Dear All,
I am sending this email out of frustration, but I hope that someone will
be able to tell me what is going on.
I am using pylab on a Debian testing box.
I have the feeling that there is some problem with pylab when I
alternate, as I am doing now, many linear and log-log plots. Very often,
pylab complains about the fact that I cannot take the log of a negative
number even if all the quantities are positive or even if I have
replaced the loglog with a linear plot...
An example (sorry but there are long arrays involved and I cannot
reproduce all my code here):
#! /usr/bin/env python
import scipy as s
import numpy as n
import pylab as p
#from rpy import r
#import distance_calc as d_calc
# now I try performing a least-square fitting
import scipy.optimize as sopt
#do my stuff here
print "my_n_clus_fit is, ", my_n_clus_fit
print "my_r_sq_fit",my_r_sq_fit
print "n_clu_rep2 is, ", n_clu_rep2
print "R_sq is", R_sq
p.plot(n_clu_rep2,R_sq,"bo",my_n_clus_fit,my_r_sq_fit, "ro")
p.xlabel('particles in cluster')
p.ylabel('R square')
#p.legend(('beta=1e-2,100 part','beta=1e-1, 100 part', 'beta=1e-1, 200
part'))
p.title('Cluster-size vs average radius of gyration')
p.grid(True)
p.savefig("R_gyr_vs_N_fit.pdf")
p.hold(False)
and the relevant part of the output is:
my_n_clus_fit is, [ 1.73717793 1.80682874 1.87927215
1.95462012 2.03298911
2.11450025 2.19927951 2.28745793 2.3791718 2.47456287
2.57377856 2.67697224 2.78430338 2.89593788 3.01204829
3.13281405 3.25842182 3.38906574 3.52494773 3.66627779
3.81327439 3.96616469 4.12518502 4.29058114 4.46260869
4.64153356 4.82763228 5.02119248 5.22251334 5.43190599
5.64969409 5.87621423 6.11181652 6.35686511 6.61173873
6.87683131 7.15255258 7.43932868 7.73760284 8.04783608
8.37050788 8.70611696 9.05518203 9.41824259 9.79585979
10.18861726 10.59712204 11.0220055 11.46392434 11.92356157
12.40162761 12.89886134 13.41603127 13.95393673 14.5134091
15.09531308 15.70054806 16.33004946 16.98479025 17.66578236
18.37407832 19.11077287 19.87700462 20.67395784 21.50286429
22.3650051 23.26171279 24.19437329 25.16442809 26.17337649
27.22277791 28.31425426 29.44949252 30.63024729 31.85834351
33.13567931 34.4642289 35.84604567 37.28326532 38.77810918
40.33288767 41.9500038 43.63195696 45.38134673 47.20087695
49.09335983 51.06172036 53.10900079 55.23836536 57.45310516
59.75664325 62.15253993 64.64449825 67.23636972 69.93216028
72.73603649 75.65233197 78.68555407 81.84039088 85.12171845]
my_r_sq_fit [ -2.44174991 -2.39098053 -2.3384157 -2.28399191
-2.22764342
-2.16930217 -2.10889768 -2.04635699 -1.98160454 -1.91456212
-1.84514875 -1.77328057 -1.69887077 -1.62182947 -1.5420636
-1.45947681 -1.37396934 -1.2854379 -1.19377555 -1.09887155
-1.00061127 -0.89887602 -0.7935429 -0.68448467 -0.5715696
-0.45466128 -0.3336185 -0.20829504 -0.07853952 0.05580481
0.19490022 0.33891475 0.48802235 0.64240314 0.80224361
0.96773685 1.13908274 1.31648829 1.50016779 1.69034312
1.88724399 2.09110827 2.30218221 2.52072078 2.74698797
2.98125709 3.22381115 3.47494314 3.73495641 4.00416505
4.28289426 4.57148072 4.87027305 5.17963218 5.49993179
5.8315588 6.1749138 6.53041155 6.89848148 7.27956821
7.67413208 8.0826497 8.50561455 8.94353755 9.3969477
9.86639271 10.35243964 10.85567563 11.37670856 11.91616783
12.47470507 13.05299499 13.65173614 14.27165177 14.91349073
15.57802832 16.26606729 16.97843877 17.71600327 18.47965174
19.27030664 20.08892307 20.93648986 21.81403087 22.72260612
23.66331314 24.63728827 25.64570804 26.68979058 27.77079711
28.89003345 30.04885158 31.24865133 32.49088201 33.77704418
35.10869149 36.48743251 37.91493273 39.3929165 40.92316917]
n_clu_rep2 is, [ 4. 5. 6. 7. 8. 10. 11. 12. 13.
