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Revisión1d212f2664a62d7de0b1737c0a60848e4fd778bd (tree)
Tiempo2008-11-13 04:28:13
Autoriselllo
Commiteriselllo

Log Message

I added a code which now properly calculates the mean coordination number in the system (the previous calculation in
plot_statistics_single.py was WRONG!!!).

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diff -r 2e85adf97c48 -r 1d212f2664a6 Python-codes/plot_coord_number.py
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/Python-codes/plot_coord_number.py Wed Nov 12 19:28:13 2008 +0000
@@ -0,0 +1,1107 @@
1+#! /usr/bin/env python
2+
3+import scipy as s
4+import numpy as n
5+import pylab as p
6+#from rpy import r
7+import distance_calc as d_calc
8+import igraph as ig
9+import calc_rad as c_rad
10+#import g2_calc as g2
11+from scipy import stats #I need this module for the linear fit
12+
13+
14+
15+
16+n_part=5000
17+density=0.01
18+
19+box_vol=n_part/density
20+
21+
22+part_diam=1. # particle diameter
23+
24+part_vol=s.pi/6.*part_diam**3.
25+
26+tot_vol=n_part*part_vol
27+
28+cluster_look = 50 # specific particle (and corresponding cluster) you want to track
29+
30+#Again, I prefer to give the number of particles and the density as input parameters
31+# and work out the box size accordingly
32+
33+collisions =1 #whether I should take statistics about the collisions or not.
34+
35+ini_config=0
36+fin_config=3000 #for large data post-processing I need to declare an initial and final
37+
38+ #configuration I want to read and post-process
39+
40+
41+by=1000 #this tells how many configurations there are in the file I am reading
42+
43+figure=0 #whether I sould print many figures or not
44+
45+
46+n_config=(fin_config-ini_config)/by # total number of configurations, which are numbered from 0 to n_config-1
47+
48+my_selection=s.arange(ini_config,fin_config,by)
49+
50+box_size=(n_part/density)**(1./3.)
51+print "the box size is, ", box_size
52+
53+threshold=1.04 #threshold to consider to particles as directly connected
54+
55+
56+save_size_save =1 #tells whether I want to save the coordinates of a cluster of a specific size
57+
58+size_save=98
59+
60+
61+
62+t_step=1. #here meant as the time-separation between adjacent configurations
63+
64+
65+
66+
67+
68+fold=0
69+
70+gyration = 1 #parameter controlling whether I want to calculate the radius of gyration
71+
72+#time=s.linspace(0.,((n_config-1)*t_step),n_config) #I define the time along the simulation
73+time=p.load("../1/time.dat")
74+
75+
76+delta_t=(time[2]-time[1])*by
77+
78+
79+time=time[my_selection] #I adapt the time to the initial and final configuration
80+
81+p.save("time_red.dat",time)
82+
83+
84+#some physical parameters of the system
85+T=0.1 #temperature
86+beta=0.1 #damping coefficient
87+v_0=0. #initial particle mean velocity
88+
89+
90+n_s_ini=2 #element in the time-sequence corresponding
91+ # to the chosen reference time for calculating the velocity autocorrelation
92+ #function
93+
94+s_ini=(n_s_ini)*t_step #I define a specific time I will need for the calculation
95+# of the autocorrelation function
96+
97+#print 'the time chosen for the velocity correlation function is, ', s_ini
98+#print 'and the corresponding time on the array is, ', time[n_s_ini]
99+
100+
101+
102+Len=box_size
103+print 'Len is, ', Len
104+
105+
106+
107+calculate=1
108+
109+if (calculate == 1):
110+
111+
112+# energy_1000=p.load("tot_energy.dat")
113+
114+# p.plot(energy_1000[:,0], energy_1000[:,1] ,linewidth=2.)
115+# p.xlabel('time')
116+# p.ylabel('energy')
117+# #p.legend(('beta=1e-2,100 part','beta=1e-1, 100 part', 'beta=1e-1, 200 part'))
118+# p.title('energy evolution')
119+# p.grid(True)
120+# p.savefig('energy_2000_particles.pdf')
121+# p.hold(False)
122+
123+# print "the std of the energy for a system with 2000 particles is, ", s.std(energy_1000[config_ini:n_config,1])
124+# print "the mean of the energy for a system with 2000 particles is, ", s.mean(energy_1000[config_ini:n_config,1])
125+
126+# print "the ratio of the two is,", s.std(energy_1000[config_ini:n_config,1])\
127+# /s.mean(energy_1000[config_ini:n_config,1])
128+# print "and the theoretical value is, ", s.sqrt(2./3.)*(1./s.sqrt(n_part))
129+
130+# tot_config=p.load("total_configuration.dat")
131+# tot_config=tot_config[(ini_config*n_part*3):(fin_config*n_part*3)]
132+ #I cut the array with the total number
133+ #of configurations for large arrays
134+
135+
136+
137+
138+# print "the length of tot_config is, ", len(tot_config)
139+# tot_config=s.reshape(tot_config,(n_config,3*n_part))
140+
141+# print "tot_config at line 10 is, ", tot_config[10,:]
142+
143+
144+# #Now I save some "raw" data for detailed analysis
145+# i=71
146+# test_arr=tot_config[i,:]
147+# test_arr=s.reshape(test_arr,(n_part,3))
148+# p.save("test_71_raw.dat",test_arr)
149+
150+
151+
152+ #Now I want to "fold" particle positions in order to be sure that they are inside
153+ # my box.
