A student asked me this and I can't find the answer. You can set the turtle's speed to 0-10. But what does that actually mean? x actions / second?
We are on Code.org, which translates its code in the lessons into Javascript, but this command is found in the Play lab, which provides no translation. I am assuming this is analogous to JS-turtle, but if you know the answer for Python Turtle, etc, I'd love to hear it.
What precisely is the turtle's speed? X actions/second? ... if you
know the answer for Python Turtle, etc, I'd love to hear it.
In standard Python, the turtle's speed() method indirectly controls the speed of the turtle by dividing up the turtle's motion into smaller or larger steps, where each step has a defined delay.
By default, if we don't mess with setworldcoordinates(), or change the default screen update delay using delay(), or tracer(), then the motion of a turtle is broken up into a number of individual steps determined by:
steps = int(distance / (3 * 1.1**speed * speed))
At the default speed (3 or 'slow'), a 100px line would be drawn in 8 steps. At the slowest speed (1 or 'slowest'), 30 steps. At a fast speed (10 or 'fast'), 1 step. (Oddly, the default speed isn't the 'normal' (6) speed!) Each step incurs a screen update delay of 10ms by default.
Using a speed of 0 ('fastest'), or turning off tracer(), avoids this process altogether and just draws lines in one step.
There's a similar logic for how the speed() setting affects the number of steps the turtle takes to rotate when you do right() or left().
https://docs.python.org/3/library/turtle.html#turtle.speed
From the docs you can see that it is just an arbitrary value.
Related
I'm trying to demodulate a signal using GNU Radio Companion. The signal is FSK (Frequency-shift keying), with mark and space frequencies at 1200 and 2200 Hz, respectively.
The data in the signal text data generated by a device called GeoStamp Audio. The device generates audio of GPS data fed into it in real time, and it can also decode that audio. I have the decoded text version of the audio for reference.
I have set up a flow graph in GNU Radio (see below), and it runs without error, but with all the variations I've tried, I still can't get the data.
The output of the flow graph should be binary (1s and 0s) that I can later convert to normal text, right?
Is it correct to feed in a wav audio file the way I am?
How can I recover the data from the demodulated signal -- am I missing something in my flow graph?
This is a FFT plot of the wav audio file before demodulation:
This is the result of the scope sink after demodulation (maybe looks promising?):
UPDATE (August 2, 2016): I'm still working on this problem (occasionally), and unfortunately still cannot retrieve the data. The result is a promising-looking string of 1's and 0's, but nothing intelligible.
If anyone has suggestions for figuring out the settings on the Polyphase Clock Sync or Clock Recovery MM blocks, or the gain on the Quad Demod block, I would greatly appreciate it.
Here is one version of an updated flow graph based on Marcus's answer (also trying other versions with polyphase clock recovery):
However, I'm still unable to recover data that makes any sense. The result is a long string of 1's and 0's, but not the right ones. I've tried tweaking nearly all the settings in all the blocks. I thought maybe the clock recovery was off, but I've tried a wide range of values with no improvement.
So, at first sight, my approach here would look something like:
What happens here is that we take the input, shift it in frequency domain so that mark and space are at +-500 Hz, and then use quadrature demod.
"Logically", we can then just make a "sign decision". I'll share the configuration of the Xlating FIR here:
Notice that the signal is first shifted so that the center frequency (middle between 2200 and 1200 Hz) ends up at 0Hz, and then filtered by a low pass (gain = 1.0, Stopband starts at 1 kHz, Passband ends at 1 kHz - 400 Hz = 600 Hz). At this point, the actual bandwidth that's still present in the signal is much lower than the sample rate, so you might also just downsample without losses (set decimation to something higher, e.g. 16), but for the sake of analysis, we won't do that.
The time sink should now show better values. Have a look at the edges; they are probably not extremely steep. For clock sync I'd hence recommend to just go and try the polyphase clock recovery instead of Müller & Mueller; chosing about any "somewhat round" pulse shape could work.
For fun and giggles, I clicked together a quick demo demod (GRC here):
which shows:
I'm trying to implement one complex algorithm using GPU. The only problem is HW limitations and maximum available feature level is 9_3.
Algorithm is basically "stereo matching"-like algorithm for two images. Because of mentioned limitations all calculations has to be performed in Vertex/Pixel shaders only (there is no computation API available). Vertex shaders are rather useless here so I considered them as pass-through vertex shaders.
Let me shortly describe the algorithm:
Take two images and calculate cost volume maps (basically conterting RGB to Grayscale -> translate right image by D and subtract it from the left image). This step is repeated around 20 times for different D which generates Texture3D.
Problem here: I cannot simply create one Pixel Shader which calculates
those 20 repetitions in one go because of size limitation of Pixel
Shader (max. 512 arithmetics), so I'm forced to call Draw() in a loop
in C++ which unnecessary involves CPU while all operations are done on
the same two images - it seems to me like I have one bottleneck here. I know that there are multiple render targets but: there are max. 8 targets (I need 20+), if I want to generate 8 results in one pixel shader I exceed it's size limit (512 arithmetic for my HW).
