def plotIsoFreqNSimpedance(ax,freq,array,flag,par='abs',colorbar=True,colorNorm='SymLog',cLevel=True,contour=True):
indUniFreq = np.where(freq==array['freq'])
x, y = array['x'][indUniFreq],array['y'][indUniFreq]
if par == 'abs':
zPlot = np.abs(array[flag][indUniFreq])
cmap = plt.get_cmap('OrRd_r')#seismic')
level = np.logspace(0,-5,31)
clevel = np.logspace(0,-4,5)
plotNorm = colors.LogNorm()
elif par == 'real':
zPlot = np.real(array[flag][indUniFreq])
cmap = plt.get_cmap('RdYlBu')
if cLevel:
level = np.concatenate((-np.logspace(0,-10,31),np.logspace(-10,0,31)))
clevel = np.concatenate((-np.logspace(0,-8,5),np.logspace(-8,0,5)))
else:
level = np.linspace(zPlot.min(),zPlot.max(),100)
clevel = np.linspace(zPlot.min(),zPlot.max(),10)
if colorNorm=='SymLog':
plotNorm = colors.SymLogNorm(1e-10,linscale=2)
else:
plotNorm = colors.Normalize()
elif par == 'imag':
zPlot = np.imag(array[flag][indUniFreq])
cmap = plt.get_cmap('RdYlBu')
level = np.concatenate((-np.logspace(0,-10,31),np.logspace(-10,0,31)))
clevel = np.concatenate((-np.logspace(0,-8,5),np.logspace(-8,0,5)))
plotNorm = colors.SymLogNorm(1e-10,linscale=2)
if cLevel:
level = np.concatenate((-np.logspace(0,-10,31),np.logspace(-10,0,31)))
clevel = np.concatenate((-np.logspace(0,-8,5),np.logspace(-8,0,5)))
else:
level = np.linspace(zPlot.min(),zPlot.max(),100)
clevel = np.linspace(zPlot.min(),zPlot.max(),10)
if colorNorm=='SymLog':
plotNorm = colors.SymLogNorm(1e-10,linscale=2)
elif colorNorm=='Lin':
plotNorm = colors.Normalize()
if contour:
cs = ax.tricontourf(x,y,zPlot,levels=level,cmap=cmap,norm=plotNorm)#,extend='both')
else:
uniX,uniY = np.unique(x),np.unique(y)
X,Y = np.meshgrid(np.append(uniX-25,uniX[-1]+25),np.append(uniY-25,uniY[-1]+25))
cs = ax.pcolor(X,Y,np.reshape(zPlot,(len(uniY),len(uniX))),cmap=cmap,norm=plotNorm)
if colorbar:
plt.colorbar(cs,cax=ax.cax,ticks=clevel,format='%1.2e')
ax.set_title(flag+' '+par,fontsize=8)
return cs
def Fit(self,Source,X=None,function='line',Guess=numpy.ones(10),Show_Residuals=True,StartP=None,EndP=None,StartX=None,EndX=None,Show_Guess=False,Figure=None,Rendering=True,RAW_Data=True,Color='r',Show_Parameters=True,Number_of_points=1000.,Bounds=[[None,None]]*10,Model=False):
"""
Source is the signal to fit
X is the XScale
function is the fitting function.
Avaible functions are : line,
poly2,
poly3,
poly4,
gauss,
exp,
double_exp,
double_exp_xoffset,
sin,
hill,
sigmoid,
power,
beta
p0 are the intitial guesses (default is a list of 1.)
StartP and EndP are the start point and the End point of the fit (in points)
StartX and EndX are the start point and the End point of the fit (in timescale units)
"""
def find_indexes(Source,Min,Max):
c=abs(numpy.array(Source)-Min)
Min_index=list(c).index(min(c))
c=abs(numpy.array(Source)-Max)
Max_index=list(c).index(min(c))
return Min_index,Max_index
begin=X[0] #original begin and end points are stored here, so the final plot will be displayed in the original workspace
end=X[-1]
if (StartP == None) and (StartX == None) :
StartP=0
StartX=0
if (EndP == None) and (EndX == None) == None:
EndP=len(Source)-1
EndX=len(Source)-1
if (StartP == None) or (EndP == None) :
StartP,EndP=find_indexes(X,StartX,EndX)
else:
StartX=X[StartP]
EndX=X[EndP]
Source=numpy.array(Source[StartP:EndP]) #if not using the whole range for the fit, truncated wave is created here
X=numpy.array(X[StartP:EndP]) #if not using the whole range for the fit, truncated timescale is created here
# Data and Xscale
Source = numpy.array(Source)
if X==None or (len(Source) != len(X)):
X = numpy.array(range(len(Source)))
# create a set of Parameters
Function=eval("self."+function) #the fitting function
Formula=eval("self."+function.capitalize())[0] #the fitting formula
Parameters_Names=eval("self."+function.capitalize())[1] ##list of the fitting parameters
for i,j in enumerate(Bounds):
if Bounds[i][0] != None:
Bounds[i][0]=numpy.float32(Bounds[i][0])
if Bounds[i][1] != None:
Bounds[i][1]=numpy.float32(Bounds[i][1])
#print Bounds
p0 = Parameters()
# do fit, here with leastsq model
if Model == False:
for i,j in enumerate(Parameters_Names):
#For each paramters, you can set value, min & max. i.e. p0.add('omega', value= 0.0, min=-numpy.pi/2., max=numpy.pi/2)
if j != '0':
if Bounds[i][0] != None and Bounds[i][1]!=None:
p0.add(j,value=Guess[i],min=Bounds[i][0],max=Bounds[i][1])
elif Bounds[i][0] !=None and Bounds[i][1]==None:
p0.add(j,value=Guess[i],min=Bounds[i][0])
elif Bounds[i][0] == None and Bounds[i][1] != None:
p0.add(j,value=Guess[i],max=Bounds[i][1])
else:
p0.add(j,value=Guess[i])
print 'Fitting in process ...'
