synapticarbors

I made a few optimizations to the cython code. The timings on my machine are:

from sequences import P53_HUMAN, P53_MOUSE
from alignment import align as py_align
from align_numpy import align as cy_align
from align_numpy2 import align as cy_align2
 
%timeit py_align(P53_HUMAN, P53_MOUSE)
1 loops, best of 3: 442 ms per loop

chaosct

# Script to clean (and sort) the bibfile exported by mendeley
# It is then apt for versioning
# Author: Carles F. Julià <carles.fernandez(AT)upf.edu>

# You will need bibtexparser package:
# $ pip install bibtexparser

# usage: cleanbib.py library.bib
# WARNING: it overwrites the original file

juanpabloaj

Total of pip packages downloaded separated by Python versions

From June 26, 2016 (python 3.5.2 release) to Aug. 31, 2016.

Python versions from 2.6 to 3.5

Without 2.7

wrwrwr

#!/usr/bin/env bash

# Path to the project directory (that should include requirements.txt),
# Files and directories within that need to be deployed.
project=../backend
contents=(module lamdba_handler.py)

# Unnecessary parts. Note that there are some inter-dependencies in SciPy,
# for example to use scipy.stats you also need scipy.linalg, scipy.integrate,
# scipy.misc, scipy.sparse, and scipy.special.

scribu

This is a quick script to convert notes from Tomboy to Evernote.

Usage

pip install -r requirements.txt
mkdir export/
python tomboy-export.py <tomboy-dir>

LegoStormtroopr

from PyQt4 import QtGui, QtCore
from FingerTabs import FingerTabWidget

import sys

app = QtGui.QApplication(sys.argv)
tabs = QtGui.QTabWidget()
tabs.setTabBar(FingerTabBarWidget(width=100,height=25))
digits = ['Thumb','Pointer','Rude','Ring','Pinky']
for i,d in enumerate(digits):

simonkamronn

from scipy.stats import norm, shapiro, kstest, anderson
import bokeh.plotting as bplt
from bokeh import layouts
from bokeh.charts import Histogram, Scatter
from bokeh.models import Span
import pandas as pd
import numpy as np


def vertical_histogram(y):

fsausset

from keras.models import Sequential
from keras.models import model_from_json
from keras import backend as K
import tensorflow as tf
from tensorflow.python.tools import freeze_graph
import os

# Load existing model.
with open("model.json",'r') as f:
    modelJSON = f.read()

nicerobot

# look and feel
caption always "%{= bb}%{+b w}%h %=%{=b rw} %l %{= db} ${USER}@%H %{= dg}%c"
hardstatus alwayslastline "%-Lw%{= BW}%50>%n%f* %t%{-}%+Lw%<"

# skip the startup message
startup_message off

# go to home dir
chdir

saliksyed

""" Deep Auto-Encoder implementation
	
	An auto-encoder works as follows:

	Data of dimension k is reduced to a lower dimension j using a matrix multiplication:
	softmax(W*x + b)  = x'
	
	where W is matrix from R^k --> R^j

	A reconstruction matrix W' maps back from R^j --> R^k