Python numpy.bool() Examples
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code examples of numpy.bool().
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Example #1
Source Project: dustmaps Author: gregreen File: leike_ensslin_2019.py License: GNU General Public License v2.0 | 6 votes |
def fetch(clobber=False): """ Downloads the 3D dust map of Leike & Ensslin (2019). Args: clobber (Optional[bool]): If ``True``, any existing file will be overwritten, even if it appears to match. If ``False`` (the default), ``fetch()`` will attempt to determine if the dataset already exists. This determination is not 100\% robust against data corruption. """ dest_dir = fname_pattern = os.path.join(data_dir(), 'leike_ensslin_2019') fname = os.path.join(dest_dir, 'simple_cube.h5') # Check if the FITS table already exists md5sum = 'f54e01c253453117e3770575bed35078' if (not clobber) and fetch_utils.check_md5sum(fname, md5sum): print('File appears to exist already. Call `fetch(clobber=True)` ' 'to force overwriting of existing file.') return # Download from the server url = 'https://zenodo.org/record/2577337/files/simple_cube.h5?download=1' fetch_utils.download_and_verify(url, md5sum, fname)
Example #2
Source Project: neuropythy Author: noahbenson File: core.py License: GNU Affero General Public License v3.0 | 6 votes |
def dataframe_select(df, *cols, **filters): ''' dataframe_select(df, k1=v1, k2=v2...) yields df after selecting all the columns in which the given keys (k1, k2, etc.) have been selected such that the associated columns in the dataframe contain only the rows whose cells match the given values. dataframe_select(df, col1, col2...) selects the given columns. dataframe_select(df, col1, col2..., k1=v1, k2=v2...) selects both. If a value is a tuple/list of 2 elements, then it is considered a range where cells must fall between the values. If value is a tuple/list of more than 2 elements or is a set of any length then it is a list of values, any one of which can match the cell. ''' ii = np.ones(len(df), dtype='bool') for (k,v) in six.iteritems(filters): vals = df[k].values if pimms.is_set(v): jj = np.isin(vals, list(v)) elif pimms.is_vector(v) and len(v) == 2: jj = (v[0] <= vals) & (vals < v[1]) elif pimms.is_vector(v): jj = np.isin(vals, list(v)) else: jj = (vals == v) ii = np.logical_and(ii, jj) if len(ii) != np.sum(ii): df = df.loc[ii] if len(cols) > 0: df = df[list(cols)] return df
Example #3
Source Project: DOTA_models Author: ringringyi File: tf_example_decoder_test.py License: Apache License 2.0 | 6 votes |
def testDecodeObjectIsCrowd(self): image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8) encoded_jpeg = self._EncodeImage(image_tensor) object_is_crowd = [0, 1] example = tf.train.Example(features=tf.train.Features(feature={ 'image/encoded': self._BytesFeature(encoded_jpeg), 'image/format': self._BytesFeature('jpeg'), 'image/object/is_crowd': self._Int64Feature(object_is_crowd), })).SerializeToString() example_decoder = tf_example_decoder.TfExampleDecoder() tensor_dict = example_decoder.decode(tf.convert_to_tensor(example)) self.assertAllEqual((tensor_dict[ fields.InputDataFields.groundtruth_is_crowd].get_shape().as_list()), [None]) with self.test_session() as sess: tensor_dict = sess.run(tensor_dict) self.assertAllEqual([bool(item) for item in object_is_crowd], tensor_dict[ fields.InputDataFields.groundtruth_is_crowd])
Example #4
Source Project: DOTA_models Author: ringringyi File: tf_example_decoder_test.py License: Apache License 2.0 | 6 votes |
