def MagXCompPlot(pkl1, pkl2):
pklfrep1 = pklrep + pkl1
pklfrep2 = pklrep + pkl2
#initially going to hardcode for intensity or magnitude
MagXarr, PhaXarr, ReXarr, ImXarr, MagYarr, PhaYarr, ReYarr, ImYarr, vtxcntarr, PixCenX, PixCenY, IntX, IntY, IntT, Ix, Iy, IT, xycoords, filename = RetrieveVars(
pklfrep1)
MagX1 = MagXarr #/max(MagXarr) #since cross and co polar are mixed up
plt.figure(facecolor='xkcd:pale green')
plt.subplot(221, facecolor='#d8dcd6')
plt.scatter(PixCenX * 1000,
PixCenY * 1000,
c=MagX1,
cmap='jet',
marker='s',
s=5)
plt.axis([-60, 60, -60, 60])
plt.axis('equal')
plt.title("FP - {}".format(pkl1), fontsize=10)
plt.subplot(222, facecolor='#d8dcd6')
MagXarr, PhaXarr, ReXarr, ImXarr, MagYarr, PhaYarr, ReYarr, ImYarr, vtxcntarr, PixCenX, PixCenY, IntX, IntY, IntT, Ix, Iy, IT, xycoords, filename = RetrieveVars(
pklfrep2)
MagX2 = MagYarr #/max(MagYarr)
plt.scatter(PixCenX * 1000,
PixCenY * 1000,
c=MagX2,
cmap='jet',
marker='s',
s=5)
plt.axis([-60, 60, -60, 60])
plt.axis('equal')
plt.title("FP - {}".format(pkl2), fontsize=10)
plt.subplot(223, facecolor='#d8dcd6')
comp = (MagX1 - MagX2) / MagX1 * 100
analysisarray = ([])
#okay so here i am finding all of the outer pixels and setting to zero
#this allows me to analyse valid pixels between grasp and modal
#maybe i should delete these elements of the array to make data analysis easier
for i in range(len(PixCenX)):
if np.sqrt(PixCenX[i]**2 + PixCenY[i]**2) > 0.05:
comp[i] = np.mean(comp)
PixCenX[i] = 0.05
PixCenY[i] = 0.05
else:
analysisarray = np.append(comp[i], analysisarray)
#print "radius test", np.sqrt(PixCenX[i]**2 + PixCenY[i]**2)
#plt.scatter(PixCenX[i]*1000,PixCenY[i]*1000, c=comp[i], cmap='jet',marker='s')
plt.scatter(PixCenX * 1000,
PixCenY * 1000,
c=comp,
cmap='jet',
marker='s',
s=5)
plt.axis([-60, 60, -60, 60])
plt.axis('equal')
plt.title("Data Comparison", fontsize=10)
plt.subplot(224, facecolor='#d8dcd6')
#do histogram here
#binarr = [-0.35, -0.25, -0.15, -0.05, 0.05, 0.015]
#binarr = [-0.325, -0.275, -0.225, -0.175, -0.125, -0.075, -0.025, 0.025, 0.075, 0.125]
#binarr = [-32.5, -27.5, -22.5, -17.5, -12.5, -7.5, -2.5, 2.5, 7.5, 12.5]
#binarr = [0, 2.5, 5, 7.5, 10, 12.5, 15, 17.5, 20, 22.5, 25]
analysisarray = np.abs(analysisarray)
#analysisarray = analysisarray[~np.isnan(analysisarray)]
print("analysis info, max, length, mean ", np.max(analysisarray),
len(analysisarray), np.mean(analysisarray))
n, bins, patches = plt.hist(analysisarray)
print("hist data ", n, bins, patches)
plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
cax = plt.axes([0.85, 0.1, 0.05, 0.8])
plt.colorbar(cax=cax, label="% Difference Comparison")
plt.show()
return
def FPComparisonPlotRAW(pkl1, pkl2):
#initially going to hardcode for intensity or magnitude
MagXarr, PhaXarr, ReXarr, ImXarr, MagYarr, PhaYarr, ReYarr, ImYarr, vtxcntarr, PixCenX, PixCenY, IntX, IntY, IntT, Ix, Iy, IT, xycoords, filename, freq = RetrieveVars(
