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• Logarithmic spacing of FFT bins

  - by Mykel Stone

  I'm trying to do the examples within the GameDev.net Beat Detection article ( http://archive.gamedev.net/archive/reference/programming/features/beatdetection/index.html ) I have no issue with performing a FFT and getting the frequency data and doing most of the article. I'm running into trouble though in the section 2.B, Enhancements and beat decision factors. in this section the author gives 3 equations numbered R10-R12 to be used to determine how many bins go into each subband: R10 - Linear increase of the width of the subband with its index R11 - We can choose for example the width of the first subband R12 - The sum of all the widths must not exceed 1024 He says the following in the article: "Once you have equations (R11) and (R12) it is fairly easy to extract 'a' and 'b', and thus to find the law of the 'wi'. This calculus of 'a' and 'b' must be made manually and 'a' and 'b' defined as constants in the source; indeed they do not vary during the song." However, I cannot seem to understand how these values are calculated...I'm probably missing something simple, but learning fourier analysis in a couple of weeks has left me Decimated-in-Mind and I cannot seem to see it.

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• Packing differently sized chunks of data into multiple bins

  - by knizz

  EDIT: It seems like this problem is called "Cutting stock problem" I need an algorithm that gives me the (space-)optimal arrangement of chunks in bins. One way would be put the bigger chunks in first. But see how that algorithm fails in this example: Chunks Bins ----------------------------- AAA BBB CC DD ( ) ( ) Algorithm Result ----------------------------- biggest first (AAABBB ) (CC ) optimal (AAACCDD) (BBB) "Biggest first" can't fit in DD. Maybe it helps to build a table like this: Size 1: --- Size 2: CC, DD Size 3: AAA, BBB Size 4: CCDD Size 5: AAACC, AAADD, BBBCC, BBBDD Size 6: AAABBB Size 7: AAACCDD, BBBCCDD Size 8: AAABBBCC, AAABBBDD Size 10: AAABBBCCDD

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• how to make a frequency function in matlab like the excel frequency function?

  - by Ben Fossen

  I have a list that is sorted and I want to know how many values are in each bin? I made bins using linspace(floor(fist_element_list), ceil(last_element_list), num_bins) Is there a built in function or an easy way to do this in Matlab? All I can think of is doing it manually. I would like a frequency function like the one in excel.

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• passing back answers in prolog

  - by AhmadAssaf

  i have this code than runs perfectly .. returns a true .. when tracing the values are ok .. but its not returning back the answer .. it acts strangely when it ends and always return empty list .. uninstantiated variable .. test :- extend(4,12,[4,3,1,2],[[1,5],[3,4],[6]],_ExtendedBins). %printing basic information about the extend(NumBins,Capacity,RemainingNumbers,BinsSoFar,_ExtendedBins) :- getNumberofBins(BinsSoFar,NumberOfBins), msort(RemainingNumbers,SortedRemaining),nl, format("Current Number of Bins is :~w\n",[NumberOfBins]), format("Allowed Capacity is :~w\n",[Capacity]), format("maximum limit in bin is :~w\n",[NumBins]), format("Trying to fit :~w\n\n",[SortedRemaining]), format("Possible Solutions :\n\n"), fitElements(NumBins,NumberOfBins, Capacity,SortedRemaining,BinsSoFar,[]). %this is were the creation for possibilities will start %will check first if the number of bins allowed is less than then %we create a new list with all the possible combinations %after that we start matching to other bins with capacity constraint fitElements(NumBins,NumberOfBins, Capacity,RemainingNumbers,Bins,ExtendedBins) :- ( NumberOfBins < NumBins -> print('Creating new set: '); print('Sorry, Cannot create New Sets')), createNewList(Capacity,RemainingNumbers,Bins,ExtendedBins). createNewList(Capacity,RemainingNumbers,Bins,ExtendedBins) :- createNewList(Capacity,RemainingNumbers,Bins,[],ExtendedBins), print(ExtendedBins). createNewList(0,Bins,Bins,ExtendedBins,ExtendedBins). createNewList(_,[],_,ExtendedBins,ExtendedBins). createNewList(Capacity,[Element|Rest],Bins,Temp,ExtendedBins) :- conjunct_to_list(Element,ListedElement), append(ListedElement,Temp,NewList), sumlist(NewList,Sum), (Sum =< Capacity, append(ListedElement,ExtendedBins,Result); Capacity = 0), createNewList(Capacity,Rest,Bins,NewList,Result). fit(0,[],ExtendedBins,ExtendedBins). fit(Capacity,[Element|Rest],Bin,ExtendedBins) :- conjunct_to_list(Element,Listed), append(Listed,Bin,NewBin), sumlist(NewBin,Sum), (Sum =< Capacity -> fit(Capacity,Rest,NewBin,ExtendedBins); Capacity = 0, append(NewBin,ExtendedBins,NewExtendedBins), print(NewExtendedBins), fit(0,[],NewBin,ExtendedBins)). %get the number of bins provided getNumberofBins(List,NumberOfBins) :- getNumberofBins(List,0,NumberOfBins). getNumberofBins([],NumberOfBins,NumberOfBins). getNumberofBins([_List|Rest],TempCount,NumberOfBins) :- NewCount is TempCount + 1, %calculate the count getNumberofBins(Rest,NewCount,NumberOfBins). %recursive call %Convert set of terms into a list - used when needed to append conjunct_to_list((A,B), L) :- !, conjunct_to_list(A, L0), conjunct_to_list(B, L1), append(L0, L1, L). conjunct_to_list(A, [A]). Greatly appreciate the help

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• sell ccv good and fresh sell cvv all country fullz info

