curve_fit function not fitting to original data perfectly
Hi I have a set of data and I fitted my data with the curve_fit function
but the line does not describe the original dataset good enough.
The curve_fit function is not close to the orginal data.
the x array has following data:
[0. 0.025 0.10333333 0.1175 0.164 0.22 0.27571429 0.27625 0.33333333 0.379 0.40545455 0.43416667 0.47769231 0.52571429 0.528 0.538125 0.56470588 0.5577777 0.59263158 0.6065 0.61190476 0.62545455 ...]
y array looks like this:
[1. 1.95 2.83 3.73 4.57 5.32 5.97 6.81 7.35 7.86 8.5 9.09 9.4 9.83 10.41 11. 11.34 11.8 ...]
My curve_fit func:
def func(x, a, b, c,):
return a*np.exp(-b*x)+c
popt, pcov = curve_fit(func,x,y, maxfev=10000)
plt.plot(x, y, ls="none", marker='.', color='grey')
plt.plot(x,func(x, *popt),'-')
plt.title("my curve")
plt.legend()
plt.show()
Below is my plot:
python matplotlib
asked Nov 23 '18 at 20:01
Zara ArshadZara Arshad
294
add a comment |
Hi I have a set of data and I fitted my data with the curve_fit function
but the line does not describe the original dataset good enough.
The curve_fit function is not close to the orginal data.
the x array has following data:
[0. 0.025 0.10333333 0.1175 0.164 0.22 0.27571429 0.27625 0.33333333 0.379 0.40545455 0.43416667 0.47769231 0.52571429 0.528 0.538125 0.56470588 0.5577777 0.59263158 0.6065 0.61190476 0.62545455 ...]
y array looks like this:
[1. 1.95 2.83 3.73 4.57 5.32 5.97 6.81 7.35 7.86 8.5 9.09 9.4 9.83 10.41 11. 11.34 11.8 ...]
My curve_fit func:
def func(x, a, b, c,):
return a*np.exp(-b*x)+c
popt, pcov = curve_fit(func,x,y, maxfev=10000)
plt.plot(x, y, ls="none", marker='.', color='grey')
plt.plot(x,func(x, *popt),'-')
plt.title("my curve")
plt.legend()
plt.show()
Below is my plot:
python matplotlib
asked Nov 23 '18 at 20:01
Zara ArshadZara Arshad
294
One could think about weighting the fit by the inverse of the point density. Also one could fit x(y) instead of y(x).
– ImportanceOfBeingErnest
Nov 23 '18 at 20:26
add a comment |
Hi I have a set of data and I fitted my data with the curve_fit function
but the line does not describe the original dataset good enough.
The curve_fit function is not close to the orginal data.
the x array has following data:
[0. 0.025 0.10333333 0.1175 0.164 0.22 0.27571429 0.27625 0.33333333 0.379 0.40545455 0.43416667 0.47769231 0.52571429 0.528 0.538125 0.56470588 0.5577777 0.59263158 0.6065 0.61190476 0.62545455 ...]
y array looks like this:
[1. 1.95 2.83 3.73 4.57 5.32 5.97 6.81 7.35 7.86 8.5 9.09 9.4 9.83 10.41 11. 11.34 11.8 ...]
My curve_fit func:
def func(x, a, b, c,):
return a*np.exp(-b*x)+c
popt, pcov = curve_fit(func,x,y, maxfev=10000)
plt.plot(x, y, ls="none", marker='.', color='grey')
plt.plot(x,func(x, *popt),'-')
plt.title("my curve")
plt.legend()
plt.show()
Below is my plot:
python matplotlib
asked Nov 23 '18 at 20:01
Zara ArshadZara Arshad
294
Hi I have a set of data and I fitted my data with the curve_fit function
but the line does not describe the original dataset good enough.
The curve_fit function is not close to the orginal data.
the x array has following data:
[0. 0.025 0.10333333 0.1175 0.164 0.22 0.27571429 0.27625 0.33333333 0.379 0.40545455 0.43416667 0.47769231 0.52571429 0.528 0.538125 0.56470588 0.5577777 0.59263158 0.6065 0.61190476 0.62545455 ...]
y array looks like this:
[1. 1.95 2.83 3.73 4.57 5.32 5.97 6.81 7.35 7.86 8.5 9.09 9.4 9.83 10.41 11. 11.34 11.8 ...]
My curve_fit func:
def func(x, a, b, c,):
return a*np.exp(-b*x)+c
popt, pcov = curve_fit(func,x,y, maxfev=10000)
plt.plot(x, y, ls="none", marker='.', color='grey')
plt.plot(x,func(x, *popt),'-')
plt.title("my curve")
plt.legend()
plt.show()
Below is my plot:
python matplotlib
python matplotlib
asked Nov 23 '18 at 20:01
Zara ArshadZara Arshad
294
asked Nov 23 '18 at 20:01
Zara ArshadZara Arshad
294
asked Nov 23 '18 at 20:01
Zara ArshadZara Arshad
294
asked Nov 23 '18 at 20:01
Zara ArshadZara Arshad
294
asked Nov 23 '18 at 20:01
Zara ArshadZara Arshad
294
294
One could think about weighting the fit by the inverse of the point density. Also one could fit x(y) instead of y(x).
