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  • Cuda driver, CPU/GPU performances issue

    - by elect
    I implemented a RNS Montgomery exponentiation in Cuda and on cpu for comparison. Everything nice everything fine. It runs on just one SM. However I am going to tell you some strange regression in both cpu/gpu performances. During the devoloping, about two month ago, I was using Cuda 5 preview on Ubuntu 11.04 64b. In this time, I reach the following performances: cpu 460ms gpu 120ms Then one day when I turn on the pc, the graphical environment didnt start. I dont know which was the problem, however I switched to the console and installed again the Cuda driver. At the following boot performances changed: cpu 310ms gpu 80ms I was like Q.Q...uhm ok, nice to see this, but I was wondering how that could be possible However, I went then in holiday for 10 days and I continued developing and optimizing on my notebook (but not the same part of the code, some additional stuff) When I was back, I just updated the source files, and performances came back to 460/120ms.. I couldnt believe it, I tried to install Cuda 5 RC, updating the video driver too... nothing changed... I checked Debug/Release, Cuda computability, but the problem seems being somewhere else.. Looking around the net I found this, I am pretty sure it must have something to do with the driver, because the performance change affected both cpu and gpu Do you have some tips/ideas/suggestions?

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  • linear algebra libraries for clusters

    - by Abruzzo Forte e Gentile
    Hi all I need to develop applications doing linear algebra + eigenvalue + linear equation solutions over a cluster of pcs ( I have a lot of machines available ). I discovered Scalapack libraries but they seem to me developed long time ago. Do you know if these are other libs available that I should learn doing math & linear algebra in a cluster? My language is C++ and off course I am newbie to this topic. Kind Regards to everybody AFG

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  • How can I reduce the amount of time it takes to fully regression test an application ready for release?

    - by DrLazer
    An app I work on is being developed with a modified version of scrum. If you are not familiar with scrum, it's just an alternative approach to a more traditional watefall model, where a series of features are worked on for a set amount of time known as a sprint. The app is written in C# and makes use of WPF. We use Visual C# 2010 Express edition as an IDE. If we work on a sprint and add in a few new features, but do not plan to release until a further sprint is complete, then regression testing is not an issue as such. We just test the new features and give the app a good once over. However, if a release is planned that our customers can download - a full regression test is factored in. In the past this wasn't a big deal, it took 3 or 4 days and the devs simply fix up any bugs found in the regression phase, but now, as the app is getting larger and larger and incorporating more and more features, the regression is spanning out for weeks. I am interested in any methods that people know of or use that can decrease this time. At the moment the only ideas I have are to either start writing Unit Tests, which I have never fully tried out in a commercial environment, or to research the possibilty of any UI Automation API's or tools that would allow me to write a program to perform a series of batch tests. I know literally nothing about the possibilities of UI automation so any information would be valuable. I don't know that much about Unit testing either, how complicated can the tests be? Is it possible to get Unit tests to use the UI? Are there any other methods I should consider? Thanks for reading, and for any advice in advance. Edit: Thanks for the information. Does anybody know of any alternatives to what has been mentioned so far (NUnit, RhinoMocks and CodedUI)?

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  • How can I reduce the amount of time it takes to fully regression test an application ready for release?

    - by DrLazer
    An app I work on is being developed with a modified version of scrum. If you are not familiar with scrum, it's just an alternative approach to a more traditional watefall model, where a series of features are worked on for a set amount of time known as a sprint. The app is written in C# and makes use of WPF. We use Visual C# 2010 Express edition as an IDE. If we work on a sprint and add in a few new features, but do not plan to release until a further sprint is complete, then regression testing is not an issue as such. We just test the new features and give the app a good once over. However, if a release is planned that our customers can download - a full regression test is factored in. In the past this wasn't a big deal, it took 3 or 4 days and the devs simply fix up any bugs found in the regression phase, but now, as the app is getting larger and larger and incorporating more and more features, the regression is spanning out for weeks. I am interested in any methods that people know of or use that can decrease this time. At the moment the only ideas I have are to either start writing Unit Tests, which I have never fully tried out in a commercial environment, or to research the possibilty of any UI Automation API's or tools that would allow me to write a program to perform a series of batch tests. I know literally nothing about the possibilities of UI automation so any information would be valuable. I don't know that much about Unit testing either, how complicated can the tests be? Is it possible to get Unit tests to use the UI? Are there any other methods I should consider? Thanks for reading, and for any advice in advance.

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  • Nested Linear layout only shows first view after being set from gone to visible in Android

    - by Adam
    Hi guys, I am developing an Android app but I'm still pretty new. I want to have a button, and when you push that button, a few TextViews and Buttons will appear. So I have a main linear layout, and then another linear layout nested inside containing the things I want hidden. I have the nested linear layout set to android:visibility="gone". The problem I am having is that it only shows the first item inside the hidden linear layout instead of all of them. The way I try to make it appear is vgAddView = (ViewGroup)findViewById(R.id.add_details); btnAche.setOnClickListener(new OnClickListener(){ public void onClick(View v){ vgAddView.setVisibility(0); } }); My XML file is this <?xml version="1.0" encoding="utf-8"?> <LinearLayout xmlns:android="http://schemas.android.com/apk/res/android" android:orientation="vertical" android:layout_width="fill_parent" android:layout_height="fill_parent" > <Button android:text="@string/but_stomach_ache" android:id="@+id/but_stomach_ache" android:layout_width="fill_parent" android:layout_height="wrap_content"> </Button> <Button android:text="@string/but_food" android:id="@+id/but_food" android:layout_width="fill_parent" android:layout_height="wrap_content"> </Button> <LinearLayout xmlns:android="http://schemas.android.com/apk/res/android" android:orientation="vertical" android:layout_width="fill_parent" android:layout_height="fill_parent" android:id="@+id/add_details" android:visibility="gone"> <TextView android:orientation="vertical" android:layout_width="fill_parent" android:layout_height="fill_parent" android:text="@string/when_happen"> </TextView> <Button android:text="@string/happen_now" android:id="@+id/happen_now" android:layout_width="fill_parent" android:layout_height="wrap_content"> </Button> </LinearLayout> </LinearLayout>

