Python numpy.array_split() 使用实例

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Example 1

def extract_optical_flow(fn, n_frames=34):
    img = dd.image.load(fn)
    if img.shape != (128*34, 128, 3):
        return []
    frames = np.array_split(img, 34, axis=0)
    grayscale_frames = [fr.mean(-1) for fr in frames]
    mags = []
    skip_frames = np.random.randint(34 - n_frames + 1)
    middle_frame = frames[np.random.randint(skip_frames, skip_frames+n_frames)]
    im0 = grayscale_frames[skip_frames]
    for f in range(1+skip_frames, 1+skip_frames+n_frames-1):
        im1 = grayscale_frames[f]
        flow = cv2.calcOpticalFlowFarneback(im0, im1,
                    None, # flow
                    0.5, # pyr_scale
                    3, # levels
                    np.random.randint(3, 20), # winsize
                    3, #iterations
                    5, #poly_n 
                    1.2, #poly_sigma
                    0 # flags
        )
        mag, ang = cv2.cartToPolar(flow[...,0], flow[...,1])
        mags.append(mag)
        im0 = im1
    mag = np.sum(mags, 0)
    mag = mag.clip(min=0)
    #norm_mag = np.tanh(mag * 10000)
    norm_mag = (mag - mag.min()) / (mag.max() - mag.min() + 1e-5)
    outputs = []
    outputs.append((middle_frame, norm_mag))
    return outputs 

Example 2

def create_agents(self, generator):
        """
        Given information on a set of countries and a generator function,
        generate the agents and assign the results to ``self.agents``.

        :type generator: DataFrame, str, int
        :param generator: A function which generates the agents.
        """
        self.generator = generator
        country_array = pd.concat([pd.Series([c] * k["Population"]) for c, k in self.df.iterrows()])
        country_array.index = range(len(country_array))
        # Garbage collect before creating new processes.
        gc.collect()
        self.agents = pd.concat(
            self.pool.imap(self._gen_agents,
                           np.array_split(country_array, self.processes * self.splits))
        )
        self.agents.index = range(len(self.agents)) 

Example 3

def create_agents(self, generator):
        """
        Given information on a set of countries and a generator function,
        generate the agents and assign the results to ``self.agents``.

        :type generator: DataFrame, str, int
        :param generator: A function which generates the agents.
        """
        self.generator = generator
        country_array = pd.concat([pd.Series([c] * k["Population"]) for c, k in self.df.iterrows()])
        country_array.index = range(len(country_array))
        # Garbage collect before creating new processes.
        gc.collect()
        self.agents = pd.concat(
            self.pool.imap(self._gen_agents,
                           np.array_split(country_array, self.processes * self.splits))
        )
        self.agents.index = range(len(self.agents)) 

Example 4

def test_latlon2pix_internals(pix_size_single, origin_point, is_flipped,
                              num_chunks, chunk_position):

    img = make_image(pix_size_single, origin_point, is_flipped,
                     num_chunks, chunk_position)
    chunk_idx = img.chunk_idx
    res_x = img._full_res[0]
    res_y = img._full_res[1]
    pix_size = (img.pixsize_x, img.pixsize_y)
    origin = (img._start_lon, img._start_lat)

    # +0.5 for centre of pixels
    lons = (np.arange(res_x) + 0.5) * pix_size[0] + origin[0]
    all_lats = (np.arange(res_y) + 0.5) * pix_size[1] + origin[1]
    lats = np.array_split(all_lats, num_chunks)[chunk_idx]

    pix_x = np.arange(res_x)
    pix_y = np.arange(lats.shape[0])

    d = np.array([[a, b] for a in lons for b in lats])
    xy = img.lonlat2pix(d)
    true_xy = np.array([[a, b] for a in pix_x for b in pix_y])
    assert np.all(xy == true_xy) 

Example 5

def test_pix2latlong(pix_size_single, origin_point, is_flipped,
                     num_chunks, chunk_position):

    img = make_image(pix_size_single, origin_point, is_flipped,
                     num_chunks, chunk_position)
    chunk_idx = img.chunk_idx
    res_x = img._full_res[0]
    res_y = img._full_res[1]
    pix_size = (img.pixsize_x, img.pixsize_y)
    origin = (img._start_lon, img._start_lat)

    true_lons = np.arange(res_x) * pix_size[0] + origin[0]
    all_lats = np.arange(res_y) * pix_size[1] + origin[1]
    true_lats = np.array_split(all_lats, num_chunks)[chunk_idx]
    true_d = np.array([[a, b] for a in true_lons for b in true_lats])

    pix_x = np.arange(res_x)
    pix_y = np.arange(img.resolution[1])  # chunk resolution

    xy = np.array([[a, b] for a in pix_x for b in pix_y])

    lonlats = img.pix2lonlat(xy)
    assert np.all(lonlats == true_d) 

