Find and extract repeating pattern in data or graph - graph

I want to find repeating pattern in graph, for example in the figure shown below (plz. click on the link, I don't know why it is not displayed), green peaks are repeated at some fixed interval. Here frequency and height of peaks are more or less same, but not exactly same. Here height and frequency are not known, but they are almost same for given repeating pattern.
I have read about motifs and Fourier transformation, but it seems too mathematical. Is there any simpler approach to solve this problem.
Click here for graph

Related

How to identify points of change in a curve?

I'm working with some datasets that have a repeated pattern like shown below, often with dozens or hundreds of these peaks. My aim is to identify the average point across all of the peaks where the curve has quickly dropped off and begun to level out (the top arrow). I also want to find the average point where the curve begins to flatten out (the bottom arrow). I'm hoping to use python to identify these points.

Finding a quantity of anything between two points in space

I'm currently working towards a 3D model of this, but I thought I would start with 2D. Basically, I have a grid of longitude and latitude with NO2 concentrations across it. What I want to produce, at least for now, is a total amount of Nitrogen Dioxide between two points. Like so:
2DGrid
Basically, These two points are at different lats and lons and as I stated I want to find the amount of something between them. The tricky thing to me is that the model data I'm working with is gridded so I need to be able to account for the amount of something along a line at the lat and lons at which that line cuts through said grid.
Another approach, and maybe a better one for my purposes, could be visualized like this:3DGrid
Ultimately, I'd like to be able to create a program (within any language honestly) that could find the amount of "something" between two points in a 3D grid. If you would like specfics, the bottom altitude is the surface, the top grid is the top of the atmosphere. The bottom point is a measurement device looking at the sun during a certain time of day (and therefore having a certain zenith and azimuth angle). I want to find the NO2 between that measurement device and the "top of the atmosphere" which in my grid is just the top altitude level (of which there are 25).
I'm rather new to coding, stack exchange, and even the subject matter I'm working with so the sparse code I've made might end up creating more clutter than purely asking the question and seeing what methods/code you might suggest?
Hopefully my question is beneficial!
Best,
Taylor
To traverse all touched cells, you can use Amanatides-Woo algorithm. It is suitable both for 2D and for 3D case.
Implementation clues
To account for quantity used from every cell, you can apply some model. For example, calculate path length inside cell (as difference of enter and exit coordinates) and divide by normalizing factor to get cell weight (for example, byCellSize*Sqrt(3) for 3D case as diagonal length).

segmenting lat/long data graph into lines/vectors

I have lat/lng data of multirotor UAV flights. There are alot of datapoints (~13k per flight) and I wish to find line segments from the data. They give me flight speed and direction. I know that most of the flights are guided missons meaning a point is given to fly to. However the exact points are unknown to me.
Here is a graph of a single flight lat/lng shifted to near (0,0) so they are visible on the same time-series graph.
I attempted to generate similar data, but there are several constraints and it may take more time to solve than working on the segmenting.
The graphs start and end nearly always at the same point.
Horisontal lines mean the UAV is stationary. These segments are expected.
Beginning and and end are always stationary for takeoff and landing.
There is some level of noise in the lines for the gps accuracy tho seemingly not that much.
Alot of data points.
The number of segments is unknown.
The noise I could calculate given the segments and least squares method to the line. Currently I'm thinking of sampling the data (to decimate it a little) and constructing lines. Merging the lines with smaller angle than x (dependant on the noise) and finding the intersection points of the lines left.
Another thought is to try and look at this problem in the frequency domain. The corners should be quite high frequency. Maybe I could make a custom filter kernel that would enable me to use a window function and win in efficency.
EDIT: Rewrote the question for more clarity and less rambling.

How to determine all line segments from a list of points generated from a mouse gesture?

