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| <h1 class="page-title">Python Patterns - An Optimization Anecdote</h1> | |
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| <h1>Python Patterns - An Optimization Anecdote</h1> | |
| The other day, a friend asked me a seemingly simple question: what's the | |
| best way to convert a list of integers into a string, presuming that the | |
| integers are ASCII values. For instance, the list [97, 98, 99] should be | |
| converted to the string 'abc'. Let's assume we want to write a function to | |
| do this.<p> | |
| </p><p>The first version I came up with was totally straightforward:</p> | |
| <pre> def f1(list): | |
| string = "" | |
| for item in list: | |
| string = string + chr(item) | |
| return string | |
| </pre> | |
| <p>That can't be the fastest way to do it, said my friend. How about this one:</p> | |
| <pre> def f2(list): | |
| return reduce(lambda string, item: string + chr(item), list, "") | |
| </pre> | |
| <p>This version performs exactly the same set of string operations as the | |
| first one, but gets rid of the for loop overhead in favor of the faster, | |
| implied loop of the reduce() function. | |
| </p><p>Sure, I replied, but it does so at the cost of a function call (the lambda | |
| function) per list item. I betcha it's slower, since function call | |
| overhead in Python is bigger than for loop overhead. | |
| </p><p>(OK, so I had already done the comparisons. f2() took 60% more time than f1(). So there :-) | |
| </p><p>Hmm, said my friend. I need this to be faster. OK, I said, how about | |
| this version: | |
| </p><pre> def f3(list): | |
| string = "" | |
| for character in map(chr, list): | |
| string = string + character | |
| return string | |
| </pre> | |
| <p>To both our surprise, f3() clocked <i>twice</i> as fast as f1()! The | |
| reason that this surprised us was twofold: first, it uses more storage (the | |
| result of map(chr, list) is another list of the same length); second, it | |
| contains two loops instead of one (the one implied by the map() function, | |
| and the for loop). | |
| </p><p>Of course, space versus time is a well-known trade-off, so the first one | |
| shouldn't have surprised us. However, how come two loops are faster than | |
| one? Two reasons. | |
| </p><p>First, in f1(), the built-in function chr() is looked up on every | |
| iteration, while in f3() it is only looked up once (as the argument to | |
| map()). This look-up is relatively expensive, I told my friend, since | |
| Python's dynamic scope rules mean that it is first looked up | |
| (unsuccessfully) in the current module's global dictionary, and then in the | |
| dictionary of built-in function (where it is found). Worse, unsuccessful | |
| dictionary lookups are (on average) a bit slower than successful ones, | |
| because of the way the hash chaining works. | |
| </p><p>The second reason why f3() is faster than f1() is that the call to | |
| chr(item), as executed by the bytecode interpreter, is probably a bit | |
| slower than when executed by the map() function - the bytecode interpreter | |
| must execute three bytecode instructions for each call (load 'chr', load | |
| 'item', call), while the map() function does it all in C. | |
| </p><p>This led us to consider a compromise, which wouldn't waste extra space, but | |
| which would speed up the lookup for the chr() function: | |
| </p><pre> def f4(list): | |
| string = "" | |
| lchr = chr | |
| for item in list: | |
| string = string + lchr(item) | |
| return string | |
| </pre> | |
| <p>As expected, f4() was slower than f3(), but only by 25%; it was about 40% | |
| faster than f1() still. This is because local variable lookups are | |
| <i>much</i> faster than global or built-in variable lookups: the Python | |
| "compiler" optimizes most function bodies so that for local variables, no | |
| dictionary lookup is necessary, but a simple array indexing operation is | |
| sufficient. The relative speed of f4() compared to f1() and f3() suggests | |
| that both reasons why f3() is faster contribute, but that the first reason | |
| (fewer lookups) is a bit more important. (To get more precise data on | |
| this, we would have to instrument the interpreter.) | |
| </p><p>Still, our best version, f3(), was only twice as fast as the most | |
| straightforward version, f1(). Could we do better? | |
| </p><p>I was worried that the quadratic behavior of the algorithm was killing | |