Example 6

def transform(self, X):
        if self.tagger is None:
            raise ValueError("Must find_motifs before you can tag anything")

        logging.info("Tagging %s data with motifs using %d workers..." % (
            str(X.shape), self.n_jobs))

        if self.n_jobs > 1:
            pool = mp.ProcessingPool(self.n_jobs)
            splits = np.array_split(X, self.n_jobs)
            tag_lists = pool.map(self._tag_motifs, splits)
            tags = list(itertools.chain.from_iterable(tag_lists))
        else:
            tags = self._tag_motifs(X)

        logging.info("All motifs have been tagged")
        return self._sparsify_tags(tags) 

Example 7

def subset_iterator(X, m, repeats=1):
    '''
    Iterates over array X in chunks of m, repeat number of times.
    Each time the order of the repeat is randomly generated.
    '''

    N, dim = X.shape
    progress = tqdm(total=repeats * int(N / m))

    for i in range(repeats):

        indices = np.random.permutation(N)

        for idx in np.array_split(indices, N // m):
            yield X[idx][:]
            progress.update()

    progress.close() 

Example 8

def _split_into_groups(y, num_groups):
    groups = [[] for _ in range(num_groups)]
    group_index = 0

    for cls in set(y):
        this_cls_indices = np.where(y == cls)[0]
        num_cls_samples = len(this_cls_indices)

        num_cls_split_groups = ceil(num_cls_samples / 500)
        split = np.array_split(this_cls_indices, num_cls_split_groups)

        for cls_group in split:
            groups[group_index] = np.hstack((groups[group_index], cls_group))
            group_index = (group_index + 1) % num_groups

    return groups 

Example 9

def get_embedding_X(img):
    '''
            Args 	: Numpy Images vector
            Returns : Embedded Matrix of length Samples, 4096
    '''
    img = img.reshape((img.shape[0], img.shape[1], img.shape[2], 1))
    sess = tf.Session()
    imgs = tf.placeholder(tf.float32, [None, None, None, None])
    vgg = vgg16(imgs, '/tmp/vgg16_weights.npz', sess)
    embs = []
    cnt = 0
    for img_batch in np.array_split(img, img.shape[0] / 1000):
    	emb = sess.run(vgg.emb, feed_dict={vgg.imgs: img_batch})
        embs.extend(emb)
        cnt += 1
        progress = round(100 * (cnt * 1000 / img.shape[0]),2)
        if(progress%10 == 0):
          print progress
    embs = np.array(embs)
    print embs.shape
    embs = np.reshape(embs,(embs.shape[0],embs.shape[1] * embs.shape[2] * embs.shape[3]))
    return embs 

Example 10

def __init__(self, pobj, just_list = False, attr='_grids',
                 round_robin=False):
        ObjectIterator.__init__(self, pobj, just_list, attr=attr)
        # pobj has to be a ParallelAnalysisInterface, so it must have a .comm
        # object.
        self._offset = pobj.comm.rank
        self._skip = pobj.comm.size
        # Note that we're doing this in advance, and with a simple means
        # of choosing them; more advanced methods will be explored later.
        if self._use_all:
            self.my_obj_ids = np.arange(len(self._objs))
        else:
            if not round_robin:
                self.my_obj_ids = np.array_split(
                                np.arange(len(self._objs)), self._skip)[self._offset]
            else:
                self.my_obj_ids = np.arange(len(self._objs))[self._offset::self._skip] 

Example 11

def iter_combinatorial_pairs(queue, num_examples, batch_size, interval,
                             num_classes, augment_positive=False):
    num_examples_per_class = num_examples // num_classes
    pairs = np.array(list(itertools.combinations(range(num_examples), 2)))

    if augment_positive:
        additional_positive_pairs = make_positive_pairs(
             num_classes, num_examples_per_class, num_classes - 1)
        pairs = np.concatenate((pairs, additional_positive_pairs))

    num_pairs = len(pairs)
    num_batches = num_pairs // batch_size
    perm = np.random.permutation(num_pairs)
    for i, batch_indexes in enumerate(np.array_split(perm, num_batches)):
        if i % interval == 0:
            x, c = queue.get()
            x = x.astype(np.float32) / 255.0
            c = c.ravel()
        indexes0, indexes1 = pairs[batch_indexes].T
        x0, x1, c0, c1 = x[indexes0], x[indexes1], c[indexes0], c[indexes1]
        t = np.int32(c0 == c1)  # 1 if x0 and x1 are same class, 0 otherwise
        yield x0, x1, t 