try:
result = minimize(Function, p0, args=(X, Source))
RenderingX=numpy.linspace(float(X[0]),float(X[-1]),num=Number_of_points)
fit = Function(result.params,RenderingX) #Rendering, with Popt, the best fitting parameters
except:
print 'Fitting failed, try other parameters or constraints'
return
print 'Fitting performed between points ',StartP,' and ',EndP, ' (',len(X),' points)'
print 'in units between: ',StartX,' and ',EndX
print '######### FITTING RESULTS ############'
print 'Parameters are :'
res=[]
for i in list(result.params):
print i, result.params[i].value
res.append(result.params[i].value)
elif Model== True:
for i,j in enumerate(Parameters_Names):
if j != '0':
p0.add(j,value=Guess[i])
RenderingX=numpy.linspace(float(X[0]),float(X[-1]),num=Number_of_points)
fit = Function(p0,RenderingX) #Rendering, with Popt, the best fitting parameters
# if Show_Parameters == True:
# for i in range(len(popt)):
# if List[i][0] != '0':
# try:
# pyplot.text(begin+((end-begin)/10), max(fit)-(i+1)*abs((max(fit)-min(fit))/(10)), r"%s = {%s} +/- {%s}" % ((str(List[i][0])),str(float(popt[i])),str(float((pcov[i][i])**0.5))))# .format(popt[i], (pcov[i][i])**0.5))
# except:
# pyplot.text(begin+((end-begin)/10), max(fit)-(i+1)*abs((max(fit)-min(fit))/(10)),'test')
#if Show_Error_Bars == True: to do
#pyplot.errorbar(X, Source, yerr = y_sigma, fmt = 'o')
# if Show_Guess == True:
# guess=Function(X,*p0)
# G,=pyplot.plot(X, guess, label='Test data',marker='o',color='g')
# if RAW_Data == True:
# pyplot.legend([S, G, F], ["Data", "initial guess", "Fit"], loc='best',fancybox=True)
# else:
# pyplot.legend([G, F], ["initial guess", "Fit"], loc='best',fancybox=True)
# else:
# if RAW_Data == True:
# pyplot.legend([S, F], ["Data", "Fit"], loc='best',fancybox=True)
# else:
# pyplot.legend([F], ["Fit"], loc='best',fancybox=True)
if Rendering == True:
pyplot.rc('axes',fc='white')
if Figure == None: #Creation of the figure. If Figure is not None, the fit is integrated in pre-existing figure
fig=pyplot.figure(facecolor='white')
else:
fig = Figure
pyplot.title('Fitting completed')
if RAW_Data == True:
S,=pyplot.plot(X, Source,color='b')
try:
if Show_Residuals == True:
final = Source + result.residual
pyplot.plot(X, final, 'r')
except UnboundLocalError: #Should be only in the Plugin, at first launch.
print 'No results'
F,=pyplot.plot(RenderingX, fit, linestyle='--',color=Color)
pyplot.xlim([begin,end])
pyplot.grid(True)
pyplot.show()
return fit,res,fig
else:
return fit
def plotIsoFreqNSDiff(
ax,
freq,
arrayList,
flag,
par="abs",
colorbar=True,
cLevel=True,
mask=None,
contourLine=True,
useLog=False,
):
indUniFreq0 = np.where(freq == arrayList[0]["freq"])
indUniFreq1 = np.where(freq == arrayList[1]["freq"])
seicmap = plt.get_cmap("RdYlBu") # seismic')
x, y = arrayList[0]["x"][indUniFreq0], arrayList[0]["y"][indUniFreq0]
if par == "abs":
if useLog:
zPlot = (np.log10(np.abs(arrayList[0][flag][indUniFreq0])) -
np.log10(np.abs(arrayList[1][flag][indUniFreq1]))
) / np.log10(np.abs(arrayList[1][flag][indUniFreq1]))
else:
zPlot = (np.abs(arrayList[0][flag][indUniFreq0]) -
np.abs(arrayList[1][flag][indUniFreq1])) / np.abs(
arrayList[1][flag][indUniFreq1])
if mask:
maskInd = np.logical_or(
np.abs(arrayList[0][flag][indUniFreq0]) < 1e-3,
np.abs(arrayList[1][flag][indUniFreq1]) < 1e-3,
)
zPlot = np.ma.array(zPlot)
zPlot[maskInd] = mask
if cLevel:
level = np.arange(-200, 201, 10)
clevel = np.arange(-200, 201, 25)
else:
level = np.linspace(zPlot.min(), zPlot.max(), 100)
clevel = np.linspace(zPlot.min(), zPlot.max(), 10)
elif par == "real":
if useLog:
zPlot = (
np.log10(np.real(arrayList[0][flag][indUniFreq0])) -
np.log10(np.real(arrayList[1][flag][indUniFreq1]))) / np.log10(
np.abs((np.real(arrayList[1][flag][indUniFreq1]))))
else:
zPlot = (np.real(arrayList[0][flag][indUniFreq0]) -
np.real(arrayList[1][flag][indUniFreq1])) / np.abs(