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  • Excel 2007: plot data points not on an axis/ force linear x-incrementation without altering integrity of non-linear data

    - by Ennapode
    In Excel, how does one go about plotting points that don't have an x component that is an x-axis label? For example, in my graph, the x-components are derived from the cosine function and aren't linear, but Excel is displaying them as if .0016 to .0062 to .0135 is an equal incrementation. How would I change this so that the x-axis has an even incrementation without altering the integrity of the points themselves? In other words, how do I plot a point with an x component independent from the x-axis label?

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  • Regression tests for T-SQL stored procedures

    - by Achim
    Hi, I would like to regression test t-sql stored procedures. My idea is to specify for each SP multiple input parameter sets. The SP should be executed with these parameters, results should be written to disc. Next time the new results should be compared with results stored before. Does anybody know a good tool for something like that? Should not be that hard to implement, but in practice you will need functionality like "ignore that column" or something like that. And I would assume that such a tool should already exist!? cheers, Achim

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  • How to determine what the Seemingly Unrelated Regression error means in R

    - by user2154571
    I'm using the systemfit() function to conduct a seemingly unrelated regression and am getting the following error: Error in solve(sigma, tol = solvetol) : Lapack routine dsptrf returned error code 1 Yet, I'm unable to find the meaningful interpretation of what the error suggests is going on. Below is some simulated code that works to show what functions I'm using (the simulated code does not produce an error). Thanks for thoughts on this error. y <- sample(seq(1:4), 100, replace = TRUE) x1 <- sample(seq(0:1), 100, replace = TRUE) -1 x2 <- sample(seq(0:1), 100, replace = TRUE) - 1 x3 <- sample(seq(1:4), 100, replace = TRUE) frame <- as.data.frame(cbind(y,x1,x2, x3)) mod_1 <- y ~ x1 + x3 + x1:x3 mod_2 <- y ~ x2 + x3 + x2:x3 output <- systemfit(list(mod_1, mod_2), data = frame, method = "SUR")

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  • Linear regression confidence intervals in SQL

    - by Matt Howells
    I'm using some fairly straight-forward SQL code to calculate the coefficients of regression (intercept and slope) of some (x,y) data points, using least-squares. This gives me a nice best-fit line through the data. However we would like to be able to see the 95% and 5% confidence intervals for the line of best-fit (the curves below). What these mean is that the true line has 95% probability of being below the upper curve and 95% probability of being above the lower curve. How can I calculate these curves? I have already read wikipedia etc. and done some googling but I haven't found understandable mathematical equations to be able to calculate this. Edit: here is the essence of what I have right now. --sample data create table #lr (x real not null, y real not null) insert into #lr values (0,1) insert into #lr values (4,9) insert into #lr values (2,5) insert into #lr values (3,7) declare @slope real declare @intercept real --calculate slope and intercept select @slope = ((count(*) * sum(x*y)) - (sum(x)*sum(y)))/ ((count(*) * sum(Power(x,2)))-Power(Sum(x),2)), @intercept = avg(y) - ((count(*) * sum(x*y)) - (sum(x)*sum(y)))/ ((count(*) * sum(Power(x,2)))-Power(Sum(x),2)) * avg(x) from #lr Thank you in advance.

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  • -moz-linear-gradient

    - by bah
    Hi, Could someone explain to me what this portion of code means? repeat scroll 0 0 #F6F6F6; I have googled a lot and only found syntax to this part -moz-linear-gradient(center top , #FFFFFF, #EFEFEF) My code: background: -moz-linear-gradient(center top , #FFFFFF, #EFEFEF) repeat scroll 0 0 #F6F6F6; Thanks!

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  • Linear Performance Scalability with HP San Solutions

    - by Berzemus
    Hi all, I need a San Solution with linear scalability in size as well as in performance. From what I know, with a Modular Smart Array solution such as the P2000/MSA-class solutions from HP, even with a dual controller initial node, I can only increase the size of it, as added nodes come controller-less, so overall performance tends to decrease. On the other hand, the P4000 (lefthand) family of solutions has each of it's nodes have it's own controller, and so when a node is added, storage capacity as well as performance increase. Am I right in all that I say, and is the P4000 the only solution, or have I forgotten something ?

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  • Linear-time algorithms for sorting vertices in polygon contours

    - by Cheery
    I figured out an algorithm that lets me turn my holed polygons into trapezoids in linear time if I have vertex indices sorted from lowest coordinate to highest. I get simple polygons as contours. They have certain order that might be exploited most of the time. So giving these conditions, is there a near-linear-time algorithm on sorting?

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  • Linear gradients library

    - by Lieven Cardoen
    Is there a place online where I can find like 16 linear gradients that match good with each other? I need them for a chart of mine and the ones generated (by Flex) aren't good enough. So, I'm kind off searching for a library of gradients (linear in my case).

