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How to draw a contour plot with two 2D array (x,y) and 0D array i.e., an scalar (z)?
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I'm trying to draw a contour plot using two 2D arrays (both has the shape (6,1) as x and y and my third data (z) is the calculated RMS between the x vector (x list has just 6 items) and y vector (y list has 600 items). RMS value is a scalar for each point. I mean I have one RMS value for each point. when I try to draw a contour plot using x, y, z the following error arises:
TypeError: Input z must be a 2D array
How to solve this problem?
Thanks in advance
import numpy
import matplotlib.pyplot as plt
from math import sqrt
def Cumulative_Sensitivity (depth, coil_spacing, coil_position):
global z
global s
z=numpy.array(depth)
s=numpy.array(coil_spacing)
if coil_position == "hcp":
cs=(4*(z/s)**2+1)**(-0.5)
cs=numpy.array(cs)
elif coil_position == "vcp":
cs=(4*(z/s)**2+1) **(0.5)-2*(z/s)
cs=numpy.array(cs)
return cs
def forward_model (sigma, depth, coil_spacing, coil_position):
global cs
global cond_true
global cond_apps
cond_true=numpy.array(sigma)
cond_apps=numpy.zeros(len(coil_spacing))
for i in range (0, len(coil_spacing)):
cs= Cumulative_Sensitivity (depth, coil_spacing[i], coil_position)
cond_app = sum(cond_true[:-1]*(cs[:-1]-cs[1:]))
cond_app = cond_app + cond_true[-1]*(cs[-1])
cond_apps[i] = cond_app
return cond_apps
# s1=30mS/m and s2=50mS/m, z=1m
sa_1=forward_model ([30, 50], [0, 1], [0.32], "hcp")
sa_2=forward_model ([30, 50], [0, 1], [0.71], "hcp")
sa_3=forward_model ([30, 50], [0, 1], [1.18], "hcp")
sa_4=forward_model ([30, 50], [0, 1], [0.32], "vcp")
sa_5=forward_model ([30, 50], [0, 1], [0.71], "vcp")
sa_6=forward_model ([30, 50], [0, 1], [1.18], "vcp")
data=numpy.array([sa_1, sa_2, sa_3, sa_4, sa_5, sa_6])
#
for i in range (10,110, 10):
for j in range (10,110, 10):
cond_HC1=forward_model ([j, i], [0, 1], [0.32], "hcp")
cond_HC2=forward_model ([j, i], [0, 1], [0.71], "hcp")
cond_HC3=forward_model ([j, i], [0, 1], [1.18], "hcp")
cond_VC1=forward_model ([j, i], [0, 1], [0.32], "vcp")
cond_VC2=forward_model ([j, i], [0, 1], [0.71], "vcp")
cond_VC3=forward_model ([j, i], [0, 1], [1.18], "vcp")
predicted=numpy.array([cond_HC1, cond_HC2, cond_HC3, cond_VC1, cond_VC2, cond_VC3])
rms=numpy.array(sqrt(sum((predicted - data)**2).mean()))
x = numpy.linspace(10,100,10)
y = numpy.linspace(10,100,10)
X,Y=numpy.meshgrid(x,y)
Z=rms
contour = plt.contour(X, Y, Z)python
asked Mar 25 at 5:47
FatimaFatima
92 bronze badges
add a comment |
I'm trying to draw a contour plot using two 2D arrays (both has the shape (6,1) as x and y and my third data (z) is the calculated RMS between the x vector (x list has just 6 items) and y vector (y list has 600 items). RMS value is a scalar for each point. I mean I have one RMS value for each point. when I try to draw a contour plot using x, y, z the following error arises:
TypeError: Input z must be a 2D array
How to solve this problem?