(np.real(arrayList[1][flag][indUniFreq1])))
if mask:
maskInd = np.logical_or(
np.abs(np.real(arrayList[0][flag][indUniFreq0])) < 1e-3,
np.abs(np.real(arrayList[1][flag][indUniFreq1])) < 1e-3,
)
zPlot = np.ma.array(zPlot)
zPlot[maskInd] = mask
if cLevel:
level = np.arange(-200, 201, 10)
clevel = np.arange(-200, 201, 25)
else:
level = np.linspace(zPlot.min(), zPlot.max(), 100)
clevel = np.linspace(zPlot.min(), zPlot.max(), 10)
elif par == "imag":
if useLog:
zPlot = (
np.log10(np.imag(arrayList[0][flag][indUniFreq0])) -
np.log10(np.imag(arrayList[1][flag][indUniFreq1]))) / np.log10(
np.abs((np.imag(arrayList[1][flag][indUniFreq1]))))
else:
zPlot = (np.imag(arrayList[0][flag][indUniFreq0]) -
np.imag(arrayList[1][flag][indUniFreq1])) / np.abs(
(np.imag(arrayList[1][flag][indUniFreq1])))
if mask:
maskInd = np.logical_or(
np.abs(np.imag(arrayList[0][flag][indUniFreq0])) < 1e-3,
np.abs(np.imag(arrayList[1][flag][indUniFreq1])) < 1e-3,
)
zPlot = np.ma.array(zPlot)
zPlot[maskInd] = mask
if cLevel:
level = np.arange(-200, 201, 10)
clevel = np.arange(-200, 201, 25)
else:
level = np.linspace(zPlot.min(), zPlot.max(), 100)
clevel = np.linspace(zPlot.min(), zPlot.max(), 10)
cs = ax.tricontourf(
x, y, zPlot * 100, levels=level * 100, cmap=seicmap,
extend="both") # ,norm=colors.SymLogNorm(1e-2,linscale=2))
if contourLine:
csl = ax.tricontour(x, y, zPlot * 100, levels=clevel * 100, colors="k")
plt.clabel(csl, fontsize=7, inline=1, fmt="%1.1e", inline_spacing=10)
if colorbar:
cb = plt.colorbar(cs, cax=ax.cax, ticks=clevel * 100, format="%1.1e")
for t in cb.ax.get_yticklabels():
t.set_rotation(60)
t.set_fontsize(8)
ax.set_title(flag + " " + par, fontsize=8)
def plotIsoFreqNSimpedance(
ax,
freq,
array,
flag,
par="abs",
colorbar=True,
colorNorm="SymLog",
cLevel=True,
contour=True,
):
indUniFreq = np.where(freq == array["freq"])
x, y = array["x"][indUniFreq], array["y"][indUniFreq]
if par == "abs":
zPlot = np.abs(array[flag][indUniFreq])
cmap = plt.get_cmap("OrRd_r") # seismic')
level = np.logspace(0, -5, 31)
clevel = np.logspace(0, -4, 5)
plotNorm = colors.LogNorm()
elif par == "real":
zPlot = np.real(array[flag][indUniFreq])
cmap = plt.get_cmap("RdYlBu")
if cLevel:
level = np.concatenate(
(-np.logspace(0, -10, 31), np.logspace(-10, 0, 31)))
clevel = np.concatenate(
(-np.logspace(0, -8, 5), np.logspace(-8, 0, 5)))
else:
level = np.linspace(zPlot.min(), zPlot.max(), 100)
clevel = np.linspace(zPlot.min(), zPlot.max(), 10)
if colorNorm == "SymLog":
plotNorm = colors.SymLogNorm(1e-10, linscale=2)
else:
plotNorm = colors.Normalize()
elif par == "imag":
zPlot = np.imag(array[flag][indUniFreq])
cmap = plt.get_cmap("RdYlBu")
level = np.concatenate(
(-np.logspace(0, -10, 31), np.logspace(-10, 0, 31)))
clevel = np.concatenate((-np.logspace(0, -8, 5), np.logspace(-8, 0,
5)))
plotNorm = colors.SymLogNorm(1e-10, linscale=2)
if cLevel:
level = np.concatenate(
(-np.logspace(0, -10, 31), np.logspace(-10, 0, 31)))
clevel = np.concatenate(
(-np.logspace(0, -8, 5), np.logspace(-8, 0, 5)))
else:
level = np.linspace(zPlot.min(), zPlot.max(), 100)
clevel = np.linspace(zPlot.min(), zPlot.max(), 10)
if colorNorm == "SymLog":
plotNorm = colors.SymLogNorm(1e-10, linscale=2)
elif colorNorm == "Lin":
plotNorm = colors.Normalize()
if contour:
cs = ax.tricontourf(x,
y,
zPlot,
levels=level,
cmap=cmap,
norm=plotNorm) # ,extend='both')
else:
uniX, uniY = np.unique(x), np.unique(y)
X, Y = np.meshgrid(np.append(uniX - 25, uniX[-1] + 25),
np.append(uniY - 25, uniY[-1] + 25))
cs = ax.pcolor(X,
Y,
np.reshape(zPlot, (len(uniY), len(uniX))),
cmap=cmap,
norm=plotNorm)
if colorbar:
plt.colorbar(cs, cax=ax.cax, ticks=clevel, format="%1.2e")
ax.set_title(flag + " " + par, fontsize=8)
return cs
def plotPsudoSectNSimpedance(
ax,
sectDict,
array,
flag,
par="abs",
colorbar=True,
colorNorm="None",
cLevel=None,
