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• C# DirectSound - Capture buffers not continuous

  - by Wizche

  Hi, I'm trying to capture raw data from my line-in using DirectSound. My problem is that, from a buffer to another the data are just inconsistent, if for example I capture a sine I see a jump from my last buffer and the new one. To detected this I use a graph widget to draw the first 500 elements of the last buffer and the 500 elements from the new one: Snapshot I initialized my buffer this way: format = new WaveFormat { SamplesPerSecond = 44100, BitsPerSample = (short)bitpersample, Channels = (short)channels, FormatTag = WaveFormatTag.Pcm }; format.BlockAlign = (short)(format.Channels * (format.BitsPerSample / 8)); format.AverageBytesPerSecond = format.SamplesPerSecond * format.BlockAlign; _dwNotifySize = Math.Max(4096, format.AverageBytesPerSecond / 8); _dwNotifySize -= _dwNotifySize % format.BlockAlign; _dwCaptureBufferSize = NUM_BUFFERS * _dwNotifySize; // my capture buffer _dwOutputBufferSize = NUM_BUFFERS * _dwNotifySize / channels; // my output buffer I set my notifications one at half the buffer and one at the end: _resetEvent = new AutoResetEvent(false); _notify = new Notify(_dwCapBuffer); bpn1 = new BufferPositionNotify(); bpn1.Offset = ((_dwCapBuffer.Caps.BufferBytes) / 2) - 1; bpn1.EventNotifyHandle = _resetEvent.SafeWaitHandle.DangerousGetHandle(); bpn2 = new BufferPositionNotify(); bpn2.Offset = (_dwCapBuffer.Caps.BufferBytes) - 1; bpn2.EventNotifyHandle = _resetEvent.SafeWaitHandle.DangerousGetHandle(); _notify.SetNotificationPositions(new BufferPositionNotify[] { bpn1, bpn2 }); observer.updateSamplerStatus("Events listener initialization complete!\r\n"); And here is how I process the events. /* Process thread */ private void eventReceived() { int offset = 0; _dwCaptureThread = new Thread((ThreadStart)delegate { _dwCapBuffer.Start(true); while (isReady) { _resetEvent.WaitOne(); // Notification received /* Read the captured buffer */ Array read = _dwCapBuffer.Read(offset, typeof(short), LockFlag.None, _dwOutputBufferSize - 1); observer.updateTextPacket("Buffer: " + count.ToString() + " # " + read.GetValue(read.Length - 1).ToString() + " # " + read.GetValue(0).ToString() + "\r\n"); /* Print last/new part of the buffer to the debug graph */ short[] graphData = new short[1001]; Array.Copy(read, graphData, 1000); db.SetBufferDebug(graphData, 500); observer.updateGraph(db.getBufferDebug()); offset = (offset + _dwOutputBufferSize) % _dwCaptureBufferSize; /* Out buffer not used */ /*_dwDevBuffer.Write(0, read, LockFlag.EntireBuffer); _dwDevBuffer.SetCurrentPosition(0); _dwDevBuffer.Play(0, BufferPlayFlags.Default);*/ } _dwCapBuffer.Stop(); }); _dwCaptureThread.Start(); } Any advise? I'm sure I'm failing somewhere in the event processing, but I cant find where. I had developed the same application using the WaveIn API and it worked well. Thanks a lot...

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• Is there a random sampling function for iphone development that can simulate a coin toss?

  - by William

  Is there a random sampling function for the iphone? For example, if you want to flip a coin that returns heads 25% of the times it's flipped, and you want to flip it and see if you get heads this time? I googled for random sampling probability for iphone and couldn't find anything.

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• How to scale JPEG images with a non-standard sampling factor in Java?

  - by HRJ

  I am using Java AWT for scaling a JPEG image, to create thumbnails. The code works fine when the image has a normal sampling factor ( 2x2,1x1,1x1 ) However, an image which has this sampling factor ( 1x1, 1x1, 1x1 ) creates problem when scaled. The colors get corrupted though the features are recognizable. The original and the thumbnail: The code I am using is roughly equivalent to: static BufferedImage awtScaleImage(BufferedImage image, int maxSize, int hint) { // We use AWT Image scaling because it has far superior quality // compared to JAI scaling. It also performs better (speed)! System.out.println("AWT Scaling image to: " + maxSize); int w = image.getWidth(); int h = image.getHeight(); float scaleFactor = 1.0f; if (w > h) scaleFactor = ((float) maxSize / (float) w); else scaleFactor = ((float) maxSize / (float) h); w = (int)(w * scaleFactor); h = (int)(h * scaleFactor); // since this code can run both headless and in a graphics context // we will just create a standard rgb image here and take the // performance hit in a non-compatible image format if any Image i = image.getScaledInstance(w, h, hint); image = new BufferedImage(w, h, BufferedImage.TYPE_INT_RGB); Graphics2D g = image.createGraphics(); g.drawImage(i, null, null); g.dispose(); i.flush(); return image; } (Code courtesy of this page ) Is there a better way to do this? Here's a test image with sampling factor of [ 1x1, 1x1, 1x1 ].

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• Russian Hydrodynamic Modeling, Prediction, and Visualization in Java

  - by Geertjan

  JSC "SamaraNIPIoil", located in Samara, Russia, provides the following applications for internal use. SimTools. Used to create & manage reservoir history schedule files for hydrodynamic models. The main features are that it lets you create/manage schedule files for models and create/manage well trajectory files to use with schedule files. DpSolver. Used to estimate permeability cubes using pore cube and results of well testing. Additionally, the user visualizes maps of vapor deposition polymerization or permeability, which can be visualized layer by layer. The base opportunities of the application are that it enables calculation of reservoir vertical heterogeneity and vertical sweep efficiency; automatic history matching of sweep efficiency; and calculations using Quantile-Quantile transformation and vizualization of permeability cube and other reservoir data. Clearly, the two applications above are NetBeans Platform applications.

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• Python: (sampling with replacement): efficient algorithm to extract the set of UNIQUE N-tuples from a set

  - by Homunculus Reticulli

  I have a set of items, from which I want to select DISSIMILAR tuples (more on the definition of dissimilar touples later). The set could contain potentially several thousand items, although typically, it would contain only a few hundreds. I am trying to write a generic algorithm that will allow me to select N items to form an N-tuple, from the original set. The new set of selected N-tuples should be DISSIMILAR. A N-tuple A is said to be DISSIMILAR to another N-tuple B if and only if: Every pair (2-tuple) that occurs in A DOES NOT appear in B Note: For this algorithm, A 2-tuple (pair) is considered SIMILAR/IDENTICAL if it contains the same elements, i.e. (x,y) is considered the same as (y,x). This is a (possible variation on the) classic Urn Problem. A trivial (pseudocode) implementation of this algorithm would be something along the lines of def fetch_unique_tuples(original_set, tuple_size): while True: # randomly select [tuple_size] items from the set to create first set # create a key or hash from the N elements and store in a set # store selected N-tuple in a container if end_condition_met: break I don't think this is the most efficient way of doing this - and though I am no algorithm theorist, I suspect that the time for this algorithm to run is NOT O(n) - in fact, its probably more likely to be O(n!). I am wondering if there is a more efficient way of implementing such an algo, and preferably, reducing the time to O(n). Actually, as Mark Byers pointed out there is a second variable m, which is the size of the number of elements being selected. This (i.e. m) will typically be between 2 and 5. Regarding examples, here would be a typical (albeit shortened) example: original_list = ['CAGG', 'CTTC', 'ACCT', 'TGCA', 'CCTG', 'CAAA', 'TGCC', 'ACTT', 'TAAT', 'CTTG', 'CGGC', 'GGCC', 'TCCT', 'ATCC', 'ACAG', 'TGAA', 'TTTG', 'ACAA', 'TGTC', 'TGGA', 'CTGC', 'GCTC', 'AGGA', 'TGCT', 'GCGC', 'GCGG', 'AAAG', 'GCTG', 'GCCG', 'ACCA', 'CTCC', 'CACG', 'CATA', 'GGGA', 'CGAG', 'CCCC', 'GGTG', 'AAGT', 'CCAC', 'AACA', 'AATA', 'CGAC', 'GGAA', 'TACC', 'AGTT', 'GTGG', 'CGCA', 'GGGG', 'GAGA', 'AGCC', 'ACCG', 'CCAT', 'AGAC', 'GGGT', 'CAGC', 'GATG', 'TTCG'] Select 3-tuples from the original list should produce a list (or set) similar to: [('CAGG', 'CTTC', 'ACCT') ('CAGG', 'TGCA', 'CCTG') ('CAGG', 'CAAA', 'TGCC') ('CAGG', 'ACTT', 'ACCT') ('CAGG', 'CTTG', 'CGGC') .... ('CTTC', 'TGCA', 'CAAA') ] [[Edit]] Actually, in constructing the example output, I have realized that the earlier definition I gave for UNIQUENESS was incorrect. I have updated my definition and have introduced a new metric of DISSIMILARITY instead, as a result of this finding.

