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Guillermo Carrasco guillermo-carrasco

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import yaml
import logging
import logging.config
with open('config.yaml', 'r') as f:
conf = yaml.load(f)
myapp = logging.getLogger('myapp')
import logging
parent_log = logging.getLogger('parent')
child_1 = logging.getLogger('parent.child_1')
child_2 = logging.getLogger('parent.child_2')
View config.yaml
version: 1
format: "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
name: myapp
class: logging.StreamHandler
View Step 1 | Create

In this step we'll create a swarm cluster from two nodes. You should've received access to two nodes in AWS.

  1. SSH in to the first node: ssh -i docker_demo.pem ubuntu@IP1
  2. Figure out the internal IP address of the node: ifconfig and check the eth0 address (10.0....)
  3. Initialize the Swarm cluster: `docker swarm init --listen-addr INTERNAL_IP --advertise-addr INTERNAL_IP
  4. Open a new tab and SSH to the other node: ssh -i docker_demo.pem ubuntu@IP2
  5. Check the internal IP of that node: ifconfig and eth0
  6. Join to the cluster with the command the previous command printed out. Before executing the command, you need to the extend it with the --listen-addr and --advertise-addr parameters (using the internal IP of the second node): docker swarm join TOKEN --listen-addr 10.0.... --advertise-addr 10.0..... node1...
  7. Get back to the first node and verify the results: docker node ls
guillermo-carrasco /
Last active Oct 13, 2015
Small scrip to trim data for cancer/normal pipeline testing in AWS
# Trim the data to the first 1.000.000 reads (4.000.000 lines)
zcat synthetic_challenge_set3_normal_NGv3_1.fq.gz | head -n 4000000 > synthetic_challenge_set3_normal_NGv3_1_trimmed.fq && gzip synthetic_challenge_set3_normal_NGv3_1_trimmed.fq
zcat synthetic_challenge_set3_normal_NGv3_2.fq.gz | head -n 4000000 > synthetic_challenge_set3_normal_NGv3_2_trimmed.fq && gzip synthetic_challenge_set3_normal_NGv3_2_trimmed.fq
zcat synthetic_challenge_set3_tumor_NGv3_1.fq.gz | head -n 4000000 > synthetic_challenge_set3_tumor_NGv3_1_trimmed.fq && gzip synthetic_challenge_set3_tumor_NGv3_1_trimmed.fq
zcat synthetic_challenge_set3_tumor_NGv3_2.fq.gz | head -n 4000000 > synthetic_challenge_set3_tumor_NGv3_2_trimmed.fq && gzip synthetic_challenge_set3_tumor_NGv3_2_trimmed.fq
View Shell provisioning warnings
==> default: Running provisioner: shell...
default: Running: /var/folders/7t/bwttrszn4dv6zr1kyy2lq98m0000gn/T/
==> default: stdin: is not a tty
==> default: discarding /home/vagrant/miniconda/bin from PATH
==> default: prepending /home/vagrant/miniconda/envs/prod/bin to PATH
==> default: /vagrant/scout/ext/backend/ Warning: Click detected the use of the unicode_literals __future__ import. This is heavily discouraged because it can introduce subtle bugs in your code. You should instead use explicit u"" literals for your unicode strings. For more information see
==> default: type=click.File('w')
==> default: /home/vagrant/miniconda/envs/prod/lib/python2.7/site-packages/vcf_parser/ Warning: Click detected the use of the unicode_literals __future__ import. This is heavily discouraged because it can introduce subtle bugs in your code. You should instead use explicit u"" literals for your unicode strin
guillermo-carrasco / gist:6826648
Created Oct 4, 2013
NAIVE implementation of the problem "Find the most common k-mer in a DNA sequence
View gist:6826648
#!/usr/bin/env python
import re
if __name__=="__main__":
with open('stepic_dataset.txt', 'r') as f:
dna = f.readline().rstrip()
k = int(f.readline().rstrip())
View gist:6623249
import logging
from logbook.queues import RedisHandler
h = RedisHandler(key='python_agent')
l = logging.getLogger('redis')
guillermo-carrasco / deinterleave.c
Created May 18, 2013
FASTQ files deinterleaver.
View deinterleave.c
Deinterleave FASQ files containing both reads from a paired-end read.
The script makes the following assumption:
* FASTQ format (4 lines per read), and the two reads (fordward and reverse) in a row.
USAGE: :/deinterleave file.fastq f.file.fastq r.file.fastq
RESULT: f.file.fastq r.file.fastq containing fordward and backward reads, respectively
Can deinterleave FASTQ files of any size, does not consume memory. The execution time is
I/O bounded.
guillermo-carrasco / simNGS_unique_ids.patch
Created May 13, 2013
Unique read IDs for simLibrary
View simNGS_unique_ids.patch
diff -ru simNGS.orig/src/simlibrary.c simNGS/src/simlibrary.c
--- simNGS.orig/src/simlibrary.c 2013-05-13 17:45:17.000000000 +0200
+++ simNGS/src/simlibrary.c 2013-05-13 18:06:41.000000000 +0200
@@ -33,12 +33,13 @@
#define QUOTE(A) Q_(A)
#define DEFAULT_COV 0.055
#define DEFAULT_OUT "fasta"
+#define DEFAULT_UNIQUE "not"
#define DEFAULT_INSERT 400
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