Python sharedmem.empty函数代码示例

def main(A):
    """ match the mean fraction by fixing prefactor A(a) and B(a) on tau
        requires 'gaussian' to be finished.
        run before convolve, though it uses functions in convolve for evaluating 
        the cost function.
    """
    global meanfractionmodel
    global varfractionmodel

    varfractionmodel = VarFractionModel(A)
    meanfractionmodel = MeanFractionModel(A)

    Nbins = 8
    zBins = numpy.linspace(2.0, 4.0, Nbins + 1, endpoint=True)
    LogLamBins = numpy.log10(1216.0 * (1 + zBins ))
    z = 0.5 * (zBins[1:] + zBins[:-1])
    Af = sharedmem.empty(z.shape)
    Bf = sharedmem.empty(z.shape)
    xmeanF = sharedmem.empty(z.shape)
    xstdF = sharedmem.empty(z.shape)
    def work(i):
        if i > 0:
            Afguess, Bfguess = Af[i-1], Bf[i-1]
        else:
            Afguess, Bfguess = (0.00015, 1.5)
        Af[i], Bf[i], xmeanF[i], xstdF[i] = fitRange(A, LogLamBins[i], LogLamBins[i + 1], 
                Afguess, Bfguess)
    map(work, range(Nbins))
    numpy.savez(A.MatchMeanFractionOutput, a=1 / (z+1), 
            Af=Af, Bf=Bf, xmeanF=xmeanF, xvarF=xstdF ** 2)

    def __init__(self, face_width, res_x, res_y, f_x, f_y, fan_position, visualize=False):

        # Initialize multiprocessing.Process parent
        multiprocessing.Process.__init__(self)

        # Exit event for stopping process
        self._exit = multiprocessing.Event()

        # Event that is set, everytime a new servo angle position has been computed
        self.newposition_event = multiprocessing.Event()

        # An array in shared memory for storing the current face position
        self._currentface = sharedmem.empty((4, 1), dtype='int16')

        # An array in shared memory for storing the current position angles
        self._currentangles = sharedmem.empty((2, 1), dtype='float')

        self._facewidth = face_width
        self._res_x = res_x
        self._res_y = res_y
        self._f_x = f_x
        self._f_y = f_y
        self._fan_position = fan_position

        # Defines whether to visualize the servo angles position
        self._visualize = visualize

def test_memory_type():
    a = sharedmem.empty(100)
    b = sharedmem.empty(100)
    assert isinstance(b, type(a))

    assert not isinstance(a + 10, type(a))
    assert not isinstance(numpy.sum(a), type(a))
    assert not isinstance(a + b, type(a))
    assert not isinstance(a * b, type(a))

    def __init__(self, config):
        def getfilename(mock):
            dir = os.path.join(config.prefix, mock)
            paramfile = os.path.join(config.prefix, mock, 'paramfile')
            c = Config(paramfile, basedir=dir)
            return os.path.join(c.datadir, 'bootstrap.npz')

        if config.UseMocks is None:
            filenames = sorted(list(glob(os.path.join(config.prefix, '[0-9]*', '*',
                'bootstrap.npz'))))
        else:
            filenames = [getfilename(mock) for mock in config.UseMocks]

        files = [ numpy.load(f)  for f in filenames]

        print 'using', len(filenames), ' files', filenames

        self.r = files[0]['r']
        self.mu = files[0]['mu']

        # b/c they all have the same cosmology
        self.eigenmodes = EigenModes(numpy.load(config.EigenModesOutput)['eigenmodes'])

        self.DQDQ, self.RQDQ, self.RQRQ = sharedmem.empty(
                [3, len(files)] + list(files[0]['DQDQ'].shape))
        self.DQDFsum1, self.RQDFsum1, self.DFDFsum1 = sharedmem.empty(
                [3, len(files)] + list(files[0]['DQDQ'].shape))
        self.DQDFsum2, self.RQDFsum2, self.DFDFsum2 = sharedmem.empty(
                [3, len(files)] + list(files[0]['DQDQ'].shape))

        self.ND, self.NR = sharedmem.empty([2, len(files)] +
                list(files[0]['Qchunksize'].shape))

        def read(i):
            file = files[i]
            self.DQDQ[i] = file['DQDQ']
            self.RQDQ[i] = file['RQDQ']
            self.RQRQ[i] = file['RQRQ']
            self.DQDFsum1[i] = file['DQDFsum1'][0]
            self.RQDFsum1[i] = file['RQDFsum1'][0]
            self.DFDFsum1[i] = file['DFDFsum1'][0]
            self.DQDFsum2[i] = file['DQDFsum2']
            self.RQDFsum2[i] = file['RQDFsum2']
            self.DFDFsum2[i] = file['DFDFsum2']
            self.ND[i] = file['Qchunksize']
            self.NR[i] = file['Rchunksize']

        chunkmap(read, range(len(files)), 1)

        self.Nchunks = self.DQDQ[0].shape[0]
        # build the currelation function on the first sample
        # use it as a template 
        self.dummy = self(0)

def getforest(A, Zmin, Zmax, RfLamMin, RfLamMax, combine=1):
    spectra = SpectraOutput(A)
    meanFred = MeanFractionMeasured(A, kind='red')
    meanFreal = MeanFractionMeasured(A, kind='real')

    combine = numpy.minimum(spectra.sightlines.Npixels.max(), combine)

