I would like to plot a csv file with gnuplot. Instead of a line, I want to use points that are evenly distributed along the path of the curve. However, the data in the csv file is not evenly distributed, e.g. like this
x,p
0,2
1,4
1.1,4.2
1.2,4.4
2.8,7.6
2.85,7.7
4,10
It should be possible to achieve this, but how?
Here is an example graph where I plot every nth point. Because my numerical solution is so good :-) you only see one line, so I would like to have markers on the one curve. But the points should be distributed equidistantly (the current distribution is only due to the nature of the analytical solution).
In the development version of gnuplot (5.5) this can be done exactly as asked. smooth path does what you would expect, and pn 7 tells it to place exactly 7 evenly spaced points.
$DATA << EOD
0,2
1,4
1.1,4.2
1.2,4.4
2.8,7.6
2.85,7.7
4,10
EOD
set log y
set key top left
set datafile separator comma
plot $DATA smooth path with lp pn 7 title "smooth path pn 7", \
$DATA with points pt 6 ps 2 title "original points"
The current version 5.4 does not offer smooth path, but if your data points are sufficiently close to lying on a smooth curve one of the other smoothing options, e.g. smooth mcs, may be acceptable.
For the record, I think this is not a good thing to do. It is dishonest to hide the actual data points while showing artificially even points instead. It misleads the viewer as to where the curve is reliable and where it may not be.
Related
I have rainfall data which I have imported as a csv file. It's 185 lines like this:
Name, Longitude, Latitude, Elevation, TotalPrecipitation
BURLINGTON, -72.932, 41.794, 505, M
BAKERSVILLE, -73.008, 41.842, 686, 42.40
BARKHAMSTED, -72.964, 41.921, 710, M
NORFOLK 2 SW, -73.221, 41.973, 1340, 44.22
Looking at the layer properties the latitude and longitude are brought in as "double" but the rainfall amounts come in as "text" so I can't contour them.
How can I get beyond this point and where do I go to do the contouring? Do I go to Vector:Contour? Will it understand M is missing data or will the Ms still exist if this is converted to "double?"
I'm a little confused. Thanks for the help.
I think I might have the idea of help.
Since you have the sort of points located randomly across some area you could do as follows:
Load CSV to your QGIS in order to set the point layer with an attribute table including your most important value, which is Total Precipitation. Let's call it the TEST layer
Processing Toolbox -> TIN Interpolation -> Select the TEST layer. As an Interpolation attribute choose "Total precipitation". Use the green "+" symbol for adding this selection. Don't forget about the Extent option, where you could define the bounds of your interpolation. Preferably I wouldn't exceed the layer I am working on. Output raster size is also important - avoid a small number of rows. Put them about 10 optionally in order to make your interpolation efficient.
https://www.qgistutorials.com/en/docs/3/interpolating_point_data.html
Main bar -> Raster -> Extraction -> Contour
In the input layer select TEST, Interval contours between lines can be 10 (10mm in your case), Attribute name - put PRECIPITATION -> click Run
Your precipitation lines are ready! Now, you can Right-Click -> Properties -> Symbology (change color) or _>Labels (provide labels based on your attribute column Total Precipitation).
I'm not a dev, I'm doing this for a school project. I'm trying to put the following dataset into a surface plot in windows gnuplot. qt type terminal, if that's important.
https://files.catbox.moe/nbc6l1.json
As you can see, it's a huge set of data. Pulled directly from an image and into a csv file, which I converted to json.
When I type in "splot 'C:\Users\tyler\ESRP Data\sampleOutput.json'", this is what I get.
As you can see, there's only a single line, when there should be something approaching an intensity chart in a 3 dimensional space. Is it a problem with the data? Do I need a specific command to do this?
It would help if you attached an example of your image data to the question, and also if you provided a link to a plot similar to the one you are trying to create. There are many different styles one might use to represent a surface. I will attempt to guess at a possible solution.
Input image (scribbled in GIMP and saved as a png image):
Gnuplot surface plot:
set border -1
unset tics
# surface represented by colored lines in 3D
# down-sample by 4x in each dimension to get an interpretable surface
set palette defined (0 "blue", 1 "white")
splot 'scribble.png' binary filetype=png every 4:4:4 using 1:2:3:3 with lines lc palette
No idea if I am asking this question in the right place, but here goes...
I have a set of equations that were calculated based on numbers ranging from 4 to 8. So an equation for when this number is 5, one for when it is 6, one for when it is 7, etc. These equations were determined from graphing a best fit line to data points in a Google Sheet graph. Here is an example of a graph...
Example...
When the number is between 6 and 6.9, this equation is used: windGust6to7 = -29.2 + (17.7 * log(windSpeed))
When the number is between 7 and 7.9, this equation is used: windGust7to8 = -70.0 + (30.8 * log(windSpeed))
I am using these equations to create an image in python, but the image is too choppy since each equation covers a range from x to x.9. In order to smooth this image out and make it more accurate, I really would need an equation for every 0.1 change in number. So an equation for 6, a different equation for 6.1, one for 6.2, etc.