contour=True,
):
indSect = np.where(sectDict.values()[0] == array[sectDict.keys()[0]])
# Define the plot axes
if "x" in sectDict.keys()[0]:
x = array["y"][indSect]
else:
x = array["x"][indSect]
y = array["freq"][indSect]
if par == "abs":
zPlot = np.abs(array[flag][indSect])
cmap = plt.get_cmap("OrRd_r") # seismic')
if cLevel:
level = np.logspace(0, -5, 31, endpoint=True)
clevel = np.logspace(0, -4, 5, endpoint=True)
else:
level = np.linspace(zPlot.min(), zPlot.max(), 100, endpoint=True)
clevel = np.linspace(zPlot.min(), zPlot.max(), 10, endpoint=True)
elif par == "ares":
zPlot = np.abs(array[flag][indSect])**2 / (8 * np.pi**2 * 10**(-7) *
array["freq"][indSect])
cmap = plt.get_cmap("RdYlBu") # seismic)
if cLevel:
zMax = np.log10(cLevel[1])
zMin = np.log10(cLevel[0])
else:
zMax = np.ceil(np.log10(np.abs(zPlot).max()))
zMin = np.floor(np.log10(np.abs(zPlot).min()))
level = np.logspace(zMin, zMax, (zMax - zMin) * 8 + 1, endpoint=True)
clevel = np.logspace(zMin, zMax, (zMax - zMin) * 2 + 1, endpoint=True)
plotNorm = colors.LogNorm()
elif par == "aphs":
zPlot = np.arctan2(array[flag][indSect].imag,
array[flag][indSect].real) * (180 / np.pi)
cmap = plt.get_cmap("RdYlBu") # seismic)
if cLevel:
zMax = cLevel[1]
zMin = cLevel[0]
else:
zMax = np.ceil(zPlot).max()
zMin = np.floor(zPlot).min()
level = np.arange(zMin, zMax + 0.1, 1)
clevel = np.arange(zMin, zMax + 0.1, 10)
plotNorm = colors.Normalize()
elif par == "real":
zPlot = np.real(array[flag][indSect])
cmap = plt.get_cmap("Spectral") # ('RdYlBu')
if cLevel:
zMax = np.log10(cLevel[1])
zMin = np.log10(cLevel[0])
else:
zMax = np.ceil(np.log10(np.abs(zPlot).max()))
zMin = np.floor(np.log10(np.abs(zPlot).min()))
level = np.concatenate((
-np.logspace(
zMax, zMin - 0.125, (zMax - zMin) * 8 + 1, endpoint=True),
np.logspace(zMin - 0.125,
zMax, (zMax - zMin) * 8 + 1,
endpoint=True),
))
clevel = np.concatenate((
-np.logspace(zMax, zMin, (zMax - zMin) * 1 + 1, endpoint=True),
np.logspace(zMin, zMax, (zMax - zMin) * 1 + 1, endpoint=True),
))
plotNorm = colors.SymLogNorm(np.abs(level).min(), linscale=0.1)
elif par == "imag":
zPlot = np.imag(array[flag][indSect])
cmap = plt.get_cmap("Spectral") # ('RdYlBu')
if cLevel:
zMax = np.log10(cLevel[1])
zMin = np.log10(cLevel[0])
else:
zMax = np.ceil(np.log10(np.abs(zPlot).max()))
zMin = np.floor(np.log10(np.abs(zPlot).min()))
level = np.concatenate((
-np.logspace(
zMax, zMin - 0.125, (zMax - zMin) * 8 + 1, endpoint=True),
np.logspace(zMin - 0.125,
zMax, (zMax - zMin) * 8 + 1,
endpoint=True),
))
clevel = np.concatenate((
-np.logspace(zMax, zMin, (zMax - zMin) * 1 + 1, endpoint=True),
np.logspace(zMin, zMax, (zMax - zMin) * 1 + 1, endpoint=True),
))
plotNorm = colors.SymLogNorm(np.abs(level).min(), linscale=0.1)
if colorNorm == "SymLog":
plotNorm = colors.SymLogNorm(np.abs(level).min(), linscale=0.1)
elif colorNorm == "Lin":
plotNorm = colors.Normalize()
elif colorNorm == "Log":
plotNorm = colors.LogNorm()
if contour:
cs = ax.tricontourf(x,
y,
zPlot,
levels=level,
cmap=cmap,
norm=plotNorm) # ,extend='both')
else:
uniX, uniY = np.unique(x), np.unique(y)
X, Y = np.meshgrid(np.append(uniX - 25, uniX[-1] + 25),
np.append(uniY - 25, uniY[-1] + 25))
cs = ax.pcolor(X,
Y,
np.reshape(zPlot, (len(uniY), len(uniX))),
cmap=cmap,
norm=plotNorm)
if colorbar:
csB = plt.colorbar(cs, cax=ax.cax, ticks=clevel, format="%1.2e")
# csB.on_mappable_changed(cs)
ax.set_title(flag + " " + par, fontsize=8)
return cs, csB
return cs, None
def Fit(self,
Source,
X=None,
function='line',
Guess=numpy.ones(10),
Show_Residuals=True,
StartP=None,
EndP=None,
StartX=None,
EndX=None,
Show_Guess=False,
Figure=None,
Rendering=True,
RAW_Data=True,
Color='r',
Show_Parameters=True,
Number_of_points=1000.,
Bounds=[[None, None]] * 10,
Model=False):
"""
Source is the signal to fit
X is the XScale
function is the fitting function.