| us. So far, we had been using a list of 256 integers as test data, | |
| since that was what my friend needed the function for. But what if it | |
| were applied to a list of two thousand characters? We'd be | |
| concatenating longer and longer strings, one character at a time. It | |
| is easy to see that, apart from overhead, to create a list of length N | |
| in this way, there are 1 + 2 + 3 + ... + (N-1) characters to be | |
| copied in total, or N*(N-1)/2, or 0.5*N**2 - 0.5*N. In addition to | |
| this, there are N string allocation operations, but for sufficiently | |
| large N, the term containing N**2 will take over. Indeed, for a list | |
| that's 8 times as long (2048 items), these functions all take much | |
| more than 8 times as long; close to 16 times as long, in fact. I | |
| didn't dare try a list of 64 times as long. | |
| </p><p>There's a general technique to avoid quadratic behavior in algorithms like | |
| this. I coded it as follows for strings of exactly 256 items: | |
| </p><pre> def f5(list): | |
| string = "" | |
| for i in range(0, 256, 16): # 0, 16, 32, 48, 64, ... | |
| s = "" | |
| for character in map(chr, list[i:i+16]): | |
| s = s + character | |
| string = string + s | |
| return string | |
| </pre> | |
| <p>Unfortunately, for a list of 256 items, this version ran a bit slower | |
| (though within 20%) of f3(). Since writing a general version would only | |
| slow it down more, we didn't bother to pursue this path any further | |
| (except that we also compared it with a variant that didn't use map(), | |
| which of course was slower again). | |
| </p><p>Finally, I tried a radically different approach: use <i>only</i> implied | |
| loops. Notice that the whole operation can be described as follows: apply | |
| chr() to each list item; then concatenate the resulting characters. We | |
| were already using an implied loop for the first part: map(). Fortunately, | |
| there are some string concatenation functions in the string module that are | |
| implemented in C. In particular, string.joinfields(list_of_strings, | |
| delimiter) concatenates a list of strings, placing a delimiter of choice | |
| between each two strings. Nothing stops us from concatenating a list of | |
| characters (which are just strings of length one in Python), using the | |
| empty string as delimiter. Lo and behold: | |
| </p><pre> import string | |
| def f6(list): | |
| return string.joinfields(map(chr, list), "") | |
| </pre> | |
| <p>This function ran four to five times as fast as our fastest contender, | |
| f3(). Moreover, it doesn't have the quadratic behavior of the other | |
| versions. | |
| </p><h3>And The Winner Is...</h3> | |
| <p>The next day, I remembered an odd corner of Python: the array module. This | |
| happens to have an operation to create an array of 1-byte wide integers | |
| from a list of Python integers, and every array can be written to a file or | |
| converted to a string as a binary data structure. Here's our function | |
| implemented using these operations: | |
| </p><pre> import array | |
| def f7(list): | |
| return array.array('B', list).tostring() | |
| </pre> | |
| <p>This is about three times as fast as f6(), or 12 to 15 times as fast as | |
| f3()! it also uses less intermediate storage - it only allocates 2 objects | |
| of N bytes (plus fixed overhead), while f6() begins by allocating a list of | |
| N items, which usually costs 4N bytes (8N bytes on a 64-bit machine) - | |
| assuming the character objects are shared with similar objects elsewhere | |
| in the program (like small integers, Python caches strings of length one | |
| in most cases). | |
| </p><p>Stop, said my friend, before you get into negative times - this is fast | |
| enough for my program. I agreed, though I had wanted to try one more | |
| approach: write the whole function in C. This could have minimal storage | |
| requirements (it would allocate a string of length N right away) and save a | |
| few instructions in the C code that I knew were there in the array module, | |
| because of its genericity (it supports integer widths of 1, 2, and 4 | |
| bytes). However, it wouldn't be able to avoid having to extract the items | |
| from the list one at a time, and to extract the C integer from them, both | |
| of which are fairly costly operations in the Python-C API, so I expected at | |
| most modest speed up compared to f7(). Given the effort of writing and | |
