When I do a scatter plot, by default it shows the axis from 0.0 to 1.0 fractions.
For example, the following graph contains a straight line that goes from (0,0) to (10m,10m), but it shows:
Detailed data generation show at: Large plot: ~20 million samples, gigabytes of data
How to make the axes show from 0 to 10 million instead?
The inspiration for this comes from this question.
Tested in VisIt 2.13.3.
Since scatter plot associates variables of potentially radically different scales, by default, it maps each variable's range into [0,1]. We have this ticket for it. You can manually change by going to scatter plot attribute's window and Apperance tab and un-checking the 'Normalize the axes to a cube' option
Related
I am writing an application for Windows 10 using Delphi 11 and Steema TeeChart pro 11 components.
I use AddXY(….) to plot a DateTime series of about 1000 values as points in Chart1 with BottomAxis set to DateTime and the Leftaxis set to float.
Thereafter I zoom in a small part of the data in Chart1 using the mouse wheel or drawing a rectangle.
Then I want to plot a new time series (AddXY(….)) of 1000 values in Chart1, keeping the zoomed in min and max values for the bottom axes, but use automatic scaling for LeftAxis. Before plotting the second series I delete the first.
The second series have the same DateTime values as the first series, but the Y values are sometimes very different compared with the first series and may not be visible in the graph.
Now the problem occurs. The Left axis will be automatic scaled based on all the data points and not on the points only falling within the zoomed X boundaries.
Does TChart have a simple method to find the min and max Y-values for the second series within the zoomed in time period. I thought of using the LeftAxis.AdjustMinMax() procedure for rescaling the left axis?
Of course I could scan through all the Y-values within the zoomed in period and calculate the min and max value. Then, how do I know the starting and ending valueindex of the data points falling within the zoomed in time period? I hoped to avoid using this method due to speed resons if number of points are > 1000.
Hopefully someone more experienced TeeChart programmer than I am can give me some suggestions of the best way to achieve this.
I‘m working on a visual data logger for my DMM, it writes every measurement to RS232 inteface. There I connect a Teensy 3.6 and collect the data points.
For each point I have the timestamp and the measured value. I will collect 10.000 readings.
I want to display the measured data on a display (800x480) in two ways. First as a rolling graph, that scrolls from right to left and shows the last minute or so. This is working fine.
Second, I want to display all collected measurements in total (max. 10k points). So I have to shrink or compress the data, but I want to preserve the shape of the curve.
To give you an idea how it should look like, please watch the video from Dave on EEV at YT (https://youtu.be/SObqPuUozNo) and skip to 41:20. There you see how another DMM is shrinking the incomming data and displays it. At about 1:01:05 10k measurements are shown on the display area of only 400px wide.
Question is, how is this done?
I’ve heard about Douglas-Pucker algorithm, but have no idea if this is the right way and how to use it on the Arduino/ Teensy platform.
Any help is very welcome, thank you....
I cannot just display all data points, because I‘m using an FT81x as display controller, and this can take only up to 2000 drawing commands per frame. And it takes more time.
Anyway, I solved the problem using the simple way.
I create bins and calculate the min and max values in this bin. Then simply draw a line between these points. Works fine!
BTW, I‘m the TO :-)
For cases where you got many more samples than pixels in x axis instead of LineTo like graph use vertical lines graph instead...
So depending on the number of samples per rendered time frame and x resolution you should compute ymin and ymax for eaxch x and render vertical line ...
something like:
xs=800;
for (x0=x,i=sample_left,y0=y1=sample[i],i<sample_right;i++)
{
x = (i-sample_left)*xs/(sample_right-sample_left);
y = sample[i]; // here add y scaling and offset
if (x0!=x) { line(x0,y0,x0,y1); x0=x; y0=y; y1=y; }
if (y0>y) y0=y;
if (y1<y) y1=y;
}
where sample[] are your stored values , sample_left,sample_right is the range to render and xs is graph x resolution. To speed up you can pre-compute the y0,y1 for each x and render that (recompute only on range or samples change) ... So as you can see you will use just xs line commands which shoul dbe fast enough. The x linear interpolation can be done without multiplication nor division if you rewrite it to integer DDA style ...