    # will combine every this many pixels
    Npixels1 = spectra.sightlines.Npixels // combine
    Offset1 = numpy.concatenate([[0], numpy.cumsum(Npixels1)])
    Npixels = Npixels1.sum()
    print Npixels1.min(), Npixels1.max()
    print spectra.sightlines.Npixels.min(), spectra.sightlines.Npixels.max()
    data = sharedmem.empty(Npixels, ('f4', 3))
    DFred, DFreal, Delta = data.T
    pos = sharedmem.empty(Npixels, ('f4', 3))
    x, y, z = pos.T
    mask = sharedmem.empty(Npixels, '?')
    id = sharedmem.empty(Npixels, 'i4')

    spectra.taured
    spectra.taureal

    def work(i):
        def combinepixels(value, method=numpy.mean):
            # reduce the number of pixels with 'method'
            return \
                method(value[:Npixels1[i] * combine]\
                .reshape([Npixels1[i]] + [combine]), 
                    axis=-1)
        sl = slice(Offset1[i], Npixels1[i] + Offset1[i])
        a = spectra.a[i]
        Fred = numpy.exp(-spectra.taured[i]) / meanFred(a) - 1
        Freal = numpy.exp(-spectra.taureal[i]) / meanFreal(a) - 1

        DFred[sl] = combinepixels(Fred)
        DFreal[sl] = combinepixels(Freal)
        Delta[sl] = combinepixels(spectra.delta[i])
        p = spectra.position(i)
        x[sl] = combinepixels(p[:, 0])
        y[sl] = combinepixels(p[:, 1])
        z[sl] = combinepixels(p[:, 2])

        m = spectra.z[i] > Zmin
        m &= spectra.z[i] < Zmax
        m &= spectra.RfLam(i) > RfLamMin
        m &= spectra.RfLam(i) < RfLamMax
        mask[sl] = combinepixels(m, method=numpy.all)
        id[sl] = i
    chunkmap(work, range(len(spectra)), 100)

    return data[mask], pos[mask], id[mask]

def main(config):
    global cov
    DB = BootstrapDB(config)

    MASK = DB.dummy.imesh >= 0
    MASK &= DB.dummy.rmesh <= config.rmax
    MASK &= DB.dummy.rmesh >= config.rmin

    print "dof in fitting", MASK.sum()
    # create a dummy to test the fitting
    p0 = [-0.2, 3.5, 1.5, 1.5]

    eigenmodes = DB.eigenmodes
    dummy = eigenmodes(p0)

    covfull = numpy.load(config.CovarianceMatrixOutput)["cov"]
    cov = covfull[MASK][:, MASK]

    print "inverting"
    INV = linalg.inv(covfull[MASK][:, MASK])
    print "inverted"

    x, chi = fit1(dummy, eigenmodes, INV, MASK)

    print "x =", x
    print "p0 = bF, bQ, BF, BQ", p0

    error = poles_err(dummy, covfull)

    fitted = sharedmem.empty((len(DB), len(p0)))
    chi = sharedmem.empty((len(DB)))
    samples, models = [], []
    sharedmem.set_debug(True)

    def work(i):
        sample = DB(i)
        print "fitting", i
        fitted[i], chi[i] = fit1(sample, eigenmodes, INV, MASK)
        model = eigenmodes(fitted[i])
        print zip(sample[0].monopole, model[0].monopole)
        return i, sample, model

    def reduce(rt):
        i, s, m = rt
        samples.append((i, s))
        models.append((i, m))

    chunkmap(work, range(len(DB)), 100, reduce=reduce)
    samples = [s for i, s in sorted(samples)]
    models = [s for i, s in sorted(models)]
    numpy.savez("fit.npz", samples=samples, models=models, fittedparameters=fitted, chi=chi, error=error)

def argsort(ar):
    min = minimum.reduce(ar)
    max = maximum.reduce(ar)
    nchunk = sharedmem.cpu_count() * 2
    #bins = numpy.linspace(min, max, nchunk, endpoint=True)
    step = 1.0 * (max - min) / nchunk
    bins = numpy.array(
            1.0 * numpy.arange(nchunk + 1) * (max - min) / nchunk + min,
            min.dtype)