Avaible functions are : line,
poly2,
poly3,
poly4,
gauss,
exp,
double_exp,
double_exp_xoffset,
sin,
hill,
sigmoid,
power,
beta
p0 are the intitial guesses (default is a list of 1.)
StartP and EndP are the start point and the End point of the fit (in points)
StartX and EndX are the start point and the End point of the fit (in timescale units)
"""
def find_indexes(Source, Min, Max):
c = abs(numpy.array(Source) - Min)
Min_index = list(c).index(min(c))
c = abs(numpy.array(Source) - Max)
Max_index = list(c).index(min(c))
return Min_index, Max_index
begin = X[
0] #original begin and end points are stored here, so the final plot will be displayed in the original workspace
end = X[-1]
if (StartP == None) and (StartX == None):
StartP = 0
StartX = 0
if (EndP == None) and (EndX == None) == None:
EndP = len(Source) - 1
EndX = len(Source) - 1
if (StartP == None) or (EndP == None):
StartP, EndP = find_indexes(X, StartX, EndX)
else:
StartX = X[StartP]
EndX = X[EndP]
Source = numpy.array(
Source[StartP:EndP]
) #if not using the whole range for the fit, truncated wave is created here
X = numpy.array(
X[StartP:EndP]
) #if not using the whole range for the fit, truncated timescale is created here
# Data and Xscale
Source = numpy.array(Source)
if X == None or (len(Source) != len(X)):
X = numpy.array(range(len(Source)))
# create a set of Parameters
Function = eval("self." + function) #the fitting function
Formula = eval("self." +
function.capitalize())[0] #the fitting formula
Parameters_Names = eval("self." + function.capitalize())[
1] ##list of the fitting parameters
for i, j in enumerate(Bounds):
if Bounds[i][0] != None:
Bounds[i][0] = numpy.float32(Bounds[i][0])
if Bounds[i][1] != None:
Bounds[i][1] = numpy.float32(Bounds[i][1])
#print Bounds
p0 = Parameters()
# do fit, here with leastsq model
if Model == False:
for i, j in enumerate(Parameters_Names):
#For each paramters, you can set value, min & max. i.e. p0.add('omega', value= 0.0, min=-numpy.pi/2., max=numpy.pi/2)
if j != '0':
if Bounds[i][0] != None and Bounds[i][1] != None:
p0.add(j,
value=Guess[i],
min=Bounds[i][0],
max=Bounds[i][1])
elif Bounds[i][0] != None and Bounds[i][1] == None:
p0.add(j, value=Guess[i], min=Bounds[i][0])
elif Bounds[i][0] == None and Bounds[i][1] != None:
p0.add(j, value=Guess[i], max=Bounds[i][1])
else:
p0.add(j, value=Guess[i])
print 'Fitting in process ...'
try:
result = minimize(Function, p0, args=(X, Source))
RenderingX = numpy.linspace(float(X[0]),
float(X[-1]),
num=Number_of_points)
fit = Function(
result.params, RenderingX
) #Rendering, with Popt, the best fitting parameters
except:
print 'Fitting failed, try other parameters or constraints'
return
print 'Fitting performed between points ', StartP, ' and ', EndP, ' (', len(
X), ' points)'
print 'in units between: ', StartX, ' and ', EndX
print '######### FITTING RESULTS ############'
print 'Parameters are :'
res = []
for i in list(result.params):
print i, result.params[i].value
res.append(result.params[i].value)
elif Model == True:
for i, j in enumerate(Parameters_Names):
if j != '0':
p0.add(j, value=Guess[i])
RenderingX = numpy.linspace(float(X[0]),
float(X[-1]),
num=Number_of_points)
fit = Function(
p0,
RenderingX) #Rendering, with Popt, the best fitting parameters
# if Show_Parameters == True:
# for i in range(len(popt)):
# if List[i][0] != '0':
# try:
# pyplot.text(begin+((end-begin)/10), max(fit)-(i+1)*abs((max(fit)-min(fit))/(10)), r"%s = {%s} +/- {%s}" % ((str(List[i][0])),str(float(popt[i])),str(float((pcov[i][i])**0.5))))# .format(popt[i], (pcov[i][i])**0.5))
# except:
# pyplot.text(begin+((end-begin)/10), max(fit)-(i+1)*abs((max(fit)-min(fit))/(10)),'test')
#if Show_Error_Bars == True: to do
#pyplot.errorbar(X, Source, yerr = y_sigma, fmt = 'o')
# if Show_Guess == True:
# guess=Function(X,*p0)
# G,=pyplot.plot(X, guess, label='Test data',marker='o',color='g')
# if RAW_Data == True:
# pyplot.legend([S, G, F], ["Data", "initial guess", "Fit"], loc='best',fancybox=True)
# else:
# pyplot.legend([G, F], ["initial guess", "Fit"], loc='best',fancybox=True)
# else:
# if RAW_Data == True:
# pyplot.legend([S, F], ["Data", "Fit"], loc='best',fancybox=True)
# else:
# pyplot.legend([F], ["Fit"], loc='best',fancybox=True)
if Rendering == True:
pyplot.rc('axes', fc='white')
if Figure == None: #Creation of the figure. If Figure is not None, the fit is integrated in pre-existing figure
fig = pyplot.figure(facecolor='white')
else:
fig = Figure
pyplot.title('Fitting completed')
if RAW_Data == True:
S, = pyplot.plot(X, Source, color='b')
try:
if Show_Residuals == True:
final = Source + result.residual
pyplot.plot(X, final, 'r')
except UnboundLocalError: #Should be only in the Plugin, at first launch.