Example 12

def get_epoch_indexes(self):
        B = self.batch_size
        K = self.num_classes
        M = self.num_per_class
        N = K * M  # number of total examples
        num_batches = M * int(K // B)  # number of batches per epoch

        indexes = np.arange(N, dtype=np.int32).reshape(K, M)
        epoch_indexes = []
        for m in range(M):
            perm = np.random.permutation(K)
            c_batches = np.array_split(perm, num_batches // M)
            for c_batch in c_batches:
                b = len(c_batch)  # actual number of examples of this batch
                indexes_anchor = M * c_batch + m

                positive_candidates = np.delete(indexes[c_batch], m, axis=1)
                indexes_positive = positive_candidates[
                    range(b), np.random.choice(M - 1, size=b)]

                epoch_indexes.append((indexes_anchor, indexes_positive))

        return epoch_indexes 

Example 13

def pre_processing(self):
        """Provide same API as Model, we split data to K folds here.
        """
        if self.random:
            mask = np.random.permutation(self.train_x.shape[0])
            train_x = self.train_x[mask]
            train_y = self.train_y[mask]
        else:
            train_x = self.train_x[:]
            train_y = self.train_y[:]

        if self.select_train_method == 'step':
            self.x_folds = [train_x[i::self.k_folds] for i in range(0, self.k_folds)]
            self.y_folds = [train_y[i::self.k_folds] for i in range(0, self.k_folds)]
        else:
            self.x_folds = np.array_split(train_x, self.k_folds)
            self.y_folds = np.array_split(train_y, self.k_folds)


        # for i in range(self.k_folds):
        #     self.x_folds[i] = self.train_x[0] + self.x_folds[i] + self.train_x[-1]
        #     self.y_folds[i] = self.train_y[0] + self.y_folds[i] + self.train_y[-1] 

Example 14

def Train(self, C, A, Y, SF):
        '''
        Train the classifier using the sample matrix A and target matrix Y
        '''
        C.fit(A, Y)
        YH = np.zeros(Y.shape, dtype = np.object)
        for i in np.array_split(np.arange(A.shape[0]), 32):   #Split up verification into chunks to prevent out of memory
            YH[i] = C.predict(A[i])
        s1 = SF(Y, YH)
        print('All:{:8.6f}'.format(s1))
        '''
        ss = ShuffleSplit(random_state = 1151)  #Use fixed state for so training can be repeated later
        trn, tst = next(ss.split(A, Y))         #Make train/test split
        mi = [8] * 1                            #Maximum number of iterations at each iter
        YH = np.zeros((A.shape[0]), dtype = np.object)
        for mic in mi:                                      #Chunk size to split dataset for CV results
            #C.SetMaxIter(mic)                               #Set the maximum number of iterations to run
            #C.fit(A[trn], Y[trn])                           #Perform training iterations
        ''' 

Example 15

def add_point(self, t, alt, az):

        self.window.append((t, alt, az))
        if self._current_window_size() < self.window_duration:
            return

        points = np.array(self.window)
        steady, current = np.array_split(points, 2)

        _, steady_cube = self.create_cube(steady)
        timestamps, current_cube = self.create_cube(current)

        t = self.denoise_and_compare_cubes(steady_cube, current_cube)
        self.trigger_criterion.append(list(t))
        self.trigger_criterion_timestamps.append(list(timestamps))

        has_triggered = self.check_trigger(t)
        new_duration = self.window_duration - self.step
        self._reduce_to_duration(new_duration) 

Example 16

def predict(self):
        if os.path.exists(DATA_QUERIES_VECTOR_NPZ) and not FORCE_LOAD:
            print('{}: loading precomputed data'.format(self.__class__.__name__))
            self.load_precomputed_data()
        else:
            self.precomputed_similarity()

        batch_size = 100
        batch_elements = math.ceil(self.queries_vector.shape[0] / batch_size)
        batch_queue = np.array_split(self.queries_vector.A, batch_elements)
        print("starting batch computation of Similarity and KNN calculation")

        # # multiple versions of calculating the prediction, some faster, some use more mem

        # prediction = self.multiprocessor_batch_calc(batch_queue)
        prediction = self.batch_calculation(batch_queue)
        # prediction = self.individual_calculation()
        # prediction = self.cosine_knn_calc()
        # prediction = self.custom_knn_calculation(prediction)

        train_avg_salary = sum(self.y_train) / len(self.y_train)
        cleaned_predictions = [x if str(x) != 'nan' else train_avg_salary for x in prediction]

        return self.y_train, cleaned_predictions 

Example 17

def load_test_data(self):
        # Remove non-mat files, and perform ascending sort
        allfiles = os.listdir(self.data_dir)
        npzfiles = []
        for idx, f in enumerate(allfiles):
            if ".npz" in f:
                npzfiles.append(os.path.join(self.data_dir, f))
        npzfiles.sort()

        # Files for validation sets
        val_files = np.array_split(npzfiles, self.n_folds)
        val_files = val_files[self.fold_idx]

        print "\n========== [Fold-{}] ==========\n".format(self.fold_idx)

        print "Load validation set:"
        data_val, label_val = self._load_npz_list_files(val_files)

        return data_val, label_val 

Example 18

def __init__(self, X, kern, Xm):
		
		super(PITC, self).__init__("PITC")
		M = np.shape(Xm)[0]
		self.M = M

		start = time.time()
		X_split = np.array_split(X, M)
		self.kern = kern
		kern_blocks = np.zeros((M),dtype=object)

		for t in xrange(M):
			nyst = Nystrom(X_split[t], kern, Xm, False)
			size = np.shape(X_split[t])[0]
			kern_blocks[t] = kern.K(X_split[t], X_split[t]) - nyst.precon  + (kern.noise)*np.identity(size)

		self.blocks = kern_blocks
		blocked = block_diag(*kern_blocks)

		self.nyst = Nystrom(X, kern, Xm, False)
		self.precon = self.nyst.precon + blocked
		self.duration = time.time() - start 

