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_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)
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()
