map.py

   1 import matplotlib as mpl
   2 import matplotlib.mlab as mlab
   3 import matplotlib.pyplot as plt
   4 import matplotlib.axes as axe
   5 import matplotlib.collections as col
   6 import numpy as np
   7 import random
   8 from pylab import *
   9 from matplotlib.pyplot import *
  10
  11 FIN = '../data/dual_bif_alpha_l_55_65_1_4_f0005.dat'
  12 FOUT = '../db5.png'
  13
  14 samples = 10
  15 ticksize = 14
  16 labelsize = 24
  17 title = "a = 1.15, w = 0.1, F = 0.00, eta1 = 1.0, eta2 = 1.5, theta = 0"
  18 xlabel = r'$\alpha$'
  19 ylabel = r'$l$'
  20
  21 xtfreq = 2
  22 ytfreq = 1
  23 xpad = 0.01
  24 ypad = 0.01
  25 ms = 30
  26 ystep = 0.1
  27 xstep = 0.1
  28 res = (1024/80,768/80) # default dpi is 80
  29 method = 'direct' # mean, direct
  30 has_y = False
  31 has_col = False
  32 has_bias = False
  33
  34 ################################################################################
  35
  36 def assign_label(color):
  37         ret_label = 'DUMMY'
  38         if (color == 'grey'):
  39                 ret_label = r'$|v| = 0$'
  40         if (color == 'blue'):
  41                 ret_label = r'$|v| = -1$'
  42         if (color == 'red'):
  43                 ret_label = r'$|v| = 1$'
  44         if (color == 'orange'):
  45                 ret_label = r'$|v| = 0.5$'
  46         if (color == 'green'):
  47                 ret_label = r'$|v| = -0.5$'
  48         if (color == 'black'):
  49                 ret_label = r'other rational $v$'
  50         if (color == 'cyan'):
  51                 ret_label = r'$v < 0$'
  52         if (color == 'magenta'):
  53                 ret_label = r'$v > 0$'
  54         return (ret_label)
  55
  56 def assign_color2(colors,xar,yar,vel,clist):
  57         print "---------------------------------------------------------"
  58         unique_colors = unique(colors)
  59         #print range(len(clist))
  60         ret_xcolors = [ [] for DUMMYVAR in range(len(clist)) ]
  61         ret_ycolors = [ [] for DUMMYVAR in range(len(clist)) ]
  62         i = 0
  63         assert len(xar) == len(yar) == len(colors)
  64         xarlen_trunc = len(xar)
  65         while i < xarlen_trunc:
  66                 tmp = []
  67                 tmpv = []
  68                 for j in range(samples):
  69                         assert i+j <= len(xar)
  70                         #assert yar[i+j] == yar[i] and xar[i+j] == xar[i]
  71                         tmp.append(colors[i+j])
  72                         tmpv.append(vel[i+j])
  73 #               print len(tmp), samples
  74                 assert len(tmp) == samples
  75                 ccount = len(unique(tmp))
  76                 if ccount == 2 and 'green' in tmp and 'orange' in tmp:
  77                         ret_xcolors[6].append(xar[i])
  78                         ret_ycolors[6].append(yar[i])
  79                 elif ccount == 2 and 'blue' in tmp and 'red' in tmp:
  80                         ret_xcolors[7].append(xar[i])
  81                         ret_ycolors[7].append(yar[i])
  82                 elif ccount == 2 and 'blue' in tmp and 'black' in tmp:
  83                         if tmp.count('blue') >= samples/2:
  84                                 ret_xcolors[2].append(xar[i])
  85                                 ret_ycolors[2].append(yar[i])
  86                         else:
  87                                 ret_xcolors[0].append(xar[i])
  88                                 ret_ycolors[0].append(yar[i])
  89                 elif ccount == 2 and 'red' in tmp and 'black' in tmp:
  90                         if tmp.count('red') >= samples/2:
  91                                 ret_xcolors[5].append(xar[i])
  92                                 ret_ycolors[5].append(yar[i])
  93                         else:
  94                                 ret_xcolors[0].append(xar[i])
  95                                 ret_ycolors[0].append(yar[i])
  96                 elif ccount == 2 and 'green' in tmp and 'black' in tmp:
  97                         if tmp.count('green') >= samples/2:
  98                                 ret_xcolors[3].append(xar[i])
  99                                 ret_ycolors[3].append(yar[i])
 100                         else:
 101                                 ret_xcolors[0].append(xar[i])
 102                                 ret_ycolors[0].append(yar[i])
 103                 elif ccount == 2 and 'orange' in tmp and 'black' in tmp:
 104                         if tmp.count('orange') >= samples/2:
 105                                 ret_xcolors[4].append(xar[i])
 106                                 ret_ycolors[4].append(yar[i])
 107                         else:
 108                                 ret_xcolors[0].append(xar[i])
 109                                 ret_ycolors[0].append(yar[i])
 110                 elif ccount == 3 and 'green' in tmp and 'orange' in tmp and 'grey':
 111                         ret_xcolors[6].append(xar[i])
 112                         ret_ycolors[6].append(yar[i])
 113                 elif ccount == 3 and 'blue' in tmp and 'red' in tmp and 'grey':
 114                         ret_xcolors[7].append(xar[i])
 115                         ret_ycolors[7].append(yar[i])
 116                 elif ccount == 4 and 'blue' in tmp and 'red' in tmp and 'green' in tmp and 'orange' in tmp:
 117                         print tmp
 118                         ret_xcolors[11].append(xar[i])
 119                         ret_ycolors[11].append(yar[i])
 120                 elif ccount == 4 and 'green' in tmp and 'orange' in tmp and 'grey' in tmp and 'black' in tmp:
 121                         ret_xcolors[6].append(xar[i])
 122                         ret_ycolors[6].append(yar[i])
 123                 elif ccount == 4 and 'blue' in tmp and 'red' in tmp and 'grey' in tmp and 'black' in tmp:
 124                         ret_xcolors[7].append(xar[i])
 125                         ret_ycolors[7].append(yar[i])
 126                 elif ccount == 1 and 'grey' in tmp:
 127                         ret_xcolors[1].append(xar[i])
 128                         ret_ycolors[1].append(yar[i])
 129                 elif ccount == 1 and 'blue' in tmp:
 130                         ret_xcolors[2].append(xar[i])
 131                         ret_ycolors[2].append(yar[i])
 132                 elif ccount == 1 and 'green' in tmp:
 133                         ret_xcolors[3].append(xar[i])
 134                         ret_ycolors[3].append(yar[i])
 135                 elif ccount == 1 and 'orange' in tmp:
 136                         ret_xcolors[4].append(xar[i])
 137                         ret_ycolors[4].append(yar[i])
 138                 elif ccount == 1 and 'red' in tmp:
 139                         ret_xcolors[5].append(xar[i])
 140                         ret_ycolors[5].append(yar[i])
 141                 elif len(unique(tmpv)) >= samples:
 142                         #print unique(tmpv)
 143                         ret_xcolors[0].append(xar[i])
 144                         ret_ycolors[0].append(yar[i])
 145                 elif ccount == 1 and 'black' in tmp:
 146                         ret_xcolors[0].append(xar[i])
 147                         ret_ycolors[0].append(yar[i])
 148                 else:
 149                         print unique(tmp)
 150                         #print unique(tmpv)
 151                         ret_xcolors[0].append(xar[i])
 152                         ret_ycolors[0].append(yar[i])
 153                 i = i+samples
 154                 #print "increment:",i
 155         for j  in range(len(clist)):
 156                 print clist[j],":",len(ret_xcolors[j])
 157         #print "unmatched velocities:"
 158         #print ret_xcolors[13], ret_ycolors[13]
 159         print "---------------------------------------------------------"
 160         return (ret_xcolors, ret_ycolors)
 161
 162 def assign_color(colors,xar,yar):
 163         print "---------------------------------------------------------"
 164         unique_colors = unique(colors)
 165         ret_xcolors = [ [] for DUMMYVAR in range(len(unique_colors)) ]
 166         ret_ycolors = [ [] for DUMMYVAR in range(len(unique_colors)) ]
 167         for j in range(len(unique_colors)):
 168                 tmpx = []
 169                 tmpy = []
 170                 k = 0
 171                 for i in range(len(colors)):
 172                         if (colors[i] == unique_colors[j]):
 173                                 k = k+1
 174                                 tmpxcolors = ret_xcolors[j]
 175                                 tmpycolors = ret_ycolors[j]
 176                                 #print tmpxcolors
 177                                 if len(tmpxcolors) > 0:
 178                                         if (tmpxcolors[-1] != xar[i]) or (tmpycolors[-1] != yar[i]):
 179                                                 #print tmpxcolors[-1],tmpycolors[-1],"is not", xar[i],yar[i],"!"