154+
155+ #f[:,:,k]=where((Vsum<=V[k+1]) & (Vsum>=V[k]), (V[k+1]-Vsum)/(V[k+1]-V[k]),\
156+ #f[:,:,k] )
157+ #f[:,:,k]=where((Vsum<=V[k]) & (Vsum>=V[k-1]),(Vsum-V[k-1])/(V[k]-V[k-1]),\
158+ #f[:,:,k])
159+
160+
161+
162+# if (fold ==1):
163+# #In the case commented below, the box goes from [-L/2,L/2] but that is proba
164+# #bly not the case in Espresso
165+
166+# #Len=box_size/2.
167+
168+
169+# # and then I do the following
170+
171+# #tot_config=s.where((tot_config>Len) & (s.remainder(tot_config,(2.*Len))<Len),\
172+# #s.remainder(tot_config,Len),tot_config)
173+
174+
175+# #tot_config=s.where((tot_config>Len) & (s.remainder(tot_config,(2.*Len))>=Len),\
176+# #(s.remainder(tot_config,Len)-Len),tot_config)
177+
178+# #tot_config=s.where((tot_config< -Len) &(s.remainder(tot_config,(-2.*Len))< -Len)\
179+# #(s.remainder(tot_config,-Len)+Len),tot_config)
180+
181+# #tot_config=s.where((tot_config< -Len) &(s.remainder(tot_config,(-2.*Len))>=-Len)\
182+# #s.remainder(tot_config,-Len),tot_config)
183+
184+
185+# #Now the case when the box is [0,L], so Len is actually the box size
186+
187+# p.save("tot_config_unfolded.dat",tot_config)
188+
189+# Len=box_size
190+
191+# tot_config=s.where(tot_config>Len,s.remainder(tot_config,Len),tot_config)
192+# tot_config=s.where(tot_config<0.,(s.remainder(tot_config,-Len)+Len),tot_config)
193+
194+# print "the max of tot_config is", tot_config.max()
195+# print "the min of tot_config is", tot_config.min()
196+
197+# p.save("tot_config_my_folding.dat",tot_config)
198+
199+# x_0=tot_config[0,0] #initial x position of all my particles
200+
201+
202+
203+# x_col=s.arange(n_part)*3
204+
205+# print "x_col is, ", x_col
206+
207+
208+# #Now I plot the motion of particle number 1; I follow the three components
209+
210+# p.plot(time,tot_config[:,0] ,linewidth=2.)
211+# p.xlabel('time')
212+# p.ylabel('x position particle 1 ')
213+# #p.legend(('beta=1e-2,100 part','beta=1e-1, 100 part', 'beta=1e-1, 200 part'))
214+# p.title('x_1 vs time')
215+# p.grid(True)
216+# p.savefig('x_position_particle_1.pdf')
217+# p.hold(False)
218+
219+# # p.save("mean_square_disp.dat", var_x_arr)
220+
221+
222+# p.plot(time,tot_config[:,1] ,linewidth=2.)
223+# p.xlabel('time')
224+# p.ylabel('y position particle 1 ')
225+# #p.legend(('beta=1e-2,100 part','beta=1e-1, 100 part', 'beta=1e-1, 200 part'))
226+# p.title('y_1 vs time')
227+# p.grid(True)
228+# p.savefig('y_position_particle_1.pdf')
229+# p.hold(False)
230+
231+# # p.save("mean_square_disp.dat", var_x_arr)
232+
233+
234+# p.plot(time,tot_config[:,2] ,linewidth=2.)
235+# p.xlabel('time')
236+# p.ylabel('z position particle 1 ')
237+# #p.legend(('beta=1e-2,100 part','beta=1e-1, 100 part', 'beta=1e-1, 200 part'))
238+# p.title('z_1 vs time')
239+# p.grid(True)
240+# p.savefig('z_position_particle_1.pdf')
241+# p.hold(False)
242+
243+# # p.save("mean_square_disp.dat", var_x_arr)
244+
245+
246+
247+
248+ #x_arr=s.zeros((n_part))
249+ #y_arr=s.zeros((n_part))
250+ #z_arr=s.zeros((n_part))
251+
252+
253+
254+ # for i in xrange(0,n_part):
255+# x_arr[:,i]=tot_config[:,3*i]
256+# y_arr[:,i]=tot_config[:,(3*i+1)]
257+# z_arr[:,i]=tot_config[:,(3*i+2)]
258+
259+
260+# print "the x coordinates are, ", x_arr
261+
262+# #Now a test: I try to calculate the radius of gyration
263+# #with a particular distance (suggestion by Yannis)
264+
265+# mean_x_arr=s.zeros(n_config)
266+
267+# mean_x_arr=x_arr.mean(axis=1)
268+
269+
270+# mean_y_arr=s.zeros(n_config)
271+
272+# mean_y_arr=y_arr.mean(axis=1)
273+
274+
275+# mean_z_arr=s.zeros(n_config)
276+
277+# mean_z_arr=z_arr.mean(axis=1)
278+
279+# var_x_arr2=s.zeros(n_config)
280+# var_y_arr2=s.zeros(n_config)
281+# var_y_arr2=s.zeros(n_config)
282+
283+
284+
285+# for i in xrange(0,n_config):
286+# for j in xrange(0,n_part):
287+# var_x_arr2[i]=var_x_arr2[i]+((x_arr[i,j]-mean_x_arr[i])- \
288+# Len*n.round((x_arr[i,j]-mean_x_arr[i])/Len))**2.