  - by underworld

  ICQ: 640240418 YH: underworld_cvv Mail: [email protected] WELCOME TO MY UNDERWORLD ! I'm is Professional seller,more than 5 years experience,i started work in 2008,i have sold cvv credit card to many customers all over the world. Selling cvv, fullz many country as: Canada,USA,Australia,UK...all And many country in Europe: Fr,Ger,Spain,Ita... I hope we will work together for a long time. Always sell cvv quality with high balance. I have a website but if you want buy cvv good price please contact me. Have Cvv with Bin or Cvv with DOB,VBV if customer claim. List Price Some Cvv (good price for good buyer) -Us: 5$ /1 -Us VBV-DOB : 8$ /1 -Us fullz : 40$ /1 -Us (amex,discover) : 8$ /1 -Ca : 10$ /1 -Ca DOB : 20$ /1 -Ca fullz : 50$ /1 -Ca with bin : 15$ /1 -Au : 10$ /1 -Au DOB : 20$ /1 -Uk : 10$ /1 -Uk DOB-VBV : 20$ /1 -Fr : 15$ /1 -Fr DOB-VBV : 25$ /1 -Ger : 18$ /1 -Ger with DOB : 25$ /1 -Spain : 15$ /1 -Spain Fullz : 40$ /1 -Ita : 15$ /1 -Ita with DOB : 25$ /1 -Japan : 15$ /1 -Japan with DOB : 25$ /1 Cvv random country -Denmark : 25$ /1 -Sweden : 20$ /1 -Switzerland : 20$ /1 -Slovakia : 20$ /1 -Netherlands : 18$ /1 -Mexico : 15 /1 -Middle East : 18$ /1 -New zeland : 18$ /1 -Asia : 15$ /1 -Ireland : 18$ /1 -Belgium : 15$ /1 -Taiwan : 15$ /1 -UAE : 20$ /1 And many country... Some Bins -Us bins: 517805,488893,492536,408181,542432,482880,374355,374372... -Ca bins: 450003,450008,451242,450060,549198,533833,519123,544612... -Uk bins: 4547,5506,5569,5404,5031,4921,5505,5506,4921,4550... -Ger bins: 492942,490762,530127... -Au bins: 543568,450605,494053,450606,456475,521893,519163... -Fr bins: 497847,497831,497841,497849,497820,497825,497833... -And others bins for others country... Format France fullz Nom : di murro Prenom : mariano Adresse : rue des caillettes Ville : Corbeil Essonnes Code Postale : 91100 Telephone : 33672492372 ========== (2eme Tape) ========== Nom de Bank : crédit agricole Nom de Carte Bancaire : di murro mariano Date de naissance : 12 / 02 / 1969 Type de carte : MasterCard Numero de carte : 5131018223855xxx Numero de compte : Date d'expiration : 10 / 2014 CVN : 336 -WARRANTY time is 12 HOURS. Any cvv purchase over 12 hours can not warranty. -If you buy over 30 cvvs, i will sell for you best price. -I will discount for you if you are reseller or you order everyday many on the next day. -I only accept payment money by PerfectMoney (PM) Western Union (WU) and MoneyGram. -I will prove to you that I am the best sellers. And make sure you will enjoy doing business with me. Contact: ICQ: 640240418 YH: underworld_cvv Mail: [email protected]

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• making binned boxplot in matplotlib with numpy and scipy in Python

  - by user248237

  I have a 2-d array containing pairs of values and I'd like to make a boxplot of the y-values by different bins of the x-values. I.e. if the array is: my_array = array([[1, 40.5], [4.5, 60], ...]]) then I'd like to bin my_array[:, 0] and then for each of the bins, produce a boxplot of the corresponding my_array[:, 1] values that fall into each box. So in the end I want the plot to contain number of bins-many box plots. I tried the following: min_x = min(my_array[:, 0]) max_x = max(my_array[:, 1]) num_bins = 3 bins = linspace(min_x, max_x, num_bins) elts_to_bins = digitize(my_array[:, 0], bins) However, this gives me values in elts_to_bins that range from 1 to 3. I thought I should get 0-based indices for the bins, and I only wanted 3 bins. I'm assuming this is due to some trickyness with how bins are represented in linspace vs. digitize. What is the easiest way to achieve this? I want num_bins-many equally spaced bins, with the first bin containing the lower half of the data and the upper bin containing the upper half... i.e., I want each data point to fall into some bin, so that I can make a boxplot. thanks.

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• How do you configure recycle bins on roaming profiles?

  - by Zombian

  I copied the following from a post on the Spiceworks forum which remained unanswered: Is there any way to place the Recycle Bin back on the desktop of a Roaming Profile with the Desktop being redirected? I have used Google and can't find a straight forward answer. I am asking for people with experience in this. This is for a Windows XP machine. I saw mention of needing to use a program such as Undelete but I'm hoping that is not the case. Further explanation: I use redirected folders and whenever a user deletes something from their desktop,my documents it doesn't show up in the recycle bin. It doesn't appear in the recycle bin on the server either. Where is this data? I doubt it is permanently deleted. Is there a way to change the recycle bin on the users' desktop to display those files? Thank you!

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• Is the Leptonica implementation of 'Modified Median Cut' not using the median at all?