 180                                                 ret_xcolors[j].append(xar[i])
 181                                                 ret_ycolors[j].append(yar[i])
 182                                 else:
 183                                         ret_xcolors[j].append(xar[i])
 184                                         ret_ycolors[j].append(yar[i])
 185                 lenc = float(len(colors))
 186                 per = 100*k/lenc
 187                 print unique_colors[j],":",len(ret_xcolors[j]),k,"(",'%.2f' % per ,"%)"
 188         print "---------------------------------------------------------"
 189         return (ret_xcolors, ret_ycolors)
 190
 191 print "reading data..."
 192 r  = mlab.csv2rec(FIN, delimiter='\t', comments='#')
 193
 194 x = r.x
 195 x_color = r.vx_color
 196 y_color = r.vy_color
 197 y = r.y
 198 vx = r.vx
 199 vy = r.vy
 200
 201 assert len(x) == len(y) == len(vx) == len(vy)
 202 print "done."
 203
 204 xmin = np.min(unique(x))
 205 xmax = np.max(unique(x))
 206 ymin = np.min(unique(y))
 207 ymax = np.max(unique(y))
 208
 209 # assumes that the step distance doesn't change over the course of time
 210 rat = xstep/ystep
 211 print "size of xstep:", xstep
 212 print "size of ystep:", ystep
 213 print "aspect ratio:",rat
 214
 215 print "different values of vx:"
 216 print unique(vx),"(",len(vx),"total)"
 217
 218 print "different values of vy:"
 219 print unique(vy),"(",len(vy),"total)"
 220
 221 # needed for assign_color2
 222 color_list = [
 223                 'black',        # 0     everything else
 224                 'gray',         # 1     v = 0
 225                 'blue',         # 2     v = -1.0
 226                 'green',        # 3     v = -0.5
 227                 'orange',       # 4     v = 0.5
 228                 'red',          # 5     v = 1.0
 229                 'khaki',        # 6     v = -0.5 && v = 0.5
 230                 'purple',       # 7     v = -1.0 && v = 1.0
 231                 'white',        # 8     not understood
 232                 'darkgoldenrod',# 9     v = +/- 0.5 & 0
 233                 'mediumpurple', # 10    v = +/- 1 1 & -0
 234                 'brown',        # 11    v = +/1 1 & +/- 0.5
 235         ]
 236
 237 label_list = [
 238                 r'other rational $v$',  # 0 black
 239                 r'$v = 0$',                     # 1 grey
 240                 r'$v = -1$',                    # 2 blue
 241                 r'$v = -\frac{1}{2}$',          # 3 green
 242                 r'$v = \frac{1}{2}$',           # 4 orange
 243                 r'$v = 1$',                     # 5 red
 244                 r'$v = \pm \frac{1}{2}$',       # 6 brown
 245                 r'$v = \pm 1$',                 # 7 purple
 246                 r'???',                         # 8 white
 247                 r'$v = \pm \frac{1}{2},$ other',# 9 darkgoldenrod
 248                 r'$v = \pm 1,$ other ',         # 10 mediumpurple
 249                 r'$v = \pm 1,\pm \frac{1}{2}$', # 11 brown
 250         ]
 251
 252 xpadmin = xmin-xstep
 253 xpadmax = xmax+xstep
 254 ypadmin = ymin-ystep
 255 ypadmax = ymax+ystep
 256 xlim = (xpadmin, xpadmax)               # small padding between plot and
 257 ylim = (ypadmin, ypadmax)               # axis so they don't overlap
 258 xticks = np.arange(xmin, xmax+xpad, xtfreq)
 259 yticks = np.arange(ymin, ymax+ypad, ytfreq)
 260
 261
 262 fig = plt.figure() #(figsize=res)
 263 #fig.suptitle(title, size=labelsize)
 264
 265 max = 0.84
 266 c = max/2
 267 rect1 = [0.1, 0.1, c, 0.9]
 268 rect2 = [c+0.1, 0.1, c, 0.9]
 269
 270 if has_y is True:
 271         ax1 = fig.add_subplot(121, aspect=rat)
 272         ax2 = fig.add_subplot(122, aspect=rat)
 273 else:
 274         ax1 = fig.add_subplot(111, aspect=rat)
 275
 276 if (method == 'mean'):
 277
 278         print "assigning colors...."
 279         xcolors_x,xcolors_y = assign_color(x_color,x,y)
 280         ycolors_x,ycolors_y = assign_color(y_color,x,y)
 281         print "done."
 282
 283         print "plotting..."
