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• Can anyone recommend a decent tool for optimizing images other than Photoshop

  - by toomanyairmiles

  Can anyone recommend a decent tool for optimising images other than adobe photoshop, the gimp etc? I'm looking to optimise images for the web preferably online and free. Basically I have a client who can't install additional software on their work PC but needs to optimise photographs and other images for their website and is presently uploading 1 or 2 Mb files. On a personal level I'm interested to see what other people are using...

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• optimizing widget painting.

  - by yan bellavance

  I am painting a widget and I want to optimize the process. Basically i will be sliding the image in the x direction and I only want to fill the newly exposed area. Is there a way to translate the pixels of a widget without calling update or using paintevent? I know of pixmaps and such but I am wondering If I can for example draw a pixmap once and then translate what I have drawn without having to paint anything else or draw pixmaps anymore.

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• Optimizing processing and management of large Java data arrays

  - by mikera

  I'm writing some pretty CPU-intensive, concurrent numerical code that will process large amounts of data stored in Java arrays (e.g. lots of double[100000]s). Some of the algorithms might run millions of times over several days so getting maximum steady-state performance is a high priority. In essence, each algorithm is a Java object that has an method API something like: public double[] runMyAlgorithm(double[] inputData); or alternatively a reference could be passed to the array to store the output data: public runMyAlgorithm(double[] inputData, double[] outputData); Given this requirement, I'm trying to determine the optimal strategy for allocating / managing array space. Frequently the algorithms will need large amounts of temporary storage space. They will also take large arrays as input and create large arrays as output. Among the options I am considering are: Always allocate new arrays as local variables whenever they are needed (e.g. new double[100000]). Probably the simplest approach, but will produce a lot of garbage. Pre-allocate temporary arrays and store them as final fields in the algorithm object - big downside would be that this would mean that only one thread could run the algorithm at any one time. Keep pre-allocated temporary arrays in ThreadLocal storage, so that a thread can use a fixed amount of temporary array space whenever it needs it. ThreadLocal would be required since multiple threads will be running the same algorithm simultaneously. Pass around lots of arrays as parameters (including the temporary arrays for the algorithm to use). Not good since it will make the algorithm API extremely ugly if the caller has to be responsible for providing temporary array space.... Allocate extremely large arrays (e.g. double[10000000]) but also provide the algorithm with offsets into the array so that different threads will use a different area of the array independently. Will obviously require some code to manage the offsets and allocation of the array ranges. Any thoughts on which approach would be best (and why)?

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• Need guidelines for optimizing WebGL performance by minimizing shader changes

  - by brainjam

  I'm trying to get an idea of the practicality of WebGL for rendering large architectural interior scenes, consisting of 100K's of triangles. These triangles are distributed over many objects, and there are many materials in the scene. On the other hand, there are no moving parts. And the materials tend to be fairly simple, mostly based on texture maps. There is a lot of texture map sharing .. for example all the chairs in scene will share a common map. There is also some multitexturing - up to three textures overlaid in a material. I've been doing a little experimentation and reading, and gather that frequently switching materials during a rendering pass will slow things down. For example, a scene with 200K triangles will have significant performance differences, depending on whether there are 10 or 1000 objects, assuming that each time an object is displayed a new material is set up. So it seems that if performance is important the scene should be sorted by materials so as to minimize material switching. What I'm looking for is guidelines on how to think of the overhead of various state changes, and where do I get the biggest bang for the buck. For example, what are the relative performance costs of, say, gl.useProgram(), gl.uniformMatrix4fv(), gl.drawElements() should I try to write ubershaders to minimize shader switching? should I try to aggregate geometry to minimize the number of gl.drawElements() calls I realize that mileage may vary depending on browser, OS, and graphics hardware. And I'm also not looking for heroic measures. Just some guidelines from people who have already had some experience in making scenes fast. I'll add that while I've had some experience with fixed-pipeline OpenGL programming in the past, I'm rather new to the WebGL/OpenGL ES 2.0 way of doing things.