| testing an extension (compared to whipping up those Python one-liners), as | |
| well as the dependency on a non-standard Python extension, I decided not to | |
| pursue this option... | |
| </p><h3>Conclusion</h3> | |
| <p>If you feel the need for speed, go for built-in functions - you can't beat | |
| a loop written in C. Check the library manual for a built-in function that | |
| does what you want. If there isn't one, here are some guidelines for loop | |
| optimization: | |
| </p><ul> | |
| <li><i>Rule number one:</i> only optimize when there is a proven speed | |
| bottleneck. Only optimize the innermost loop. (This rule is independent | |
| of Python, but it doesn't hurt repeating it, since it can save a lot of | |
| work. :-) | |
| </li><li>Small is beautiful. Given Python's hefty charges for bytecode | |
| instructions and variable look-up, it rarely pays off to add extra tests | |
| to save a little bit of work. | |
| </li><li>Use intrinsic operations. An implied loop in map() is faster than an | |
| explicit for loop; a while loop with an explicit loop counter is even | |
| slower. | |
| </li><li>Avoid calling functions written in Python in your inner loop. This | |
| includes lambdas. In-lining the inner loop can save a lot of time. | |
| </li><li>Local variables are faster than globals; if you use a global constant | |
| in a loop, copy it to a local variable before the loop. And in Python, | |
| function names (global or built-in) are also global constants! | |
| </li><li>Try to use map(), filter() or reduce() to replace an explicit for loop, | |
| but only if you can use a built-in function: map with a built-in function | |
| beats for loop, but a for loop with in-line code beats map with a lambda | |
| function! | |
| </li><li>Check your algorithms for quadratic behavior. But notice that a more | |
| complex algorithm only pays off for large N - for small N, the complexity | |
| doesn't pay off. In our case, 256 turned out to be small enough that the | |
| simpler version was still a tad faster. Your mileage may vary - this is | |
| worth investigating. | |
| </li><li>And last but not least: collect data. Python's excellent profile | |
| module can quickly show the bottleneck in your code. if you're considering | |
| different versions of an algorithm, test it in a tight loop using the | |
| time.clock() function. | |
| </li></ul> | |
| <p>By the way, here's the timing function that I used. it calls a function f | |
| n*10 times with argument a, and prints the function name followed by the | |
| time it took, rounded to milliseconds. The 10 repeated calls are done to | |
| minimize the loop overhead of the timing function itself. You could go | |
| even further and make 100 calls... Also note that the expression range(n) | |
| is calculated outside the timing brackets - another trick to minimize the | |
| overhead caused by the timing function. If you are worried about this | |
| overhead, you can calibrate it by calling the timing function with a | |
| do-nothing function. | |
| </p><pre> import time | |
| def timing(f, n, a): | |
| print f.__name__, | |
| r = range(n) | |
| t1 = time.clock() | |
| for i in r: | |
| f(a); f(a); f(a); f(a); f(a); f(a); f(a); f(a); f(a); f(a) | |
| t2 = time.clock() | |
| print round(t2-t1, 3) | |
| </pre> | |
| <h3>Epilogue</h3> | |
| <p>A few days later, my friend was back with the question: how do you do | |
| the reverse operation? I.e. create a list of integer ASCII values | |
| from a string. Oh no, here we go again, it flashed through my | |
| mind... | |
| </p><p>But this time, it was relatively painless. There are two candidates, | |
| the obvious: | |
| </p><pre> def g1(string): | |
| return map(ord, string) | |
| </pre> | |
| <p>and the somewhat less obvious: | |
| </p><pre> import array | |
| def g2(string): | |
| return array.array('b', string).tolist() | |
| </pre> | |
| <p>Timing these reveals that g2() is about five times as fast as g1(). | |
| There's a catch though: g2() returns integers in the range -128..127, | |
| while g1() returns integers in the range 0..255. If you need the | |
| positive integers, g1() is going to be faster than anything | |
| postprocessing you could do on the result from g2(). (Note: since | |
| this essay was written, the 'B' typecode was added to the array | |
| module, which stores unsigned bytes, so there's no reason to prefer | |
| g1() any more.) | |
