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 collections
   6 import numpy as np
   7 import random
   8 from pylab import *
   9 from matplotlib.pyplot import *
  10
  11 FIN = 'dual_bif.dat'
  12 FOUT = 'map_alphaltheta45f01.png'
  13
  14 samples = 3
  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 tfreq = 10
  21 ms = 40
  22 res = (1440/80,900/80) # default dpi is 80
  23 method = 'direct' # mean, direct
  24
  25 ################################################################################
  26
  27 def assign_label(color):
  28         ret_label = 'DUMMY'
  29         if (color == 'grey'):
  30                 ret_label = r'$|v| = 0$'
  31         if (color == 'blue'):
  32                 ret_label = r'$|v| = -1$'
  33         if (color == 'red'):
  34                 ret_label = r'$|v| = 1$'
  35         if (color == 'orange'):
  36                 ret_label = r'$|v| = 0.5$'
  37         if (color == 'green'):
  38                 ret_label = r'$|v| = -0.5$'
  39         if (color == 'black'):
  40                 ret_label = r'other rational $v$'
  41         return (ret_label)
  42
  43 def assign_color2(colors,xar,yar):
  44         """
  45         number  velocity
  46         0       everything else
  47         1       v = 0
  48         2       v = -1.0
  49         3       v = -0.5
  50         4       v = 0.5
  51         5       v = 1.0
  52         6       v = -0.5 && v = 0.5
  53         7       v = -1.0 && v = 1.0
  54         """
  55         #print "---------------------------------------------------------"
  56         unique_colors = unique(colors)
  57         ret_xcolors = [ [] for DUMMYVAR in range(8) ]
  58         ret_ycolors = [ [] for DUMMYVAR in range(8) ]
  59         i = 0
  60         assert len(xar) == len(yar) == len(colors)
  61         xarlen_trunc = len(xar)
  62         while (i < xarlen_trunc):
  63                 #print len(xar)
  64                 #print xarlen_trunc
  65                 tmp = []
  66                 for j in range(samples):
  67                         #print i,j,i+j
  68                         assert i+j <= len(xar)
  69                         assert yar[i+j] == yar[i] and xar[i+j] == xar[i]
  70                         tmp.append(colors[i+j])
  71                 assert len(tmp) == samples
  72                 if 'green' in tmp and 'orange' in tmp:
  73                         ret_xcolors[6].append(xar[i])
  74                         ret_ycolors[6].append(yar[i])
  75                 elif 'blue' in tmp and 'red' in tmp:
  76                         ret_xcolors[7].append(xar[i])
  77                         ret_ycolors[7].append(yar[i])
  78                 elif len(unique(tmp)) == 1 and 'grey' in tmp:
  79                         ret_xcolors[1].append(xar[i])
  80                         ret_ycolors[1].append(yar[i])
  81                 elif len(unique(tmp)) == 1 and 'blue' in tmp:
  82                         ret_xcolors[2].append(xar[i])
  83                         ret_ycolors[2].append(yar[i])
  84                 elif len(unique(tmp)) == 1 and 'green' in tmp:
  85                         ret_xcolors[3].append(xar[i])
  86                         ret_ycolors[3].append(yar[i])
  87                 elif len(unique(tmp)) == 1 and 'orange' in tmp:
  88                         ret_xcolors[4].append(xar[i])
  89                         ret_ycolors[4].append(yar[i])
  90                 elif len(unique(tmp)) == 1 and 'red' in tmp:
  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                 i = i+samples
  97                 #print "increment:",i
  98         #print "---------------------------------------------------------"
  99         return (ret_xcolors, ret_ycolors)
 100
 101 def assign_color(colors,xar,yar):
 102         print "---------------------------------------------------------"
 103         unique_colors = unique(colors)
 104         ret_xcolors = [ [] for DUMMYVAR in range(len(unique_colors)) ]
 105         ret_ycolors = [ [] for DUMMYVAR in range(len(unique_colors)) ]
 106         for j in range(len(unique_colors)):
 107                 tmpx = []
 108                 tmpy = []
 109                 k = 0
 110                 for i in range(len(colors)):
 111                         if (colors[i] == unique_colors[j]):
 112                                 k = k+1
 113                                 tmpxcolors = ret_xcolors[j]
 114                                 tmpycolors = ret_ycolors[j]
 115                                 #print tmpxcolors
 116                                 if len(tmpxcolors) > 0:
 117                                         if (tmpxcolors[-1] != xar[i]) or (tmpycolors[-1] != yar[i]):
 118                                                 #print tmpxcolors[-1],tmpycolors[-1],"is not", xar[i],yar[i],"!"