def testDecodeObjectDifficult(self): image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8) encoded_jpeg = self._EncodeImage(image_tensor) object_difficult = [0, 1] example = tf.train.Example(features=tf.train.Features(feature={ 'image/encoded': self._BytesFeature(encoded_jpeg), 'image/format': self._BytesFeature('jpeg'), 'image/object/difficult': self._Int64Feature(object_difficult), })).SerializeToString() example_decoder = tf_example_decoder.TfExampleDecoder() tensor_dict = example_decoder.decode(tf.convert_to_tensor(example)) self.assertAllEqual((tensor_dict[ fields.InputDataFields.groundtruth_difficult].get_shape().as_list()), [None]) with self.test_session() as sess: tensor_dict = sess.run(tensor_dict) self.assertAllEqual([bool(item) for item in object_difficult], tensor_dict[ fields.InputDataFields.groundtruth_difficult])
Example #5
Source Project: cs294-112_hws Author: xuwd11 File: dqn_utils.py License: MIT License | 6 votes |
def store_effect(self, idx, action, reward, done): """Store effects of action taken after obeserving frame stored at index idx. The reason `store_frame` and `store_effect` is broken up into two functions is so that once can call `encode_recent_observation` in between. Paramters --------- idx: int Index in buffer of recently observed frame (returned by `store_frame`). action: int Action that was performed upon observing this frame. reward: float Reward that was received when the actions was performed. done: bool True if episode was finished after performing that action. """ self.action[idx] = action self.reward[idx] = reward self.done[idx] = done
Example #6
Source Project: QCElemental Author: MolSSI File: test_scipy_hungarian.py License: BSD 3-Clause "New" or "Revised" License | 6 votes |
def test_linear_sum_assignment_input_validation(): assert_raises(ValueError, linear_sum_assignment, [1, 2, 3]) C = [[1, 2, 3], [4, 5, 6]] assert_array_equal(linear_sum_assignment(C), linear_sum_assignment(np.asarray(C))) # assert_array_equal(linear_sum_assignment(C), # linear_sum_assignment(matrix(C))) I = np.identity(3) assert_array_equal(linear_sum_assignment(I.astype(np.bool)), linear_sum_assignment(I)) assert_raises(ValueError, linear_sum_assignment, I.astype(str)) I[0][0] = np.nan assert_raises(ValueError, linear_sum_assignment, I) I = np.identity(3) I[1][1] = np.inf assert_raises(ValueError, linear_sum_assignment, I)
Example #7
Source Project: lirpg Author: Hwhitetooth File: buffer.py License: MIT License | 6 votes |
def put(self, enc_obs, actions, rewards, mus, dones, masks): # enc_obs [nenv, (nsteps + nstack), nh, nw, nc] # actions, rewards, dones [nenv, nsteps] # mus [nenv, nsteps, nact] if self.enc_obs is None: self.enc_obs = np.empty([self.size] + list(enc_obs.shape), dtype=np.uint8) self.actions = np.empty([self.size] + list(actions.shape), dtype=np.int32) self.rewards = np.empty([self.size] + list(rewards.shape), dtype=np.float32) self.mus = np.empty([self.size] + list(mus.shape), dtype=np.float32) self.dones = np.empty([self.size] + list(dones.shape), dtype=np.bool) self.masks = np.empty([self.size] + list(masks.shape), dtype=np.bool) self.enc_obs[self.next_idx] = enc_obs self.actions[self.next_idx] = actions self.rewards[self.next_idx] = rewards self.mus[self.next_idx] = mus self.dones[self.next_idx] = dones self.masks[self.next_idx] = masks self.next_idx = (self.next_idx + 1) % self.size self.num_in_buffer = min(self.size, self.num_in_buffer + 1)
Example #8
Source Project: HardRLWithYoutube Author: MaxSobolMark File: buffer.py License: MIT License | 6 votes |
def put(self, enc_obs, actions, rewards, mus, dones, masks): # enc_obs [nenv, (nsteps + nstack), nh, nw, nc] # actions, rewards, dones [nenv, nsteps] # mus [nenv, nsteps, nact] if self.enc_obs is None: self.enc_obs = np.empty([self.size] + list(enc_obs.shape), dtype=np.uint8) self.actions = np.empty([self.size] + list(actions.shape), dtype=np.int32) self.rewards = np.empty([self.size] + list(rewards.shape), dtype=np.float32) self.mus = np.empty([self.size] + list(mus.shape), dtype=np.float32) self.dones = np.empty([self.size] + list(dones.shape), dtype=np.bool) self.masks = np.empty([self.size] + list(masks.shape), dtype=np.bool) self.enc_obs[self.next_idx] = enc_obs self.actions[self.next_idx] = actions self.rewards[self.next_idx] = rewards self.mus[self.next_idx] = mus self.dones[self.next_idx] = dones self.masks[self.next_idx] = masks self.next_idx = (self.next_idx + 1) % self.size self.num_in_buffer = min(self.size, self.num_in_buffer + 1)