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• Optimizing a shared buffer in a producer/consumer multithreaded environment

  - by Etan

  I have some project where I have a single producer thread which writes events into a buffer, and an additional single consumer thread which takes events from the buffer. My goal is to optimize this thing for a single machine to achieve maximum throughput. Currently, I am using some simple lock-free ring buffer (lock-free is possible since I have only one consumer and one producer thread and therefore the pointers are only updated by a single thread). #define BUF_SIZE 32768 struct buf_t { volatile int writepos; volatile void * buffer[BUF_SIZE]; volatile int readpos;) }; void produce (buf_t *b, void * e) { int next = (b->writepos+1) % BUF_SIZE; while (b->readpos == next); // queue is full. wait b->buffer[b->writepos] = e; b->writepos = next; } void * consume (buf_t *b) { while (b->readpos == b->writepos); // nothing to consume. wait int next = (b->readpos+1) % BUF_SIZE; void * res = b->buffer[b->readpos]; b->readpos = next; return res; } buf_t *alloc () { buf_t *b = (buf_t *)malloc(sizeof(buf_t)); b->writepos = 0; b->readpos = 0; return b; } However, this implementation is not yet fast enough and should be optimized further. I've tried with different BUF_SIZE values and got some speed-up. Additionaly, I've moved writepos before the buffer and readpos after the buffer to ensure that both variables are on different cache lines which resulted also in some speed. What I need is a speedup of about 400 %. Do you have any ideas how I could achieve this using things like padding etc?

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• Optimizing Python code with many attribute and dictionary lookups

  - by gotgenes

  I have written a program in Python which spends a large amount of time looking up attributes of objects and values from dictionary keys. I would like to know if there's any way I can optimize these lookup times, potentially with a C extension, to reduce the time of execution, or if I need to simply re-implement the program in a compiled language. The program implements some algorithms using a graph. It runs prohibitively slowly on our data sets, so I profiled the code with cProfile using a reduced data set that could actually complete. The vast majority of the time is being burned in one function, and specifically in two statements, generator expressions, within the function: The generator expression at line 202 is neighbors_in_selected_nodes = (neighbor for neighbor in node_neighbors if neighbor in selected_nodes) and the generator expression at line 204 is neighbor_z_scores = (interaction_graph.node[neighbor]['weight'] for neighbor in neighbors_in_selected_nodes) The source code for this function of context provided below. selected_nodes is a set of nodes in the interaction_graph, which is a NetworkX Graph instance. node_neighbors is an iterator from Graph.neighbors_iter(). Graph itself uses dictionaries for storing nodes and edges. Its Graph.node attribute is a dictionary which stores nodes and their attributes (e.g., 'weight') in dictionaries belonging to each node. Each of these lookups should be amortized constant time (i.e., O(1)), however, I am still paying a large penalty for the lookups. Is there some way which I can speed up these lookups (e.g., by writing parts of this as a C extension), or do I need to move the program to a compiled language? Below is the full source code for the function that provides the context; the vast majority of execution time is spent within this function. def calculate_node_z_prime( node, interaction_graph, selected_nodes ): """Calculates a z'-score for a given node. The z'-score is based on the z-scores (weights) of the neighbors of the given node, and proportional to the z-score (weight) of the given node. Specifically, we find the maximum z-score of all neighbors of the given node that are also members of the given set of selected nodes, multiply this z-score by the z-score of the given node, and return this value as the z'-score for the given node. If the given node has no neighbors in the interaction graph, the z'-score is defined as zero. Returns the z'-score as zero or a positive floating point value. :Parameters: - `node`: the node for which to compute the z-prime score - `interaction_graph`: graph containing the gene-gene or gene product-gene product interactions - `selected_nodes`: a `set` of nodes fitting some criterion of interest (e.g., annotated with a term of interest) """ node_neighbors = interaction_graph.neighbors_iter(node) neighbors_in_selected_nodes = (neighbor for neighbor in node_neighbors if neighbor in selected_nodes) neighbor_z_scores = (interaction_graph.node[neighbor]['weight'] for neighbor in neighbors_in_selected_nodes) try: max_z_score = max(neighbor_z_scores) # max() throws a ValueError if its argument has no elements; in this # case, we need to set the max_z_score to zero except ValueError, e: # Check to make certain max() raised this error if 'max()' in e.args[0]: max_z_score = 0 else: raise e z_prime = interaction_graph.node[node]['weight'] * max_z_score return z_prime Here are the top couple of calls according to cProfiler, sorted by time. ncalls tottime percall cumtime percall filename:lineno(function) 156067701 352.313 0.000 642.072 0.000 bpln_contextual.py:204(<genexpr>) 156067701 289.759 0.000 289.759 0.000 bpln_contextual.py:202(<genexpr>) 13963893 174.047 0.000 816.119 0.000 {max} 13963885 69.804 0.000 936.754 0.000 bpln_contextual.py:171(calculate_node_z_prime) 7116883 61.982 0.000 61.982 0.000 {method 'update' of 'set' objects}