 119                                                 ret_xcolors[j].append(xar[i])
 120                                                 ret_ycolors[j].append(yar[i])
 121                                 else:
 122                                         ret_xcolors[j].append(xar[i])
 123                                         ret_ycolors[j].append(yar[i])
 124                 lenc = float(len(colors))
 125                 per = 100*k/lenc
 126                 print unique_colors[j],":",len(ret_xcolors[j]),k,"(",'%.2f' % per ,"%)"
 127         print "---------------------------------------------------------"
 128         return (ret_xcolors, ret_ycolors)
 129
 130 r  = mlab.csv2rec(FIN, delimiter='\t', comments='#')
 131
 132 x = r.x
 133 x_color = r.vx_color
 134 y_color = r.vy_color
 135 y = r.y
 136 vx = r.vx
 137 vy = r.vy
 138
 139 assert len(x) == len(y) == len(vx) == len(vy)
 140
 141 xmin = np.min(unique(x))
 142 xmax = np.max(unique(x))
 143 ymin = np.min(unique(y))
 144 ymax = np.max(unique(y))
 145
 146 # assumes that the step distance doesn't change over the course of time
 147 ystep = 0.1
 148 xstep = 1
 149 rat = xstep/ystep
 150 print "size of xstep:", xstep
 151 print "size of ystep:", ystep
 152 print "aspect ratio:",rat
 153
 154 print "different values of vx:"
 155 print unique(vx),"(",len(vx),"total)"
 156
 157 print "different values of vy:"
 158 print unique(vy),"(",len(vy),"total)"
 159
 160 # needed for assign_color2
 161 color_list = [
 162                 'black',        # 0     everything else
 163                 'grey',         # 1     v = 0
 164                 'blue',         # 2     v = -1.0
 165                 'green',        # 3     v = -0.5
 166                 'orange',       # 4     v = 0.5
 167                 'red',          # 5     v = 1.0
 168                 'teal',         # 6     v = -0.5 && v = 0.5
 169                 'purple'        # 7     v = -1.0 && v = 1.0
 170         ]
 171
 172 label_list = [
 173                 r'other rational $v$',  # 0 black
 174                 r'$v = 0$',             # 1 grey
 175                 r'$v| = -1$',           # 2 blue
 176                 r'$v = -0.5$',          # 3 green
 177                 r'$v = 0.5$',           # 4 orange
 178                 r'$v = 1.0$',           # 5 red
 179                 r'$v = \pm 0.5$',       # 6 teal
 180                 r'$v = \pm 1.0$'        # 7 purple
 181         ]
 182
 183 xpadmin = xmin-xstep
 184 xpadmax = xmax+xstep
 185 ypadmin = ymin-ystep
 186 ypadmax = ymax+ystep
 187 xlim = (xpadmin, xpadmax)               # small padding between plot and
 188 ylim = (ypadmin, ypadmax)               # axis so they don't overlap
 189 xticks = np.linspace(xmin, xmax, tfreq)
 190 yticks = np.linspace(ymin, ymax+ystep, tfreq)
 191
 192
 193 fig = plt.figure(figsize=res)
 194 fig.suptitle(title, size=labelsize)
 195
 196 max = 0.84
 197 c = max/2
 198 rect1 = [0.1, 0.1, c, 0.9]
 199 rect2 = [c+0.1, 0.1, c, 0.9]
 200
 201 ax1 = fig.add_axes(rect1, aspect=rat)
 202 ax2 = fig.add_axes(rect2, aspect=rat)
 203
 204 if (method == 'mean'):