Example #9
Source Project: py360convert Author: sunset1995 File: utils.py License: MIT License | 6 votes |
def equirect_facetype(h, w): ''' 0F 1R 2B 3L 4U 5D ''' tp = np.roll(np.arange(4).repeat(w // 4)[None, :].repeat(h, 0), 3 * w // 8, 1) # Prepare ceil mask mask = np.zeros((h, w // 4), np.bool) idx = np.linspace(-np.pi, np.pi, w // 4) / 4 idx = h // 2 - np.round(np.arctan(np.cos(idx)) * h / np.pi).astype(int) for i, j in enumerate(idx): mask[:j, i] = 1 mask = np.roll(np.concatenate([mask] * 4, 1), 3 * w // 8, 1) tp[mask] = 4 tp[np.flip(mask, 0)] = 5 return tp.astype(np.int32)
Example #10
Source Project: Modeling-Cloth Author: the3dadvantage File: ModelingCloth.py License: MIT License | 6 votes |
def get_poly_centers(ob, type=np.float32, mesh=None): mod = False m_count = len(ob.modifiers) if m_count > 0: show = np.zeros(m_count, dtype=np.bool) ren_set = np.copy(show) ob.modifiers.foreach_get('show_render', show) ob.modifiers.foreach_set('show_render', ren_set) mod = True p_count = len(mesh.polygons) center = np.zeros(p_count * 3, dtype=type) mesh.polygons.foreach_get('center', center) center.shape = (p_count, 3) if mod: ob.modifiers.foreach_set('show_render', show) return center
Example #11
Source Project: Modeling-Cloth Author: the3dadvantage File: ModelingCloth.py License: MIT License | 6 votes |
def get_poly_normals(ob, type=np.float32, mesh=None): mod = False m_count = len(ob.modifiers) if m_count > 0: show = np.zeros(m_count, dtype=np.bool) ren_set = np.copy(show) ob.modifiers.foreach_get('show_render', show) ob.modifiers.foreach_set('show_render', ren_set) mod = True p_count = len(mesh.polygons) normal = np.zeros(p_count * 3, dtype=type) mesh.polygons.foreach_get('normal', normal) normal.shape = (p_count, 3) if mod: ob.modifiers.foreach_set('show_render', show) return normal
Example #12
Source Project: Modeling-Cloth Author: the3dadvantage File: ModelingCloth.py License: MIT License | 6 votes |
def get_v_normals(ob, arr, mesh): """Since we're reading from a shape key we have to use a proxy mesh.""" mod = False m_count = len(ob.modifiers) if m_count > 0: show = np.zeros(m_count, dtype=np.bool) ren_set = np.copy(show) ob.modifiers.foreach_get('show_render', show) ob.modifiers.foreach_set('show_render', ren_set) mod = True #v_count = len(mesh.vertices) #normal = np.zeros(v_count * 3)#, dtype=type) mesh.vertices.foreach_get('normal', arr.ravel()) #normal.shape = (v_count, 3) if mod: ob.modifiers.foreach_set('show_render', show)
Example #13
Source Project: Modeling-Cloth Author: the3dadvantage File: ModelingCloth.py License: MIT License | 6 votes |
def triangle_bounds_check(tri_co, co_min, co_max, idxer, fudge): """Returns a bool aray indexing the triangles that intersect the bounds of the object""" # min check cull step 1 tri_min = np.min(tri_co, axis=1) - fudge check_min = co_max > tri_min in_min = np.all(check_min, axis=1) # max check cull step 2 idx = idxer[in_min] tri_max = np.max(tri_co[in_min], axis=1) + fudge check_max = tri_max > co_min in_max = np.all(check_max, axis=1) in_min[idx[~in_max]] = False return in_min, tri_min[in_min], tri_max[in_max] # can reuse the min and max
Example #14
Source Project: Modeling-Cloth Author: the3dadvantage File: ModelingCloth.py License: MIT License | 6 votes |
def tri_back_check(co, tri_min, tri_max, idxer, fudge): """Returns a bool aray indexing the vertices that intersect the bounds of the culled triangles""" # min check cull step 1 tb_min = np.min(tri_min, axis=0) - fudge check_min = co > tb_min in_min = np.all(check_min, axis=1) idx = idxer[in_min] # max check cull step 2 tb_max = np.max(tri_max, axis=0) + fudge check_max = co[in_min] < tb_max in_max = np.all(check_max, axis=1) in_min[idx[~in_max]] = False return in_min # ------------------------------------------------------- # -------------------------------------------------------