    dig = digitize(ar, bins)
    binlength = bincount(dig, minlength=len(bins) + 1)
    binoffset = numpy.cumsum(binlength)
    out = sharedmem.empty(len(ar), dtype='intp')

    with sharedmem.MapReduce() as pool:
        def work(i):
            # we can do this a lot faster
            # but already having pretty good speed.
            ind = numpy.nonzero(dig == i + 1)[0]
            myar = ar[ind]
            out[binoffset[i]:binoffset[i+1]] = ind[myar.argsort()]
        pool.map(work, range(nchunk)) 

    return out

    def call(self, args, axis=0, out=None, chunksize=1024 * 1024, **kwargs):
        """ axis is the axis to chop it off.
            if self.altreduce is set, the results will
            be reduced with altreduce and returned
            otherwise will be saved to out, then return out.
        """
        if self.altreduce is not None:
            ret = [None]
        else:
            if out is None :
                if self.outdtype is not None:
                    dtype = self.outdtype
                else:
                    try:
                        dtype = numpy.result_type(*[args[i] for i in self.ins] * 2)
                    except:
                        dtype = None
                out = sharedmem.empty(
                        numpy.broadcast(*[args[i] for i in self.ins] * 2).shape,
                        dtype=dtype)
        if axis != 0:
            for i in self.ins:
                args[i] = numpy.rollaxis(args[i], axis)
            out = numpy.rollaxis(out, axis)
        size = numpy.max([len(args[i]) for i in self.ins])
        with sharedmem.MapReduce() as pool:
            def work(i):
                sl = slice(i, i+chunksize)
                myargs = args[:]
                for j in self.ins:
                    try: 
                        tmp = myargs[j][sl]
                        a, b, c = sl.indices(len(args[j]))
                        myargs[j] = tmp
                    except Exception as e:
                        print tmp
                        print j, e
                        pass
                if b == a: return None
                rt = self.ufunc(*myargs, **kwargs)
                if self.altreduce is not None:
                    return rt
                else:
                    out[sl] = rt
            def reduce(rt):
                if self.altreduce is None:
                    return
                if ret[0] is None:
                    ret[0] = rt
                elif rt is not None:
                    ret[0] = self.altreduce(ret[0], rt)

            pool.map(work, range(0, size, chunksize), reduce=reduce)

        if self.altreduce is None:
            if axis != 0:
                out = numpy.rollaxis(out, 0, axis + 1)
            return out                
        else:
            return ret[0]

def test_critical():
    t = sharedmem.empty((), dtype='i8')
    t[...] = 0
    # FIXME: if the system has one core then this will never fail,
    # even if the critical section is not 
    with sharedmem.MapReduce(np=8) as pool:
        def work(i):
            with pool.critical:
                t[...] = 1
                if i != 30:
                    time.sleep(0.01)
                assert_equal(t, 1)
                t[...] = 0

        pool.map(work, range(16))

        def work(i):
            t[...] = 1
            if i != 30:
                time.sleep(0.01)
            assert_equal(t, 1)
            t[...] = 0

        try:
            pool.map(work, range(16))
        except sharedmem.SlaveException as e:
            assert isinstance(e.reason, AssertionError)
            return 
    raise AssertionError("Shall not reach here.")

def generate_shared_array(unshared_arr,dtype):

    r"""Creates synchronized shared arrays from numpy arrays.

    The function takes a numpy array `unshared_arr` and returns a shared
    memory object, `shared_arr`.  The user also specifies the data-type of
    the values in the array with the `dataType` argument.  See
    multiprocessing.Array and ctypes for details on shared memory arrays and
    the data-types.

    Parameters
    ----------
    unshared_arr : ndarray
        Array_like means all those objects -- lists, nested lists, etc. --
        that can be converted to an array.  We can also refer to
        variables like `var1`.
    dtype : ctypes instance
        The data-type specificed has to be an instance of the ctypes library.
        See ctypes for details.

    Returns
    -------
    shared_arr : synchronized shared array
        An array that is read accessible from multiple processes/threads.
    """

    shared_arr = sharedmem.empty(unshared_arr.shape, dtype=dtype)
    shared_arr[:] = unshared_arr[:]
    return shared_arr

def main(config):
    DB = BootstrapDB(config)
    Ndof = len(DB.dummy.compress())

    if mpicov.world.rank == 0:
        numpy.random.seed(9999)
        seeds = numpy.random.randint(low=0, high=99999999999, size=config.BigN)
    else:
        seeds = []

    myseeds = mpicov.world.scatter(numpy.array_split(seeds, mpicov.world.size))

    print 'This Task = ', mpicov.world.rank, 'Number of samples = ', \
            len(myseeds), 'seed0 =', myseeds[0]
    myxi = sharedmem.empty((len(myseeds), Ndof), dtype='f8')