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  • Regressing panel data in SAS.

    - by John
    Hey Guys, thanks to your help I succesfully managed all my databases! I am now looking at a panel data set on which I have to regress. Since I only started my Phd this semester together with the econometrics courses I am still new to many statistic applications and regression methods. I want to do a simple regression as in Y = x1 x2 x3 etc, now I already browsed through some literature and found that for panel data it's common to do a fixed effects regression. Also, my Y variable only has positive values so I was thinking in the direction of a Tobit model? I'm doing some research concerning the coverage of analysts in the financial business. My independent variable is the coverage of analysts on a certain firm, so per observation i have 1 analyst and 1 firm, together with different characteristics(market cap and betas etc) of the firm. All this data is monthly. As coverage cannot become negative (only 0) I was thinking of a Tobit model? Do you guys have any ideas what would be a good regression method? Or have some good sources (e books, written books, through university I have access to almost anything concerning my field of work) of information (cause I do have to learn these things for future research)? Thanks!

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  • Screening (multi)collinearity in a reggresion model

    - by aL3xa
    I hope that this one is not going to be "ask-and-answer" question... here goes: (multi)collinearity refers to extremely high correlations between predictors in the regression model. How to cure them... well, sometimes you don't need to "cure" collinearity, since it doesn't affect regression model itself, but interpretation of an effect of individual predictors. One way to spot collinearity is to put each predictor as a dependent variable, and other predictors as independent variables, determine R2, and if it's larger than .9 (or .95), we can consider predictor redundant. This is one "method"... what about other approaches? Some of them are time consuming, like excluding predictors from model and watching for b-coefficient changes - they should be noticeably different. Of course, we must always bare in mind specific context/goal of analysis... Sometimes, only remedy is to repeat a research, but right now, I'm interested in various ways of screening redundant predictors when (multi)collinearity occurs in a regression model.

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  • NET Math Libraries

    - by JoshReuben
    NET Mathematical Libraries   .NET Builder for Matlab The MathWorks Inc. - http://www.mathworks.com/products/netbuilder/ MATLAB Builder NE generates MATLAB based .NET and COM components royalty-free deployment creates the components by encrypting MATLAB functions and generating either a .NET or COM wrapper around them. .NET/Link for Mathematica www.wolfram.com a product that 2-way integrates Mathematica and Microsoft's .NET platform call .NET from Mathematica - use arbitrary .NET types directly from the Mathematica language. use and control the Mathematica kernel from a .NET program. turns Mathematica into a scripting shell to leverage the computational services of Mathematica. write custom front ends for Mathematica or use Mathematica as a computational engine for another program comes with full source code. Leverages MathLink - a Wolfram Research's protocol for sending data and commands back and forth between Mathematica and other programs. .NET/Link abstracts the low-level details of the MathLink C API. Extreme Optimization http://www.extremeoptimization.com/ a collection of general-purpose mathematical and statistical classes built for the.NET framework. It combines a math library, a vector and matrix library, and a statistics library in one package. download the trial of version 4.0 to try it out. Multi-core ready - Full support for Task Parallel Library features including cancellation. Broad base of algorithms covering a wide range of numerical techniques, including: linear algebra (BLAS and LAPACK routines), numerical analysis (integration and differentiation), equation solvers. Mathematics leverages parallelism using .NET 4.0's Task Parallel Library. Basic math: Complex numbers, 'special functions' like Gamma and Bessel functions, numerical differentiation. Solving equations: Solve equations in one variable, or solve systems of linear or nonlinear equations. Curve fitting: Linear and nonlinear curve fitting, cubic splines, polynomials, orthogonal polynomials. Optimization: find the minimum or maximum of a function in one or more variables, linear programming and mixed integer programming. Numerical integration: Compute integrals over finite or infinite intervals, over 2D and higher dimensional regions. Integrate systems of ordinary differential equations (ODE's). Fast Fourier Transforms: 1D and 2D FFT's using managed or fast native code (32 and 64 bit) BigInteger, BigRational, and BigFloat: Perform operations with arbitrary precision. Vector and Matrix Library Real and complex vectors and matrices. Single and double precision for elements. Structured matrix types: including triangular, symmetrical and band matrices. Sparse matrices. Matrix factorizations: LU decomposition, QR decomposition, singular value decomposition, Cholesky decomposition, eigenvalue decomposition. Portability and performance: Calculations can be done in 100% managed code, or in hand-optimized processor-specific native code (32 and 64 bit). Statistics Data manipulation: Sort and filter data, process missing values, remove outliers, etc. Supports .NET data binding. Statistical Models: Simple, multiple, nonlinear, logistic, Poisson regression. Generalized Linear Models. One and two-way ANOVA. Hypothesis Tests: 12 14 hypothesis tests, including the z-test, t-test, F-test, runs test, and more advanced tests, such as the Anderson-Darling test for normality, one and two-sample Kolmogorov-Smirnov test, and Levene's test for homogeneity of variances. Multivariate Statistics: K-means cluster analysis, hierarchical cluster analysis, principal component analysis (PCA), multivariate probability distributions. Statistical Distributions: 25 29 continuous and discrete statistical distributions, including uniform, Poisson, normal, lognormal, Weibull and Gumbel (extreme value) distributions. Random numbers: Random variates from any distribution, 4 high-quality random number generators, low discrepancy sequences, shufflers. New in version 4.0 (November, 2010) Support for .NET Framework Version 4.0 and Visual Studio 2010 TPL Parallellized – multicore ready sparse linear program solver - can solve problems with more than 1 million variables. Mixed integer linear programming using a branch and bound algorithm. special functions: hypergeometric, Riemann zeta, elliptic integrals, Frensel functions, Dawson's integral. Full set of window functions for FFT's. Product  Price Update subscription Single Developer License $999  $399  Team License (3 developers) $1999  $799  Department License (8 developers) $3999  $1599  Site License (Unlimited developers in one physical location) $7999  $3199    NMath http://www.centerspace.net .NET math and statistics libraries matrix and vector classes random number generators Fast Fourier Transforms (FFTs) numerical integration linear programming linear regression curve and surface fitting optimization hypothesis tests analysis of variance (ANOVA) probability distributions principal component analysis cluster analysis built on the Intel Math Kernel Library (MKL), which contains highly-optimized, extensively-threaded versions of BLAS (Basic Linear Algebra Subroutines) and LAPACK (Linear Algebra PACKage). Product  Price Update subscription Single Developer License $1295 $388 Team License (5 developers) $5180 $1554   DotNumerics http://www.dotnumerics.com/NumericalLibraries/Default.aspx free DotNumerics is a website dedicated to numerical computing for .NET that includes a C# Numerical Library for .NET containing algorithms for Linear Algebra, Differential Equations and Optimization problems. The Linear Algebra library includes CSLapack, CSBlas and CSEispack, ports from Fortran to C# of LAPACK, BLAS and EISPACK, respectively. Linear Algebra (CSLapack, CSBlas and CSEispack). Systems of linear equations, eigenvalue problems, least-squares solutions of linear systems and singular value problems. Differential Equations. Initial-value problem for nonstiff and stiff ordinary differential equations ODEs (explicit Runge-Kutta, implicit Runge-Kutta, Gear's BDF and Adams-Moulton). Optimization. Unconstrained and bounded constrained optimization of multivariate functions (L-BFGS-B, Truncated Newton and Simplex methods).   Math.NET Numerics http://numerics.mathdotnet.com/ free an open source numerical library - includes special functions, linear algebra, probability models, random numbers, interpolation, integral transforms. A merger of dnAnalytics with Math.NET Iridium in addition to a purely managed implementation will also support native hardware optimization. constants & special functions complex type support real and complex, dense and sparse linear algebra (with LU, QR, eigenvalues, ... decompositions) non-uniform probability distributions, multivariate distributions, sample generation alternative uniform random number generators descriptive statistics, including order statistics various interpolation methods, including barycentric approaches and splines numerical function integration (quadrature) routines integral transforms, like fourier transform (FFT) with arbitrary lengths support, and hartley spectral-space aware sequence manipulation (signal processing) combinatorics, polynomials, quaternions, basic number theory. parallelized where appropriate, to leverage multi-core and multi-processor systems fully managed or (if available) using native libraries (Intel MKL, ACMS, CUDA, FFTW) provides a native facade for F# developers

