Kozo Nishida kozo2

mrknさんのworkflowのrubydownでの試行

sudo gem install specific_install

gem install daru-apiclient
gem install daru-view

require 'daru/apiclient'

	library(clusterProfiler)
	library(DT)
	library(tibble)

	pathway <- character(0)
	cids <- list()

	for(i in 1:length(mset_SMPDB_format_KEGG)) {
	row = mset_SMPDB_format_KEGG[[i]]
	pathway <- c(pathway, row[[2]])

	from __future__ import absolute_import

	import pandas as pd
	from pandas.api.types import is_numeric_dtype
	import numpy as np

	import plotly.graph_objs as go

	KEGG_PATHWAY_ID = "rn01100"

	# IPI data from ftp://ftp.ebi.ac.uk/pub/databases/IPI/last_release/current/ipi.genes.MOUSE.xrefs.gz
	library(tidyverse)
	ipi_genes_MOUSE <- read.delim("ipi.genes.MOUSE.xrefs", "\t", escape_double = FALSE, col_names = FALSE, trim_ws = TRUE, skip = 1)
	ipi2ncbigene <- ipi_genes_MOUSE %>% select(9,10) %>%
	separate(X9, into=c("NCBI_GENEID", "GENE_NAME")) %>%
	separate_rows(X10) %>%
	filter(X10 != "") %>%
	drop_na(NCBI_GENEID) %>%
	distinct()

	jobs:
	- job: Linux
	pool:
	vmImage: 'ubuntu-16.04'
	steps:
	- script: echo hello from Linux
	- bash: \|
	git submodule init && git submodule update
	sudo apt-get update && sudo apt-get install -y wget bzip2 python3-pip libgsl-dev libboost-dev cmake
	sudo python3 -m pip install -U pip setuptools

	FROM ubuntu
	USER root
	RUN apt-get update && apt-get install -y git python3 python3-pip cmake libboost-dev libgsl-dev curl && \
	curl -LO https://support.hdfgroup.org/ftp/HDF5/releases/hdf5-1.10/hdf5-1.10.5/src/hdf5-1.10.5.tar.bz2 && \
	tar xf hdf5-1.10.5.tar.bz2 && \
	cd ./hdf5-1.10.5 && ./configure --enable-cxx --prefix=/opt/hdf5 && make && make install && \
	export PATH=/opt/hdf5/bin:${PATH} && \
	export LD_LIBRARY_PATH=/opt/hdf5/lib:${LD_LIBRARY_PATH} && \
	export INCLUDE_PATH=/opt/hdf5/include:${INCLUDE_PATH} && \
	cd && git clone git://github.com/ecell/ecell4-base && cd ecell4-base && git checkout -t origin/v2.0 && \

	# azure_batch.py
	# Copyright (c) 2017 Kazunari Kaizu
	# Released under the GNU General Public License

	# python_tutorial_client.py
	# Copyright (c) 2017 Microsoft Corporation
	# Released under the MIT license

	from __future__ import print_function
	import datetime

	require "numo/narray"
	require "rumale"
	samples = Numo::DFloat[[1, 2], [1, 4], [1, 0],[10, 2], [10, 4], [10, 0]]
	analyzer = Rumale::Clustering::KMeans.new(n_clusters: 2, max_iter: 50)
	cluster_labels = analyzer.fit_predict(samples)