289+
290+
291+# var_x_arr2=var_x_arr2/n_part
292+# print 'var_x_arr2 is, ', var_x_arr2
293+# p.save( "test_config_before_manipulation.dat",x_arr[600,:])
294+
295+
296+
297+# max_x_dist=s.zeros(n_config)
298+
299+
300+# #for i in xrange(0, n_config):
301+# #mean_x_arr[i]=s.mean(x_arr[i,:])
302+# #var_x_arr[i]=s.var(x_arr[i,:])
303+
304+# mean_x_arr[:]=s.mean(x_arr,axis=1)
305+# var_x_arr[:]=s.var(x_arr,axis=1)
306+# max_x_dist[:]=x_arr.max(axis=1)-x_arr.min(axis=1)
307+
308+# print "mean_x_arr is, ", mean_x_arr
309+# print "var_x_arr is, ", var_x_arr
310+
311+
312+# if (fold==1):
313+
314+# p.plot(time, max_x_dist ,linewidth=2.)
315+# p.xlabel('time')
316+# p.ylabel('x-dimension of aggregate ')
317+# p.title('x-maximum stretch vs time [folded]')
318+# p.grid(True)
319+# p.savefig('max_x_distance_folded.pdf')
320+# p.hold(False)
321+
322+# p.save("max_x_distance_folded.dat", max_x_dist)
323+
324+
325+# elif (fold==0):
326+# p.plot(time, max_x_dist ,linewidth=2.)
327+# p.xlabel('time')
328+# p.ylabel('x-dimension of aggregate ')
329+# p.title('x-maximum stretch vs time[unfolded]')
330+# p.grid(True)
331+# p.savefig('max_x_distance_unfolded.pdf')
332+# p.hold(False)
333+
334+# p.save("max_x_distance_unfolded.dat", max_x_dist)
335+
336+
337+
338+
339+
340+# p.plot(time, var_x_arr ,linewidth=2.)
341+# p.xlabel('time')
342+# p.ylabel('mean square displacement ')
343+# #p.legend(('beta=1e-2,100 part','beta=1e-1, 100 part', 'beta=1e-1, 200 part'))
344+# p.title('VAR(x) vs time')
345+# p.grid(True)
346+# p.savefig('mean_square_disp.pdf')
347+# p.hold(False)
348+
349+# p.save("mean_square_disp.dat", var_x_arr)
350+
351+
352+
353+# p.plot(time, s.sqrt(var_x_arr) ,linewidth=2.)
354+# p.xlabel('time')
355+# p.ylabel('mean square displacement ')
356+# #p.legend(('beta=1e-2,100 part','beta=1e-1, 100 part', 'beta=1e-1, 200 part'))
357+# p.title('std(x) vs time')
358+# p.grid(True)
359+# p.savefig('std_of_x_position.pdf')
360+# p.hold(False)
361+
362+# p.save("std_of_x_position.dat", s.sqrt(var_x_arr))
363+
364+
365+
366+
367+# p.plot(time, mean_x_arr ,linewidth=2.)
368+# p.xlabel('time')
369+# p.ylabel('mean x position ')
370+# #p.legend(('beta=1e-2,100 part','beta=1e-1, 100 part', 'beta=1e-1, 200 part'))
371+# p.title('mean(x) vs time')
372+# p.grid(True)
373+# p.savefig('mean_x_position.pdf')
374+# p.hold(False)
375+
376+# p.save("mean_x_position.dat", mean_x_arr)
377+
378+
379+# #I now calculate the radius of gyration
380+
381+# if (gyration ==1):
382+
383+# mean_y_arr=s.zeros(n_config)
384+# mean_z_arr=s.zeros(n_config)
385+
386+
387+
388+
389+# var_y_arr[:]=s.var(y_arr,axis=1)
390+# var_z_arr[:]=s.var(z_arr,axis=1)
391+
392+# mean_y_arr[:]=s.mean(y_arr,axis=1)
393+# mean_z_arr[:]=s.mean(z_arr,axis=1)
394+
395+
396+# r_gyr=s.sqrt(var_x_arr+var_y_arr+var_z_arr)
397+
398+# p.save("r_gyr_my_post_processing.dat",r_gyr)
399+
400+# p.plot(time, r_gyr ,linewidth=2.)
401+# p.xlabel('time')
402+# p.ylabel('Radius of gyration')
403+# p.title('Radius of gyration vs time')
404+# p.grid(True)
405+# p.savefig('radius of gyration.pdf')
406+# p.hold(False)
407+
408+# #Now I want to calculate the distance of the centre of
409+# #mass of my aggregate from the origin
410+# dist_origin=s.sqrt(mean_x_arr**2.+mean_y_arr**2.+mean_z_arr**2.)
411+
412+# p.plot(time, dist_origin ,linewidth=2.)
413+# p.xlabel('time')
414+# p.ylabel('Mean distance from origin')
415+# p.title('Mean distance vs time')
416+# p.grid(True)
417+# p.savefig('mean_distance_from_origin.pdf')
418+# p.hold(False)
419+
420+
421+
422+# #Now some plots of the std's along the other 2 directions
423+# p.plot(time, s.sqrt(var_y_arr) ,linewidth=2.)