pkl1)
IntX1 = IT / max(IT) # cx and co mixed
print("pkl1 max intensity ", max(IT))
plt.figure(facecolor='xkcd:pale green')
plt.subplot(221, facecolor='#d8dcd6')
plt.scatter(xycoords[:, 1] * 1000,
xycoords[:, 0] * 1000,
c=IntX1,
cmap='jet',
marker='s')
plt.axis([-60, 60, -60, 60])
plt.axis('equal')
plt.title("pkl1", fontsize=10)
#delete vars
del MagXarr, PhaXarr, ReXarr, ImXarr, MagYarr, PhaYarr, ReYarr, ImYarr, vtxcntarr, PixCenX, PixCenY, IntX, IntY, IntT, Ix, Iy, IT, xycoords, filename, freq
MagXarr, PhaXarr, ReXarr, ImXarr, MagYarr, PhaYarr, ReYarr, ImYarr, vtxcntarr, PixCenX, PixCenY, IntX, IntY, IntT, Ix, Iy, IT, xycoords, filename, freq = RetrieveVars(
pkl2)
IntX2 = IT / max(IT) # norm to first plot
print("pkl2 max intensity ", max(IT))
plt.subplot(222, facecolor='#d8dcd6')
plt.scatter(xycoords[:, 0] * 1000,
xycoords[:, 1] * 1000,
c=IntX2,
cmap='jet',
marker='s')
plt.axis([-60, 60, -60, 60])
plt.axis('equal')
plt.title("pkl2", fontsize=10)
#initialise comparison array and plot
#IntX1[IntX1 == 0] = min(IntX1)
#IntX2[IntX2 == 0] = min(IntX1)
comp = ((IntX1 - IntX2) / IntX1) * 100
plt.subplot(223, facecolor='#d8dcd6')
plt.scatter(xycoords[:, 0] * 1000,
xycoords[:, 1] * 1000,
c=comp,
cmap='RdYlGn',
marker='s') #RdPu, YlGnBu
plt.axis([-60, 60, -60, 60])
plt.axis('equal')
plt.title("Data Comparison", fontsize=10)
#Now do histogram
plt.subplot(224, facecolor='#d8dcd6')
print("analysis info, max, length, mean ", np.max(comp), len(comp),
np.mean(comp))
n, bins, patches = plt.hist(comp)
print("hist data ", n, bins, patches)
#Set colorbar
plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
cax = plt.axes([0.85, 0.1, 0.05, 0.8])
plt.colorbar(cax=cax, label="Model % Diff Comparison")
plt.show()
return
def IntensityXPlot(plotfname):
pklrep = '/home/james/files4CSFPA/qbdataioOUTFILES/' + plotfname
######################IntensityX plot
MagXarr, PhaXarr, ReXarr, ImXarr, MagYarr, PhaYarr, ReYarr, ImYarr, vtxcntarr, PixCenX, PixCenY, IntX, IntY, IntT, Ix, Iy, IT, xycoords, filename = RetrieveVars(
pklrep)
plt.figure(facecolor='xkcd:pale green')
plt.subplot(121, facecolor='#d8dcd6') #xkcd reference for this colour
plt.scatter(PixCenX,
PixCenY,
c=IntX / max(IntX),
s=25,
cmap='jet',
marker='s')
plt.axis([-0.06, 0.06, -0.06, 0.06])
plt.axis('equal')
plt.title("{} as Bolometers Intensity X dir".format(plotfname),
fontsize=10)
plt.subplot(122, facecolor='#d8dcd6') #xkcd reference for this colour
plt.scatter(xycoords[:, 0],
xycoords[:, 1],
c=Ix / max(Ix),
cmap='jet',
marker='.')
plt.axis([-0.06, 0.06, -0.06, 0.06])
plt.axis('equal')
plt.title("{}".format(plotfname), fontsize=10)
plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
cax = plt.axes([0.85, 0.1, 0.05, 0.8])
plt.colorbar(cax=cax, label="Intensity X")
plt.show()
return
def PhaYCompPlot(pkl1, pkl2):
#initially going to hardcode for intensity or magnitude
#Is this a Pha Y plot?