Thanks in advance
import numpy
import matplotlib.pyplot as plt
from math import sqrt
def Cumulative_Sensitivity (depth, coil_spacing, coil_position):
global z
global s
z=numpy.array(depth)
s=numpy.array(coil_spacing)
if coil_position == "hcp":
cs=(4*(z/s)**2+1)**(-0.5)
cs=numpy.array(cs)
elif coil_position == "vcp":
cs=(4*(z/s)**2+1) **(0.5)-2*(z/s)
cs=numpy.array(cs)
return cs
def forward_model (sigma, depth, coil_spacing, coil_position):
global cs
global cond_true
global cond_apps
cond_true=numpy.array(sigma)
cond_apps=numpy.zeros(len(coil_spacing))
for i in range (0, len(coil_spacing)):
cs= Cumulative_Sensitivity (depth, coil_spacing[i], coil_position)
cond_app = sum(cond_true[:-1]*(cs[:-1]-cs[1:]))
cond_app = cond_app + cond_true[-1]*(cs[-1])
cond_apps[i] = cond_app
return cond_apps
# s1=30mS/m and s2=50mS/m, z=1m
sa_1=forward_model ([30, 50], [0, 1], [0.32], "hcp")
sa_2=forward_model ([30, 50], [0, 1], [0.71], "hcp")
sa_3=forward_model ([30, 50], [0, 1], [1.18], "hcp")
sa_4=forward_model ([30, 50], [0, 1], [0.32], "vcp")
sa_5=forward_model ([30, 50], [0, 1], [0.71], "vcp")
sa_6=forward_model ([30, 50], [0, 1], [1.18], "vcp")
data=numpy.array([sa_1, sa_2, sa_3, sa_4, sa_5, sa_6])
#
for i in range (10,110, 10):
for j in range (10,110, 10):
cond_HC1=forward_model ([j, i], [0, 1], [0.32], "hcp")
cond_HC2=forward_model ([j, i], [0, 1], [0.71], "hcp")
cond_HC3=forward_model ([j, i], [0, 1], [1.18], "hcp")
cond_VC1=forward_model ([j, i], [0, 1], [0.32], "vcp")
cond_VC2=forward_model ([j, i], [0, 1], [0.71], "vcp")
cond_VC3=forward_model ([j, i], [0, 1], [1.18], "vcp")
predicted=numpy.array([cond_HC1, cond_HC2, cond_HC3, cond_VC1, cond_VC2, cond_VC3])
rms=numpy.array(sqrt(sum((predicted - data)**2).mean()))
x = numpy.linspace(10,100,10)
y = numpy.linspace(10,100,10)
X,Y=numpy.meshgrid(x,y)
Z=rms
contour = plt.contour(X, Y, Z)python
asked Mar 25 at 5:47
FatimaFatima
92 bronze badges
please show some code
– David Jones
Mar 25 at 8:59
add a comment |
I'm trying to draw a contour plot using two 2D arrays (both has the shape (6,1) as x and y and my third data (z) is the calculated RMS between the x vector (x list has just 6 items) and y vector (y list has 600 items). RMS value is a scalar for each point. I mean I have one RMS value for each point. when I try to draw a contour plot using x, y, z the following error arises:
TypeError: Input z must be a 2D array
How to solve this problem?
Thanks in advance
import numpy
import matplotlib.pyplot as plt
from math import sqrt
def Cumulative_Sensitivity (depth, coil_spacing, coil_position):
global z
global s
z=numpy.array(depth)
s=numpy.array(coil_spacing)
if coil_position == "hcp":
cs=(4*(z/s)**2+1)**(-0.5)
cs=numpy.array(cs)
elif coil_position == "vcp":
cs=(4*(z/s)**2+1) **(0.5)-2*(z/s)
cs=numpy.array(cs)
return cs
def forward_model (sigma, depth, coil_spacing, coil_position):
global cs
global cond_true
global cond_apps
cond_true=numpy.array(sigma)
cond_apps=numpy.zeros(len(coil_spacing))
for i in range (0, len(coil_spacing)):
cs= Cumulative_Sensitivity (depth, coil_spacing[i], coil_position)
cond_app = sum(cond_true[:-1]*(cs[:-1]-cs[1:]))
cond_app = cond_app + cond_true[-1]*(cs[-1])
cond_apps[i] = cond_app
return cond_apps
# s1=30mS/m and s2=50mS/m, z=1m
sa_1=forward_model ([30, 50], [0, 1], [0.32], "hcp")
sa_2=forward_model ([30, 50], [0, 1], [0.71], "hcp")
sa_3=forward_model ([30, 50], [0, 1], [1.18], "hcp")
sa_4=forward_model ([30, 50], [0, 1], [0.32], "vcp")
sa_5=forward_model ([30, 50], [0, 1], [0.71], "vcp")
sa_6=forward_model ([30, 50], [0, 1], [1.18], "vcp")
data=numpy.array([sa_1, sa_2, sa_3, sa_4, sa_5, sa_6])
#
for i in range (10,110, 10):
for j in range (10,110, 10):
cond_HC1=forward_model ([j, i], [0, 1], [0.32], "hcp")
cond_HC2=forward_model ([j, i], [0, 1], [0.71], "hcp")
cond_HC3=forward_model ([j, i], [0, 1], [1.18], "hcp")
cond_VC1=forward_model ([j, i], [0, 1], [0.32], "vcp")
cond_VC2=forward_model ([j, i], [0, 1], [0.71], "vcp")
cond_VC3=forward_model ([j, i], [0, 1], [1.18], "vcp")
predicted=numpy.array([cond_HC1, cond_HC2, cond_HC3, cond_VC1, cond_VC2, cond_VC3])
rms=numpy.array(sqrt(sum((predicted - data)**2).mean()))
x = numpy.linspace(10,100,10)
y = numpy.linspace(10,100,10)
X,Y=numpy.meshgrid(x,y)
Z=rms
contour = plt.contour(X, Y, Z)python
asked Mar 25 at 5:47
FatimaFatima
92 bronze badges
I'm trying to draw a contour plot using two 2D arrays (both has the shape (6,1) as x and y and my third data (z) is the calculated RMS between the x vector (x list has just 6 items) and y vector (y list has 600 items). RMS value is a scalar for each point. I mean I have one RMS value for each point. when I try to draw a contour plot using x, y, z the following error arises:
TypeError: Input z must be a 2D array
How to solve this problem?
