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  • How can I get nVidia CUDA or OpenCL working on a laptop with nVidia discrete card/Intel Integrated Graphics?

    - by PeterDC
    Background: I'm a 3D artist (as a hobby) and have recently started using Ubuntu 12.04 LTS as a dual-boot with Windows 7. It's running on my a fairly new 64-bit Toshiba laptop with an nVidia GeForce GT 540M GPU (graphics card). It also, however has Intel Integrated Graphics (which I suspect Ubuntu's been using). So, when I render my 3D scenes to images on Windows, I am able to choose between using my CPU or my nVidia GPU (faster). From the 3D application, I can set the GPU to use either CUDA or OpenCL. In Ubuntu, there's no GPU option. After doing (too much?) research on the issues with Linux and the nVidia Optimus technology, I am slightly more enlightened, but a lot more confused. I don't care one bit about the Optimus technology, as battery life is not by any means an issue for me. Here's my question: What can I do to be able to use CUDA-utilizing programs (such as Blender) on my nVidia GPU in Ubuntu? Will I need nVidia drivers? (I have heard they don't play nicely with Optimus setups on Linux.) Is there at least a way to use OpenCL on my GPU in Ubuntu?

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  • Is it possible to plot a single density over a discrete variable?

    - by mattrepl
    The x-axis is time broken up into time intervals. There is an interval column in the data frame that specifies the time for each row. Plotting a histogram or line using geom_histogram and geom_freqpoly works great, but I'd like to use geom_density to get a filled area. Perhaps there is a better way to achieve this. Right now, if I use geom_density, curves are created for each discrete factor level instead of smoothing over all of them.

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  • How do I fake 2 discrete monitors using a DualHead2Go?

    - by Sietse
    I just got a [Matrox Dualhead2Go|http://www.matrox.com/graphics/en/products/gxm/dh2go/] for use with my MacBook Pro. I realise that the reason it works is that it fakes 1 big (wide) monitor. I also kind of depended on the software that came with it to trick OSX into accepting it as 2 monitors. Turns out the support is kind of lame: it just adds shortcuts for maximizing the window to whatever screen you want. And it even gets that wrong, since my dock doesn't auto-hide, but it doesn't take it in account while resizing, causing my window do end up "behind" my dock. (I've made a AppleScript that does the resize correctly, that I'll post below). There's two glaring issues this causes: Full screen (video, etc.) takes up both monitors, and dialogs just pops up in the middle. Is there a way to trick OSX, or at least a way to fix these issues?

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  • How to force my laptop to use the discrete GPU?

    - by Anton Roth
    My laptop (Asus X7BSV) is stuck using only the integrated GPU. It has a nVidia GT540M as well, but I cannot get it to work. I am using a Windows 7 x64 with latest drivers. This occurred after I attached an external USB monitor I need for work, and since then I cannot swap back to the nVidia GPU (dxdiag for example says that the primary GPU is the Intel integrated one). Asus support asked me to completely reinstall the system, but that is something I do not want to do. I checked the BIOS, there is no option as to what GPU to use. The nVidia card itself is working, since I can use CUDA on it, and it worked with a Ubuntu Live CD. In the nVidia software management I tried setting it to global setting, high performance GPU (nVidia), but that had no impact. What am I missing here? I did uninstall/delete all drivers and software related to the external monitor, but that did not help.

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  • How do I disable a Nvidia 9600GT on MacBookPro 5.1?

    - by Gjan
    i finally put up a Dual Boot with Ubuntu and Lion on my old MacBookPro 5.1 As reported in many cases the discrete graphics card is turned on all the time consuming a lot of power and thus heating up the laptop. Since the discrete graphics card does not support the nvidia optimus technology, the corresponding packge nvidia-prime does not help in this case. Therefore my question is, how to manually disable the discrete graphics card Nvdidia 9600GT ? Preferably a 'switch-on-the-run' version, but a 'set-on-boot' would be totally fine!

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  • guvcview recording video and audio out of synchronisation in Ubuntu 10.10

