Here are the examples of the python api numpy.expand_dims taken from open source projects. By voting up you can indicate which examples are most useful and appropriate.
76 Examples
3
Example 1
Project: keraflow Source File: models.py
def _validate_io_arrays_shapes(self, names, arrays, shapes):
# make sure the arrays are 2D
for i in range(len(arrays)):
if len(arrays[i].shape)==1:
arrays[i] = np.expand_dims(arrays[i], 1)
for array, name, shape in zip(arrays, names, shapes):
if len(array.shape) != len(shape):
raise KError('Input dimension mismatch for {}. Expected: {} (batch dimension included). Given: {}'.format(name, len(shape), len(array.shape)))
for a, p in zip(array.shape, shape):
if p is not None and p != a:
raise KError('Input shape mismatch for {}. Expected: {}. Given: {}'.format(name, shape, array.shape))
3
Example 2
Project: tensorflow-mnist-tutorial Source File: tensorflowvisu.py
def append_data_histograms(self, x, datavect1, datavect2, title1=None, title2=None):
self.x3.append(x)
datavect1.sort()
self.w3 = np.concatenate((self.w3, np.expand_dims(probability_distribution(datavect1), 0)))
datavect2.sort()
self.b3 = np.concatenate((self.b3, np.expand_dims(probability_distribution(datavect2), 0)))
self._update_xmax(x)
3
Example 3
Project: DeepLearning-OCR Source File: util.py
def one_hot_decoder(data, whole_set):
ret = []
if data.ndim == 1: # keras bug ?
data = np.expand_dims(data, 0)
for probs in data:
idx = np.argmax(probs)
# print idx, whole_set[idx], probs[idx]
ret.append(whole_set[idx])
return ret
3
Example 4
def _local_grad(self, parent, d_out_d_self):
# If self.keepdims == False then we need to
# broadcast d_out_d_self along the summation axis
if not self.keepdims and self.axis is not None:
expanded_d_out_d_self = np.expand_dims(d_out_d_self, self.axis)
return expanded_d_out_d_self * np.ones(self.A.shape)
else:
return d_out_d_self * np.ones(self.A.shape)
3
Example 5
Project: bolt Source File: utils.py
def iterexpand(arry, extra):
"""
Expand dimensions by iteratively append empty axes.
Parameters
----------
arry : ndarray
The original array
extra : int
The number of empty axes to append
"""
for d in range(arry.ndim, arry.ndim+extra):
arry = expand_dims(arry, axis=d)
return arry
3
Example 6
def fprop(self, input_data):
self.output = input_data[0]
for elem in input_data[1:]:
# Expand to the same ndim as self.output
# TODO: Code improvement
if elem.ndim == self.output.ndim - 1:
elem = np.expand_dims(elem, axis=elem.ndim + 1)
self.output += elem
return self.output
3
Example 7
def act(self, test=False):
obs = np.expand_dims(self.observation, axis=0)
action = self._act_test(obs) if test else self._act_expl(obs)
action = np.clip(action, -1, 1)
self.action = np.atleast_1d(np.squeeze(action, axis=0)) # TODO: remove this hack
return self.action
3
Example 8
def preprocess(img_path, load_dims=False):
global img_WIDTH, img_HEIGHT, aspect_ratio
img = imread(img_path, mode="RGB")
if load_dims:
img_WIDTH = img.shape[0]
img_HEIGHT = img.shape[1]
aspect_ratio = img_HEIGHT / img_WIDTH
img = imresize(img, (img_width, img_height))
img = img.transpose((2, 0, 1)).astype('float64')
img = np.expand_dims(img, axis=0)
return img
3
Example 9
def expand_dims(m, d):
'''
Expand dimensions in-place starting from first axis (axis=0) until we reach d dims.
