Jeffrey McLarty¶

Smuggle - Log, catalogue, and move python objects via pickling
Sympa - Symbolic math for pandas
RRSM - Readable run-time state machine
PySter - Python *anywhere
Trump - coming soon...

Smuggle 0.2.0¶

Log, catalogue, and move python objects via pickling¶

from smuggle import Smuggler, Payload # Reduce development costs and downtime!

# Create a safe place to smuggle objects to...
MySmuggler = Smuggler("C:\MyObjectLogFolder")

# Create some picklable objects...
aList = [1,2,3]
aDict = {'a' : 1, 'b' : 2, 'c' : 3}

# Use this in inconvenient code paths to augment logging and errors...
MySmuggler.smuggle(MyList=aList,MyDict=aDict,NoteToSelf="This is cool")

# Send this to an e-mail, log, or the screen. 
print(MySmuggler.passphrases())


import pickle

# NoteToSelf of type 'str' was smuggled at 22:57:51, 2015/03/04
#   'This is cool'
NoteToSelf = pickle.load(open(r"C:\MyObjectLogFolder\NoteToSelf-2015-03-04-22-57-51.smug","rb"))

# MyList of type 'list' was smuggled at 22:57:51, 2015/03/04
#   [1, 2, 3]
MyList = pickle.load(open(r"C:\MyObjectLogFolder\MyList-2015-03-04-22-57-51.smug","rb"))

# MyDict of type 'dict' was smuggled at 22:57:51, 2015/03/04
#   {'a': 1, 'c': 3, 'b': 2}
MyDict = pickle.load(open(r"C:\MyObjectLogFolder\MyDict-2015-03-04-22-57-51.smug","rb"))

# There are a few systematic ways to access the pickled objects
MyPayload = Payload("C:\MyObjectLogFolder")
MyPayload.astimevardict2D()

{datetime.datetime(2015, 3, 4, 22, 28, 43): {'MyDict': {'a': 1,
   'b': 2,
   'c': 3},
  'MyList': [1, 2, 3],
  'NoteToSelf': 'This is cool'},
 datetime.datetime(2015, 3, 4, 22, 30, 48): {'MyDict': {'a': 1,
   'b': 2,
   'c': 3},
  'MyList': [1, 2, 3],
  'NoteToSelf': 'This is cool'},
 datetime.datetime(2015, 3, 4, 22, 57, 51): {'MyDict': {'a': 1,
   'b': 2,
   'c': 3},
  'MyList': [1, 2, 3],
  'NoteToSelf': 'This is cool'}}

Sympa 0.1.0¶

Symbolic math for pandas¶

from sympa import domath

#We need some other stuff to show off sympa
from sympy import *
from sympy.interactive.printing import init_printing
init_printing()
import pandas as pd

#Set up some data (ints, floats, and irrational!)
df = pd.DataFrame({'x' : range(7)})
df['x'] = df.x * 10
df['y'] = df.x * 0.05
df['r'] = pi * df['y'] / 2
df

x,y,r,rn1,x1,xn1,yn1 = symbols('x y r r_-1 x_1 x_-1 y_-1')
f = x + xn1
g = x**y + x*y*3.0 + (x1 * 0.5 + xn1 * pi / (yn1 + 1.0))**2
h = sin(r)
i = sin(r) / cos(rn1 + 1)

f, g, h, i

# This is where the magic happens...
# ...the symbol names link to column names, and the subscripts handle shifts properly.
df['f'] = domath(df,f)
df['g'] = domath(df,g)
df['h'] = domath(df,h)
df['i'] = domath(df,i)

df # Look mom, no lambda!

RRSM 0.2.0¶

Readable run-time state machine¶

from rrsm import StateMachine

This isn't exactly for iPython Notebook. It's more for prototyping classes in a way that makes it harder to write undocumented code. Just by using this class, you have no choice but to document the states properly.

#Instantiation works with a list of the form ['state', ...]
#or a dictionary of the form {'state' : value, ...}
SM = StateMachine(['cool', 'off', 'on', 'warm']) #Initializes to the first state, "cool".
#checking state works against strings or integers:
SM == 'cool'

True

#Change the state
SM('warm')
#using attributes to check state:
SM == SM.cool

False

SM.current_state, SM.current_code

('warm', 3)

PySter Beta¶

Python *anywhere¶

Please see the README, and watch the video below in HD @ vimeo

from IPython.lib.display import VimeoVideo
VimeoVideo(121615286,1056,594)

	x	y	r
0	0	0.0	0
1	10	0.5	0.25*pi
2	20	1.0	0.5*pi
3	30	1.5	0.75*pi
4	40	2.0	1.0*pi
5	50	2.5	1.25*pi
6	60	3.0	1.5*pi

	x	y	r	f	g	h	i
0	0	0.0	0	NaN	NaN	0.000000e+00	NaN
1	10	0.5	0.25*pi	10	118.162278	7.071068e-01	1.308724e+00
2	20	1.0	0.5*pi	30	1371.967615	1.000000e+00	-4.695752e+00
3	30	1.5	0.75*pi	50	2942.914269	7.071068e-01	-8.403222e-01
4	40	2.0	1.0*pi	70	5771.178626	1.224647e-16	-1.253398e-16
5	50	2.5	1.25*pi	90	23220.539991	-7.071068e-01	1.308724e+00
6	60	3.0	1.5*pi	110	NaN	-1.000000e+00	-4.695752e+00