Look at LSB init scripts for more information.
Copy to /etc/init.d
:
# replace "$YOUR_SERVICE_NAME" with your service's name (whenever it's not enough obvious)
# | |
# CONFIGURATION FOR USING SMS KANNEL WITH RAPIDSMS | |
# | |
# For any modifications to this file, see Kannel User Guide | |
# If that does not help, see Kannel web page (http://www.kannel.org) and | |
# various online help and mailing list archives | |
# | |
# Notes on those who base their configuration on this: | |
# 1) check security issues! (allowed IPs, passwords and ports) | |
# 2) groups cannot have empty rows inside them! |
############# init parent django project settings | |
from os import path | |
import sys | |
sys.path.append(path.dirname(path.dirname(path.abspath(__file__)))) | |
import settings | |
from django.core.management import setup_environ | |
setup_environ(settings) | |
############### |
1. pip install -r reqs.pip | |
2. server.py | |
3. open client.html in browser | |
4. redis-cli publish push '123456' | |
5. check browser console |
""" | |
Two things are wrong with Django's default `SECRET_KEY` system: | |
1. It is not random but pseudo-random | |
2. It saves and displays the SECRET_KEY in `settings.py` | |
This snippet | |
1. uses `SystemRandom()` instead to generate a random key | |
2. saves a local `secret.txt` |
"""Information Retrieval metrics | |
Useful Resources: | |
http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt | |
http://www.nii.ac.jp/TechReports/05-014E.pdf | |
http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf | |
http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf | |
Learning to Rank for Information Retrieval (Tie-Yan Liu) | |
""" | |
import numpy as np |
Look at LSB init scripts for more information.
Copy to /etc/init.d
:
# replace "$YOUR_SERVICE_NAME" with your service's name (whenever it's not enough obvious)
"""making a dataframe""" | |
df = pd.DataFrame([[1, 2], [3, 4]], columns=list('AB')) | |
"""quick way to create an interesting data frame to try things out""" | |
df = pd.DataFrame(np.random.randn(5, 4), columns=['a', 'b', 'c', 'd']) | |
"""convert a dictionary into a DataFrame""" | |
"""make the keys into columns""" | |
df = pd.DataFrame(dic, index=[0]) |
class BigForeignKey(models.ForeignKey): | |
def db_type(self, connection): | |
""" Adds support for foreign keys to big integers as primary keys. | |
""" | |
rel_field = self.rel.get_related_field() | |
if (isinstance(rel_field, BigAutoField) or | |
(not connection.features.related_fields_match_type and | |
isinstance(rel_field, (BigIntegerField, )))): |
A personal diary of DataFrame munging over the years.
Convert Series datatype to numeric (will error if column has non-numeric values)
(h/t @makmanalp)
from scipy.spatial.distance import pdist, squareform | |
import numpy as np | |
from numbapro import jit, float32 | |
def distcorr(X, Y): | |
""" Compute the distance correlation function | |
>>> a = [1,2,3,4,5] | |
>>> b = np.array([1,2,9,4,4]) |