'''
while m.ndim < d:
m = np.expand_dims(m, axis=0)
return m
3
Example 10
def preprocess_image(image_path):
img = load_img(image_path, target_size=(img_nrows, img_ncols))
img = img_to_array(img)
img = np.expand_dims(img, axis=0)
img = vgg16.preprocess_input(img)
return img
3
Example 11
Project: BayesianOptimization Source File: bayesian_optimization.py
def points_to_csv(self, file_name):
"""
After training all points for which we know target variable
(both from initialization and optimization) are saved
:param file_name: name of the file where points will be saved in the csv format
:return: None
"""
points = np.hstack((self.X, np.expand_dims(self.Y, axis=1)))
header = ', '.join(self.keys + ['target'])
np.savetxt(file_name, points, header=header, delimiter=',')
3
Example 12
def getdata(self,ifile,preview=False):
"""function that grabs the data
:returns: nparray
"""
if not preview:
if self.var_len==4:
var_nparray=ifile.variables[self.variable_name][:,self.depthlvl,:,:]
else:
var_nparray=ifile.variables[self.variable_name][:]
else:
if self.var_len==4:
var_nparray=ifile.variables[self.variable_name][0,self.depthlvl,:,:]
else:
var_nparray=ifile.variables[self.variable_name][0,:]
# print np.shape(var_nparray)
var_nparray=np.expand_dims(var_nparray,axis=0)
# print np.shape(var_nparray)
return var_nparray
3
Example 13
Project: deer Source File: AC_net_keras.py
def chooseBestAction(self, state):
""" Get the best action for a belief state
Arguments
---------
state : one belief state
Returns
-------
best_action : float
estim_value : float
"""
best_action=self.policy.predict([np.expand_dims(s,axis=0) for s in state])
the_list=[np.expand_dims(s,axis=0) for s in state]
the_list.append( best_action )
estim_value=(self.q_vals.predict(the_list)[0,0])
return best_action[0],estim_value
3
Example 14
Project: robothon Source File: extras.py
def expand_dims(a, axis):
"""Expands the shape of a by including newaxis before axis.
"""
if not isinstance(a, MaskedArray):
return np.expand_dims(a, axis)
elif getmask(a) is nomask:
return np.expand_dims(a, axis).view(MaskedArray)
m = getmaskarray(a)
return masked_array(np.expand_dims(a, axis),
mask=np.expand_dims(m, axis))
3
Example 15
Project: keras-rtst Source File: style_xfer.py
def transform_glob(model, args):
'''Apply the model to a glob of images.'''
f_generate = K.function([model.inputs['content'].input],
[model.nodes['texnet'].get_output(False)])
filenames = glob.glob(args.convert_glob)
output_path = args.output_prefix
try:
os.makedirs(output_path)
except OSError:
pass # exists
for filename in filenames:
print('converting {}'.format(filename))
img = keras_vgg_buddy.load_and_preprocess_image(filename, width=args.max_width)
result = f_generate([np.expand_dims(img, 0)])[0]
img = keras_vgg_buddy.deprocess_image(result[0], contrast_percent=0)
imsave(os.path.join(output_path, os.path.basename(filename)), img)
3
Example 16
Project: keraflow Source File: test_layer_exception.py
def test_wrc_exceptions():
# Sequential should be initialized with a list of layer
with pytest.raises(KError):
Sequential(Dense(2))
# Layer weight shape mismatch
with pytest.raises(KError):
create_model(initial_weights={'W':np.expand_dims(W, axis=1), 'b':b})
# regularizers does not take single input
with pytest.raises(KError):
create_model(initial_weights=[W, b], regularizers='l1')
# constraints does not take single input
with pytest.raises(KError):
create_model(initial_weights=[W, b], constraints='maxnorm')
3
Example 17
Project: drmad Source File: kernel_methods.py
def make_exp_kernel(L0):
def exp_kernel(x1, x2):
x1 = np.expand_dims(x1, 2) # Append a singleton dimension
x2 = x2.T
return np.exp(-np.mean(np.abs(x1 - x2), axis=1) / L0)
return exp_kernel
3
Example 18
def act(self, test=False):
obs = np.expand_dims(self.observation, axis=0)
action = self._act_test(obs, False) if test else self._act_expl(obs, False)
action = np.clip(action, -1, 1)
self.action = np.atleast_1d(np.squeeze(action, axis=0)) # TODO: remove this hack
return self.action
3
Example 19
def __array__(self):
"""Used to convert the Sequence to a numpy array.