  - by TheCodeJunkie

  I'm playing around a bit with image processing and decided to read up on how color quantization worked and after a bit of reading I found the Modified Median Cut Quantization algorithm. I've been reading the code of the C implementation in Leptonica library and came across something I thought was a bit odd. Now I want to stress that I am far from an expert in this area, not am I a math-head, so I am predicting that this all comes down to me not understanding all of it and not that the implementation of the algorithm is wrong at all. The algorithm states that the vbox should be split along the lagest axis and that it should be split using the following logic The largest axis is divided by locating the bin with the median pixel (by population), selecting the longer side, and dividing in the center of that side. We could have simply put the bin with the median pixel in the shorter side, but in the early stages of subdivision, this tends to put low density clusters (that are not considered in the subdivision) in the same vbox as part of a high density cluster that will outvote it in median vbox color, even with future median-based subdivisions. The algorithm used here is particularly important in early subdivisions, and 3is useful for giving visible but low population color clusters their own vbox. This has little effect on the subdivision of high density clusters, which ultimately will have roughly equal population in their vboxes. For the sake of the argument, let's assume that we have a vbox that we are in the process of splitting and that the red axis is the largest. In the Leptonica algorithm, on line 01297, the code appears to do the following Iterate over all the possible green and blue variations of the red color For each iteration it adds to the total number of pixels (population) it's found along the red axis For each red color it sum up the population of the current red and the previous ones, thus storing an accumulated value, for each red note: when I say 'red' I mean each point along the axis that is covered by the iteration, the actual color may not be red but contains a certain amount of red So for the sake of illustration, assume we have 9 "bins" along the red axis and that they have the following populations 4 8 20 16 1 9 12 8 8 After the iteration of all red bins, the partialsum array will contain the following count for the bins mentioned above 4 12 32 48 49 58 70 78 86 And total would have a value of 86 Once that's done it's time to perform the actual median cut and for the red axis this is performed on line 01346 It iterates over bins and check they accumulated sum. And here's the part that throws me of from the description of the algorithm. It looks for the first bin that has a value that is greater than total/2 Wouldn't total/2 mean that it is looking for a bin that has a value that is greater than the average value and not the median ? The median for the above bins would be 49 The use of 43 or 49 could potentially have a huge impact on how the boxes are split, even though the algorithm then proceeds by moving to the center of the larger side of where the matched value was.. Another thing that puzzles me a bit is that the paper specified that the bin with the median value should be located, but does not mention how to proceed if there are an even number of bins.. the median would be the result of (a+b)/2 and it's not guaranteed that any of the bins contains that population count. So this is what makes me thing that there are some approximations going on that are negligible because of how the split actually takes part at the center of the larger side of the selected bin. Sorry if it got a bit long winded, but I wanted to be as thoroughas I could because it's been driving me nuts for a couple of days now ;)

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• question about Doug Lea's malloc

  - by hatorade

  http://gee.cs.oswego.edu/dl/html/malloc.html in making his array for the segmented bins, he makes bins for 8 - 512 using multiples of 8 (so 63 bins, but only the last 62 are ever used). how does he determine the size for the remaining 63 bins? He mentions they are logarithmically spaced, but is there some sort of equation he used to do that optimally?

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• vectorized approach to binning with numpy/scipy in Python

  - by user248237

  I am binning a 2d array (x by y) in Python into the bins of its x value (given in "bins"), using np.digitize: elements_to_bins = digitize(vals, bins) where "vals" is a 2d array, i.e.: vals = array([[1, v1], [2, v2], ...]). elements_to_bins just says what bin each element falls into. What I then want to do is get a list whose length is the number of bins in "bins", and each element returns the y-dimension of "vals" that falls into that bin. I do it this way right now: points_by_bins = [] for curr_bin in range(min(elements_to_bins), max(elements_to_bins) + 1): curr_indx = where(elements_to_bins == curr_bin)[0] curr_bin_vals = vals[:, curr_indx] points_by_bins.append(curr_bin_vals) is there a more elegant/simpler way to do this? All I need is a list of of lists of the y-values that fall into each bin. thanks.

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• sell skimmed dump+pin([email protected])wu transfer,bank transfer,paypal+mailpass

  - by bestseller

  http://megareserve.blogspot.com///////// [email protected]////gmail:[email protected] Sell, Cvv, Bank Logins, Tracks, PayPal, Transfers, WU, Credit Cards, Card, Hacks, Citi, Boa, Visa, MasterCard, Amex, American Express, Make Money Fast, Stolen, Cc, C++, Adder, Western, Union, Banks, Of, America, Wellsfargo, Liberty, Reserve, Gram, Mg, LR, AlertPay ..PAYMENT METHOD LIBERTYRESERVE AND WESTERNUNION ONLY........ Ccv EU is $ 6 per ccv (Visa + Master) Ccv EU is $ 7 per ccv (Amex + Discover) Ccv Au is $ 6 per ccv Ccv Italy is 15 $ per cc sweden 12$ spain 10$ france 12$ Ccv US is $ 1.5 per ccv (Visa) Ccv US is $ 2 per ccv (master) Ccv US is $ 3 per ccv (Amex + Discover) Ccv UK is $ 5 per ccv (Visa + Master) Ccv UK is $ 6 per ccv (Amex + swith) Ccv Ca is $ 6 per ccv (Visa+ Master) Ccv Ca is $ 9 per ccv (Visa Business + Visa Gold) Ccv Germany is 14$ Per Ccv Ccv DOB with US is 15 $ per ccv Ccv DOB with UK is 19 $ per ccv Ccv DOB + BIN with UK 25$ per ccv Ccv US full info is 40 $ per ccv Ccv UK full info is 60 $ per ccv 1 Uk check bins= 12.5$/1cvv 1 Sock live = 1$/1sock live 5day yahoo:[email protected] gmail:[email protected] Balance In Chase:.........70K To 155K ========300$ Balance In Wachovia:.........24K To 80K==========180$ Balance In Boa..........75K To 450K==========400$ Balance In Credit Union:.........Any Amount:=========420$ Balance In Hallifax..........ANY AMOUNT=========420$ Balance In Compass..........ANY AMOUNT=========400$ Balance In Wellsfargo..........ANY AMOUNT=========400$ Balance In Barclays..........80K To 100K=========550$ Balance In Abbey:.........82K ==========650$ Balance in Hsbc:.........50K========650$ and more 1 Paypal with pass email = 50$/paypal 1 Paypal don't have pass email = 20$/Paypal 1 Banklogin us or uk (personel)= 550$ yahoo :[email protected] gmail :[email protected] Track 1/2 Visa Classic, MasterCard Standart US - 13$ UK - 17$ EU - 24$ AU - 26$ Track 1/2 Visa Gold | Platinum | Business | Signature, MasterCard Gold | Platinum US - 17$ UK - 20$ EU - 28$ AU - 30$ Bank transfer Balance 71.000$ CITIBANK SOUTH DAKOTA, N.A. Balance 65.000$ Wachovia: 76.000$ Abbey: 65.000£ Hsbc : 87.000$ Hallifax : 97.000£ Barclays: 110.000£ AHLI UNITED BANK --- 80.000£ LLOYDSTSB ---------- 100.000£ BANK OF SCOTLAND --- 123.000£ BOA ----------210.000$ UBS ---------- 152.000$ RBC BANK ------ 245.000$ BANK OF CANADA -------- 78.000$ BDC ---------- 281.000$ BANK LAURENTIENNE ----- 241.000$ please no test yahoo: [email protected] gmail: [email protected] website ; http://megareserve.blogspot.com Prices For Bin and Its List: 5434, 5419, 4670,374288,545140,454634,3791 with d.o.b,4049,4462,4921.4929.46274547,5506,5569,5404,5031,4921, 5505,5506,4921,4550 ,4552,4988,5186,4462,4543,4567 ,4539,5301,4929,5521 , 4291,5051,4975,5413 5255 4563,4547 4505,4563 5413 5255,5521,5506,4921,4929,54609 7,5609,54609,4543, 4975,5432,5187 ,4973,4627,4049,4779,426565,55 05, 5549, 5404, 5434, 5419, 4670,456730,541361, 451105,4670,5505, 5549, 5404, UK Nomall NO BINS(Serial) with DOB is :10$ UK with BINS(Serial) with DOB is :15$ UK Nomall no BINS(Serial) no DOB is: 6$ UK with BINS(Serial) is :12$ Please do not request : cc for TEST and FREE. DON'T CONTACT ME IF YOU NOT READY NEED TO BUY .