| </p><h3>Sample Code</h3> | |
| <ul> | |
| <li><a href="f.py">timing f1() through f7()</a> | |
| </li><li><a href="g.py">timing g1() and g2()</a> | |
| </li></ul> | |
| </article> | |
| </section> | |
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| <h1 class="page-title">Python Patterns - An Optimization Anecdote</h1> | |
| </header> | |
| <h1>Python Patterns - An Optimization Anecdote</h1> | |
| The other day, a friend asked me a seemingly simple question: what's the | |
| best way to convert a list of integers into a string, presuming that the | |
| integers are ASCII values. For instance, the list [97, 98, 99] should be | |
| converted to the string 'abc'. Let's assume we want to write a function to | |
| do this.<p> | |
| </p><p>The first version I came up with was totally straightforward:</p> | |
| <pre> def f1(list): | |
| string = "" | |
| for item in list: | |
| string = string + chr(item) | |
| return string | |
| </pre> | |
| <p>That can't be the fastest way to do it, said my friend. How about this one:</p> | |
| <pre> def f2(list): | |
| return reduce(lambda string, item: string + chr(item), list, "") | |
| </pre> | |
| <p>This version performs exactly the same set of string operations as the | |
| first one, but gets rid of the for loop overhead in favor of the faster, | |
| implied loop of the reduce() function. | |
| </p><p>Sure, I replied, but it does so at the cost of a function call (the lambda | |
| function) per list item. I betcha it's slower, since function call | |
| overhead in Python is bigger than for loop overhead. | |
| </p><p>(OK, so I had already done the comparisons. f2() took 60% more time than f1(). So there :-) | |
| </p><p>Hmm, said my friend. I need this to be faster. OK, I said, how about | |
| this version: | |
| </p><pre> def f3(list): | |
| string = "" | |
| for character in map(chr, list): | |
| string = string + character | |
| return string | |
| </pre> | |
| <p>To both our surprise, f3() clocked <i>twice</i> as fast as f1()! The | |
| reason that this surprised us was twofold: first, it uses more storage (the | |
| result of map(chr, list) is another list of the same length); second, it | |
| contains two loops instead of one (the one implied by the map() function, | |
| and the for loop). | |
| </p><p>Of course, space versus time is a well-known trade-off, so the first one | |
| shouldn't have surprised us. However, how come two loops are faster than | |
| one? Two reasons. | |
| </p><p>First, in f1(), the built-in function chr() is looked up on every | |
| iteration, while in f3() it is only looked up once (as the argument to | |
| map()). This look-up is relatively expensive, I told my friend, since | |
| Python's dynamic scope rules mean that it is first looked up | |
| (unsuccessfully) in the current module's global dictionary, and then in the | |
| dictionary of built-in function (where it is found). Worse, unsuccessful | |
| dictionary lookups are (on average) a bit slower than successful ones, | |
| because of the way the hash chaining works. | |
| </p><p>The second reason why f3() is faster than f1() is that the call to | |
| chr(item), as executed by the bytecode interpreter, is probably a bit | |
| slower than when executed by the map() function - the bytecode interpreter | |
| must execute three bytecode instructions for each call (load 'chr', load | |
| 'item', call), while the map() function does it all in C. | |
| </p><p>This led us to consider a compromise, which wouldn't waste extra space, but | |
| which would speed up the lookup for the chr() function: | |
| </p><pre> def f4(list): | |
| string = "" | |
| lchr = chr | |
| for item in list: | |
| string = string + lchr(item) | |
| return string | |
| </pre> | |
| <p>As expected, f4() was slower than f3(), but only by 25%; it was about 40% | |
| faster than f1() still. This is because local variable lookups are | |
| <i>much</i> faster than global or built-in variable lookups: the Python | |
| "compiler" optimizes most function bodies so that for local variables, no | |
| dictionary lookup is necessary, but a simple array indexing operation is | |
| sufficient. The relative speed of f4() compared to f1() and f3() suggests | |
| that both reasons why f3() is faster contribute, but that the first reason | |
| (fewer lookups) is a bit more important. (To get more precise data on | |
| this, we would have to instrument the interpreter.) | |