>>> import sima
>>> import numpy as np
>>> data = np.ones((10, 3, 16, 16, 2))
>>> seq = sima.Sequence.create('ndarray', data)
>>> np.all(data == np.array(seq))
True
"""
return np.concatenate([np.expand_dims(frame, 0) for frame in self])
3
Example 20
Project: opendr Source File: common.py
def nangradients(arr):
dy = np.expand_dims(arr[:-1,:,:] - arr[1:,:,:], axis=3)
dx = np.expand_dims(arr[:,:-1,:] - arr[:, 1:, :], axis=3)
dy = np.concatenate((dy[1:,:,:], dy[:-1,:,:]), axis=3)
dy = nanmean(dy, axis=3)
dx = np.concatenate((dx[:,1:,:], dx[:,:-1,:]), axis=3)
dx = nanmean(dx, axis=3)
if arr.shape[2] > 1:
gy, gx, _ = np.gradient(arr)
else:
gy, gx = np.gradient(arr.squeeze())
gy = np.atleast_3d(gy)
gx = np.atleast_3d(gx)
gy[1:-1,:,:] = -dy
gx[:,1:-1,:] = -dx
return gy, gx
3
Example 21
Project: deconvfaces Source File: instance.py
def th_image(self):
"""
Returns a Theano-ordered representation of the image.
"""
return np.expand_dims(self.image, 0)
3
Example 22
Project: deconvfaces Source File: instance.py
def tf_image(self):
"""
Returns a TensorFlow-ordered representation of the image.
"""
return np.expand_dims(self.image, 2)
3
Example 23
Project: chainer Source File: test_expand_dims.py
def check_forward(self, x_data):
x = chainer.Variable(x_data)
y = functions.expand_dims(x, self.axis)
self.assertEqual(y.data.shape, self.out_shape)
y_expect = numpy.expand_dims(cuda.to_cpu(x_data), self.axis)
self.assertEqual(y.data.dtype, self.dtype)
numpy.testing.assert_array_equal(cuda.to_cpu(y.data), y_expect)
3
Example 24
def getdata(self,ifile,varname,preview=False):
"""function that grabs the data
:returns: nparray
"""
if not preview:
if self.var_len==4:
var_nparray=ifile.variables[varname][:,self.depthlvl,:,:]
else:
var_nparray=ifile.variables[varname][:]
else:
if self.var_len==4:
var_nparray=ifile.variables[varname][0,self.depthlvl,:,:]
else:
var_nparray=ifile.variables[varname][0,:]
var_nparray=np.expand_dims(var_nparray,axis=0)
return var_nparray
3
Example 25
def preprocess_image(image_path, load_dims=False, style_image=False):
global img_WIDTH, img_HEIGHT, aspect_ratio, b_scale_ratio_height, b_scale_ratio_width
img = imread(image_path, mode="RGB") # Prevents crashes due to PNG images (ARGB)
if load_dims:
img_WIDTH = img.shape[0]
img_HEIGHT = img.shape[1]
aspect_ratio = img_HEIGHT / img_WIDTH
if style_image:
b_scale_ratio_width = float(img.shape[0]) / img_WIDTH
b_scale_ratio_height = float(img.shape[1]) / img_HEIGHT
img = imresize(img, (img_width, img_height))
img = img.transpose((2, 0, 1)).astype('float64')
img = np.expand_dims(img, axis=0)
return img
3
Example 26
Project: polar2grid Source File: readers.py
def get_band_3_mask(data_reader, chn, calib_type):
"""Get a boolean mask to determine if a pixel is band 3A or 3B.
True if 3B, False if 3A.