 205
 206         xcolors_x,xcolors_y = assign_color(x_color,x,y)
 207         ycolors_x,ycolors_y = assign_color(y_color,x,y)
 208
 209         for k in range(len(unique(x_color))):
 210                 x_xtli = xcolors_x[k]
 211                 x_ytli = xcolors_y[k]
 212                 labelx = assign_label(unique(x_color)[k])
 213                 ax1.scatter(x_xtli, x_ytli, marker='s', lw=0, s=ms, alpha=0.5,
 214                                 c = unique(x_color)[k], label=labelx)
 215
 216         for k in range(len(unique(y_color))):
 217                 y_xtli = ycolors_x[k]
 218                 y_ytli = ycolors_y[k]
 219                 labely = assign_label(unique(y_color)[k])
 220                 ax2.scatter(y_xtli, y_ytli, marker='s', lw=0, s=ms, alpha=0.5,
 221                                 c = unique(y_color)[k], label=labely)
 222
 223 elif (method == 'direct'):
 224
 225         xcolors_x,xcolors_y = assign_color2(x_color,x,y)
 226         ycolors_x,ycolors_y = assign_color2(y_color,x,y)
 227
 228         for k in range(len(color_list)):
 229                 x_xtli = xcolors_x[k]
 230                 x_ytli = xcolors_y[k]
 231                 #labelx = assign_label(color_list)[k]
 232                 ax1.scatter(x_xtli, x_ytli, marker='s', lw=0, s=ms, alpha=0.5,
 233                                 c = color_list[k], label=label_list[k])
 234
 235         for k in range(len(color_list)):
 236                 y_xtli = ycolors_x[k]
 237                 y_ytli = ycolors_y[k]
 238                 #labely = assign_label(color_list[k])
 239                 ax2.scatter(y_xtli, y_ytli, marker='s', lw=0, s=ms, alpha=0.5,
 240                                 c = color_list[k], label=label_list[k])
 241
 242 else:
 243         print "fatal error: specify a valid method"
 244
 245 leg1 = ax1.legend(loc=0, shadow=True, fancybox=True, scatterpoints=1,
 246                 markerscale=1)
 247 leg2 = ax2.legend(loc=0, shadow=True, fancybox=True, scatterpoints=1,
 248                 markerscale=1)
 249 for t in leg1.get_texts():
 250         t.set_fontsize('small')
 251 for t in leg2.get_texts():
 252         t.set_fontsize('small')
 253
 254 ax1.set_title(r'$v_x$', size=labelsize)
 255 ax1.set_xlim(xlim)
 256 ax1.set_ylim(ylim)
 257 ax1.set_xlabel(xlabel, size=labelsize)
 258 ax1.set_ylabel(ylabel, size=labelsize)
 259 ax1.set_xticks(xticks, minor=False)
 260 ax1.set_yticks(yticks, minor=False)
 261 ax1.set_xticklabels(ax1.get_xticks(), size=ticksize)
 262 ax1.set_yticklabels(ax1.get_yticks(), size=ticksize)
 263
 264 ax2.set_title(r'$v_y$', size=labelsize)
 265 ax2.set_xlim(xlim)
 266 ax2.set_ylim(ylim)
 267 ax2.set_xlabel(xlabel, size=labelsize)
 268 ax2.set_ylabel('', size=labelsize)
 269 ax2.set_xticks(xticks, minor=False)
 270 ax2.set_yticks(yticks, minor=False)
 271 ax2.set_xticklabels(ax2.get_xticks(), size=ticksize)
 272 ax2.set_yticklabels('', size=ticksize)
 273
 274 leg1.get_frame().set_alpha(0.75)
 275 leg2.get_frame().set_alpha(0.75)
 276
 277 plt.subplots_adjust(hspace=0)
 278 plt.savefig(FOUT)
 279 plt.show()
 280
 281
 282
 283