| </p><p>Still, our best version, f3(), was only twice as fast as the most | |
| straightforward version, f1(). Could we do better? | |
| </p><p>I was worried that the quadratic behavior of the algorithm was killing | |
| us. So far, we had been using a list of 256 integers as test data, | |
| since that was what my friend needed the function for. But what if it | |
| were applied to a list of two thousand characters? We'd be | |
| concatenating longer and longer strings, one character at a time. It | |
| is easy to see that, apart from overhead, to create a list of length N | |
| in this way, there are 1 + 2 + 3 + ... + (N-1) characters to be | |
| copied in total, or N*(N-1)/2, or 0.5*N**2 - 0.5*N. In addition to | |
| this, there are N string allocation operations, but for sufficiently | |
| large N, the term containing N**2 will take over. Indeed, for a list | |
| that's 8 times as long (2048 items), these functions all take much | |
| more than 8 times as long; close to 16 times as long, in fact. I | |
| didn't dare try a list of 64 times as long. | |
| </p><p>There's a general technique to avoid quadratic behavior in algorithms like | |
| this. I coded it as follows for strings of exactly 256 items: | |
| </p><pre> def f5(list): | |
| string = "" | |
| for i in range(0, 256, 16): # 0, 16, 32, 48, 64, ... | |
| s = "" | |
| for character in map(chr, list[i:i+16]): | |
| s = s + character | |
| string = string + s | |
| return string | |
| </pre> | |
| <p>Unfortunately, for a list of 256 items, this version ran a bit slower | |
| (though within 20%) of f3(). Since writing a general version would only | |
| slow it down more, we didn't bother to pursue this path any further | |
| (except that we also compared it with a variant that didn't use map(), | |
| which of course was slower again). | |
| </p><p>Finally, I tried a radically different approach: use <i>only</i> implied | |
| loops. Notice that the whole operation can be described as follows: apply | |
| chr() to each list item; then concatenate the resulting characters. We | |
| were already using an implied loop for the first part: map(). Fortunately, | |
| there are some string concatenation functions in the string module that are | |
| implemented in C. In particular, string.joinfields(list_of_strings, | |
| delimiter) concatenates a list of strings, placing a delimiter of choice | |
| between each two strings. Nothing stops us from concatenating a list of | |
| characters (which are just strings of length one in Python), using the | |
| empty string as delimiter. Lo and behold: | |
| </p><pre> import string | |
| def f6(list): | |
| return string.joinfields(map(chr, list), "") | |
| </pre> | |
| <p>This function ran four to five times as fast as our fastest contender, | |
| f3(). Moreover, it doesn't have the quadratic behavior of the other | |
| versions. | |
| </p><h3>And The Winner Is...</h3> | |
| <p>The next day, I remembered an odd corner of Python: the array module. This | |
| happens to have an operation to create an array of 1-byte wide integers | |
| from a list of Python integers, and every array can be written to a file or | |
| converted to a string as a binary data structure. Here's our function | |
| implemented using these operations: | |
| </p><pre> import array | |
| def f7(list): | |
| return array.array('B', list).tostring() | |
| </pre> | |
| <p>This is about three times as fast as f6(), or 12 to 15 times as fast as | |
| f3()! it also uses less intermediate storage - it only allocates 2 objects | |
| of N bytes (plus fixed overhead), while f6() begins by allocating a list of | |
| N items, which usually costs 4N bytes (8N bytes on a 64-bit machine) - | |
| assuming the character objects are shared with similar objects elsewhere | |
| in the program (like small integers, Python caches strings of length one | |
| in most cases). | |
| </p><p>Stop, said my friend, before you get into negative times - this is fast | |
| enough for my program. I agreed, though I had wanted to try one more | |
| approach: write the whole function in C. This could have minimal storage | |
| requirements (it would allocate a string of length N right away) and save a | |
| few instructions in the C code that I knew were there in the array module, | |
| because of its genericity (it supports integer widths of 1, 2, and 4 | |
| bytes). However, it wouldn't be able to avoid having to extract the items | |