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• bin_at in dlmalloc

  - by chunhui

  In glibc malloc.c or dlmalloc It said "repositioning tricks"As in blew, and use this trick in bin_at. bins is a array,the space is allocated when av(struct malloc_state) is allocated.doesn't it? the sizeof(bin[i]) is less then sizeof(struct malloc_chunk*)? Who can describe this trick for me? I can't understand the bin_at macro.why they get the bins address use this method?how it works? Very thanks,and sorry for my poor English. /* To simplify use in double-linked lists, each bin header acts as a malloc_chunk. This avoids special-casing for headers. But to conserve space and improve locality, we allocate only the fd/bk pointers of bins, and then use repositioning tricks to treat these as the fields of a malloc_chunk*. */ typedef struct malloc_chunk* mbinptr; /* addressing -- note that bin_at(0) does not exist */ #define bin_at(m, i) \ (mbinptr) (((char *) &((m)->bins[((i) - 1) * 2])) \ - offsetof (struct malloc_chunk, fd)) The malloc_chunk struct like this: struct malloc_chunk { INTERNAL_SIZE_T prev_size; /* Size of previous chunk (if free). */ INTERNAL_SIZE_T size; /* Size in bytes, including overhead. */ struct malloc_chunk* fd; /* double links -- used only if free. */ struct malloc_chunk* bk; /* Only used for large blocks: pointer to next larger size. */ struct malloc_chunk* fd_nextsize; /* double links -- used only if free. */ struct malloc_chunk* bk_nextsize; }; And the bin type like this: typedef struct malloc_chunk* mbinptr; struct malloc_state { /* Serialize access. */ mutex_t mutex; /* Flags (formerly in max_fast). */ int flags; #if THREAD_STATS /* Statistics for locking. Only used if THREAD_STATS is defined. */ long stat_lock_direct, stat_lock_loop, stat_lock_wait; #endif /* Fastbins */ mfastbinptr fastbinsY[NFASTBINS]; /* Base of the topmost chunk -- not otherwise kept in a bin */ mchunkptr top; /* The remainder from the most recent split of a small request */ mchunkptr last_remainder; /* Normal bins packed as described above */ mchunkptr bins[NBINS * 2 - 2]; /* Bitmap of bins */ unsigned int binmap[BINMAPSIZE]; /* Linked list */ struct malloc_state *next; #ifdef PER_THREAD /* Linked list for free arenas. */ struct malloc_state *next_free; #endif /* Memory allocated from the system in this arena. */ INTERNAL_SIZE_T system_mem; INTERNAL_SIZE_T max_system_mem; };

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• Why this friend function can't access a private member of the class?

  - by Alceu Costa

  I am getting the following error when I try to access bins private member of the GHistogram class from within the extractHistogram() implementation: error: 'QVector<double> MyNamespace::GHistogram::bins' is private error: within this context Where the 'within this context' error points to the extractHistogram() implementation. Does anyone knows what's wrong with my friend function declaration? Here's the code: namespace MyNamespace{ class GHistogram { public: GHistogram(qint32 numberOfBins); qint32 getNumberOfBins(); /** * Returns the frequency of the value i. */ double getValueAt(qint32 i); friend GHistogram * Gbdi::extractHistogram(GImage *image, qint32 numberOfBins); private: QVector<double> bins; }; GHistogram * extractHistogram(GImage * image, qint32 numberOfBins); }

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• Matlab: plotting frequency distribution with a curve

  - by Kaly

  I have to plot 10 frequency distributions on one graph. In order to keep things tidy, I would like to avoid making a histogram with bins and would prefer having lines that follow the contour of each histogram plot. I tried the following [counts, bins] = hist(data); plot(bins, counts) But this gives me a very inexact and jagged line. I read about ksdensity, which gives me a nice curve, but it changes the scaling of my y-axis and I need to be able to read the frequencies from the y-axis. Can you recommend anything else?

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• How can I test that my hash function is good in terms of max-load?

