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I'm quite new to CAD/Solidworks, and am having a trouble understanding how exactly I would model the figure shown in the diagram.
As circled, the top part of the ramp has a width of 0.38, and towards the bottom, the width becomes 0.25. Are there any tools I can use to make the "width" or extrude length of the figure change gradually along the slope? Also, it's not very clear to me where exactly on the ramp the width becomes 0.25; would I just assume that it measures 0.25 at the very end of the slope, or would I just approximate?
The width of the ramp is 0.25 period. It doesn't change at the top. The circled 0.38 is the width of the hidden tab symmetrical to the other 0.38 width tab on the other side.
For that matter, the thickness of every features on this particular part is 0.25 everywhere.
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Few days ago we (a student group) were acquainted with Octave, and one of the first tasks were to build some functions graphs. I'm stuck on the task of plotting the cos(x) and tan(x) graphs on a same figure on a segment from -2 pi to 2 pi, as the tan(x) function breaks at points of pi/2, which is clearly visible on the graph. According to the task this must not happen.
So, the question is how to correctly plot the tan(x) function graph without using incomprehensible to an Octave beginner ways?
Our code should look something like this: code example
I thought maybe removing half values of pi from the x range would cause the tan(x) to behave properly, but I don’t know how to. Anyway, there might be another option, which I’m too unaware of. So I am hope for your help.
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The general task is to binarize the image so that only the brightest spots remain. But adaptive binarization and the Otsu method do not give an acceptable result due to light traces (shown in the image).
I think that you need to go through the entire image with a small window that will highlight a local minimum in the area.I am counting on the fact that with the correct selection of the threshold, only light spots will remain that need to be found. It should be. But I do not know how to apply the standard opencv threshold function in sliding windows.
UPD:After the proposed adaptive threshold, the image looks like this. Not perfect, but much closer to what I need.It seems that a combination of threshold functions does not always give a better result than a single one.
This is the command:
outputimg = cv.adaptiveThreshold(img,255,cv.ADAPTIVE_THRESH_MEAN_C,cv.THRESH_BINARY,11,0)
further explanation and examples: https://docs.opencv.org/3.4/d7/d4d/tutorial_py_thresholding.html
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Here I have an image of two objects/stars:
I have hundreds of images like this one, from NASA MAST Archive. (The corners are not stars, just errors, one star is on the top, the other one is on the bottom).
What algorithm should I use to determine the number of objects (in this case stars) in one picture? For a human, it is pretty obvious that there are two objects, but I want to implement this detection in Python.
For reference, here is a picture with one star only:
(The pictures are produced from FITS files with PyKE.)
You can apply a threshold and use open cv to analyze the number of connected components (groups).
For example :
import cv2
src = cv2.imread('/path/to/your/image')
ret, thresh = cv2.threshold(src,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
connectivity = 8 #also diagonal neighbors, choose 4 if you want just horizontal and vertical neighbors.
# Analysis of the binary image
output = cv2.connectedComponentsWithStats(thresh, connectivity, cv2.CV_32S)
n_groups=output[2].max()
To get rid of the noises you can decide that you don't take into account groups with less than TH number of connected pixels (from the images you uploaded as an example I would choose something like TH=4).
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I need to check for complete separation. I am using SPSS and need to know what steps I have to take to get the grahpic on this site. Can someone help me?
SPSS does not provide that probability curve (SAS and Stata can do that). However, plotting the 1/0 outcome against the continuous predictor, and observe how the two horizontal data lines overlap may be enough to give you some hint.
If you have enough data, you can also first separate your data by different groups (for example, 10 equal groups split by your continuous predictors), and the compute each group's mean (aka probability of "yes" to outcome), and join the points. That line should approximate the curve in the illustration you provide.
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How do I convert a video of something oscillating to a frequency I can synthesize and tune my guitar to?
You can get a first order approximation to the oscillation by watching the video and using a stopwatch. Hit start on the stopwatch and start counting oscillations. When you reach 10 oscillations, hit stop. Divide the time by 10 and you have seconds/cycle. Take its reciprocal to get cycles/second, or Hz.
But if you could see it oscillating, and not a blur of motion, the frequency was probably < 5 Hz. We don't see very well at more than 20 Hz (hence the > 20 frames/sec for video). Conversely, we don't hear very well below 20 Hz. Maybe you mean you'll tune your guitar to a harmonic of the flax frequency (disclaimer, not a musician).
Also, this question is probably more suited for http://dsp.stackexchange.com.
If the object is not moving with respect to the camera you could grep a pixel (or perhaps an averaged area of pixels) at the border of the object. Generate a time series from that: pixel(time). This time series you could fourier transform and get the peak frequency from that, which should respond to the frequency of the wobbling.