MagXarr, PhaXarr, ReXarr, ImXarr, MagYarr, PhaYarr, ReYarr, ImYarr, vtxcntarr, PixCenX, PixCenY, IntX, IntY, IntT, Ix, Iy, IT, xycoords, filename = RetrieveVars(
pkl1)
PhaY1 = PhaYarr / max(PhaYarr) # cross and co polar mixed up
plt.figure(facecolor='xkcd:pale green')
plt.subplot(221, facecolor='#d8dcd6')
plt.scatter(PixCenX * 1000,
PixCenY * 1000,
c=PhaY1,
cmap='jet',
marker='s',
s=5)
plt.axis([-60, 60, -60, 60])
plt.axis('equal')
plt.title("FP - {}".format(pkl1), fontsize=10)
plt.subplot(222, facecolor='#d8dcd6')
MagXarr, PhaXarr, ReXarr, ImXarr, MagYarr, PhaYarr, ReYarr, ImYarr, vtxcntarr, PixCenX, PixCenY, IntX, IntY, IntT, Ix, Iy, IT, xycoords, filename = RetrieveVars(
pkl2)
PhaY2 = PhaYarr / max(PhaYarr)
plt.scatter(PixCenX * 1000,
PixCenY * 1000,
c=PhaY2,
cmap='jet',
marker='s',
s=5)
plt.axis([-60, 60, -60, 60])
plt.axis('equal')
plt.title("FP - {}".format(pkl2), fontsize=10)
plt.subplot(223, facecolor='#d8dcd6')
PhaY1[PhaY1 == 0] = 0.000001
PhaY2[PhaY2 == 0] = 0.000001
comp = PhaY1 / PhaY2
analysisarray = ([])
#okay so here i am finding all of the outer pixels and setting to zero
#this allows me to analyse valid pixels between grasp and modal
#maybe i should delete these elements of the array to make data analysis easier
for i in range(len(PixCenX)):
if np.sqrt(PixCenX[i]**2 + PixCenY[i]**2) > 0.05:
comp[i] = 0
PixCenX[i] = 0.05
PixCenY[i] = 0.05
else:
analysisarray = np.append(comp[i], analysisarray)
#print "radius test", np.sqrt(PixCenX[i]**2 + PixCenY[i]**2)
#plt.scatter(PixCenX[i]*1000,PixCenY[i]*1000, c=comp[i], cmap='jet',marker='s')
plt.scatter(PixCenX * 1000,
PixCenY * 1000,
c=comp,
cmap='jet',
marker='s',
s=5)
plt.axis([-60, 60, -60, 60])
plt.axis('equal')
plt.title("Data Comparison", fontsize=10)
plt.subplot(224, facecolor='#d8dcd6')
#analysisarray = np.abs(analysisarray)
analysisarray = analysisarray[~np.isnan(analysisarray)]
#print "analysis info, max, length, mean", np.max(analysisarray), len(analysisarray), np.mean(analysisarray)
binarr = [-3, -2, -1, 0, 1, 2, 3]
n, bins, patches = plt.hist(analysisarray, bins=binarr)
print("hist data ", n, bins, patches)
plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
cax = plt.axes([0.85, 0.1, 0.05, 0.8])
plt.colorbar(cax=cax, label="% Difference Comparison")
plt.show()
return
def PhaYPlot():
MagXarr, PhaXarr, ReXarr, ImXarr, MagYarr, PhaYarr, ReYarr, ImYarr, vtxcntarr, PixCenX, PixCenY, IntX, IntY, IntT, Ix, Iy, IT, xycoords, filename = RetrieveVars(
)
#load raw data from file
dataCF = np.loadtxt(filename, skiprows=1)
############################### plot normalised data ################
plt.figure()
plt.subplot(121)
plt.scatter(PixCenX * 1000,
PixCenY * 1000,
c=PhaYarr / max(PhaYarr),
cmap='plasma',
marker='s')
plt.axis([-0.06, 0.06, -0.06, 0.06])
plt.axis('equal')
plt.title("Phase Y CF Source as Bolometers", fontsize=10)
plt.subplot(122)
plt.scatter(xycoords[:, 0],
xycoords[:, 1],
c=dataCF[:, 7] / (max(dataCF[:, 7])),
cmap='plasma',
marker='.')
