Timeline for Python Implementation of Canopy Structural Dynamic Model using scipy.optimize.least_squares
Current License: CC BY-SA 4.0
10 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Nov 5, 2024 at 6:59 | vote | accept | ABC | ||
| Nov 4, 2024 at 12:32 | history | edited | Reinderien | CC BY-SA 4.0 | grammar, Wikipedia links |
| Nov 3, 2024 at 18:52 | answer | added | Reinderien | timeline score: 4 | |
| Jan 27, 2020 at 9:40 | comment | added | ABC | Also, the data sets should be cast from float32 to float64. | |
| Jan 27, 2020 at 9:01 | comment | added | ABC | I found that there were no overflow and other warnings when constrain the model parameters to vary in certain bounds. I set the bounds parameter of least_squares like this: bounds=([1, 500, 1500, 3, 0.001, 0.001, 0.5], [50, 2000, 3000, 9, 0.1, 0.1, 1.5]). | |
| Jan 24, 2020 at 14:37 | comment | added | ABC | @Graipher thanks for the explanation. This equation is popular in the literature in my field. I have had some success in finding the parameters with Excel Solver but using another data set. But implementation in Python seems to be quite difficult; or maybe impossible? | |
| Jan 24, 2020 at 13:48 | comment | added | Graipher | An overflow in np.exp(-x[4] * (tt - x[0] - x[1]))) ** x[6]) does not sound so unexpected, x[6] does not even need to be very large, depending on the other parameters, e.g. np.exp(2**10) = inf. And then 1./np.exp(-2**10) is a divide by zero. | |
| Jan 24, 2020 at 8:22 | comment | added | greybeard | Welcome to CodeReview@SE. I see your question close to the border between what is and isn't on topic here: please explicitly express one or more concerns about the code presented. | |
| Jan 24, 2020 at 8:00 | review | First posts | |||
| Jan 24, 2020 at 8:22 | |||||
| Jan 24, 2020 at 7:56 | history | asked | ABC | CC BY-SA 4.0 |