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  • How to choose an integer linear programming solver ?

    - by Cassie
    Hi all, I am newbie for integer linear programming. I plan to use a integer linear programming solver to solve my combinatorial optimization problem. I am more familiar with C++/object oriented programming on an IDE. Now I am using NetBeans with Cygwin to write my applications most of time. May I ask if there is an easy use ILP solver for me? Or it depends on the problem I want to solve ? I am trying to do some resources mapping optimization. Please let me know if any further information is required. Thank you very much, Cassie.

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  • How to choose a integer linear programming solver ?

    - by Cassie
    Hi all, I am newbie for integer linear programming. I plan to use a integer linear programming solver to solve my combinational optimization problem. I am more familiar with C++/object oriented programming on an IDE. Now I am using NetBeans with Cygwin to write my applications most of time. May I ask if there is an easy use ILP solver for me? Or does it depend on the problem I want to solve ? I am trying to do some resources mapping optimization. please let me know if any further information is required. Thank you very much, Cassie.

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  • How to find minimum of nonlinear, multivariate function using Newton's method (code not linear algeb

    - by Norman Ramsey
    I'm trying to do some parameter estimation and want to choose parameter estimates that minimize the square error in a predicted equation over about 30 variables. If the equation were linear, I would just compute the 30 partial derivatives, set them all to zero, and use a linear-equation solver. But unfortunately the equation is nonlinear and so are its derivatives. If the equation were over a single variable, I would just use Newton's method (also known as Newton-Raphson). The Web is rich in examples and code to implement Newton's method for functions of a single variable. Given that I have about 30 variables, how can I program a numeric solution to this problem using Newton's method? I have the equation in closed form and can compute the first and second derivatives, but I don't know quite how to proceed from there. I have found a large number of treatments on the web, but they quickly get into heavy matrix notation. I've found something moderately helpful on Wikipedia, but I'm having trouble translating it into code. Where I'm worried about breaking down is in the matrix algebra and matrix inversions. I can invert a matrix with a linear-equation solver but I'm worried about getting the right rows and columns, avoiding transposition errors, and so on. To be quite concrete: I want to work with tables mapping variables to their values. I can write a function of such a table that returns the square error given such a table as argument. I can also create functions that return a partial derivative with respect to any given variable. I have a reasonable starting estimate for the values in the table, so I'm not worried about convergence. I'm not sure how to write the loop that uses an estimate (table of value for each variable), the function, and a table of partial-derivative functions to produce a new estimate. That last is what I'd like help with. Any direct help or pointers to good sources will be warmly appreciated. Edit: Since I have the first and second derivatives in closed form, I would like to take advantage of them and avoid more slowly converging methods like simplex searches.

