I am searched the mailing list and web without success with this problem. I am getting unexpected behaviour when using savefig in the eps format.

The pdf renders the figure as it appears in the plot figure however the alpha for the patches is lost when saving as eps (see code below).

Any help would be greatly appreciated.

Kind Regards,

Kurt

matplotlib '0.98.5.2'

python 2.6.2

ubuntu 9.04

#!/usr/bin/env python

# -*- coding: utf-8 -*-

from numpy import *

from pylab import plot, show, grid, xlabel, ylabel, axhspan, axvspan, savefig

from scipy.optimize import leastsq, fsolve

sample_day = array([0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,\

22,23,24,25,26,27,28,29,30,31])

sample_measurement = array([0,0,0,0,0,0,0,0.02190,0.04910,0.06540,0.08170,\

0.10930,0.13650,0.15850,0.20200,0.33320,0.52000,0.66110,0.78710,0.85250,\

0.89070,0.91270,0.92890,0.94560,0.96180,0.97280,0.97280,0.97810,0.97810,\

0.98370,0.98370,0.98370])

## Logistic function

logistic = lambda a, x: (a[0] + (a[1]-a[0])/(1 + (a[2]/x)**a[3]))

## Linear first order function

linear_first_order = lambda a, x: (a[0]*x + a[1])

## Column leach model function

column_leach_model = lambda a, x: (array([zeros(len(x)), linear_first_order(a[0:2],x), logistic(a[2:len(a)],x)]).max(0))

## Error function

e = lambda a, x, y: (column_leach_model(a,x)-y)

## Initial conditions

a0 = [0.022,-0.13, 0.0,0.987,15.5,10.3]

## Least-squares regression

a, cov_x, infodict, mesg ,success = leastsq(e, a0, args=(sample_day,sample_measurement), full_output=1)

## Intercept of the linear and logistic functions

intercept = lambda x, a: (logistic(a[2:len(a)],x) - linear_first_order(a[0:2],x))

xint = fsolve(intercept, a[4], args=(a))

def plot_fit():

# Create a time series data set to evaluate the regression model against

x0 = linspace(0,-a[1]/a[0])

x1 = linspace(-a[1]/a[0], xint)

x2 = logspace(log10(xint), log10(31.))

xsample = array([x0,x1,x2]).flatten()

# Evaluate the regression model

y = column_leach_model(a, xsample)

# Plot the experimental data and the regression model results

plot(sample_day, sample_measurement, marker='o', linestyle='none')

xlabel("Duration [days]")

ylabel("Fraction Recovered [-]")

plot(xsample, y, linewidth=2)

patch1 = axvspan(0, -a[1]/a[0], facecolor='.1', alpha=0.25)

patch2 = axvspan(-a[1]/a[0], xint, facecolor='g', alpha=0.25)

patch3 = axvspan(xint, 35, facecolor='b', alpha=0.25)

grid("on")

savefig('data_model.eps')

savefig('data_model.pdf')

plot_fit()

show()

## ···

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