Python numpy 模块，asfortranarray() 实例源码

我们从Python开源项目中，提取了以下50个代码示例，用于说明如何使用numpy.asfortranarray()。

项目：enet-keras 作者：PavlosMelissinos | 项目源码 | 文件源码

def mask_to_mscoco(alpha, annotations, img_id, mode='rle'):
        if mode == 'rle':
            in_ = np.reshape(np.asfortranarray(alpha), (alpha.shape[0], alpha.shape[1], 1))
            in_ = np.asfortranarray(in_)
            rle = mask.encode(in_)
            segmentation = rle[0]
        else:
            raise ValueError('Unknown mask mode "{}"'.format(mode))
        for idx, c in enumerate(np.unique(alpha)):
            area = mask.area(rle).tolist()
            if isinstance(area, list):
                area = area[0]
            bbox = mask.toBbox(rle).tolist()
            if isinstance(bbox[0], list):
                bbox = bbox[0]
            annotation = {
                'area': area,
                'bbox': bbox,
                'category_id': c,
                'id': len(annotations)+idx,
                'image_id': img_id,
                'iscrowd': 0,
                'segmentation': segmentation}
            annotations.append(annotation)
        return annotations

项目：paragraph2vec 作者：thunlp | 项目源码 | 文件源码

def initialize(self, corpus):
        """
        Initialize the random projection matrix.
        """
        if self.id2word is None:
            logger.info("no word id mapping provided; initializing from corpus, assuming identity")
            self.id2word = utils.dict_from_corpus(corpus)
            self.num_terms = len(self.id2word)
        else:
            self.num_terms = 1 + max([-1] + self.id2word.keys())

        shape = self.num_topics, self.num_terms
        logger.info("constructing %s random matrix" % str(shape))
        # Now construct the projection matrix itself.
        # Here i use a particular form, derived in "Achlioptas: Database-friendly random projection",
        # and his (1) scenario of Theorem 1.1 in particular (all entries are +1/-1).
        randmat = 1 - 2 * numpy.random.binomial(1, 0.5, shape) # convert from 0/1 to +1/-1
        self.projection = numpy.asfortranarray(randmat, dtype=numpy.float32) # convert from int32 to floats, for faster multiplications

项目：veros 作者：dionhaefner | 项目源码 | 文件源码

def set_attribute(self, attribute, value):
        if isinstance(value, np.ndarray):
            getattr(self.veros_new, attribute)[...] = value
        else:
            setattr(self.veros_new, attribute, value)
        for module in self.legacy_modules:
            module_handle = getattr(self.veros_legacy, module)
            if hasattr(module_handle, attribute):
                try:
                    v = np.asfortranarray(value.copy2numpy())
                except AttributeError:
                    v = np.asfortranarray(value)
                setattr(module_handle, attribute, v)
                assert np.all(value == getattr(module_handle, attribute)), attribute
                return
        raise AttributeError("Legacy pyOM has no attribute {}".format(attribute))

项目：chainer-fcis 作者：knorth55 | 项目源码 | 文件源码

def _create_ann(whole_m, lbl, sc, img_id, ann_id, crw=None, ar=None):
    H, W = whole_m.shape
    if crw is None:
        crw = False
    whole_m = np.asfortranarray(whole_m.astype(np.uint8))
    rle = mask_tools.encode(whole_m)
    # Surprisingly, ground truth ar can be different from area(rle)
    if ar is None:
        ar = mask_tools.area(rle)
    ann = {
        'image_id': img_id, 'category_id': lbl,
        'segmentation': rle,
        'area': ar,
        'id': ann_id,
        'iscrowd': crw}
    if sc is not None:
        ann.update({'score': sc})
    return ann

项目：topical_word_embeddings 作者：thunlp | 项目源码 | 文件源码

def initialize(self, corpus):
        """
        Initialize the random projection matrix.
        """
        if self.id2word is None:
            logger.info("no word id mapping provided; initializing from corpus, assuming identity")
            self.id2word = utils.dict_from_corpus(corpus)
            self.num_terms = len(self.id2word)
        else:
            self.num_terms = 1 + max([-1] + self.id2word.keys())

        shape = self.num_topics, self.num_terms
        logger.info("constructing %s random matrix" % str(shape))
        # Now construct the projection matrix itself.
        # Here i use a particular form, derived in "Achlioptas: Database-friendly random projection",
        # and his (1) scenario of Theorem 1.1 in particular (all entries are +1/-1).
        randmat = 1 - 2 * numpy.random.binomial(1, 0.5, shape) # convert from 0/1 to +1/-1
        self.projection = numpy.asfortranarray(randmat, dtype=numpy.float32) # convert from int32 to floats, for faster multiplications

