From 7e2e7ac40b2985cdbf7d9efd036d098eb5c6fef4 Mon Sep 17 00:00:00 2001 From: janko Date: Wed, 25 May 2022 14:09:21 +0200 Subject: [PATCH] Remove julia 1.2 from CI, update README --- .github/workflows/ci.yml | 1 - README.md | 2 +- src/hiton.jl | 8 +++++--- 3 files changed, 6 insertions(+), 5 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index e10ecaf..2fd37c1 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -13,7 +13,6 @@ jobs: fail-fast: false matrix: version: - - '1.2' - '1.6' # current LTS - '1' # automatically expands to the latest stable 1.x release of Julia os: diff --git a/README.md b/README.md index 9ded9f7..9588566 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,7 @@ FlashWeave predicts ecological interactions between microbes from large-scale co ## Installation ## -To install Julia, please follow instructions on https://github.com/JuliaLang/julia. The preferred way is to obtain a binary from https://julialang.org/downloads/. Make sure you install Julia 1.2 or above, the versions currently supported by FlashWeave. +To install Julia, please follow instructions on https://github.com/JuliaLang/julia. The preferred way is to obtain a binary from https://julialang.org/downloads/. Make sure you install Julia 1.6 or above, the versions currently supported by FlashWeave. In an interactive Julia session, you can then install FlashWeave after typing `]` via diff --git a/src/hiton.jl b/src/hiton.jl index c58f98f..be482b3 100644 --- a/src/hiton.jl +++ b/src/hiton.jl @@ -400,7 +400,9 @@ end ## convenience function for learning local neighborhoods function si_HITON_PC(T, data; test_name, levels=isdiscrete(test_name) ? get_levels(data) : Int[], - max_vals=isdiscrete(test_name) ? get_max_vals(data) : Int[], cor_mat=zeros(Float32, 0, 0), kwargs...) -univar_res = FlashWeave.pw_univar_neighbors(data, test_name=test_name, levels=levels, max_vals=max_vals, cor_mat=cor_mat) -FlashWeave.si_HITON_PC(T, data, levels, max_vals, cor_mat; test_name=test_name, univar_nbrs=univar_res[T], kwargs...) + max_vals=isdiscrete(test_name) ? get_max_vals(data) : Int[], cor_mat=zeros(Float32, 0, 0), n_obs_min=0, kwargs...) + univar_res = pw_univar_neighbors(data, test_name=test_name, levels=levels, max_vals=max_vals, cor_mat=cor_mat, + n_obs_min=n_obs_min) + si_HITON_PC(T, data, levels, max_vals, cor_mat; test_name=test_name, univar_nbrs=univar_res[T], n_obs_min=n_obs_min, + kwargs...) end