– ImportanceOfBeingErnest
Nov 23 '18 at 20:26
add a comment |
One could think about weighting the fit by the inverse of the point density. Also one could fit x(y) instead of y(x).
– ImportanceOfBeingErnest
Nov 23 '18 at 20:26
One could think about weighting the fit by the inverse of the point density. Also one could fit x(y) instead of y(x).
– ImportanceOfBeingErnest
Nov 23 '18 at 20:26
One could think about weighting the fit by the inverse of the point density. Also one could fit x(y) instead of y(x).
– ImportanceOfBeingErnest
Nov 23 '18 at 20:26
add a comment |
1 Answer
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votes
As far as I can see you are trying to fit an exponential curve to your data. Most of your data is concentrated on the upper right and hence the algorithm tries to fit it best-possible to that part.
answered Nov 23 '18 at 20:04
Thomas LangThomas Lang
44627
thanks , but how can I fit my curve for the lower left part?
– Zara Arshad
Nov 23 '18 at 20:12
1
If you know that your data will look like the example you've provided, I suggest using a polynomial model and a least-squares fit. This works reasonably well in practice. Edit: If you insist on an exponential model, maybe logarithmize your data and try a linear model then.
– Thomas Lang
Nov 23 '18 at 20:14
add a comment |
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1 Answer
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active
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1 Answer
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active
oldest
votes
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oldest
votes
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oldest
votes
As far as I can see you are trying to fit an exponential curve to your data. Most of your data is concentrated on the upper right and hence the algorithm tries to fit it best-possible to that part.
answered Nov 23 '18 at 20:04
Thomas LangThomas Lang
44627
thanks , but how can I fit my curve for the lower left part?
– Zara Arshad
Nov 23 '18 at 20:12
1
If you know that your data will look like the example you've provided, I suggest using a polynomial model and a least-squares fit. This works reasonably well in practice. Edit: If you insist on an exponential model, maybe logarithmize your data and try a linear model then.
– Thomas Lang
Nov 23 '18 at 20:14
add a comment |
As far as I can see you are trying to fit an exponential curve to your data. Most of your data is concentrated on the upper right and hence the algorithm tries to fit it best-possible to that part.
answered Nov 23 '18 at 20:04
Thomas LangThomas Lang
44627
thanks , but how can I fit my curve for the lower left part?
– Zara Arshad
Nov 23 '18 at 20:12
1
If you know that your data will look like the example you've provided, I suggest using a polynomial model and a least-squares fit. This works reasonably well in practice. Edit: If you insist on an exponential model, maybe logarithmize your data and try a linear model then.
– Thomas Lang
Nov 23 '18 at 20:14
add a comment |
As far as I can see you are trying to fit an exponential curve to your data. Most of your data is concentrated on the upper right and hence the algorithm tries to fit it best-possible to that part.
answered Nov 23 '18 at 20:04
Thomas LangThomas Lang
44627
As far as I can see you are trying to fit an exponential curve to your data. Most of your data is concentrated on the upper right and hence the algorithm tries to fit it best-possible to that part.
answered Nov 23 '18 at 20:04
Thomas LangThomas Lang
44627
answered Nov 23 '18 at 20:04
Thomas LangThomas Lang
44627
answered Nov 23 '18 at 20:04
Thomas LangThomas Lang
44627
answered Nov 23 '18 at 20:04
Thomas LangThomas Lang
44627
44627
thanks , but how can I fit my curve for the lower left part?
– Zara Arshad
Nov 23 '18 at 20:12
1
If you know that your data will look like the example you've provided, I suggest using a polynomial model and a least-squares fit. This works reasonably well in practice. Edit: If you insist on an exponential model, maybe logarithmize your data and try a linear model then.
– Thomas Lang
Nov 23 '18 at 20:14
add a comment |
thanks , but how can I fit my curve for the lower left part?
– Zara Arshad
Nov 23 '18 at 20:12
1
If you know that your data will look like the example you've provided, I suggest using a polynomial model and a least-squares fit. This works reasonably well in practice. Edit: If you insist on an exponential model, maybe logarithmize your data and try a linear model then.
– Thomas Lang
Nov 23 '18 at 20:14
thanks , but how can I fit my curve for the lower left part?
– Zara Arshad
Nov 23 '18 at 20:12
thanks , but how can I fit my curve for the lower left part?
– Zara Arshad
Nov 23 '18 at 20:12
1
1
If you know that your data will look like the example you've provided, I suggest using a polynomial model and a least-squares fit. This works reasonably well in practice. Edit: If you insist on an exponential model, maybe logarithmize your data and try a linear model then.
– Thomas Lang
Nov 23 '18 at 20:14
If you know that your data will look like the example you've provided, I suggest using a polynomial model and a least-squares fit. This works reasonably well in practice. Edit: If you insist on an exponential model, maybe logarithmize your data and try a linear model then.
– Thomas Lang
Nov 23 '18 at 20:14
add a comment |
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One could think about weighting the fit by the inverse of the point density. Also one could fit x(y) instead of y(x).
– ImportanceOfBeingErnest
Nov 23 '18 at 20:26