def plotIsoStaImpedance(ax, loc, array, flag, par='abs',
pSym='s', pColor=None, addLabel='', zorder=1):
appResFact = 1/(8*np.pi**2*10**(-7))
treshold = 1.0 # 1 meter
indUniSta = np.sqrt(np.sum((array[['x','y']].view((float,2))-loc)**2,axis=1)) < treshold
freq = array['freq'][indUniSta]
if par == 'abs':
zPlot = np.abs(array[flag][indUniSta])
elif par == 'real':
zPlot = np.real(array[flag][indUniSta])
elif par == 'imag':
zPlot = np.imag(array[flag][indUniSta])
elif par == 'res':
zPlot = (appResFact/freq)*np.abs(array[flag][indUniSta])**2
elif par == 'phs':
zPlot = np.arctan2(array[flag][indUniSta].imag,array[flag][indUniSta].real)*(180/np.pi)
if not pColor:
if 'xx' in flag:
lab = 'XX'
pColor = 'g'
elif 'xy' in flag:
lab = 'XY'
pColor = 'r'
elif 'yx' in flag:
lab = 'YX'
pColor = 'b'
elif 'yy' in flag:
lab = 'YY'
pColor = 'y'
ax.plot(freq,zPlot,color=pColor,marker=pSym,label=flag+addLabel,zorder=zorder)
def plotIsoStaImpedance(ax,
loc,
array,
flag,
par="abs",
pSym="s",
pColor=None,
addLabel="",
zorder=1):
appResFact = 1 / (8 * np.pi**2 * 10**(-7))
treshold = 1.0 # 1 meter
indUniSta = (np.sqrt(
np.sum((array[["x", "y"]].copy().view(
(float, 2)) - loc)**2, axis=1)) < treshold)
freq = array["freq"][indUniSta]
if par == "abs":
zPlot = np.abs(array[flag][indUniSta])
elif par == "real":
zPlot = np.real(array[flag][indUniSta])
elif par == "imag":
zPlot = np.imag(array[flag][indUniSta])
elif par == "res":
zPlot = (appResFact / freq) * np.abs(array[flag][indUniSta])**2
elif par == "phs":
zPlot = np.arctan2(array[flag][indUniSta].imag,
array[flag][indUniSta].real) * (180 / np.pi)
if not pColor:
if "xx" in flag:
lab = "XX"
pColor = "g"
elif "xy" in flag:
lab = "XY"
pColor = "r"
elif "yx" in flag:
lab = "YX"
pColor = "b"
elif "yy" in flag:
lab = "YY"
pColor = "y"
ax.plot(freq,
zPlot,
color=pColor,
marker=pSym,
label=flag + addLabel,
zorder=zorder)
def plotIsoStaImpedance(ax,
loc,
array,
flag,
par='abs',
pSym='s',
pColor=None,
addLabel='',
zorder=1):
appResFact = 1 / (8 * np.pi**2 * 10**(-7))
treshold = 1.0 # 1 meter
indUniSta = np.sqrt(
np.sum((array[['x', 'y']].view(
(float, 2)) - loc)**2, axis=1)) < treshold
freq = array['freq'][indUniSta]
if par == 'abs':
zPlot = np.abs(array[flag][indUniSta])
elif par == 'real':
zPlot = np.real(array[flag][indUniSta])
elif par == 'imag':
zPlot = np.imag(array[flag][indUniSta])
elif par == 'res':
zPlot = (appResFact / freq) * np.abs(array[flag][indUniSta])**2
elif par == 'phs':
zPlot = np.arctan2(array[flag][indUniSta].imag,
array[flag][indUniSta].real) * (180 / np.pi)
if not pColor:
if 'xx' in flag:
lab = 'XX'
pColor = 'g'
elif 'xy' in flag:
lab = 'XY'
pColor = 'r'
elif 'yx' in flag:
lab = 'YX'
pColor = 'b'
elif 'yy' in flag:
lab = 'YY'
pColor = 'y'
ax.plot(freq,
zPlot,
color=pColor,
marker=pSym,
label=flag + addLabel,
zorder=zorder)
def update_v(): global detv detv=0 for i in xrange(1,len(x),-1): for j in xrange(1,len(y),-1): if (i==len(x)/3or i==len(x)*2/3) and len(y)/3<j<len(y)*2/3: continue tmp=(v[i-1][j]+v[i+1][j]+v[i][j-1]+v[i][j+1])/4 detv+=numpy.abs(tmp-v[i][j]) v[i][j]=tmp
def update_V():
global detV
detV = 0
for i in xrange(1, len(X) - 1):
for j in xrange(1, len(Y) - 1):
if (i==len(X)/3 or i==len(X)*2/3) and len(Y)/3<j<len(Y)*2/3:
continue
tmp = (V[i-1][j] + V[i+1][j] + V[i][j-1] + V[i][j+1]) / 4
detV += numpy.abs(tmp - V[i][j])
