I have run the cyclone case from the OpenFOAM tutorials and want to view it using the builtin paraFOAM viewer which is based on Paraview 5.4.0.
The simulation has a number of particles in the diameter range of [2e-5, 1e-4] and i would like to scale the size of particles with the diameter array provided with the results.
To do this i select the Point Gaussian representation for the lagrangian fields (kinematiccloud), select Advanced properties, and select 'Scale by data array' after which the diameter array is chosen by default (although its not possible to change it to another field, which I suspect is a bug) but all the particles disappear from the view, as can be seen in the following screenshot:
My guess is that i need to chose proper values of the Gaussian radius and for the scale transfer function but there is no documentation to which it should be set. I have tried trial-and-error but i cannot find any settings for which i can get the particles back and have them render at different sizes.
Can someone enlighten me on how to set the Gaussian radius and scale transfer function properly?
The PointGaussian has just been improved and configuration is now automatic. You may want to try the last release of ParaView.
More info here :
https://blog.kitware.com/major-improvements-on-the-point-gaussian-representation/
Related
The measurement tool of the viewer has calibration tool. It requires that user selects two points in the viewer and define the distance with proper units.
My plan is that I will have the points defined in my model at a fixed distance. I will not need user input for this. How do I add the distance, unit, and size so as to programmatically set the calibration?
Edit: The workaround.
I need that the default units be meters and it should correctly show 1 meter on the model to 1 meter as measured by measurement tool.
For the time being, what I did is -
I manually calibrate the model using calibrate tool to meters by picking two known points in the model.
Then I used this to get the scale factor -
var measureExtension =NOP_VIEWER.getExtension('Autodesk.Measure')
var factor = measureExtension.getCalibrationFactor()
(I used the above code lines in the developer console of the browser while interacting with the viewer simultaneously.)
which gave me this value factor = 0.039369.
I am adding this scale factor in my code once the model is loaded again.
measureExtension.calibrateByScale('m', 0.039369)
This seems to solve the issue for the models that I have with me.
I know this will break once I have some different model with different default units. Please let me know if someone has a better solution.
I'm taking a quick guess by looking at the viewer3D.js source:
var measureExt = viewer.getExtension('Autodesk.Measure')
// pick from available values:
// 'decimal-ft'
// 'ft-and-fractional-in'
// 'ft-and-decimal-in'
// 'decimal-in'
// 'fractional-in'
// 'm'
// 'cm'
// 'mm'
// 'm-and-cm'
measureExt.calibrate('decimal-in', 10)
I was asked to create a leg follower robot (I already did it) and in the second part of this assignment I have to develop a Kalman filter in order to improve the following process of the robot. The robot gets from the person the distance where she is to the robot and also the angle (it is a relative angle, because the reference is the robot itself, not absolute x-y coordinates)
About this assignment I have a serious doubt. Everything I have read, every sample I have seen about kalman filter has been in one dimension (a car running distance or a rock falling from a building) and according to the task I would have to apply it in 2 dimensions. Is it possible to apply a kalman filter like this?
If it is possible to calculate kalman filter in 2 dimensions then I would understand that what is asked to do is to follow the legs in a linnearized way, despite a person walks weirdly (with random movements) --> About this I have the doubt of how to establish the function of the state matrix, could anyone please tell me how to do it or to tell me where I can find more information about this?
thanks.
Well you should read up on Kalman Filter. Basically what it does is estimate a state through its mean and variance separately. The state can be whatever you want. You can have local coordinates in your state but also global coordinates.
Note that the latter will certainly result in nonlinear system dynamics, in which case you could use the Extended Kalman Filter, or to be more correct the continuous-discrete Kalman Filter, where you treat the system dynamics in a continuous manner and the measurements in discrete time.
Example with global coordinates:
Assuming you have a small cubic mass which can drive forward with velocity v. You could simply model the dynamics in local coordinates only, where your state s would be s = [v], which is a linear model.
But, you could also incorporate the global coordinates x and y, assuming we are moving on a plane only. Then you would have s = [x, y, phi, v]'. We need phi to keep track of the current orientation since the cube can only move forward in respect to its orientation of course. Let's define phi as the angle between the cube's forward direction and the x-axis. Or in other words: With phi=0 the cube would move along the x-axis, with phi=90° it would move along the y-axis.
The nonlinear system dynamics with global coordinates can then be written as
s_dot = [x_dot, y_dot, phi_dot, v_dot]'
with
x_dot = cos(phi) * v
y_dot = sin(phi) * v
phi_dot = ...
v_dot = ... (Newton's Law)
In EKF (Extended Kalman Filter) Prediction step you would use the (discretized) equations above to predict the mean of the state in the first step of and the linearized (and discretized) equations for prediction of the Variance.