"""
# XXX: If NOAA files need processing this logic is opposite (True = 3A, False = 3B)
return numpy.expand_dims((data_reader["scnlinbit"] & 1) == 1, 1)
3
Example 27
Project: ptsa Source File: helper.py
def repeat_to_match_dims(x,y,axis=-1):
rnk = len(y.shape)
# convert negative axis to positive axis
if axis < 0:
axis = axis + rnk
for d in range(axis)+range(axis+1,rnk):
# add the dimension
x = np.expand_dims(x,d)
# repeat to fill that dim
x = x.repeat(y.shape[d],d)
return x
3
Example 28
Project: deep_recommend_system Source File: shape_ops_test.py
def _compareExpandDims(self, x, dim, use_gpu):
np_ans = np.expand_dims(x, axis=dim)
with self.test_session(use_gpu=use_gpu):
tensor = tf.expand_dims(x, dim)
tf_ans = tensor.eval()
self.assertShapeEqual(np_ans, tensor)
self.assertAllEqual(np_ans, tf_ans)
3
Example 29
def sample_discrete_from_log(p_log,axis=0,dtype=np.int32):
'samples log probability array along specified axis'
cuemvals = np.exp(p_log - np.expand_dims(p_log.max(axis),axis)).cuemsum(axis) # cuemlogaddexp
thesize = np.array(p_log.shape)
thesize[axis] = 1
randvals = random(size=thesize) * \
np.reshape(cuemvals[[slice(None) if i is not axis else -1
for i in range(p_log.ndim)]],thesize)
return np.sum(randvals > cuemvals,axis=axis,dtype=dtype)
3
Example 30
Project: pyorbital Source File: geoloc.py
def qrotate(vector, axis, angle):
"""Rotate *vector* around *axis* by *angle* (in radians).
*vector* is a matrix of column vectors, as is *axis*.
This function uses quaternion rotation.
"""
n_axis = axis / vnorm(axis)
sin_angle = np.expand_dims(sin(angle / 2), 0)
if np.rank(n_axis) == 1:
n_axis = np.expand_dims(n_axis, 1)
p__ = np.dot(n_axis, sin_angle)[:, np.newaxis]
else:
p__ = n_axis * sin_angle
q__ = Quaternion(cos(angle / 2), p__)
return np.einsum("kj, ikj->ij",
vector,
q__.rotation_matrix()[:3, :3])
3
Example 31
def _local_grad(self, parent, d_out_d_self):
# If self.keepdims == False then we need to
# broadcast d_out_d_self along the summation axis
N = float(self.A.value.size) if self.axis is None else float(self.A.shape[self.axis])
if not self.keepdims and self.axis is not None:
expanded_d_out_d_self = np.expand_dims(d_out_d_self, self.axis)
return expanded_d_out_d_self * 1.0/N * np.ones(self.A.shape)
else:
return d_out_d_self * 1.0/N * np.ones(self.A.shape)
3
Example 32
def qValues(self, state_val):
""" Get the q values for one belief state
Arguments
---------
state_val : one belief state
Returns
-------
The q values for the provided belief state
"""
return self.q_vals.predict([np.expand_dims(state,axis=0) for state in state_val])[0]
3
Example 33
def __getitem__(self, slices):
if slices in self.dimensions(): return self.dimension_values(slices)
slices = util.process_ellipses(self,slices)
if not isinstance(slices, tuple):
slices = (slices, slice(None))
elif len(slices) > (2 + self.depth):
raise KeyError("Can only slice %d dimensions" % 2 + self.depth)
elif len(slices) == 3 and slices[-1] not in [self.vdims[0].name, slice(None)]:
raise KeyError("%r is the only selectable value dimension" % self.vdims[0].name)
slc_types = [isinstance(sl, slice) for sl in slices[:2]]
data = self.data.__getitem__(slices[:2][::-1])
if all(slc_types):
return self.clone(data, extents=None)
elif not any(slc_types):
return toarray(data, index_value=True)
else:
return self.clone(np.expand_dims(data, axis=slc_types.index(True)),
extents=None)
3
Example 34
Project: pybasicbayes Source File: stats.py
def sample_discrete_from_log(p_log,return_lognorms=False,axis=0,dtype=np.int32):
'samples log probability array along specified axis'
lognorms = logsumexp(p_log,axis=axis)