Example #15
Source Project: Modeling-Cloth Author: the3dadvantage File: UVShape.py License: MIT License | 6 votes |
def basic_unwrap(): ob = bpy.context.object mode = ob.mode data = ob.data key = ob.active_shape_key_index bpy.ops.object.mode_set(mode='OBJECT') layers = [i.name for i in ob.data.uv_layers] if "UV_Shape_key" not in layers: bpy.ops.mesh.uv_texture_add() ob.data.uv_layers[len(ob.data.uv_layers) - 1].name = 'UV_Shape_key' ob.data.uv_layers.active_index = len(ob.data.uv_layers) - 1 ob.active_shape_key_index = 0 data.vertices.foreach_set('select', np.ones(len(data.vertices), dtype=np.bool)) bpy.ops.object.mode_set(mode='EDIT') bpy.ops.uv.unwrap(method='ANGLE_BASED', margin=0.0635838) bpy.ops.object.mode_set(mode=mode) ob.active_shape_key_index = key
Example #16
Source Project: object_detector_app Author: datitran File: tf_example_decoder_test.py License: MIT License | 6 votes |
def testDecodeObjectIsCrowd(self): image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8) encoded_jpeg = self._EncodeImage(image_tensor) object_is_crowd = [0, 1] example = tf.train.Example(features=tf.train.Features(feature={ 'image/encoded': self._BytesFeature(encoded_jpeg), 'image/format': self._BytesFeature('jpeg'), 'image/object/is_crowd': self._Int64Feature(object_is_crowd), })).SerializeToString() example_decoder = tf_example_decoder.TfExampleDecoder() tensor_dict = example_decoder.Decode(tf.convert_to_tensor(example)) self.assertAllEqual((tensor_dict[ fields.InputDataFields.groundtruth_is_crowd].get_shape().as_list()), [None]) with self.test_session() as sess: tensor_dict = sess.run(tensor_dict) self.assertAllEqual([bool(item) for item in object_is_crowd], tensor_dict[ fields.InputDataFields.groundtruth_is_crowd])
Example #17
Source Project: object_detector_app Author: datitran File: tf_example_decoder_test.py License: MIT License | 6 votes |
def testDecodeObjectDifficult(self): image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8) encoded_jpeg = self._EncodeImage(image_tensor) object_difficult = [0, 1] example = tf.train.Example(features=tf.train.Features(feature={ 'image/encoded': self._BytesFeature(encoded_jpeg), 'image/format': self._BytesFeature('jpeg'), 'image/object/difficult': self._Int64Feature(object_difficult), })).SerializeToString() example_decoder = tf_example_decoder.TfExampleDecoder() tensor_dict = example_decoder.Decode(tf.convert_to_tensor(example)) self.assertAllEqual((tensor_dict[ fields.InputDataFields.groundtruth_difficult].get_shape().as_list()), [None]) with self.test_session() as sess: tensor_dict = sess.run(tensor_dict) self.assertAllEqual([bool(item) for item in object_difficult], tensor_dict[ fields.InputDataFields.groundtruth_difficult])
Example #18
Source Project: ICDAR-2019-SROIE Author: zzzDavid File: text_proposal_graph_builder.py License: MIT License | 6 votes |
def build_graph(self, text_proposals, scores, im_size): self.text_proposals = text_proposals self.scores = scores self.im_size = im_size self.heights = text_proposals[:, 3] - text_proposals[:, 1] + 1 boxes_table = [[] for _ in range(self.im_size[1])] for index, box in enumerate(text_proposals): boxes_table[int(box[0])].append(index) self.boxes_table = boxes_table graph = np.zeros((text_proposals.shape[0], text_proposals.shape[0]), np.bool) for index, box in enumerate(text_proposals): successions = self.get_successions(index) if len(successions) == 0: continue succession_index = successions[np.argmax(scores[successions])] if self.is_succession_node(index, succession_index): # NOTE: a box can have multiple successions(precursors) if multiple successions(precursors) # have equal scores. graph[index, succession_index] = True return Graph(graph)