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  • CSS repeat pattern with linear gradient

    - by luca
    I'm on my first approach with photoshop patterns.I'm buildin a webpage where I want to use my pattern to give a nice effect to my webpage background. The pattern I found is 120x120 px If I was done here I should use this css: background-imag:url(mypattern.jpg); background-repeat:repeat; But Im not done.Id like to *add to my page's background a linear gradient(dir=top/down col=light-blue/green) with the pattern fill layer on top of it, with blending mode=darken *. This is the final effect: I come to the point. QUESTION: Combining linear vertical-gradient effect and my 120x120 pattern is it possible to find a pattern that I could use to repeat itself endlessly both vertical and horizontal??which is a common solution in this case? Hope It's clear thanks Luca

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  • Converting linear colors to SRGB shows banding in FFmpeg

    - by user1863947
    When I convert an EXR file sequence with x264 using FFmpeg and convert the colorspace from linear to SRGB (with gamma 0.45454545) I get some heavy banding issues (most visible on a dark gradient). Here is the ffmpeg command I use: C:/ffmpeg.exe -y -i C:/seq_v001.%04d.exr -vf lutrgb=r=gammaval(0.45454545):g=gammaval(0.45454545):b=gammaval(0.45454545) -vcodec libx264 -pix_fmt yuv420p -preset slow -crf 18 -r 25 C:/out.mov Here is the output: ffmpeg version N-47062-g26c531c Copyright (c) 2000-2012 the FFmpeg developers built on Nov 25 2012 12:25:21 with gcc 4.7.2 (GCC) configuration: --enable-gpl --enable-version3 --disable-pthreads --enable-runtime-cpudetect --enable-avisynth --enable-bzlib --enable-frei0r --enable-libass --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libfreetype --enable-libgsm --enable-libmp3lame --enable-libnut --enable-libopenjpeg --enable-libopus --enable-librtmp --enable-libschroedinger --enable-libspeex --enable-libtheora --enable-libutvideo --enable-libvo-aacenc --enable-libvo-amrwbenc --enable-libvorbis --enable-libvpx --enable-libx264 --enable-libxavs --enable-libxvid --enable-zlib libavutil 52. 9.100 / 52. 9.100 libavcodec 54. 77.100 / 54. 77.100 libavformat 54. 37.100 / 54. 37.100 libavdevice 54. 3.100 / 54. 3.100 libavfilter 3. 23.102 / 3. 23.102 libswscale 2. 1.102 / 2. 1.102 libswresample 0. 17.101 / 0. 17.101 libpostproc 52. 2.100 / 52. 2.100 Input #0, image2, from 'C:/seq_v001.%04d.exr': Duration: 00:00:09.60, start: 0.000000, bitrate: N/A Stream #0:0: Video: exr, rgb48le, 960x540 [SAR 1:1 DAR 16:9], 25 fps, 25 tbr, 25 tbn, 25 tbc [libx264 @ 0000000004d11540] using SAR=1/1 [libx264 @ 0000000004d11540] using cpu capabilities: MMX2 SSE2Fast SSSE3 FastShuffle SSE4.2 [libx264 @ 0000000004d11540] profile High, level 3.1 [libx264 @ 0000000004d11540] 264 - core 128 r2216 198a7ea - H.264/MPEG-4 AVC codec - Copyleft 2003-2012 - http://www.videolan.org/x264.html - options: cabac=1 ref=5 deblock=1:0:0 analyse=0x3:0x113 me=umh subme=8 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=-2 threads=18 lookahead_threads=3 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=2 b_bias=0 direct=3 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=25 scenecut=40 intra_refresh=0 rc_lookahead=50 rc=crf mbtree=1 crf=18.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00 Output #0, mov, to 'C:/out.mov': Metadata: encoder : Lavf54.37.100 Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv420p, 960x540 [SAR 1:1 DAR 16:9], q=-1--1, 12800 tbn, 25 tbc Stream mapping: Stream #0:0 -> #0:0 (exr -> libx264) Press [q] to stop, [?] for help [exr @ 000000000dff8c40] Found more than one compression attribute [exr @ 000000000dff90c0] Found more than one compression attribute [exr @ 000000000dff9520] Found more than one compression attribute [exr @ 000000000dff9960] Found more than one compression attribute [exr @ 000000000dff9dc0] Found more than one compression attribute [exr @ 000000000dffa200] Found more than one compression attribute [exr @ 000000000dffa660] Found more than one compression attribute [exr @ 000000000dffaaa0] Found more than one compression attribute [exr @ 000000000dffaf00] Found more than one compression attribute [exr @ 000000000dffb340] Found more than one compression attribute [exr @ 000000000dffb7a0] Found more than one compression attribute [exr @ 000000000dffbbe0] Found more than one compression attribute [exr @ 000000000dffc040] Found more than one compression attribute [exr @ 000000000dff8c40] Found more than one compression attribute [exr @ 000000000dff90c0] Found more than one compression attribute frame= 16 fps=0.0 q=0.0 size= 0kB time=00:00:00.00 bitrate= 0.0kbits/s Found more than one compression attribute [exr @ 000000000dff9960] Found more than one compression attribute [exr @ 000000000dff9dc0] Found more than one compression attribute [exr @ 000000000dffa200] Found more than one compression attribute [exr @ 000000000dffa660] Found more than one compression attribute [exr @ 000000000dffaaa0] Found more than one compression attribute [exr @ 000000000dffaf00] Found more than one compression attribute [exr @ 000000000dffb340] Found more than one compression attribute [exr @ 000000000dffb7a0] Found more than one compression attribute [exr @ 000000000dffbbe0] Found more