Arthur Mwai bolshoibooze

## kannel.conf
#
# 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!

## export _as_ excel

from django.core.exceptions import PermissionDenied
from django.http import HttpResponse
from pyExcelerator import *


def export_as_xls(modeladmin, request, queryset):
    """
    Generic xls export admin action.
    usage:actions = [export_as_xls]

## secret-key-gen.py
"""
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`

## gist:17adacf7436783faba3c
//simple_list_item_1
<TextView xmlns:android="http://schemas.android.com/apk/res/android"
    android:id="@android:id/text1"
    style="?android:attr/listItemFirstLineStyle"
    android:paddingTop="2dip"
    android:paddingBottom="3dip"
    android:layout_width="fill_parent"
    android:layout_height="wrap_content" />

//simple_list_item_2

## export_as_excel.py
#One of those handy scripts, see:http://djangotricks.blogspot.co.ke/2013/12/how-to-export-data-as-excel.html


from .models import *
from django.http import *

"""
# Quick 'how-to' guide
# model in question
class Duplicate(models.Model):

## get_pepperstone_data.py
import urllib2

"""
#Usage:
in a python shell
>> import get_pepperstone_data
>> get_pepperstone_data.run()

# Notes:
1: Selection of pairs is highly opinionated

## useful_pandas_snippets.py
#List unique values in a DataFrame column
pd.unique(df.column_name.ravel())

#Convert Series datatype to numeric, getting rid of any non-numeric values
df['col'] = df['col'].astype(str).convert_objects(convert_numeric=True)

#Grab DataFrame rows where column has certain values
valuelist = ['value1', 'value2', 'value3']
df = df[df.column.isin(value_list)]

## note.md

      
              4 files
            
          
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                bolshoibooze
                / note.md
            
            
              Created
              October 14, 2015 10:48
                — forked from fyears/note.md
            
              
                how to install scipy numpy matplotlib ipython in virtualenv
              
          
    if you are using linux, unix, os x:
pip install -U setuptools
pip install -U pip

pip install numpy
pip install scipy
pip install matplotlib
#pip install PySide


## Python concave hull ( alpha shape ) .md

      
              1 file
            
          
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                bolshoibooze
                / Python concave hull ( alpha shape ) .md
            
            
              Created
              October 15, 2015 13:12
                — forked from hellpanderrr/Python concave hull ( alpha shape ) .md
            
              
                Concave hull in python using scipy and networkx
              
          
    from scipy.spatial import Delaunay
import networkx as nx
 
points = [ [0,0],[0,50],[50,50],[50,0],[0,400],[0,450],[50,400],[50,450],[700,300],[700,350],[750,300],[750,350],
          [900,600],[950,650],[950,600],[900,650]
]
def concave(points,alpha_x=150,alpha_y=250):
  points = [(i[0],i[1]) if type(i) <> tuple else i for i in points]
  de = Delaunay(points)

  
## rank_metrics.py
"""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
	#
	# 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!

	from django.core.exceptions import PermissionDenied
	from django.http import HttpResponse
	from pyExcelerator import *


	def export_as_xls(modeladmin, request, queryset):
	"""
	Generic xls export admin action.
	usage:actions = [export_as_xls]
	"""
	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`
	//simple_list_item_1
	<TextView xmlns:android="http://schemas.android.com/apk/res/android"
	android:id="@android:id/text1"
	style="?android:attr/listItemFirstLineStyle"
	android:paddingTop="2dip"
	android:paddingBottom="3dip"
	android:layout_width="fill_parent"
	android:layout_height="wrap_content" />

	//simple_list_item_2
	#One of those handy scripts, see:http://djangotricks.blogspot.co.ke/2013/12/how-to-export-data-as-excel.html


	from .models import *
	from django.http import *

	"""
	# Quick 'how-to' guide
	# model in question
	class Duplicate(models.Model):
	import urllib2

	"""
	#Usage:
	in a python shell
	>> import get_pepperstone_data
	>> get_pepperstone_data.run()

	# Notes:
	1: Selection of pairs is highly opinionated
	#List unique values in a DataFrame column
	pd.unique(df.column_name.ravel())

	#Convert Series datatype to numeric, getting rid of any non-numeric values
	df['col'] = df['col'].astype(str).convert_objects(convert_numeric=True)

	#Grab DataFrame rows where column has certain values
	valuelist = ['value1', 'value2', 'value3']
	df = df[df.column.isin(value_list)]
	"""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