| from the list one at a time, and to extract the C integer from them, both | |
| of which are fairly costly operations in the Python-C API, so I expected at | |
| most modest speed up compared to f7(). Given the effort of writing and | |
| testing an extension (compared to whipping up those Python one-liners), as | |
| well as the dependency on a non-standard Python extension, I decided not to | |
| pursue this option... | |
| </p><h3>Conclusion</h3> | |
| <p>If you feel the need for speed, go for built-in functions - you can't beat | |
| a loop written in C. Check the library manual for a built-in function that | |
| does what you want. If there isn't one, here are some guidelines for loop | |
| optimization: | |
| </p><ul> | |
| <li><i>Rule number one:</i> only optimize when there is a proven speed | |
| bottleneck. Only optimize the innermost loop. (This rule is independent | |
| of Python, but it doesn't hurt repeating it, since it can save a lot of | |
| work. :-) | |
| </li><li>Small is beautiful. Given Python's hefty charges for bytecode | |
| instructions and variable look-up, it rarely pays off to add extra tests | |
| to save a little bit of work. | |
| </li><li>Use intrinsic operations. An implied loop in map() is faster than an | |
| explicit for loop; a while loop with an explicit loop counter is even | |
| slower. | |
| </li><li>Avoid calling functions written in Python in your inner loop. This | |
| includes lambdas. In-lining the inner loop can save a lot of time. | |
| </li><li>Local variables are faster than globals; if you use a global constant | |
| in a loop, copy it to a local variable before the loop. And in Python, | |
| function names (global or built-in) are also global constants! | |
| </li><li>Try to use map(), filter() or reduce() to replace an explicit for loop, | |
| but only if you can use a built-in function: map with a built-in function | |
| beats for loop, but a for loop with in-line code beats map with a lambda | |
| function! | |
| </li><li>Check your algorithms for quadratic behavior. But notice that a more | |
| complex algorithm only pays off for large N - for small N, the complexity | |
| doesn't pay off. In our case, 256 turned out to be small enough that the | |
| simpler version was still a tad faster. Your mileage may vary - this is | |
| worth investigating. | |
| </li><li>And last but not least: collect data. Python's excellent profile | |
| module can quickly show the bottleneck in your code. if you're considering | |
| different versions of an algorithm, test it in a tight loop using the | |
| time.clock() function. | |
| </li></ul> | |
| <p>By the way, here's the timing function that I used. it calls a function f | |
| n*10 times with argument a, and prints the function name followed by the | |
| time it took, rounded to milliseconds. The 10 repeated calls are done to | |
| minimize the loop overhead of the timing function itself. You could go | |
| even further and make 100 calls... Also note that the expression range(n) | |
| is calculated outside the timing brackets - another trick to minimize the | |
| overhead caused by the timing function. If you are worried about this | |
| overhead, you can calibrate it by calling the timing function with a | |
| do-nothing function. | |
| </p><pre> import time | |
| def timing(f, n, a): | |
| print f.__name__, | |
| r = range(n) | |
| t1 = time.clock() | |
| for i in r: | |
| f(a); f(a); f(a); f(a); f(a); f(a); f(a); f(a); f(a); f(a) | |
| t2 = time.clock() | |
| print round(t2-t1, 3) | |
| </pre> | |
| <h3>Epilogue</h3> | |
| <p>A few days later, my friend was back with the question: how do you do | |
| the reverse operation? I.e. create a list of integer ASCII values | |
| from a string. Oh no, here we go again, it flashed through my | |
| mind... | |
| </p><p>But this time, it was relatively painless. There are two candidates, | |
| the obvious: | |
| </p><pre> def g1(string): | |
| return map(ord, string) | |
| </pre> | |
| <p>and the somewhat less obvious: | |
| </p><pre> import array | |
| def g2(string): | |
| return array.array('b', string).tolist() | |
| </pre> | |
| <p>Timing these reveals that g2() is about five times as fast as g1(). | |
| There's a catch though: g2() returns integers in the range -128..127, | |
| while g1() returns integers in the range 0..255. If you need the | |
| positive integers, g1() is going to be faster than anything | |
| postprocessing you could do on the result from g2(). (Note: since | |
