def save_plotSpectrum(y,Fs,image_name): """ Plots a Single-Sided Amplitude Spectrum of y(t) """ fig = Figure(linewidth=0.0) fig.set_size_inches(fig_width,fig_length, forward=True) Figure.subplots_adjust(fig, left = fig_left, right = fig_right, bottom = fig_bottom, top = fig_top, hspace = fig_hspace) n = len(y) # length of the signal _subplot = fig.add_subplot(2,1,1) print "Fi" _subplot.plot(arange(0,n),y) xlabel('Time') ylabel('Amplitude') _subploti_2=fig.add_subplot(2,1,2) k = arange(n) T = n/Fs frq = k/T # two sides frequency range frq = frq[range(n/2)] # one side frequency range Y = fft(y)/n # fft computing and normalization Y = Y[range(n/2)] _subplot_2.plot(frq,abs(Y),'r') # plotting the spectrum xlabel('Freq (Hz)') ylabel('|Y(freq)|') print "here" canvas = FigureCanvasAgg(fig) if '.eps' in outfile_name: canvas.print_eps(outfile_name, dpi = 110) if '.png' in outfile_name: canvas.print_figure(outfile_name, dpi = 110)
def print_image(x, y, x2, y2, outfile_name): fig = Figure(linewidth=0.0) fig.set_size_inches(fig_width, fig_length, forward=True) Figure.subplots_adjust(fig, left=fig_left, right=fig_right, bottom=fig_bottom, top=fig_top, hspace=fig_hspace) _subplot = fig.add_subplot(2, 1, 1) _subplot.set_title('Detection of Source generating non-wifi Interference') _subplot.plot(x, y, color='b') _subplot.set_xlabel('Time') _subplot.set_ylabel('Error Counts') # _subplot.set_ylim([0,1]) _subplot2 = fig.add_subplot(2, 1, 2) _subplot2.plot(x2, y2, color='r') # plotting the spectrum _subplot2.set_ylabel('Entropy') _subplot2.set_xlabel('Time') _subplot2.set_ylim([0, 1]) canvas = FigureCanvasAgg(fig) if '.eps' in outfile_name: canvas.print_eps(outfile_name, dpi=110) if '.png' in outfile_name: canvas.print_figure(outfile_name, dpi=110)
def template_plotter(x_axis_label, y_axis_label,x_axes=[],x_ticks=[],title, outfile_name): fig = Figure(linewidth=0.0) fig.set_size_inches(fig_width,fig_length, forward=True) Figure.subplots_adjust(fig, left = fig_left, right = fig_right, bottom = fig_bottom, top = fig_top, hspace = fig_hspace) _subplot = fig.add_subplot(1,1,1) _subplot.boxplot(x_axis,notch=0,sym='+',vert=1, whis=1.5) #_subplot.plot(x,y,color='b', linestyle='--', marker='o' ,label='labels') a =[i for i in range(1,len(x_ticks)+1)] _subplot.set_xticklabels(x_ticks) _subplot.set_xticks(a) labels=_subplot.get_xticklabels() for label in labels: label.set_rotation(30) _subplot.set_ylabel(y_axis_label,fontsize=36) _subplot.set_xlabel(x_axis_label) #_subplot.set_ylim() #_subplot.set_xlim() _subplot.set_title(title) _subplot.legend(loc='upper left',prop=LEGEND_PROP ,bbox_to_anchor=(0.5, -0.05)) canvas = FigureCanvasAgg(fig) if '.eps' in outfile_name: canvas.print_eps(outfile_name, dpi = 110) if '.png' in outfile_name: canvas.print_figure(outfile_name, dpi = 110) outfile_name='EpsilonvsMTU.pdf' if '.pdf' in outfile_name: canvas.print_figure(outfile_name, dpi = 110)