V[i][j] = tmp
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)
# file_for_decompression = np.transpose(file_for_decompression)
return np.array(row_idft)
if __name__ == '__main__':
types = ['.jpg'] # Making the list of possible file types
output_path = 'AlbertBadretdinovOutputs'
create_folder(output_path)
for file_path in glob.glob(
"input" + "/*",
recursive=True): # And for every file of the type do
file = mpimg.imread(file_path)
compressed = fftc(file)
compressed_output_path = output_path + '/' + file_path[
6:-4] + 'Compressed' + types[0]
comp_save = np.log(np.abs(compressed))
mpimg.imsave(compressed_output_path,
comp_save,
cmap='gray',
vmin=0,
vmax=np.amax(comp_save))
decompressed = fftdc(compressed)
decompressed_output_path = output_path + '/' + file_path[
6:-4] + 'Decompressed' + types[0]
decomp_save = np.log(np.abs(decompressed))
mpimg.imsave(decompressed_output_path,
decomp_save,
cmap='gray',
vmin=0,
vmax=decomp_save.max())
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 plotPsudoSectNSDiff(
ax,
sectDict,
arrayList,
flag,
par="abs",
colorbar=True,
colorNorm="SymLog",
cLevel=None,
contour=True,
mask=None,
useLog=False,
):
def sortInArr(arr):
return np.sort(arr, order=["freq", "x", "y", "z"])
# Find the index for the slice
indSect0 = np.where(
sectDict.values()[0] == arrayList[0][sectDict.keys()[0]])
indSect1 = np.where(
sectDict.values()[0] == arrayList[1][sectDict.keys()[0]])
# Extract and sort the mats
arr0 = sortInArr(arrayList[0][indSect0])
arr1 = sortInArr(arrayList[1][indSect1])
# Define the plot axes
if "x" in sectDict.keys()[0]:
x0 = arr0["y"]
x1 = arr1["y"]
else:
x0 = arr0["x"]
x1 = arr1["x"]
y0 = arr0["freq"]
y1 = arr1["freq"]
if par == "abs":
if useLog:
zPlot = (np.log10(np.abs(arr0[flag])) - np.log10(np.abs(
arr1[flag]))) / np.log10(np.abs(arr1[flag]))
else:
zPlot = (np.abs(arr0[flag]) - np.abs(arr1[flag])) / np.abs(
arr1[flag])
if mask:
maskInd = np.logical_or(
np.abs(arr0[flag]) < 1e-3,
np.abs(arr1[flag]) < 1e-3)
zPlot = np.ma.array(zPlot)
zPlot[maskInd] = mask
cmap = plt.get_cmap("RdYlBu") # seismic)
elif par == "ares":
arF = 1 / (8 * np.pi**2 * 10**(-7))
if useLog:
zPlot = (np.log10(
(arF / arr0["freq"]) * np.abs(arr0[flag])**2) - np.log10(
(arF / arr1["freq"]) * np.abs(arr1[flag])**2)) / np.log10(
(arF / arr1["freq"]) * np.abs(arr1[flag])**2)
else:
zPlot = ((arF / arr0["freq"]) * np.abs(arr0[flag])**2 -
(arF / arr1["freq"]) * np.abs(arr1[flag])**2) / (
(arF / arr1["freq"]) * np.abs(arr1[flag])**2)
if mask:
maskInd = np.logical_or(
np.abs(arr0[flag]) < 1e-3,
np.abs(arr1[flag]) < 1e-3)
zPlot = np.ma.array(zPlot)
zPlot[maskInd] = mask
cmap = plt.get_cmap("Spectral") # seismic)
elif par == "aphs":
if useLog:
zPlot = (np.log10(
np.arctan2(arr0[flag].imag, arr0[flag].real) *
(180 / np.pi)) - np.log10(
np.arctan2(arr1[flag].imag, arr1[flag].real) *
(180 / np.pi))) / np.log10(
np.arctan2(arr1[flag].imag, arr1[flag].real) *
(180 / np.pi))
else:
zPlot = (np.arctan2(arr0[flag].imag, arr0[flag].real) *
(180 / np.pi) -
np.arctan2(arr1[flag].imag, arr1[flag].real) *
(180 / np.pi)) / (
np.arctan2(arr1[flag].imag, arr1[flag].real) *
(180 / np.pi))
if mask:
maskInd = np.logical_or(
np.abs(arr0[flag]) < 1e-3,
np.abs(arr1[flag]) < 1e-3)
zPlot = np.ma.array(zPlot)
zPlot[maskInd] = mask
cmap = plt.get_cmap("Spectral") # seismic)