Example #19
Source Project: pyscf Author: pyscf File: mp2.py License: Apache License 2.0 | 6 votes |
def get_frozen_mask(mp): '''Get boolean mask for the restricted reference orbitals. In the returned boolean (mask) array of frozen orbital indices, the element is False if it corresonds to the frozen orbital. ''' moidx = numpy.ones(mp.mo_occ.size, dtype=numpy.bool) if mp._nmo is not None: moidx[mp._nmo:] = False elif mp.frozen is None: pass elif isinstance(mp.frozen, (int, numpy.integer)): moidx[:mp.frozen] = False elif len(mp.frozen) > 0: moidx[list(mp.frozen)] = False else: raise NotImplementedError return moidx
Example #20
Source Project: visual_turing_test-tutorial Author: mateuszmalinowski File: input_output_space.py License: MIT License | 6 votes |
def shift_with_index_vector(X, index, size, time_axis, value=1, dtype=np.bool): """ Shifts X along time_axis, and inserts a one-hot vector at the first column at this axis. In: X - n-array index - index for value, the other elements of the corresponding vector are 0 time_axis - axis where shifting happens value - value to place at index; by default 1 dtype - type of the new vector; by default np.bool """ tmp = np.zeros(size, dtype=dtype) tmp[..., index] = value return shift(X, tmp, time_axis)
Example #21
Source Project: me-ica Author: ME-ICA File: tedana.py License: GNU Lesser General Public License v2.1 | 6 votes |
def makeadmask(cdat,min=True,getsum=False): nx,ny,nz,Ne,nt = cdat.shape mask = np.ones((nx,ny,nz),dtype=np.bool) if min: mask = cdat[:,:,:,:,:].prod(axis=-1).prod(-1)!=0 return mask else: #Make a map of longest echo that a voxel can be sampled with, #with minimum value of map as X value of voxel that has median #value in the 1st echo. N.b. larger factor leads to bias to lower TEs emeans = cdat[:,:,:,:,:].mean(-1) medv = emeans[:,:,:,0] == stats.scoreatpercentile(emeans[:,:,:,0][emeans[:,:,:,0]!=0],33,interpolation_method='higher') lthrs = np.squeeze(np.array([ emeans[:,:,:,ee][medv]/3 for ee in range(Ne) ])) if len(lthrs.shape)==1: lthrs = np.atleast_2d(lthrs).T lthrs = lthrs[:,lthrs.sum(0).argmax()] mthr = np.ones([nx,ny,nz,ne]) for ee in range(Ne): mthr[:,:,:,ee]*=lthrs[ee] mthr = np.abs(emeans[:,:,:,:])>mthr masksum = np.array(mthr,dtype=np.int).sum(-1) mask = masksum!=0 if getsum: return mask,masksum else: return mask
Example #22
Source Project: radiometric_normalization Author: planetlabs File: gimage.py License: Apache License 2.0 | 6 votes |
def read_alpha_and_band_count(gdal_ds, last_band_alpha=False): logging.info('GImage: Initial band count: {}'.format( gdal_ds.RasterCount)) last_band = gdal_ds.GetRasterBand(gdal_ds.RasterCount) if last_band.GetColorInterpretation() == gdal.GCI_AlphaBand: logging.info('GImage: Alpha band found, reducing band count') alpha = last_band.ReadAsArray().astype(numpy.bool) band_count = gdal_ds.RasterCount - 1 elif last_band_alpha: logging.info( 'GImage: Forcing last band to be an alpha band, reducing band ' 'count') alpha = last_band.ReadAsArray().astype(numpy.bool) band_count = gdal_ds.RasterCount - 1 else: logging.info('GImage: No alpha band found') alpha = numpy.ones( (gdal_ds.RasterYSize, gdal_ds.RasterXSize), dtype=numpy.bool) band_count = gdal_ds.RasterCount return alpha, band_count
Example #23
Source Project: radiometric_normalization Author: planetlabs File: utils.py License: Apache License 2.0 | 6 votes |
def pixel_list_to_array(pixel_locations, shape): ''' Transforms a list of pixel locations into a 2D array. :param tuple pixel_locations: A tuple of two lists representing the x and y coordinates of the locations of a set of pixels (i.e. the output of numpy.nonzero(valid_pixels) where valid_pixels is a 2D boolean array representing the pixel locations) :param list active_pixels: A list the same length as the x and y coordinate lists within pixel_locations representing whether a pixel location should be represented in the mask or not :param tuple shape: The shape of the output array consisting of a tuple of (height, width) :returns: A 2-D boolean array representing active pixels ''' mask = numpy.zeros(shape, dtype=numpy.bool) mask[pixel_locations] = True return mask