Example 19

def __init__(self, X, kern, Xm):
		
		super(PITC, self).__init__("PITC")
		M = np.shape(Xm)[0]
		self.M = M

		start = time.time()
		X_split = np.array_split(X, M)
		self.kern = kern
		kern_blocks = np.zeros((M),dtype=object)

		for t in xrange(M):
			nyst = Nystrom(X_split[t], kern, Xm, False)
			size = np.shape(X_split[t])[0]
			kern_blocks[t] = kern.K(X_split[t], X_split[t]) - nyst.precon  + (kern.noise)*np.identity(size)

		self.blocks = kern_blocks
		blocked = block_diag(*kern_blocks)

		self.nyst = Nystrom(X, kern, Xm, False)
		self.precon = self.nyst.precon + blocked
		self.duration = time.time() - start 

Example 20

def _read_image_as_array(path, dtype, load_size, crop_size, flip):
    f = Image.open(path)

    A, B = numpy.array_split(numpy.asarray(f), 2, axis=1)
    if hasattr(f, 'close'):
        f.close()
        
    A = _resize(A, load_size, Image.BILINEAR, dtype)
    B = _resize(B, load_size, Image.NEAREST, dtype)

    sx, sy = numpy.random.randint(0, load_size-crop_size, 2)
    A = _crop(A, sx, sy, crop_size)
    B = _crop(B, sx, sy, crop_size)
    
    if flip and numpy.random.rand() > 0.5:
        A = numpy.fliplr(A)
        B = numpy.fliplr(B)

    return A.transpose(2, 0, 1), B.transpose(2, 0, 1) 

Example 21

def setup_figure():

    f = plt.figure(figsize=(7, 5))

    mat_grid = plt.GridSpec(2, 6, .07, .52, .98, .95, .15, .20)
    mat_axes = [f.add_subplot(spec) for spec in mat_grid]
    sticks_axes, rest_axes = np.array_split(mat_axes, 2)

    scatter_grid = plt.GridSpec(1, 6, .07, .30, .98, .49, .15, .05)
    scatter_axes = [f.add_subplot(spec) for spec in scatter_grid]

    kde_grid = plt.GridSpec(1, 6, .07, .07, .98, .21, .15, .05)
    kde_axes = [f.add_subplot(spec) for spec in kde_grid]

    cbar_ax = f.add_axes([.04, .62, .015, .26])

    return f, sticks_axes, rest_axes, scatter_axes, kde_axes, cbar_ax 

Example 22

def partitions(min_val, max_val, n):

    """
    Get start/stop boundaries for N partitions.

    Args:
        min_val (int): The starting value.
        max_val (int): The last value.
        n (int): The number of partitions.
    """

    pts = np.array_split(np.arange(min_val, max_val+1), n)

    bounds = []
    for pt in pts:
        bounds.append((int(pt[0]), int(pt[-1])))

    return bounds 

Example 23

def fit(self, X, y):
        """Fit a series of independent estimators to the dataset.

        Parameters
        ----------
        X : array, shape (n_samples, n_features, n_estimators)
            The training input samples. For each data slice, a clone estimator
            is fitted independently.
        y : array, shape (n_samples,)
            The target values.

        Returns
        -------
        self : object
            Return self.
        """
        self._check_Xy(X, y)
        self.estimators_ = list()
        # For fitting, the parallelization is across estimators.
        parallel, p_func, n_jobs = parallel_func(_sl_fit, self.n_jobs)
        estimators = parallel(
            p_func(self.base_estimator, split, y)
            for split in np.array_split(X, n_jobs, axis=-1))
        self.estimators_ = np.concatenate(estimators, 0)
        return self 

Example 24

def _transform(self, X, method):
        """Aux. function to make parallel predictions/transformation."""
        self._check_Xy(X)
        method = _check_method(self.base_estimator, method)
        if X.shape[-1] != len(self.estimators_):
            raise ValueError('The number of estimators does not match '
                             'X.shape[2]')
        # For predictions/transforms the parallelization is across the data and
        # not across the estimators to avoid memory load.
        parallel, p_func, n_jobs = parallel_func(_sl_transform, self.n_jobs)
        X_splits = np.array_split(X, n_jobs, axis=-1)
        est_splits = np.array_split(self.estimators_, n_jobs)
        y_pred = parallel(p_func(est, x, method)
                          for (est, x) in zip(est_splits, X_splits))

        if n_jobs > 1:
            y_pred = np.concatenate(y_pred, axis=1)
        else:
            y_pred = y_pred[0]
        return y_pred 