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• Optimizing tasks to reduce CPU in a trading application

  - by Joel

  Hello, I have designed a trading application that handles customers stocks investment portfolio. I am using two datastore kinds: Stocks - Contains unique stock name and its daily percent change. UserTransactions - Contains information regarding a specific purchase of a stock made by a user : the value of the purchase along with a reference to Stock for the current purchase. db.Model python modules: class Stocks (db.Model): stockname = db.StringProperty(multiline=True) dailyPercentChange=db.FloatProperty(default=1.0) class UserTransactions (db.Model): buyer = db.UserProperty() value=db.FloatProperty() stockref = db.ReferenceProperty(Stocks) Once an hour I need to update the database: update the daily percent change in Stocks and then update the value of all entities in UserTransactions that refer to that stock. The following python module iterates over all the stocks, update the dailyPercentChange property, and invoke a task to go over all UserTransactions entities which refer to the stock and update their value: Stocks.py # Iterate over all stocks in datastore for stock in Stocks.all(): # update daily percent change in datastore db.run_in_transaction(updateStockTxn, stock.key()) # create a task to update all user transactions entities referring to this stock taskqueue.add(url='/task', params={'stock_key': str(stock.key(), 'value' : self.request.get ('some_val_for_stock') }) def updateStockTxn(stock_key): #fetch the stock again - necessary to avoid concurrency updates stock = db.get(stock_key) stock.dailyPercentChange= data.get('some_val_for_stock') # I get this value from outside ... some more calculations here ... stock.put() Task.py (/task) # Amount of transaction per task amountPerCall=10 stock=db.get(self.request.get("stock_key")) # Get all user transactions which point to current stock user_transaction_query=stock.usertransactions_set cursor=self.request.get("cursor") if cursor: user_transaction_query.with_cursor(cursor) # Spawn another task if more than 10 transactions are in datastore transactions = user_transaction_query.fetch(amountPerCall) if len(transactions)==amountPerCall: taskqueue.add(url='/task', params={'stock_key': str(stock.key(), 'value' : self.request.get ('some_val_for_stock'), 'cursor': user_transaction_query.cursor() }) # Iterate over all transaction pointing to stock and update their value for transaction in transactions: db.run_in_transaction(updateUserTransactionTxn, transaction.key()) def updateUserTransactionTxn(transaction_key): #fetch the transaction again - necessary to avoid concurrency updates transaction = db.get(transaction_key) transaction.value= transaction.value* self.request.get ('some_val_for_stock') db.put(transaction) The problem: Currently the system works great, but the problem is that it is not scaling well… I have around 100 Stocks with 300 User Transactions, and I run the update every hour. In the dashboard, I see that the task.py takes around 65% of the CPU (Stock.py takes around 20%-30%) and I am using almost all of the 6.5 free CPU hours given to me by app engine. I have no problem to enable billing and pay for additional CPU, but the problem is the scaling of the system… Using 6.5 CPU hours for 100 stocks is very poor. I was wondering, given the requirements of the system as mentioned above, if there is a better and more efficient implementation (or just a small change that can help with the current implemntation) than the one presented here. Thanks!! Joel

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• Optimizing comparison instruction count (PDP-11)

  - by lego69

  For PDP-11, how can I change the following snippet of assembly so that it's only two instructions, yet does the same work as these four? tst r0 blt label cmp r0, #75 bgt label

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• Optimizing sorting container of objects with heap-allocated buffers - how to avoid hard-copying buff