424+# p.xlabel('time')
425+# p.ylabel('mean square displacement ')
426+# p.title('std(y) vs time')
427+# p.grid(True)
428+# p.savefig('std_of_y_position.pdf')
429+# p.hold(False)
430+
431+# p.save("std_of_y_position.dat", s.sqrt(var_y_arr))
432+
433+
434+# p.plot(time, s.sqrt(var_z_arr) ,linewidth=2.)
435+# p.xlabel('time')
436+# p.ylabel('mean square displacement ')
437+# p.title('std(z) vs time')
438+# p.grid(True)
439+# p.savefig('std_of_z_position.pdf')
440+# p.hold(False)
441+
442+# p.save("std_of_z_position.dat", s.sqrt(var_z_arr))
443+
444+
445+
446+# p.plot(time, mean_y_arr ,linewidth=2.)
447+# p.xlabel('time')
448+# p.ylabel('mean y position ')
449+# p.title('mean(y) vs time')
450+# p.grid(True)
451+# p.savefig('mean_y_position.pdf')
452+# p.hold(False)
453+
454+# p.save("mean_y_position.dat", mean_y_arr)
455+
456+
457+
458+
459+# p.plot(time, mean_z_arr ,linewidth=2.)
460+# p.xlabel('time')
461+# p.ylabel('mean z position ')
462+# p.title('mean(z) vs time')
463+# p.grid(True)
464+# p.savefig('mean_z_position.pdf')
465+# p.hold(False)
466+
467+# p.save("mean_z_position.dat", mean_z_arr)
468+
469+
470+# #Now some comparative plots:
471+
472+
473+# p.plot(time, mean_x_arr,time, mean_y_arr,time, mean_z_arr,linewidth=2.)
474+# p.xlabel('time')
475+# p.ylabel('mean position')
476+# p.legend(('mean x','mean y','mean z',))
477+# p.title('Evolution mean position')
478+# p.grid(True)
479+# p.savefig('mean_position_comparison.pdf')
480+# p.hold(False)
481+
482+
483+# p.plot(time, var_x_arr,time, var_y_arr,time, var_z_arr,linewidth=2.)
484+# p.xlabel('time')
485+# p.ylabel('mean square displacement')
486+# p.legend(('var x','var y','var z',))
487+# p.title('Evolution mean square displacement')
488+# p.grid(True)
489+# p.savefig('mean_square_displacement_comparison.pdf')
490+# p.hold(False)
491+
492+
493+
494+# p.plot(time, s.sqrt(var_x_arr),time, s.sqrt(var_y_arr),time,\
495+# s.sqrt(var_z_arr),linewidth=2.)
496+# p.xlabel('time')
497+# p.ylabel('std of displacement')
498+# p.legend(('std x','std y','std z',))
499+# p.title('Evolution std of displacement')
500+# p.grid(True)
501+# p.savefig('std_displacement_comparison.pdf')
502+# p.hold(False)
503+
504+
505+
506+
507+
508+# # Now I compare my calculations with the one made
509+# # by espresso:
510+
511+# r_gyr_esp=p.load("rgyr.dat")
512+
513+# p.plot(time, r_gyr, r_gyr_esp[:,0], r_gyr_esp[:,1],linewidth=2.)
514+# p.xlabel('time')
515+# p.ylabel('Radius of gyration')
516+# p.legend(('my_postprocessing','espresso'))
517+# p.title('Radius of gyration vs time')
518+# p.grid(True)
519+# p.savefig('radius_of_gyration_comparison.pdf')
520+# p.hold(False)
521+
522+
523+
524+
525+
526+# # Now I do pretty much the same thing with the velocity
527+
528+# tot_config_vel=p.load("total_configuration_vel.dat")
529+
530+
531+# if (gyration ==1):
532+# r_gyr=s.zeros(n_config)
533+# y_arr=s.zeros((n_config,n_part))
534+# z_arr=s.zeros((n_config,n_part))
535+
536+# var_y_arr=s.zeros(n_config)
537+# var_z_arr=s.zeros(n_config)
538+
539+# for i in xrange(0,n_part):
540+# y_arr[:,i]=tot_config[:,(3*i+1)]
541+# z_arr[:,i]=tot_config[:,(3*i+2)]
542+
543+# var_y_arr[:]=s.var(y_arr,axis=1)
544+# var_z_arr[:]=s.var(z_arr,axis=1)
545+
546+
547+
548+# print "the length of tot_config is, ", len(tot_config)
549+# tot_config_vel=s.reshape(tot_config_vel,(n_config,3*n_part))
550+
551+# #print "tot_config at line 10 is, ", tot_config[10,:]
552+
553+
554+# v_0=tot_config_vel[0,0] #initial x position of all my particles
555+# v_arr=s.zeros((n_config,n_part))
556+# print 'OK creating v_arr'
557+# v_col=s.arange(n_part)*3
558+
559+# #print "x_col is, ", x_col
560+
561+# for i in xrange(0,n_part):
562+# v_arr[:,i]=tot_config_vel[:,3*i]
563+
564+# mean_v_arr=s.zeros(n_config)
565+# var_v_arr=s.zeros(n_config)
566+
567+# # for i in xrange(0, n_config):
568+# # mean_v_arr[i]=s.mean(v_arr[i,:])
569+# #var_v_arr[i]=s.var(v_arr[i,:])
570+
571+# mean_v_arr[:]=s.mean(v_arr,axis=1)
572+# var_v_arr[:]=s.var(v_arr,axis=1)
573+
574+
575+
576+
577+# #print "mean_v_arr is, ", mean_v_arr
578+# #print "var_v_arr is, ", var_v_arr
579+
580+# #time=s.linspace(0.,(n_config*t_step),n_config)
581+
582+# p.plot(time, var_v_arr ,linewidth=2.)