  - by philcolbourn

  I have read through various papers on the 'Balls and Bins' problem and it seems that if a hash function is working right (ie. it is effectively a random distribution) then the following should/must be true if I hash n values into a hash table with n slots (or bins): Probability that a bin is empty, for large n is 1/e. Expected number of empty bins is n/e. Probability that a bin has k collisions is <= 1/k!. Probability that a bin has at least k collisions is <= (e/k)**k. These look easy to check. But the max-load test (the maximum number of collisions with high probability) is usually stated vaguely. Most texts state that the maximum number of collisions in any bin is O( ln(n) / ln(ln(n)) ). Some say it is 3*ln(n) / ln(ln(n)). Other papers mix ln and log - usually without defining them, or state that log is log base e and then use ln elsewhere. Is ln the log to base e or 2 and is this max-load formula right and how big should n be to run a test? This lecture seems to cover it best, but I am no mathematician. http://pages.cs.wisc.edu/~shuchi/courses/787-F07/scribe-notes/lecture07.pdf BTW, with high probability seems to mean 1 - 1/n.

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• Database for managing large volumes of (system) metrics

  - by symcbean

  Hi, I'm looking at building a system for managing and reporting stats on web page performance. I'll be collecting a lot more stats than are available in the standard log formats (approx 20 metrics) but compared to most types of database applications, the base data structure will be very simple. My problem is that I'll be accumulating a lot of data - in the region of 100,000 records (i.e. sets of metrics) per hour. Of course, resources are very limited! So that its possible to sensibly interact with the data, I'd need to consolidate each metric into one minute bins, broken down by URL, then for anything more than 1 day old, consolidated into 10 minute bins, then at 1 week, hourly bins. At the front end, I want to provide a view (prefereably as plots) of the last hour of data, with the facility for users to drill up/down through defined hierarchies of URLs (which do not always map directly to the hierarchy expressed in the path of the URL) and to view different time frames. Rather than coding all this myself and using a relational database, I was wondering if there were tools available which would facilitate both the management of the data and the reporting. I had a look at Mondrian however I can't see from the documentation I've looked at whether it's possible to drop the more granular information while maintaining the consolidated views of the data. RRDTool looks promising in terms of managing the data consolidation, but seems to be rather limited in terms of querying the dataset as a multi-dimensional/relational database. What else whould I be looking at?

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• Analyse frequencies of date ranges in Google Spreadsheet

  - by wnstnsmth

  I have a Google Spreadsheet where I would like to compute occurrences of date ranges. As you can see in my sheet, there is a column date_utc+1 which contains almost random date data. What I would like to do is put the date values into bins of 6 hours each, i.e., 12/5/2012 23:57:04 until 12/6/2012 0:03:17 would be in the first bin, 12/6/2012 11:20:53 until 12/6/2012 17:17:07 in the second bin, and so forth. Then, I would like to count the occurrence of those bins, such as bin_from bin_to freq ----------------------------------------------- 12/5/2012 23:57:04 12/6/2012 0:03:17 2 12/6/2012 11:20:53 12/6/2012 17:17:07 19 ... ... ... Hope it is clear what I mean. Partial hints are very welcome as well since I am pretty new to spreadsheeting.

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• Analyse frequencies of date ranges in Google Drive

  - by wnstnsmth

  I have a Google Drive spreadsheet where I would like to compute occurrences of date ranges. As you can see in my sheet, there is a column date_utc+1 which contains almost random date data. https://docs.google.com/spreadsheet/ccc?key=0AhqMXeYxWMD_dGRkVGRqbkR3c05mWUdhYkJWcFo2Mmc What I would like to do is 1) put the date values into bins of 6 hours each, i.e. 12/5/2012 23:57:04 until 12/6/2012 0:03:17 would be in the first bin, 12/6/2012 11:20:53 until 12/6/2012 17:17:07 in the second bin, and so forth. Then, I would like to count the occurrence of those bins, such as bin_from bin_to freq ----------------------------------------------- 12/5/2012 23:57:04 12/6/2012 0:03:17 2 12/6/2012 11:20:53 12/6/2012 17:17:07 19 ... ... ... Hope it is clear what I mean. Partial hints are very welcome as well since I am pretty new to spreadsheeting.

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• data handling with javascript

  - by Vincent Warmerdam

  Python has a very neat package called pandas which allows for quick data transformation; tables, aggregation, that sort of thing. A lot of these types of functionality can also be found in the python itertools module. The plyR package in R is also very similar. Usually one woud use this functionality to produce a table which is later visualized with a plot. I am personally very fond of d3, and I would like to allow the user to first indicate what type of data aggregation he wants on the dataset before it is visualized. The visualisation in question involves making a heatmap where the user gets to select the size of the bins of the heatmap beforehand (I want d3 to project this through leaflet). I want to visually select the ideal size of the bins for the heatmap. The way I work now is that I take the dataset, aggregate it with python and then manually load it in d3. This is a process that takes a lot of human effort and I was wondering if the data aggregation can be done through the javascript of the browser. I couldn't find a package for javascript specifically built for data, suggesting (to me) that this is a bad idea and that one should not use javascript for the data handling. Is there a good module/package for javascript to handle data aggregation? Is it a good/bad idea to do the data aggregation in javascript (performance wise)?

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• Ho to make Histogram Normalize and Equalize in java using JAI library?