  - by Kache4

  I was making sure I knew how to do the op= and copy constructor correctly in order to sort() properly, so I wrote up a test case. After getting it to work, I realized that the op= was hard-copying all the data_. I figure if I wanted to sort a container with this structure (its elements have heap allocated char buffer arrays), it'd be faster to just swap the pointers around. Is there a way to do that? Would I have to write my own sort/swap function? #include <deque> //#include <string> //#include <utility> //#include <cstdlib> #include <cstring> #include <iostream> //#include <algorithm> // I use sort(), so why does this still compile when commented out? #include <boost/filesystem.hpp> #include <boost/foreach.hpp> using namespace std; namespace fs = boost::filesystem; class Page { public: // constructor Page(const char* path, const char* data, int size) : path_(fs::path(path)), size_(size), data_(new char[size]) { // cout << "Creating Page..." << endl; strncpy(data_, data, size); // cout << "done creating Page..." << endl; } // copy constructor Page(const Page& other) : path_(fs::path(other.path())), size_(other.size()), data_(new char[other.size()]) { // cout << "Copying Page..." << endl; strncpy(data_, other.data(), size_); // cout << "done copying Page..." << endl; } // destructor ~Page() { delete[] data_; } // accessors const fs::path& path() const { return path_; } const char* data() const { return data_; } int size() const { return size_; } // operators Page& operator = (const Page& other) { if (this == &other) return *this; char* newImage = new char[other.size()]; strncpy(newImage, other.data(), other.size()); delete[] data_; data_ = newImage; path_ = fs::path(other.path()); size_ = other.size(); return *this; } bool operator < (const Page& other) const { return path_ < other.path(); } private: fs::path path_; int size_; char* data_; }; class Book { public: Book(const char* path) : path_(fs::path(path)) { cout << "Creating Book..." << endl; cout << "pushing back #1" << endl; pages_.push_back(Page("image1.jpg", "firstImageData", 14)); cout << "pushing back #3" << endl; pages_.push_back(Page("image3.jpg", "thirdImageData", 14)); cout << "pushing back #2" << endl; pages_.push_back(Page("image2.jpg", "secondImageData", 15)); cout << "testing operator <" << endl; cout << pages_[0].path().string() << (pages_[0] < pages_[1]? " < " : " > ") << pages_[1].path().string() << endl; cout << pages_[1].path().string() << (pages_[1] < pages_[2]? " < " : " > ") << pages_[2].path().string() << endl; cout << pages_[0].path().string() << (pages_[0] < pages_[2]? " < " : " > ") << pages_[2].path().string() << endl; cout << "sorting" << endl; BOOST_FOREACH (Page p, pages_) cout << p.path().string() << endl; sort(pages_.begin(), pages_.end()); cout << "done sorting\n"; BOOST_FOREACH (Page p, pages_) cout << p.path().string() << endl; cout << "checking datas" << endl; BOOST_FOREACH (Page p, pages_) { char data[p.size() + 1]; strncpy((char*)&data, p.data(), p.size()); data[p.size()] = '\0'; cout << p.path().string() << " " << data << endl; } cout << "done Creating Book" << endl; } private: deque<Page> pages_; fs::path path_; }; int main() { Book* book = new Book("/some/path/"); }

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• Optimizing an iphone app for 3G in landscape with opengl, camera, quartz

  - by Joey

  I have an iphone app that basically uses the camera, an opengl layer, and UIViews (some drawing with Quartz). It runs ok on 3GS, but on the 3G it is unusable. Particularly, when I press a UIButton, it literally takes sometimes 10 seconds to register the press. Shark doesn't do me much good because it crashes when I try to profile even a tiny portion, and I've tried turning off some of the layers to see if they might be obvious contributors to the lag. I've noticed that turning off the camera really helps. I'm wondering if anyone has any familiarity with this and might suggest some likely causes. I had issues with extreme slowdown from running my app in landscape mode and using transforms, so considered that might be a cause, but I'm wondering if hoping for a 3G to run something with all of the above elements is just not really possible considering the camera seems to really cost a lot. The fact that the buttons are horribly delayed in their response makes me think there is something fundamental that I might be missing.

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• Optimizing an embedded SELECT query in mySQL

  - by Crazy Serb

  Ok, here's a query that I am running right now on a table that has 45,000 records and is 65MB in size... and is just about to get bigger and bigger (so I gotta think of the future performance as well here): SELECT count(payment_id) as signup_count, sum(amount) as signup_amount FROM payments p WHERE tm_completed BETWEEN '2009-05-01' AND '2009-05-30' AND completed > 0 AND tm_completed IS NOT NULL AND member_id NOT IN (SELECT p2.member_id FROM payments p2 WHERE p2.completed=1 AND p2.tm_completed < '2009-05-01' AND p2.tm_completed IS NOT NULL GROUP BY p2.member_id) And as you might or might not imagine - it chokes the mysql server to a standstill... What it does is - it simply pulls the number of new users who signed up, have at least one "completed" payment, tm_completed is not empty (as it is only populated for completed payments), and (the embedded Select) that member has never had a "completed" payment before - meaning he's a new member (just because the system does rebills and whatnot, and this is the only way to sort of differentiate between an existing member who just got rebilled and a new member who got billed for the first time). Now, is there any possible way to optimize this query to use less resources or something, and to stop taking my mysql resources down on their knees...? Am I missing any info to clarify this any further? Let me know... EDIT: Here are the indexes already on that table: PRIMARY PRIMARY 46757 payment_id member_id INDEX 23378 member_id payer_id INDEX 11689 payer_id coupon_id INDEX 1 coupon_id tm_added INDEX 46757 tm_added, product_id tm_completed INDEX 46757 tm_completed, product_id

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• Database structure, optimizing table with lots of rows

  - by aepheus

  I have a table with a lot of rows. The structure is something like this: UserID | ItemID | Item Data... Would I see any gains in query time if I separated this into a table per user, or per smaller group of users? Queries are always single user getting/modifying a selection of items.