Currently I am interning at a software company and one of my tasks has been to implement the recognition of mouse gestures. One of the senior developers helped me get started and provided code/projects that uses the $1 Unistroke Recognizer http://depts.washington.edu/aimgroup/proj/dollar/. I get, in a broad way, what the $1 Unistroke Recognizer is doing and how it works but am a bit overwhelmed with trying to understand all of the internals/finer details of it.
My problem is that I am trying to recognize the gesture of moving the mouse downards, then upwards. The $1 Unistroke Recognizer determines that the gesture I created was a downwards gesture, which is infact what it ought to do. What I really would like it to do is say "I recognize a downards gesture AND THEN an upwards gesture."
I do not know if the lack of understanding of the $1 Unistroke Recognizer completely is causing me to scratch my head, but does anyone have any ideas as to how to recognize two different gestures from moving the mouse downwards then upwards?
Here is my idea that I thought might help me but would love for someone who is an expert or even knows just a bit more than me to let me know what you think. Any help or resources that you know of would be greatly appreciated.
How My Application Currently Works:
The way that my current application works is that I capture points from where the mouse cursor is while the user holds down the left mouse button. A list of points then gets feed to a the gesture recognizer and it then spits out what it thinks to be the best shape/gesture that cooresponds to the captured points.
My Idea:
What I wanted to do is before I feed the points to the gesture recognizer is to somehow go through all the points and break them down into separate lines or curves. This way I could feed each line/curve in one at a time and from the basic movements of down, up, left, right, diagonals, and curves I could determine the final shape/gesture.
One way I thought would be good in determining if there are separate lines in my list of points is sampling groups of points and looking at their slope. If the slope of a sampled group of points differed X% from some other group of sampled points then it would be safe to assume that there is indeed a separate line present.
What I Think Are Possible Problems In My Thinking:
Where do I determine the end of a line and the start of a separate line? If I was to use the idea of checking the slope of a group of points and then determined that there was a separate line present that doesn't mean I nessecarily found the slope of a separate line. For example if you were to draw a straight edged "L" with a right angle and sample the slope of the points around the corner of the "L" you would see that the slope would give resonable indication that there is a separate line present but those points don't correspond to the start of a separate line.
How to deal with the ever changing slope of a curved line? The gesture recognizer that I use handles curves already in the way I want it too. But I don't want my method that I use to determine separate lines keep on looking for these so called separate lines in a curve because its slope is changing all the time when I sample groups of points. Would I just stop sampling points once the slope changed more than X% so many times in a row?
I'm not using the correct "type" of math for determining separate lines. Math isn't my strongest subject but I did do some research. I tried to look into Dot Products and see if that would point me in some direction, but I don't know if it will. Has anyone used Dot Prodcuts for doing something like this or some other method?
Final Thoughts, Remarks, And Thanks:
Part of my problem I feel like is that I don't know how to compeletly ask my question. I wouldn't be surprised if this problem has already been asked (in one way or another) and a solution exist that can be Googled. But my search results on Google didn't provide any solutions as I just don't know exactly how to ask my question yet. If you feel like it is confusing please let me know where and why and I will help clarify it. In doing so maybe my searches on Google will become more precise and I will be able to find a solution.
I just want to say thanks again for reading my post. I know its long but didn't really know where else to ask it. Imma talk with some other people around the office but all of my best solutions I have used throughout school have come from the StackOverflow community so I owe much thanks to you.
Edits To This Post:
(7/6 4:00 PM) Another idea I thought about was comparing all the points before a Min/Max point. For example, if I moved the mouse downards then upwards, my starting point would be the current Max point while the point where I start moving the mouse back upwards would be my min point. I could then go ahead and look to see if there are any points after the min point and if so say that there could be a new potential line. I dunno how well this will work on other shapes like stars but thats another thing Im going to look into. Has anyone done something similar to this before?
If your problem can be narrowed down to breaking apart a general curve into straight or smoothly curved partial lines then you could try this.
Comparing the slope of the segments and identifying breaking points where it is greater then some threshold would work in a very simplified case. Imagine a perfectly formed L-shape where you have a right angle between two straight lines. Obviously the corner point would be the only one where the slope difference is above the threshold as long as the threshold is between 0 and 90 degrees, and thus a identifiable breaking point.
However, the vertical and horizontal lines may be slightly curved so the threshold would need to be large enough for these small differences in slope to be ignored as breaking points. You'd also have to decide how sharp a corner the algorithm should pick up as a break. is 90 deg or higher required, or is even 30 deg enough? This is an important question.
Finally, to make this robust I would not be satisfied comparing the slopes of two adjacent segments. Hands may shake, corners may be smoothed out and the ideal conditions to find straight lines and sharp corners will probably never occur. For each point investigated for a break I would take the average slope of the N previous segments and compare it to the average slope of the N following segments. This can be efficiently implemented using a running mean. By choosing a good sample number N (depending on the accuracy of the input, the total number of points, etc) the algorithm can avoid the noise and make better detections.
Basically the algorithm would be:
For each investigated point (beginning N points into the sequence and ending N points before the end.)
Compute average slope of the N previous segments.
Compute average slope of the N next segments.
If the difference of the averages is greater than the Threshold, mark current point as a breaking point.
This is quite off the top of my head. You'd have to try it in your application.
if you work with absolute angles, like upwards and downwards, you can simply take the absolute slope between two points (not necessarily adjacent) to determine if it's RIGHT, LEFT, UP, DOWN (if that is enough of a distinction)
the art is to find a distance between points so that the angle is not random (with 1px, the angle will be a multiple of 45°)
There is a firefox plugin for Navigation using mouse gestures that works very well. I think it's FireGestures, but I'm not sure. I guess you can get some inspiration from that one
Additional thought: If you draw a shape by connectiong successive points, then connecting back to the first point, the ratio between the area and the final line segment's length is also an indicator for the gesture's "edginess"
If you are just interested in up/down/left/right, a first approximation is to check 45 degree segments of a circle. This is easily done by checking the the horizontal difference between (successive) points against the vertical difference between points.
Say you have a greater positive horizontal difference than vertical difference, then that would be 'RIGHT'.
The only difficulty then comes for example, in distinguishing UP/DOWN from UP/RIGHT/DOWN. But this could be done by distances between points. If you determine that the mouse has moved RIGHT for less than 20 pixels say, then you can ignore that movement.

How to detect a trend inside unsteady data (e.g. Trendly)?

I was wondering what kind of model / method / technique Trendly might use to achieve this model:
[It tries to find the moments where significant changes set in and ignores random movements]
Any pointers very welcome! :)
I've never seen 'Trendly', and don't know anything about it, but if I wanted to produce that red line from that blue line, in an algorithmic fashion, I would try:
Fourier the whole data set
Choose a block size longer than the period of the dominant frequency
Divide the data up into blocks of the chosen size
Compare adjacent ones with a statistical test of some sort.
Where the test says two blocks belong to the same underlying distribution, merge them.
If any were merged, go back to 4.
Red trend line is the mean of each block.
A simple "median" function could produce smoother curves over a mostly un-smooth curve.
Otherwise, a brute-force or genetic algorithm could be used; attempting to find the way to split the data into sections, so that more sections = worse solution, and less accuracy of the lines = worse solution.
Another way would be like this: Start at the beginning. As soon as the line moves outside of some radius (3 above or 3 below the first, for instance) set the new height to an average of the current line's height and the previous marker.
If you keep doing that, it would ignore small fluctuations. However, if the fluctuation was large enough, it would still effect it.

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