14. 15. 16.
17. 18. 19. 20. 21. 22. 23. 24. 25. 27. 28. 30.
31. 32. 33. 34. 35. 36. 37. 38. 40. 41. 42. 43.
44. 45. 46. 47. 48. 50. 51. 54. 55. 56. 57. 58.
61. 62. 63. 67. 68. 72. 74. 77. 85. 86. 89. 91.
92. 93. 99. 108. 113. 114. 120. 145. 196.]
R_sq is [ 0.41290231 0.63219481 0.50921663 0.60545032
0.71055003
1.24577182 1.01656939 1.35542652 1.68938719 1.96960734
1.8587407 1.25335718 3.38501487 1.73960942 2.66786771
0.78304002 1.9472814 2.59527354 2.82129068 1.95855193
3.97830788 2.42530912 4.77505413 8.87282622 2.6755995
3.53707475 1.36507753 3.87191511 6.63215895 3.45373196
8.22450598 5.15409505 3.28241933 11.51525185 1.11042409
4.69324094 4.337342 7.0230309 6.98027636 2.10322888
3.63283609 3.69895425 2.39382279 24.86394803 6.14874073
6.59253038 7.7958188 20.11261061 9.22805741 1.60984999
4.08915341 13.37680959 10.7330271 9.19988106 18.86681803
16.01119253 16.7679522 12.83288125 28.15284186 2.96382532
28.60613768 10.38346233 24.59404975 3.06332058 120.46780399
29.27275139 25.00389411 17.40801449 21.23510666]
Traceback (most recent call last):
File "./r_gyr.py", line 322, in ?
p.plot(n_clu_rep2,R_sq,"bo",my_n_clus_fit,my_r_sq_fit, "ro")
File "/usr/lib/python2.4/site-packages/matplotlib/pylab.py", line
2028, in plot
ret = gca().plot(*args, **kwargs)
File "/usr/lib/python2.4/site-packages/matplotlib/axes.py", line 2540,
in plot
self.autoscale_view(scalex=scalex, scaley=scaley)
File "/usr/lib/python2.4/site-packages/matplotlib/axes.py", line 1207,
in autoscale_view
self.set_xlim(XL)
File "/usr/lib/python2.4/site-packages/matplotlib/axes.py", line 1493,
in set_xlim
raise ValueError('Cannot set nonpositive limits with log transform')
ValueError: Cannot set nonpositive limits with log transform
But there is no log transform here! True, before I was plotting
n_clu_rep2 and R_sq in a loglog plot, but this should not be a problem.
Is there anything wrong with:
p.plot(n_clu_rep2,R_sq,"bo",my_n_clus_fit,my_r_sq_fit, "ro")
p.xlabel('particles in cluster')
p.ylabel('R square')
#p.legend(('beta=1e-2,100 part','beta=1e-1, 100 part', 'beta=1e-1, 200
part'))
p.title('Cluster-size vs average radius of gyration')
p.grid(True)
p.savefig("R_gyr_vs_N_fit.pdf")
p.hold(False)
?? It looks like pylab is not able to "forget" the previous plots.
Any help here is really appreciated.
Many thanks
Lorenzo
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