Then I need to calculate for each of calculated textures box filter with windows where r > 9.
Another problem here: Because window is so big I need to split box filtering into two Pixel Shaders (vertical and horizontal direction separately) because loops unrolling stage results with very long code. Manual implementation of those loops won't help cuz still it would create to big pixel shader. So another bottleneck here - CPU needs to be involved to pass results from temp texture (result of V pass) to the second pass (H pass).
Then in next step some arithmetic operations are applied for each pair of results from 1st step and 2nd step.
I haven't reach yet here with my development so no idea what kind of bottlenecks are waiting for me here.
Then minimal D (value of parameter from 1st step) is taken for each pixel based on pixel value from step 3.
... same as in step 3.
Here basically is VERY simple graph showing my current implementation (excluding steps 3 and 4).
Red dots/circles/whatever are temporary buffers (textures) where partial results are stored and at every red dot CPU is getting involved.
Question 1: Isn't it possible somehow to let GPU know how to perform each branch form up to the bottom without involving CPU and leading to bottleneck? I.e. to program sequence of graphics pipelines in one go and then let the GPU do it's job.
One additional question about render-to-texture thing: Does all textures resides in GPU memory all the time even between Draw() method calls and Pixel/Vertex shaders switching? Or there is any transfer from GPU to CPU happening... Cuz this may be another issue here which leads to bottleneck.
Any help would be appreciated!
Thank you in advance.
Best regards,
Lukasz
Writing computational algorithms in pixel shaders can be very difficult. Writing such algorithms for 9_3 target can be impossible. Too much restrictions. But, well, I think I know how to workaround your problems.
1. Shader repetition
First of all, it is unclear, what do you call "bottleneck" here. Yes, theoretically, draw calls in for loop is a performance loss. But does it bottleneck? Does your application really looses performance here? How much? Only profilers (CPU and GPU) can answer. But to run it, you must first complete your algorithm (stages 3 and 4). So, I'd better stick with current solution, and started to implement whole algorithm, then profile and than fix performance issues.
But, if you feel ready to tweaks... Common "repetition" technology is instancing. You can create one more vertex buffer (called instance buffer), which will contains parameters not for each vertex, but for one draw instance. Then you do all the stuff with one DrawInstanced() call.
For you first stage, instance buffer can contain your D value and index of target Texture3D layer. You can pass-through them from vertex shader.
As always, you have a tradeof here: simplicity of code to (probably) performance.
2. Multi-pass rendering
CPU needs to be involved to pass results from temp texture (result of
V pass) to the second pass (H pass)
Typically, you do chaining like this, so no CPU involved:
// Pass 1: from pTexture0 to pTexture1
// ...set up pipeline state for Pass1 here...
pContext->PSSetShaderResources(slot, 1, pTexture0); // source
pContext->OMSetRenderTargets(1, pTexture1, 0); // target
pContext->Draw(...);
// Pass 2: from pTexture1 to pTexture2
// ...set up pipeline state for Pass1 here...
pContext->PSSetShaderResources(slot, 1, pTexture1); // previous target is now source
pContext->OMSetRenderTargets(1, pTexture2, 0);
pContext->Draw(...);
// Pass 3: ...
Note, that pTexture1 must have both D3D11_BIND_SHADER_RESOURCE and D3D11_BIND_RENDER_TARGET flags. You can have multiple input textures and multiple render targets. Just make sure, that every next pass knows what previous pass outputs.
And if previous pass uses more resources than current, don't forget to unbind unneeded, to prevent hard-to-find errors:
pContext->PSSetShaderResources(2, 1, 0);
pContext->PSSetShaderResources(3, 1, 0);
pContext->PSSetShaderResources(4, 1, 0);
// Only 0 and 1 texture slots will be used
3. Resource data location
Does all textures resides in GPU memory all the time even between
Draw() method calls and Pixel/Vertex shaders switching?
We can never know that. Driver chooses appropriate location for resources. But if you have resources created with DEFAULT usage and 0 CPU access flag, you can be almost sure it will always be in video memory.
Hope it helps. Happy coding!
I'am non-programmer, trying to assess the time spent in (opencv-)functions. We have an AD-converter which comes with a counter that is able to count external signals (e.g. from a function generator) with a frequency of 1 MHz = 1 µs resolution. The actual counter status can be queried with a function cbIn32(..., unsigned long *pointertovalue).
So my idea was to query the counter status before and after calling the function of interest and to calculate then the difference. However, doubts came up when I calcultated the difference without a function call in between, which revealed rel. high fluctuations (values between 80 and 400 µs or so). I wondered, if calculating the average time for calling cbIn32() (approx. 180 µs) and substract this from the putative time spent in the function of interest is a valid solution.
So my first two questions:
Is that approach generally feasible or useless?
Where do the fluctuations come from?
Alternatively, we tried using getTickCount(), which seemed to deliver reasonable values. But checking forums revealed that it has a low resolution of about 10 ms, which would be insatisfactory (100 µs resolution would be appreciated). However, the values we got were in the sub-ms range.
This brings me to the next questions:
How can the time assessed for a function with getTickCount() be in the microseconds range, when the resolution is around 10 ms?