    def work(i):
        rng = numpy.random.RandomState(myseeds[i])
        choice = rng.choice(len(DB), size=DB.Nchunks)
        sample = DB(choice)
        myxi[i][...] = sample.compress()

    print 'build samples'
    chunkmap(work, range(len(myxi)), 100)
    print 'done samples'

    print 'covariance matrix'
    cov = mpicov.cov(myxi, rowvar=0, ddof=0)
    print 'done covariance matrix'

    print numpy.nanmin(numpy.diag(cov))
    if mpicov.world.rank == 0:
        numpy.savez(config.CovarianceMatrixOutput, cov=cov,
                BigN=config.BigN, dummy=DB.dummy,
                xi_cov=DB.dummy.copy().uncompress(myxi[0]), r=DB.r, mu=DB.mu)

def create_video_pipe(video, name=None, read_ahead=False):
    """ creates the two ends of a video pipe.
    
    The typical use case is
    
    def worker_process(self, video):
        ''' worker process processing a video '''
        expensive_function(video)
        
    if __name__ == '__main__':
        # load a video file
        video = VideoFile('test.mov')
        # create the video pipe
        sender, receiver = create_video_pipe(video)
        # create the worker process
        proc = multiprocessing.Process(target=worker_process,
                                       args=(receiver,))
        proc.start()
        sender.start()   
    
    """
    # create the pipe used for communication
    pipe_sender, pipe_receiver = mp.Pipe(duplex=True)
    # create the buffer in memory that is used for passing frames
    frame_buffer = sharedmem.empty(video.shape[1:], np.uint8)
    
    # create the two ends of the video pipe
    sender = VideoPipeSender(video, pipe_sender, frame_buffer,
                             name, read_ahead)
    receiver = VideoPipeReceiver(pipe_receiver, frame_buffer,
                                 video.video_format, name)
    
    return sender, receiver

def test_local():
    t = sharedmem.empty(800)
    with sharedmem.MapReduce(np=4) as pool:
        def work(i):
            time.sleep(0.1 * numpy.random.uniform())
            with pool.ordered:
                t[i] = pool.local.rank
        pool.map(work, range(800))
    assert_equal(numpy.unique(t), range(4))

def test_sum():
    """ 
        Integrate [0, ... 1.0) with rectangle rule. 
        Compare results from 
        1. direct sum of 'xdx' (filled by subprocesses)
        2. 'shmsum', cummulated by partial sums on each process
        3. sum of partial sums from each process.

    """
    xdx = sharedmem.empty(1024 * 1024 * 128, dtype='f8')
    shmsum = sharedmem.empty((), dtype='f8')

    shmsum[...] = 0.0

    with sharedmem.MapReduce() as pool:

        def work(i):
            s = slice (i, i + chunksize)
            start, end, step = s.indices(len(xdx))

            dx = 1.0 / len(xdx)

            myxdx = numpy.arange(start, end, step) \
                    * 1.0 / len(xdx) * dx

            xdx[s] = myxdx

            a = xdx[s].sum(dtype='f8')

            with pool.critical:
                shmsum[...] += a

            return i, a

        def reduce(i, a):
            # print('chunk', i, 'done', 'local sum', a)
            return a

        chunksize = 1024 * 1024

        r = pool.map(work, range(0, len(xdx), chunksize), reduce=reduce)

    assert_almost_equal(numpy.sum(r, dtype='f8'), shmsum)
    assert_almost_equal(numpy.sum(xdx, dtype='f8'), shmsum)

    def __init__(self, x, y, scale_factor=1.1, minsize=(60, 60),
                 classifier='haarcascade_frontalface_alt2.xml',
                 use_lowpass=True, lowpass_rc=50,
                 visualize=False):

        # Initialize multiprocessing.Process parent
        multiprocessing.Process.__init__(self)

        # Exit event for stopping process
        self._exit = multiprocessing.Event()

        # Event that is set, everytime a face is detected
        self.newface_event = multiprocessing.Event()

        # Event that pauses the main loop if set
        self._pause_event = multiprocessing.Event()

        # An array in shared memory to store the current image frame
        self._currentframe = sharedmem.empty((y, x), dtype='uint8')

        # Set camera parameters
        self._x = x
        self._y = y

        # Set parameters for face detection algorithm
        self._scale_factor = scale_factor
        self._minsize = minsize
        self._classifier_file = classifier
        self._use_lowpass = use_lowpass
        self._lowpass_rc = lowpass_rc

        # Defines whether to visualize the camera output
        self._visualize = visualize

        # A tuple for storing the current width and height of a face
        self._currentface = sharedmem.empty((4, 1), dtype='float')

        # A tuple for storing the last width and height of a face
        self._lastface = sharedmem.empty((4, 1), dtype='float')

        # Setup a multiscale classifier
        self._classifier = cv2.CascadeClassifier(self._classifier_file)