Cheers
Script used to create here: import matplotlib.pyplot as plt import matplotlib.ticker as tick from numpy import load, sqrt, shape, size, loadtxt, transpose def clear_spines(ax): ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') def set_spineLineWidth(ax, lineWidth): for i in ax.spines.keys(): ax.spines[i].set_linewidth(lineWidth) def showOnlySomeTicks(x, pos): s = str(int(x)) if x == 5000: return '5e3'#'%.0e' % x return '' plt.close('all') golden_mean = (sqrt(5)-1.0)/2.0 # Aesthetic ratio fig_width = fig_width_pt*inches_per_pt # width in inches fig_height = fig_width*golden_mean # height in inches fig_size = [fig_width,fig_height] tick_size = 9 fontlabel_size = 10.5params = {'backend': 'wxAgg', 'axes.labelsize': fontlabel_size, 'text.fontsize': fontlabel_size, 'legend.fontsize': fontlabel_size, 'xtick.labelsize': tick_size, 'ytick.labelsize': tick_size, 'text.usetex': True, 'figure.figsize': fig_size}
plt.rcParams.update(params) sizeX = storeMat[0].size fig = plt.figure(1) #figure(num=None, figsize=(8, 6), dpi=80, facecolor='w', edgecolor='k') #fig.set_size_inches(fig_size) plt.clf() ax = plt.axes([0.145,0.18,0.95-0.155,0.95-0.2]) plt.plot(storeMat[0][::2],storeMat[1][::2]/300.*100,'ko',markersize=3.5) #plt.plot(storeMat[0][::2],storeMat[1][::2]/300.*100,'k') plt.ylim(0,102) plt.xlabel('Number of Channels') plt.ylabel('Recognition Accuracy') set_spineLineWidth(ax,spineLineWidth) clear_spines(ax) ax.yaxis.set_ticks_position('left') ax.xaxis.set_ticks_position('bottom') #ax.xaxis.set_minor_formatter(tick.FuncFormatter(showOnlySomeTicks)) #plt.legend() for i in outExt: plt.savefig('lineVersion/'+outFile+i, dpi = mydpi) -- ________________________ Jeffrey Spencer jeffspenc...@gmail.com
<<attachment: RecogVsChannels.resized.png>>
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