plt.axis([-0.06, 0.06, -0.06, 0.06])
plt.axis('equal')
plt.title("Phase Y CF Source - MODAL", fontsize=10)
plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
cax = plt.axes([0.85, 0.1, 0.05, 0.8])
plt.colorbar(cax=cax, label="Phase Y")
plt.show()
return
def TotIntCompPlot(pkl1, pkl2):
#initially going to hardcode for intensity or magnitude
MagXarr, PhaXarr, ReXarr, ImXarr, MagYarr, PhaYarr, ReYarr, ImYarr, vtxcntarr, PixCenX, PixCenY, IntX, IntY, IntT, Ix, Iy, IT, xycoords, filename = RetrieveVars(
pkl1)
IntT1 = IntT / max(IntT)
plt.figure(facecolor='xkcd:pale green')
plt.subplot(221, facecolor='#d8dcd6')
plt.scatter(PixCenX * 1000,
PixCenY * 1000,
c=IntT1,
cmap='jet',
marker='s',
s=5)
plt.axis([-60, 60, -60, 60])
plt.axis('equal')
plt.title("FP - {}".format(pkl1), fontsize=10)
plt.subplot(222, facecolor='#d8dcd6')
MagXarr, PhaXarr, ReXarr, ImXarr, MagYarr, PhaYarr, ReYarr, ImYarr, vtxcntarr, PixCenX, PixCenY, IntX, IntY, IntT, Ix, Iy, IT, xycoords, filename = RetrieveVars(
pkl2)
IntT2 = IntT / max(IntT) #Normalise to first files peak
plt.scatter(PixCenX * 1000,
PixCenY * 1000,
c=IntT2,
cmap='jet',
marker='s',
s=5)
plt.axis([-60, 60, -60, 60])
plt.axis('equal')
plt.title("FP - {}".format(pkl2), fontsize=10)
plt.subplot(223, facecolor='#d8dcd6')
IntT1[IntT1 == 0] = 0.000001
IntT2[IntT2 == 0] = 0.000001
comp = ((IntT1 - IntT2) / IntT1) * 100 #can delete this % conversion
analysisarray = ([])
#okay so here i am finding all of the outer pixels and setting to zero
#this allows me to analyse valid pixels between grasp and modal
#maybe i should delete these elements of the array to make data analysis easier
for i in range(len(PixCenX)):
if np.sqrt(PixCenX[i]**2 + PixCenY[i]**2) > 0.05:
comp[i] = np.mean(comp)
PixCenX[i] = 0.05
PixCenY[i] = 0.05
else:
analysisarray = np.append(comp[i], analysisarray)
#print "radius test", np.sqrt(PixCenX[i]**2 + PixCenY[i]**2)
#plt.scatter(PixCenX[i]*1000,PixCenY[i]*1000, c=comp[i], cmap='jet',marker='s')
plt.scatter(PixCenX * 1000,
PixCenY * 1000,
c=comp,
cmap='RdPu',
marker='s',
s=5)
plt.axis([-60, 60, -60, 60])
plt.axis('equal')
plt.title("Data Comparison", fontsize=10)
plt.subplot(224, facecolor='#d8dcd6')
#do histogram here
#binarr = [-0.35, -0.25, -0.15, -0.05, 0.05, 0.015]
#binarr = [-0.325, -0.275, -0.225, -0.175, -0.125, -0.075, -0.025, 0.025, 0.075, 0.125]
#binarr = [-32.5, -27.5, -22.5, -17.5, -12.5, -7.5, -2.5, 2.5, 7.5, 12.5]
#binarr = [0, 2.5, 5, 7.5, 10, 12.5, 15, 17.5, 20, 22.5, 25]
#binarr = [0, 1, 2, 3, 4, 5, 6, 7, 8]
#comp = np.abs(comp)
#analysisarray = np.abs(analysisarray)
print("analysis info, max, length, mean ", np.max(analysisarray),
len(analysisarray), np.mean(analysisarray))
n, bins, patches = plt.hist(analysisarray)
print("hist data ", n, bins, patches)
plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