  - by Jay

  I m making App in java using Swing component and JAI library. I make histogram of black and white or gray scale image.Is this method of making histogram correct? iif it is correct then how can i do normalization and Equalization of histogram in my App in java using JAI library?my code is below. in my code i make BufferedImage object and then make and plot histogram of that image . enter code here import java.awt.Color; import java.awt.Graphics; import java.awt.image.BufferedImage; import java.io.IOException; import javax.media.jai.JAI; import javax.media.jai.PlanarImage; import javax.swing.*; public class FinalHistogram extends JPanel { static int[] bins = new int[256]; static int[] newBins = new int[256]; static int x1 = 0, y1 = 0; static PlanarImage image = JAI.create("fileload", "alp_finger.tiff"); static BufferedImage bi = image.getAsBufferedImage(); FinalHistogram(int[] pbins) { for (int i = 0; i < 256; i++) { bins[i] = pbins[i]; newBins[i] = 0; } repaint(); } @Override protected void paintComponent(Graphics g) { for (int i = 0; i < 256; i++) { g.drawLine(150 + i, 300, 150 + i, 300 - (bins[i] / 300)); if (i == 0 || i == 255) { String sr = new Integer((i)).toString(); g.drawString(sr, 150 + i, 305); } System.out.println("bin[" + i + "]===" + bins[i]); } } public static void main(String[] args) throws IOException { int[] sbins = new int[256]; int pixel = 0; int k = 0; for (int x = 0; x < bi.getWidth(); x++) { for (int y = 0; y < bi.getHeight(); y++) { pixel = bi.getRaster().getSample(x, y, 0); k = (int) (pixel / 256); sbins[k]++; //pixel = bi.getRGB(x, y) & 0x000000ff; //k=pixel; //int[] pixels = m_image.getRGB(0, 0, m_image.getWidth(), m_image.getHeight(), null, 0, m_image.getWidth()); //short currentValue = 0; //int red,green,blue; //for(int i = 0; i<pixels.length; i++){ //red = (pixels[i] >> 16) & 0x000000FF; //green = (pixels[i] >>8 ) & 0x000000FF; //blue = pixels[i] & 0x000000FF; //currentValue = (short)((red + green + blue) / 3); //Current value gives the average //Disregard the alpha //assert(currentValue >= 0 && currentValue <= 255); //Something is awfully wrong if this goes off... //m_histogramArray[currentValue] += 1; //Increment the specific value of the array //} } } JTabbedPane jtp = new JTabbedPane(); jtp.addTab("Histogram", new JScrollPane(new FinalHistogram(sbins))); JFrame frame = new JFrame(); frame.setSize(500, 500); frame.add(new JScrollPane(jtp)); frame.setVisible(true); frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE); } }

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• DSP - Filtering in the frequency domain via FFT

  - by Trap

  I've been playing around a little with the Exocortex implementation of the FFT, but I'm having some problems. Whenever I modify the amplitudes of the frequency bins before calling the iFFT the resulting signal contains some clicks and pops, especially when low frequencies are present in the signal (like drums or basses). However, this does not happen if I attenuate all the bins by the same factor. Let me put an example of the output buffer of a 4-sample FFT: // Bin 0 (DC) FFTOut[0] = 0.0000610351563 FFTOut[1] = 0.0 // Bin 1 FFTOut[2] = 0.000331878662 FFTOut[3] = 0.000629425049 // Bin 2 FFTOut[4] = -0.0000381469727 FFTOut[5] = 0.0 // Bin 3, this is the first and only negative frequency bin. FFTOut[6] = 0.000331878662 FFTOut[7] = -0.000629425049 The output is composed of pairs of floats, each representing the real and imaginay parts of a single bin. So, bin 0 (array indexes 0, 1) would represent the real and imaginary parts of the DC frequency. As you can see, bins 1 and 3 both have the same values, (except for the sign of the Im part), so I guess bin 3 is the first negative frequency, and finally indexes (4, 5) would be the last positive frequency bin. Then to attenuate the frequency bin 1 this is what I do: // Attenuate the 'positive' bin FFTOut[2] *= 0.5; FFTOut[3] *= 0.5; // Attenuate its corresponding negative bin. FFTOut[6] *= 0.5; FFTOut[7] *= 0.5; For the actual tests I'm using a 1024-length FFT and I always provide all the samples so no 0-padding is needed. // Attenuate var halfSize = fftWindowLength / 2; float leftFreq = 0f; float rightFreq = 22050f; for( var c = 1; c < halfSize; c++ ) { var freq = c * (44100d / halfSize); // Calc. positive and negative frequency indexes. var k = c * 2; var nk = (fftWindowLength - c) * 2; // This kind of attenuation corresponds to a high-pass filter. // The attenuation at the transition band is linearly applied, could // this be the cause of the distortion of low frequencies? var attn = (freq < leftFreq) ? 0 : (freq < rightFreq) ? ((freq - leftFreq) / (rightFreq - leftFreq)) : 1; // Attenuate positive and negative bins. mFFTOut[ k ] *= (float)attn; mFFTOut[ k + 1 ] *= (float)attn; mFFTOut[ nk ] *= (float)attn; mFFTOut[ nk + 1 ] *= (float)attn; } Obviously I'm doing something wrong but can't figure out what. I don't want to use the FFT output as a means to generate a set of FIR coefficients since I'm trying to implement a very basic dynamic equalizer. What's the correct way to filter in the frequency domain? what I'm missing? Thanks in advance.

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• Best Allocation Algorithm

  - by shaju

  Hi, I am looking for a best allocation algorithm for the below scenario. We have requirement for say 18 pieces. I have the stock in my shelf as follows. Bin A - 10 Bin B - 6 Bin C - 3 Bin D - 4 Algorithm should propose the bins in the following order Bin A(10) , Bin D (4), Bin C (3) Real scenario we have n number of bins with different quantities.We need to find the optimal combination. Objective is to maxmize the allocation quantity. Can you please help. Regards, Shaju