    - by SIJAR
    I finally got Guvcview, a great software for Logitech webcam and it does all the stuff that one wants out of it. But I'm not satisfy with the video recording, video and audio out of synchronisation also video seems to be in slow motion. Please help so that I can tweak in and get a good video recording with the webcam. Below is the log of Guvcview ------------------------------------------------------------------------------- guvcview 1.4.1 video_device: /dev/video0 vid_sleep: 0 cap_meth: 1 resolution: 640 x 480 windowsize: 1024 x 715 vert pane: 578 spin behavior: 0 mode: mjpg fps: 1/25 Display Fps: 0 bpp: 0 hwaccel: 1 avi_format: 4 sound: 1 sound Device: 4 sound samp rate: 0 sound Channels: 0 Sound delay: 0 nanosec Sound Format: 85 Pan Step: 2 degrees Tilt Step: 2 degrees Video Filter Flags: 0 image inc: 0 profile(default):/home/sijar/default.gpfl starting portaudio... bt_audio_service_open: connect() failed: Connection refused (111) bt_audio_service_open: connect() failed: Connection refused (111) bt_audio_service_open: connect() failed: Connection refused (111) bt_audio_service_open: connect() failed: Connection refused (111) Cannot connect to server socket err = No such file or directory Cannot connect to server socket jack server is not running or cannot be started language catalog= dir:/usr/share/locale type:UTF-8 lang:en_US.utf8 cat:guvcview.mo mjpg: setting format to 1196444237 capture method = 1 video device: /dev/video0 libv4lconvert: warning more framesizes then I can handle! libv4lconvert: warning more framesizes then I can handle! /dev/video0 - device 1 libv4lconvert: warning more framesizes then I can handle! libv4lconvert: warning more framesizes then I can handle! Init. UVC Camera (046d:0825) (location: usb-0000:00:1d.7-5) { pixelformat = 'YUYV', description = 'YUV 4:2:2 (YUYV)' } { discrete: width = 640, height = 480 } Time interval between frame: 1/30, 1/25, 1/20, 1/15, 1/10, 1/5, { discrete: width = 160, height = 120 } Time interval between frame: 1/30, 1/25, 1/20, 1/15, 1/10, 1/5, { discrete: width = 176, height = 144 } Time interval between frame: 1/30, 1/25, 1/20, 1/15, 1/10, 1/5, { discrete: width = 320, height = 176 } Time interval between frame: 1/30, 1/25, 1/20, 1/15, 1/10, 1/5, { discrete: width = 320, height = 240 } Time interval between frame: 1/30, 1/25, 1/20, 1/15, 1/10, 1/5, { discrete: width = 352, height = 288 } Time interval between frame: 1/30, 1/25, 1/20, 1/15, 1/10, 1/5, { discrete: width = 432, height = 240 } Time interval between frame: 1/30, 1/25, 1/20, 1/15, 1/10, 1/5, { discrete: width = 544, height = 288 } Time interval between frame: 1/30, 1/25, 1/20, 1/15, 1/10, 1/5, { discrete: width = 640, height = 360 } Time interval between frame: 1/30, 1/25, 1/20, 1/15, 1/10, 1/5, ... repeats a couple of times ... vid:046d pid:0825 driver:uvcvideo Adding control for Pan (relative) UVCIOC_CTRL_ADD - Error: Operation not permitted checking format: 1196444237 VIDIOC_G_COMP:: Invalid argument compression control not supported fps is set to 1/25 drawing controls control[0]: 0x980900 Brightness, 0:255:1, default 128 control[0]: 0x980901 Contrast, 0:255:1, default 32 control[0]: 0x980902 Saturation, 0:255:1, default 32 control[0]: 0x98090c White Balance Temperature, Auto, 0:1:1, default 1 control[0]: 0x980913 Gain, 0:255:1, default 0 control[0]: 0x980918 Power Line Frequency, 0:2:1, default 2 control[0]: 0x98091a White Balance Temperature, 0:10000:10, default 4000 control[0]: 0x98091b Sharpness, 0:255:1, default 24 control[0]: 0x98091c Backlight Compensation, 0:1:1, default 1 control[0]: 0x9a0901 Exposure, Auto, 0:3:1, default 3 control[0]: 0x9a0902 Exposure (Absolute), 1:10000:1, default 166 control[0]: 0x9a0903 Exposure, Auto Priority, 0:1:1, default 0 resolutions of format(2) = 19 frame rates of 1º resolution=6 Def. Res: 0 numb. fps:6 --------------------------------------- device #0 Name = Intel 82801DB-ICH4: Intel 82801DB-ICH4 (hw:0,0) Host API = ALSA Max inputs = 2, Max outputs = 2 Def. low input latency = 0.012 Def. low output latency = 0.012 Def. high input latency = 0.046 Def. high output latency = 0.046 Def. sample rate = 44100.00 --------------------------------------- device #1 Name = Intel 82801DB-ICH4: Intel 82801DB-ICH4 - MIC ADC (hw:0,1) Host API = ALSA Max inputs = 2, Max outputs = 0 Def. low input latency = 0.011 Def. low output latency = -1.000 Def. high input latency = 0.043 Def. high output latency = -1.000 Def. sample rate = 48000.00 --------------------------------------- device #2 Name = Intel 82801DB-ICH4: Intel 82801DB-ICH4 - MIC2 ADC (hw:0,2) Host API = ALSA Max inputs = 2, Max outputs = 0 Def. low input latency = 0.011 Def. low output latency = -1.000 Def. high input latency = 0.043 Def. high output latency = -1.000 Def. sample rate = 48000.00 --------------------------------------- device #3 Name = Intel 82801DB-ICH4: Intel 82801DB-ICH4 - ADC2 (hw:0,3) Host API = ALSA Max inputs = 2, Max outputs = 0 Def. low input latency = 0.011 Def. low output latency = -1.000 Def. high input latency = 0.043 Def. high output latency = -1.000 Def. sample rate = 48000.00 --------------------------------------- device #4 Name = Intel 82801DB-ICH4: Intel 82801DB-ICH4 - IEC958 (hw:0,4) Host API = ALSA Max inputs = 0, Max outputs = 2 Def. low input latency = -1.000 Def. low output latency = 0.011 Def. high input latency = -1.000 Def. high output latency = 0.043 Def. sample rate = 48000.00 --------------------------------------- device #5 Name = USB Device 0x46d:0x825: USB Audio (hw:1,0) Host API = ALSA Max inputs = 1, Max outputs = 0 Def. low input latency = 0.011 Def. low output latency = -1.000 Def. high input latency = 0.043 Def. high output latency = -1.000 Def. sample rate = 48000.00 --------------------------------------- device #6 Name = front Host API = ALSA Max inputs = 0, Max outputs = 2 Def. low input latency = -1.000 Def. low output latency = 0.012 Def. high input latency = -1.000 Def. high output latency = 0.046 Def. sample rate = 44100.00 --------------------------------------- device #7 Name = iec958 Host API = ALSA Max inputs = 0, Max outputs = 2 Def. low input latency = -1.000 Def. low output latency = 0.011 Def. high input latency = -1.000 Def. high output latency = 0.043 Def. sample rate = 48000.00 --------------------------------------- device #8 Name = spdif Host API = ALSA Max inputs = 0, Max outputs = 2 Def. low input latency = -1.000 Def. low output latency = 0.011 Def. high input latency = -1.000 Def. high output latency = 0.043 Def. sample rate = 48000.00 --------------------------------------- device #9 Name = pulse Host API = ALSA Max inputs = 32, Max outputs = 32 Def. low input latency = 0.012 Def. low output latency = 0.012 Def. high input latency = 0.046 Def. high output latency = 0.046 Def. sample rate = 44100.00 --------------------------------------- device #10 Name = dmix Host API = ALSA Max inputs = 0, Max outputs = 2 Def. low input latency = -1.000 Def. low output latency = 0.043 Def. high input latency = -1.000 Def. high output latency = 0.043 Def. sample rate = 48000.00 --------------------------------------- device #11 [ Default Input, Default Output ] Name = default Host API = ALSA Max inputs = 32, Max outputs = 32 Def. low input latency = 0.012 Def. low output latency = 0.012 Def. high input latency = 0.046 Def. high output latency = 0.046 Def. sample rate = 44100.00 ---------------------------------------------- SampleRate:0 Channels:0 Video driver: x11 A window manager is available VIDIOC_S_EXT_CTRLS for multiple controls failed (error -1) using VIDIOC_S_CTRL for user class controls control(0x0098091a) "White Balance Temperature" failed to set (error -1) VIDIOC_S_EXT_CTRLS for multiple controls failed (error -1) using VIDIOC_S_EXT_CTRLS on single controls for class: 0x009a0000 control(0x009a0902) "Exposure (Absolute)" failed to set (error -1) VIDIOC_S_EXT_CTRLS for multiple controls failed (error -1) using VIDIOC_S_CTRL for user class controls control(0x0098091a) "White Balance Temperature" failed to set (error -1) VIDIOC_S_EXT_CTRLS for multiple controls failed (error -1) using VIDIOC_S_EXT_CTRLS on single controls for class: 0x009a0000 control(0x009a0902) "Exposure (Absolute)" failed to set (error -1) Cap Video toggled: 1 (/home/sijar/Videos/Webcam) 25371756K bytes free on a total of 39908968K (used: 36 %) treshold=51200K using audio codec: 0x0055 Audio frame size is 1152 samples for selected codec IO thread started...OK [libx264 @ 0x8cbd8b0]using cpu capabilities: MMX2 SSE2 Cache64 [libx264 @ 0x8cbd8b0]profile Baseline, level 3.0 [libx264 @ 0x8cbd8b0]non-strictly-monotonic PTS shift sound by -9 ms shift sound by -9 ms shift sound by -9 ms AUDIO: droping audio data AUDIO: droping audio data AUDIO: droping audio data AUDIO: droping audio data AUDIO: droping audio data ... repeats a couple of times ... AUDIO: droping audio data (/home/sijar/Videos/Webcam) 25371748K bytes free on a total of 39908968K (used: 36 %) treshold=51200K AUDIO: droping audio data AUDIO: droping audio data ... repeats a couple of times ... Cap Video toggled: 0 Shuting Down IO Thread AUDIO: droping audio data stop= 4426644744000 start=4416533023000 VIDEO: 146 frames in 10111.000000 ms = 14.439719 fps Stoping audio stream Closing audio stream... close avi Last message repeated 145 times [libx264 @ 0x8cbd8b0]frame I:2 Avg QP:14.10 size: 24492 [libx264 @ 0x8cbd8b0]frame P:103 Avg QP:16.06 size: 20715 [libx264 @ 0x8cbd8b0]mb I I16..4: 48.4% 0.0% 51.6% [libx264 @ 0x8cbd8b0]mb P I16..4: 57.5% 0.0% 0.0% P16..4: 40.2% 0.0% 0.0% 0.0% 0.0% skip: 2.3% [libx264 @ 0x8cbd8b0]final ratefactor: 62.05 [libx264 @ 0x8cbd8b0]coded y,uvDC,uvAC intra: 79.7% 92.2% 68.4% inter: 62.4% 87.5% 48.0% [libx264 @ 0x8cbd8b0]i16 v,h,dc,p: 23% 17% 41% 19% [libx264 @ 0x8cbd8b0]i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 30% 24% 26% 2% 5% 3% 3% 3% 4% [libx264 @ 0x8cbd8b0]i8c dc,h,v,p: 53% 20% 23% 4% [libx264 @ 0x8cbd8b0]ref P L0: 63.0% 37.0% [libx264 @ 0x8cbd8b0]kb/s:-0.00 total frames encoded: 0 total audio frames encoded: 0 IO thread finished...OK IO Thread finished enabling controls Cap Video toggled: 1 (/home/sijar/Videos/Webcam) 25379744K bytes free on a total of 39908968K (used: 36 %) treshold=51200K using audio codec: 0x0055 Audio frame size is 1152 samples for selected codec IO thread started...OK [libx264 @ 0x8cfba20]using cpu capabilities: MMX2 SSE2 Cache64 [libx264 @ 0x8cfba20]profile Baseline, level 3.0 [libx264 @ 0x8cfba20]non-strictly-monotonic PTS shift sound by -236 ms shift sound by -236 ms shift sound by -236 ms (/home/sijar/Videos/Webcam) 25377044K bytes free on a total of 39908968K (used: 36 %) treshold=51200K (/home/sijar/Videos/Webcam) 25373408K bytes free on a total of 39908968K (used: 36 %) treshold=51200K AUDIO: droping audio data AUDIO: droping audio data AUDIO: droping audio data AUDIO: droping audio data AUDIO: droping audio data AUDIO: droping audio data ... repeats a couple of times ... (/home/sijar/Videos/Webcam) 25370696K bytes free on a total of 39908968K (used: 36 %) treshold=51200K AUDIO: droping audio data AUDIO: droping audio data AUDIO: droping audio data ... repeats a couple of times ... (/home/sijar/Videos/Webcam) 25367680K bytes free on a total of 39908968K (used: 36 %) treshold=51200K (/home/sijar/Videos/Webcam) 25364052K bytes free on a total of 39908968K (used: 36 %) treshold=51200K (/home/sijar/Videos/Webcam) 25360312K bytes free on a total of 39908968K (used: 36 %) treshold=51200K (/home/sijar/Videos/Webcam) 25356628K bytes free on a total of 39908968K (used: 36 %) treshold=51200K (/home/sijar/Videos/Webcam) 25352908K bytes free on a total of 39908968K (used: 36 %) treshold=51200K (/home/sijar/Videos/Webcam) 25349316K bytes free on a total of 39908968K (used: 36 %) treshold=51200K (/home/sijar/Videos/Webcam) 25345552K bytes free on a total of 39908968K (used: 36 %) treshold=51200K (/home/sijar/Videos/Webcam) 25341828K bytes free on a total of 39908968K (used: 36 %) treshold=51200K (/home/sijar/Videos/Webcam) 25338092K bytes free on a total of 39908968K (used: 36 %) treshold=51200K (/home/sijar/Videos/Webcam) 25334412K bytes free on a total of 39908968K (used: 36 %) treshold=51200K Cap Video toggled: 0 Shuting Down IO Thread stop= 4708817235000 start=4578624714000 VIDEO: 1604 frames in 130192.000000 ms = 12.320265 fps Stoping audio stream Closing audio stream... close avi Last message repeated 1603 times [libx264 @ 0x8cfba20]frame I:16 Avg QP:14.78 size: 42627 [libx264 @ 0x8cfba20]frame P:1547 Avg QP:16.44 size: 28599 [libx264 @ 0x8cfba20]mb I I16..4: 21.6% 0.0% 78.4% [libx264 @ 0x8cfba20]mb P I16..4: 28.1% 0.0% 0.0% P16..4: 70.5% 0.0% 0.0% 0.0% 0.0% skip: 1.4% [libx264 @ 0x8cfba20]final ratefactor: 88.17 [libx264 @ 0x8cfba20]coded y,uvDC,uvAC intra: 74.4% 95.8% 83.2% inter: 75.2% 94.6% 69.2% [libx264 @ 0x8cfba20]i16 v,h,dc,p: 27% 17% 40% 16% [libx264 @ 0x8cfba20]i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 25% 25% 21% 3% 6% 4% 5% 4% 7% [libx264 @ 0x8cfba20]i8c dc,h,v,p: 61% 18% 18% 4% [libx264 @ 0x8cfba20]ref P L0: 64.0% 36.0% [libx264 @ 0x8cfba20]kb/s:-0.00 total frames encoded: 0 total audio frames encoded: 0 IO thread finished...OK IO Thread finished enabling controls Shuting Down Thread Thread terminated... cleaning Thread allocations: 100% SDL Quit Video Thread finished write /home/sijar/.guvcviewrc OK free audio mutex closed v4l2 strutures free controls free controls - vidState cleaned allocations - 100% Closing portaudio ...OK Closing GTK... OK