There are two things to keep in mind when you decide what your state vector s should look like:
You might be tempted to use my linear example s = [v] and then integrate the velocity outside of the Kalman Filter in order to obtain the global coordinate estimates. This would work, but you would lose the awesomeness of the Kalman Filter since you would only integrate the mean of the state, not its variance. In other words, you would have no idea what the current uncertainties for your global coordinates are.
The second step of the Kalman Filter, the measurement or correction update, requires that you can describe your sensor output as a function of your states. So you may have to add states to your representation just so that you can express your measurements correctly as z[k] = h(s[k], w[k]) where z are measurements and w is a noise vector with Gaussian distribution.
I have a Google Map and a server sends a list of objects that have a position with a small radius (100m max). I need to quickly be able to know if a position is colliding with something in the list and draw on the map everything.
I'm thinking I should use a Quadtree (very useful in 2D collisions for games) but my issue is I'm not limited to a screen but to the earth !
Sure, if I have 100 objects it's not a problem but at any time the server can send me new objects that I need to add to the list and so my Quadtree could drastically change or become unbalanced.
What should I do ? Should I still use a Quadtree and modify the entire tree if a new element is added outside of the current boundaries ? Should I set the boundaries to the max latitude longitude (but could have issue with double precision) ? Or does someone knows a better data structure for that type of problem ?
rXp
To avoid issues with double precision, especially at the splitting border of a quad cell, it is advisable to use integer coordinates in the quad tree.
convert double lat/lon to int by multiplying with 1E6, this results in a precision of about 10cm.
You can use a space-filling-curve, for example a z curve.
I have a series of data and need to detect peak values in the series within a certain number of readings (window size) and excluding a certain level of background "noise." I also need to capture the starting and stopping points of the appreciable curves (ie, when it starts ticking up and then when it stops ticking down).
The data are high precision floats.
Here's a quick sketch that captures the most common scenarios that I'm up against visually:
One method I attempted was to pass a window of size X along the curve going backwards to detect the peaks. It started off working well, but I missed a lot of conditions initially not anticipated. Another method I started to work out was a growing window that would discover the longer duration curves. Yet another approach used a more calculus based approach that watches for some velocity / gradient aspects. None seemed to hit the sweet spot, probably due to my lack of experience in statistical analysis.
Perhaps I need to use some kind of a statistical analysis package to cover my bases vs writing my own algorithm? Or would there be an efficient method for tackling this directly with SQL with some kind of local max techniques? I'm simply not sure how to approach this efficiently. Each method I try it seems that I keep missing various thresholds, detecting too many peak values or not capturing entire events (reporting a peak datapoint too early in the reading process).
Ultimately this is implemented in Ruby and so if you could advise as to the most efficient and correct way to approach this problem with Ruby that would be appreciated, however I'm open to a language agnostic algorithmic approach as well. Or is there a certain library that would address the various issues I'm up against in this scenario of detecting the maximum peaks?
my idea is simple, after get your windows of interest you will need find all the peaks in this window, you can just compare the last value with the next , after this you will have where the peaks occur and you can decide where are the best peak.
I wrote one simple source in matlab to show my idea!
My example are in wave from audio file :-)
waveFile='Chick_eco.wav';
[y, fs, nbits]=wavread(waveFile);
subplot(2,2,1); plot(y); legend('Original signal');
startIndex=15000;
WindowSize=100;
endIndex=startIndex+WindowSize-1;
frame = y(startIndex:endIndex);
nframe=length(frame)
%find the peaks
peaks = zeros(nframe,1);
k=3;
while(k <= nframe - 1)
y1 = frame(k - 1);
y2 = frame(k);
y3 = frame(k + 1);
if (y2 > 0)
if (y2 > y1 && y2 >= y3)
peaks(k)=frame(k);
end
end
k=k+1;
end
peaks2=peaks;
peaks2(peaks2<=0)=nan;
subplot(2,2,2); plot(frame); legend('Get Window Length = 100');
subplot(2,2,3); plot(peaks); legend('Where are the PEAKS');
subplot(2,2,4); plot(frame); legend('Peaks in the Window');
hold on; plot(peaks2, '*');
for j = 1 : nframe
if (peaks(j) > 0)
fprintf('Local=%i\n', j);
fprintf('Value=%i\n', peaks(j));
end
end
%Where the Local Maxima occur
[maxivalue, maxi]=max(peaks)
you can see all the peaks and where it occurs
Local=37
Value=3.266296e-001
Local=51
Value=4.333496e-002
Local=65
Value=5.049438e-001
Local=80
Value=4.286804e-001
Local=84
Value=3.110046e-001
I'll propose a couple of different ideas. One is to use discrete wavelets, the other is to use the geographer's concept of prominence.
