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• comparison between string literal

  - by wiso

  This very simple code: #include <iostream> using namespace std; void exec(char* option) { cout << "option is " << option << endl; if (option == "foo") cout << "option foo"; else if (option == "bar") cout << "opzion bar"; else cout << "???"; cout << endl; } int main() { char opt[] = "foo"; exec(opt); return 0; } generate two warning: comparison with string literal results in unspecified behaviour. Can you explain why exactly this code doesn't work, but if I change char opt[] to char *opt it works, but generates the warning? Is it related to the \0 termination? What is the difference between the two declaration of opt? What if I use const qualifier? The solution is to use std::string?

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• auto vectorization on double and ffast-math

  - by wiso

  Why is mandatory to use -ffast-math with g++ to achieve vectorization of loop using doubles? I don't like ffast-math because I don't want to loose precision.

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• do I need to use partial?

  - by wiso

  I've a general function, for example (only a simplified example): def do_operation(operation, a, b, name): print name do_something_more(a,b,name, operation(a,b)) def operation_x(a,b): return a**2 + b def operation_y(a,b): return a**10 - b/2. and some data: data = {"first": {"name": "first summation", "a": 10, "b": 20, "operation": operation_x}, "second": {"name": "second summation", "a": 20, "b": 50, "operation": operation_y}, "third": {"name": "third summation", "a": 20, "b": 50, "operation": operation_x}, # <-- operation_x again } now I can do: what_to_do = ("first", "third") # this comes from command line for sum_id in what_to_do: do_operation(data["operation"], data["a"], data["b"], data["name"]) or maybe it's better if I use functools.partial? from functools import partial do_operation_one = do_operation(name=data["first"]["name"], operation=data["first"]["operation"], a=data["first"]["a"], b=data["first"]["b"]) do_operation_two = do_operation(name=data["second"]["name"], operation=data["second"]["operation"] a=data["second"]["a"], b=data["second"]["b"]) do_operation_three = do_operation(name=data["third"]["name"], operation=data["third"]["operation"] a=data["third"]["a"], b=data["third"]["b"]) do_dictionary = { "first": do_operation_one, "second": do_operation_two, "third": do_operation_three } for what in what_to_do: do_dictionary[what]()

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• scoping problem in recursive closure

  - by wiso

  why this work: def function1(): a = 10 def function2(): print a function2() but this not: def function1(): a = 10 def function2(): print a a -= 1 if a>0: function2() function2() error: UnboundLocalError: local variable 'a' referenced before assignment

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• inheritance from str or int

  - by wiso

  Why I have problem creating a class the inherite from str (or also int) class C(str): def __init__(self, a, b): str.__init__(self,a) self.b = b C("a", "B") TypeError: str() takes at most 1 argument (2 given) tha same appened if I try to use int instead of str, but it works with custom classes. I need to use __new__ instead of __init__? why?

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• redefine __and__ operator

  - by wiso

  Why I can't redefine the __and__ operator? class Cut(object): def __init__(self, cut): self.cut = cut def __and__(self, other): return Cut("(" + self.cut + ") && (" + other.cut + ")") a = Cut("a>0") b = cut("b>0") c = a and b print c.cut() I want (a>0) && (b>0), but I got b, that the usual behaviour of and

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• call multiple c++ functions in python using threads

  - by wiso

  Suppose I have a C(++) function taking an integer, and it is bound to (C)python with python api, so I can call it from python: import c_module c_module.f(10) now, I want to parallelize it. The problem is: how does the GIL work in this case? Suppose I have a queue of numbers to be processed, and some workers (threading.Thread) working in parallel, each of them calling c_module.f(number) where number is taken from a queue. The difference with the usual case, when GIL lock the interpreter, is that now you don't need the interpreter to evaluate c_module.f because it is compiled. So the question is: in this case the processing is really parallel?

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• add a decorate function to a class

  - by wiso

  I have a decorated function (simplified version): class Memoize: def __init__(self, function): self.function = function self.memoized = {} def __call__(self, *args, **kwds): hash = args try: return self.memoized[hash] except KeyError: self.memoized[hash] = self.function(*args) return self.memoized[hash] @Memoize def _DrawPlot(self, options): do something... now I want to add this method to a pre-esisting class. ROOT.TChain.DrawPlot = _DrawPlot when I call this method: chain = TChain() chain.DrawPlot(opts) I got: self.memoized[hash] = self.function(*args) TypeError: _DrawPlot() takes exactly 2 arguments (1 given) why doesn't it propagate self?

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• cpu floating operations cost

  - by wiso

  I'm interesting in the time cost on a modern desktop cpu of some floating point operations in order to optimize a mathematical evaluation. In particular I'm interested on the comparison between complex operations like exp, log and simple operation like +, *, /. I tried to search for these information, but I can't find a source.