Thanks in advance
import numpy
import matplotlib.pyplot as plt
from math import sqrt
def Cumulative_Sensitivity (depth, coil_spacing, coil_position):
global z
global s
z=numpy.array(depth)
s=numpy.array(coil_spacing)
if coil_position == "hcp":
cs=(4*(z/s)**2+1)**(-0.5)
cs=numpy.array(cs)
elif coil_position == "vcp":
cs=(4*(z/s)**2+1) **(0.5)-2*(z/s)
cs=numpy.array(cs)
return cs
def forward_model (sigma, depth, coil_spacing, coil_position):
global cs
global cond_true
global cond_apps
cond_true=numpy.array(sigma)
cond_apps=numpy.zeros(len(coil_spacing))
for i in range (0, len(coil_spacing)):
cs= Cumulative_Sensitivity (depth, coil_spacing[i], coil_position)
cond_app = sum(cond_true[:-1]*(cs[:-1]-cs[1:]))
cond_app = cond_app + cond_true[-1]*(cs[-1])
cond_apps[i] = cond_app
return cond_apps
# s1=30mS/m and s2=50mS/m, z=1m
sa_1=forward_model ([30, 50], [0, 1], [0.32], "hcp")
sa_2=forward_model ([30, 50], [0, 1], [0.71], "hcp")
sa_3=forward_model ([30, 50], [0, 1], [1.18], "hcp")
sa_4=forward_model ([30, 50], [0, 1], [0.32], "vcp")
sa_5=forward_model ([30, 50], [0, 1], [0.71], "vcp")
sa_6=forward_model ([30, 50], [0, 1], [1.18], "vcp")
data=numpy.array([sa_1, sa_2, sa_3, sa_4, sa_5, sa_6])
#
for i in range (10,110, 10):
for j in range (10,110, 10):
cond_HC1=forward_model ([j, i], [0, 1], [0.32], "hcp")
cond_HC2=forward_model ([j, i], [0, 1], [0.71], "hcp")
cond_HC3=forward_model ([j, i], [0, 1], [1.18], "hcp")
cond_VC1=forward_model ([j, i], [0, 1], [0.32], "vcp")
cond_VC2=forward_model ([j, i], [0, 1], [0.71], "vcp")
cond_VC3=forward_model ([j, i], [0, 1], [1.18], "vcp")
predicted=numpy.array([cond_HC1, cond_HC2, cond_HC3, cond_VC1, cond_VC2, cond_VC3])
rms=numpy.array(sqrt(sum((predicted - data)**2).mean()))
x = numpy.linspace(10,100,10)
y = numpy.linspace(10,100,10)
X,Y=numpy.meshgrid(x,y)
Z=rms
contour = plt.contour(X, Y, Z)import numpy
import matplotlib.pyplot as plt
from math import sqrt
def Cumulative_Sensitivity (depth, coil_spacing, coil_position):
global z
global s
z=numpy.array(depth)
s=numpy.array(coil_spacing)
if coil_position == "hcp":
cs=(4*(z/s)**2+1)**(-0.5)
cs=numpy.array(cs)
elif coil_position == "vcp":
cs=(4*(z/s)**2+1) **(0.5)-2*(z/s)
cs=numpy.array(cs)
return cs
def forward_model (sigma, depth, coil_spacing, coil_position):
global cs
global cond_true
global cond_apps
cond_true=numpy.array(sigma)
cond_apps=numpy.zeros(len(coil_spacing))
for i in range (0, len(coil_spacing)):
cs= Cumulative_Sensitivity (depth, coil_spacing[i], coil_position)
cond_app = sum(cond_true[:-1]*(cs[:-1]-cs[1:]))
cond_app = cond_app + cond_true[-1]*(cs[-1])
cond_apps[i] = cond_app
return cond_apps
# s1=30mS/m and s2=50mS/m, z=1m
sa_1=forward_model ([30, 50], [0, 1], [0.32], "hcp")
sa_2=forward_model ([30, 50], [0, 1], [0.71], "hcp")
sa_3=forward_model ([30, 50], [0, 1], [1.18], "hcp")
sa_4=forward_model ([30, 50], [0, 1], [0.32], "vcp")
sa_5=forward_model ([30, 50], [0, 1], [0.71], "vcp")
sa_6=forward_model ([30, 50], [0, 1], [1.18], "vcp")
data=numpy.array([sa_1, sa_2, sa_3, sa_4, sa_5, sa_6])
#
for i in range (10,110, 10):
for j in range (10,110, 10):