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• Python: (sampling with replacement): efficient algorithm to extract the set of DISSIMILAR N-tuples from a set

  - by Homunculus Reticulli

  I have a set of items, from which I want to select DISSIMILAR tuples (more on the definition of dissimilar touples later). The set could contain potentially several thousand items, although typically, it would contain only a few hundreds. I am trying to write a generic algorithm that will allow me to select N items to form an N-tuple, from the original set. The new set of selected N-tuples should be DISSIMILAR. A N-tuple A is said to be DISSIMILAR to another N-tuple B if and only if: Every pair (2-tuple) that occurs in A DOES NOT appear in B Note: For this algorithm, A 2-tuple (pair) is considered SIMILAR/IDENTICAL if it contains the same elements, i.e. (x,y) is considered the same as (y,x). This is a (possible variation on the) classic Urn Problem. A trivial (pseudocode) implementation of this algorithm would be something along the lines of def fetch_unique_tuples(original_set, tuple_size): while True: # randomly select [tuple_size] items from the set to create first set # create a key or hash from the N elements and store in a set # store selected N-tuple in a container if end_condition_met: break I don't think this is the most efficient way of doing this - and though I am no algorithm theorist, I suspect that the time for this algorithm to run is NOT O(n) - in fact, its probably more likely to be O(n!). I am wondering if there is a more efficient way of implementing such an algo, and preferably, reducing the time to O(n). Actually, as Mark Byers pointed out there is a second variable m, which is the size of the number of elements being selected. This (i.e. m) will typically be between 2 and 5. Regarding examples, here would be a typical (albeit shortened) example: original_list = ['CAGG', 'CTTC', 'ACCT', 'TGCA', 'CCTG', 'CAAA', 'TGCC', 'ACTT', 'TAAT', 'CTTG', 'CGGC', 'GGCC', 'TCCT', 'ATCC', 'ACAG', 'TGAA', 'TTTG', 'ACAA', 'TGTC', 'TGGA', 'CTGC', 'GCTC', 'AGGA', 'TGCT', 'GCGC', 'GCGG', 'AAAG', 'GCTG', 'GCCG', 'ACCA', 'CTCC', 'CACG', 'CATA', 'GGGA', 'CGAG', 'CCCC', 'GGTG', 'AAGT', 'CCAC', 'AACA', 'AATA', 'CGAC', 'GGAA', 'TACC', 'AGTT', 'GTGG', 'CGCA', 'GGGG', 'GAGA', 'AGCC', 'ACCG', 'CCAT', 'AGAC', 'GGGT', 'CAGC', 'GATG', 'TTCG'] # Select 3-tuples from the original list should produce a list (or set) similar to: [('CAGG', 'CTTC', 'ACCT') ('CAGG', 'TGCA', 'CCTG') ('CAGG', 'CAAA', 'TGCC') ('CAGG', 'ACTT', 'ACCT') ('CAGG', 'CTTG', 'CGGC') .... ('CTTC', 'TGCA', 'CAAA') ] [[Edit]] Actually, in constructing the example output, I have realized that the earlier definition I gave for UNIQUENESS was incorrect. I have updated my definition and have introduced a new metric of DISSIMILARITY instead, as a result of this finding.

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• How would i down-sample a .wav file then reconstruct it using nyquist? - in matlab [closed]

  - by martin

  This is all done in MatLab 2010 My objective is to show the results of: undersampling, nyquist rate/ oversampling First i need to downsample the .wav file to get an incomplete/ or impartial data stream that i can then reconstuct. Heres the flow chart of what im going to be doing So the flow is analog signal - sampling analog filter - ADC - resample down - resample up - DAC - reconstruction analog filter what needs to be achieved: F= Frequency F(Hz=1/s) E.x. 100Hz = 1000 (Cyc/sec) F(s)= 1/(2f) Example problem: 1000 hz = Highest frequency 1/2(1000hz) = 1/2000 = 5x10(-3) sec/cyc or a sampling rate of 5ms This is my first signal processing project using matlab. what i have so far. % Fs = frequency sampled (44100hz or the sampling frequency of a cd) [test,fs]=wavread('test.wav'); % loads the .wav file left=test(:,1); % Plot of the .wav signal time vs. strength time=(1/44100)*length(left); t=linspace(0,time,length(left)); plot(t,left) xlabel('time (sec)'); ylabel('relative signal strength') **%this is were i would need to sample it at the different frequecys (both above and below and at) nyquist frequency.*I think.*** soundsc(left,fs) % shows the resaultant audio file , which is the same as original ( only at or above nyquist frequency however) Can anyone tell me how to make it better, and how to do the various sampling at different frequencies?

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• SQL SERVER – Update Statistics are Sampled By Default

  - by pinaldave

  After reading my earlier post SQL SERVER – Create Primary Key with Specific Name when Creating Table on Statistics, I have received another question by a blog reader. The question is as follows: Question: Are the statistics sampled by default? Answer: Yes. The sampling rate can be specified by the user and it can be anywhere between a very low value to 100%. Let us do a small experiment to verify if the auto update on statistics is left on. Also, let’s examine a very large table that is created and statistics by default- whether the statistics are sampled or not. USE [AdventureWorks] GO -- Create Table CREATE TABLE [dbo].[StatsTest]( [ID] [int] IDENTITY(1,1) NOT NULL, [FirstName] [varchar](100) NULL, [LastName] [varchar](100) NULL, [City] [varchar](100) NULL, CONSTRAINT [PK_StatsTest] PRIMARY KEY CLUSTERED ([ID] ASC) ) ON [PRIMARY] GO -- Insert 1 Million Rows INSERT INTO [dbo].[StatsTest] (FirstName,LastName,City) SELECT TOP 1000000 'Bob', CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%2 = 1 THEN 'Smith' ELSE 'Brown' END, CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 1 THEN 'New York' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 5 THEN 'San Marino' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 3 THEN 'Los Angeles' ELSE 'Houston' END FROM sys.all_objects a CROSS JOIN sys.all_objects b GO -- Update the statistics UPDATE STATISTICS [dbo].[StatsTest] GO -- Shows the statistics DBCC SHOW_STATISTICS ("StatsTest"PK_StatsTest) GO -- Clean up DROP TABLE [dbo].[StatsTest] GO Now let us observe the result of the DBCC SHOW_STATISTICS. The result shows that Resultset is for sure sampling for a large dataset. The percentage of sampling is based on data distribution as well as the kind of data in the table. Before dropping the table, let us check first the size of the table. The size of the table is 35 MB. Now, let us run the above code with lesser number of the rows. USE [AdventureWorks] GO -- Create Table CREATE TABLE [dbo].[StatsTest]( [ID] [int] IDENTITY(1,1) NOT NULL, [FirstName] [varchar](100) NULL, [LastName] [varchar](100) NULL, [City] [varchar](100) NULL, CONSTRAINT [PK_StatsTest] PRIMARY KEY CLUSTERED ([ID] ASC) ) ON [PRIMARY] GO -- Insert 1 Hundred Thousand Rows INSERT INTO [dbo].[StatsTest] (FirstName,LastName,City) SELECT TOP 100000 'Bob', CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%2 = 1 THEN 'Smith' ELSE 'Brown' END, CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 1 THEN 'New York' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 5 THEN 'San Marino' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 3 THEN 'Los Angeles' ELSE 'Houston' END FROM sys.all_objects a CROSS JOIN sys.all_objects b GO -- Update the statistics UPDATE STATISTICS [dbo].[StatsTest] GO -- Shows the statistics DBCC SHOW_STATISTICS ("StatsTest"PK_StatsTest) GO -- Clean up DROP TABLE [dbo].[StatsTest] GO You can see that Rows Sampled is just the same as Rows of the table. In this case, the sample rate is 100%. Before dropping the table, let us also check the size of the table. The size of the table is less than 4 MB. Let us compare the Result set just for a valid reference. Test 1: Total Rows: 1000000, Rows Sampled: 255420, Size of the Table: 35.516 MB Test 2: Total Rows: 100000, Rows Sampled: 100000, Size of the Table: 3.555 MB The reason behind the sample in the Test1 is that the data space is larger than 8 MB, and therefore it uses more than 1024 data pages. If the data space is smaller than 8 MB and uses less than 1024 data pages, then the sampling does not happen. Sampling aids in reducing excessive data scan; however, sometimes it reduces the accuracy of the data as well. Please note that this is just a sample test and there is no way it can be claimed as a benchmark test. The result can be dissimilar on different machines. There are lots of other information can be included when talking about this subject. I will write detail post covering all the subject very soon. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, SQL, SQL Authority, SQL Index, SQL Optimization, SQL Performance, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology Tagged: SQL Statistics

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• Is this a correct Interlocked synchronization design?

  - by Dan Bryant

  I have a system that takes Samples. I have multiple client threads in the application that are interested in these Samples, but the actual process of taking a Sample can only occur in one context. It's fast enough that it's okay for it to block the calling process until Sampling is done, but slow enough that I don't want multiple threads piling up requests. I came up with this design (stripped down to minimal details): public class Sample { private static Sample _lastSample; private static int _isSampling; public static Sample TakeSample(AutomationManager automation) { //Only start sampling if not already sampling in some other context if (Interlocked.CompareExchange(ref _isSampling, 0, 1) == 0) { try { Sample sample = new Sample(); sample.PerformSampling(automation); _lastSample = sample; } finally { //We're done sampling _isSampling = 0; } } return _lastSample; } private void PerformSampling(AutomationManager automation) { //Lots of stuff going on that shouldn't be run in more than one context at the same time } } Is this safe for use in the scenario I described?

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• multiple move operations and data processes in work thread

  - by younevertell

  main thread-- start workthread--StartStage(get list of positions for data process) -- move to one position -- data sampling*strong text*-- data collection--data analysis------data sampling*strong text* basically, work thread does the data sampling*strong text*-- data collection--data analysis------data sampling*strong text* loop for one positioin until press stop or target is obtained. my questions: After work thread finishs the loop for one positioin, it would end itself. now how to make the work thread moves to the next position to do the data process loop after work thread finish one position work, would not end itself until data process for all the positions are done? Thanks in advance!

