The following are code examples for showing how to use . They are extracted from open source Python projects. You can vote up the examples you like or vote down the exmaples you don’t like. You can also save this page to your account.
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))) 