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• Optimizing Vector elements swaps using CUDA

  - by Orion Nebula

  Hi all, Since I am new to cuda .. I need your kind help I have this long vector, for each group of 24 elements, I need to do the following: for the first 12 elements, the even numbered elements are multiplied by -1, for the second 12 elements, the odd numbered elements are multiplied by -1 then the following swap takes place: Graph: because I don't yet have enough points, I couldn't post the image so here it is: http://www.freeimagehosting.net/image.php?e4b88fb666.png I have written this piece of code, and wonder if you could help me further optimize it to solve for divergence or bank conflicts .. //subvector is a multiple of 24, Mds and Nds are shared memory _shared_ double Mds[subVector]; _shared_ double Nds[subVector]; int tx = threadIdx.x; int tx_mod = tx ^ 0x0001; int basex = __umul24(blockDim.x, blockIdx.x); Mds[tx] = M.elements[basex + tx]; __syncthreads(); // flip the signs if (tx < (tx/24)*24 + 12) { //if < 12 and even if ((tx & 0x0001)==0) Mds[tx] = -Mds[tx]; } else if (tx < (tx/24)*24 + 24) { //if >12 and < 24 and odd if ((tx & 0x0001)==1) Mds[tx] = -Mds[tx]; } __syncthreads(); if (tx < (tx/24)*24 + 6) { //for the first 6 elements .. swap with last six in the 24elements group (see graph) Nds[tx] = Mds[tx_mod + 18]; Mds [tx_mod + 18] = Mds [tx]; Mds[tx] = Nds[tx]; } else if (tx < (tx/24)*24 + 12) { // for the second 6 elements .. swp with next adjacent group (see graph) Nds[tx] = Mds[tx_mod + 6]; Mds [tx_mod + 6] = Mds [tx]; Mds[tx] = Nds[tx]; } __syncthreads(); Thanks in advance ..

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• Optimizing tiled maps in cocos2d-iphone

  - by Omega

  My cocos2d-iphone game has tiled maps. The tilesets textures are rather big. I got around 5 tilesets and each one is 2048x2048 (retina). My maps are around 80x80. They have around 8 layers and each one is obviously using one tileset. The frame rate falls (it goes around 30 sometimes. I know 30 is rather aceptable, but still, I want 50+). So given that textures are huge I can't afford to make many layers (since each one loads a texture of these). So how about I divide my tileset textures into much smaller tilesets (like 1024x1024 each)? That will allow me to use many more layers for my maps, right? Are there any other tips for huge retina display tile maps?

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• Optimizing python link matching regular expression

  - by Matt

  I have a regular expression, links = re.compile('<a(.+?)href=(?:"|\')?((?:https?://|/)[^\'"]+)(?:"|\')?(.*?)>(.+?)</a>',re.I).findall(data) to find links in some html, it is taking a long time on certain html, any optimization advice? One that it chokes on is http://freeyourmindonline.net/Blog/

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• Optimizing a bin-placement algorithm

  - by user258651

  Alright, I've got two collections, and I need to place elements from collection1 into the bins (elements) of collection2, based on whether their value falls within a given bin's range. For a concrete example, assume I have a sorted collection objects (bins) which have an int range ([1...4], [5..10], etc). I need to determine the range an int falls in, and place it in the appropriate bin. foreach(element n in collection1) { foreach(bin m in collection2) { if (m.inRange(n)) { m.add(n); break; } } } So the obvious NxM complexity algorithm is there, but I really would like to see Nxlog(M). To do this I'd like to use BinarySearch in place of the inner foreach loop. To use BinarySearch, I need to implement an IComparer class to do the searching for me. The problem I'm running into is this approach would require me to make an IComparer.Compare function that compares two different types of objects (an element to its bin), and that doesn't seem possible or correct. So I'm asking, how should I write this algorithm?