| this essay was written, the 'B' typecode was added to the array | |
| module, which stores unsigned bytes, so there's no reason to prefer | |
| g1() any more.) | |
| </p><h3>Sample Code</h3> | |
| <ul> | |
| <li><a href="f.py">timing f1() through f7()</a> | |
| </li><li><a href="g.py">timing g1() and g2()</a> | |
| </li></ul> | |
| </article> | |
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| <\!--<li class="tier-1 element-3"><a href="#"><span class="say-no-more">Website</span> Colophon</a></li>--> | |
| </ul> | |
| <div class="copyright"> | |
| <p><small> | |
| <span class="pre">Copyright ©2001-2015.</span> | |
| <span class="pre"><a href="/psf-landing/">Python Software Foundation</a></span> | |
| <span class="pre"><a href="/about/legal/">Legal Statements</a></span> | |
| <span class="pre"><a href="/privacy/">Privacy Policy</a></span> | |
| </small></p> | |
| </div> | |
| </div><\!-- end .container --> | |
| </div><\!-- end .site-base --> | |
| </footer> | |
| </div><\!-- end #touchnav-wrapper --> | |
| <script src="//ajax.googleapis.com/ajax/libs/jquery/1.8.2/jquery.min.js"><\/script> | |
| <script>window.jQuery || document.write('<script src="/static/js/libs/jquery-1.8.2.min.js"><\/script>')<\/script> | |
| <script src="/static/js/libs/masonry.pkgd.min.js"><\/script> | |
| <script type="text/javascript" src="/static/js/main-min.js" charset="utf-8"><\/script> | |
| <\!--[if lte IE 7]> | |
| <script type="text/javascript" src="/static/js/plugins/IE8-min.js" charset="utf-8"><\/script> | |
| <![endif]--> | |
| <\!--[if lte IE 8]> | |
| <script type="text/javascript" src="/static/js/plugins/getComputedStyle-min.js" charset="utf-8"><\/script> | |
| <![endif]--> | |
| <iframe id="rufous-sandbox" scrolling="no" frameborder="0" allowtransparency="true" style="display: none;"></iframe></body></html></script> | |
| <script id="jsbin-source-javascript" type="text/javascript"> | |
| addlib = (url) => | |
| console.log "." | |
| e = document.createElement "script" | |
| e.type = "text/javascript" | |
| e.src = url | |
| console.log e | |
| document.body.appendChild e | |
| x = -> | |
| o = 1e3 | |
| console.log "Waiting", o, "ms, to check", e.src, "loading status." | |
| return -> | |
| console.log "Waited", o, "ms. jQuery.fn.jquery ->", jQuery.fn.jquery | |
| setTimeout x(), o | |
| # addlib "https://code.jquery.com/jquery-2.1.4.min.js" | |
| flatmap = (l,f) => | |
| r = [] | |
| l.forEach (e) => | |
| if Array.isArray e | |
| then Array.prototype.push.apply r, (flatmap e,f) | |
| else r.push (f e) | |
| r | |
| neigh = (d) => | |
| ps = $(d).parents().get() | |
| flatmap ps, (e) => $(e).siblings() | |
| readable = (d) => | |
| n = neigh d | |
| l = neigh.length | |
| if l > 1000 then alert "#{l} siblings!" else n.forEach (e) => $(e).toggle()</script></body></html> |
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| var addlib; | |
| addlib = (function(_this) { | |
| return function(url) { | |
| var e, x; | |
| console.log("."); | |
| e = document.createElement("script"); | |
| e.type = "text/javascript"; | |
| e.src = url; | |
| console.log(e); | |
| document.body.appendChild(e); | |
| var o; | |
| o = 1e3; | |
| x = function() { | |
| console.log("Waiting", o, "ms, to check", e.src, "loading status."); | |
| return function() { | |
| return console.log("Waited", o, "ms. jQuery.fn.jquery ->", jQuery.fn.jquery); | |
| }; | |
| }; | |
| return setTimeout(x(), o); | |
| }; | |
| })(this); | |
| addlib("https://code.jquery.com/jquery-2.1.4.min.js"); | |
| var flatmap, neigh, readable; | |
| flatmap = (function(_this) { | |
| return function(l, f) { | |
| var r; | |
| r = []; | |
| l.forEach(function(e) { | |
| if (Array.isArray(e)) { | |
| return Array.prototype.push.apply(r, flatmap(e, f)); | |
| } else { | |
| return r.push(f(e)); | |
| } | |
| }); | |
| return r; | |
| }; | |
| })(this); | |
| neigh = (function(_this) { | |
| return function(d) { | |
| var ps; | |
| ps = $(d).parents().get(); | |
| return flatmap(ps, function(e) { | |
| return $(e).siblings(); | |
| }); | |
| }; | |
| })(this); | |
| readable = (function(_this) { | |
| return function(d) { | |
| var l, n; | |
| n = neigh(d); | |
| l = neigh.length; | |
| if (l > 1000) { | |
| return alert("" + l + " siblings!"); | |
| } else { | |
| return n.forEach(function(e) { | |
| return $(e).toggle(); | |
| }); | |
| } | |
| }; | |
| })(this); |
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