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• How would i down-sample a .wav file then reconstruct it using nyquist? - in matlab [closed]

  - by martin

  Possible Duplicate: How would i down-sample a .wav file then reconstruct it using nyquist? - in matlab This is all done in MatLab 2010 My objective is to show the results of: undersampling, nyquist rate/ oversampling First i need to downsample the .wav file to get an incomplete/ or impartial data stream that i can then reconstuct. Heres the flow chart of what im going to be doing So the flow is analog signal - sampling analog filter - ADC - resample down - resample up - DAC - reconstruction analog filter what needs to be achieved: F= Frequency F(Hz=1/s) E.x. 100Hz = 1000 (Cyc/sec) F(s)= 1/(2f) Example problem: 1000 hz = Highest frequency 1/2(1000hz) = 1/2000 = 5x10(-3) sec/cyc or a sampling rate of 5ms This is my first signal processing project using matlab. what i have so far. % Fs = frequency sampled (44100hz or the sampling frequency of a cd) [test,fs]=wavread('test.wav'); % loads the .wav file left=test(:,1); % Plot of the .wav signal time vs. strength time=(1/44100)*length(left); t=linspace(0,time,length(left)); plot(t,left) xlabel('time (sec)'); ylabel('relative signal strength') **%this is were i would need to sample it at the different frequecys (both above and below and at) nyquist frequency.*I think.*** soundsc(left,fs) % shows the resaultant audio file , which is the same as original ( only at or above nyquist frequency however) Can anyone tell me how to make it better, and how to do the various sampling at different frequencies?

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• Explaining Explain Plan Notes for Auto DOP

  - by jean-pierre.dijcks

  I've recently gotten some questions around "why do I not see a parallel plan" while Auto DOP is on (I think)...? It is probably worthwhile to quickly go over some of the ways to find out what Auto DOP was thinking. In general, there is no need to go tracing sessions and look under the hood. The thing to start with is to do an explain plan on your statement and to look at the parameter settings on the system. Parameter Settings to Look At First and foremost, make sure that parallel_degree_policy = AUTO. If you have that parameter set to LIMITED you will not have queuing and we will only do the auto magic if your objects are set to default parallel (so no degree specified). Next you want to look at the value of parallel_degree_limit. It is typically set to CPU, which in default settings equates to the Default DOP of the system. If you are testing Auto DOP itself and the impact it has on performance you may want to leave it at this CPU setting. If you are running concurrent statements you may want to give this some more thoughts. See here for more information. In general, do stick with either CPU or with a specific number. For now avoid the IO setting as I've seen some mixed results with that... In 11.2.0.2 you should also check that IO Calibrate has been run. Best to simply do a: SQL> select * from V$IO_CALIBRATION_STATUS; STATUS        CALIBRATION_TIME ------------- ---------------------------------------------------------------- READY         04-JAN-11 10.04.13.104 AM You should see that your IO Calibrate is READY and therefore Auto DOP is ready. In any case, if you did not run the IO Calibrate step you will get the following note in the explain plan: Note -----    - automatic DOP: skipped because of IO calibrate statistics are missing One more note on calibrate_io, if you do not have asynchronous IO enabled you will see:  ERROR at line 1: ORA-56708: Could not find any datafiles with asynchronous i/o capability ORA-06512: at "SYS.DBMS_RMIN", line 463 ORA-06512: at "SYS.DBMS_RESOURCE_MANAGER", line 1296 ORA-06512: at line 7 While this is changed in some fixes to the calibrate procedure, you should really consider switching asynchronous IO on for your data warehouse. Explain Plan Explanation To see the notes that are shown and explained here (and the above little snippet ) you can use a simple explain plan mechanism. There should  be no need to add +parallel etc. explain plan for <statement> SELECT PLAN_TABLE_OUTPUT FROM TABLE(DBMS_XPLAN.DISPLAY()); Auto DOP The note structure displaying why Auto DOP did not work (with the exception noted above on IO Calibrate) is like this: Automatic degree of parallelism is disabled: <reason> These are the reason codes: Parameter -  parallel_degree_policy = manual which will not allow Auto DOP to kick in  Hint - One of the following hints are used NOPARALLEL, PARALLEL(1), PARALLEL(MANUAL) Outline - A SQL outline of an older version (before 11.2) is used SQL property restriction - The statement type does not allow for parallel processing Rule-based mode - Instead of the Cost Based Optimizer the system is using the RBO Recursive SQL statement - The statement type does not allow for parallel processing pq disabled/pdml disabled/pddl disabled - For some reason (alter session?) parallelism is disabled Limited mode but no parallel objects referenced - your parallel_degree_policy = LIMITED and no objects in the statement are decorated with the default PARALLEL degree. In most cases all objects have a specific degree in which case Auto DOP will honor that degree. Parallel Degree Limited When Auto DOP does it works you may see the cap you imposed with parallel_degree_limit showing up in the note section of the explain plan: Note -----    - automatic DOP: Computed Degree of Parallelism is 16 because of degree limit This is an obvious indication that your are being capped for this statement. There is one quite interesting one that happens when you are being capped at DOP = 1. First of you get a serial plan and the note changes slightly in that it does not indicate it is being capped (we hope to update the note at some point in time to be more specific). It right now looks like this: Note -----    - automatic DOP: Computed Degree of Parallelism is 1 Dynamic Sampling With 11.2.0.2 you will start seeing another interesting change in parallel plans, and since we are talking about the note section here, I figured we throw this in for good measure. If we deem the parallel (!) statement complex enough, we will enact dynamic sampling on your query. This happens as long as you did not change the default for dynamic sampling on the system. The note looks like this: Note ----- - dynamic sampling used for this statement (level=5)

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• Why does multiplying texture coordinates scale the texture?

  - by manning18

  I'm having trouble visualizing this geometrically - why is it that multiplying the U,V coordinates of a texture coordinate has the effect of scaling that texture by that factor? eg if you scaled the texture coordinates by a factor of 3 ..then doesn't this mean that if you had texture coordinates 0,1 and 0,2 ...you'd be sampling 0,3 and 0,6 in the U,V texture space of 0..1? How does that make it bigger eg HLSL: tex2D(textureSampler, TexCoords*3) Integers make it smaller, decimals make it bigger I mean I understand intuitively if you added to the U,V coordinates, as that is simply an offset into the sampling range, but what's the case with multiplication? I have a feeling when someone explains this to me I'm going to be feeling mighty stupid

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• Why can't a blendShader sample anything but the current coordinate of the background image?

  - by Triynko

  In Flash, you can set a DisplayObject's blendShader property to a pixel shader (flash.shaders.Shader class). The mechanism is nice, because Flash automatically provides your Shader with two input images, including the background surface and the foreground display object's bitmap. The problem is that at runtime, the shader doesn't allow you to sample the background anywhere but under the current output coordinate. If you try to sample other coordinates, it just returns the color of the current coordinate instead, ignoring the coordinates you specified. This seems to occur only at runtime, because it works properly in the Pixel Bender toolkit. This limitation makes it impossible to simulate, for example, the Aero Glass effect in Windows Vista/7, because you cannot sample the background properly for blurring. I must mention that it is possible to create the effect in Flash through manual composition techniques, but it's hard to determine when it actually needs updated, because Flash does not provide information about when a particular area of the screen or a particular display object needs re-rendered. For example, you may have a fixed glass surface with objects moving underneath it that don't dispatch events when they move. The only alternative is to re-render the glass bar every frame, which is inefficient, which is why I am trying to do it through a blendShader so Flash determines when it needs rendered automatically. Is there a technical reason for this limitation, or is it an oversight of some sort? Does anyone know of a workaround, or a way I could provide my manual composition implementation with information about when it needs re-rendered? The limitation is mentioned with no explanation in the last note in this page: http://help.adobe.com/en_US/as3/dev/WSB19E965E-CCD2-4174-8077-8E5D0141A4A8.html It says: "Note: When a Pixel Bender shader program is run as a blend in Flash Player or AIR, the sampling and outCoord() functions behave differently than in other contexts.In a blend, a sampling function will always return the current pixel being evaluated by the shader. You cannot, for example, use add an offset to outCoord() in order to sample a neighboring pixel. Likewise, if you use the outCoord() function outside a sampling function, its coordinates always evaluate to 0. You cannot, for example, use the position of a pixel to influence how the blended images are combined."

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• Is there any online programmer's community, focusing on core game development?

  - by kasperov

  I am looking for a stricktly/mostly programming oriented game community, focusing on core graphics, middleware, and research. Any suggestions? Edit: I am specifically looking for people/community/group, having expertise in core game engine design/programming, directx/opengl reservoir.(And specifically targetting the programming part only).(The platforms can be anything from PC to xbox360/ps3/wii and even 3ds.)

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• kAudioSessionProperty_CurrentHardwareSampleRate input/output

  - by iter

  kAudioSessionProperty_CurrentHardwareSampleRate seems to describe the input sampling rate. I wonder if there is a way to determine the available output sampling rate on an iPhone / iPad (iPhone supports 44.1K; iPad, 48K). http://developer.apple.com/iphone/library/documentation/AudioToolbox/Reference/AudioSessionServicesReference/Reference/reference.html#//apple_ref/doc/c_ref/kAudioSessionProperty_CurrentHardwareSampleRate

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• Sample uniformly at random from an n-dimensional unit simplex.