583+# p.xlabel('time')
584+# p.ylabel('velocity variance')
585+# #p.legend(('beta=1e-2,100 part','beta=1e-1, 100 part', 'beta=1e-1, 200 part'))
586+# p.title('VAR(v) vs time')
587+# p.grid(True)
588+# p.savefig('velocity_variance.pdf')
589+# p.hold(False)
590+
591+# p.save("velocity_variance.dat", var_v_arr)
592+
593+# #Now I want to calculate the autocorrelation function.
594+# #First, I need to fix an intial time. I choose something which is well past the
595+# #ballistic regime
596+
597+
598+
599+# #Now I start calculating the velocity autocorrelation function
600+
601+# vel_autocor=s.zeros(n_config)
602+
603+
604+
605+# for i in xrange(0, n_config):
606+# vel_autocor[i]=s.mean(v_arr[i]*v_arr[n_s_ini])
607+
608+
609+# p.plot(time, vel_autocor ,linewidth=2.)
610+# p.xlabel('time')
611+# p.ylabel('<v(s)v(t)> ')
612+# #p.legend(('beta=1e-2,100 part','beta=1e-1, 100 part', 'beta=1e-1, 200 part'))
613+# p.title('Velocity correlation')
614+# p.grid(True)
615+# p.savefig('velocity_correlation_numerics.pdf')
616+# p.hold(False)
617+
618+
619+# p.save("velocity_autocorrelation.dat", vel_autocor)
620+
621+
622+
623+
624+
625+
626+# #Now I try reconstructing the structure of the aggregate.
627+
628+# #first a test case
629+
630+# my_conf=tot_config[919,:]
631+
632+# print 'my_conf is, ', my_conf
633+
634+
635+# #Now I want to reconstruct the structure of the aggregate in 3D
636+# # I choose particle zero as a reference
637+
638+# x_test=s.zeros(3)
639+# y_test=s.zeros(3)
640+# z_test=s.zeros(3)
641+
642+# for i in xrange(0,3):
643+# x_test[i]=my_conf[3*i]
644+# y_test[i]=my_conf[3*i+1]
645+# z_test[i]=my_conf[3*i+2]
646+
647+
648+# print "x_test is, ", x_test
649+# print "y_test is, ", y_test
650+# print "z_test is, ", z_test
651+
652+# #OK reading the files
653+
654+
655+
656+# pos_0=s.zeros(3) #I initialized the positions of the three particles
657+# pos_1=s.zeros(3)
658+# pos_2=s.zeros(3)
659+
660+# pos_1[0]=r_ij[0] #Now I have given the x-position of particles 1 and 2 wrt particle 0
661+# pos_2[0]=r_ij[1]
662+
663+
664+# #Now I do the same for the y coord!
665+
666+# count_int=0
667+
668+# for i in xrange(0,(n_part-1)):
669+# for j in xrange((i+1),n_part):
670+# r_ij[count_int]=y_test[i]-y_test[j]
671+# r_ij[count_int]=r_ij[count_int]-Len*n.round(r_ij[count_int]/Len)
672+# r_ij[count_int]=-r_ij[count_int] #I have better reverse the signs now.
673+# print 'i and j are, ', (i+1), (j+1)
674+
675+# count_int=count_int+1
676+
677+# print 'r_ij is, ', r_ij
678+
679+
680+# pos_1[1]=r_ij[0] #Now I have given the x-position of particles 1 and 2 wrt particle 0
681+# pos_2[1]=r_ij[1]
682+
683+
684+
685+# #Now I do the same for the z coord!
686+
687+# count_int=0
688+
689+# for i in xrange(0,(n_part-1)):
690+# for j in xrange((i+1),n_part):
691+# r_ij[count_int]=z_test[i]-z_test[j]
692+# r_ij[count_int]=r_ij[count_int]-Len*n.round(r_ij[count_int]/Len)
693+# r_ij[count_int]=-r_ij[count_int] #I have better reverse the signs now.
694+# print 'i and j are, ', (i+1), (j+1)
695+
696+# count_int=count_int+1
697+
698+# print 'r_ij is, ', r_ij
699+
700+
701+# pos_1[2]=r_ij[0] #Now I have given the x-position of particles 1 and 2 wrt particle 0
702+# pos_2[2]=r_ij[1]
703+
704+# print 'pos_0 is,', pos_0
705+# print 'pos_1 is,', pos_1
706+# print 'pos_2 is,', pos_2
707+
708+# #now I can calculate the radius of gyration!