cond_HC1=forward_model ([j, i], [0, 1], [0.32], "hcp")
cond_HC2=forward_model ([j, i], [0, 1], [0.71], "hcp")
cond_HC3=forward_model ([j, i], [0, 1], [1.18], "hcp")
cond_VC1=forward_model ([j, i], [0, 1], [0.32], "vcp")
cond_VC2=forward_model ([j, i], [0, 1], [0.71], "vcp")
cond_VC3=forward_model ([j, i], [0, 1], [1.18], "vcp")
predicted=numpy.array([cond_HC1, cond_HC2, cond_HC3, cond_VC1, cond_VC2, cond_VC3])
rms=numpy.array(sqrt(sum((predicted - data)**2).mean()))
x = numpy.linspace(10,100,10)
y = numpy.linspace(10,100,10)
X,Y=numpy.meshgrid(x,y)
Z=rms
contour = plt.contour(X, Y, Z)import numpy
import matplotlib.pyplot as plt
from math import sqrt
def Cumulative_Sensitivity (depth, coil_spacing, coil_position):
global z
global s
z=numpy.array(depth)
s=numpy.array(coil_spacing)
if coil_position == "hcp":
cs=(4*(z/s)**2+1)**(-0.5)
cs=numpy.array(cs)
elif coil_position == "vcp":
cs=(4*(z/s)**2+1) **(0.5)-2*(z/s)
cs=numpy.array(cs)
return cs
def forward_model (sigma, depth, coil_spacing, coil_position):
global cs
global cond_true
global cond_apps
cond_true=numpy.array(sigma)
cond_apps=numpy.zeros(len(coil_spacing))
for i in range (0, len(coil_spacing)):
cs= Cumulative_Sensitivity (depth, coil_spacing[i], coil_position)
cond_app = sum(cond_true[:-1]*(cs[:-1]-cs[1:]))
cond_app = cond_app + cond_true[-1]*(cs[-1])
cond_apps[i] = cond_app
return cond_apps
# s1=30mS/m and s2=50mS/m, z=1m
sa_1=forward_model ([30, 50], [0, 1], [0.32], "hcp")
sa_2=forward_model ([30, 50], [0, 1], [0.71], "hcp")
sa_3=forward_model ([30, 50], [0, 1], [1.18], "hcp")
sa_4=forward_model ([30, 50], [0, 1], [0.32], "vcp")
sa_5=forward_model ([30, 50], [0, 1], [0.71], "vcp")
sa_6=forward_model ([30, 50], [0, 1], [1.18], "vcp")
data=numpy.array([sa_1, sa_2, sa_3, sa_4, sa_5, sa_6])
#
for i in range (10,110, 10):
for j in range (10,110, 10):
cond_HC1=forward_model ([j, i], [0, 1], [0.32], "hcp")
cond_HC2=forward_model ([j, i], [0, 1], [0.71], "hcp")
cond_HC3=forward_model ([j, i], [0, 1], [1.18], "hcp")
cond_VC1=forward_model ([j, i], [0, 1], [0.32], "vcp")
cond_VC2=forward_model ([j, i], [0, 1], [0.71], "vcp")
cond_VC3=forward_model ([j, i], [0, 1], [1.18], "vcp")
predicted=numpy.array([cond_HC1, cond_HC2, cond_HC3, cond_VC1, cond_VC2, cond_VC3])
rms=numpy.array(sqrt(sum((predicted - data)**2).mean()))
x = numpy.linspace(10,100,10)
y = numpy.linspace(10,100,10)
X,Y=numpy.meshgrid(x,y)
Z=rms
contour = plt.contour(X, Y, Z)python
python
asked Mar 25 at 5:47
FatimaFatima
92 bronze badges
asked Mar 25 at 5:47
FatimaFatima
92 bronze badges
edited Mar 26 at 9:11
Fatima
asked Mar 25 at 5:47
FatimaFatima
92 bronze badges
asked Mar 25 at 5:47
FatimaFatima
92 bronze badges
asked Mar 25 at 5:47
FatimaFatima
92 bronze badges
92 bronze badges
please show some code
– David Jones
Mar 25 at 8:59
add a comment |
please show some code
– David Jones
Mar 25 at 8:59
please show some code
– David Jones
Mar 25 at 8:59
please show some code
– David Jones
Mar 25 at 8:59
add a comment |
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Required, but never shown
please show some code
– David Jones
Mar 25 at 8:59