local extrema pgfplots - smoothHow can I automatically mark local extrema with pgfplots and scatter?plotting...
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local extrema pgfplots - smooth
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Is it possible to change this code for a "smooth" plot?
How can I automatically mark local extrema with pgfplots and scatter?
MWE
documentclass{scrartcl}
usepackage[utf8]{inputenc}
usepackage[ngerman]{babel}
usepackage{pgfplots}
%-min/max-code https://tex.stackexchange.com/questions/55349/how-can-i-automatically-mark-local-extrema-with-pgfplots-and-scatter
begin{document}
begin{tikzpicture}
begin{axis}[axis lines*=middle,after end axis/.code={draw [thick, dashed, gray] (maximum) --({axis cs:0,0}-|maximum);}]
addplot +[mark max,smooth,no markers] file {titra.dat};
end{axis}
end{tikzpicture}
end{document}
titra.dat
0 5
2 5.5
4 5
4.2 5
4.4 10
4.6 15
4.8 25
5 60
5.2 100
5.4 225
5.6 315
5.8 300
6 100
6.2 35
6.4 8.33
pgfplots
edited 8 hours ago
Glorfindel
269139
add a comment |
Is it possible to change this code for a "smooth" plot?
How can I automatically mark local extrema with pgfplots and scatter?
MWE
documentclass{scrartcl}
usepackage[utf8]{inputenc}
usepackage[ngerman]{babel}
usepackage{pgfplots}
%-min/max-code https://tex.stackexchange.com/questions/55349/how-can-i-automatically-mark-local-extrema-with-pgfplots-and-scatter
begin{document}
begin{tikzpicture}
begin{axis}[axis lines*=middle,after end axis/.code={draw [thick, dashed, gray] (maximum) --({axis cs:0,0}-|maximum);}]
addplot +[mark max,smooth,no markers] file {titra.dat};
end{axis}
end{tikzpicture}
end{document}
titra.dat
0 5
2 5.5
4 5
4.2 5
4.4 10
4.6 15
4.8 25
5 60
5.2 100
5.4 225
5.6 315
5.8 300
6 100
6.2 35
6.4 8.33
pgfplots
edited 8 hours ago
Glorfindel
269139
5
No, you can't adapt that solution to work with curves produced using thesmoothoption. I wouldn't recommend usingsmoothwhen you're using measured data, especially not if the data changes as sharply as yours. You don't know what algorithm is being used for the smoothing, so you shouldn't let it influence your analysis. If you want to find the most likely maximum, you should either use a defensible mathematical model of your process and fit that to your data points, or just use the data point with the largest value.
– Jake
May 6 '13 at 11:05
add a comment |
Is it possible to change this code for a "smooth" plot?
How can I automatically mark local extrema with pgfplots and scatter?
MWE
documentclass{scrartcl}
usepackage[utf8]{inputenc}
usepackage[ngerman]{babel}
usepackage{pgfplots}
%-min/max-code https://tex.stackexchange.com/questions/55349/how-can-i-automatically-mark-local-extrema-with-pgfplots-and-scatter
begin{document}
begin{tikzpicture}
begin{axis}[axis lines*=middle,after end axis/.code={draw [thick, dashed, gray] (maximum) --({axis cs:0,0}-|maximum);}]
addplot +[mark max,smooth,no markers] file {titra.dat};
end{axis}
end{tikzpicture}
end{document}
titra.dat
0 5
2 5.5
4 5
4.2 5
4.4 10
4.6 15
4.8 25
5 60
5.2 100
5.4 225
5.6 315
5.8 300
6 100
6.2 35
6.4 8.33
pgfplots
edited 8 hours ago
Glorfindel
269139
Is it possible to change this code for a "smooth" plot?
How can I automatically mark local extrema with pgfplots and scatter?
MWE
documentclass{scrartcl}
usepackage[utf8]{inputenc}
usepackage[ngerman]{babel}
usepackage{pgfplots}
%-min/max-code https://tex.stackexchange.com/questions/55349/how-can-i-automatically-mark-local-extrema-with-pgfplots-and-scatter
begin{document}
begin{tikzpicture}
begin{axis}[axis lines*=middle,after end axis/.code={draw [thick, dashed, gray] (maximum) --({axis cs:0,0}-|maximum);}]
addplot +[mark max,smooth,no markers] file {titra.dat};
end{axis}
end{tikzpicture}
end{document}
titra.dat
0 5
2 5.5
4 5
4.2 5
4.4 10
4.6 15
4.8 25
5 60
5.2 100
5.4 225
5.6 315
5.8 300
6 100
6.2 35
6.4 8.33
pgfplots
pgfplots
edited 8 hours ago
Glorfindel
269139
edited 8 hours ago
Glorfindel
269139
edited 8 hours ago
Glorfindel
269139
edited 8 hours ago
Glorfindel
269139
edited 8 hours ago
Glorfindel
269139
269139
asked May 6 '13 at 10:40
user29463
5
No, you can't adapt that solution to work with curves produced using thesmoothoption. I wouldn't recommend usingsmoothwhen you're using measured data, especially not if the data changes as sharply as yours. You don't know what algorithm is being used for the smoothing, so you shouldn't let it influence your analysis. If you want to find the most likely maximum, you should either use a defensible mathematical model of your process and fit that to your data points, or just use the data point with the largest value.