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• Optimizing a memoization decorator not increase call stack

  - by Tyler Crompton

  I have a very, very basic memoization decorator that I need to optimize below: def memoize(function): memos = {} def wrapper(*args): try: return memos[args] except KeyError: pass result = function(*args) memos[args] = result return result return wrapper The goal is to make this so that it doesn't add on to the call stack. It actually doubles it right now. I realize that I can embed this on a function by function basis, but that is not desired as I would like a global solution for memoizing. Any ideas?

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• Optimizing Dijkstra for dense graph?

  - by Jason

  Is there another way to calculate the shortest path for a near complete graph other than Dijkstra? I have about 8,000 nodes and about 18 million edges. I've gone through the thread "a to b on map" and decided to use Dijkstra. I wrote my script in Perl using the Boost::Graph library. But the result isn't what I expected. It took about 10+ minutes to calculate one shortest path using the call $graph-dijkstra_shortest_path($start_node,$end_node); I understand there are a lot of edges and it may be the reason behind the slow running time. Am I dead in the water? Is there any other way to speed this up?

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• Book on optimizing complex real world webservices architecture ?

  - by asksuperuser

  Is there any book that goes beyond "how to create a webservice" but still for java or .net.

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• Optimizing Mysql to avoid redundancy but still have fast access to calculable data

  - by diglettpotato

  An example for the sake of the question: I have a database which contains users, questions, and answers. Each user has a score which can be calculated using the data from the questions and answers tables. Therefore if I had a score field in the users table, it would be redundant. However, if I don't use a score field, then calculating the score every time would significantly slow down the website. My current solution is to put it in a score field, and then have a cron running every few hours which recalculates everybody's score, and updates the field. Is there a better way to handle this?

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• Optimizing BeautifulSoup (Python) code

  - by user283405

  I have code that uses the BeautifulSoup library for parsing, but it is very slow. The code is written in such a way that threads cannot be used. Can anyone help me with this? I am using BeautifulSoup for parsing and than save into a DB. If I comment out the save statement, it still takes a long time, so there is no problem with the database. def parse(self,text): soup = BeautifulSoup(text) arr = soup.findAll('tbody') for i in range(0,len(arr)-1): data=Data() soup2 = BeautifulSoup(str(arr[i])) arr2 = soup2.findAll('td') c=0 for j in arr2: if str(j).find("<a href=") > 0: data.sourceURL = self.getAttributeValue(str(j),'<a href="') else: if c == 2: data.Hits=j.renderContents() #and few others... c = c+1 data.save() Any suggestions? Note: I already ask this question here but that was closed due to incomplete information.

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• Optimizing a fluid grid layout

  - by user1815176

  I recently just added a grid layout, but I can't figure out how to make my links work. The grid that I used is the 1140 one at http://cssgrid.net/. I studied the source code of that website, and tried to make my page like theirs, but when I put everything in it made mine worse, and the grid didn't even work. This is how my website is supposed to look http://spencedesign.netau.net/singaporehome.html and this is how it does http://spencedesign.netau.net/home.html And when you reduce the size, it doesn't look like it's supposed to. When you minimize it I want the pictures(links) to be two per row, then one per row depending on how small the page is. I also want the quote to turn into different rows when it is too small for it. But I can't figure out how to make the page look normal regularly let alone make it look good with a smaller browser. Thanks!

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• Optimizing python code performance when importing zipped csv to a mongo collection