 284         for k in range(len(unique(x_color))):
 285                 x_xtli = xcolors_x[k]
 286                 x_ytli = xcolors_y[k]
 287                 labelx = assign_label(unique(x_color)[k])
 288                 ax1.scatter(x_xtli, x_ytli, marker='s', lw=0, s=ms, alpha=0.5,
 289                                 c = unique(x_color)[k], label=labelx,
 290                                 edgecolor='face')
 291         if (has_y is True):
 292                 for k in range(len(unique(y_color))):
 293                         y_xtli = ycolors_x[k]
 294                         y_ytli = ycolors_y[k]
 295                         labely = assign_label(unique(y_color)[k])
 296                         ax2.scatter(y_xtli, y_ytli, marker='s', lw=0,
 297                                         s=ms, alpha=0.5, c =
 298                                         unique(y_color)[k],
 299                                         label=labely, edgecolor='none')
 300
 301         print "done."
 302
 303 elif (method == 'direct'):
 304
 305         print "assigning colors..."
 306         xcolors_x,xcolors_y = assign_color2(x_color,x,y,vx,color_list)
 307         ycolors_x,ycolors_y = assign_color2(y_color,x,y,vy,color_list)
 308         print "done."
 309
 310         #for i in range(8):
 311         #       print i, xcolors_x[i],xcolors_y[i];
 312
 313         print "plotting..."
 314         for k in range(len(xcolors_x)):
 315                 if len(xcolors_x[k]) > 0:
 316                         x_xtli = xcolors_x[k]
 317                         x_ytli = xcolors_y[k]
 318                         ax1.scatter(x_xtli, x_ytli, marker='s', lw=0,
 319
 320                                         s=ms, alpha=1, c=color_list[k],
 321                                         label=label_list[k])
 322
 323         if (has_y is True):
 324                 for k in range(len(ycolors_x)):
 325                         if len(ycolors_x[k]) > 0:
 326                                 y_xtli = ycolors_x[k]
 327                                 y_ytli = ycolors_y[k]
 328                                 ax2.scatter(y_xtli, y_ytli, marker='s',
 329                                                 lw=0, s=ms, alpha=1,
 330                                                 c=color_list[k],
 331                                                 label=label_list[k])
 332
 333         print "done."
 334 else:
 335         print "fatal error: specify a valid method"
 336
 337 leg1 = ax1.legend(loc=0, shadow=True,
 338                 fancybox=True, scatterpoints=1, markerscale=1,
 339                 borderaxespad=0.)
 340 for t in leg1.get_texts():
 341         t.set_fontsize('small')
 342
 343 if has_y is True:
 344         leg2 = ax2.legend(loc=2, shadow=True,
 345                         fancybox=True, scatterpoints=1, markerscale=1,
 346                         borderaxespad=0.)
 347         for t in leg2.get_texts():
 348                 t.set_fontsize('small')
 349
 350
 351 ax1.set_title(r'$v_x$', size=labelsize)
 352 ax1.set_xlim(xlim)
 353 ax1.set_ylim(ylim)
 354 ax1.set_xlabel(xlabel, size=labelsize)
 355 ax1.set_ylabel(ylabel, size=labelsize)
 356 ax1.set_xticks(xticks, minor=False)
 357 ax1.set_yticks(yticks, minor=False)
 358 ax1.set_xticklabels(ax1.get_xticks(), size=ticksize)
 359 ax1.set_yticklabels(ax1.get_yticks(), size=ticksize)
 360
 361 if has_y is True:
 362         ax2.set_title(r'$v_y$', size=labelsize)
 363         ax2.set_xlim(xlim)
 364         ax2.set_ylim(ylim)
 365         ax2.set_xlabel(xlabel, size=labelsize)
 366         ax2.set_ylabel('', size=labelsize)
 367         ax2.set_xticks(xticks, minor=False)
 368         ax2.set_yticks(yticks, minor=False)
 369         ax2.set_xticklabels(ax2.get_xticks(), size=ticksize)
 370         ax2.set_yticklabels('', size=ticksize)
 371
 372 if (has_col is True):
 373         s1 = np.arange(54.5,66,0.5)
 374         collection = col.BrokenBarHCollection.span_where(s1, ymin=0.95, ymax=4.05,
 375                 where=s1>54, facecolor='None', edgecolor='red')
 376         ax1.add_collection(collection)
 377
 378
 379 leg1.get_frame().set_alpha(0.75)
 380
 381 if has_y is True:
 382         leg2.get_frame().set_alpha(0.75)
 383
 384
 385 plt.subplots_adjust(hspace=0)
 386 print "flushing into a file..."
 387 plt.savefig(FOUT)
 388 print "done."
 389 #plt.show()
 390
 391
 392
 393