792cd1074b7ee87cf7ebaa39462721301ef7 | Jupyter | 104 | 8 | # %%
%alias chiro runoak -i sqlite:obo:chiro
# %%
chiro relationships -p obo:chiro#inhibitor_of
# %%
|
122d0f9851c26a0fc580dce6f8e74a33cb2779ba58fa5522eb32a74c266200da | Jupyter | 251 | 17 | # %% [markdown]
# # Schema.org Tutorial
# %%
%alias schema runoak -i ../../db/schema-dot-org.db
# %%
schema tree schema:Article --down
# %%
schema viz schema:Article --down -o output/Article.png
# %% [markdown]
# 
# %%
|
8c1700d419273844cc9b8209e6cd081ebce484fd38740fb8de527d1efb8287d5 | Jupyter | 304 | 18 | # %% [markdown]
# # Validate candidate TPA sites
# %%
%load_ext autoreload
%autoreload 2
# %%
from validate_map import Validator
# %%
# define input file to read, and output file to write
fin = 'tamil_nadu_candidates.geojson'
fout = 'tamil_nadu_validated.geojson'
v = Validator(fin, fout)
# %%
v.map
|
87fa9cefae2997ae0922d01bc52c9a81e320508d2944a5a2f1817b9905142885 | Jupyter | 325 | 20 | # %%
from palmari import Acquisition, Experiment, ImagePipeline
from glob import glob
import os
import palmari
# %%
exp = Experiment(data_folder="../../data/raw/",export_folder="../../data/processed/czi",file_pattern=".czi")
# %%
exp.index_df
# %%
ip = ImagePipeline.default_with_name("test")
# %%
ip.process(exp)
#... |
323f89ed1c03dadbf9a8e95b5d1a96bae1ea5a043f31ce522587c08602dbe8ca | Jupyter | 352 | 29 | # %%
alias go runoak -i sqlite:obo:go
# %%
alias eccode runoak -i sqlite:obo:eccode
# %%
eccode statistics
# %%
eccode relationships EC:2.1.1.133
# %%
eccode tree -p i EC:2.1.1.133
# %%
go mappings EC:2.1.1.133 -O sssom
# %%
go mappings EC:2.1.1.- -O sssom
# %% [markdown]
# TODO - eliminate blank nodes
# %%
go ... |
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