Scipy ValueError: object too deep for desired array with optimize.leastsq
I am trying to fit my 3D data with linear 3D function Z = ax+by+c. I import the data with pandas:
dataframe = pd.read_csv('3d_data.csv',names=['x','y','z'],header=0)
print(dataframe)
x y z
0 52.830740 7.812507 0.000000
1 44.647931 61.031381 8.827942
2 38.725318 0.707952 52.857968
3 0.000000 31.026271 17.743218
4 57.137854 51.291656 61.546131
5 46.341341 3.394429 26.462564
6 3.440893 46.333864 70.440650
I have done some digging and found that the best way to fit 3D data it is to use optimize from scipy with the model equation and residual function:
def model_calc(parameter, x, y):
a, b, c = parameter
return a*x + b*y + c
def residual(parameter, data, x, y):
res =
for _x in x:
for _y in y:
res.append(data-model_calc(parameter,x,y))
return res
I fit the data with:
params0 = [0.1, -0.2,1.]
result = scipy.optimize.leastsq(residual,params0,(dataframe['z'],dataframe['x'],dataframe['y']))
fittedParams = result[0]
But the result is a ValueError:
ValueError: object too deep for desired array [...]
minpack.error: Result from function call is not a proper array of floats.
I was trying to minimize the residual function to give only single value or single np.array but it didn't help. I don't know where is the problem and if maybe the search space for parameters it is not too complex. I would be very grateful for some hints!
python numpy scipy data-fitting function-fitting
add a comment |
I am trying to fit my 3D data with linear 3D function Z = ax+by+c. I import the data with pandas:
dataframe = pd.read_csv('3d_data.csv',names=['x','y','z'],header=0)
print(dataframe)
x y z
0 52.830740 7.812507 0.000000
1 44.647931 61.031381 8.827942
2 38.725318 0.707952 52.857968
3 0.000000 31.026271 17.743218
4 57.137854 51.291656 61.546131
5 46.341341 3.394429 26.462564
6 3.440893 46.333864 70.440650
I have done some digging and found that the best way to fit 3D data it is to use optimize from scipy with the model equation and residual function:
def model_calc(parameter, x, y):
a, b, c = parameter
return a*x + b*y + c
def residual(parameter, data, x, y):
res =
for _x in x:
for _y in y:
res.append(data-model_calc(parameter,x,y))
return res
I fit the data with:
params0 = [0.1, -0.2,1.]
result = scipy.optimize.leastsq(residual,params0,(dataframe['z'],dataframe['x'],dataframe['y']))
fittedParams = result[0]
But the result is a ValueError:
ValueError: object too deep for desired array [...]
minpack.error: Result from function call is not a proper array of floats.
I was trying to minimize the residual function to give only single value or single np.array but it didn't help. I don't know where is the problem and if maybe the search space for parameters it is not too complex. I would be very grateful for some hints!
python numpy scipy data-fitting function-fitting
add a comment |
I am trying to fit my 3D data with linear 3D function Z = ax+by+c. I import the data with pandas:
dataframe = pd.read_csv('3d_data.csv',names=['x','y','z'],header=0)
print(dataframe)
x y z
0 52.830740 7.812507 0.000000
1 44.647931 61.031381 8.827942
2 38.725318 0.707952 52.857968
3 0.000000 31.026271 17.743218
4 57.137854 51.291656 61.546131
5 46.341341 3.394429 26.462564
6 3.440893 46.333864 70.440650
I have done some digging and found that the best way to fit 3D data it is to use optimize from scipy with the model equation and residual function:
def model_calc(parameter, x, y):
a, b, c = parameter
return a*x + b*y + c
def residual(parameter, data, x, y):
res =
for _x in x:
for _y in y:
res.append(data-model_calc(parameter,x,y))
return res
I fit the data with:
params0 = [0.1, -0.2,1.]
result = scipy.optimize.leastsq(residual,params0,(dataframe['z'],dataframe['x'],dataframe['y']))
fittedParams = result[0]
But the result is a ValueError:
ValueError: object too deep for desired array [...]
minpack.error: Result from function call is not a proper array of floats.
I was trying to minimize the residual function to give only single value or single np.array but it didn't help. I don't know where is the problem and if maybe the search space for parameters it is not too complex. I would be very grateful for some hints!
python numpy scipy data-fitting function-fitting
I am trying to fit my 3D data with linear 3D function Z = ax+by+c. I import the data with pandas:
dataframe = pd.read_csv('3d_data.csv',names=['x','y','z'],header=0)
print(dataframe)
x y z
0 52.830740 7.812507 0.000000
1 44.647931 61.031381 8.827942
2 38.725318 0.707952 52.857968
3 0.000000 31.026271 17.743218
4 57.137854 51.291656 61.546131
5 46.341341 3.394429 26.462564
6 3.440893 46.333864 70.440650
I have done some digging and found that the best way to fit 3D data it is to use optimize from scipy with the model equation and residual function:
def model_calc(parameter, x, y):
a, b, c = parameter
return a*x + b*y + c
def residual(parameter, data, x, y):
res =
for _x in x:
for _y in y:
res.append(data-model_calc(parameter,x,y))
return res
I fit the data with:
params0 = [0.1, -0.2,1.]
result = scipy.optimize.leastsq(residual,params0,(dataframe['z'],dataframe['x'],dataframe['y']))
fittedParams = result[0]
But the result is a ValueError:
ValueError: object too deep for desired array [...]
minpack.error: Result from function call is not a proper array of floats.