709+
710+# x_test[0]=pos_0[0]
711+# x_test[1]=pos_1[0]
712+# x_test[2]=pos_2[0]
713+
714+
715+# y_test[0]=pos_0[1]
716+# y_test[1]=pos_1[1]
717+# y_test[2]=pos_2[1]
718+
719+
720+# z_test[0]=pos_0[2]
721+# z_test[1]=pos_1[2]
722+# z_test[2]=pos_2[2]
723+
724+
725+# R_g=s.sqrt(s.var(x_test)+s.var(y_test)+s.var(z_test))
726+
727+# print "the correct radius of gyration is, ", R_g
728+
729+#############################################################
730+#############################################################
731+ #Now I start counting the number of aggregates in each saved configuration
732+ #First I re-build the configuration of the system with the "correct" x_arr,y_arr,z_arr
733+
734+ # I turned the following bit into a comment since I am using the original coord as
735+ #returned by Espresso to start with
736+
737+
738+
739+
740+# #I re-use the tot_config array for this purpose
741+
742+# for i in xrange(0,n_part):
743+# tot_config[:,3*i]=x_arr[:,i]
744+# tot_config[:,(3*i+1)]=y_arr[:,i]
745+# tot_config[:,(3*i+2)]=z_arr[:,i]
746+
747+ #p.save("test_calculating_graph.dat",tot_config[71:73,:])
748+
749+
750+
751+
752+#Now a function to count the collisions
753+#despite the name, it counts only the number of collisions which took place; it does not know anything
754+#about the direct calculation of the kernel.
755+
756+
757+ def kernel_calc(A):
758+
759+ d = {}
760+ for r in A:
761+ t = tuple(r)
762+ d[t] = d.get(t,0) + 1
763+
764+ # The dict d now has the counts of the unique rows of A.
765+
766+ B = n.array(d.keys()) # The unique rows of A
767+ C = n.array(d.values()) # The counts of the unique rows
768+
769+ return B,C
770+
771+
772+ #The following function actually takes care of calculating the elements of the kernel matrix from the
773+ #statistics on the collisions.
774+
775+
776+
777+ def kernel_entries_normalized(B2, dist, C2, box_vol, delta_t):
778+ dim=s.shape(B2)
779+ print "dim is, ", dim
780+
781+ n_i=s.zeros(dim[0])
782+ n_j=s.zeros(dim[0])
783+
784+
785+
786+ for i in xrange(len(B2[:,0])):
787+
788+ n_i[i]=len(s.where(dist==B2[i,0])[0])
789+ n_j[i]=len(s.where(dist==B2[i,1])[0])
790+
791+ n_i=n_i/box_vol
792+ n_j=n_j/box_vol
793+
794+
795+
796+ kernel_list=C2/(n_i*n_j*delta_t*box_vol) #I do not get the whole kernel matrix,
797+ #but only the matrix elements for the collisions which actually took place
798+
799+ return kernel_list
800+
801+
802+
803+
804+ #Now a function to calculate the radius of gyration
805+ def calc_radius(x_arr,y_arr,z_arr,Len):
806+ #here x_arr is one-dimensional corresponding to a single configuration
807+ r_0j=s.zeros((len(x_arr)-1))
808+ for j in xrange(1,len(x_arr)): #so, particle zero is now the reference particle
809+ r_0j[j-1]=x_arr[0]-x_arr[j]
810+ r_0j[j-1]=-(r_0j[j-1]-Len*n.round(r_0j[j-1]/Len))
811+ #r_ij[count_int]=-r_ij[count_int] #I have better reverse the signs now.
812+ #print 'i and j are, ', (i+1), (j+1)
813+
814+
815+ #Now I re-define the x_arr in order to be able to take tha variance correctly
816+ x_arr[0]=0.
817+ x_arr[1:n_part]=r_0j
818+
819+ #var_x_arr[:]=s.var(r_0j, axis=1)
820+ var_x_arr=s.var(x_arr)
821+
822+ for j in xrange(1,len(y_arr)): #so, particle zero is now the reference particle
823+ r_0j[j-1]=y_arr[0]-y_arr[j]
824+ r_0j[j-1]=-(r_0j[j-1]-Len*n.round(r_0j[j-1]/Len))
825+ #r_ij[count_int]=-r_ij[count_int] #I have better reverse the signs now.
826+ #print 'i and j are, ', (i+1), (j+1)
827+
828+
829+ #Now I re-define the x_arr in order to be able to take tha variance correctly
830+ y_arr[0]=0.
831+ y_arr[1:n_part]=r_0j
832+
833+ #var_x_arr[:]=s.var(r_0j, axis=1)
834+ var_y_arr=s.var(y_arr)
835+
836+
837+
838+ for j in xrange(1,len(z_arr)): #so, particle zero is now the reference particle
839+ r_0j[j-1]=z_arr[0]-z_arr[j]
840+ r_0j[j-1]=-(r_0j[j-1]-Len*n.round(r_0j[j-1]/Len))
841+ #r_ij[count_int]=-r_ij[count_int] #I have better reverse the signs now.
842+ #print 'i and j are, ', (i+1), (j+1)
843+
844+
845+ #Now I re-define the x_arr in order to be able to take tha variance correctly
846+ z_arr[0]=0.