Should I trust the obtained values or not?
I also tried it with gprof, but it gave me "no time accumulated", although I am sure that the time spent in a function containing opencv-related calls is at least a few milliseconds. I even tried rebuilding opencv with ENABLE_PROFILING=ON, but same result. I read somewhere that you need to build static opencv libraries to enable profiling, but I am not sure if this would improve the situation. So the question here is:
What do I have to do so that gprof also "sees" opencv functions?
Next alternative would be the QueryPerformanceCounter() function of the WINAPI. I don't how to use it, but I would fight my way through, if you recommend it. Question to that approach:
Will it be problematic because of multiple cores?
If yes, is there an "easy" way to handle that problem?
I also tried it with verysleepy, but it exits somehow to early (worked fine with other .exe).
Newbie-friendly answers would be very, very appreciated. My goal is to find the easiest approach with highest precision. I'm working on Win7 64bit, Eclipse with MinGW.
Thx for your help...
I am trying to find the best way to solve the problem below:
Problem
I have (up to) 100,000 Lat/Lng points in Set A
I have (up to) 2000 Lat/Lng points in Set B
I need to find the nearest neighbour of points in set B to points in Set A.
Once they have been paired - I then need to calculate their distance which will be:
2000 Set A points to 2000 Set B Points.
These points are "in memory" they do not come from a database - they are the result of other calculations done the in the system.
Current Solution
Using a KDTree implementation in Ruby I can create a KDTree lookup that will match the points I have. I then use a haversine method in Ruby to calculate the distance of the points when they are paired.
KDtree code: Ruby KDTree Code
haversine Code: Haversine Code
Platform
I am running jruby - with rails as the web framework.
Issue
Its slow! Like 30 to 40 seconds slow... I think the main bottle neck is in the KDtree, but the point look up takes a long time too (i think). At smaller numbers in Set B its quick but the higher the number of points in Set B it gets a lot quicker.
The Question
Would anyone think of doing this differently? Is there something I am missing. I think a Java library might be a lot quicker, but how would I implement this, and which one would I use (Not strong in Java - I use Jruby for multithreading ruby code in the JVM)
Is it possible to persist the information to a database? Because then you can use GeoKit, which leverages a geo-aware database (MySQL, Postgres > 8.1, etc) so that you can do this:
Location.find(:all, :origin =>[37.792,-122.393], :within=>10, :order=>"distance asc")
Also, you can find the distance between two points, etc. The response time will be more on par with a DB query, and much faster than what you're seeing.
Just an idea in my mind. If you round your lat/long's to two decimal places then all the points with-in 1.11 km's will be the same. See this for more details. I'm not 100% sure about it but may be it works for you. Off-course for areas near the pols, this will not work as longitude shrinks there.
To speed up the distance calculation between two lat/long's, you can calculate euclidean distance by using simple distance formula rather than geographical distance. This distance will not be accurate off-course but will speed up your process.
Firstly , For those of your who dont know - Anytime Algorithm is an algorithm that get as input the amount of time it can run and it should give the best solution it can on that time.
Weighted A* is the same as A* with one diffrence in the f function :
(where g is the path cost upto node , and h is the heuristic to the end of path until reaching a goal)
Original = f(node) = g(node) + h(node)
Weighted = f(node) = (1-w)g(node) +h(node)
My anytime algorithm runs Weighted A* with decaring weight from 1 to 0.5 until it reaches the time limit.
My problem is that most of the time , it takes alot time until this it reaches a solution , and if given somthing like 10 seconds it usaully doesnt find solution while other algorithms like anytime beam finds one in 0.0001 seconds.
Any ideas what to do?
If I were you I'd throw the unbounded heuristic away. Admissible heuristics are much better in that given a weight value for a solution you've found, you can say that it is at most 1/weight times the length of an optimal solution.
A big problem when implementing A* derivatives is the data structures. When I implemented a bidirectional search, just changing from array lists to a combination of hash augmented priority queues and array lists on demand, cut the runtime cost by three orders of magnitude - literally.
The main problem is that most of the papers only give pseudo-code for the algorithm using set logic - it's up to you to actually figure out how to represent the sets in your code. Don't be afraid of using multiple ADTs for a single list, i.e. your open list. I'm not 100% sure on Anytime Weighted A*, I've done other derivatives such as Anytime Dynamic A* and Anytime Repairing A*, not AWA* though.
Another issue is when you set the g-value too low, sometimes it can take far longer to find any solution that it would if it were a higher g-value. A common pitfall is forgetting to check your closed list for duplicate states, thus ending up in a (infinite if your g-value gets reduced to 0) loop. I'd try starting with something reasonably higher than 0 if you're getting quick results with a beam search.
Some pseudo-code would likely help here! Anyhow these are just my thoughts on the matter, you may have solved it already - if so good on you :)
Beam search is not complete since it prunes unfavorable states whereas A* search is complete. Depending on what problem you are solving, if incompleteness does not prevent you from finding a solution (usually many correct paths exist from origin to destination), then go for Beam search, otherwise, stay with AWA*. However, you can always run both in parallel if there are sufficient hardware resources.