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  • Converting from samplerate/cutoff frequency to pi-radians/sample in a discrete time sampled IIR filter system.

    - by Fake Name
    I am working on doing some digital filter work using Python and Numpy/Scipy. I'm using scipy.signal.iirdesign to generate my filter coefficents, but it requires the filter passband coefficents in a format I am not familiar with wp, ws : float Passband and stopband edge frequencies, normalized from 0 to 1 (1 corresponds to pi radians / sample). For example: Lowpass: wp = 0.2, ws = 0.3 Highpass: wp = 0.3, ws = 0.2 (from here) I'm not familiar with digital filters (I'm coming from a hardware design background). In an analog context, I would determine the desired slope and the 3db down point, and calculate component values from that. In this context, how do I take a known sample rate, a desired corner frequency, and a desired rolloff, and calculate the wp, ws values from that? (This might be more appropriate for math.stackexchange. I'm not sure)

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  • Interpolating height for a point inside a grid based on a discrete height function.

    - by fastrack20
    Hi, I have been wracking my brain to come up with a solution to this problem. I have a lookup table that returns height values for various points (x,z) on the grid. For instance I can calculate the height at A, B, C and D in Figure 1. However, I am looking for a way to interpolate the height at P (which has a known (x,z)). The lookup table only has values at the grid intervals, and P lies between these intervals. I am trying to calculate values s and t such that: A'(s) = A + s(C-A) B'(t) = B + t(P-B) I would then use the these two equations to find the intersection point of B'(t) with A'(s) to find a point X on the line A-C. With this I can calculate the height at this point X and with that the height at point P. My issue lies in calculating the values for s and t. Any help would be greatly appreciated.

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  • Is it safe to have NVidia graphics always on on a Linux laptop, or do I risk overheating?

    - by codeape
    I'm getting a Lenovo T520 with two graphics cards: Integrated Intel HD 3000 Discrete NVidia NVS 4200M In BIOS, I can adjust which card(s) to use: Integrated only Discrete only Both (NVidia optimus) Since optimus is not well supported under Linux, I wonder if it is OK to set up the system to use the NVidia card all the time. I have read somewhere that a laptop risks overheating if using a discrete graphics card all the time. Is this true? Does someone have any experience to share?

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  • Parallelism in .NET – Part 12, More on Task Decomposition

    - by Reed
    Many tasks can be decomposed using a Data Decomposition approach, but often, this is not appropriate.  Frequently, decomposing the problem into distinctive tasks that must be performed is a more natural abstraction. However, as I mentioned in Part 1, Task Decomposition tends to be a bit more difficult than data decomposition, and can require a bit more effort.  Before we being parallelizing our algorithm based on the tasks being performed, we need to decompose our problem, and take special care of certain considerations such as ordering and grouping of tasks. Up to this point in this series, I’ve focused on parallelization techniques which are most appropriate when a problem space can be decomposed by data.  Using PLINQ and the Parallel class, I’ve shown how problem spaces where there is a collection of data, and each element needs to be processed, can potentially be parallelized. However, there are many other routines where this is not appropriate.  Often, instead of working on a collection of data, there is a single piece of data which must be processed using an algorithm or series of algorithms.  Here, there is no collection of data, but there may still be opportunities for parallelism. As I mentioned before, in cases like this, the approach is to look at your overall routine, and decompose your problem space based on tasks.  The idea here is to look for discrete “tasks,” individual pieces of work which can be conceptually thought of as a single operation. Let’s revisit the example I used in Part 1, an application startup path.  Say we want our program, at startup, to do a bunch of individual actions, or “tasks”.  The following is our list of duties we must perform right at startup: Display a splash screen Request a license from our license manager Check for an update to the software from our web server If an update is available, download it Setup our menu structure based on our current license Open and display our main, welcome Window Hide the splash screen The first step in Task Decomposition is breaking up the problem space into discrete tasks. This, naturally, can be abstracted as seven discrete tasks.  In the serial version of our program, if we were to diagram this, the general process would appear as: These tasks, obviously, provide some opportunities for parallelism.  Before we can parallelize this routine, we need to analyze these tasks, and find any dependencies between tasks.  In this case, our dependencies include: The splash screen must be displayed first, and as quickly as possible. We can’t download an update before we see whether one exists. Our menu structure depends on our license, so we must check for the license before setting up the menus. Since our welcome screen will notify the user of an update, we can’t show it until we’ve downloaded the update. Since our welcome screen includes menus that are customized based off the licensing, we can’t display it until we’ve received a license. We can’t hide the splash until our welcome screen is displayed. By listing our dependencies, we start to see the natural ordering that must occur for the tasks to be processed correctly. The second step in Task Decomposition is determining the dependencies between tasks, and ordering tasks based on their dependencies. Looking at these tasks, and looking at all the dependencies, we quickly see that even a simple decomposition such as this one can get quite complicated.  In order to simplify the problem of defining the dependencies, it’s often a useful practice to group our tasks into larger, discrete tasks.  The goal when grouping tasks is that you want to make each task “group” have as few dependencies as possible to other tasks or groups, and then work out the dependencies within that group.  Typically, this works best when any external dependency is based on the “last” task within the group when it’s ordered, although that is not a firm requirement.  This process is often called Grouping Tasks.  In our case, we can easily group together tasks, effectively turning this into four discrete task groups: 1. Show our splash screen – This needs to be left as its own task.  First, multiple things depend on this task, mainly because we want this to start before any other action, and start as quickly as possible. 2. Check for Update and Download the Update if it Exists - These two tasks logically group together.  We know we only download an update if the update exists, so that naturally follows.  This task has one dependency as an input, and other tasks only rely on the final task within this group. 3. Request a License, and then Setup the Menus – Here, we can group these two tasks together.  Although we mentioned that our welcome screen depends on the license returned, it also depends on setting up the menu, which is the final task here.  Setting up our menus cannot happen until after our license is requested.  By grouping these together, we further reduce our problem space. 4. Display welcome and hide splash - Finally, we can display our welcome window and hide our splash screen.  This task group depends on all three previous task groups – it cannot happen until all three of the previous groups have completed. By grouping the tasks together, we reduce our problem space, and can naturally see a pattern for how this process can be parallelized.  The diagram below shows one approach: The orange boxes show each task group, with each task represented within.  We can, now, effectively take these tasks, and run a large portion of this process in parallel, including the portions which may be the most time consuming.  We’ve now created two parallel paths which our process execution can follow, hopefully speeding up the application startup time dramatically. The main point to remember here is that, when decomposing your problem space by tasks, you need to: Define each discrete action as an individual Task Discover dependencies between your tasks Group tasks based on their dependencies Order the tasks and groups of tasks

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  • ubuntu integrated graphics suspend + hybrid graphics

    - by kapad
    My laptop uses a hybrid graphics hardware. Using the vgaswitcheroo I am able to power off either card and switch between them correctly. The issue is that the system wakes up/resumes from suspend correctly only when using the discrete graphics, or atleast the discrete graphics must be powered on even if the display is connected to the onboard intel card. Has anyone faced this issues? Is there any workaround? I only want to use the integrated card and not the discrete amd card. System Info Intel HD 3000 ATI mobility radeon HD 7600M Ubuntu 12.10

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  • Thinkpad T530 with Optimus and Docking Station

    - by Vic Boudolf
    I have a Lenovo Thinkpad T530 with Optimus video, which is not supported on 12.04.1. I don't normally need the discrete (nVidia) graphics, so I turn it off in the BIOS settings to achieve longer battery life (and so that the screen dimmer will work), but when placed in the docking station, the integrated (Intel) graphics don't power the HDMI ports. (The VGA port does work, but I want to focus on the HDMI.) This means I have to change the BIOS settings constantly. Is there any way to have the system detect the docking station and power up/enable the discrete graphics accordingly? I don't need to do it on the fly. Just at startup. This post suggests that bumblebee can turn the discrete graphics on and off for specific applications, but I just want to turn it on or off. [2 suggests that vga_switcheroo will not work with nVidia Optimus.

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  • vgaswitcheroo - switch GPU based on load?