than one compression attribute [exr @ 000000000dffc040] Found more than one compression attribute [exr @ 000000000dff8c40] Found more than one compression attribute [exr @ 000000000dff90c0] Found more than one compression attribute [exr @ 000000000dff9520] Found more than one compression attribute [exr @ 000000000dff9960] Found more than one compression attribute [exr @ 000000000dff9dc0] Found more than one compression attribute [exr @ 000000000dffa200] Found more than one compression attribute [exr @ 000000000dffa660] Found more than one compression attribute frame= 34 fps= 33 q=0.0 size= 0kB time=00:00:00.00 bitrate= 0.0kbits/s Found more than one compression attribute [exr @ 000000000dffaf00] Found more than one compression attribute [exr @ 000000000dffb340] Found more than one compression attribute [exr @ 000000000dffb7a0] Found more than one compression attribute [exr @ 000000000dffbbe0] Found more than one compression attribute [exr @ 000000000dffc040] Found more than one compression attribute [exr @ 000000000dff8c40] Found more than one compression attribute [exr @ 000000000dff90c0] Found more than one compression attribute [exr @ 000000000dff9520] Found more than one compression attribute [exr @ 000000000dff9960] Found more than one compression attribute [exr @ 000000000dff9dc0] Found more than one compression attribute [exr @ 000000000dffa200] Found more than one compression attribute [exr @ 000000000dffa660] Found more than one compression attribute [exr @ 000000000dffaaa0] Found more than one compression attribute [exr @ 000000000dffaf00] Found more than one compression attribute [exr @ 000000000dffb340] Found more than one compression attribute [exr @ 000000000dffb7a0] Found more than one compression attribute [exr @ 000000000dffbbe0] Found more than one compression attribute frame= 52 fps= 34 q=0.0 size= 0kB time=00:00:00.00 bitrate= 0.0kbits/s Found more than one compression attribute [exr @ 000000000dff8c40] Found more than one compression attribute [exr @ 000000000dff90c0] Found more than one compression attribute [exr @ 000000000dff9520] Found more than one compression attribute [exr @ 000000000dff9960] Found more than one compression attribute [exr @ 000000000dff9dc0] Found more than one compression attribute [exr @ 000000000dffa200] Found more than one compression attribute [exr @ 000000000dffa660] Found more than one compression attribute [exr @ 000000000dffaaa0] Found more than one compression attribute [exr @ 000000000dffaf00] Found more than one compression attribute [exr @ 000000000dffb340] Found more than one compression attribute [exr @ 000000000dffb7a0] Found more than one compression attribute [exr @ 000000000dffbbe0] Found more than one compression attribute [exr @ 000000000dffc040] Found more than one compression attribute [exr @ 000000000dff8c40] Found more than one compression attribute [exr @ 000000000dff90c0] Found more than one compression attribute frame= 68 fps= 34 q=0.0 size= 0kB time=00:00:00.00 bitrate= 0.0kbits/s Found more than one compression attribute [exr @ 000000000dff9960] Found more than one compression attribute [exr @ 000000000dff9dc0] Found more than one compression attribute [exr @ 000000000dffa200] Found more than one compression attribute [exr @ 000000000dffa660] Found more than one compression attribute [exr @ 000000000dffaaa0] Found more than one compression attribute [exr @ 000000000dffaf00] Found more than one compression attribute [exr @ 000000000dffb340] Found more than one compression attribute [exr @ 000000000dffb7a0] Found more than one compression attribute [exr @ 000000000dffbbe0] Found more than one compression attribute [exr @ 000000000dffc040] Found more than one compression attribute [exr @ 000000000dff8c40] Found more than one compression attribute [exr @ 000000000dff90c0] Found more than one compression attribute [exr @ 000000000dff9520] Found more than one compression attribute [exr @ 000000000dff9960] Found more than one compression attribute [exr @ 000000000dff9dc0] Found more than one compression attribute [exr @ 000000000dffa200] Found more than one compression attribute frame= 85 fps= 33 q=23.0 size= 47kB time=00:00:00.44 bitrate= 867.5kbits/s Found more than one compression attribute [exr @ 000000000dffaaa0] Found more than one compression attribute [exr @ 000000000dffaf00] Found more than one compression attribute [exr @ 000000000dffb340] Found more than one compression attribute [exr @ 000000000dffb7a0] Found more than one compression attribute [exr @ 000000000dffbbe0] Found more than one compression attribute [exr @ 000000000dffc040] Found more than one compression attribute [exr @ 000000000dff8c40] Found more than one compression attribute [exr @ 000000000dff90c0] Found more than one compression attribute [exr @ 000000000dff9520] Found more than one compression attribute [exr @ 000000000dff9960] Found more than one compression attribute [exr @ 000000000dff9dc0] Found more than one compression attribute [exr @ 000000000dffa200] Found more than one compression attribute [exr @ 000000000dffa660] Found more than one compression attribute [exr @ 000000000dffaaa0] Found more than one compression attribute [exr @ 000000000dffaf00] Found more than one compression attribute [exr @ 000000000dffb340] Found more than one compression attribute [exr @ 