  - by dreeves

  Sampling uniformly at random from an n-dimensional unit simplex is the fancy way to say that you want n random numbers such that they are all non-negative, they sum to one, and every possible vector of n non-negative numbers that sum to one are equally likely. In the n=2 case you want to sample uniformly from the segment of the line x+y=1 (ie, y=1-x) that is in the positive quadrant. In the n=3 case you're sampling from the triangle-shaped part of the plane x+y+z=1 that is in the positive octant of R3: (Image from http://en.wikipedia.org/wiki/Simplex.) Note that picking n uniform random numbers and then normalizing them so they sum to one does not work. You end up with a bias towards less extreme numbers. Similarly, picking n-1 uniform random numbers and then taking the nth to be one minus the sum of them also introduces bias. Wikipedia gives two algorithms to do this correctly: http://en.wikipedia.org/wiki/Simplex#Random_sampling (Though the second one currently claims to only be correct in practice, not in theory. I'm hoping to clean that up or clarify it when I understand this better. I initially stuck in a "WARNING: such-and-such paper claims the following is wrong" on that Wikipedia page and someone else turned it into the "works only in practice" caveat.) Finally, the question: What do you consider the best implementation of simplex sampling in Mathematica (preferably with empirical confirmation that it's correct)? Related questions http://stackoverflow.com/questions/2171074/generating-a-probability-distribution http://stackoverflow.com/questions/3007975/java-random-percentages

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• Figuring out the performance limitation of an ADC on a PIC microcontroller

  - by AKE

  I'm spec-ing the suitability of a microcontroller like PIC for an analog-to-digital application. This would be preferable to using external A/D chips. To do that, I've had to run through some computations, pulling the relevant parameters from the datasheets. I'm not sure I've got it right -- would appreciate a check! Here's the simplest example: PIC10F220 is the simplest possible PIC with an ADC. Runs at clock speed of 8MHz. Has an instruction cycle of 0.5us (4 clock steps per instruction) So: Taking Tacq = 6.06 us (acquisition time for ADC, assume chip temp. = 50*C) [datasheet p34] Taking Fosc = 8MHz (? clock speed) Taking divisor = 4 (4 clock steps per CPU instruction) This gives TAD = 0.5us (TAD = 1/(Fosc/divisor) ) Conversion time is 13*TAD [datasheet p31] This gives conversion time 6.5us ADC duration is then 12.56 us [? Tacq + 13*TAD] Assuming at least 2 instructions for load/store: This is another 1 us [0.5 us per instruction] Which would give max sampling rate of 73.7 ksps (1/13.56) Supposing 8 more instructions for real-time processing: This is another 4 us Thus, total ADC/handling time = 17.56us (12.56us + 1us + 4us) So expected upper sampling rate is 56.9 ksps. Nyquist frequency for this sampling rate is therefore 28 kHz. If this is right, it suggests the (theoretical) performance suitability of this chip's A/D is for signals that are bandlimited to 28 kHz. Is this a correct interpretation of the information given in the data sheet? Any pointers would be much appreciated! AKE

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• Figuring out the Nyquist performance limitation of an ADC on an example PIC microcontroller

  - by AKE

  I'm spec-ing the suitability of a dsPIC microcontroller for an analog-to-digital application. This would be preferable to using dedicated A/D chips and a separate dedicated DSP chip. To do that, I've had to run through some computations, pulling the relevant parameters from the datasheets. I'm not sure I've got it right -- would appreciate a check! (EDITED NOTE: The PIC10F220 in the example below was selected ONLY to walk through a simple example to check that I'm interpreting Tacq, Fosc, TAD, and divisor correctly in working through this sort of Nyquist analysis. The actual chips I'm considering for the design are the dsPIC33FJ128MC804 (with 16b A/D) or dsPIC30F3014 (with 12b A/D).) A simple example: PIC10F220 is the simplest possible PIC with an ADC Runs at clock speed of 8MHz. Has an instruction cycle of 0.5us (4 clock steps per instruction) So: Taking Tacq = 6.06 us (acquisition time for ADC, assume chip temp. = 50*C) [datasheet p34] Taking Fosc = 8MHz (? clock speed) Taking divisor = 4 (4 clock steps per CPU instruction) This gives TAD = 0.5us (TAD = 1/(Fosc/divisor) ) Conversion time is 13*TAD [datasheet p31] This gives conversion time 6.5us ADC duration is then 12.56 us [? Tacq + 13*TAD] Assuming at least 2 instructions for load/store: This is another 1 us [0.5 us per instruction] Which would give max sampling rate of 73.7 ksps (1/13.56) Supposing 8 more instructions for real-time processing: This is another 4 us Thus, total ADC/handling time = 17.56us (12.56us + 1us + 4us) So expected upper sampling rate is 56.9 ksps. Nyquist frequency for this sampling rate is therefore 28 kHz. If this is right, it suggests the (theoretical) performance suitability of this chip's A/D is for signals that are bandlimited to 28 kHz. Is this a correct interpretation of the information given in the data sheet in obtaining the Nyquist performance limit? Any opinions on the noise susceptibility of ADCs in PIC / dsPIC chips would be much appreciated! AKE

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• How to create a "retro" pixel shader for transformed 2D sprites that maintains pixel fidelity?

  - by David Gouveia

  The image below shows two sprites rendered with point sampling on top of a background: The left skull has no rotation/scaling applied to it, so every pixel matches perfectly with the background. The right skull is rotated/scaled, and this results in larger pixels that are no longer axis aligned. How could I develop a pixel shader that would render the transformed sprite on the right with axis aligned pixels of the same size as the rest of the scene? This might be related to how sprite scaling was implemented in old games such as Monkey Island, because that's the effect I'm trying to achieve, but with rotation added. Edit As per kaoD's suggestions, I tried to address the problem as a post-process. The easiest approach was to render to a separate render target first (downsampled to match the desired pixel size) and then upscale it when rendering a second time. It did address my requirements above. First I tried doing it Linear -> Point and the result was this: There's no distortion but the result looks blurred and it loses most of the highlights colors. In my opinion it breaks the retro look I needed. The second time I tried Point -> Point and the result was this: Despite the distortion, I think that might be good enough for my needs, although it does look better as a still image than in motion. To demonstrate, here's a video of the effect, although YouTube filtered the pixels out of it: http://youtu.be/hqokk58KFmI However, I'll leave the question open for a few more days in case someone comes up with a better sampling solution that maintains the crisp look while decreasing the amount of distortion when moving.

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• OpenGL ES 2.0 texture distortion on large geometry

  - by Spruce

  OpenGL ES 2.0 has serious precision issues with texture sampling - I've seen topics with a similar problem, but I haven't seen a real solution to this "distorted OpenGL ES 2.0 texture" problem yet. This is not related to the texture's image format or OpenGL color buffers, it seems like it's a precision error. I don't know what specifically causes the precision to fail - it doesn't seem like it's just the size of geometry that causes this distortion, because simply scaling vertex position passed to the the vertex shader does not solve the issue. Here are some examples of the texture distortion: Distorted Texture (on OpenGL ES 2.0): http://i47.tinypic.com/3322h6d.png What the texture normally looks like (also on OpenGL ES 2.0): http://i49.tinypic.com/b4jc6c.png The texture issue is limited to small scale geometry on OpenGL ES 2.0, otherwise the texture sampling appears normal, but the grainy effect gradually worsens the further the vertex data is from the origin of XYZ(0,0,0) These texture issues do not occur on desktop OpenGL (works fine under Windows XP, Windows 7, and Mac OS X) I've only seen the problem occur on Android, iPhone, or WebGL(which is similar to OpenGL ES 2.0) All textures are power of 2 but the problem still occurs Scaling the vertex data - The values of a vertex's X Y Z location are in the range of: -65536 to +65536 floating point I realized this was large, so I tried dividing the vertex positions by 1024 to shrink the geometry and hopefully get more accurate floating point precision, but this didn't fix or lessen the texture distortion issue Scaling the modelview or scaling the projection matrix does not help Changing texture filtering options does not help Disabling mipmapping, or using GL_NEAREST/GL_LINEAR does nothing Enabling/disabling anisotropic does nothing The banding effect still occurs even when using GL_CLAMP Dividing the texture coords passed to the vertex shader and then multiplying them back to the correct values in the fragment shader, also does not work precision highp sampler2D, highp float, highp int - in the fragment or the vertex shader didn't change anything (lowp/mediump did not work either) I'm thinking this problem has to have been solved at one point - Seeing that OpenGL ES 2.0 -based games have been able to render large-scale, highly detailed geometry

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• ?12c database ????Adaptive Execution Plans ????????

  - by Liu Maclean(???)