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• numpy array C api

  - by wiso

  I have a C++ function returning a std::vector and I want to use it in python, so I'm using the C numpy api: static PyObject * py_integrate(PyObject *self, PyObject *args){ ... std::vector<double> integral; cpp_function(integral); // this change integral npy_intp size = {integral.size()}; PyObject *out = PyArray_SimpleNewFromData(1, &size, NPY_DOUBLE, &(integral[0])); return out; } when I call it from python, if I do import matplotlib.pyplot as plt a = py_integrate(parameters) print a fig = plt.figure() ax = fig.add_subplot(111) ax.plot(a) print a the first print is ok, the values are correct, but when I plot a they are not, and in particular in the second print I see very strange values like 1E-308 1E-308 ... or 0 0 0 ... as an unitialized memory. I don't understand why the first print is ok.

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• optimize output value using a class and public member

  - by wiso

  Suppose you have a function, and you call it a lot of times, every time the function return a big object. I've optimized the problem using a functor that return void, and store the returning value in a public member: #include <vector> const int N = 100; std::vector<double> fun(const std::vector<double> & v, const int n) { std::vector<double> output = v; output[n] *= output[n]; return output; } class F { public: F() : output(N) {}; std::vector<double> output; void operator()(const std::vector<double> & v, const int n) { output = v; output[n] *= n; } }; int main() { std::vector<double> start(N,10.); std::vector<double> end(N); double a; // first solution for (unsigned long int i = 0; i != 10000000; ++i) a = fun(start, 2)[3]; // second solution F f; for (unsigned long int i = 0; i != 10000000; ++i) { f(start, 2); a = f.output[3]; } } Yes, I can use inline or optimize in an other way this problem, but here I want to stress on this problem: with the functor I declare and construct the output variable output only one time, using the function I do that every time it is called. The second solution is two time faster than the first with g++ -O1 or g++ -O2. What do you think about it, is it an ugly optimization?

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• define global in a python module from C api

  - by wiso

  Sorry for the trivial question, but I can't find this infomation from the manual. I am developping a module for python using C api; how can I create a variabile that is seen as global from python? For example if my module is module I want to create a variable g that do this job: import module print module.g in particular g is an integer. Solution from Alex Martelli PyObject *m = Py_InitModule("mymodule", mymoduleMethods); PyObject *v = PyLong_FromLong((long) 23); PyObject_SetAttrString(m, "L", v); Py_DECREF(v);

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• optimization math computation (multiplication and summing)

  - by wiso

  Suppose you want to compute the sum of the square of the differences of items: $\sum_{i=1}^{N-1} (x_i - x_{i+1})^2$, the simplest code (the input is std::vector<double> xs, the ouput sum2) is: double sum2 = 0.; double prev = xs[0]; for (vector::const_iterator i = xs.begin() + 1; i != xs.end(); ++i) { sum2 += (prev - (*i)) * (prev - (*i)); // only 1 - with compiler optimization prev = (*i); } I hope that the compiler do the optimization in the comment above. If N is the length of xs you have N-1 multiplications and 2N-3 sums (sums means + or -). Now suppose you know this variable: sum = $x_1^2 + x_N^2 + 2 sum_{i=2}^{N-1} x_i^2$ Expanding the binomial square: $sum_i^{N-1} (x_i-x_{i+1})^2 = sum - 2\sum_{i=1}^{N-1} x_i x_{i+1}$ so the code becomes: double sum2 = 0.; double prev = xs[0]; for (vector::const_iterator i = xs.begin() + 1; i != xs.end(); ++i) { sum2 += (*i) * prev; prev = (*i); } sum2 = -sum2 * 2. + sum; Here I have N multiplications and N-1 additions. In my case N is about 100. Well, compiling with g++ -O2 I got no speed up (I try calling the inlined function 2M times), why?

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• reuse generators

  - by wiso

  I need to check the central limit with dices. Rool D dices. Sum the results. Repeat the same thing for N times. Change D and repeat. There's no need to store random values so I want to use only generators. The problem is that generators are consuming, I can't resuging them more times. Now my code use explicit for and I don't like it. dice_numbers = (1, 2, 10, 100, 1000) repetitions = 10000 for dice_number in dice_numbers: # how many dice to sum sum_container = [] for r in range(repetitions): rool_sum = sum((random.randint(1,6) for _ in range(dice_number))) sum_container.append(rool_sum) plot_histogram(sum_container) I want to create something like for r in repetitions: rools_generator = (random.randint(1,6) for _ in range(dice_number) sum_generator = (sum(rools_generator) for _ in range(r)) but the second time I resuse rools_generator it is condumed. I need to construct generator class?

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