cax = plt.axes([0.85, 0.1, 0.05, 0.8])
plt.colorbar(cax=cax, label="% Difference Comparison - Total Intensity")
plt.show()
return
def MagXPlot(plotfname):
#load saved variables
MagXarr, PhaXarr, ReXarr, ImXarr, MagYarr, PhaYarr, ReYarr, ImYarr, vtxcntarr, PixCenX, PixCenY, IntX, IntY, IntT, Ix, Iy, IT, xycoords, filename = RetrieveVars(
plotfname)
#load raw data from file
dataCF = np.loadtxt(filename, skiprows=1)
plt.figure(facecolor='xkcd:pale green')
plt.subplot(121, facecolor='#d8dcd6')
plt.scatter(PixCenX,
PixCenY,
c=MagXarr / max(MagXarr),
s=25,
cmap='jet',
marker='s')
plt.axis([-0.055, 0.055, -0.055, 0.055])
plt.axis('equal')
plt.title("{} as Bolometers".format(plotfname), fontsize=10)
#plt.plot(0, 0, 'o', mfc='none',markersize=57.16*2,color='black')
plt.subplot(122, facecolor='#d8dcd6')
plt.scatter(xycoords[:, 0],
xycoords[:, 1],
c=dataCF[:, 4] / (max(dataCF[:, 4])),
cmap='jet',
marker='.')
#plt.scatter(xycoords[:,0],xycoords[:,1], c=MagXarr/(max(MagXarr)), cmap='plasma',marker='.')
#plt.plot(0, 0, 'o', mfc='none',markersize=57.16*2,color='black')
plt.axis([-0.055, 0.055, -0.055, 0.055])
plt.axis('equal')
plt.title("Source - {}".format(filename), fontsize=10)
plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
cax = plt.axes([0.85, 0.1, 0.05, 0.8])
plt.colorbar(cax=cax, label="Mag X")
plt.show()
return
def TotalIntensityPlot():
MagXarr, PhaXarr, ReXarr, ImXarr, MagYarr, PhaYarr, ReYarr, ImYarr, vtxcntarr, PixCenX, PixCenY, IntX, IntY, IntT, Ix, Iy, IT, xycoords, filename = RetrieveVars(
)
######################Total Intensity plot - Normalised
plt.figure()
plt.subplot(121)
plt.scatter(PixCenX * 1000,
PixCenY * 1000,
c=IntT[:] / max(IntT[:]),
cmap='plasma',
marker='s')
plt.axis([-60, 60, -60, 60])
plt.axis('equal')
plt.title("CF1 Source as Bolometers Total Instensity", fontsize=10)
plt.subplot(122)
plt.scatter(xycoords[:, 0],
xycoords[:, 1],
c=IT[:] / max(IT[:]),
cmap='plasma',
marker='.')
plt.axis([-60, 60, -60, 60])
plt.axis('equal')
plt.title("CF1 Source - MODAL", fontsize=10)
plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
cax = plt.axes([0.85, 0.1, 0.05, 0.8])
plt.colorbar(cax=cax, label="Intensity")
plt.show()
os.system('spd-say "BING! BING! BING!"')
return
def IntensityXPlot():
######################IntensityX plot
MagXarr, PhaXarr, ReXarr, ImXarr, MagYarr, PhaYarr, ReYarr, ImYarr, vtxcntarr, PixCenX, PixCenY, IntX, IntY, IntT, Ix, Iy, IT, xycoords, filename = RetrieveVars(
)
plt.figure()
plt.subplot(121)
plt.scatter(PixCenX * 1000,
PixCenY * 1000,
c=IntX / max(IntX),
s=8,
cmap='plasma',
marker='s')
plt.axis([-0.06, 0.06, -0.06, 0.06])
plt.axis('equal')
plt.title("CF1 Source as Bolometers Intensity X dir", fontsize=10)
plt.subplot(122)
plt.scatter(xycoords[:, 0],
xycoords[:, 1],
c=Ix / max(Ix),
cmap='plasma',
marker='.')