Example 25

def _yield_minibatches_idx(self, n_batches, data_ary, shuffle=True):
            indices = np.arange(data_ary.shape[0])
            if shuffle:
                indices = np.random.permutation(indices)
            if n_batches > 1:
                remainder = data_ary.shape[0] % n_batches

                if remainder:
                    minis = np.array_split(indices[:-remainder], n_batches)
                    minis[-1] = np.concatenate((minis[-1],
                                                indices[-remainder:]),
                                               axis=0)
                else:
                    minis = np.array_split(indices, n_batches)

            else:
                minis = (indices,)

            for idx_batch in minis:
                yield idx_batch 

Example 26

def test_mini_batch_k_means_random_init_partial_fit():
    km = MiniBatchKMeans(n_clusters=n_clusters, init="random", random_state=42)

    # use the partial_fit API for online learning
    for X_minibatch in np.array_split(X, 10):
        km.partial_fit(X_minibatch)

    # compute the labeling on the complete dataset
    labels = km.predict(X)
    assert_equal(v_measure_score(true_labels, labels), 1.0) 

Example 27

def binned_batch_stream(target_statistics, batch_size, n_batches, n_bins=64):
    hist, bins = np.histogram(target_statistics, bins=n_bins)
    indx = np.argsort(target_statistics)
    indicies_categories = np.array_split(indx, np.cumsum(hist)[:-1])

    per_category = batch_size / n_bins

    weight_correction = (np.float64(hist) / per_category).astype('float32')
    wc = np.repeat(weight_correction, per_category)

    for i in xrange(n_batches):
      sample = [
        np.random.choice(ind, size=per_category, replace=True)
        for ind in indicies_categories
        ]

      yield np.hstack(sample), wc 

Example 28

def binned_batch_stream(target_statistics, batch_size, n_batches, n_bins=64):
  hist, bins = np.histogram(target_statistics, bins=n_bins)
  indx = np.argsort(target_statistics)
  indicies_categories = np.array_split(indx, np.cumsum(hist)[:-1])
  n_samples = target_statistics.shape[0]

  per_category = batch_size / n_bins

  weight_correction = (n_bins * np.float64(hist) / n_samples).astype('float32')
  wc = np.repeat(weight_correction, per_category)

  for i in xrange(n_batches):
    sample = [
      np.random.choice(ind, size=per_category, replace=True)
      for ind in indicies_categories
    ]

    yield np.hstack(sample), wc 

Example 29

def test_shape_factors(self):
        """
        Tests for :func:`array_split.split.shape_factors`.
        """
        f = shape_factors(4, 2)
        self.assertTrue(_np.all(f == 2))

        f = shape_factors(4, 1)
        self.assertTrue(_np.all(f == 4))

        f = shape_factors(5, 2)
        self.assertTrue(_np.all(f == [1, 5]))

        f = shape_factors(6, 2)
        self.assertTrue(_np.all(f == [2, 3]))

        f = shape_factors(6, 3)
        self.assertTrue(_np.all(f == [1, 2, 3])) 

Example 30

def scale(boxlist, y_scale, x_scale):
  """Scale box coordinates in x and y dimensions.

  Args:
    boxlist: BoxList holding N boxes
    y_scale: float
    x_scale: float

  Returns:
    boxlist: BoxList holding N boxes
  """
  y_min, x_min, y_max, x_max = np.array_split(boxlist.get(), 4, axis=1)
  y_min = y_scale * y_min
  y_max = y_scale * y_max
  x_min = x_scale * x_min
  x_max = x_scale * x_max
  scaled_boxlist = np_box_list.BoxList(np.hstack([y_min, x_min, y_max, x_max]))

  fields = boxlist.get_extra_fields()
  for field in fields:
    extra_field_data = boxlist.get_field(field)
    scaled_boxlist.add_field(field, extra_field_data)

  return scaled_boxlist 

Example 31

def iterbatches(arrays, num_batches=None, batch_size=None, shuffle=True, include_final_partial_batch=True):
    assert (num_batches is None) != (batch_size is None), 'Provide num_batches or batch_size, but not both'
    arrays = tuple(map(np.asarray, arrays))
    n = arrays[0].shape[0]
    assert all(a.shape[0] == n for a in arrays[1:])
    inds = np.arange(n)
    if shuffle: np.random.shuffle(inds)
    sections = np.arange(0, n, batch_size)[1:] if num_batches is None else num_batches
    for batch_inds in np.array_split(inds, sections):
        if include_final_partial_batch or len(batch_inds) == batch_size:
            yield tuple(a[batch_inds] for a in arrays) 