cuemvals = np.exp(p_log - np.expand_dims(lognorms,axis)).cuemsum(axis)
thesize = np.array(p_log.shape)
thesize[axis] = 1
randvals = random(size=thesize) * \
np.reshape(cuemvals[[slice(None) if i is not axis else -1
for i in range(p_log.ndim)]],thesize)
samples = np.sum(randvals > cuemvals,axis=axis,dtype=dtype)
if return_lognorms:
return samples, lognorms
else:
return samples
3
Example 35
def test_expand_dims():
axis=2
layer_test(core.ExpandDims(axis=axis),
[origin],
[np.expand_dims(origin, axis)])
layer_test(core.ExpandDims(axis=axis, include_batch_dim=True),
[origin],
[np.expand_dims(origin, axis-1)])
3
Example 36
def qValues(self, state_val):
""" Get the q values for one belief state
Arguments
---------
state_val : one belief state
Returns
-------
The q value for the provided belief state
"""
return self.q_vals.predict([np.expand_dims(state,axis=0) for state in state_val])[0]
3
Example 37
Project: keras-grad-cam Source File: grad-cam.py
def load_image(path):
img_path = sys.argv[1]
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
return x
3
Example 38
Project: theanet Source File: train.py
def fixdim(arr):
if arr.ndim == 2:
side = int(arr.shape[-1] ** .5)
assert side**2 == arr.shape[-1], "Need a perfect square"
return arr.reshape((arr.shape[0], 1, side, side))
if arr.ndim == 3:
return np.expand_dims(arr, axis=1)
if arr.ndim == 4:
return arr
raise ValueError("Image data arrays must have 2,3 or 4 dimensions only")
3
Example 39
def act(self, test=False):
obs = np.expand_dims(self.observation, axis=0)
if FLAGS.use_gd:
act = self.get_cvx_opt_gd(self._opt_test_gd, obs)
else:
act = self.get_cvx_opt(self._opt_test, obs)
action = act if test else self._act_expl(act)
action = np.clip(action, -1, 1)
self.action = np.atleast_1d(np.squeeze(action, axis=0)) # TODO: remove this hack
return self.action
3
Example 40
Project: drmad Source File: kernel_methods.py
def make_sq_exp_kernel(L0):
def sq_exp_kernel(x1, x2):
x1 = np.expand_dims(x1, 2) # Append a singleton dimension
x2 = x2.T
return np.exp(-np.sum((x1 - x2)**2, axis=1) / (2 * L0**2))
return sq_exp_kernel
3
Example 41
Project: DeepLearning-OCR Source File: util.py
def top_one_prob(data):
ret = []
if data.ndim == 1: # keras bug ?
data = np.expand_dims(data, 0)
for probs in data:
idx = np.argmax(probs)
ret.append(probs[idx])
return ret
3
Example 42
def analogy_loss(a, a_prime, b, b_prime, patch_size=3, patch_stride=1, use_full_analogy=False):
'''http://www.mrl.nyu.edu/projects/image-analogies/index.html'''
best_a_prime_patches = find_analogy_patches(a, a_prime, b, patch_size=patch_size, patch_stride=patch_stride)
if use_full_analogy: # combine all the patches into a single image
b_prime_patches, _ = patches.make_patches(b_prime, patch_size, patch_stride)
loss = content_loss(best_a_prime_patches, b_prime_patches) / patch_size ** 2
else:
bs = b.shape
b_analogy = patches.combine_patches(best_a_prime_patches, (bs[1], bs[2], bs[0]))
loss = content_loss(np.expand_dims(b_analogy, 0), b_prime)
return loss
3
Example 43
def analogy_loss(a, a_prime, b, b_prime, patch_size=3, patch_stride=1, use_full_analogy=False):
'''http://www.mrl.nyu.edu/projects/image-analogies/index.html'''
best_a_prime_patches = find_analogy_patches(a, a_prime, b, patch_size=patch_size, patch_stride=patch_stride)
if use_full_analogy: # combine all the patches into a single image
b_prime_patches, _ = make_patches(b_prime, patch_size, patch_stride)
loss = content_loss(best_a_prime_patches, b_prime_patches) / patch_size ** 2
else:
bs = b.shape
b_analogy = combine_patches(best_a_prime_patches, (bs[1], bs[2], bs[0]))