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• Tripwire help Required

  - by ramaperumal

  I have created the policy file in Tripwire and also I have created the rules as well mentioned below: /opt/jboss/server/gis/conf -> $(SEC_CONFIG) +aipm +c+g+a+i+s+t+u+l+M; /usr/local/gtech/eseries/ -> $(SEC_CONFIG) +a+c+g+i+s+t+u+l+M ; After running the integrity check the output should be a(Access timestamp),c (Inode timestamp (create/modify),g (File owner's group ID),i (Inode number),s (File size),t (time stamp),u (File owner's user ID),l(File is increasing in size (a "growing file"),M (MD5 hash value). I am getting the output as below: [root@xxsi1242 tripwire]# tripwire --check Parsing policy file: /etc/tripwire/tw.pol *** Processing Unix File System *** Performing integrity check... Wrote report file: /var/lib/tripwire/report/xxsi1242.gtk.gtech.com-20131106-053812.twr Open Source Tripwire(R) 2.4.1 Integrity Check Report Report generated by: root Report created on: Wed 06 Nov 2013 05:38:12 AM EST Database last updated on: Wed 06 Nov 2013 05:31:17 AM EST =============================================================================== Report Summary: =============================================================================== Host name: xxsi1242.gtk.gtech.com Host IP address: 156.24.65.171 Host ID: None Policy file used: /etc/tripwire/tw.pol Configuration file used: /etc/tripwire/tw.cfg Database file used: /var/lib/tripwire/xxsi1242.gtk.gtech.com.twd Command line used: tripwire --check =============================================================================== Rule Summary: =============================================================================== ------------------------------------------------------------------------------- Section: Unix File System ------------------------------------------------------------------------------- Rule Name Severity Level Added Removed Modified --------- -------------- ----- ------- -------- Invariant Directories 66 0 0 0 Temporary directories 33 0 0 0 * Tripwire Data Files 100 0 0 1 Tech Stack 100 0 0 0 User binaries 66 0 0 0 Tripwire Binaries 100 0 0 0 * CLPS bins 100 0 0 2 CLPS Configuration files 100 0 0 0 ESCommon 100 0 0 0 Shell Binaries 100 0 0 0 OS executables and libraries 100 0 0 0 Security Control 100 0 0 0 ESCommon Configuration 100 0 0 0 (/etc/gtech/escommon) Total objects scanned: 12358 Total violations found: 3 =============================================================================== Object Summary: =============================================================================== ------------------------------------------------------------------------------- # Section: Unix File System ------------------------------------------------------------------------------- ------------------------------------------------------------------------------- Rule Name: Tripwire Data Files (/etc/tripwire/tw.pol) Severity Level: 100 ------------------------------------------------------------------------------- Modified: "/etc/tripwire/tw.pol" ------------------------------------------------------------------------------- Rule Name: CLPS bins (/opt/jboss/server) Severity Level: 100 ------------------------------------------------------------------------------- Modified: "/opt/jboss/server/esapps1/data/hypersonic/localDB.lck" "/opt/jboss/server/gis/data/hypersonic/localDB.lck" =============================================================================== Error Report: =============================================================================== No Errors ------------------------------------------------------------------------------- *** End of report *** Note: In the output I only am getting the files which are modified. I need the detail output for this. But unfortunately I am not getting what I expected. Please help me to proced further.

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• Compare images after canny edge detection in OpenCV (C++)

  - by typoknig

  Hi all, I am working on an OpenCV project and I need to compare some images after canny has been applied to both of them. Before the canny was applied I had the gray scale images populating a histogram and then I compared the histograms, but when canny is added to the images the histogram does not populate. I have read that a canny image can populate a histogram, but have not found a way to make it happen. I do not necessairly need to keep using the histograms, I just want to know the best way to compare two canny images. SSCCE below for you to chew on. I have poached and patched about 75% of this code from books and various sites on the internet, so props to those guys... // SLC (Histogram).cpp : Defines the entry point for the console application. #include "stdafx.h" #include <cxcore.h> #include <cv.h> #include <cvaux.h> #include <highgui.h> #include <stdio.h> #include <sstream> #include <iostream> using namespace std; IplImage* image1= 0; IplImage* imgHistogram1 = 0; IplImage* gray1= 0; CvHistogram* hist1; int main(){ CvCapture* capture = cvCaptureFromCAM(0); if(!cvQueryFrame(capture)){ cout<<"Video capture failed, please check the camera."<<endl; } else{ cout<<"Video camera capture successful!"<<endl; }; CvSize sz = cvGetSize(cvQueryFrame(capture)); IplImage* image = cvCreateImage(sz, 8, 3); IplImage* imgHistogram = 0; IplImage* gray = 0; CvHistogram* hist; cvNamedWindow("Image Source",1); cvNamedWindow("gray", 1); cvNamedWindow("Histogram",1); cvNamedWindow("BG", 1); cvNamedWindow("FG", 1); cvNamedWindow("Canny",1); cvNamedWindow("Canny1", 1); image1 = cvLoadImage("image bin/use this image.jpg");// an image has to load here or the program will not run //size of the histogram -1D histogram int bins1 = 256; int hsize1[] = {bins1}; //max and min value of the histogram float max_value1 = 0, min_value1 = 0; //value and normalized value float value1; int normalized1; //ranges - grayscale 0 to 256 float xranges1[] = { 0, 256 }; float* ranges1[] = { xranges1 }; //create an 8 bit single channel image to hold a //grayscale version of the original picture gray1 = cvCreateImage( cvGetSize(image1), 8, 1 ); cvCvtColor( image1, gray1, CV_BGR2GRAY ); IplImage* canny1 = cvCreateImage(cvGetSize(gray1), 8, 1 ); cvCanny( gray1, canny1, 55, 175, 3 ); //Create 3 windows to show the results cvNamedWindow("original1",1); cvNamedWindow("gray1",1); cvNamedWindow("histogram1",1); //planes to obtain the histogram, in this case just one IplImage* planes1[] = { canny1 }; //get the histogram and some info about it hist1 = cvCreateHist( 1, hsize1, CV_HIST_ARRAY, ranges1,1); cvCalcHist( planes1, hist1, 0, NULL); cvGetMinMaxHistValue( hist1, &min_value1, &max_value1); printf("min: %f, max: %f\n", min_value1, max_value1); //create an 8 bits single channel image to hold the histogram //paint it white imgHistogram1 = cvCreateImage(cvSize(bins1, 50),8,1); cvRectangle(imgHistogram1, cvPoint(0,0), cvPoint(256,50), CV_RGB(255,255,255),-1); //draw the histogram :P for(int i=0; i < bins1; i++){ value1 = cvQueryHistValue_1D( hist1, i); normalized1 = cvRound(value1*50/max_value1); cvLine(imgHistogram1,cvPoint(i,50), cvPoint(i,50-normalized1), CV_RGB(0,0,0)); } //show the image results cvShowImage( "original1", image1 ); cvShowImage( "gray1", gray1 ); cvShowImage( "histogram1", imgHistogram1 ); cvShowImage( "Canny1", canny1); CvBGStatModel* bg_model = cvCreateFGDStatModel( image ); for(;;){ image = cvQueryFrame(capture); cvUpdateBGStatModel( image, bg_model ); //Size of the histogram -1D histogram int bins = 256; int hsize[] = {bins}; //Max and min value of the histogram float max_value = 0, min_value = 0; //Value and normalized value float value; int normalized; //Ranges - grayscale 0 to 256 float xranges[] = {0, 256}; float* ranges[] = {xranges}; //Create an 8 bit single channel image to hold a grayscale version of the original picture gray = cvCreateImage(cvGetSize(image), 8, 1); cvCvtColor(image, gray, CV_BGR2GRAY); IplImage* canny = cvCreateImage(cvGetSize(gray), 8, 1 ); cvCanny( gray, canny, 55, 175, 3 );//55, 175, 3 with direct light //Planes to obtain the histogram, in this case just one IplImage* planes[] = {canny}; //Get the histogram and some info about it hist = cvCreateHist(1, hsize, CV_HIST_ARRAY, ranges,1); cvCalcHist(planes, hist, 0, NULL); cvGetMinMaxHistValue(hist, &min_value, &max_value); //printf("Minimum Histogram Value: %f, Maximum Histogram Value: %f\n", min_value, max_value); //Create an 8 bits single channel image to hold the histogram and paint it white imgHistogram = cvCreateImage(cvSize(bins, 50),8,3); cvRectangle(imgHistogram, cvPoint(0,0), cvPoint(256,50), CV_RGB(255,255,255),-1); //Draw the histogram for(int i=0; i < bins; i++){ value = cvQueryHistValue_1D(hist, i); normalized = cvRound(value*50/max_value); cvLine(imgHistogram,cvPoint(i,50), cvPoint(i,50-normalized), CV_RGB(0,0,0)); } double correlation = cvCompareHist (hist1, hist, CV_COMP_CORREL); double chisquare = cvCompareHist (hist1, hist, CV_COMP_CHISQR); double intersection = cvCompareHist (hist1, hist, CV_COMP_INTERSECT); double bhattacharyya = cvCompareHist (hist1, hist, CV_COMP_BHATTACHARYYA); double difference = (1 - correlation) + chisquare + (1 - intersection) + bhattacharyya; printf("correlation: %f\n", correlation); printf("chi-square: %f\n", chisquare); printf("intersection: %f\n", intersection); printf("bhattacharyya: %f\n", bhattacharyya); printf("difference: %f\n", difference); cvShowImage("Image Source", image); cvShowImage("gray", gray); cvShowImage("Histogram", imgHistogram); cvShowImage( "Canny", canny); cvShowImage("BG", bg_model->background); cvShowImage("FG", bg_model->foreground); //Page 19 paragraph 3 of "Learning OpenCV" tells us why we DO NOT use "cvReleaseImage(&image)" in this section cvReleaseImage(&imgHistogram); cvReleaseImage(&gray); cvReleaseHist(&hist); cvReleaseImage(&canny); char c = cvWaitKey(10); //if ASCII key 27 (esc) is pressed then loop breaks if(c==27) break; } cvReleaseBGStatModel( &bg_model ); cvReleaseImage(&image); cvReleaseCapture(&capture); cvDestroyAllWindows(); }