print 'No results'
F, = pyplot.plot(RenderingX, fit, linestyle='--', color=Color)
pyplot.xlim([begin, end])
pyplot.grid(True)
pyplot.show()
return fit, res, fig
else:
return fit
def plotIsoFreqNStipper(ax,
freq,
array,
flag,
par='abs',
colorbar=True,
colorNorm='SymLog',
cLevel=True,
contour=True):
indUniFreq = np.where(freq == array['freq'])
x, y = array['x'][indUniFreq], array['y'][indUniFreq]
if par == 'abs':
cmap = plt.get_cmap('OrRd_r') #seismic')
zPlot = np.abs(array[flag][indUniFreq])
if cLevel:
level = np.logspace(-4, 0, 33)
clevel = np.logspace(-4, 0, 5)
else:
level = np.linspace(zPlot.min(), zPlot.max(), 100)
clevel = np.linspace(zPlot.min(), zPlot.max(), 10)
if colorNorm == 'SymLog':
plotNorm = colors.LogNorm()
else:
plotNorm = colors.Normalize()
elif par == 'real':
cmap = plt.get_cmap('RdYlBu')
zPlot = np.real(array[flag][indUniFreq])
if cLevel:
level = np.concatenate(
(-np.logspace(0, -4, 33), np.logspace(-4, 0, 33)))
clevel = np.concatenate(
(-np.logspace(0, -4, 5), np.logspace(-4, 0, 5)))
else:
level = np.linspace(zPlot.min(), zPlot.max(), 100)
clevel = np.linspace(zPlot.min(), zPlot.max(), 10)
if colorNorm == 'SymLog':
plotNorm = colors.SymLogNorm(1e-4, linscale=2)
else:
plotNorm = colors.Normalize()
elif par == 'imag':
cmap = plt.get_cmap('RdYlBu')
zPlot = np.imag(array[flag][indUniFreq])
if cLevel:
level = np.concatenate(
(-np.logspace(0, -4, 33), np.logspace(-4, 0, 33)))
clevel = np.concatenate(
(-np.logspace(0, -4, 5), np.logspace(-4, 0, 5)))
else:
level = np.linspace(zPlot.min(), zPlot.max(), 100)
clevel = np.linspace(zPlot.min(), zPlot.max(), 10)
if colorNorm == 'SymLog':
plotNorm = colors.SymLogNorm(1e-4, linscale=2)
else:
plotNorm = colors.Normalize()
if contour:
cs = ax.tricontourf(x,
y,
zPlot,
levels=level,
cmap=cmap,
norm=plotNorm) #,extend='both')
else:
uniX, uniY = np.unique(x), np.unique(y)
X, Y = np.meshgrid(np.append(uniX - 25, uniX[-1] + 25),
np.append(uniY - 25, uniY[-1] + 25))
cs = ax.pcolor(X,
Y,
np.reshape(zPlot, (len(uniY), len(uniX))),
levels=level,
cmap=cmap,
norm=plotNorm,
edgecolors='k',
linewidths=0.5)
if colorbar:
plt.colorbar(cs, cax=ax.cax, ticks=clevel, format='%1.2e')
ax.set_title(flag + ' ' + par, fontsize=8)
def plotIsoFreqNSDiff(ax,freq,arrayList,flag,par='abs',colorbar=True,cLevel=True,mask=None,contourLine=True,useLog=False):
indUniFreq0 = np.where(freq==arrayList[0]['freq'])
indUniFreq1 = np.where(freq==arrayList[1]['freq'])
seicmap = plt.get_cmap('RdYlBu')#seismic')
x, y = arrayList[0]['x'][indUniFreq0],arrayList[0]['y'][indUniFreq0]
if par == 'abs':
if useLog:
zPlot = (np.log10(np.abs(arrayList[0][flag][indUniFreq0])) - np.log10(np.abs(arrayList[1][flag][indUniFreq1])))/np.log10(np.abs(arrayList[1][flag][indUniFreq1]))
else:
zPlot = (np.abs(arrayList[0][flag][indUniFreq0]) - np.abs(arrayList[1][flag][indUniFreq1]))/np.abs(arrayList[1][flag][indUniFreq1])
if mask:
maskInd = np.logical_or(np.abs(arrayList[0][flag][indUniFreq0])< 1e-3,np.abs(arrayList[1][flag][indUniFreq1]) < 1e-3)
zPlot = np.ma.array(zPlot)
zPlot[maskInd] = mask
if cLevel:
level = np.arange(-200,201,10)
clevel = np.arange(-200,201,25)
else:
level = np.linspace(zPlot.min(),zPlot.max(),100)
clevel = np.linspace(zPlot.min(),zPlot.max(),10)
elif par == 'real':
if useLog:
zPlot = (np.log10(np.real(arrayList[0][flag][indUniFreq0])) -np.log10(np.real(arrayList[1][flag][indUniFreq1])))/np.log10(np.abs((np.real(arrayList[1][flag][indUniFreq1]))))
else:
zPlot = (np.real(arrayList[0][flag][indUniFreq0]) -np.real(arrayList[1][flag][indUniFreq1]))/np.abs((np.real(arrayList[1][flag][indUniFreq1])))
if mask:
maskInd = np.logical_or(np.abs(np.real(arrayList[0][flag][indUniFreq0])) < 1e-3,np.abs(np.real(arrayList[1][flag][indUniFreq1])) < 1e-3)
zPlot = np.ma.array(zPlot)
zPlot[maskInd] = mask
if cLevel:
level = np.arange(-200,201,10)