000000000dffb7a0] Found more than one compression attribute [exr @ 000000000dffbbe0] Found more than one compression attribute frame= 104 fps= 34 q=23.0 size= 94kB time=00:00:01.20 bitrate= 640.3kbits/s Found more than one compression attribute [exr @ 000000000dff8c40] Found more than one compression attribute [exr @ 000000000dff90c0] Found more than one compression attribute [exr @ 000000000dff9520] Found more than one compression attribute [exr @ 000000000dff9960] Found more than one compression attribute [exr @ 000000000dff9dc0] Found more than one compression attribute [exr @ 000000000dffa200] Found more than one compression attribute [exr @ 000000000dffa660] Found more than one compression attribute [exr @ 000000000dffaaa0] Found more than one compression attribute [exr @ 000000000dffaf00] Found more than one compression attribute [exr @ 000000000dffb340] Found more than one compression attribute [exr @ 000000000dffb7a0] Found more than one compression attribute [exr @ 000000000dffbbe0] Found more than one compression attribute [exr @ 000000000dffc040] Found more than one compression attribute [exr @ 000000000dff8c40] Found more than one compression attribute [exr @ 000000000dff90c0] Found more than one compression attribute [exr @ 000000000dff9520] Found more than one compression attribute frame= 121 fps= 34 q=23.0 size= 133kB time=00:00:01.88 bitrate= 577.8kbits/s Found more than one compression attribute [exr @ 000000000dff9dc0] Found more than one compression attribute [exr @ 000000000dffa200] Found more than one compression attribute [exr @ 000000000dffa660] Found more than one compression attribute [exr @ 000000000dffaaa0] Found more than one compression attribute [exr @ 000000000dffaf00] Found more than one compression attribute [exr @ 000000000dffb340] Found more than one compression attribute [exr @ 000000000dffb7a0] Found more than one compression attribute [exr @ 000000000dffbbe0] Found more than one compression attribute [exr @ 000000000dffc040] Found more than one compression attribute [exr @ 000000000dff8c40] Found more than one compression attribute [exr @ 000000000dff90c0] Found more than one compression attribute [exr @ 000000000dff9520] Found more than one compression attribute [exr @ 000000000dff9960] Found more than one compression attribute [exr @ 000000000dff9dc0] Found more than one compression attribute [exr @ 000000000dffa200] Found more than one compression attribute [exr @ 000000000dffa660] Found more than one compression attribute [exr @ 000000000dffaaa0] Found more than one compression attribute frame= 139 fps= 34 q=23.0 size= 172kB time=00:00:02.60 bitrate= 543.4kbits/s Found more than one compression attribute [exr @ 000000000dffb340] Found more than one compression attribute [exr @ 000000000dffb7a0] Found more than one compression attribute [exr @ 000000000dffbbe0] Found more than one compression attribute [exr @ 000000000dffc040] Found more than one compression attribute [exr @ 000000000dff8c40] Found more than one compression attribute [exr @ 000000000dff90c0] Found more than one compression attribute [exr @ 000000000dff9520] Found more than one compression attribute [exr @ 000000000dff9960] Found more than one compression attribute [exr @ 000000000dff9dc0] Found more than one compression attribute [exr @ 000000000dffa200] Found more than one compression attribute [exr @ 000000000dffa660] Found more than one compression attribute [exr @ 000000000dffaaa0] Found more than one compression attribute [exr @ 000000000dffaf00] Found more than one compression attribute [exr @ 000000000dffb340] Found more than one compression attribute [exr @ 000000000dffb7a0] Found more than one compression attribute [exr @ 000000000dffbbe0] Found more than one compression attribute [exr @ 000000000dffc040] Found more than one compression attribute frame= 157 fps= 34 q=23.0 size= 213kB time=00:00:03.32 bitrate= 525.6kbits/s Found more than one compression attribute [exr @ 000000000dff90c0] Found more than one compression attribute [exr @ 000000000dff9520] Found more than one compression attribute [exr @ 000000000dff9960] Found more than one compression attribute [exr @ 000000000dff9dc0] Found more than one compression attribute [exr @ 000000000dffa200] Found more than one compression attribute [exr @ 000000000dffa660] Found more than one compression attribute [exr @ 000000000dffaaa0] Found more than one compression attribute [exr @ 000000000dffaf00] Found more than one compression attribute [exr @ 000000000dffb340] Found more than one compression attribute [exr @ 000000000dffb7a0] Found more than one compression attribute [exr @ 000000000dffbbe0] Found more than one compression attribute [exr @ 000000000dffc040] Found more than one compression attribute [exr @ 000000000dff8c40] Found more than one compression attribute [exr @ 000000000dff90c0] Found more than one compression attribute [exr @ 000000000dff9520] Found more than one compression attribute [exr @ 000000000dff9960] Found more than one compression attribute [exr @ 000000000dff9dc0] Found more than one compression attribute frame= 175 fps= 34 q=23.0 size= 254kB time=00:00:04.04 bitrate= 516.0kbits/s Found more than one compression attribute [exr @ 000000000dffa660] Found more than one compression attribute [exr @ 000000000dffaaa0] Found more than one