  12c R1 ????SQL??????- Adaptive Execution Plans ????????,???????optimizer ??????(runtime)???????????????, ????????????????????? SQL???????? ????????????, ?????????????????????????????????????????????????????????????adaptive plan ????????????????????????????????????,?????subplan???????????????????? ??????, ???????? ???????????????,?????????, ?????? ???????????????”???”????, ???????????????????buffer ???????  ????????????,?????,??????????????????? ???optimizer ?????????????????????????,?????????????????????????????????????????plan???? ??12C?????????????, ???????????????????,?????? ???????????? ????????????2???: Dynamic Plans????: ???????????????????????;??????,???optimizer??????????subplans??????????????, ???????????????????,?????????????? Reoptimization????: ?Dynamic Plans????,Reoptimization??????????????????????Reoptimization??,?????????????????????????,??reoptimization????? OPTIMIZER_ADAPTIVE_REPORTING_ONLY ???? report-only????????????????TRUE,?????????report-only????,???????????????,??????????????? Dynamic Plans ??????????????,????????????????????????, ?????????????,???????????,????????????????????????????????????????? ?????????????final plan??????????????default plan, ??final plan?default plan???????,????????????? subplan ???????????????,???????????????????????? ??????,???????statistics collector ?buffer???????????statistics collector?????????????????,???????????????????????????? ?????????????????????????????????????????,??????????,?????????????? ???????????,???????buffer???? ???????????????,?????????????????????????????,??????buffer,??????final plan? ????????,???????????????????????,????????????????? ?V$SQL??????IS_RESOLVED_DYNAMIC_PLAN??????????final plan???default plan? ??????dynamic plan ???????SQL PLAN directives?????? declare cursor PLAN_DIRECTIVE_IDS is select directive_id from DBA_SQL_PLAN_DIRECTIVES; begin for z in PLAN_DIRECTIVE_IDS loop DBMS_SPD.DROP_SQL_PLAN_DIRECTIVE(z.directive_id); end loop; end; / explain plan for select /*MALCEAN*/ product_name from oe.order_items o, oe.product_information p where o.unit_price=15 and quantity>1 and p.product_id=o.product_id; select * from table(dbms_xplan.display()); Plan hash value: 1255158658 www.askmaclean.com ------------------------------------------------------------------------------------------------------- | Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | ------------------------------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 4 | 128 | 7 (0)| 00:00:01 | | 1 | NESTED LOOPS | | | | | | | 2 | NESTED LOOPS | | 4 | 128 | 7 (0)| 00:00:01 | |* 3 | TABLE ACCESS FULL | ORDER_ITEMS | 4 | 48 | 3 (0)| 00:00:01 | |* 4 | INDEX UNIQUE SCAN | PRODUCT_INFORMATION_PK | 1 | | 0 (0)| 00:00:01 | | 5 | TABLE ACCESS BY INDEX ROWID| PRODUCT_INFORMATION | 1 | 20 | 1 (0)| 00:00:01 | ------------------------------------------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 3 - filter("O"."UNIT_PRICE"=15 AND "QUANTITY">1) 4 - access("P"."PRODUCT_ID"="O"."PRODUCT_ID") alter session set events '10053 trace name context forever,level 1'; OR alter session set events 'trace[SQL_Plan_Directive] disk highest'; select /*MALCEAN*/ product_name from oe.order_items o, oe.product_information p where o.unit_price=15 and quantity>1 and p.product_id=o.product_id; ---------------------------------------------------------------+-----------------------------------+ | Id | Operation | Name | Rows | Bytes | Cost | Time | ---------------------------------------------------------------+-----------------------------------+ | 0 | SELECT STATEMENT | | | | 7 | | | 1 | HASH JOIN | | 4 | 128 | 7 | 00:00:01 | | 2 | NESTED LOOPS | | | | | | | 3 | NESTED LOOPS | | 4 | 128 | 7 | 00:00:01 | | 4 | STATISTICS COLLECTOR | | | | | | | 5 | TABLE ACCESS FULL | ORDER_ITEMS | 4 | 48 | 3 | 00:00:01 | | 6 | INDEX UNIQUE SCAN | PRODUCT_INFORMATION_PK| 1 | | 0 | | | 7 | TABLE ACCESS BY INDEX ROWID | PRODUCT_INFORMATION | 1 | 20 | 1 | 00:00:01 | | 8 | TABLE ACCESS FULL | PRODUCT_INFORMATION | 1 | 20 | 1 | 00:00:01 | ---------------------------------------------------------------+-----------------------------------+ Predicate Information: ---------------------- 1 - access("P"."PRODUCT_ID"="O"."PRODUCT_ID") 5 - filter(("O"."UNIT_PRICE"=15 AND "QUANTITY">1)) 6 - access("P"."PRODUCT_ID"="O"."PRODUCT_ID") ===================================== SPD: BEGIN context at statement level ===================================== Stmt: ******* UNPARSED QUERY IS ******* SELECT /*+ OPT_ESTIMATE (@"SEL$1" JOIN ("P"@"SEL$1" "O"@"SEL$1") ROWS=13.000000 ) OPT_ESTIMATE (@"SEL$1" TABLE "O"@"SEL$1" ROWS=13.000000 ) */ "P"."PRODUCT_NAME" "PRODUCT_NAME" FROM "OE"."ORDER_ITEMS" "O","OE"."PRODUCT_INFORMATION" "P" WHERE "O"."UNIT_PRICE"=15 AND "O"."QUANTITY">1 AND "P"."PRODUCT_ID"="O"."PRODUCT_ID" Objects referenced in the statement PRODUCT_INFORMATION[P] 92194, type = 1 ORDER_ITEMS[O] 92197, type = 1 Objects in the hash table Hash table Object 92197, type = 1, ownerid = 6573730143572393221: No Dynamic Sampling Directives for the object Hash table Object 92194, type = 1, ownerid = 17822962561575639002: No Dynamic Sampling Directives for the object Return code in qosdInitDirCtx: ENBLD =================================== SPD: END context at statement level =================================== ======================================= SPD: BEGIN context at query block level ======================================= Query Block SEL$1 (#0) Return code in qosdSetupDirCtx4QB: NOCTX ===================================== SPD: END context at query block level ===================================== SPD: Return code in qosdDSDirSetup: NOCTX, estType = TABLE SPD: Generating finding id: type = 1, reason = 1, objcnt = 1, obItr = 0, objid = 92197, objtyp = 1, vecsize = 6, colvec = [4, 5, ], fid = 2896834833840853267 SPD: Inserted felem, fid=2896834833840853267, ftype = 1, freason = 1, dtype = 0, dstate = 0, dflag = 0, ver = YES, keep = YES SPD: qosdCreateFindingSingTab retCode = CREATED, fid = 2896834833840853267 SPD: qosdCreateDirCmp retCode = CREATED, fid = 2896834833840853267 SPD: Return code in qosdDSDirSetup: NOCTX, estType = TABLE SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = JOIN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SKIP_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = JOIN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Generating finding id: type = 1, reason = 1, objcnt = 1, obItr = 0, objid = 92197, objtyp = 1, vecsize = 6, colvec = [4, 5, ], fid = 2896834833840853267 SPD: Modified felem, fid=2896834833840853267, ftype = 1, freason = 1, dtype = 0, dstate = 0, dflag = 0, ver = YES, keep = YES SPD: Generating finding id: type = 1, reason = 1, objcnt = 1, obItr = 0, objid = 92194, objtyp = 1, vecsize = 2, colvec = [1, ], fid = 5618517328604016300 SPD: Modified felem, fid=5618517328604016300, ftype = 1, freason = 1, dtype = 0, dstate = 0, dflag = 0, ver = NO, keep = NO SPD: Generating finding id: type = 1, reason = 1, objcnt = 1, obItr = 0, objid = 92194, objtyp = 1, vecsize = 2, colvec = [1, ], fid = 1142802697078608149 SPD: Modified felem, fid=1142802697078608149, ftype = 1, freason = 1, dtype = 0, dstate = 0, dflag = 0, ver = NO, keep = NO SPD: Generating finding id: type = 1, reason = 2, objcnt = 2, obItr = 0, objid = 92194, objtyp = 1, vecsize = 0, obItr = 1, objid = 92197, objtyp = 1, vecsize = 0, fid = 1437680122701058051 SPD: Modified felem, fid=1437680122701058051, ftype = 1, freason = 2, dtype = 0, dstate = 0, dflag = 0, ver = NO, keep = NO select * from table(dbms_xplan.display_cursor(format=>'report')) ; ????report????adaptive plan Adaptive plan: ------------- This cursor has an adaptive plan, but adaptive plans are enabled for reporting mode only.  The plan that would be executed if adaptive plans were enabled is displayed below. ------------------------------------------------------------------------------------------ | Id  | Operation          | Name                | Rows  | Bytes | Cost (%CPU)| Time     | ------------------------------------------------------------------------------------------ |   0 | SELECT STATEMENT   |                     |       |       |     7 (100)|          | |*  1 |  HASH JOIN         |                     |     4 |   128 |     7   (0)| 00:00:01 | |*  2 |   TABLE ACCESS FULL| ORDER_ITEMS         |     4 |    48 |     3   (0)| 00:00:01 | |   3 |   TABLE ACCESS FULL| PRODUCT_INFORMATION |     1 |    20 |     1   (0)| 00:00:01 | ------------------------------------------------------------------------------------------ SQL> select SQL_ID,IS_RESOLVED_DYNAMIC_PLAN,sql_text from v$SQL WHERE SQL_TEXT like '%MALCEAN%' and sql_text not like '%like%'; SQL_ID IS -------------------------- -- SQL_TEXT -------------------------------------------------------------------------------- 6ydj1bn1bng17 Y select /*MALCEAN*/ product_name from oe.order_items o, oe.product_information p where o.unit_price=15 and quantity>1 and p.product_id=o.product_id ???? explain plan for ????default plan, ??????optimizer???final plan,??V$SQL.IS_RESOLVED_DYNAMIC_PLAN???Y,????????????? DBA_SQL_PLAN_DIRECTIVES?????????????SQL PLAN DIRECTIVES, ???12c? ???MMON?????DML ???column usage??????????,????SMON??? MMON????SGA??PLAN DIRECTIVES??? ?????DBMS_SPD.flush_sql_plan_directive???? select directive_id,type,reason from DBA_SQL_PLAN_DIRECTIVES / DIRECTIVE_ID TYPE REASON ----------------------------------- -------------------------------- ----------------------------- 10321283028317893030 DYNAMIC_SAMPLING JOIN CARDINALITY MISESTIMATE 4757086536465754886 DYNAMIC_SAMPLING JOIN CARDINALITY MISESTIMATE 16085268038103121260 DYNAMIC_SAMPLING JOIN CARDINALITY MISESTIMATE SQL> set pages 9999 SQL> set lines 300 SQL> col state format a5 SQL> col subobject_name format a11 SQL> col col_name format a11 SQL> col object_name format a13 SQL> select d.directive_id, o.object_type, o.object_name, o.subobject_name col_name, d.type, d.state, d.reason 2 from dba_sql_plan_directives d, dba_sql_plan_dir_objects o 3 where d.DIRECTIVE_ID=o.DIRECTIVE_ID 4 and o.object_name in ('ORDER_ITEMS') 5 order by d.directive_id; DIRECTIVE_ID OBJECT_TYPE OBJECT_NAME COL_NAME TYPE STATE REASON ------------ ------------ ------------- ----------- -------------------------------- ----- ------------------------------------- --- 1.8156E+19 COLUMN ORDER_ITEMS UNIT_PRICE DYNAMIC_SAMPLING NEW SINGLE TABLE CARDINALITY MISESTIMATE 1.8156E+19 TABLE ORDER_ITEMS DYNAMIC_SAMPLING NEW SINGLE TABLE CARDINALITY MISESTIMATE 1.8156E+19 COLUMN ORDER_ITEMS QUANTITY DYNAMIC_SAMPLING NEW SINGLE TABLE CARDINALITY MISESTIMATE DBA_SQL_PLAN_DIRECTIVES????? _BASE_OPT_DIRECTIVE ? _BASE_OPT_FINDING SELECT d.dir_own#, d.dir_id, d.f_id, decode(type, 1, 'DYNAMIC_SAMPLING', 'UNKNOWN'), decode(state, 1, 'NEW', 2, 'MISSING_STATS', 3, 'HAS_STATS', 4, 'CANDIDATE', 5, 'PERMANENT', 6, 'DISABLED', 'UNKNOWN'), decode(bitand(flags, 1), 1, 'YES', 'NO'), cast(d.created as timestamp), cast(d.last_modified as timestamp), -- Please see QOSD_DAYS_TO_UPDATE and QOSD_PLUS_SECONDS for more details -- about 6.5 cast(d.last_used as timestamp) - NUMTODSINTERVAL(6.5, 'day') FROM sys.opt_directive$ d ??dbms_spd??? SQL PLAN DIRECTIVES, SQL PLAN DIRECTIVES???retention ???53?: Package: DBMS_SPD This package provides subprograms for managing Sql Plan Directives(SPD). SPD are objects generated automatically by Oracle server. For example, if server detects that the single table cardinality estimated by optimizer is off from the actual number of rows returned when accessing the table, it will automatically create a directive to do dynamic sampling for the table. When any Sql statement referencing the table is compiled, optimizer will perform dynamic sampling for the table to get more accurate estimate. Notes: DBMSL_SPD is a invoker-rights package. The invoker requires ADMINISTER SQL MANAGEMENT OBJECT privilege for executing most of the subprograms of this package. Also the subprograms commit the current transaction (if any), perform the operation and commit it again. DBA view dba_sql_plan_directives shows all the directives created in the system and the view dba_sql_plan_dir_objects displays the objects that are included in the directives. -- Default value for SPD_RETENTION_WEEKS SPD_RETENTION_WEEKS_DEFAULT CONSTANT varchar2(4) := '53'; | STATE : NEW : Newly created directive. | : MISSING_STATS : The directive objects do not | have relevant stats. | : HAS_STATS : The objects have stats. | : PERMANENT : A permanent directive. Server | evaluated effectiveness and these | directives are useful. | | AUTO_DROP : YES : Directive will be dropped | automatically if not | used for SPD_RETENTION_WEEKS. | This is the default behavior. | NO : Directive will not be dropped | automatically. Procedure: flush_sql_plan_directive This procedure allows manually flushing the Sql Plan directives that are automatically recorded in SGA memory while executing sql statements. The information recorded in SGA are periodically flushed by oracle background processes. This procedure just provides a way to flush the information manually. ????”_optimizer_dynamic_plans”(enable dynamic plans)????????,???TRUE??DYNAMIC PLAN? ???FALSE???????????? ????,Dynamic Plan????????????Nested Loop?Hash Join???case ,????????Nested loop???????????HASH JOIN,?HASH JOIN????????????????? ????????subplan?????,???? pass?? ?join method???,?????STATISTICS COLLECTOR???cardinality?,???????HASH JOIN?????Nested Loop,????????????subplan?????access path; ???????Sales??????????????????,????HASH JOIN,??SUBPLAN??customers?????????;?????Nested Loop,???????cust_id?????Range Scan+Access by Rowid? Cardinality feedback Cardinality feedback????????11.2????,????????re-optimization???;  ???????????,Cardinality feedback?????????????????????????? ???????????????????,?????????????????,??????????Cardinality feedback????????????? ????????????????????????? ??????????????Cardinality feedback ??: ????????,???????????,??????????,????????????????selectivity ??? ????????????: ??????,?????????????????????????????????,??????????????????? ????????????????????????????????????????,?????????????????????????? ?????????,???????????????,?????????? ??????????Cardinality ????,??????join Cardinality ????????? Cardinality feedback???????cursor?,?Cursor???aged out????? SELECT /*+ gather_plan_statistics */ product_name FROM order_items o, product_information p WHERE o.unit_price = 15 AND quantity > 1 AND p.product_id = o.product_id Plan hash value: 1553478007 ---------------------------------------------------------------------------------------------------------------------------------------- | Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | Reads | OMem | 1Mem | Used-Mem | ---------------------------------------------------------------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 1 | | 13 |00:00:00.01 | 24 | 20 | | | | |* 1 | HASH JOIN | | 1 | 4 | 13 |00:00:00.01 | 24 | 20 | 2061K| 2061K| 429K (0)| |* 2 | TABLE ACCESS FULL| ORDER_ITEMS | 1 | 4 | 13 |00:00:00.01 | 7 | 6 | | | | | 3 | TABLE ACCESS FULL| PRODUCT_INFORMATION | 1 | 1 | 288 |00:00:00.01 | 17 | 14 | | | | ---------------------------------------------------------------------------------------------------------------------------------------- SELECT /*+ gather_plan_statistics */ product_name FROM order_items o, product_information p WHERE o.unit_price = 15 AND quantity > 1 AND p.product_id = o.product_id Plan hash value: 1553478007 ------------------------------------------------------------------------------------------------------------------------------- | Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | OMem | 1Mem | Used-Mem | ------------------------------------------------------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 1 | | 13 |00:00:00.01 | 24 | | | | |* 1 | HASH JOIN | | 1 | 13 | 13 |00:00:00.01 | 24 | 2061K| 2061K| 413K (0)| |* 2 | TABLE ACCESS FULL| ORDER_ITEMS | 1 | 13 | 13 |00:00:00.01 | 7 | | | | | 3 | TABLE ACCESS FULL| PRODUCT_INFORMATION | 1 | 288 | 288 |00:00:00.01 | 17 | | | | ------------------------------------------------------------------------------------------------------------------------------- Note ----- - statistics feedback used for this statement SQL> select count(*) from v$SQL where SQL_ID='cz0hg2zkvd10y'; COUNT(*) ---------- 2 SQL>select sql_ID,USE_FEEDBACK_STATS FROM V$SQL_SHARED_CURSOR where USE_FEEDBACK_STATS ='Y'; SQL_ID U ------------- - cz0hg2zkvd10y Y ????????Cardinality feedback????,???????????????????????????,????????????order_items???????? ????2??????plan hash value??(??????????),?????2????child cursor??????gather_plan_statistics???actual : A-ROWS  estimate :E-ROWS????????? Automatic Re-optimization ???dynamic plan, Re-optimization???????????????  ?  ??????????????? ????????????????????????????????  ???????????,??????????????, ???????????????????? ???????????  Re-optimization??, ????????????????????? Re-optimization????dynamic plan??????????  dynamic plan????????????????????, ???????????????????? ????,??????????join order ??????????????,?????????????join order????? ??????,????????Re-optimization, ??Re-optimization ??????????????????? ?Oracle database 12c?,join statistics?????????????????????,??????????????????????Re-optimization???????????adaptive cursor sharing????? ????????????????,???????????? ????? ???????statistics collectors ????????????????????Re-optimization??????2?????????????,???????????????? ??????????????Re-optimization?????,?????????????????????? ???v$SQL??????IS_REOPTIMIZABLE?????????????????????Re-optimization,??????????Re-optimization???,?????Re-optimization ,???????reporting????? IS_REOPTIMIZABLE VARCHAR2(1) This columns shows whether the next execution matching this child cursor will trigger a reoptimization. The values are:   Y: If the next execution will trigger a reoptimization R: If the child cursor contains reoptimization information, but will not trigger reoptimization because the cursor was compiled in reporting mode N: If the child cursor has no reoptimization information ??1: select plan_table_output from table (dbms_xplan.display_cursor('gwf99gfnm0t7g',NULL,'ALLSTATS LAST')); SQL_ID  gwf99gfnm0t7g, child number 0 ------------------------------------- SELECT /*+ SFTEST gather_plan_statistics */ o.order_id, v.product_name FROM  orders o,   ( SELECT order_id, product_name FROM order_items o, product_information p     WHERE  p.product_id = o.product_id AND list_price < 50 AND min_price < 40  ) v WHERE o.order_id = v.order_id Plan hash value: 1906736282 ------------------------------------------------------------------------------------------------------------------------------------------- | Id  | Operation             | Name                | Starts | E-Rows | A-Rows |   A-Time   | Buffers | Reads  |  OMem |  1Mem | Used-Mem | ------------------------------------------------------------------------------------------------------------------------------------------- |   0 | SELECT STATEMENT      |                     |      1 |        |    269 |00:00:00.02 |    1336 |     18 |       |       |          | |   1 |  NESTED LOOPS         |                     |      1 |      1 |    269 |00:00:00.02 |    1336 |     18 |       |       |          | |   2 |   MERGE JOIN CARTESIAN|                     |      1 |      4 |   9135 |00:00:00.02 |      34 |     15 |       |       |          | |*  3 |    TABLE ACCESS FULL  | PRODUCT_INFORMATION |      1 |      1 |     87 |00:00:00.01 |      33 |     14 |       |       |          | |   4 |    BUFFER SORT        |                     |     87 |    105 |   9135 |00:00:00.01 |       1 |      1 |  4096 |  4096 | 4096  (0)| |   5 |     INDEX FULL SCAN   | ORDER_PK            |      1 |    105 |    105 |00:00:00.01 |       1 |      1 |       |       |          | |*  6 |   INDEX UNIQUE SCAN   | ORDER_ITEMS_UK      |   9135 |      1 |    269 |00:00:00.01 |    1302 |      3 |       |       |          | ------------------------------------------------------------------------------------------------------------------------------------------- Predicate Information (identified by operation id): ---------------------------------------------------    3 - filter(("MIN_PRICE"<40 AND "LIST_PRICE"<50))    6 - access("O"."ORDER_ID"="ORDER_ID" AND "P"."PRODUCT_ID"="O"."PRODUCT_ID") SQL_ID  gwf99gfnm0t7g, child number 1 ------------------------------------- SELECT /*+ SFTEST gather_plan_statistics */ o.order_id, v.product_name FROM  orders o,   ( SELECT