  - by mark

  I need to import a zipped csv into a mongo collection, but there is a catch - every record contains a timestamp in Pacific Time, which must be converted to the local time corresponding to the (longitude,latitude) pair found in the same record. The code looks like so: def read_csv_zip(path, timezones): with ZipFile(path) as z, z.open(z.namelist()[0]) as input: csv_rows = csv.reader(input) header = csv_rows.next() check,converters = get_aux_stuff(header) for csv_row in csv_rows: if check(csv_row): row = { converter[0]:converter[1](value) for converter, value in zip(converters, csv_row) if allow_field(converter) } ts = row['ts'] lng, lat = row['loc'] found_tz_entry = timezones.find_one(SON({'loc': {'$within': {'$box': [[lng-tz_lookup_radius, lat-tz_lookup_radius],[lng+tz_lookup_radius, lat+tz_lookup_radius]]}}})) if found_tz_entry: tz_name = found_tz_entry['tz'] local_ts = ts.astimezone(timezone(tz_name)).replace(tzinfo=None) row['tz'] = tz_name else: local_ts = (ts.astimezone(utc) + timedelta(hours = int(lng/15))).replace(tzinfo = None) row['local_ts'] = local_ts yield row def insert_documents(collection, source, batch_size): while True: items = list(itertools.islice(source, batch_size)) if len(items) == 0: break; try: collection.insert(items) except: for item in items: try: collection.insert(item) except Exception as exc: print("Failed to insert record {0} - {1}".format(item['_id'], exc)) def main(zip_path): with Connection() as connection: data = connection.mydb.data timezones = connection.timezones.data insert_documents(data, read_csv_zip(zip_path, timezones), 1000) The code proceeds as follows: Every record read from the csv is checked and converted to a dictionary, where some fields may be skipped, some titles be renamed (from those appearing in the csv header), some values may be converted (to datetime, to integers, to floats. etc ...) For each record read from the csv, a lookup is made into the timezones collection to map the record location to the respective time zone. If the mapping is successful - that timezone is used to convert the record timestamp (pacific time) to the respective local timestamp. If no mapping is found - a rough approximation is calculated. The timezones collection is appropriately indexed, of course - calling explain() confirms it. The process is slow. Naturally, having to query the timezones collection for every record kills the performance. I am looking for advises on how to improve it. Thanks. EDIT The timezones collection contains 8176040 records, each containing four values: > db.data.findOne() { "_id" : 3038814, "loc" : [ 1.48333, 42.5 ], "tz" : "Europe/Andorra" } EDIT2 OK, I have compiled a release build of http://toblerity.github.com/rtree/ and configured the rtree package. Then I have created an rtree dat/idx pair of files corresponding to my timezones collection. So, instead of calling collection.find_one I call index.intersection. Surprisingly, not only there is no improvement, but it works even more slowly now! May be rtree could be fine tuned to load the entire dat/idx pair into RAM (704M), but I do not know how to do it. Until then, it is not an alternative. In general, I think the solution should involve parallelization of the task. EDIT3 Profile output when using collection.find_one: >>> p.sort_stats('cumulative').print_stats(10) Tue Apr 10 14:28:39 2012 ImportDataIntoMongo.profile 64549590 function calls (64549180 primitive calls) in 1231.257 seconds Ordered by: cumulative time List reduced from 730 to 10 due to restriction <10> ncalls tottime percall cumtime percall filename:lineno(function) 1 0.012 0.012 1231.257 1231.257 ImportDataIntoMongo.py:1(<module>) 1 0.001 0.001 1230.959 1230.959 ImportDataIntoMongo.py:187(main) 1 853.558 853.558 853.558 853.558 {raw_input} 1 0.598 0.598 370.510 370.510 ImportDataIntoMongo.py:165(insert_documents) 343407 9.965 0.000 359.034 0.001 ImportDataIntoMongo.py:137(read_csv_zip) 343408 2.927 0.000 287.035 0.001 c:\python27\lib\site-packages\pymongo\collection.py:489(find_one) 343408 1.842 0.000 274.803 0.001 c:\python27\lib\site-packages\pymongo\cursor.py:699(next) 343408 2.542 0.000 271.212 0.001 c:\python27\lib\site-packages\pymongo\cursor.py:644(_refresh) 343408 4.512 0.000 253.673 0.001 c:\python27\lib\site-packages\pymongo\cursor.py:605(__send_message) 343408 0.971 0.000 242.078 0.001 c:\python27\lib\site-packages\pymongo\connection.py:871(_send_message_with_response) Profile output when using index.intersection: >>> p.sort_stats('cumulative').print_stats(10) Wed Apr 11 16:21:31 2012 ImportDataIntoMongo.profile 41542960 function calls (41542536 primitive calls) in 2889.164 seconds Ordered by: cumulative time List reduced from 778 to 10 due to restriction <10> ncalls tottime percall cumtime percall filename:lineno(function) 1 0.028 0.028 2889.164 2889.164 ImportDataIntoMongo.py:1(<module>) 1 0.017 0.017 2888.679 2888.679 ImportDataIntoMongo.py:202(main) 1 2365.526 2365.526 2365.526 2365.526 {raw_input} 1 0.766 0.766 502.817 502.817 ImportDataIntoMongo.py:180(insert_documents) 343407 9.147 0.000 491.433 0.001 ImportDataIntoMongo.py:152(read_csv_zip) 343406 0.571 0.000 391.394 0.001 c:\python27\lib\site-packages\rtree-0.7.0-py2.7.egg\rtree\index.py:384(intersection) 343406 379.957 0.001 390.824 0.001 c:\python27\lib\site-packages\rtree-0.7.0-py2.7.egg\rtree\index.py:435(_intersection_obj) 686513 22.616 0.000 38.705 0.000 c:\python27\lib\site-packages\rtree-0.7.0-py2.7.egg\rtree\index.py:451(_get_objects) 343406 6.134 0.000 33.326 0.000 ImportDataIntoMongo.py:162(<dictcomp>) 346 0.396 0.001 30.665 0.089 c:\python27\lib\site-packages\pymongo\collection.py:240(insert) EDIT4 I have parallelized the code, but the results are still not very encouraging. I am convinced it could be done better. See my own answer to this question for details.

