title: CDF, matplotlib - not enough colors for plot, python
body: <p>Here is needed to plot CDF for 8 different functions in one plot. The problem that it gives just 7 different colors and the 8 one gives just first blue color again. How to make 8 different colors?</p>
  
  <p>Here is the script:</p>
  
  <pre><code>locerror_2d=[Scan_Around[1],Triangle_Around[1],M_shape_Around[1],Hilbert_Around[1],Scan_SbS[1],Triangle_SbS[1],M_shape_SbS[1],Hilbert_SbS[1]]
  
  
  # N = len(locerror_2d[0]) #same for all ( here, I hope so... )
  # N1=len(locerror_2d[2])
  H_cent,h_cent1 = np.histogram( locerror_2d[0], bins = 10, normed = True ) # Random Walk Centroid
  hy_cent = np.cumsum(H_cent)*(h_cent1[1] - h_cent1[0])
  
  H_1st,h_1st = np.histogram( locerror_2d[1], bins = 10, normed = True ) # Random Walk Weighterd
  hy_1st = np.cumsum(H_1st)*(h_1st[1] - h_1st[0])
  
  H_2nd,h_2nd = np.histogram( locerror_2d[2], bins = 10, normed = True ) # Circle Walk Centroid
  hy_2nd = np.cumsum(H_2nd)*(h_2nd[1] - h_2nd[0])
  
  H_3rd,h_3rd = np.histogram( locerror_2d[3], bins = 10, normed = True ) # Circle Walk Weighterd
  hy_3rd = np.cumsum(H_3rd)*(h_3rd[1] - h_3rd[0])
  
  H_mm,h_mm = np.histogram( locerror_2d[4], bins = 10, normed = True ) # G Walk Centroid
  hy_mm = np.cumsum(H_mm)*(h_mm[1] - h_mm[0])
  
  H_shr,h_shr = np.histogram( locerror_2d[5], bins = 10, normed = True ) # G Walk Weighterd
  hy_shr = np.cumsum(H_shr)*(h_shr[1] - h_shr[0])
  
  H_s,h_s = np.histogram( locerror_2d[6], bins = 10, normed = True ) # G Walk Weighterd
  hy_s = np.cumsum(H_s)*(h_s[1] - h_s[0])
  
  H_sh,h_sh = np.histogram( locerror_2d[7], bins = 10, normed = True ) # G Walk Weighterd
  hy_sh = np.cumsum(H_sh)*(h_sh[1] - h_sh[0])
  
  
  plt.hold(True)
  ddd_hist_cent, = plt.plot(h_cent1[1:], hy_cent,label="Scan_Around")        # centroid
  ddd_hist_1st, = plt.plot(h_1st[1:], hy_1st,label='Triangle_Around')       #Gradient
  ddd_circ_cent, = plt.plot(h_2nd[1:], hy_cent,label="M_shape_around")        # centroid
  ddd_circ_wei, = plt.plot(h_3rd[1:], hy_1st,label='Hilbert_Around')       #Gradient
  ddd_g_cent, = plt.plot(h_mm[1:], hy_cent,label="Scan_SbS")        # centroid
  ddd_g_wei, = plt.plot(h_shr[1:], hy_1st,label='Triangle_SbS')       #Gradient
  ddd_g_w, = plt.plot(h_s[1:], hy_cent,label='M_shape_SbS')
  ddd_g_we, = plt.plot(h_sh[1:], hy_1st,label='Hilbert_SbS')
  
  plt.hold(False)
  
  plt.rc('legend',**{'fontsize':10})
  plt.legend(handles=[ddd_hist_cent, ddd_hist_1st, ddd_circ_cent, ddd_circ_wei, ddd_g_cent,ddd_g_wei, ddd_g_w],loc='center left', bbox_to_anchor=(0.75, 0.18)) #no trilateration here
  plt.ylabel('Probability')
  plt.xlabel('Localization Error, m')
  plt.ylim(ymax = 1.1, ymin = 0)
  plt.title('Path Planning Algorithms')
  plt.grid()
  plt.show()
  </code></pre>
  
  <p>Thank you</p>