compression attribute [exr @ 000000000dffaf00] Found more than one compression attribute [exr @ 000000000dffb340] Found more than one compression attribute [exr @ 000000000dffb7a0] Found more than one compression attribute [exr @ 000000000dffbbe0] Found more than one compression attribute [exr @ 000000000dffc040] Found more than one compression attribute [exr @ 000000000dff8c40] Found more than one compression attribute [exr @ 000000000dff90c0] Found more than one compression attribute [exr @ 000000000dff9520] Found more than one compression attribute [exr @ 000000000dff9960] Found more than one compression attribute [exr @ 000000000dff9dc0] Found more than one compression attribute [exr @ 000000000dffa200] Found more than one compression attribute [exr @ 000000000dffa660] Found more than one compression attribute [exr @ 000000000dffaaa0] Found more than one compression attribute [exr @ 000000000dffaf00] Found more than one compression attribute [exr @ 000000000dffb340] Found more than one compression attribute frame= 193 fps= 35 q=23.0 size= 287kB time=00:00:04.76 bitrate= 494.6kbits/s Found more than one compression attribute [exr @ 000000000dffbbe0] Found more than one compression attribute [exr @ 000000000dffc040] Found more than one compression attribute [exr @ 000000000dff8c40] Found more than one compression attribute [exr @ 000000000dff90c0] Found more than one compression attribute [exr @ 000000000dff9520] Found more than one compression attribute [exr @ 000000000dff9960] Found more than one compression attribute [exr @ 000000000dff9dc0] Found more than one compression attribute [exr @ 000000000dffa200] Found more than one compression attribute [exr @ 000000000dffa660] Found more than one compression attribute [exr @ 000000000dffaaa0] Found more than one compression attribute [exr @ 000000000dffaf00] Found more than one compression attribute [exr @ 000000000dffb340] Found more than one compression attribute [exr @ 000000000dffb7a0] Found more than one compression attribute [exr @ 000000000dffbbe0] Found more than one compression attribute [exr @ 000000000dffc040] Found more than one compression attribute [exr @ 000000000dff8c40] Found more than one compression attribute [exr @ 000000000dff90c0] Found more than one compression attribute frame= 211 fps= 35 q=23.0 size= 332kB time=00:00:05.48 bitrate= 496.4kbits/s Found more than one compression attribute [exr @ 000000000dff9960] Found more than one compression attribute [exr @ 000000000dff9dc0] Found more than one compression attribute [exr @ 000000000dffa200] Found more than one compression attribute [exr @ 000000000dffa660] Found more than one compression attribute [exr @ 000000000dffaaa0] Found more than one compression attribute [exr @ 000000000dffaf00] Found more than one compression attribute [exr @ 000000000dffb340] Found more than one compression attribute [exr @ 000000000dffb7a0] Found more than one compression attribute [exr @ 000000000dffbbe0] Found more than one compression attribute [exr @ 000000000dffc040] Found more than one compression attribute [exr @ 000000000dff8c40] Found more than one compression attribute [exr @ 000000000dff90c0] Found more than one compression attribute [exr @ 000000000dff9520] Found more than one compression attribute [exr @ 000000000dff9960] Found more than one compression attribute [exr @ 000000000dff9dc0] Found more than one compression attribute [exr @ 000000000dffa200] Found more than one compression attribute frame= 228 fps= 34 q=23.0 size= 421kB time=00:00:06.16 bitrate= 559.8kbits/s frame= 240 fps= 32 q=-1.0 Lsize= 708kB time=00:00:09.52 bitrate= 609.3kbits/s video:705kB audio:0kB subtitle:0 global headers:0kB muxing overhead 0.505636% [libx264 @ 0000000004d11540] frame I:2 Avg QP:15.07 size: 18186 [libx264 @ 0000000004d11540] frame P:73 Avg QP:16.51 size: 3719 [libx264 @ 0000000004d11540] frame B:165 Avg QP:18.38 size: 2502 [libx264 @ 0000000004d11540] consecutive B-frames: 2.5% 3.3% 42.5% 51.7% [libx264 @ 0000000004d11540] mb I I16..4: 46.2% 33.3% 20.4% [libx264 @ 0000000004d11540] mb P I16..4: 6.8% 2.0% 0.6% P16..4: 29.4% 10.5% 4.6% 0.0% 0.0% skip:46.1% [libx264 @ 0000000004d11540] mb B I16..4: 1.8% 0.7% 0.2% B16..8: 40.9% 6.5% 0.3% direct: 1.2% skip:48.5% L0:52.0% L1:47.5% BI: 0.5% [libx264 @ 0000000004d11540] 8x8 transform intra:24.7% inter:81.3% [libx264 @ 0000000004d11540] direct mvs spatial:93.3% temporal:6.7% [libx264 @ 0000000004d11540] coded y,uvDC,uvAC intra: 10.7% 31.4% 24.9% inter: 2.3% 9.0% 2.9% [libx264 @ 0000000004d11540] i16 v,h,dc,p: 83% 11% 6% 1% [libx264 @ 0000000004d11540] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 9% 9% 52% 6% 4% 4% 5% 5% 5% [libx264 @ 0000000004d11540] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 22% 11% 44% 5% 4% 3% 3% 4% 3% [libx264 @ 0000000004d11540] i8c dc,h,v,p: 69% 15% 15% 2% [libx264 @ 0000000004d11540] Weighted P-Frames: Y:0.0% UV:0.0% [libx264 @ 0000000004d11540] ref P L0: 48.9% 0.1% 16.8% 17.0% 11.3% 5.8% [libx264 @ 0000000004d11540] ref B L0: 57.7% 21.9% 13.9% 6.4% [libx264 @ 0000000004d11540] ref B L1: 82.4% 17.6% [libx264 @ 0000000004d11540] kb/s:600.61 For me it looks like it converts the video first and afterwards applies the gamma correction on 8-bit clipped video. Does someone have an idea?

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