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• Optimizing this "Boundarize" method for Numerics in Ruby

  - by mstksg

  I'm extending Numerics with a method I call "Boundarize" for lack of better name; I'm sure there are actually real names for this. But its basic purpose is to reset a given point to be within a boundary. That is, "wrapping" a point around the boundary; if the area is betweeon 0 and 100, if the point goes to -1, -1.boundarize(0,100) = 99 (going one too far to the negative "wraps" the point around to one from the max). 102.boundarize(0,100) = 2 It's a very simple function to implement; when the number is below the minimum, simply add (max-min) until it's in the boundary. If the number is above the maximum, simply subtract (max-min) until it's in the boundary. One thing I also need to account for is that, there are cases where I don't want to include the minimum in the range, and cases where I don't want to include the maximum in the range. This is specified as an argument. However, I fear that my current implementation is horribly, terribly, grossly inefficient. And because every time something moves on the screen, it has to re-run this, this is one of the bottlenecks of my application. Anyone have any ideas? module Boundarizer def boundarize min=0,max=1,allow_min=true,allow_max=false raise "Improper boundaries #{min}/#{max}" if min >= max new_num = self if allow_min while new_num < min new_num += (max-min) end else while new_num <= min new_num += (max-min) end end if allow_max while new_num > max new_num -= (max-min) end else while new_num >= max new_num -= (max-min) end end return new_num end end class Numeric include Boundarizer end

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• optimizing the jquery in django

  - by sankar

  I have done the following code which actually dynamically generate the values in the third list box using ajax and jquery concepts. Thoug it works, i need to optimize it. Below is the code i am working on now. <html> <head> <title>Comparison based on ID Number</title> <script src="http://code.jquery.com/jquery-latest.js"></script> </head> <body> {% if form.errors %} <p style="color: red;"> Please correct the error{{ form.errors|pluralize }} below. </p> {% endif %} <form action="/idnumber/" method="post" align= "center">{% csrf_token %} <p>Select different id numbers and the name of the <b> Measurement Group </b>to start the comparison</p> <table align = "center"> <tr> <th><label for="id_criteria_1">Id Number 1:</label></th> <th> {{ form.idnumber_1 }} </th> </tr> <tr> <th><label for="id_criteria_2">Id Number 2:</label></th> <th> {{ form.idnumber_2 }} </th> </tr> <tr> <th><label for="group_name_list">Group Name:</label></th> <th><select name="group_name_list" id="id_group_name_list"> <option>Select</option> </select> </th> </tr> <script> $('#id_idnumber_2').change( function get_group_names() { var value1 = $('#id_idnumber_1').attr('value'); var value2 = $(this).attr('value'); alert(value1); alert(value2); var request = $.ajax({ url: "/getGroupnamesforIDnumber/", type: "GET", data: {idnumber_1 : value1,idnumber_2 : value2}, dataType: "json", success: function(data) { alert(data); var myselect = document.getElementById("group_name_list"); document.getElementById("group_name_list").options.length = 1; var length_of_data = data.length; alert(length_of_data); try{ for(var i = 0;i < length_of_data; i++) { myselect.add(new Option(data[i].group_name, "i"), myselect.options[i]) //add new option to beginning of "sample" } } catch(e){ //in IE, try the below version instead of add() for(var i = 0;i < length_of_data; i++) { myselect.add(new Option(data[i].group_name, data[i].group_name)) //add new option to end of "sample" } } } }); }); </script> <tr align = "center"><th><input type="submit" value="Submit"></th></tr> </table> </form> </body> </html> everything works fine but there is a little problem in my code. (ie) my ajax function calls only when there is a change in the select list 2 (ie) 'id_number_2'. I want to make it call in such a way that which ever select box, the third list box should be updated automatically. Can anyone please help me on this with a possible logical solution

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