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 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 cannyEdgeDetectorFilter(self, img, sigma=1, t_low=0.01, t_high=0.2):
im = img.convert('L')
img = np.array(im, dtype=float)
# 1) Convolve gaussian kernel with gradient
# gaussian kernel
halfSize = 3 * sigma
maskSize = 2 * halfSize + 1
mat = np.ones((maskSize, maskSize)) / (float)(2 * np.pi * (sigma**2))
xyRange = np.arange(-halfSize, halfSize + 1)
xx, yy = np.meshgrid(xyRange, xyRange)
x2y2 = (xx**2 + yy**2)
exp_part = np.exp(-(x2y2 / (2.0 * (sigma**2))))
gSig = mat * exp_part
gx, gy = self.drogEdgeDetectorFilter(gSig, ret_grad=True, pillow=False)
# 2) Magnitude and Angles
# apply kernels for Ix & Iy
Ix = cv2.filter2D(img, -1, gx)
Iy = cv2.filter2D(img, -1, gy)
# compute magnitude
mag = np.sqrt(Ix**2 + Iy**2)
# normalize magnitude image
normMag = my_Normalize(mag)
# compute orientation of gradient
orient = np.arctan2(Iy, Ix)
# round elements of orient
orientRows = orient.shape[0]
orientCols = orient.shape[1]
# 3) Non maximum suppression
for i in range(0, orientRows):
for j in range(0, orientCols):
if normMag[i, j] > t_low:
# case 0
if (orient[i, j] > (-np.pi / 8) and orient[i, j] <=
(np.pi / 8)):
orient[i, j] = 0
elif (orient[i, j] > (7 * np.pi / 8)
and orient[i, j] <= np.pi):
orient[i, j] = 0
elif (orient[i, j] >= -np.pi and orient[i, j] <
(-7 * np.pi / 8)):
orient[i, j] = 0
# case 1
elif (orient[i, j] > (np.pi / 8) and orient[i, j] <=
(3 * np.pi / 8)):
orient[i, j] = 3
elif (orient[i, j] >= (-7 * np.pi / 8) and orient[i, j] <
(-5 * np.pi / 8)):
orient[i, j] = 3
# case 2
elif (orient[i, j] > (3 * np.pi / 8) and orient[i, j] <=
(5 * np.pi / 8)):
orient[i, j] = 2
elif (orient[i, j] >= (-5 * np.pi / 4) and orient[i, j] <
(-3 * np.pi / 8)):
orient[i, j] = 2
# case 3
elif (orient[i, j] > (5 * np.pi / 8) and orient[i, j] <=
(7 * np.pi / 8)):
orient[i, j] = 1
elif (orient[i, j] >= (-3 * np.pi / 8) and orient[i, j] <
(-np.pi / 8)):
orient[i, j] = 1
mag = normMag
mag_thin = np.zeros(mag.shape)
for i in range(mag.shape[0] - 1):
for j in range(mag.shape[1] - 1):
if mag[i][j] < t_low:
continue
if orient[i][j] == 0:
if mag[i][j] > mag[i][j - 1] and mag[i][j] >= mag[i][j +
1]:
mag_thin[i][j] = mag[i][j]
if orient[i][j] == 1:
if mag[i][j] > mag[i - 1][j + 1] and mag[i][j] >= mag[
i + 1][j - 1]:
mag_thin[i][j] = mag[i][j]
if orient[i][j] == 2:
if mag[i][j] > mag[i - 1][j] and mag[i][j] >= mag[i +
1][j]:
mag_thin[i][j] = mag[i][j]
if orient[i][j] == 3:
if mag[i][j] > mag[i - 1][j - 1] and mag[i][j] >= mag[
i + 1][j + 1]:
mag_thin[i][j] = mag[i][j]
# 4) Thresholding and edge linking
result_binary = np.zeros(mag_thin.shape)
tHigh = t_high
tLow = t_low
# forward scan
for i in range(0, mag_thin.shape[0] - 1): # rows
for j in range(0, mag_thin.shape[1] - 1): # columns
if mag_thin[i][j] >= tHigh:
if mag_thin[i][j + 1] >= tLow: # right
mag_thin[i][j + 1] = tHigh
if mag_thin[i + 1][j + 1] >= tLow: # bottom right
mag_thin[i + 1][j + 1] = tHigh
if mag_thin[i + 1][j] >= tLow: # bottom
mag_thin[i + 1][j] = tHigh
if mag_thin[i + 1][j - 1] >= tLow: # bottom left
mag_thin[i + 1][j - 1] = tHigh
# backwards scan
for i in range(mag_thin.shape[0] - 2, 0, -1): # rows
for j in range(mag_thin.shape[1] - 2, 0, -1): # columns
if mag_thin[i][j] >= tHigh:
if mag_thin[i][j - 1] > tLow: # left
mag_thin[i][j - 1] = tHigh
if mag_thin[i - 1][j - 1]: # top left
mag_thin[i - 1][j - 1] = tHigh
if mag_thin[i - 1][j] > tLow: # top
mag_thin[i - 1][j] = tHigh
if mag_thin[i - 1][j + 1] > tLow: # top right
mag_thin[i - 1][j + 1] = tHigh
# fill in result_binary
for i in range(0, mag_thin.shape[0] - 1): # rows
for j in range(0, mag_thin.shape[1] - 1): # columns
if mag_thin[i][j] >= tHigh:
result_binary[i][j] = 255 # set to 255 for >= tHigh
img = Image.fromarray(result_binary)
return img
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