847+ z_arr[1:n_part]=r_0j
848+
849+ #var_x_arr[:]=s.var(r_0j, axis=1)
850+ var_z_arr=s.var(z_arr)
851+
852+ radius=s.sqrt(var_x_arr+var_y_arr+var_z_arr)
853+ return radius
854+
855+
856+
857+
858+ #Now a function to calculate the positions of the particles in a single cluster
859+ def calc_coord_single(x_arr,y_arr,z_arr,Len):
860+
861+ position_array=s.zeros((len(x_arr),3))
862+
863+ #here x_arr is one-dimensional corresponding to a single configuration
864+ r_0j=s.zeros((len(x_arr)-1))
865+ for j in xrange(1,len(x_arr)): #so, particle zero is now the reference particle
866+ r_0j[j-1]=x_arr[0]-x_arr[j]
867+ r_0j[j-1]=-(r_0j[j-1]-Len*n.round(r_0j[j-1]/Len))
868+ #r_ij[count_int]=-r_ij[count_int] #I have better reverse the signs now.
869+ #print 'i and j are, ', (i+1), (j+1)
870+
871+
872+ #Now I re-define the x_arr in order to be able to take tha variance correctly
873+ x_arr[0]=0.
874+ x_arr[1:n_part]=r_0j
875+
876+ #var_x_arr[:]=s.var(r_0j, axis=1)
877+ #var_x_arr=s.var(x_arr)
878+
879+ position_array[:,0]=x_arr
880+
881+ for j in xrange(1,len(y_arr)): #so, particle zero is now the reference particle
882+ r_0j[j-1]=y_arr[0]-y_arr[j]
883+ r_0j[j-1]=-(r_0j[j-1]-Len*n.round(r_0j[j-1]/Len))
884+ #r_ij[count_int]=-r_ij[count_int] #I have better reverse the signs now.
885+ #print 'i and j are, ', (i+1), (j+1)
886+
887+
888+ #Now I re-define the x_arr in order to be able to take tha variance correctly
889+ y_arr[0]=0.
890+ y_arr[1:n_part]=r_0j
891+
892+ #var_x_arr[:]=s.var(r_0j, axis=1)
893+ #var_y_arr=s.var(y_arr)
894+
895+ position_array[:,1]=y_arr
896+
897+
898+ for j in xrange(1,len(z_arr)): #so, particle zero is now the reference particle
899+ r_0j[j-1]=z_arr[0]-z_arr[j]
900+ r_0j[j-1]=-(r_0j[j-1]-Len*n.round(r_0j[j-1]/Len))
901+ #r_ij[count_int]=-r_ij[count_int] #I have better reverse the signs now.
902+ #print 'i and j are, ', (i+1), (j+1)
903+
904+
905+ #Now I re-define the x_arr in order to be able to take tha variance correctly
906+ z_arr[0]=0.
907+ z_arr[1:n_part]=r_0j
908+
909+ #var_x_arr[:]=s.var(r_0j, axis=1)
910+ #var_z_arr=s.var(z_arr)
911+
912+ position_array[:,2]=z_arr
913+
914+
915+# radius=s.sqrt(var_x_arr+var_y_arr+var_z_arr)
916+
917+ return position_array
918+
919+
920+
921+
922+
923+ #Now I try loading the R script
924+
925+ #r.source("cluster_functions.R")
926+
927+ #I now calculate the number of clusters in each configuration
928+
929+ n_clusters=s.zeros(n_config)
930+ mean_dist_part_single_cluster=s.zeros(n_config)
931+
932+ overall_coord_number=s.zeros(n_config)
933+ v_aver=s.zeros(n_config)
934+ size_single_cluster=s.zeros(n_config)
935+ r_gyr_single_cluster=s.zeros(n_config)
936+ coord_number_single_cluster=s.zeros(n_config)
937+
938+ correct_coord_number=s.zeros(n_config)
939+
940+
941+# min_dist=s.zeros(n_config)
942+ dist_mat=s.zeros((n_part,n_part))
943+ for i in xrange(0,n_config):
944+ read_pos="../1/read_pos_%1d"%my_selection[i]
945+ print "read_pos is, ", read_pos
946+ #cluster_name="cluster_dist%05d"%my_selection[i]
947+ test_arr=p.load(read_pos)
948+ #test_arr=s.reshape(test_arr,(n_part,3))
949+# if (i==14):
950+# p.save("test_14.dat",test_arr)
951+ #dist_mat=r.distance(test_arr)
952+ x_pos=test_arr[:,0]
953+ y_pos=test_arr[:,1]
954+ z_pos=test_arr[:,2]
955+ dist_mat=d_calc.distance_calc(x_pos,y_pos,z_pos, box_size)
956+# if (i==71):
957+# p.save("distance_matrix_71.dat",dist_mat)
958+# p.save("x_pos_71.dat",x_pos)
959+
960+
961+
962+ #clust_struc= (r.mycluster2(dist_mat,threshold)) #a cumbersome
963+ #way to make sure I have an array even when clust_struct is a single
964+ #number (i.e. I have only a cluster)
965+# print'clust_struct is, ', clust_struc
966+# n_clusters[i]=r.mycluster(dist_mat,threshold)
967+
968+
969+# > Lorenzo,
970+# > > if your graph is `g` then
971+# > > degree(g)
972+# > > gives the number of direct neighbors of each vertex (or particle).