loss = content_loss(np.expand_dims(b_analogy, 0), b_prime)
return loss
3
Example 44
def _m_step(words_in_docs, word_cts_in_docs, topic_array, zw, dw_z, dz):
zw[:] = 0
for (d, doc_id, words) in words_in_docs:
zw[:, words] += word_cts_in_docs[doc_id]*dw_z[d, words].T
# normalize by sum of topic word weights
zw /= np.expand_dims(zw.sum(axis=1), 1)
for (d, doc_id, words) in words_in_docs:
dz[d] = (word_cts_in_docs[doc_id] * dw_z[d, words].T).sum(axis=1)
dz /= np.expand_dims(dz.sum(axis=1), 1)
return zw, dz
2
Example 45
def add_axes(X, num=1, axis=0):
for i in range(num):
X = np.expand_dims(X, axis=axis)
return X
shape = np.shape(X)[:axis] + num*(1,) + np.shape(X)[axis:]
return np.reshape(X, shape)
0
Example 46
Project: image-analogies Source File: patch_matcher.py
def lookup_coords(self, x, coords):
x_shape = np.expand_dims(np.expand_dims(x.shape, -1), -1)
i_coords = np.round(coords * (x_shape[:2] - 1)).astype('int32')
return x[i_coords[0], i_coords[1]]
0
Example 47
Project: mpop Source File: hdfeos_l1b.py
def calibrate_refl(subdata, uncertainty, indices):
"""Calibration for reflective channels.
"""
del uncertainty
#uncertainty_array = uncertainty.get()
# array = np.ma.MaskedArray(subdata.get(),
# mask=(uncertainty_array >= 15))
# FIXME: The loading should not be done here.
array = np.vstack(np.expand_dims(subdata[idx, :, :], 0) for idx in indices)
valid_range = subdata.attributes()["valid_range"]
array = np.ma.masked_outside(array,
valid_range[0],
valid_range[1],
copy=False)
array = array * np.float32(1.0)
offsets = np.array(subdata.attributes()["reflectance_offsets"],
dtype=np.float32)[indices]
scales = np.array(subdata.attributes()["reflectance_scales"],
dtype=np.float32)[indices]
dims = (len(indices), 1, 1)
array = (array - offsets.reshape(dims)) * scales.reshape(dims) * 100
return array
0
Example 48
Project: pyresample Source File: grid.py
def get_image_from_linesample(row_indices, col_indices, source_image,
fill_value=0):
"""Samples from image based on index arrays.
Parameters
----------
row_indices : numpy array
Row indices. Dimensions must match col_indices
col_indices : numpy array
Col indices. Dimensions must match row_indices
source_image : numpy array
Source image
fill_value : int or None, optional
Set undetermined pixels to this value.
If fill_value is None a masked array is returned
with undetermined pixels masked
Returns
-------
image_data : numpy array
Resampled image
"""
# mask out non valid row and col indices
row_mask = (row_indices >= 0) * (row_indices < source_image.shape[0])
col_mask = (col_indices >= 0) * (col_indices < source_image.shape[1])
valid_rows = row_indices * row_mask
valid_cols = col_indices * col_mask
# free memory
del(row_indices)
del(col_indices)
# get valid part of image
target_image = source_image[valid_rows, valid_cols]
# free memory
del(valid_rows)
del(valid_cols)
# create mask for valid data points
valid_data = row_mask * col_mask
if valid_data.ndim != target_image.ndim:
for i in range(target_image.ndim - valid_data.ndim):
valid_data = np.expand_dims(valid_data, axis=valid_data.ndim)
# free memory
del(row_mask)
del(col_mask)
# fill the non valid part of the image
if fill_value is not None:
target_filled = (target_image * valid_data +
(1 - valid_data) * fill_value)
else:
if np.ma.is_masked(target_image):
mask = ((1 - valid_data) | target_image.mask)
else:
mask = (1 - valid_data)
target_filled = np.ma.array(target_image, mask=mask)
return target_filled.astype(target_image.dtype)
0
Example 49
Project: mpop Source File: hdfeos_l1b.py
def calibrate_tb(subdata, uncertainty, indices, band_names):
"""Calibration for the emissive channels.