Wavelets: Apply some sort of wavelet decomposition to your data. There are multiple choices, with Daubechies wavelets being the most widely used. You want the low frequency peaks. Zero out the high frequency wavelet elements, reconstruct your data, and look for local extrema.
Prominence: Those noisy peaks and valleys are of key interest to geographers. They want to know exactly which of a mountain's multiple little peaks is tallest, the exact location of the lowest point in the valley. Find the local minima and maxima in your data set. You should have a sequence of min/max/min/max/.../min. (You might want to add an arbitrary end points that are lower than your global minimum.) Consider a min/max/min sequence. Classify each of these triples per the difference between the max and the larger of the two minima. Make a reduced sequence that replaces the smallest of these triples with the smaller of the two minima. Iterate until you get down to a single min/max/min triple. In your example, you want the next layer down, the min/max/min/max/min sequence.
Note: I'm going to describe the algorithmic steps as if each pass were distinct. Obviously, in a specific implementation, you can combine steps where it makes sense for your application. For the purposes of my explanation, it makes the text a little more clear.
I'm going to make some assumptions about your problem:
The windows of interest (the signals that you are looking for) cover a fraction of the entire data space (i.e., it's not one long signal).
The windows have significant scope (i.e., they aren't one pixel wide on your picture).
The windows have a minimum peak of interest (i.e., even if the signal exceeds the background noise, the peak must have an additional signal excess of the background).
The windows will never overlap (i.e., each can be examined as a distinct sub-problem out of context of the rest of the signal).
Given those, you can first look through your data stream for a set of windows of interest. You can do this by making a first pass through the data: moving from left to right, look for noise threshold crossing points. If the signal was below the noise floor and exceeds it on the next sample, that's a candidate starting point for a window (vice versa for the candidate end point).
Now make a pass through your candidate windows: compare the scope and contents of each window with the values defined above. To use your picture as an example, the small peaks on the left of the image barely exceed the noise floor and do so for too short a time. However, the window in the center of the screen clearly has a wide time extent and a significant max value. Keep the windows that meet your minimum criteria, discard those that are trivial.
Now to examine your remaining windows in detail (remember, they can be treated individually). The peak is easy to find: pass through the window and keep the local max. With respect to the leading and trailing edges of the signal, you can see n the picture that you have a window that's slightly larger than the actual point at which the signal exceeds the noise floor. In this case, you can use a finite difference approximation to calculate the first derivative of the signal. You know that the leading edge will be somewhat to the left of the window on the chart: look for a point at which the first derivative exceeds a positive noise floor of its own (the slope turns upwards sharply). Do the same for the trailing edge (which will always be to the right of the window).
Result: a set of time windows, the leading and trailing edges of the signals and the peak that occured in that window.
It looks like the definition of a window is the range of x over which y is above the threshold. So use that to determine the size of the window. Within that, locate the largest value, thus finding the peak.
If that fails, then what additional criteria do you have for defining a region of interest? You may need to nail down your implicit assumptions to more than 'that looks like a peak to me'.
I have been using the MBRWithin function for quite a lot of times. Suddenly I notice on google map this POINT(101.11857 4.34475) is out of the geo fence which I specify but it still give a value of 1 in mysql any reason or tweaking need to be done?
SELECT MBRWithin(GeomFromText('POINT(101.11857 4.34475)'),GeomFromText('POLYGON((101.12112522125244 4.3531723687957164,101.11846446990967 4.351417913665312,101.13138198852539 4.336397898951581,101.13477230072021 4.33211863778494,101.14065170288086 4.321933898868271,101.14992141723633 4.306699328215635,101.15455627441406 4.30978050198082,101.1397933959961 4.334600612212089,101.12112522125244 4.3531723687957164,101.12112522125244 4.3531723687957164))')) As geoFenceStatus
MySQL 5.6.1 and later have exact geometry algorithms in addition to the earlier functions that only operated on MBR.
You can use ST_WITHIN rather than MBR_WITHIN. See documentation. Like this
SELECT ST_Within(GeomFromText('POINT(101.11857 4.34475)'),
GeomFromText('POLYGON((101.12112522125244 4.3531723687957164,101.11846446990967
4.351417913665312,101.13138198852539 4.336397898951581,101.13477230072021
4.33211863778494,101.14065170288086 4.321933898868271,101.14992141723633
4.306699328215635,101.15455627441406 4.30978050198082,101.1397933959961
4.334600612212089,101.12112522125244 4.3531723687957164,101.12112522125244
4.3531723687957164))')) As geoFenceStatus
MBRWithin() will return results based on the minimum bounding rectangle of it's parameters. Your polygon contains both larger and smaller values for both coordinates than the point, so it will be within the polygon's MBR.
MySQL has no built-in point in polygon algorithm, so you'll either have to roll your own or find one elsewhere. This one seems to be a good candidate.