973+# Just to translate it to Python: g.degree() gives the number of direct
974+# neighbors of each vertex. If your graph is directed, you may only want
975+# to count only the outgoing or the incoming edges: g.degree(igraph.OUT)
976+# or g.degree(igraph.IN)
977+
978+# > > So
979+# > > mean(degree(g))
980+# In Python: sum(g.degree()) / float(g.vcount())
981+
982+# > > (and to turn an adjacency matrix `am` into an igraph object `g` just
983+# > > use "g <- graph.adjacency(am)")
984+# In Python: g = Graph.Adjacency(matrix)
985+# e.g. g = Graph.Adjacency([[0,1,0],[1,0,1],[0,1,0]])
986+
987+
988+ cluster_obj=ig.Graph.Adjacency((dist_mat <= threshold).tolist(),\
989+ ig.ADJ_UNDIRECTED)
990+
991+ # Now I start the calculation of the coordination number
992+
993+ coord_list=cluster_obj.degree() #Now I have a list with the number of 1rst
994+ #neughbors of each particle
995+
996+ #coord_arr=s.zeros(len(coord_list))
997+
998+
999+
1000+# Lorenzo, i'm not sure why you would like to use adjacency matrices,
1001+# igraph graphs are much better in most cases, espacially if the graph
1002+# is large. Here is how to calculate the mean degree per connected
1003+# component in R, assuming your graph is 'g':
1004+
1005+# comps <- decompose.graph(g)
1006+# sapply(comps, function(x) mean(degree(x)))
1007+
1008+# Gabor
1009+
1010+
1011+
1012+# In Python: starting with your graph, you obtain a VertexClustering
1013+# object somehow (I assume you already have it). Now, cl[0] gives the
1014+# vertex IDs in the first component, cl[1] gives the vertex IDs in the
1015+# second and so on. If the original graph is called g, then
1016+# g.degree(cl[0]) gives the degrees of those vertices, so the average
1017+# degrees in each connected component are as follows (I'm simply
1018+# iterating over the components in a for loop):
1019+
1020+# cl=g.components()
1021+# for idx, component in enumerate(cl):
1022+# degrees = g.degree(component)
1023+# print "Component #%d: %s" % (idx, sum(degrees)/float(len(degrees)))
1024+
1025+# -- T.
1026+
1027+
1028+
1029+
1030+ #I need to get an array out of the list of the list of 1st neighbors
1031+
1032+ #for l in xrange(len(coord_list)):
1033+ #coord_arr[l]=coord_list[l]
1034+
1035+ #print "coord_list is, ", coord_list
1036+ coord_arr=s.asarray(coord_list)-2 #Important! I need this correction!
1037+
1038+ cluster_obj.simplify()
1039+ clustering=cluster_obj.clusters()
1040+ #n_clusters[i]=p.load("nc_temp.dat")
1041+ n_clusters[i]=len(clustering)
1042+ print "the total number of clusters and my_config are,", n_clusters[i], my_selection[i]
1043+
1044+ my_cluster=clustering.sizes()
1045+
1046+ #Now I create the array which will contain all the cluster sizes by changing the previous
1047+ #list into a scipy array.
1048+
1049+
1050+
1051+ my_cluster=s.asarray(my_cluster)
1052+
1053+
1054+
1055+ #Now I re-organize the particles in my configuration
1056+ #by putting together those which are in the same
1057+ #cluster
1058+ clust_struc=clustering.membership
1059+ clust_struc=s.asarray(clust_struc)
1060+ #if (i==0):
1061+# print "clust_struc[4727] and my_selection[i] are, ", clust_struc[4727], my_selection[i]
1062+# print "clust_struc[29] and my_selection[i] are, ", clust_struc[29], my_selection[i]
1063+
1064+
1065+ part_in_clust=s.argsort(clust_struc)
1066+ if (i ==0 ):
1067+ #cluster_record_old=part_in_clust
1068+ part_in_clust_old=s.copy(part_in_clust)
1069+ len_my_cluster_old=len(my_cluster)
1070+ my_cluster_old=s.copy(my_cluster)
1071+
1072+
1073+
1074+ coord_number_dist=s.zeros(len(my_cluster)) #this will contain the distribution of
1075+ #the coordination numbers
1076+
1077+
1078+ my_sum=s.cumsum(my_cluster)
1079+ f=s.arange(1) #simply 0 but as an array!
1080+ my_lim=s.concatenate((f,my_sum))
1081+ if (i==0):
1082+ my_lim_old=my_lim
1083+
1084+
1085+
1086+ for m in xrange(0,len(my_cluster)):
1087+ #if (abs(my_lim[m]-my_lim[m+1])<2):
1088+ # r_gyr_dist[m]=0.0 # r_gyr_dist has already been initialized to zero!
1089+ if (abs(my_lim[m]-my_lim[m+1])>=2):
1090+
1091+ x_pos2=x_pos[part_in_clust[my_lim[m]:my_lim[m+1]]]
1092+ y_pos2=y_pos[part_in_clust[my_lim[m]:my_lim[m+1]]]
1093+ z_pos2=z_pos[part_in_clust[my_lim[m]:my_lim[m+1]]]
1094+
1095+
1096+ coord_clust=coord_arr[part_in_clust[my_lim[m]:my_lim[m+1]]]
1097+ #print "coord_clust is, ", coord_clust
1098+ coord_number_dist[m]=s.mean(coord_clust)
1099+
1100+ correct_coord_number[i]=coord_number_dist.mean() #i.e. : associate to each cluster a coordination number and
1101+ #then take the average on the set of coordination numbers
1102+ # (one per cluster)
1103+
1104+p.save("coordination_numbers_averaged.dat", correct_coord_number )
1105+
1106+
1107+print "So far so good"