def print_figure(fig, destination_directory, filename, extension): canvas = FigureCanvasAgg(fig) if extension == '.png': canvas.print_figure(os.path.join(destination_directory, \ filename + extension), dpi = 110) elif extension == '.eps': canvas.print_eps(os.path.join(destination_directory,\ filename + extension), dpi = 110)
def print_image(x,y,x2,y2,outfile_name): fig = Figure(linewidth=0.0) fig.set_size_inches(fig_width,fig_length, forward=True) Figure.subplots_adjust(fig, left = fig_left, right = fig_right, bottom = fig_bottom, top = fig_top, hspace = fig_hspace) _subplot = fig.add_subplot(2,1,1) _subplot.set_title('Detection of Source generating non-wifi Interference') _subplot.plot(x,y,color='b') _subplot.set_xlabel('Time') _subplot.set_ylabel('Error Counts') # _subplot.set_ylim([0,1]) _subplot2=fig.add_subplot(2,1,2) _subplot2.plot(x2,y2,color='r') # plotting the spectrum _subplot2.set_ylabel('Entropy') _subplot2.set_xlabel('Time') _subplot2.set_ylim([0,1]) canvas = FigureCanvasAgg(fig) if '.eps' in outfile_name: canvas.print_eps(outfile_name, dpi = 110) if '.png' in outfile_name: canvas.print_figure(outfile_name, dpi = 110)
def save_plotSpectrum(y, Fs, image_name): """ Plots a Single-Sided Amplitude Spectrum of y(t) """ fig = Figure(linewidth=0.0) fig.set_size_inches(fig_width, fig_length, forward=True) Figure.subplots_adjust(fig, left=fig_left, right=fig_right, bottom=fig_bottom, top=fig_top, hspace=fig_hspace) n = len(y) # length of the signal _subplot = fig.add_subplot(2, 1, 1) print "Fi" _subplot.plot(arange(0, n), y) xlabel('Time') ylabel('Amplitude') _subploti_2 = fig.add_subplot(2, 1, 2) k = arange(n) T = n / Fs frq = k / T # two sides frequency range frq = frq[range(n / 2)] # one side frequency range Y = fft(y) / n # fft computing and normalization Y = Y[range(n / 2)] _subplot_2.plot(frq, abs(Y), 'r') # plotting the spectrum xlabel('Freq (Hz)') ylabel('|Y(freq)|') print "here" canvas = FigureCanvasAgg(fig) if '.eps' in outfile_name: canvas.print_eps(outfile_name, dpi=110) if '.png' in outfile_name: canvas.print_figure(outfile_name, dpi=110)
def save_to_eps(fig, output_file): fig.set_facecolor("#FFFFFF") canvas = FigureCanvasAgg(fig) canvas.print_eps(output_file, dpi=72)
temp = [d.temp for d in data] lux = [d.lux for d in data] fig = matplotlib.figure.Figure() hplot = fig.add_subplot(3,1,1) tplot = fig.add_subplot(3,1,2) lplot = fig.add_subplot(3,1,3) hplot.plot_date(dates, hum) tplot.plot_date(dates, temp) lplot.plot_date(dates, lux) hplot.xaxis.set_major_locator(matplotlib.dates.DayLocator()) hplot.xaxis.set_minor_locator(matplotlib.dates.HourLocator(numpy.arange(0,24,1))) hplot.xaxis.set_major_formatter(matplotlib.dates.DateFormatter('%Y-%m-%d')) tplot.xaxis.set_major_locator(matplotlib.dates.DayLocator()) tplot.xaxis.set_minor_locator(matplotlib.dates.HourLocator(numpy.arange(0,24,1))) tplot.xaxis.set_major_formatter(matplotlib.dates.DateFormatter('%Y-%m-%d')) lplot.xaxis.set_major_locator(matplotlib.dates.DayLocator()) lplot.xaxis.set_minor_locator(matplotlib.dates.HourLocator(numpy.arange(0,24,1))) lplot.xaxis.set_major_formatter(matplotlib.dates.DateFormatter('%Y-%m-%d')) canvas = FigureCanvasAgg(fig) canvas.print_eps(output, dpi=110) of = open('export.csv', 'w') of.write('Humidity,Temperature,Luminosity,Date\n') for d in data: of.write('%.2f,%.2f,%d,%s\n' % (d.hum, d.temp, d.lux / 20000.0, d.time.strftime('%m/%d/%Y %H:%M'))) of.close()