项目：topical_word_embeddings 作者：thunlp | 项目源码 | 文件源码

def initialize(self, corpus):
        """
        Initialize the random projection matrix.
        """
        if self.id2word is None:
            logger.info("no word id mapping provided; initializing from corpus, assuming identity")
            self.id2word = utils.dict_from_corpus(corpus)
            self.num_terms = len(self.id2word)
        else:
            self.num_terms = 1 + max([-1] + self.id2word.keys())

        shape = self.num_topics, self.num_terms
        logger.info("constructing %s random matrix" % str(shape))
        # Now construct the projection matrix itself.
        # Here i use a particular form, derived in "Achlioptas: Database-friendly random projection",
        # and his (1) scenario of Theorem 1.1 in particular (all entries are +1/-1).
        randmat = 1 - 2 * numpy.random.binomial(1, 0.5, shape) # convert from 0/1 to +1/-1
        self.projection = numpy.asfortranarray(randmat, dtype=numpy.float32) # convert from int32 to floats, for faster multiplications

项目：topical_word_embeddings 作者：thunlp | 项目源码 | 文件源码

def initialize(self, corpus):
        """
        Initialize the random projection matrix.
        """
        if self.id2word is None:
            logger.info("no word id mapping provided; initializing from corpus, assuming identity")
            self.id2word = utils.dict_from_corpus(corpus)
            self.num_terms = len(self.id2word)
        else:
            self.num_terms = 1 + max([-1] + self.id2word.keys())

        shape = self.num_topics, self.num_terms
        logger.info("constructing %s random matrix" % str(shape))
        # Now construct the projection matrix itself.
        # Here i use a particular form, derived in "Achlioptas: Database-friendly random projection",
        # and his (1) scenario of Theorem 1.1 in particular (all entries are +1/-1).
        randmat = 1 - 2 * numpy.random.binomial(1, 0.5, shape) # convert from 0/1 to +1/-1
        self.projection = numpy.asfortranarray(randmat, dtype=numpy.float32) # convert from int32 to floats, for faster multiplications

项目：yt 作者：yt-project | 项目源码 | 文件源码

def deposit(self, positions, fields = None, method = None,
                kernel_name = 'cubic'):
        # Here we perform our particle deposition.
        if fields is None: fields = []
        cls = getattr(particle_deposit, "deposit_%s" % method, None)
        if cls is None:
            raise YTParticleDepositionNotImplemented(method)
        nz = self.nz
        nvals = (nz, nz, nz, self.ires.size)
        # We allocate number of zones, not number of octs
        op = cls(nvals, kernel_name)
        op.initialize()
        mylog.debug("Depositing %s (%s^3) particles into %s Root Mesh",
            positions.shape[0], positions.shape[0]**0.3333333, nvals[-1])
        pos = np.array(positions, dtype="float64")
        f64 = [np.array(f, dtype="float64") for f in fields]
        self.oct_handler.deposit(op, self.base_selector, pos, f64)
        vals = op.finalize()
        if vals is None: return
        return np.asfortranarray(vals)

项目：yt 作者：yt-project | 项目源码 | 文件源码

def _init_kd_tree(self):
        """
        Builds the kd tree of grid center points.
        """
        # Grid cell centers.
        mylog.info("Multigrid: Building kD-Tree.")
        xp = self.ds["x"]
        yp = self.ds["y"]
        zp = self.ds["z"]
        fKD.pos = np.asfortranarray(np.empty((3,xp.size), dtype='float64'))
        # Normalize the grid points only within the kdtree.
        fKD.pos[0, :] = xp[:] / self.period[0]
        fKD.pos[1, :] = yp[:] / self.period[1]
        fKD.pos[2, :] = zp[:] / self.period[2]
        fKD.nn = 1
        fKD.sort = False
        fKD.rearrange = True
        create_tree(0)

项目：yt 作者：yt-project | 项目源码 | 文件源码

def _find_nearest_cell(self, points):
        """
        Finds the closest grid cell for each point in a vectorized manner.
        """
        if self.nlevels == 0:
            pos = (points - self.ds.left_edge) / self.width
            n = (self.sizes[2] * pos[:,2]).astype('int32')
            n += self.sizes[2] * (self.sizes[1] * pos[:,1]).astype('int32')
            n += self.sizes[2] * self.sizes[1] * (self.sizes[0] * pos[:,0]).astype('int32')
        else:
            # Normalize the points to a 1-period for use only within the kdtree.
            points[:, 0] = points[:, 0] / self.period[0]
            points[:, 1] = points[:, 1] / self.period[1]
            points[:, 2] = points[:, 2] / self.period[2]
            fKD.qv_many = points.T
            fKD.nn_tags = np.asfortranarray(np.empty((1, points.shape[0]), dtype='int64'))
            fKD.find_many_nn_nearest_neighbors()
            # The -1 is for fortran counting.
            n = fKD.nn_tags[0,:] - 1
        return n