Example 32

def _gen_init_n_blocks(na, nb, ka, kb):
        num_nodes_a = np.arange(na)
        n_blocks_a = map(len, np.array_split(num_nodes_a, ka))
        num_nodes_b = np.arange(nb)
        n_blocks_b = map(len, np.array_split(num_nodes_b, kb))

        n_blocks_ = " ".join(map(str, n_blocks_a)) + " " + " ".join(map(str, n_blocks_b))

        return n_blocks_ 

Example 33

def gen_equal_partition(n, total):
    all_nodes = np.arange(total)
    n_blocks = list(map(len, np.array_split(all_nodes, n)))

    return n_blocks 

Example 34

def run_par(self, function, **kwargs):
        """
        Run a function on the agents in parallel.
        """
        columns = kwargs["columns"] if "columns" in kwargs else self.agents.columns
        # Garbage collect before creating new processes.
        gc.collect()
        return pd.concat(self.pool.imap(partial(function, **kwargs),
                                        np.array_split(self.agents[columns],
                                                       self.processes * self.splits))) 

Example 35

def run_par(self, function, **kwargs):
        """
        Run a function on the agents in parallel.
        """
        columns = kwargs["columns"] if "columns" in kwargs else self.agents.columns
        # Garbage collect before creating new processes.
        gc.collect()
        return pd.concat(self.pool.imap(partial(function, **kwargs),
                                        np.array_split(self.agents[columns],
                                                       self.processes * self.splits))) 

Example 36

def split_in_chunks(minibatch, num_splits, flatten_keys=['labels']):
    '''Return the splits per device

    Return a list of dictionaries, one per device. Each dictionary
    contains, for each key, the values that should be allocated on its
    device.
    '''
    # Split the value of each key into chunks
    for k, v in minibatch.iteritems():
        minibatch[k] = np.array_split(v, num_splits)
        if any(k == v for v in flatten_keys):
            minibatch[k] = [el.flatten() for el in minibatch[k]]
    return map(dict, zip(*[[(k, v) for v in value]
                           for k, value in minibatch.items()])) 

Example 37

def chunk_iterator(dataset, chunk_size=1000):
    chunk_indices = np.array_split(np.arange(len(dataset)),
                                    len(dataset)/chunk_size)
    for chunk_ixs in chunk_indices:
        chunk = dataset[chunk_ixs]
        yield (chunk_ixs, chunk)
    raise StopIteration 

Example 38

def array_split(ary, indices_or_sections, axis=0):
    """Splits an array into multiple sub arrays along a given axis.

    This function is almost equivalent to :func:`cupy.split`. The only
    difference is that this function allows an integer sections that does not
    evenly divide the axis.

    .. seealso:: :func:`cupy.split` for more detail, :func:`numpy.array_split`

    """
    return core.array_split(ary, indices_or_sections, axis) 

Example 39

def split(ary, indices_or_sections, axis=0):
    """Splits an array into multiple sub arrays along a given axis.

    Args:
        ary (cupy.ndarray): Array to split.
        indices_or_sections (int or sequence of ints): A value indicating how
            to divide the axis. If it is an integer, then is treated as the
            number of sections, and the axis is evenly divided. Otherwise,
            the integers indicate indices to split at. Note that the sequence
            on the device memory is not allowed.
        axis (int): Axis along which the array is split.

    Returns:
        A list of sub arrays. Each array is a view of the corresponding input
        array.

    .. seealso:: :func:`numpy.split`

    """
    if ary.ndim <= axis:
        raise IndexError('Axis exceeds ndim')
    size = ary.shape[axis]

    if numpy.isscalar(indices_or_sections):
        if size % indices_or_sections != 0:
            raise ValueError(
                'indices_or_sections must divide the size along the axes.\n'
                'If you want to split the array into non-equally-sized '
                'arrays, use array_split instead.')
    return array_split(ary, indices_or_sections, axis) 

Example 40

def iterbatches(arrays, *, num_batches=None, batch_size=None, shuffle=True, include_final_partial_batch=True):
    assert (num_batches is None) != (batch_size is None), 'Provide num_batches or batch_size, but not both'
    arrays = tuple(map(np.asarray, arrays))
    n = arrays[0].shape[0]
    assert all(a.shape[0] == n for a in arrays[1:])
    inds = np.arange(n)
    if shuffle: np.random.shuffle(inds)
    sections = np.arange(0, n, batch_size)[1:] if num_batches is None else num_batches
    for batch_inds in np.array_split(inds, sections):
        if include_final_partial_batch or len(batch_inds) == batch_size:
            yield tuple(a[batch_inds] for a in arrays) 

Example 41

def trim_data(data, resolution):
        r = []
        for i in numpy.array_split(data, resolution):
            if len(i) > 0:
                r.append(numpy.average(i))
        return r 

Example 42

def test_latlon2pix_edges(pix_size_single, origin_point, is_flipped,
                          num_chunks, chunk_position):

    img = make_image(pix_size_single, origin_point, is_flipped,
                     num_chunks, chunk_position)
    chunk_idx = img.chunk_idx
    res_x = img._full_res[0]
    res_y = img._full_res[1]
    pix_size = (img.pixsize_x, img.pixsize_y)
    origin = (img._start_lon, img._start_lat)