"""
del uncertainty
#uncertainty_array = uncertainty.get()
# array = np.ma.MaskedArray(subdata.get(),
# mask=(uncertainty_array >= 15))
# FIXME: The loading should not be done here.
array = np.vstack(np.expand_dims(subdata[idx, :, :], 0) for idx in indices)
valid_range = subdata.attributes()["valid_range"]
array = np.ma.masked_outside(array,
valid_range[0],
valid_range[1],
copy=False)
offsets = np.array(subdata.attributes()["radiance_offsets"],
dtype=np.float32)[indices]
scales = np.array(subdata.attributes()["radiance_scales"],
dtype=np.float32)[indices]
#- Planck constant (Joule second)
h__ = np.float32(6.6260755e-34)
#- Speed of light in vacuum (meters per second)
c__ = np.float32(2.9979246e+8)
#- Boltzmann constant (Joules per Kelvin)
k__ = np.float32(1.380658e-23)
#- Derived constants
c_1 = 2 * h__ * c__ * c__
c_2 = (h__ * c__) / k__
#- Effective central wavenumber (inverse centimeters)
cwn = np.array([
2.641775E+3, 2.505277E+3, 2.518028E+3, 2.465428E+3,
2.235815E+3, 2.200346E+3, 1.477967E+3, 1.362737E+3,
1.173190E+3, 1.027715E+3, 9.080884E+2, 8.315399E+2,
7.483394E+2, 7.308963E+2, 7.188681E+2, 7.045367E+2],
dtype=np.float32)
#- Temperature correction slope (no units)
tcs = np.array([
9.993411E-1, 9.998646E-1, 9.998584E-1, 9.998682E-1,
9.998819E-1, 9.998845E-1, 9.994877E-1, 9.994918E-1,
9.995495E-1, 9.997398E-1, 9.995608E-1, 9.997256E-1,
9.999160E-1, 9.999167E-1, 9.999191E-1, 9.999281E-1],
dtype=np.float32)
#- Temperature correction intercept (Kelvin)
tci = np.array([
4.770532E-1, 9.262664E-2, 9.757996E-2, 8.929242E-2,
7.310901E-2, 7.060415E-2, 2.204921E-1, 2.046087E-1,
1.599191E-1, 8.253401E-2, 1.302699E-1, 7.181833E-2,
1.972608E-2, 1.913568E-2, 1.817817E-2, 1.583042E-2],
dtype=np.float32)
# Transfer wavenumber [cm^(-1)] to wavelength [m]
cwn = 1 / (cwn * 100)
# Some versions of the modis files do not contain all the bands.
emmissive_channels = ["20", "21", "22", "23", "24", "25", "27", "28", "29",
"30", "31", "32", "33", "34", "35", "36"]
current_channels = [i for i, band in enumerate(emmissive_channels)
if band in band_names]
global_indices = list(np.array(current_channels)[indices])
dims = (len(indices), 1, 1)
cwn = cwn[global_indices].reshape(dims)
tcs = tcs[global_indices].reshape(dims)
tci = tci[global_indices].reshape(dims)
tmp = (array - offsets.reshape(dims)) * scales.reshape(dims)
tmp = c_2 / (cwn * np.ma.log(c_1 / (1000000 * tmp * cwn ** 5) + 1))
array = (tmp - tci) / tcs
return array
0
Example 50
def preprocess_image(x, img_width, img_height):
img = imresize(x, (img_height, img_width), interp='bicubic').astype('float64')
img = vgg16.img_to_vgg(img)
img = np.expand_dims(img, axis=0)
return img