Timeline for Compute a numerical derivative
Current License: CC BY-SA 3.0
9 events
| when toggle format | what | by | license | comment | |
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| Apr 6, 2018 at 11:42 | comment | added | 301_Moved_Permanently |
*varargs is just the Python notation to mean variable number of arguments, just call np.gradient(y, x) Python will figure it out just fine.
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| Apr 6, 2018 at 11:37 | comment | added | user127168 | Out of curiosity, is there an advantage to using *varargs as an argument including x rather than having x as an argument by itself? | |
| Apr 6, 2018 at 9:46 | answer | added | Maarten Fabré | timeline score: 4 | |
| Apr 6, 2018 at 9:24 | vote | accept | CommunityBot | ||
| Apr 6, 2018 at 9:10 | answer | added | 301_Moved_Permanently | timeline score: 6 | |
| Apr 6, 2018 at 8:43 | comment | added | 301_Moved_Permanently | yes, by default numpy consider each point on the x axis to be spaced by one unit, unless you tell it otherwise. | |
| Apr 6, 2018 at 8:41 | comment | added | user127168 | Ahhh, that is varargs... I can try that in a little bit. | |
| Apr 6, 2018 at 8:39 | comment | added | 301_Moved_Permanently |
Did you consider passing x as the second parameter of np.gradient?
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| Apr 6, 2018 at 6:23 | history | asked | user127168 | CC BY-SA 3.0 |