    - by Primož Kralj
    Is it possible to make vgaswitcheroo switch between GPU based on load? For example, when computer would be in idle it would turn off discrete and use only integrated; when enough load would accumulate on it it would automatically turn on discrete graphics and disable integrated. My second question is whether there is any GUI applet or something that I could use to manually switch between GPUs - instead of manually echoing in switch file? Edit: I found answer to last question at https://help.ubuntu.com/community/HybridGraphics

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  • Continuous Physics Engine's Collision Detection Techniques

    - by Griffin
    I'm working on a purely continuous physics engine, and I need to choose algorithms for broad and narrow phase collision detection. "Purely continuous" means I never do intersection tests, but instead want to find ways to catch every collision before it happens, and put each into "planned collisions" stack that is ordered by TOI. Broad Phase The only continuous broad-phase method I can think of is encasing each body in a circle and testing if each circle will ever overlap another. This seems horribly inefficient however, and lacks any culling. I have no idea what continuous analogs might exist for today's discrete collision culling methods such as quad-trees either. How might I go about preventing inappropriate and pointless broad test's such as a discrete engine does? Narrow Phase I've managed to adapt the narrow SAT to a continuous check rather than discrete, but I'm sure there's other better algorithms out there in papers or sites you guys might have come across. What various fast or accurate algorithm's do you suggest I use and what are the advantages / disatvantages of each? Final Note: I say techniques and not algorithms because I have not yet decided on how I will store different polygons which might be concave, convex, round, or even have holes. I plan to make a decision on this based on what the algorithm requires (for instance if I choose an algorithm that breaks down a polygon into triangles or convex shapes I will simply store the polygon data in this form).

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  • LOD in modern games

    - by Firas Assaad
    I'm currently working on my master's thesis about LOD and mesh simplification, and I've been reading many academic papers and articles about the subject. However, I can't find enough information about how LOD is being used in modern games. I know many games use some sort of dynamic LOD for terrain, but what about elsewhere? Level of Detail for 3D Graphics for example points out that discrete LOD (where artists prepare several models in advance) is widely used because of the performance overhead of continuous LOD. That book was published in 2002 however, and I'm wondering if things are different now. There has been some research in performing dynamic LOD using the geometry shader (this paper for example, with its implementation in ShaderX6), would that be used in a modern game? To summarize, my question is about the state of LOD in modern video games, what algorithms are used and why? In particular, is view dependent continuous simplification used or does the runtime overhead make using discrete models with proper blending and impostors a more attractive solution? If discrete models are used, is an algorithm used (e.g. vertex clustering) to generate them offline, do artists manually create the models, or perhaps a combination of both methods is used?

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  • Parallelism in .NET – Part 1, Decomposition

    - by Reed
    The first step in designing any parallelized system is Decomposition.  Decomposition is nothing more than taking a problem space and breaking it into discrete parts.  When we want to work in parallel, we need to have at least two separate things that we are trying to run.  We do this by taking our problem and decomposing it into parts. There are two common abstractions that are useful when discussing parallel decomposition: Data Decomposition and Task Decomposition.  These two abstractions allow us to think about our problem in a way that helps leads us to correct decision making in terms of the algorithms we’ll use to parallelize our routine. To start, I will make a couple of minor points. I’d like to stress that Decomposition has nothing to do with specific algorithms or techniques.  It’s about how you approach and think about the problem, not how you solve the problem using a specific tool, technique, or library.  Decomposing the problem is about constructing the appropriate mental model: once this is done, you can choose the appropriate design and tools, which is a subject for future posts. Decomposition, being unrelated to tools or specific techniques, is not specific to .NET in any way.  This should be the first step to parallelizing a problem, and is valid using any framework, language, or toolset.  However, this gives us a starting point – without a proper understanding of decomposition, it is difficult to understand the proper usage of specific classes and tools within the .NET framework. Data Decomposition is often the simpler abstraction to use when trying to parallelize a routine.  In order to decompose our problem domain by data, we take our entire set of data and break it into smaller, discrete portions, or chunks.  We then work on each chunk in the data set in parallel. This is particularly useful if we can process each element of data independently of the rest of the data.  In a situation like this, there are some wonderfully simple techniques we can use to take advantage of our data.  By decomposing our domain by data, we can very simply parallelize our routines.  In general, we, as developers, should be always searching for data that can be decomposed. Finding data to decompose if fairly simple, in many instances.  Data decomposition is typically used with collections of data.  Any time you have a collection of items, and you’re going to perform work on or with each of the items, you potentially have a situation where parallelism can be exploited.  This is fairly easy to do in practice: look for iteration statements in your code, such as for and foreach. Granted, every for loop is not a candidate to be parallelized.  If the collection is being modified as it’s iterated, or the processing of elements depends on other elements, the iteration block may need to be processed in serial.  However, if this is not the case, data decomposition may be possible. Let’s look at one example of how we might use data decomposition.  Suppose we were working with an image, and we were applying a simple contrast stretching filter.  When we go to apply the filter, once we know the minimum and maximum values, we can apply this to each pixel independently of the other pixels.  This means that we can easily decompose this problem based off data – we will do the same operation, in parallel, on individual chunks of data (each pixel). Task Decomposition, on the other hand, is focused on the individual tasks that need to be performed instead of focusing on the data.  In order to decompose our problem domain by tasks, we need to think about our algorithm in terms of discrete operations, or tasks, which can then later be parallelized. Task decomposition, in practice, can be a bit more tricky than data decomposition.  Here, we need to look at what our algorithm actually does, and how it performs its actions.  Once we have all of the basic steps taken into account, we can try to analyze them and determine whether there are any constraints in terms of shared data or ordering.  There are no simple things to look for in terms of finding tasks we can decompose for parallelism; every algorithm is unique in terms of its tasks, so every algorithm will have unique opportunities for task decomposition. For example, say we want our software to perform some customized actions on startup, prior to showing our main screen.  Perhaps we want to check for proper licensing, notify the user if the license is not valid, and also check for updates to the program.  Once we verify the license, and that there are no updates, we’ll start normally.  In this case, we can decompose this problem into tasks – we have a few tasks, but there are at least two discrete, independent tasks (check licensing, check for updates) which we can perform in parallel.  Once those are completed, we will continue on with our other tasks. One final note – Data Decomposition and Task Decomposition are not mutually exclusive.  Often, you’ll mix the two approaches while trying to parallelize a single routine.  It’s possible to decompose your problem based off data, then further decompose the processing of each element of data based on tasks.  This just provides a framework for thinking about our algorithms, and for discussing the problem.

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  • Ubuntu 11.10 doesn't detect Intel integrated graphics (i7-2670QM CPU)

    - by Telmo Marques
    The laptop I'm using is an MSI GT683DX-847PT that comes with an NVIDIA GeForce GTX570M discrete GPU, and an Intel Core i7-2670QM CPU. According to Intel's description of the Core i7-2670QM CPU, it has an HD Graphics 3000 integrated GPU. The problem is that the Intel integrated graphics GPU doesn't come up in lspci nor in lshw, only the NVIDIA GPU shows up. Here is the output of both commands: sudo lspci: http://pastebin.com/raw.php?i=9AZg8bJy sudo lshw: http://pastebin.com/raw.php?i=6cAMFQsY I was counting on having two GPU's to run CUDA programs on the discrete NVIDIA GPU, while X was handled by the integrated Intel GPU, to prevent kernel execution timeout. Why doesn't the Intel HD Graphics 3000 GPU show up? Any tests I could make to verify the presence of an integrated GPU?

