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@terrycojones
terrycojones / factor
Created May 19, 2019
A factoring program written in dc
View factor
[[neither]plsx]sn[c2pla2/sallx]se[ladv1+sm0=nla1=ncla2=pla2%0=elfx]sl
[ldlm<pclald%0=cld2+sdlfx]sf[lap[is prime.]plsx]sp[ldplald/salfx]sc
[[enter X : ]P?dsa0>n3sdllx]ss[[negative]plsx]snlsx
View elpy.el
(elpy-enable)
(pyvenv-workon "37")
(setq elpy-test-runner 'elpy-test-trial-runner)
(setq
elpy-test-trial-runner-command
(list (expand-file-name (format "~/.virtualenvs/%s/bin/trial" pyvenv-virtual-env-name)) "--rterrors"))
(when (require 'flycheck nil t)
(setq elpy-modules (delq 'elpy-module-flymake elpy-modules))
(add-hook 'elpy-mode-hook 'flycheck-mode))
View gist:1cb893b1725740d9df26596a841c6bac
$ echo $i
$ set i aaaxy
$ echo $i
untrackedfiles
$ echo $FISH_VERSION
2.7.1-1829-g742fde0d
# Whereas:
View gor4.py
from gor4 import GOR4
_GOR4 = None
def predictions(sequence):
global _GOR4
if _GOR4 is None:
_GOR4 = GOR4()
return _GOR4.predict(sequence)['predictions']
View wigless.txt
"My dear fellow," Burlingame said caustically, "we sit on a blind rock
careening through space; we are all of us rushing headlong to the
grave. Think you the worms will care, when anon they make a meal of
you, whether you spent your moment sighing wigless in your chamber, or
sacked the golden towns of Montezuma? Lookee, the day's nigh spent;
'tis gone careening into time forever. Not a tale's length since we
lined our bowels with dinner, and already they growl for more. We are
dying men Ebenezer: i'faith, there's time for nought but bold
resolves!"
View gist:d0d2f232bafb857257b4462932dd8fa7
ls summary-proteins-* | \
proteins-to-pathogens.py \
--format fastq \
--html \
--minProteinFraction 0.1 \
--pathogenType bacterial \
--proteinFastaFilename $HOME/rdsbt/root/share/ncbi/bacterial-refseq/20180609-bacteria.nonredundant_protein.fasta.gz \
--pathogenPanelFilename bacteria.png \
--sampleIndexFilename samples.index \
--pathogenIndexFilename pathogens.index \
View gist:1953690f960e3da1d2e307cf918b8453
from sklearn.metrics import mutual_info_score
from sklearn.metrics.cluster import entropy
def normalized_information_distance(c1, c2):
"""
Calculate Normalized Information Distance
Taken from Vinh, Epps, and Bailey, (2010). Information Theoretic Measures
for Clusterings Comparison: Variants, Properties, Normalization and
Correction for Chance, JMLR
View gist:39c738d04f342843adb8afd995924cb4
[color]
ui = auto
[color "branch"]
current = yellow reverse
local = yellow
remote = green
[color "diff"]
meta = yellow bold
frag = magenta bold
old = red bold
View locale.txt
$ locale
LANG=en_GB.UTF-8
LC_CTYPE="en_GB.UTF-8"
LC_NUMERIC=de_DE.UTF-8
LC_TIME=de_DE.UTF-8
LC_COLLATE="en_GB.UTF-8"
LC_MONETARY=de_DE.UTF-8
LC_MESSAGES="en_GB.UTF-8"
LC_PAPER=de_DE.UTF-8
LC_NAME=de_DE.UTF-8
View tcr6-exif.txt
ExifTool Version Number : 8.98
File Name : tcr6.jpg
Directory : /Users/terry/Downloads
File Size : 40 kB
File Modification Date/Time : 2018:01:12 02:41:28+00:00
File Permissions : rw-rw-rw-
File Type : JPEG
MIME Type : image/jpeg
JFIF Version : 1.02
Resolution Unit : None
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