– Jake
May 6 '13 at 11:05
add a comment |
5
No, you can't adapt that solution to work with curves produced using thesmoothoption. I wouldn't recommend usingsmoothwhen you're using measured data, especially not if the data changes as sharply as yours. You don't know what algorithm is being used for the smoothing, so you shouldn't let it influence your analysis. If you want to find the most likely maximum, you should either use a defensible mathematical model of your process and fit that to your data points, or just use the data point with the largest value.
– Jake
May 6 '13 at 11:05
5
5
No, you can't adapt that solution to work with curves produced using the
smooth option. I wouldn't recommend using smooth when you're using measured data, especially not if the data changes as sharply as yours. You don't know what algorithm is being used for the smoothing, so you shouldn't let it influence your analysis. If you want to find the most likely maximum, you should either use a defensible mathematical model of your process and fit that to your data points, or just use the data point with the largest value.– Jake
May 6 '13 at 11:05
No, you can't adapt that solution to work with curves produced using the
smooth option. I wouldn't recommend using smooth when you're using measured data, especially not if the data changes as sharply as yours. You don't know what algorithm is being used for the smoothing, so you shouldn't let it influence your analysis. If you want to find the most likely maximum, you should either use a defensible mathematical model of your process and fit that to your data points, or just use the data point with the largest value.– Jake
May 6 '13 at 11:05
add a comment |
1 Answer
1
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oldest
votes
No, you can't adapt that solution to work with curves produced using the smooth option. I wouldn't recommend using smooth when you're using measured data, especially not if the data changes as sharply as yours. You don't know what algorithm is being used for the smoothing, so you shouldn't let it influence your analysis. If you want to find the most likely maximum, you should either use a defensible mathematical model of your process and fit that to your data points, or just use the data point with the largest value
answered Aug 15 '13 at 21:30
JakeJake
196k24660765
add a comment |
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votes
No, you can't adapt that solution to work with curves produced using the smooth option. I wouldn't recommend using smooth when you're using measured data, especially not if the data changes as sharply as yours. You don't know what algorithm is being used for the smoothing, so you shouldn't let it influence your analysis. If you want to find the most likely maximum, you should either use a defensible mathematical model of your process and fit that to your data points, or just use the data point with the largest value
answered Aug 15 '13 at 21:30
JakeJake
196k24660765
add a comment |
No, you can't adapt that solution to work with curves produced using the smooth option. I wouldn't recommend using smooth when you're using measured data, especially not if the data changes as sharply as yours. You don't know what algorithm is being used for the smoothing, so you shouldn't let it influence your analysis. If you want to find the most likely maximum, you should either use a defensible mathematical model of your process and fit that to your data points, or just use the data point with the largest value
answered Aug 15 '13 at 21:30
JakeJake
196k24660765
add a comment |
No, you can't adapt that solution to work with curves produced using the smooth option. I wouldn't recommend using smooth when you're using measured data, especially not if the data changes as sharply as yours. You don't know what algorithm is being used for the smoothing, so you shouldn't let it influence your analysis. If you want to find the most likely maximum, you should either use a defensible mathematical model of your process and fit that to your data points, or just use the data point with the largest value
answered Aug 15 '13 at 21:30
JakeJake
196k24660765
No, you can't adapt that solution to work with curves produced using the smooth option. I wouldn't recommend using smooth when you're using measured data, especially not if the data changes as sharply as yours. You don't know what algorithm is being used for the smoothing, so you shouldn't let it influence your analysis. If you want to find the most likely maximum, you should either use a defensible mathematical model of your process and fit that to your data points, or just use the data point with the largest value
answered Aug 15 '13 at 21:30
JakeJake
196k24660765
answered Aug 15 '13 at 21:30
JakeJake
196k24660765
answered Aug 15 '13 at 21:30
JakeJake
196k24660765
answered Aug 15 '13 at 21:30
JakeJake
196k24660765
196k24660765
add a comment |
add a comment |
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5
No, you can't adapt that solution to work with curves produced using the
smoothoption. I wouldn't recommend usingsmoothwhen you're using measured data, especially not if the data changes as sharply as yours. You don't know what algorithm is being used for the smoothing, so you shouldn't let it influence your analysis. If you want to find the most likely maximum, you should either use a defensible mathematical model of your process and fit that to your data points, or just use the data point with the largest value.– Jake
May 6 '13 at 11:05