elif par == "real":
if useLog:
zPlot = (np.log10(arr0[flag].real) -
np.log10(arr1[flag].real)) / np.log10(arr1[flag].real)
else:
zPlot = (arr0[flag].real - arr1[flag].real) / arr1[flag].real
if mask:
maskInd = np.logical_or(arr0[flag].real < 1e-3,
arr1[flag].real < 1e-3)
zPlot = np.ma.array(zPlot)
zPlot[maskInd] = mask
cmap = plt.get_cmap("Spectral") # ('Spectral')
elif par == "imag":
if useLog:
zPlot = (np.log10(arr0[flag].imag) -
np.log10(arr1[flag].imag)) / np.log10(arr1[flag].imag)
else:
zPlot = (arr0[flag].imag - arr1[flag].imag) / arr1[flag].imag
if mask:
maskInd = np.logical_or(arr0[flag].imag < 1e-3,
arr1[flag].imag < 1e-3)
zPlot = np.ma.array(zPlot)
zPlot[maskInd] = mask
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()))
if colorNorm == "SymLog":
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 colorNorm == "Lin":
if cLevel:
level = np.arange(cLevel[0], cLevel[1] + 0.1,
(cLevel[1] - cLevel[0]) / 50.0)
clevel = np.arange(cLevel[0], cLevel[1] + 0.1,
(cLevel[1] - cLevel[0]) / 10.0)
else:
level = np.arange(zPlot.min(), zPlot.max(),
(zPlot.max() - zPlot.min()) / 50.0)
clevel = np.arange(zPlot.min(), zPlot.max(),
(zPlot.max() - zPlot.min()) / 10.0)
plotNorm = colors.Normalize()
elif colorNorm == "Log":
level = np.logspace(zMin - 0.125,
zMax, (zMax - zMin) * 8 + 1,
endpoint=True)
clevel = np.logspace(zMin, zMax, (zMax - zMin) * 2 + 1, endpoint=True)
plotNorm = colors.LogNorm()
if contour:
cs = ax.tricontourf(
x0,
y0,
zPlot * 100,
levels=level * 100,
cmap=cmap,
norm=plotNorm,
extend="both",
) # ,extend='both')
else:
uniX, uniY = np.unique(x0), np.unique(y0)
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 * 100, format="%1.2e")
# csB.on_mappable_changed(cs)
ax.set_title(flag + " " + par + " diff", fontsize=8)
return cs, csB
return cs, None
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 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 plotPsudoSectNSDiff(ax,sectDict,arrayList,flag,par='abs',colorbar=True,colorNorm='SymLog',cLevel=None,contour=True,mask=None,useLog=False):
def sortInArr(arr):
return np.sort(arr,order=['freq','x','y','z'])
# Find the index for the slice
indSect0 = np.where(sectDict.values()[0]==arrayList[0][sectDict.keys()[0]])
indSect1 = np.where(sectDict.values()[0]==arrayList[1][sectDict.keys()[0]])
# Extract and sort the mats
arr0 = sortInArr(arrayList[0][indSect0])
arr1 = sortInArr(arrayList[1][indSect1])
# Define the plot axes
if 'x' in sectDict.keys()[0]:
x0 = arr0['y']
x1 = arr1['y']
else:
x0 = arr0['x']
x1 = arr1['x']
y0 = arr0['freq']
y1 = arr1['freq']
if par == 'abs':
if useLog:
zPlot = (np.log10(np.abs(arr0[flag])) - np.log10(np.abs(arr1[flag])))/np.log10(np.abs(arr1[flag]))
else:
zPlot = (np.abs(arr0[flag]) - np.abs(arr1[flag]))/np.abs(arr1[flag])
if mask:
maskInd = np.logical_or(np.abs(arr0[flag])< 1e-3,np.abs(arr1[flag]) < 1e-3)
zPlot = np.ma.array(zPlot)
zPlot[maskInd] = mask
cmap = plt.get_cmap('RdYlBu')#seismic)
elif par == 'ares':
arF = 1/(8*np.pi**2*10**(-7))
if useLog:
zPlot = (np.log10((arF/arr0['freq'])*np.abs(arr0[flag])**2) - np.log10((arF/arr1['freq'])*np.abs(arr1[flag])**2))/np.log10((arF/arr1['freq'])*np.abs(arr1[flag])**2)
else:
zPlot = ((arF/arr0['freq'])*np.abs(arr0[flag])**2 - (arF/arr1['freq'])*np.abs(arr1[flag])**2)/((arF/arr1['freq'])*np.abs(arr1[flag])**2)
if mask:
maskInd = np.logical_or(np.abs(arr0[flag])< 1e-3,np.abs(arr1[flag]) < 1e-3)
zPlot = np.ma.array(zPlot)
zPlot[maskInd] = mask
cmap = plt.get_cmap('Spectral')#seismic)
elif par == 'aphs':
if useLog:
zPlot = (np.log10(np.arctan2(arr0[flag].imag,arr0[flag].real)*(180/np.pi)) - np.log10(np.arctan2(arr1[flag].imag,arr1[flag].real)*(180/np.pi)) )/np.log10(np.arctan2(arr1[flag].imag,arr1[flag].real)*(180/np.pi))
else:
zPlot = ( np.arctan2(arr0[flag].imag,arr0[flag].real)*(180/np.pi) - np.arctan2(arr1[flag].imag,arr1[flag].real)*(180/np.pi) )/(np.arctan2(arr1[flag].imag,arr1[flag].real)*(180/np.pi))
if mask:
maskInd = np.logical_or(np.abs(arr0[flag])< 1e-3,np.abs(arr1[flag]) < 1e-3)
zPlot = np.ma.array(zPlot)
zPlot[maskInd] = mask
cmap = plt.get_cmap('Spectral')#seismic)
elif par == 'real':
if useLog:
zPlot = (np.log10(arr0[flag].real) - np.log10(arr1[flag].real))/np.log10(arr1[flag].real)
else:
zPlot = (arr0[flag].real - arr1[flag].real)/arr1[flag].real
if mask:
maskInd = np.logical_or(arr0[flag].real< 1e-3,arr1[flag].real < 1e-3)
zPlot = np.ma.array(zPlot)
zPlot[maskInd] = mask
cmap = plt.get_cmap('Spectral') #('Spectral')
elif par == 'imag':
if useLog:
zPlot = (np.log10(arr0[flag].imag) - np.log10(arr1[flag].imag))/np.log10(arr1[flag].imag)
else:
zPlot = (arr0[flag].imag - arr1[flag].imag)/arr1[flag].imag
if mask:
maskInd = np.logical_or(arr0[flag].imag< 1e-3,arr1[flag].imag < 1e-3)
zPlot = np.ma.array(zPlot)
zPlot[maskInd] = mask
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())))
if colorNorm=='SymLog':
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 colorNorm=='Lin':
if cLevel:
level = np.arange(cLevel[0],cLevel[1]+.1,(cLevel[1] - cLevel[0])/50.)
clevel = np.arange(cLevel[0],cLevel[1]+.1,(cLevel[1] - cLevel[0])/10.)
else:
level = np.arange(zPlot.min(),zPlot.max(),(zPlot.max() - zPlot.min())/50.)
clevel = np.arange(zPlot.min(),zPlot.max(),(zPlot.max() - zPlot.min())/10.)
plotNorm = colors.Normalize()
elif colorNorm=='Log':
level = np.logspace(zMin-.125,zMax,(zMax-zMin)*8+1,endpoint=True)
clevel = np.logspace(zMin,zMax,(zMax-zMin)*2+1,endpoint=True)
plotNorm = colors.LogNorm()
if contour:
cs = ax.tricontourf(x0,y0,zPlot*100,levels=level*100,cmap=cmap,norm=plotNorm,extend='both')#,extend='both')
else:
uniX,uniY = np.unique(x0),np.unique(y0)
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*100,format='%1.2e')
# csB.on_mappable_changed(cs)
ax.set_title(flag+' '+par + ' diff',fontsize=8)
return cs, csB
return cs,None
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
x = np.array([[0, 0, 0, 0], [0, 0, 0, 1], [0, 0, 1, 0], [0, 1, 0, 0],
[1, 0, 0, 0], [0, 0, 1, 1], [0, 1, 1, 0], [1, 1, 0, 0],
[1, 0, 0, 1], [1, 1, 1, 1]])
y = np.array([[0, 1, 0, 0, 0, 1, 0, 0, 1, 1]]).T
w = np.random.random((4, 5))
w2 = np.random.random((5, 5))
w3 = np.random.random((5, 1))
for j in xrange(50000):
a2 = 1 / (1 + np.exp(-((np.dot(x, w) + 1))))
a3 = 1 / (1 + np.exp(-((np.dot(a2, w2) + 1))))
a4 = 1 / (1 + np.exp(-(np.dot(a3, w3) + 1)))
graphicalDataError.append(np.mean(np.abs(y - a4)))
graphicalDataEpochs.append(j)
print(y.shape, a4.shape)
a4delta = (y - a4) * (a4 * (1 - a4))
a3delta = a4delta.dot(w3.T) * (a3 * (1 - a3))
a2delta = a3delta.dot(w2.T) * (a2 * (1 - a2))
w3 += a3.T.dot(a4delta)
w2 += a2.T.dot(a3delta)
w += x.T.dot(a2delta)
print(a4)
x = [0, 1, 1, 1]