order_id, product_name FROM order_items o, product_information p     WHERE  p.product_id = o.product_id AND list_price < 50 AND min_price < 40  ) v WHERE o.order_id = v.order_id Plan hash value: 35479787 -------------------------------------------------------------------------------------------------------------------------------------------- | Id  | Operation              | Name                | Starts | E-Rows | A-Rows |   A-Time   | Buffers | Reads  |  OMem |  1Mem | Used-Mem | -------------------------------------------------------------------------------------------------------------------------------------------- |   0 | SELECT STATEMENT       |                     |      1 |        |    269 |00:00:00.01 |      63 |      3 |       |       |          | |   1 |  NESTED LOOPS          |                     |      1 |    269 |    269 |00:00:00.01 |      63 |      3 |       |       |          | |*  2 |   HASH JOIN            |                     |      1 |    313 |    269 |00:00:00.01 |      42 |      3 |  1321K|  1321K| 1234K (0)| |*  3 |    TABLE ACCESS FULL   | PRODUCT_INFORMATION |      1 |     87 |     87 |00:00:00.01 |      16 |      0 |       |       |          | |   4 |    INDEX FAST FULL SCAN| ORDER_ITEMS_UK      |      1 |    665 |    665 |00:00:00.01 |      26 |      3 |       |       |          | |*  5 |   INDEX UNIQUE SCAN    | ORDER_PK            |    269 |      1 |    269 |00:00:00.01 |      21 |      0 |       |       |          | -------------------------------------------------------------------------------------------------------------------------------------------- Predicate Information (identified by operation id): ---------------------------------------------------    2 - access("P"."PRODUCT_ID"="O"."PRODUCT_ID")    3 - filter(("MIN_PRICE"<40 AND "LIST_PRICE"<50))    5 - access("O"."ORDER_ID"="ORDER_ID") Note -----    - statistics feedback used for this statement    SQL> select IS_REOPTIMIZABLE,child_number FROM V$SQL  A where A.SQL_ID='gwf99gfnm0t7g'; IS CHILD_NUMBER -- ------------ Y             0 N             1    1* select child_number,other_xml From v$SQL_PLAN  where SQL_ID='gwf99gfnm0t7g' and other_xml is not nul SQL> / CHILD_NUMBER OTHER_XML ------------ --------------------------------------------------------------------------------            1 <other_xml><info type="cardinality_feedback">yes</info><info type="db_version">1              2.1.0.1</info><info type="parse_schema"><![CDATA["OE"]]></info><info type="plan_              hash">35479787</info><info type="plan_hash_2">3382491761</info><outline_data><hi              nt><![CDATA[IGNORE_OPTIM_EMBEDDED_HINTS]]></hint><hint><![CDATA[OPTIMIZER_FEATUR              ES_ENABLE('12.1.0.1')]]></hint><hint><![CDATA[DB_VERSION('12.1.0.1')]]></hint><h              int><![CDATA[ALL_ROWS]]></hint><hint><![CDATA[OUTLINE_LEAF(@"SEL$F5BB74E1")]]></              hint><hint><![CDATA[MERGE(@"SEL$2")]]></hint><hint><![CDATA[OUTLINE(@"SEL$1")]]>              </hint><hint><![CDATA[OUTLINE(@"SEL$2")]]></hint><hint><![CDATA[FULL(@"SEL$F5BB7              4E1" "P"@"SEL$2")]]></hint><hint><![CDATA[INDEX_FFS(@"SEL$F5BB74E1" "O"@"SEL$2"              ("ORDER_ITEMS"."ORDER_ID" "ORDER_ITEMS"."PRODUCT_ID"))]]></hint><hint><![CDATA[I              NDEX(@"SEL$F5BB74E1" "O"@"SEL$1" ("ORDERS"."ORDER_ID"))]]></hint><hint><![CDATA[              LEADING(@"SEL$F5BB74E1" "P"@"SEL$2" "O"@"SEL$2" "O"@"SEL$1")]]></hint><hint><![C              DATA[USE_HASH(@"SEL$F5BB74E1" "O"@"SEL$2")]]></hint><hint><![CDATA[USE_NL(@"SEL$              F5BB74E1" "O"@"SEL$1")]]></hint></outline_data></other_xml>            0 <other_xml><info type="db_version">12.1.0.1</info><info type="parse_schema"><![C              DATA["OE"]]></info><info type="plan_hash">1906736282</info><info type="plan_hash              _2">2579473118</info><outline_data><hint><![CDATA[IGNORE_OPTIM_EMBEDDED_HINTS]]>              </hint><hint><![CDATA[OPTIMIZER_FEATURES_ENABLE('12.1.0.1')]]></hint><hint><![CD              ATA[DB_VERSION('12.1.0.1')]]></hint><hint><![CDATA[ALL_ROWS]]></hint><hint><![CD              ATA[OUTLINE_LEAF(@"SEL$F5BB74E1")]]></hint><hint><![CDATA[MERGE(@"SEL$2")]]></hi              nt><hint><![CDATA[OUTLINE(@"SEL$1")]]></hint><hint><![CDATA[OUTLINE(@"SEL$2")]]>              </hint><hint><![CDATA[FULL(@"SEL$F5BB74E1" "P"@"SEL$2")]]></hint><hint><![CDATA[              INDEX(@"SEL$F5BB74E1" "O"@"SEL$1" ("ORDERS"."ORDER_ID"))]]></hint><hint><![CDATA              [INDEX(@"SEL$F5BB74E1" "O"@"SEL$2" ("ORDER_ITEMS"."ORDER_ID" "ORDER_ITEMS"."PROD              UCT_ID"))]]></hint><hint><![CDATA[LEADING(@"SEL$F5BB74E1" "P"@"SEL$2" "O"@"SEL$1              " "O"@"SEL$2")]]></hint><hint><![CDATA[USE_MERGE_CARTESIAN(@"SEL$F5BB74E1" "O"@"              SEL$1")]]></hint><hint><![CDATA[USE_NL(@"SEL$F5BB74E1" "O"@"SEL$2")]]></hint></o              utline_data></other_xml> ??2: SELECT /*+gather_plan_statistics*/ * FROM customers WHERE cust_state_province='CA' AND country_id='US'; SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR(FORMAT=>'ALLSTATS LAST')); PLAN_TABLE_OUTPUT ------------------------------------- SQL_ID b74nw722wjvy3, child number 0 ------------------------------------- select /*+gather_plan_statistics*/ * from customers where CUST_STATE_PROVINCE='CA' and country_id='US' Plan hash value: 1683234692 -------------------------------------------------------------------------------------------------- | Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | Reads | -------------------------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 1 | | 29 |00:00:00.01 | 17 | 14 | |* 1 | TABLE ACCESS FULL| CUSTOMERS | 1 | 8 | 29 |00:00:00.01 | 17 | 14 | -------------------------------------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 1 - filter(("CUST_STATE_PROVINCE"='CA' AND "COUNTRY_ID"='US')) SELECT SQL_ID, CHILD_NUMBER, SQL_TEXT, IS_REOPTIMIZABLE FROM V$SQL WHERE SQL_TEXT LIKE 'SELECT /*+gather_plan_statistics*/%'; SQL_ID CHILD_NUMBER SQL_TEXT I ------------- ------------ ----------- - b74nw722wjvy3 0 select /*+g Y ather_plan_ statistics* / * from cu stomers whe re CUST_STA TE_PROVINCE ='CA' and c ountry_id=' US' EXEC DBMS_SPD.FLUSH_SQL_PLAN_DIRECTIVE; SELECT TO_CHAR(d.DIRECTIVE_ID) dir_id, o.OWNER, o.OBJECT_NAME, o.SUBOBJECT_NAME col_name, o.OBJECT_TYPE, d.TYPE, d.STATE, d.REASON FROM DBA_SQL_PLAN_DIRECTIVES d, DBA_SQL_PLAN_DIR_OBJECTS o WHERE d.DIRECTIVE_ID=o.DIRECTIVE_ID AND o.OWNER IN ('SH') ORDER BY 1,2,3,4,5; DIR_ID OWNER OBJECT_NAME COL_NAME OBJECT TYPE STATE REASON ----------------------- ----- ------------- ----------- ------ ---------------- ----- ------------------------ 1484026771529551585 SH CUSTOMERS COUNTRY_ID COLUMN DYNAMIC_SAMPLING NEW SINGLE TABLE CARDINALITY MISESTIMATE 1484026771529551585 SH CUSTOMERS CUST_STATE_ COLUMN DYNAMIC_SAMPLING NEW SINGLE TABLE CARDINALITY PROVINCE MISESTIMATE 1484026771529551585 SH CUSTOMERS TABLE DYNAMIC_SAMPLING NEW SINGLE TABLE CARDINALITY MISESTIMATE SELECT /*+gather_plan_statistics*/ * FROM customers WHERE cust_state_province='CA' AND country_id='US'; ELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR(FORMAT=>'ALLSTATS LAST')); PLAN_TABLE_OUTPUT ------------------------------------- SQL_ID b74nw722wjvy3, child number 1 ------------------------------------- select /*+gather_plan_statistics*/ * from customers where CUST_STATE_PROVINCE='CA' and country_id='US' Plan hash value: 1683234692 ----------------------------------------------------------------------------------------- | Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | ----------------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 1 | | 29 |00:00:00.01 | 17 | |* 1 | TABLE ACCESS FULL| CUSTOMERS | 1 | 29 | 29 |00:00:00.01 | 17 | ----------------------------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 1 - filter(("CUST_STATE_PROVINCE"='CA' AND "COUNTRY_ID"='US')) Note ----- - cardinality feedback used for this statement SELECT SQL_ID, CHILD_NUMBER, SQL_TEXT, IS_REOPTIMIZABLE FROM V$SQL WHERE SQL_TEXT LIKE 'SELECT /*+gather_plan_statistics*/%'; SQL_ID CHILD_NUMBER SQL_TEXT I ------------- ------------ ----------- - b74nw722wjvy3 0 select /*+g Y ather_plan_ statistics* / * from cu stomers whe re CUST_STA TE_PROVINCE ='CA' and c ountry_id=' US' b74nw722wjvy3 1 select /*+g N ather_plan_ statistics* / * from cu stomers whe re CUST_STA TE_PROVINCE ='CA' and c ountry_id=' US' SELECT /*+gather_plan_statistics*/ CUST_EMAIL FROM CUSTOMERS WHERE CUST_STATE_PROVINCE='MA' AND COUNTRY_ID='US'; SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR(FORMAT=>'ALLSTATS LAST')); PLAN_TABLE_OUTPUT ------------------------------------- SQL_ID 3tk6hj3nkcs2u, child number 0 ------------------------------------- Select /*+gather_plan_statistics*/ cust_email From customers Where cust_state_province='MA' And country_id='US' Plan hash value: 1683234692 ------------------------------------------------------------------------------- |Id | Operation | Name | Starts|E-Rows|A-Rows| A-Time |Buffers| ------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 1 | | 2 |00:00:00.01| 16 | |*1 | TABLE ACCESS FULL| CUSTOMERS | 1 | 2| 2 |00:00:00.01| 16 | ----------------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 1 - filter(("CUST_STATE_PROVINCE"='MA' AND "COUNTRY_ID"='US')) Note ----- - dynamic sampling used for this statement (level=2) - 1 Sql Plan Directive used for this statement EXEC DBMS_SPD.FLUSH_SQL_PLAN_DIRECTIVE; SELECT TO_CHAR(d.DIRECTIVE_ID) dir_id, o.OWNER, o.OBJECT_NAME, o.SUBOBJECT_NAME col_name, o.OBJECT_TYPE, d.TYPE, d.STATE, d.REASON FROM DBA_SQL_PLAN_DIRECTIVES d, DBA_SQL_PLAN_DIR_OBJECTS o WHERE d.DIRECTIVE_ID=o.DIRECTIVE_ID AND o.OWNER IN ('SH') ORDER BY 1,2,3,4,5; DIR_ID OW OBJECT_NA COL_NAME OBJECT TYPE STATE REASON ------------------- -- --------- ---------- ------- --------------- ------------- ------------------------ 1484026771529551585 SH CUSTOMERS COUNTRY_ID COLUMN DYNAMIC_SAMPLING MISSING_STATS SINGLE TABLE CARDINALITY MISESTIMATE 1484026771529551585 SH CUSTOMERS CUST_STATE_ COLUMN DYNAMIC_SAMPLING MISSING_STATS SINGLE TABLE CARDINALITY PROVINCE MISESTIMATE 1484026771529551585 SH CUSTOMERS TABLE DYNAMIC_SAMPLING MISSING_STATS SINGLE TABLE CARDINALITY MISESTIMATE