    # compute chunks
    lons = np.arange(res_x + 1) * pix_size[0] + origin[0]  # right edge +1
    all_lats = np.arange(res_y) * pix_size[1] + origin[1]
    lats_chunks = np.array_split(all_lats, num_chunks)[chunk_idx]
    pix_x = np.concatenate((np.arange(res_x), [res_x - 1]))
    pix_y_chunks = range(lats_chunks.shape[0])
    if chunk_position == 'end':
        pix_y = np.concatenate((pix_y_chunks, [pix_y_chunks[-1]]))
        lats = np.concatenate((lats_chunks, [res_y * pix_size[1] + origin[1]]))
    else:
        pix_y = pix_y_chunks
        lats = lats_chunks

    d = np.array([[a, b] for a in lons for b in lats])
    xy = img.lonlat2pix(d)
    true_xy = np.array([[a, b] for a in pix_x for b in pix_y])
    assert np.all(xy == true_xy) 

Example 43

def split_cfold(nsamples, k=5, seed=None):
    """
    Function that returns indices for splitting data into random folds.

    Parameters
    ----------
    nsamples: int
        the number of samples in the dataset
    k: int, optional
        the number of folds
    seed: int, optional
        random seed to provide to numpy

    Returns
    -------
    cvinds: list
        list of arrays of length k, each with approximate shape (nsamples /
        k,) of indices. These indices are randomly permuted (without
        replacement) of assignments to each fold.
    cvassigns: ndarray
        array of shape (nsamples,) with each element in [0, k), that can be
        used to assign data to a fold. This corresponds to the indices of
        cvinds.

    """
    np.random.seed(seed)
    pindeces = np.random.permutation(nsamples)
    cvinds = np.array_split(pindeces, k)

    cvassigns = np.zeros(nsamples, dtype=int)
    for n, inds in enumerate(cvinds):
        cvassigns[inds] = n

    return cvinds, cvassigns 

Example 44

def fit(self, x, y, *args, **kwargs):

        # set a different random seed for each thread
        np.random.seed(self.random_state + mpiops.chunk_index)

        if self.parallel:
            process_rfs = np.array_split(range(self.forests),
                                         mpiops.chunks)[mpiops.chunk_index]
        else:
            process_rfs = range(self.forests)

        for t in process_rfs:
            print('training forest {} using '
                  'process {}'.format(t, mpiops.chunk_index))

            # change random state in each forest
            self.kwargs['random_state'] = np.random.randint(0, 10000)
            rf = RandomForestTransformed(
                target_transform=self.target_transform,
                n_estimators=self.n_estimators,
                **self.kwargs
                )
            rf.fit(x, y)
            if self.parallel:  # used in training
                pk_f = join(self.temp_dir,
                            'rf_model_{}.pk'.format(t))
            else:  # used when parallel is false, i.e., during x-val
                pk_f = join(self.temp_dir,
                            'rf_model_{}_{}.pk'.format(t, mpiops.chunk_index))
            with open(pk_f, 'wb') as fp:
                pickle.dump(rf, fp)
        if self.parallel:
            mpiops.comm.barrier()
        # Mark that we are now trained
        self._trained = True 

Example 45

def kmean_distance2(x, C):
    """Compute squared euclidian distance to the nearest cluster centre

    Parameters
    ----------
    x : ndarray
        (n, d) array of n d-dimensional points
    C : ndarray
        (k, d) array of k cluster centres

    Returns
    -------
    d2_x : ndarray
        (n,) length array of distances from each x to the nearest centre
    """
    # To save memory we partition the computation
    nsplits = max(1, int(x.shape[0]/distance_partition_size))
    splits = np.array_split(x, nsplits)
    d2_x = np.empty(x.shape[0])
    idx = 0
    for x_i in splits:
        n_i = x_i.shape[0]
        D2_x = scipy.spatial.distance.cdist(x_i, C, metric='sqeuclidean')
        d2_x[idx:idx + n_i] = np.amin(D2_x, axis=1)
        idx += n_i
    return d2_x 

Example 46

def compute_weights(x, C):
    """Number of points in x assigned to each centre c in C

    Parameters
    ----------
    x : ndarray
        (n, d) array of n d-dimensional points
    C : ndarray
        (k, d) array of k cluster centres

    Returns
    -------
    weights : ndarray
        (k,) length array giving number of x closest to each c in C
    """
    nsplits = max(1, int(x.shape[0]/distance_partition_size))
    splits = np.array_split(x, nsplits)
    closests = np.empty(x.shape[0], dtype=int)
    idx = 0
    for x_i in splits:
        n_i = x_i.shape[0]
        D2_x = scipy.spatial.distance.cdist(x_i, C, metric='sqeuclidean')
        closests[idx: idx+n_i] = np.argmin(D2_x, axis=1)
        idx += n_i
    weights = np.bincount(closests, minlength=C.shape[0])
    return weights 

Example 47

def reseed_point(X, C, index):
    """ Re-initialise the centre of a class if it loses all its members

    This should almost never happen. If it does, find the point furthest
    from all the other cluster centres and use that. Maybe a bad idea but
    a decent first pass

