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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