I was trying to minimize the residual function to give only single value or single np.array but it didn't help. I don't know where is the problem and if maybe the search space for parameters it is not too complex. I would be very grateful for some hints!
python numpy scipy data-fitting function-fitting
python numpy scipy data-fitting function-fitting
asked Nov 12 at 14:16
Dawid
147110
147110
add a comment |
add a comment |
1 Answer
1
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votes
If you are fitting parameters to a function, you can use curve_fit. Here's an implementation:
from scipy.optimize import curve_fit
def model_calc(X, a, b, c):
x, y = X
return a*x + b*y + c
p0 = [0.1, -0.2, 1.]
popt, pcov = curve_fit(model_calc, (dataframe.x, dataframe.y), dataframe.z, p0) #popt is the fit, pcov is the covariance matrix (see the docs)
Note that your sintax must be if the form f(X, a, b, c), where X can be a 2D vector (See this post).
(Another approach)
If you know your fit is going to be linear, you can use numpy.linalg.lstsq
. See here. Example solution:
import numpy as np
from numpy.linalg import lstsq
A = np.vstack((dataframe.x, dataframe.y, np.ones_like(dataframe.y))).T
B = dataframe.z
a, b, c = lstsq(A, B)[0]
Your answer works perfectly! Thank you so much!
– Dawid
Nov 13 at 8:42
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
If you are fitting parameters to a function, you can use curve_fit. Here's an implementation:
from scipy.optimize import curve_fit
def model_calc(X, a, b, c):
x, y = X
return a*x + b*y + c
p0 = [0.1, -0.2, 1.]
popt, pcov = curve_fit(model_calc, (dataframe.x, dataframe.y), dataframe.z, p0) #popt is the fit, pcov is the covariance matrix (see the docs)
Note that your sintax must be if the form f(X, a, b, c), where X can be a 2D vector (See this post).
(Another approach)
If you know your fit is going to be linear, you can use numpy.linalg.lstsq
. See here. Example solution:
import numpy as np
from numpy.linalg import lstsq
A = np.vstack((dataframe.x, dataframe.y, np.ones_like(dataframe.y))).T
B = dataframe.z
a, b, c = lstsq(A, B)[0]
Your answer works perfectly! Thank you so much!
– Dawid
Nov 13 at 8:42
add a comment |
If you are fitting parameters to a function, you can use curve_fit. Here's an implementation:
from scipy.optimize import curve_fit
def model_calc(X, a, b, c):
x, y = X
return a*x + b*y + c
p0 = [0.1, -0.2, 1.]
popt, pcov = curve_fit(model_calc, (dataframe.x, dataframe.y), dataframe.z, p0) #popt is the fit, pcov is the covariance matrix (see the docs)
Note that your sintax must be if the form f(X, a, b, c), where X can be a 2D vector (See this post).
(Another approach)
If you know your fit is going to be linear, you can use numpy.linalg.lstsq
. See here. Example solution:
import numpy as np
from numpy.linalg import lstsq
A = np.vstack((dataframe.x, dataframe.y, np.ones_like(dataframe.y))).T
B = dataframe.z
a, b, c = lstsq(A, B)[0]
Your answer works perfectly! Thank you so much!
– Dawid
Nov 13 at 8:42
add a comment |
If you are fitting parameters to a function, you can use curve_fit. Here's an implementation:
from scipy.optimize import curve_fit
def model_calc(X, a, b, c):
x, y = X
return a*x + b*y + c
p0 = [0.1, -0.2, 1.]
popt, pcov = curve_fit(model_calc, (dataframe.x, dataframe.y), dataframe.z, p0) #popt is the fit, pcov is the covariance matrix (see the docs)
Note that your sintax must be if the form f(X, a, b, c), where X can be a 2D vector (See this post).
(Another approach)
If you know your fit is going to be linear, you can use numpy.linalg.lstsq
. See here. Example solution:
import numpy as np
from numpy.linalg import lstsq
A = np.vstack((dataframe.x, dataframe.y, np.ones_like(dataframe.y))).T
B = dataframe.z
a, b, c = lstsq(A, B)[0]
If you are fitting parameters to a function, you can use curve_fit. Here's an implementation:
from scipy.optimize import curve_fit
def model_calc(X, a, b, c):
x, y = X
return a*x + b*y + c
p0 = [0.1, -0.2, 1.]
popt, pcov = curve_fit(model_calc, (dataframe.x, dataframe.y), dataframe.z, p0) #popt is the fit, pcov is the covariance matrix (see the docs)
Note that your sintax must be if the form f(X, a, b, c), where X can be a 2D vector (See this post).
(Another approach)
If you know your fit is going to be linear, you can use numpy.linalg.lstsq
. See here. Example solution:
import numpy as np
from numpy.linalg import lstsq
A = np.vstack((dataframe.x, dataframe.y, np.ones_like(dataframe.y))).T
B = dataframe.z
a, b, c = lstsq(A, B)[0]
answered Nov 12 at 16:41
Mstaino
72439
72439
Your answer works perfectly! Thank you so much!
– Dawid
Nov 13 at 8:42
add a comment |
Your answer works perfectly! Thank you so much!
– Dawid
Nov 13 at 8:42
Your answer works perfectly! Thank you so much!
– Dawid
Nov 13 at 8:42
Your answer works perfectly! Thank you so much!
– Dawid
Nov 13 at 8:42
add a comment |
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