clevel = np.arange(-200,201,25)
else:
level = np.linspace(zPlot.min(),zPlot.max(),100)
clevel = np.linspace(zPlot.min(),zPlot.max(),10)
elif par == 'imag':
if useLog:
zPlot = (np.log10(np.imag(arrayList[0][flag][indUniFreq0])) -np.log10(np.imag(arrayList[1][flag][indUniFreq1])))/np.log10(np.abs((np.imag(arrayList[1][flag][indUniFreq1]))))
else:
zPlot = (np.imag(arrayList[0][flag][indUniFreq0]) -np.imag(arrayList[1][flag][indUniFreq1]))/np.abs((np.imag(arrayList[1][flag][indUniFreq1])))
if mask:
maskInd = np.logical_or(np.abs(np.imag(arrayList[0][flag][indUniFreq0])) < 1e-3,np.abs(np.imag(arrayList[1][flag][indUniFreq1])) < 1e-3)
zPlot = np.ma.array(zPlot)
zPlot[maskInd] = mask
if cLevel:
level = np.arange(-200,201,10)
clevel = np.arange(-200,201,25)
else:
level = np.linspace(zPlot.min(),zPlot.max(),100)
clevel = np.linspace(zPlot.min(),zPlot.max(),10)
cs = ax.tricontourf(x,y,zPlot*100,levels=level*100,cmap=seicmap,extend='both') #,norm=colors.SymLogNorm(1e-2,linscale=2))
if contourLine:
csl = ax.tricontour(x,y,zPlot*100,levels=clevel*100,colors='k')
plt.clabel(csl, fontsize=7, inline=1,fmt='%1.1e',inline_spacing=10)
if colorbar:
cb = plt.colorbar(cs,cax=ax.cax,ticks=clevel*100,format='%1.1e')
for t in cb.ax.get_yticklabels():
t.set_rotation(60)
t.set_fontsize(8)
ax.set_title(flag+' '+par,fontsize=8)
def plotPsudoSectNSimpedance(ax,sectDict,array,flag,par='abs',colorbar=True,colorNorm='None',cLevel=None,contour=True):
indSect = np.where(sectDict.values()[0]==array[sectDict.keys()[0]])
# Define the plot axes
if 'x' in sectDict.keys()[0]:
x = array['y'][indSect]
else:
x = array['x'][indSect]
y = array['freq'][indSect]
if par == 'abs':
zPlot = np.abs(array[flag][indSect])
cmap = plt.get_cmap('OrRd_r')#seismic')
if cLevel:
level = np.logspace(0,-5,31,endpoint=True)
clevel = np.logspace(0,-4,5,endpoint=True)
else:
level = np.linspace(zPlot.min(),zPlot.max(),100,endpoint=True)
clevel = np.linspace(zPlot.min(),zPlot.max(),10,endpoint=True)
elif par == 'ares':
zPlot = np.abs(array[flag][indSect])**2/(8*np.pi**2*10**(-7)*array['freq'][indSect])
cmap = plt.get_cmap('RdYlBu')#seismic)
if cLevel:
zMax = np.log10(cLevel[1])
zMin = np.log10(cLevel[0])
else:
zMax = (np.ceil(np.log10(np.abs(zPlot).max())))
zMin = (np.floor(np.log10(np.abs(zPlot).min())))
level = np.logspace(zMin,zMax,(zMax-zMin)*8+1,endpoint=True)
clevel = np.logspace(zMin,zMax,(zMax-zMin)*2+1,endpoint=True)
plotNorm = colors.LogNorm()
elif par == 'aphs':
zPlot = np.arctan2(array[flag][indSect].imag,array[flag][indSect].real)*(180/np.pi)
cmap = plt.get_cmap('RdYlBu')#seismic)
if cLevel:
zMax = cLevel[1]
zMin = cLevel[0]
else:
zMax = (np.ceil(zPlot).max())
zMin = (np.floor(zPlot).min())
level = np.arange(zMin,zMax+.1,1)
clevel = np.arange(zMin,zMax+.1,10)
plotNorm = colors.Normalize()
elif par == 'real':
zPlot = np.real(array[flag][indSect])
cmap = plt.get_cmap('Spectral') #('RdYlBu')
if cLevel:
zMax = np.log10(cLevel[1])
zMin = np.log10(cLevel[0])
else:
zMax = (np.ceil(np.log10(np.abs(zPlot).max())))
zMin = (np.floor(np.log10(np.abs(zPlot).min())))
level = np.concatenate((-np.logspace(zMax,zMin-.125,(zMax-zMin)*8+1,endpoint=True),np.logspace(zMin-.125,zMax,(zMax-zMin)*8+1,endpoint=True)))
clevel = np.concatenate((-np.logspace(zMax,zMin,(zMax-zMin)*1+1,endpoint=True),np.logspace(zMin,zMax,(zMax-zMin)*1+1,endpoint=True)))
plotNorm = colors.SymLogNorm(np.abs(level).min(),linscale=0.1)
elif par == 'imag':
zPlot = np.imag(array[flag][indSect])
cmap = plt.get_cmap('Spectral') #('RdYlBu')
if cLevel:
zMax = np.log10(cLevel[1])
zMin = np.log10(cLevel[0])
else:
zMax = (np.ceil(np.log10(np.abs(zPlot).max())))
zMin = (np.floor(np.log10(np.abs(zPlot).min())))
level = np.concatenate((-np.logspace(zMax,zMin-.125,(zMax-zMin)*8+1,endpoint=True),np.logspace(zMin-.125,zMax,(zMax-zMin)*8+1,endpoint=True)))