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  • Dual Monitor/Xinerama not working; cannot even detect on-board graphic card

    - by Steven H
    I have Kubuntu 12.04 and two identical VGA monitors. One I plugged via DVI-VGA adapter into the DVI port of my discrete AMD Radeon HD 6670, the other into the VGA port of my on-board graphic card (Radeon HD 6410D). After installing Kubuntu I got a black screen, so I booted with nomodeset and installed AMD's catalyst drivers but only the monitor plugged into the discrete graphic card worked. Using lspci I saw that the on-board graphics was not listed. Then I found in the BIOS settings the options "Surround View" and "Onboard Dual Link DVI" both disabled. After enabling both, the on-board graphics card shows up in lspci but in amdcccle, it only shows as [Uknown display]Uknown adapter. When I try to enable xinerama, I get a black screen after rebooting on both monitors. I tried several options and hints from the web but nothing worked so far. I also reinstalled the AMD drivers several times. What should I do?

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  • Windows 8 freezes after every other reboot on Lenovo W520 after about 10 seconds

    - by John Nevermore
    I have a Lenovo W520 laptop with i7-2760QM, intel 520 SSD and Nvidia Quadro 1000m. When i boot the PC with discrete graphics SET in BIOS, the computer totally freezes and the only thing left to do is reboot. This only happens with NVidia drivers for Windows 8 x64 installed (I've tried about 4 different drivers on Nvidia's site). When i boot the PC with integrated graphics set in BIOS, there is a momentary "hickup" after about 10 seconds (instead of freezing) and then everything is working fine. When i boot the PC with discrete graphics ON and no Nvidia drivers installed, the same thing happens as described above with integrated graphics. I've tried doing 1) bcdedit /set disabledynamictick yes 2) Disabling VT-x in BIOS (Seriously would prefer not to disable it, since i use VM-s almost every day) but no dice. The only thing that worked was to enable the Hyper-v feature. I was then able to boot properly with discrete graphics and Nvidia drivers installed, but since i use VMWare for VM development this was no solution (VMWare complained about not being able to launch because of Hyper-v being installed). I followed the instructions in this tutorial, to be able to run VMWare. Then the computer just booted into a black screen past Windows logo. How to boot Windows 8 x64 without freezing with Quadro 1000M enabled, Nvidia Drivers installed and Hyper-v feature preferably disabled ?

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  • A Guided Tour of Complexity

    - by JoshReuben
    I just re-read Complexity – A Guided Tour by Melanie Mitchell , protégé of Douglas Hofstadter ( author of “Gödel, Escher, Bach”) http://www.amazon.com/Complexity-Guided-Tour-Melanie-Mitchell/dp/0199798109/ref=sr_1_1?ie=UTF8&qid=1339744329&sr=8-1 here are some notes and links:   Evolved from Cybernetics, General Systems Theory, Synergetics some interesting transdisciplinary fields to investigate: Chaos Theory - http://en.wikipedia.org/wiki/Chaos_theory – small differences in initial conditions (such as those due to rounding errors in numerical computation) yield widely diverging outcomes for chaotic systems, rendering long-term prediction impossible. System Dynamics / Cybernetics - http://en.wikipedia.org/wiki/System_Dynamics – study of how feedback changes system behavior Network Theory - http://en.wikipedia.org/wiki/Network_theory – leverage Graph Theory to analyze symmetric  / asymmetric relations between discrete objects Algebraic Topology - http://en.wikipedia.org/wiki/Algebraic_topology – leverage abstract algebra to analyze topological spaces There are limits to deterministic systems & to computation. Chaos Theory definitely applies to training an ANN (artificial neural network) – different weights will emerge depending upon the random selection of the training set. In recursive Non-Linear systems http://en.wikipedia.org/wiki/Nonlinear_system – output is not directly inferable from input. E.g. a Logistic map: Xt+1 = R Xt(1-Xt) Different types of bifurcations, attractor states and oscillations may occur – e.g. a Lorenz Attractor http://en.wikipedia.org/wiki/Lorenz_system Feigenbaum Constants http://en.wikipedia.org/wiki/Feigenbaum_constants express ratios in a bifurcation diagram for a non-linear map – the convergent limit of R (the rate of period-doubling bifurcations) is 4.6692016 Maxwell’s Demon - http://en.wikipedia.org/wiki/Maxwell%27s_demon - the Second Law of Thermodynamics has only a statistical certainty – the universe (and thus information) tends towards entropy. While any computation can theoretically be done without expending energy, with finite memory, the act of erasing memory is permanent and increases entropy. Life & thought is a counter-example to the universe’s tendency towards entropy. Leo Szilard and later Claude Shannon came up with the Information Theory of Entropy - http://en.wikipedia.org/wiki/Entropy_(information_theory) whereby Shannon entropy quantifies the expected value of a message’s information in bits in order to determine channel capacity and leverage Coding Theory (compression analysis). Ludwig Boltzmann came up with Statistical Mechanics - http://en.wikipedia.org/wiki/Statistical_mechanics – whereby our Newtonian perception of continuous reality is a probabilistic and statistical aggregate of many discrete quantum microstates. This is relevant for Quantum Information Theory http://en.wikipedia.org/wiki/Quantum_information and the Physics of Information - http://en.wikipedia.org/wiki/Physical_information. Hilbert’s Problems http://en.wikipedia.org/wiki/Hilbert's_problems pondered whether mathematics is complete, consistent, and decidable (the Decision Problem – http://en.wikipedia.org/wiki/Entscheidungsproblem – is there always an algorithm that can determine whether a statement is true).  Godel’s Incompleteness Theorems http://en.wikipedia.org/wiki/G%C3%B6del's_incompleteness_theorems  proved that mathematics cannot be both complete and consistent (e.g. “This statement is not provable”). Turing through the use of Turing Machines (http://en.wikipedia.org/wiki/Turing_machine symbol processors that can prove mathematical statements) and Universal Turing Machines (http://en.wikipedia.org/wiki/Universal_Turing_machine Turing Machines that can emulate other any Turing Machine via accepting programs as well as data as input symbols) that computation is limited by demonstrating the Halting Problem http://en.wikipedia.org/wiki/Halting_problem (is is not possible to know when a program will complete – you cannot build an infinite loop detector). You may be used to thinking of 1 / 2 / 3 dimensional systems, but Fractal http://en.wikipedia.org/wiki/Fractal systems are defined by self-similarity & have non-integer Hausdorff Dimensions !!!  http://en.wikipedia.org/wiki/List_of_fractals_by_Hausdorff_dimension – the fractal dimension quantifies the number of copies of a self similar object at each level of detail – eg Koch Snowflake - http://en.wikipedia.org/wiki/Koch_snowflake Definitions of complexity: size, Shannon entropy, Algorithmic Information Content (http://en.wikipedia.org/wiki/Algorithmic_information_theory - size of shortest program that can generate a description of an object) Logical depth (amount of info processed), thermodynamic depth (resources required). Complexity is statistical and fractal. John Von Neumann’s other machine was the Self-Reproducing Automaton http://en.wikipedia.org/wiki/Self-replicating_machine  . Cellular Automata http://en.wikipedia.org/wiki/Cellular_automaton are alternative form of Universal Turing machine to traditional Von Neumann machines where grid cells are locally synchronized with their neighbors according to a rule. Conway’s Game of Life http://en.wikipedia.org/wiki/Conway's_Game_of_Life demonstrates various emergent constructs such as “Glider Guns” and “Spaceships”. Cellular Automatons are not practical because logical ops require a large number of cells – wasteful & inefficient. There are no compilers or general program languages available for Cellular Automatons (as far as I am aware). Random Boolean Networks http://en.wikipedia.org/wiki/Boolean_network are extensions of cellular automata where nodes are connected at random (not to spatial neighbors) and each node has its own rule –> they demonstrate the emergence of complex  & self organized behavior. Stephen Wolfram’s (creator of Mathematica, so give him the benefit of the doubt) New Kind of Science http://en.wikipedia.org/wiki/A_New_Kind_of_Science proposes the universe may be a discrete Finite State Automata http://en.wikipedia.org/wiki/Finite-state_machine whereby reality emerges from simple rules. I am 2/3 through this book. It is feasible that the universe is quantum discrete at the plank scale and that it computes itself – Digital Physics: http://en.wikipedia.org/wiki/Digital_physics – a simulated reality? Anyway, all behavior is supposedly derived from simple algorithmic rules & falls into 4 patterns: uniform , nested / cyclical, random (Rule 30 http://en.wikipedia.org/wiki/Rule_30) & mixed (Rule 110 - http://en.wikipedia.org/wiki/Rule_110 localized structures – it is this that is interesting). interaction between colliding propagating signal inputs is then information processing. Wolfram proposes the Principle of Computational Equivalence - http://mathworld.wolfram.com/PrincipleofComputationalEquivalence.html - all processes that are not obviously simple can be viewed as computations of equivalent sophistication. Meaning in information may emerge from analogy & conceptual slippages – see the CopyCat program: http://cognitrn.psych.indiana.edu/rgoldsto/courses/concepts/copycat.pdf Scale Free Networks http://en.wikipedia.org/wiki/Scale-free_network have a distribution governed by a Power Law (http://en.wikipedia.org/wiki/Power_law - much more common than Normal Distribution). They are characterized by hubs (resilience to random deletion of nodes), heterogeneity of degree values, self similarity, & small world structure. They grow via preferential attachment http://en.wikipedia.org/wiki/Preferential_attachment – tipping points triggered by positive feedback loops. 2 theories of cascading system failures in complex systems are Self-Organized Criticality http://en.wikipedia.org/wiki/Self-organized_criticality and Highly Optimized Tolerance http://en.wikipedia.org/wiki/Highly_optimized_tolerance. Computational Mechanics http://en.wikipedia.org/wiki/Computational_mechanics – use of computational methods to study phenomena governed by the principles of mechanics. This book is a great intuition pump, but does not cover the more mathematical subject of Computational Complexity Theory – http://en.wikipedia.org/wiki/Computational_complexity_theory I am currently reading this book on this subject: http://www.amazon.com/Computational-Complexity-Christos-H-Papadimitriou/dp/0201530821/ref=pd_sim_b_1   stay tuned for that review!