These QAs might interest you:
plotting real time Data on (qwt )Oscillocope
i don't really understand FFT and sample rates
[note]
After a second look The deleted answer is with the same approach as this (got deleted by review probably before the edit which transformed it from not an answer (comment) to the correct answer) so I voted for undelete even if it is considerably lower quality than mine but was posted sooner.
I am working on a project which make use of Python-2.7, pygtk and matplotlib-2 on windows.
My app records data from COM port and plot it like a live plot. I need to put major and minor ticks for grid purpose.
My X-axis data is time duration in seconds.
ax1.set_xticks(np.arange(x_range[0],x_range[1],0.45))
ax1.set_xticks(np.arange(x_range[0],x_range[1],0.09),minor=True)
major ticks # 0.45 sec.
minor ticks # 0.09 sec
x_range[0] is minimum value of x axis data
x_range1 is maximum value of x axis data
Setting ticks this way is taking more time and also it takes lot of time when I plot entire data.
Is there any faster way to set ticks using matplotlib?
Note: I have totally 7 different data sets to be plotted on different subplots.
Sample plot image is given blow.
In following image, you can see grid properly. This plot window is also with a scroll bar at bottom. Total plot size depends on total time of data recording.
I want to match symmetry of object contours. I tried using matchShape(), computeDistance(), Humoments() from opencv 3.0 library. But none of them is close to what I want.
Following are the images on which I am working.
Good Shape-1
Defected
I expect to get highest value of dis-symmetry for image-2(named Defected)
You can do it yourself, by using several simple tools:
Find the centroid
Use PCA (http://docs.opencv.org/master/d1/dee/tutorial_introduction_to_pca.html#gsc.tab=0) to find the main axis
Rotate the shape so that the main axis points up
In each row, count the number of pixels on either side of the center and compare (There should be a single center coordinate, probably using the median value of all rows centers)
You can tune your own thresholds to fit the problem.
Let's say I have a list of values and I have already chunked them into groups to make a histogram.
Since Excel doesn't have histograms, I made a bar plot using the groups I developed. Specifically, I have the frequencies 2 6 12 10 2 and it produces the bar plot you see below.
Next, I want to add a normal distribution (line plot) with a mean of 0.136 and standard deviation of 0.497 on top of this histogram. How can I do this in excel? I need the axis to line up such that it takes up the width of the bar plot. Otherwise, you get something like I've attached.
But...the normal should be overlayed on the bar plot. How can I get this effect?
There are two main part to this answer:
First, I reverse-engineered the grouped data to come up with an appropriate mean and standard deviation on this scale.
Second, I employed some chart trickery to make the normal distribution curve look right when superimposed on the column chart. I used Excel 2007 for this; hopefully you have the same options available in your version.
Part 1: Reverse-Engineer
The column B formulae are:
Last Point =MAX(A2:A6)
Mean =SUMPRODUCT(B2:B6,A2:A6)/SUM(B2:B6)
E(x^2f) =SUMPRODUCT(A2:A6^2,B2:B6)
E(xf)^2 =SUMPRODUCT(A2:A6,B2:B6)^2
E(f) =SUM(B2:B6)
Variance =B10-B11/B12
StDev =SQRT(B13/(B12-1))
Part 2: Chart Trickery
Data table:
Column D is just an incremental counter. This will be the number of data points in the normal distribution curve.
E2 =D2/$B$8 etc.
F2 =NORMDIST(E2,$B$9,$B$14,FALSE) etc.
Chart:
Now, add Columns E:F to the chart. You will need to massage a few things:
Change the series to be an X-Y plot. This might require some editing of the chart series to force a single series to use your desired X and Y values.
Change the series to use the secondary axes (both X and Y).
Change the secondary X-axis range to 0.5-5.5 (i.e., 0.5 on either side of the column chart category values). This will effectively align the primary and secondary X-axes.
Change the secondary Y-axis range to 0-1
Format the X-Y series appearance to taste (I suggest removing value markers).
The result so far:
Lastly, you can remove the tick marks and labels on the secondary axes to clean up the look.
Postscript: Thanks to John Peltier for innumerable charting inspirations over the years.