    Parameters
    ----------
    X : ndarray
        (n, d) array of points
    C : ndarray
        (k, d) array of cluster centres
    index : int >= 0
        index between 0..k-1 of the cluster that has lost it's points

    Returns
    -------
    new_point : ndarray
        d-dimensional point for replacing the empty cluster centre.
    """
    log.info("Reseeding class with no members")
    nsplits = max(1, int(X.shape[0]/distance_partition_size))
    splits = np.array_split(X, nsplits)
    empty_index = np.ones(C.shape[0], dtype=bool)
    empty_index[index] = False
    local_candidate = None
    local_cost = 1e23
    for x_i in splits:
        D2_x = scipy.spatial.distance.cdist(x_i, C, metric='sqeuclidean')
        costs = np.sum(D2_x[:, empty_index], axis=1)
        potential_idx = np.argmax(costs)
        potential_cost = costs[potential_idx]
        if potential_cost < local_cost:
            local_candidate = x_i[potential_idx]
            local_cost = potential_cost
    best_pernode = mpiops.comm.allgather(local_cost)
    best_node = np.argmax(best_pernode)
    new_point = mpiops.comm.bcast(local_candidate, root=best_node)
    return new_point 

Example 48

def __init__(self, shape, bbox, crs, name, n_subchunks, outputdir,
                 band_tags=None):
        # affine
        self.A, _, _ = image.bbox2affine(bbox[1, 0], bbox[0, 0],
                                         bbox[0, 1], bbox[1, 1],
                                         shape[0], shape[1])
        self.shape = shape
        self.outbands = len(band_tags)
        self.bbox = bbox
        self.name = name
        self.outputdir = outputdir
        self.n_subchunks = n_subchunks
        self.sub_starts = [k[0] for k in np.array_split(
                           np.arange(self.shape[1]),
                           mpiops.chunks * self.n_subchunks)]

        # file tags don't have spaces
        if band_tags:
            file_tags = ["_".join(k.lower().split()) for k in band_tags]
        else:
            file_tags = [str(k) for k in range(self.outbands)]
            band_tags = file_tags

        if mpiops.chunk_index == 0:
            # create a file for each band
            self.files = []
            for band in range(self.outbands):
                output_filename = os.path.join(outputdir, name + "_" +
                                               file_tags[band] + ".tif")
                f = rasterio.open(output_filename, 'w', driver='GTiff',
                                  width=self.shape[0], height=self.shape[1],
                                  dtype=np.float32, count=1,
                                  crs=crs,
                                  transform=self.A,
                                  nodata=self.nodata_value)
                f.update_tags(1, image_type=band_tags[band])
                self.files.append(f) 

Example 49

def gdalaverage(input_dir, out_dir, size):
    """
    average data using gdal's averaging method.
    Parameters
    ----------
    input_dir: str
        input dir name of the tifs that needs to be averaged
    out_dir: str
        output dir name
    size: int, optional
        size of kernel
    Returns
    -------

    """
    input_dir = abspath(input_dir)
    log.info('Reading tifs from {}'.format(input_dir))
    tifs = glob.glob(join(input_dir, '*.tif'))

    process_tifs = np.array_split(tifs, mpiops.chunks)[mpiops.chunk_index]

    for tif in process_tifs:
        data_set = gdal.Open(tif, gdal.GA_ReadOnly)
        # band = data_set.GetRasterBand(1)
        # data_type = gdal.GetDataTypeName(band.DataType)
        # data = band.ReadAsArray()
        # no_data_val = band.GetNoDataValue()
        # averaged_data = filter_data(data, size, no_data_val)
        log.info('Calculated average for {}'.format(basename(tif)))

        output_file = join(out_dir, 'average_' + basename(tif))
        src_gt = data_set.GetGeoTransform()
        tmp_file = '/tmp/tmp_{}.tif'.format(mpiops.chunk_index)
        resample_cmd = [TRANSLATE] + [tif, tmp_file] + \
            ['-tr', str(src_gt[1]*size), str(src_gt[1]*size)] + \
            ['-r', 'bilinear']
        check_call(resample_cmd)
        rollback_cmd = [TRANSLATE] + [tmp_file, output_file] + \
            ['-tr', str(src_gt[1]), str(src_gt[1])]
        check_call(rollback_cmd)
        log.info('Finished converting {}'.format(basename(tif))) 

Example 50

def mean(input_dir, out_dir, size, func, partitions, mask):
    input_dir = abspath(input_dir)
    if isdir(input_dir):
        log.info('Reading tifs from {}'.format(input_dir))
        tifs = glob.glob(join(input_dir, '*.tif'))
    else:
        assert isfile(input_dir)
        tifs = [input_dir]

    process_tifs = np.array_split(tifs, mpiops.chunks)[mpiops.chunk_index]

    for tif in process_tifs:
        log.info('Starting to average {}'.format(basename(tif)))
        treat_file(tif, out_dir, size, func, partitions, mask)
        log.info('Finished averaging {}'.format(basename(tif))) 
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