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  • creating a color coded time chart using colorbar and colormaps in python

    - by Rusty
    I'm trying to make a time tracking chart based on a daily time tracking file that I used. I wrote code that crawls through my files and generates a few lists. endTimes is a list of times that a particular activity ends in minutes going from 0 at midnight the first day of the month to however many minutes are in a month. labels is a list of labels for the times listed in endTimes. It is one shorter than endtimes since the trackers don't have any data about before 0 minute. Most labels are repeats. categories contains every unique value of labels in order of how well I regard that time. I want to create a colorbar or a stack of colorbars (1 for eachday) that will depict how I spend my time for a month and put a color associated with each label. Each value in categories will have a color associated. More blue for more good. More red for more bad. It is already in order for the jet colormap to be right, but I need to get desecrate color values evenly spaced out for each value in categories. Then I figure the next step would be to convert that to a listed colormap to use for the colorbar based on how the labels associated with the categories. I think this is the right way to do it, but I am not sure. I am not sure how to associate the labels with color values. Here is the last part of my code so far. I found one function to make a discrete colormaps. It does, but it isn't what I am looking for and I am not sure what is happening. Thanks for the help! # now I need to develop the graph import numpy as np from matplotlib import pyplot,mpl import matplotlib from scipy import interpolate from scipy import * def contains(thelist,name): # checks if the current list of categories contains the one just read for val in thelist: if val == name: return True return False def getCategories(lastFile): ''' must determine the colors to use I would like to make a gradient so that the better the task, the closer to blue bad labels will recieve colors closer to blue read the last file given for the information on how I feel the order should be then just keep them in the order of how good they are in the tracker use a color range and develop discrete values for each category by evenly spacing them out any time not found should assume to be sleep sleep should be white ''' tracker = open(lastFile+'.txt') # open the last file # find all the categories categories = [] for line in tracker: pos = line.find(':') # does it have a : or a ? if pos==-1: pos=line.find('?') if pos != -1: # ignore if no : or ? name = line[0:pos].strip() # split at the : or ? if contains(categories,name)==False: # if the category is new categories.append(name) # make a new one return categories # find good values in order of last day newlabels=[] for val in getCategories(lastDay): if contains(labels,val): newlabels.append(val) categories=newlabels # convert discrete colormap to listed colormap python for ii,val in enumerate(labels): if contains(categories,val)==False: labels[ii]='sleep' # create a figure fig = pyplot.figure() axes = [] for x in range(endTimes[-1]%(24*60)): ax = fig.add_axes([0.05, 0.65, 0.9, 0.15]) axes.append(ax) # figure out the colors to use # stole this function to make a discrete colormap # http://www.scipy.org/Cookbook/Matplotlib/ColormapTransformations def cmap_discretize(cmap, N): """Return a discrete colormap from the continuous colormap cmap. cmap: colormap instance, eg. cm.jet. N: Number of colors. Example x = resize(arange(100), (5,100)) djet = cmap_discretize(cm.jet, 5) imshow(x, cmap=djet) """ cdict = cmap._segmentdata.copy() # N colors colors_i = np.linspace(0,1.,N) # N+1 indices indices = np.linspace(0,1.,N+1) for key in ('red','green','blue'): # Find the N colors D = np.array(cdict[key]) I = interpolate.interp1d(D[:,0], D[:,1]) colors = I(colors_i) # Place these colors at the correct indices. A = zeros((N+1,3), float) A[:,0] = indices A[1:,1] = colors A[:-1,2] = colors # Create a tuple for the dictionary. L = [] for l in A: L.append(tuple(l)) cdict[key] = tuple(L) # Return colormap object. return matplotlib.colors.LinearSegmentedColormap('colormap',cdict,1024) # jet colormap goes from blue to red (good to bad) cmap = cmap_discretize(mpl.cm.jet, len(categories)) cmap.set_over('0.25') cmap.set_under('0.75') #norm = mpl.colors.Normalize(endTimes,cmap.N) print endTimes print labels # make a color list by matching labels to a picture #norm = mpl.colors.ListedColormap(colorList) cb1 = mpl.colorbar.ColorbarBase(axes[0],cmap=cmap ,orientation='horizontal' ,boundaries=endTimes ,ticks=endTimes ,spacing='proportional') pyplot.show()