lines = p.loglog(getXvals(times_L), getYvals(times_L), 'ro-', getXvals(times_L_ideal), getYvals(times_L_ideal), 'r:', \ getXvals(times_E), getYvals(times_E), 'bo-', getXvals(times_E_ideal), getYvals(times_E_ideal), 'b:', \ getXvals(times_B), getYvals(times_B), 'go-', getXvals(times_B_ideal), getYvals(times_B_ideal), 'g:', \ getXvals(times_M), getYvals(times_M), 'ko-', getXvals(times_M_ideal), getYvals(times_M_ideal), 'k:', \ getXvals(times_Lm), getYvals(times_Lm), 'mo-', getXvals(times_Lm_ideal), getYvals(times_Lm_ideal), 'm:') p.set_ylabel('Simulation time for 1 sec of net activity (sec)', fontsize=14) p.set_xlabel('Number of processors', fontsize=14) lines[0].set_label('Legion') lines[2].set_label('PadraigPC') lines[4].set_label('Bernal') lines[6].set_label('Matthau') lines[8].set_label('Lemmon') legend() fig.set_figheight(8) fig.set_figwidth(12) #plt.print_figure() canvas = FigureCanvas(fig) canvas.print_eps('Performance.eps') print dir(fig) plt.show()
pArr = [] data_x = np.arange(0.0, 100.0, 1) data_y = np.arange(0.0, 100.0, 1) for alab in legendColors: # p = Rectangle((0, 0), 1, 1, fc="r") # l = Line2D(ax, data_x, data_y , color=plotcolors[alab % len(plotcolors)], ls='-', lw=2) l = Rectangle((0, 0), 1, 1, fc=plotcolors[alab % len(plotcolors)]) pArr.append(l) #pArr.append( ax.plot(data_x, data_y, color=plotcolors[colorParams % len(plotcolors)], linestyle='-', marker='', markerfacecolor='None', markeredgecolor='#000000' ) ) legend(pArr, legendText) canvas = FigureCanvas(fig) targetFile = targetList[0] graphicsFilename = targetFile graphicsFilename = graphicsFilename.replace('.txt', '') #canvas.print_eps(graphicsFilename+'.svg') canvas.print_eps(graphicsFilename + '.eps') #canvas.print_pdf(graphicsFilename+'.pdf') canvas.print_png(graphicsFilename + '.png') plt.close(fig) # for each file sent as argument create a plot for filidx in range(0, len(targetList)): targetFile = targetList[filidx] # read the x values if (os.path.exists(targetFile)): # read the y values data_y = [] data_x = [] t = 0 if (dtfileFlag and os.path.exists(dtfileParams)): file_x = open(dtfileParams, 'rU')
pArr = [] data_x = np.arange(0.0, 100.0, 1) data_y = np.arange(0.0, 100.0, 1) for alab in legendColors: # p = Rectangle((0, 0), 1, 1, fc="r") # l = Line2D(ax, data_x, data_y , color=plotcolors[alab % len(plotcolors)], ls='-', lw=2) l = Rectangle((0, 0), 1, 1, fc=plotcolors[alab % len(plotcolors)]) pArr.append(l) #pArr.append( ax.plot(data_x, data_y, color=plotcolors[colorParams % len(plotcolors)], linestyle='-', marker='', markerfacecolor='None', markeredgecolor='#000000' ) ) legend(pArr, legendText) canvas = FigureCanvas(fig) targetFile = targetList[0] graphicsFilename = targetFile graphicsFilename = graphicsFilename.replace('.txt','') #canvas.print_eps(graphicsFilename+'.svg') canvas.print_eps(graphicsFilename+'.eps') #canvas.print_pdf(graphicsFilename+'.pdf') canvas.print_png(graphicsFilename+'.png') plt.close(fig) # for each file sent as argument create a plot for filidx in range(0,len(targetList)): targetFile = targetList[filidx] # read the x values if (os.path.exists(targetFile) ): # read the y values data_y = [] data_x = [] t = 0 if (dtfileFlag and os.path.exists(dtfileParams)): file_x = open(dtfileParams,'rU')
p.set_ylabel('Simulation time for 1 sec of net activity (sec)', fontsize=14) p.set_xlabel('Number of processors', fontsize=14) lines[0].set_label('Legion') lines[2].set_label('PadraigPC') lines[4].set_label('Bernal') lines[6].set_label('Matthau') lines[8].set_label('Lemmon') legend() fig.set_figheight(8) fig.set_figwidth(12) #plt.print_figure() canvas = FigureCanvas(fig) canvas.print_eps('Performance.eps') print dir(fig) plt.show()
#done with creation #width=0.35 #import numpy as np #ind = np.arange(len(homes)) #rects=_subplot.bar(ind,percentile,width,color='blue') #_subplot.set_ylabel('Delay(90th percentile)') g=[] for i in z : g.append(str(i)) #_subplot.set_xticks(ind+width) #_subplot.set_xticklabels(g) #_subplot.legend(loc=0, prop=LEGEND_PROP, bbox_to_anchor=(0.1,- 0.05), scatterpoints=1) #aray.append(rects[0]) #legend_elem=file.strip('pickle.') #legend_elem=legend_elem.strip('^rate_') #legend.append(legend_elem) #print legend #print "========" #print aray labels = _subplot.get_xticklabels() for label in labels: label.set_rotation(30) _subplot.legend(loc=0, prop=LEGEND_PROP,bbox_to_anchor=(0.1,- 0.05)) canvas = FigureCanvasAgg(fig) if '.eps' in outfile_name: canvas.print_eps(outfile_name, dpi = 110) if '.png' in outfile_name: canvas.print_figure(outfile_name, dpi = 110)
_subplot.set_ylabel('Delay(mean) between frames in microseconds') ''' g=[] aray=[] for i in z : g.append(str(i)) _subplot.set_xticks(ind+width) _subplot.set_xticklabels(g) #_subplot.legend(loc=0, prop=LEGEND_PROP, bbox_to_anchor=(0.1,- 0.05), scatterpoints=1) aray.append(rects[0]) _subplot.set_yscale('log') #legend_elem=file.strip('pickle.') #legend_elem=legend_elem.strip('^rate_') #legend.append(legend_elem) #print legend #print "========" #print aray #if len(aray) > 0: # _subplot.legend(aray,legend)#,bbox_to_anchor=(0.1,- 0.05)) labels = _subplot.get_xticklabels() for label in labels: label.set_rotation(30) canvas = FigureCanvasAgg(fig) if '.eps' in outfile_name: canvas.print_eps(outfile_name, dpi = 110) if '.png' in outfile_name: canvas.print_figure(outfile_name, dpi = 110)
getXvals(times_Lm_ideal), getYvals(times_Lm_ideal), "m:", ) p.set_ylabel("Simulation time for 1 sec of net activity (sec)", fontsize=14) p.set_xlabel("Number of processors", fontsize=14) lines[0].set_label("Lemmon") lines[2].set_label("Matthau") lines[4].set_label("Both") legend() fig.set_figheight(8) fig.set_figwidth(12) # plt.print_figure() canvas = FigureCanvas(fig) canvas.print_eps("Performance.eps") # print dir(fig) plt.show()