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• Can anyone give me a sample DSP script in C/C++

  - by Andrew

  Im working on a (Audio) DSP project and just wondering if there are any sample (Open source) DSP example that are written in c or c++, for my MSP430 Chip. I just want something as a guideline so i can program my own script using the ACD and DCA on my board for sampling. http://focus.ti.com/docs/toolsw/folders/print/msp-exp430f5438.html Thats my board, MSP430F5438 Experimenter Board, from what i herd it can run dsp script via the USB connection with the computer. Im using CCS ( From TI, code composer studio) and Octave/Matlab. Just any DSP example scripts or sites that will help me create my own would be appreciated. What im tying to do, Partial audio (sampled) track -- Nyquist rate sampling -- over- and undersampling -- reconstruction of the audio track.

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• 4th Annual Hartford Code Camp - The Code Camp Manifesto lives on!

  - by SB Chatterjee

  It is amazing that Thom Robbins' blog posting back in December 2004 laid the foundation of the Code Camps that have grown world-wide - there is at least one every week-end in some country (unscientific tweets stats sampling). This week end, we at the Connecticut .NET Developers Group had the 4th Annual Hartford Code Camp and it was well attended with 120+ attendees with ~30 sessions. Our thanks to the Speakers from near and far who made our event a success.

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• Taking fixed direction on hemisphere and project to normal (openGL)

  - by Maik Xhani

  I am trying to perform sampling using hemisphere around a surface normal. I want to experiment with fixed directions (and maybe jitter slightly between frames). So I have those directions: vec3 sampleDirections[6] = {vec3(0.0f, 1.0f, 0.0f), vec3(0.0f, 0.5f, 0.866025f), vec3(0.823639f, 0.5f, 0.267617f), vec3(0.509037f, 0.5f, -0.700629f), vec3(-0.509037f, 0.5f, -0.700629), vec3(-0.823639f, 0.5f, 0.267617f)}; now I want the first direction to be projected on the normal and the others accordingly. I tried these 2 codes, both failing. This is what I used for random sampling (it doesn't seem to work well, the samples seem to be biased towards a certain direction) and I just used one of the fixed directions instead of s (here is the code of the random sample, when i used it with the fixed direction i didn't use theta and phi). vec3 CosWeightedRandomHemisphereDirection( vec3 n, float rand1, float rand2 ) float theta = acos(sqrt(1.0f-rand1)); float phi = 6.283185f * rand2; vec3 s = vec3(sin(theta) * cos(phi), sin(theta) * sin(phi), cos(theta)); vec3 v = normalize(cross(n,vec3(0.0072, 1.0, 0.0034))); vec3 u = cross(v, n); u = s.x*u; v = s.y*v; vec3 w = s.z*n; vec3 direction = u+v+w; return normalize(direction); } ** EDIT ** This is the new code vec3 FixedHemisphereDirection( vec3 n, vec3 sampleDir) { vec3 x; vec3 z; if(abs(n.x) < abs(n.y)){ if(abs(n.x) < abs(n.z)){ x = vec3(1.0f,0.0f,0.0f); }else{ x = vec3(0.0f,0.0f,1.0f); } }else{ if(abs(n.y) < abs(n.z)){ x = vec3(0.0f,1.0f,0.0f); }else{ x = vec3(0.0f,0.0f,1.0f); } } z = normalize(cross(x,n)); x = cross(n,z); mat3 M = mat3( x.x, n.x, z.x, x.y, n.y, z.y, x.z, n.z, z.z); return M*sampleDir; } So if my n = (0,0,1); and my sampleDir = (0,1,0); shouldn't the M*sampleDir be (0,0,1)? Cause that is what I was expecting.

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