Here is an example output image that is created using the current equations:
So my question is: Is there a way to find the relationship between the two example equations I gave above in order to use that to create a smoother looking image?
This is not about logarithms; for the purposes of this derivation, log(windspeed) is a constant term. Rather, you're trying to find a fit for your mapping:
6 (-29.2, 17.7)
7 (-70.0, 30.8)
...
... and all of the other numbers you have already. You need to determine two basic search paramteres:
(1) Where in each range is your function an exact fit? For instance, for the first one, is it exactly correct at 6.0, 6.5, 7.0, or elsewhere? Change the left-hand column to reflect that point.
(2) What sort of fit do you want? You are basically fitting a pair of parameterized equations, one for each coefficient:
x y x y
6 -29.2 6 17.7
7 -70.0 7 30.8
For each of these, you want to find the coefficients of a good matching function. This is a large field of statistical and algebraic study. Since you have four ranges, you will have four points for each function. It is straightforward to fit a cubic equation to each set of points in Cartesian space. However, the resulting function may not be as smooth as you like; in such a case, you may well find that a 4th- or 5th- degree function fits better, or perhaps something exponential, depending on the actual distribution of your points.
You need to work with your own problem objectives and do a little more research into function fitting. Once you determine the desired characteristics, look into scikit for fitting functions to do the heavy computational work for you.
I'm trying to develop a model to recognize new gestures with the Myo Armband. (It's an armband that possesses 8 electrical sensors and can recognize 5 hand gestures). I'd like to record the sensors' raw data for a new gesture and feed it to a model so it can recognize it.
I'm new to machine/deep learning and I'm using CNTK. I'm wondering what would be the best way to do it.
I'm struggling to understand how to create the trainer. The input data looks like something like that I'm thinking about using 20 sets of these 8 values (they're between -127 and 127). So one label is the output of 20 sets of values.
I don't really know how to do that, I've seen tutorials where images are linked with their label but it's not the same idea. And even after the training is done, how can I avoid the model to recognize this one gesture whatever I do since it's the only one it's been trained for.
An easy way to get you started would be to create 161 columns (8 columns for each of the 20 time steps + the designated label). You would rearrange the columns like
emg1_t01, emg2_t01, emg3_t01, ..., emg8_t20, gesture_id
This will give you the right 2D format to use different algorithms in sklearn as well as a feed forward neural network in CNTK. You would use the first 160 columns to predict the 161th one.
Once you have that working you can model your data to better represent the natural time series order it contains. You would move away from a 2D shape and instead create a 3D array to represent your data.
The first axis shows the number of samples
The second axis shows the number of time steps (20)
The thirst axis shows the number of sensors (8)
With this shape you're all set to use a 1D convolutional model (CNN) in CNTK that traverses the time axis to learn local patterns from one step to the next.
You might also want to look into RNNs which are often used to work with time series data. However, RNNs are sometimes hard to train and a recent paper suggests that CNNs should be the natural starting point to work with sequence data.
For example , if I want to plot Sin(z) where z is a complex variable , how I will achieve it in either Octave or Maxima?
I don't know about Octave, but here is a message about that, with some code you can try in Maxima: https://www.ma.utexas.edu/pipermail/maxima/2007/006644.html
There may be more specific information for wxMaxima -- you can try their user forum: https://sourceforge.net/p/wxmaxima/discussion/435775/
(referring Octave 4.0.0)
How do you want to try to represent the output of the function? Plotting either the real or imaginary parts of the output can be done fairly simply using a 3-dimensional graph, where the x and y axes are the real and imaginary components of z, and the vertical axis is either the real or imaginary values of sin(z). Producing those are fairly simple in Octave. Here's a link to a script you can save and run to show an example.
Simply change the g = exp(f) line to g = sin(f).
Octave-help mailing list example
Note that the imaginary part plot is commented out. Just switch the # between the different plot commands if you want to see that part.
Now, are you instead looking for options to map the Z plane (z=x+iy) to the W plane (w=u+iv) and represent closed contours mapped by w=sin(z)? in that case you'll need to do parametric plotting as described on this FIT site. There is a link to his Matlab program at the bottom of the explanation that provides one method of using color coding to match z->w plane contour mapping.
Those m-files are written for Matlab, so a few things do not work, but the basic plotting is compatible with Octave 4.0.0. (the top level ss13.m file will fail on calls to flops and imwrite)
But, if you put your desired function in myfun13.m for f, df and d2f, (sin(z), cos(z), -sin(z) respectively), then run cvplot13, you'll get color maps showing the correspondence between z and w planes.
wxMaxima has a plot3d that can do it. Since the expression to plot is in terms of x and y, I plotted the function's magnitude with abs(f(x+%i*y)):
plot3d(abs((x+%i*y-3)*(x+%i*y-5)*(x+%i*y-6)), [x,2,7], [y,-1,1], [grid,100,100], [z,0,5])$