clevel = np.concatenate((-np.logspace(zMax,zMin,(zMax-zMin)*1+1,endpoint=True),np.logspace(zMin,zMax,(zMax-zMin)*1+1,endpoint=True)))
plotNorm = colors.SymLogNorm(np.abs(level).min(),linscale=0.1)
if colorNorm=='SymLog':
plotNorm = colors.SymLogNorm(np.abs(level).min(),linscale=0.1)
elif colorNorm=='Lin':
plotNorm = colors.Normalize()
elif colorNorm=='Log':
plotNorm = colors.LogNorm()
if contour:
cs = ax.tricontourf(x,y,zPlot,levels=level,cmap=cmap,norm=plotNorm)#,extend='both')
else:
uniX,uniY = np.unique(x),np.unique(y)
X,Y = np.meshgrid(np.append(uniX-25,uniX[-1]+25),np.append(uniY-25,uniY[-1]+25))
cs = ax.pcolor(X,Y,np.reshape(zPlot,(len(uniY),len(uniX))),cmap=cmap,norm=plotNorm)
if colorbar:
csB = plt.colorbar(cs,cax=ax.cax,ticks=clevel,format='%1.2e')
# csB.on_mappable_changed(cs)
ax.set_title(flag+' '+par,fontsize=8)
return cs, csB
return cs,None
#!/usr/bin/python3
from matplotlib import pyplot, numpy
from math import pi, e
SAMPLES_N = 80
ARG_MAX = 2 * pi
arguments = list(numpy.linspace(0, ARG_MAX, SAMPLES_N + 1))
y = []
for x in arguments:
y.append(e ** (1j * x))
y = numpy.array(y)
# Flat unit circle plot
figure = pyplot.figure()
ax = figure.add_subplot(121, xlabel="Re", ylabel="Im", title="Unit circle")
ax.scatter(y.real, y.imag, numpy.linspace(0, ARG_MAX, SAMPLES_N + 1) * 3)
ax.grid(True)
# 3D helix plot
ax2 = figure.add_subplot(122, projection='3d', xlabel="Re", ylabel="θ", zlabel="Im", title="Complex exponential")
ax2.plot(y.real, arguments, y.imag)
pyplot.show()
print(supervivientes)
while (i < maximo):
r1 = random.randint(0, 3)
r2 = random.randint(0, 3)
return otraGeneracion
def f(x):
return (-50 * x * x) + 500 * x
def f_dev(x):
return (-100 * x) + 500
sample = np.linspace(-50, 100, num=30)
mutacion = 25
rangoSupervivencia = 4
poblacionInicial = generarPoblacion(8, 20, -10)
i = 1
index = -1
while (i < 50):
j = 0
r = 0
print("Generacion " + str(i))
print(poblacionInicial[0])
for x in poblacionInicial[0]:
res = f_dev(x)
poblacionInicial[1].append(res)
#!/usr/bin/python3
#Exercise 1.4 in Owen's "Practical Signal Processing"
from matplotlib import pyplot, numpy, colors
from math import pi, e, radians
#Unit circle
SAMPLES_N = 160
ARG_MAX = 2 * pi
t = list(numpy.linspace(0, ARG_MAX, SAMPLES_N + 1))
y = list(map(lambda x: e**(1j * x), t))
y = numpy.array(y)
#Handles and labels for the legend
labels = [r'$e^{j\theta}$', 'Series A', 'Series B', 'Series C']
handles = []
#Plot circle
FIG_SIZE = 8 # Side of a square
fig, ax = pyplot.subplots(figsize=(FIG_SIZE, FIG_SIZE))
handles += ax.plot(y.real, y.imag)
ax.set(xlabel="Re", ylabel="Im", title="Unit circle")
#Series of measurement data
data = [[12, 15, 13, 9, 16], [358, 1, 359, 355, 2], [210, 290, 10, 90, 170]]
#Let measurements 'degrade'
weights = [5, 4, 3, 2, 1]
#Averaging results
from matplotlib import numpy as np
from matplotlib import pyplot as plt
import scipy.optimize as opt
def twoD_Gaussian((x, y), amplitude, xo, yo, sigma_x, sigma_y, theta, offset):
xo = float(xo)
yo = float(yo)
a = (np.cos(theta)**2)/(2*sigma_x**2) + (np.sin(theta)**2)/(2*sigma_y**2)
b = -(np.sin(2*theta))/(4*sigma_x**2) + (np.sin(2*theta))/(4*sigma_y**2)
c = (np.sin(theta)**2)/(2*sigma_x**2) + (np.cos(theta)**2)/(2*sigma_y**2)
g = offset + amplitude*np.exp( - (a*((x-xo)**2) + 2*b*(x-xo)*(y-yo)
+ c*((y-yo)**2)))
return g.ravel()
# Create x and y indices
x = np.linspace(0, 200, 201)
y = np.linspace(0, 200, 201)
x, y = np.meshgrid(x, y)
#create data
data = twoD_Gaussian((x, y), 3, 100, 100, 20, 40, 0, 10)
# plot twoD_Gaussian data generated above
plt.figure()
plt.imshow(data.reshape(201, 201))
plt.colorbar()
# add some noise to the data and try to fit the data generated beforehand
initial_guess = (3,100,100,20,40,0,10)
data_noisy = data + 0.2*np.random.normal(size=data.shape)