plt.axis([-0.06, 0.06, -0.06, 0.06])
plt.axis('equal')
plt.title("CF1 Source - MODAL", fontsize=10)
plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
cax = plt.axes([0.85, 0.1, 0.05, 0.8])
plt.colorbar(cax=cax, label="Intensity X")
plt.show()
return
def PoynICompPlot(mpath, gpath):
#prep MODAL data
df = pd.read_csv(mpath, sep='\t', header=0)
garea, parea = GetMODALGridPixArea(mpath)
#prep GRASP
MagXarr, PhaXarr, ReXarr, ImXarr, MagYarr, PhaYarr, ReYarr, ImYarr, vtxcntarr, PixCenX, PixCenY, IntX, IntY, IntT, Ix, Iy, IT, xycoords, filename = RetrieveVars(
gpath)
gfile = os.path.basename(gpath)
GPow = GridPowerCalc(gfile)
#calculate %diff
comp = ((abs(df.PoynZ * parea) - abs(GPow)) / abs(GPow)) * 100
#comp = np.isclose(GPow, df.PoynZ*parea, atol=1e-6)
fourpi = 4 * np.pi
#do multi plots
fig = plt.figure(facecolor='xkcd:pale green')
fig.suptitle("FP Comparison")
ax1 = fig.add_subplot(221, facecolor='#d8dcd6', aspect='equal')
ax1.set_title("MODAL FP from Poynting Z")
sc = ax1.scatter(df.X, df.Y, c=df.PoynZ * parea, cmap='jet', marker='.')
cbar = fig.colorbar(sc, label="MODAL Poynting (W)")
ax2 = fig.add_subplot(222, facecolor='#d8dcd6', aspect='equal')
ax2.set_title("GRASP FP from Total Intensity")
sc = ax2.scatter(xycoords[:, 1] * 1000,
xycoords[:, 0] * 1000,
c=GPow,
cmap='jet',
marker='.')
cbar = fig.colorbar(sc, label="GRASP Total Intensity (W)")
#plot %diff between models
ax3 = fig.add_subplot(223, facecolor='#d8dcd6', aspect='equal')
ax3.set_title("Difference between Softwares")
sc = ax3.scatter(xycoords[:, 1] * 1000,
xycoords[:, 0] * 1000,
c=comp,
cmap='RdPu',
marker='.') #Both useful PiYG & RdPu
cbar = fig.colorbar(sc, label="Difference")
#plot histogram of %diff per pixel
ax4 = fig.add_subplot(224, facecolor='#d8dcd6')
ax4.set_title("% Difference Histogram")
n, bins, patches = ax4.hist(comp)
print "hist data", n, bins, patches
#calcualte sum of power
print "Poynting Sum W = ", sum(
df.PoynZ *
parea), "% Diff from 1 W = ", ((sum(df.PoynZ * parea) - 1) / 1) * 100
print "modal values: max, mean ", max(df.PoynZ * parea), np.mean(df.PoynZ *
parea)
print "grasp values: max, mean ", max(GPow), np.mean(GPow)
return
def TotalIntensityPlot(plotfname):
pklrep = '/home/james/files4CSFPA/qbdataioOUTFILES/' + plotfname
MagXarr, PhaXarr, ReXarr, ImXarr, MagYarr, PhaYarr, ReYarr, ImYarr, vtxcntarr, PixCenX, PixCenY, IntX, IntY, IntT, Ix, Iy, IT, xycoords, filename = RetrieveVars(
pklrep)
######################Total Intensity plot - Normalised
TESPower = TESPowerCalc(plotfname)
GPow = GridPowerCalc(plotfname)
plt.figure()
plt.subplot(121)
plt.scatter(PixCenX * 1000,
PixCenY * 1000,
c=TESPower,
cmap='jet',
marker='s')
plt.axis([-60, 60, -60, 60])
plt.axis('equal')
plt.title("Bolometers Total Intensity", fontsize=10)
plt.subplot(122)
plt.scatter(xycoords[:, 1],
xycoords[:, 0],
c=GPow,
cmap='jet',
marker='.',
s=1)
plt.axis([-60, 60, -60, 60])
plt.axis('equal')
plt.title("Model Power Data", fontsize=10)
plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
cax = plt.axes([0.85, 0.1, 0.05, 0.8])
plt.colorbar(cax=cax, label="Intensity (1 W Source)")
plt.show()
os.system('spd-say "BING! BING! BING!"')
return