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  • Integrated webcam in lenovo t410 not working with 12.04

    - by kristianp
    I have a Lenovo T410 with an inbuilt webcam and I haven't been able to get the webcam working. I tried skype, cheese, both just give me a black window. The microphone works fine with skype, by the way. Can anyone provide any clues please? The webcam is enabled in the bios, but there is no light indicating the webcam is on (not sure if there should be, though). I tried this on Kubuntu 11.10 and have upgraded to 12.04 with the same results. The Fn-F6 keyboard combination doens't seem to do anything either. EDIT: I got the webcam replaced, it looks like it was a hardware problem, because it works fine now. Thanks guys. $ ls /dev/v4l/* /dev/v4l/by-id: usb-Chicony_Electronics_Co.__Ltd._Integrated_Camera-video-index0 /dev/v4l/by-path: pci-0000:00:1a.0-usb-0:1.6:1.0-video-index0 And lsusb: $ lsusb Bus 001 Device 001: ID 1d6b:0002 Linux Foundation 2.0 root hub Bus 002 Device 001: ID 1d6b:0002 Linux Foundation 2.0 root hub Bus 001 Device 002: ID 8087:0020 Intel Corp. Integrated Rate Matching Hub Bus 002 Device 002: ID 8087:0020 Intel Corp. Integrated Rate Matching Hub Bus 001 Device 003: ID 147e:2016 Upek Biometric Touchchip/Touchstrip Fingerprint Sensor Bus 001 Device 004: ID 0a5c:217f Broadcom Corp. Bluetooth Controller Bus 001 Device 005: ID 17ef:480f Lenovo Integrated Webcam [R5U877] Bus 002 Device 003: ID 05c6:9204 Qualcomm, Inc. Bus 002 Device 004: ID 17ef:1003 Lenovo Integrated Smart Card Reader Here is the output from guvcview, minus lots of lines describing the available capture formats. It says "unable to start with minimum setup. Please reconnect your camera.". guvcview 1.5.3 ALSA lib pcm_dmix.c:1018:(snd_pcm_dmix_open) unable to open slave ALSA lib pcm.c:2217:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.rear ALSA lib pcm.c:2217:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.center_lfe ALSA lib pcm.c:2217:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.side ALSA lib audio/pcm_bluetooth.c:1614:(audioservice_expect) BT_GET_CAPABILITIES failed : Input/output error(5) ALSA lib audio/pcm_bluetooth.c:1614:(audioservice_expect) BT_GET_CAPABILITIES failed : Input/output error(5) ALSA lib audio/pcm_bluetooth.c:1614:(audioservice_expect) BT_GET_CAPABILITIES failed : Input/output error(5) ALSA lib audio/pcm_bluetooth.c:1614:(audioservice_expect) BT_GET_CAPABILITIES failed : Input/output error(5) ALSA lib pcm_dmix.c:957:(snd_pcm_dmix_open) The dmix plugin supports only playback stream ALSA lib pcm_dmix.c:1018:(snd_pcm_dmix_open) unable to open slave Cannot connect to server socket err = No such file or directory Cannot connect to server socket jack server is not running or cannot be started video device: /dev/video0 Init. Integrated Camera (location: usb-0000:00:1a.0-1.6) { pixelformat = 'YUYV', description = 'YUV 4:2:2 (YUYV)' } { discrete: width = 640, height = 480 } Time interval between frame: 1/30, .... { discrete: width = 1600, height = 1200 } Time interval between frame: 1/15, vid:17ef pid:480f driver:uvcvideo checking format: 1196444237 libv4l2: error setting pixformat: Device or resource busy VIDIOC_S_FORMAT - Unable to set format: Device or resource busy Init v4L2 failed !! Init video returned -2 trying minimum setup ... video device: /dev/video0 Init. Integrated Camera (location: usb-0000:00:1a.0-1.6) { pixelformat = 'YUYV', description = 'YUV 4:2:2 (YUYV)' } { discrete: width = 640, height = 480 } .... vid:17ef pid:480f driver:uvcvideo checking format: 1448695129 libv4l2: error setting pixformat: Device or resource busy VIDIOC_S_FORMAT - Unable to set format: Device or resource busy Init v4L2 failed !! ERROR: Minimum Setup Failed. Exiting... VIDIOC_REQBUFS - Failed to delete buffers: Invalid argument (errno 22) cleaned allocations - 100% Closing portaudio ...OK Terminated.

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  • WCF - separating service contracts and partial deriving?

    - by dwhittenburg
    So, I've seperated my WCF service contracts into discrete contracts for re-use. I use to have IOneServiceContract that contained 3 functions: Function1, Function2, Function3. I've seperated this service contract into two discrete service contracts: IServiceContract1 and IServiceContract2. IServiceContract1 contains Function1 and IServiceContract2 contains Function2 and Function3. This will allow me to re-use the discrete IServiceContract1 and/or IServiceContract2 to build a new service contract that represents the contract for the public service. Knowing this...and hopefully I haven't messed up the description so that you can't follow the rest... I have two services IService1 and IService2. IService1 implements IServiceContract1 and IServiceContract2. This works perfect as IService1 needs to implement all of the functions: Function1, Function2, Function3. IService2 however doesn't need to implement all of the functions of IServiceContract2, only Function1. Is there a way for IService2 to partially implement the contract? I know that sounds ridiculous. Is the correct way to handle this situation to try and logically separate IServiceContract2 so that IService2 only has to implement the pieces that it needs? Thanks

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  • How to get AMD Catalyst working on Arch x86_64

    - by gh403
    I've got a Dell Inspiron 15R 7520 with AMD's hybrid "PowerXpress" graphics. The integrated graphics card is (if I understand it correctly) integrated with the i7-3612QM processor, and the discrete graphics card is a "Southern Islands" Radeon HD 7730M. The integrated graphics work perfectly under Arch. However, the discrete graphics don't. I have tried several different methods, and the one that seems to get me the farthest with the least effort is the AUR package catalyst-total-pxp. After installing, rebooting, and issuing the commands # aticonfig --initial # pxp_switch_catalyst amd # X X completely fails to start. The X log can be found here. I don't understand what is failing; potentially, it has something to do with the way my card is hooked up--I think it's muxless, but I really don't know. What is the matter here? Any help would be appreciated.

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