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// bitvec = "0.22.3" | |
use bitvec::prelude::*; | |
// These are two functions I'd like to template the input and output types of, | |
// function bodies are identical | |
fn bslice_to_int(bv: &BitSlice::<Lsb0, u8>) -> u32 { | |
let mut int = 0; | |
for bit in bv { | |
int = int << 1; |
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From 95ea1abb942e8da86faa1f1856fed316dcd95985 Mon Sep 17 00:00:00 2001 | |
From: John Lees <[email protected]> | |
Date: Tue, 20 Jul 2021 15:44:19 +0100 | |
Subject: [PATCH] Update CUDA version | |
--- | |
docker/Dockerfile | 8 ++++---- | |
1 file changed, 4 insertions(+), 4 deletions(-) | |
diff --git a/docker/Dockerfile b/docker/Dockerfile |
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I0719 14:43:06.996543 139929070653184 run_docker.py:180] E0719 13:43:06.994233 139663816972096 hhblits.py:141] - 11:45:50.511 INFO: Searching 65983866 column state sequences. | |
I0719 14:43:06.996851 139929070653184 run_docker.py:180] E0719 13:43:06.994395 139663816972096 hhblits.py:141] - 11:45:51.265 INFO: Searching 15161831 column state sequences. | |
I0719 14:43:06.997076 139929070653184 run_docker.py:180] E0719 13:43:06.994539 139663816972096 hhblits.py:141] - 11:45:51.326 INFO: /mnt/fasta_path_0/dltA.fa is in A2M, A3M or FASTA format | |
I0719 14:43:06.997292 139929070653184 run_docker.py:180] E0719 13:43:06.994683 139663816972096 hhblits.py:141] - 11:45:51.327 INFO: Iteration 1 | |
I0719 14:43:06.997498 139929070653184 run_docker.py:180] E0719 13:43:06.994824 139663816972096 hhblits.py:141] - 11:45:51.559 INFO: Prefiltering database | |
I0719 14:43:06.997703 139929070653184 run_docker.py:180] E0719 13:43:06.994960 139663816972096 hhblits.py:141] - 11:55:13.366 INFO: HMMs passed 1st prefilter (gapless profile-profile alig |
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import json | |
import random | |
import string | |
# Download from https://github.com/dwyl/english-words/raw/master/words_dictionary.json | |
with open('words_dictionary.json', 'r') as word_list: | |
real_words = json.load(word_list) | |
vowels = ["a", "e", "i", "o", "u"] | |
trouble = ["q", "x", "y"] |
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matrix_pow <- function(x, n) { | |
if (abs(n - round(n)) < .Machine$double.eps^0.5) { | |
stop(sprintf("Power %.1f is not an integer", n)) | |
} | |
if (n < 0) { | |
return(matrix_exp(solve(x), -n)) | |
} else if (n == 0) { | |
return(1) | |
} else if (n == 1) { | |
return(x) |
We can make this file beautiful and searchable if this error is corrected: It looks like row 10 should actually have 11 columns, instead of 5 in line 9.
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COG,Gene name,Average_aa_length,Aligned_sites,dS,dN,omega,Sites,pi_aa,dN_rate,pi_gene | |
CLS00002,adcB,268,268,56.5,10.5,0.0656766113998045,NA,0.000751449436378113,0.0391791044776119,0.201388448949334 | |
CLS00003,adcC,234,234,50.5,14.5,0.134629916080151,NA,0.00391277647376286,0.061965811965812,0.915589694860509 | |
CLS00004,adcR,146.941558441558,166,11,4,0.188155217627637,NA,0.00141473858168382,0.0240963855421687,0.207883891980019 | |
CLS00005,dltD,420.982142857143,425,65,80,0.45168151934188,NA,0.00272578563815855,0.188235294117647,1.14750707892121 | |
CLS00006,dltC,79,79,5,4,0.561513140654798,NA,0.0012429013818335,0.0506329113924051,0.0981892091648464 | |
CLS00007,dltB,413.631493506493,422,84,84,0.429553902002596,NA,0.00524836591836688,0.199052132701422,2.17088943328267 | |
CLS00013,glpF,233.165584415584,234,102,32,0.143771320439321,NA,0.0118706308630385,0.136752136752137,2.76782258256203 | |
CLS00014,glpO,607.709415584416,608,413.75,128,0.164681667531972,NA,0.00827269833662607,0.210526315789474,5.0273966714572 | |
CLS00022,phoB,217,217,86.6 |
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CLS | Gene | KC distance (lambda = 0) | KC distance (lambda = 1) | |
---|---|---|---|---|
CLS00002 | adcB | 7960.19308811036 | 7.36117345073605 | |
CLS00003 | adcC | 5691.7166127628 | 2.62408261905232 | |
CLS00004 | adcR | 68944.9760968847 | 1.01831498716277 | |
CLS00005 | dltD | 6031.18935202668 | 8.28319801733685 | |
CLS00006 | dltC | 85402.9472149527 | 1.40643165294022 | |
CLS00007 | dltB | 6084.44787963542 | 7.20462600697612 | |
CLS00013 | glpF | 4540.60722811388 | 5.18730142556587 | |
CLS00014 | glpO | 2720.14006992287 | 10.7387600382768 | |
CLS00022 | phoB | 22900.4934226317 | 41.118579712978 |
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locus_id | module_number | gene_name | |
---|---|---|---|
SPV_0086 | NA | SPV_0086 | |
SPV_0480 | NA | SPV_0480 | |
SPV_0481 | NA | SPV_0481 | |
SPV_0982 | NA | SPV_0982 | |
SPV_1013 | NA | SPV_1013 | |
SPV_1364 | NA | SPV_1364 | |
SPV_1793 | NA | SPV_1793 | |
SPV_2310 | NA | SPV_2310 | |
SPV_1772 | NA | acyP |
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require(WGCNA) | |
# See: | |
# https://labs.genetics.ucla.edu/horvath/CoexpressionNetwork/Rpackages/WGCNA/Tutorials/FemaleLiver-02-networkConstr-man.pdf | |
SPV_2_name = read.delim("SPV_2_name.txt", header=FALSE) | |
colnames(SPV_2_name) = c("SPV", "gene") | |
# co-expression matrix from Veening lab | |
# https://www.biorxiv.org/content/early/2018/03/22/283739 |
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#!/usr/bin/env python | |
'''Python implementation of Firth regression by John Lees | |
See https://www.ncbi.nlm.nih.gov/pubmed/12758140''' | |
def firth_likelihood(beta, logit): | |
return -(logit.loglike(beta) + 0.5*np.log(np.linalg.det(-logit.hessian(beta)))) | |
# Do firth regression | |
# Note information = -hessian, for some reason available but not implemented in statsmodels | |
def fit_firth(y, X, start_vec=None, step_limit=1000, convergence_limit=0.0001): |
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