tkf

#!/usr/bin/env python

import matplotlib.pyplot as plt
import numpy

class SublotsForReuse(object):
    def __init__(self, *args, **kwds):
        self.fig = plt.figure(*args, **kwds)
        self.ax = []

tkf

#!/usr/bin/env python

import matplotlib.pyplot as plt
import numpy

class SublotsForReuse(object):
    def __init__(self, *args, **kwds):
        self.fig = plt.figure(*args, **kwds)
        self.ax = []

tkf

import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid import AxesGrid
import numpy

class MultiSeqPlotter(object):
    """
    Reuseable multi-sequence plotter for plotting some time series
    of some variables. Time series are column-wise and variables are
    row-wise.
    """

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list_func = [ lambda x: numpy.sin(x),
              lambda x: numpy.sin(0.1*x),
              lambda x: numpy.sin(x)*numpy.sin(0.1*x) ]
list_tmax = [30,50,70,100,200]
dt = 1
nrows_ncols = (len(list_func), len(list_tmax))

num_steps = sum(list_tmax) * dt
num_rows  = len(list_func)

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# -*- coding: utf-8 -*-
import os
import cgi
## import cgitb
## cgitb.enable()

caldir_path = "../path/to/cal/"
calset = set(['cal1','cal2','cal3',])

fs = cgi.FieldStorage()

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"""
x[t] = fp(u)[t] = tanh(v[t])
v[t] = mean( u[t-T+1:t+1] ) = ( u[t-T+1] + u[t-T+2] + ... + u[t-1] + u[t] )/T

E = sum( 0.5 * (x - y)**2 )
dE/dv[t] = (x[t] - y[t]) * (1 - x[t]**2)
dE/du[t] = mean( dE/dv[t:t+T] )
         = ( dE/dv[t+T-1] + dE/dv[t+T-2] + ... + dE/dv[t+1] + dE/dv[t] )/T

See result here: http://www.flickr.com/photos/arataka/tags/up20091213135244/show/

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"""
x[t] = fp(u)[t]
     = mean( v[t-T+1:t+1] ) = ( v[t-T+1] + v[t-T+2] + ... + v[t-1] + v[t] )/T
v[t] = tanh(u[t])

E = sum( 0.5 * (x - y)**2 )
dE/dx[t] = x[t] - y[t]
dE/dv[t] = ( dE/dx[t+T-1] + dE/dx[t+T-2] + ... + dE/dx[t+1] + dE/dx[t] )/T
dE/du[t] = dE/dv[t] * (1 - x[t]**2)

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ALL = \
	rnn_ca1d-gcc-O2 rnn_ca1d-gcc-O3 rnn_ca1d-icc-O2 rnn_ca1d-icc-O3 \
	rnn_ca2d-gcc-O2 rnn_ca2d-gcc-O3 rnn_ca2d-icc-O2 rnn_ca2d-icc-O3 \
	rnn_opt0-gcc-O2 rnn_opt0-gcc-O3 rnn_opt0-icc-O2 rnn_opt0-icc-O3 \
	rnn_opt1-gcc-O2 rnn_opt1-gcc-O3 rnn_opt1-icc-O2 rnn_opt1-icc-O3 \
	rnn_opt2-gcc-O2 rnn_opt2-gcc-O3 rnn_opt2-icc-O2 rnn_opt2-icc-O3 \
	rnn_opt3-gcc-O2 rnn_opt3-gcc-O3 rnn_opt3-icc-O2 rnn_opt3-icc-O3 \
	rnn_opt4-gcc-O2 rnn_opt4-gcc-O3 rnn_opt4-icc-O2 rnn_opt4-icc-O3 \

#ALL = \

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import math

from pylab import *
import random
def mixture_gaussian(i):
    pi_0 = 0.3
    if random.random() < pi_0: 
        return random.gauss(-5, 1)
    else:
        return random.gauss( 5, 4)

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import re

FORMAT_PATTERN_GROUPED = re.compile("{([^}]+):([^}]+)}")
FORMAT_PATTERN = re.compile("{[^}]+:[^}]+}")

def find_variables(formatstr, strlist):
    """
    Extract values from fixed formatted string.
    (e.g., '1' and '1000.0' from '001_1E3.txt')