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• DSP - Problems using the inverse Fast Fourier Transform

  - by Trap

  I've been playing around a little with the Exocortex implementation of the FFT, but I'm having some problems. First, after calculating the inverse FFT of an unchanged frequency spectrum obtained by a previous forward FFT, one would expect to get the original signal back, but this is not the case. I had to figure out that I needed to scale the FFT output to about 1 / fftLength to get the amplitudes ok. Why is this? Second, whenever I modify the amplitudes of the frequency bins before calling the iFFT the signal gets distorted at low frequencies. However, this does not happen if I attenuate all the bins by the same factor. Let me put a very simplified example of the output buffer of a 4-sample FFT: // Bin 0 (DC) FFTOut[0] = 0.0000610351563 FFTOut[1] = 0.0 // Bin 1 FFTOut[2] = 0.000331878662 FFTOut[3] = 0.000629425049 // Central bin FFTOut[4] = -0.0000381469727 FFTOut[5] = 0.0 // Bin 3, this is a negative frequency bin. FFTOut[6] = 0.000331878662 FFTOut[7] = -0.000629425049 The output is composed of pairs of floats, each representing the real and imaginay parts of a single bin. So, bin 0 (array indexes 0, 1) would represent the real and imaginary parts of the DC frequency. As you can see, bins 1 and 3 both have the same values, (except for the sign of the Im part), so I guess these are the negative frequency values, and finally indexes (4, 5) would be the central frequency bin. To attenuate the frequency bin 1 this is what I do: // Attenuate the 'positive' bin FFTOut[2] *= 0.5; FFTOut[3] *= 0.5; // Attenuate its corresponding negative bin. FFTOut[6] *= 0.5; FFTOut[7] *= 0.5; For the actual tests I'm using a 1024-length FFT and I always provide all the samples so no 0-padding is needed. // Attenuate var halfSize = fftWindowLength / 2; float leftFreq = 0f; float rightFreq = 22050f; for( var c = 1; c < halfSize; c++ ) { var freq = c * (44100d / halfSize); // Calc. positive and negative frequency locations. var k = c * 2; var nk = (fftWindowLength - c) * 2; // This kind of attenuation corresponds to a high-pass filter. // The attenuation at the transition band is linearly applied, could // this be the cause of the distortion of low frequencies? var attn = (freq < leftFreq) ? 0 : (freq < rightFreq) ? ((freq - leftFreq) / (rightFreq - leftFreq)) : 1; mFFTOut[ k ] *= (float)attn; mFFTOut[ k + 1 ] *= (float)attn; mFFTOut[ nk ] *= (float)attn; mFFTOut[ nk + 1 ] *= (float)attn; } Obviously I'm doing something wrong but can't figure out what or where.

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