Example #24
Source Project: radiometric_normalization Author: planetlabs File: transformation_tests.py License: Apache License 2.0 | 6 votes |
def test_generate_linear_relationship(self): test_candidate = numpy.array( [[1, 2, 3, 4], [1, 3, 4, 4]]) test_reference = numpy.array( [[2, 4, 4, 3], [2, 6, 8, 8]]) test_pifs = numpy.array([[1, 1, 0, 0], [0, 0, 1, 1]], dtype=numpy.bool) transform = transformation.generate_linear_relationship( test_reference, test_candidate, test_pifs) expected_gain = 0.5 self.assertEqual(transform.gain, expected_gain) expected_offset = 0 self.assertEqual(transform.offset, expected_offset)
Example #25
Source Project: radiometric_normalization Author: planetlabs File: gimage_tests.py License: Apache License 2.0 | 6 votes |
def test_save_with_compress(self): output_file = 'test_save_with_compress.tif' test_band = numpy.array([[5, 2, 2], [1, 6, 8]], dtype=numpy.uint16) test_alpha = numpy.array([[0, 0, 0], [1, 1, 1]], dtype=numpy.bool) test_gimage = gimage.GImage([test_band, test_band, test_band], test_alpha, self.metadata) gimage.save(test_gimage, output_file, compress=True) result_gimg = gimage.load(output_file) numpy.testing.assert_array_equal(result_gimg.bands[0], test_band) numpy.testing.assert_array_equal(result_gimg.bands[1], test_band) numpy.testing.assert_array_equal(result_gimg.bands[2], test_band) numpy.testing.assert_array_equal(result_gimg.alpha, test_alpha) self.assertEqual(result_gimg.metadata, self.metadata) os.unlink(output_file)
Example #26
Source Project: dustmaps Author: gregreen File: leike_ensslin_2019.py License: GNU General Public License v2.0 | 5 votes |
def _coords2idx(self, coords): c = coords.transform_to('galactic').represent_as('cartesian') idx = np.empty((3,) + c.shape, dtype='i4') mask = np.zeros(c.shape, dtype=np.bool) for i,x in enumerate((c.x, c.y, c.z)): idx[i,...] = np.floor(x.to('pc').value + 300) * 256/600. mask |= (idx[i] < 0) | (idx[i] >= self._shape[i]) for i in range(3): idx[i, mask] = -1 return idx, mask
Example #27
Source Project: dustmaps Author: gregreen File: bayestar.py License: GNU General Public License v2.0 | 5 votes |
def lb2pix(nside, l, b, nest=True): """ Converts Galactic (l, b) to HEALPix pixel index. Args: nside (:obj:`int`): The HEALPix :obj:`nside` parameter. l (:obj:`float`, or array of :obj:`float`): Galactic longitude, in degrees. b (:obj:`float`, or array of :obj:`float`): Galactic latitude, in degrees. nest (Optional[:obj:`bool`]): If :obj:`True` (the default), nested pixel ordering will be used. If :obj:`False`, ring ordering will be used. Returns: The HEALPix pixel index or indices. Has the same shape as the input :obj:`l` and :obj:`b`. """ theta = np.radians(90. - b) phi = np.radians(l) if not hasattr(l, '__len__'): if (b < -90.) or (b > 90.): return -1 pix_idx = hp.pixelfunc.ang2pix(nside, theta, phi, nest=nest) return pix_idx idx = (b >= -90.) & (b <= 90.) pix_idx = np.empty(l.shape, dtype='i8') pix_idx[idx] = hp.pixelfunc.ang2pix(nside, theta[idx], phi[idx], nest=nest) pix_idx[~idx] = -1 return pix_idx
Example #28
Source Project: mmdetection Author: open-mmlab File: structures.py License: Apache License 2.0 | 5 votes |
def polygon_to_bitmap(polygons, height, width): """Convert masks from the form of polygons to bitmaps. Args: polygons (list[ndarray]): masks in polygon representation height (int): mask height width (int): mask width Return: ndarray: the converted masks in bitmap representation """ rles = maskUtils.frPyObjects(polygons, height, width) rle = maskUtils.merge(rles) bitmap_mask = maskUtils.decode(rle).astype(np.bool) return bitmap_mask
Example #29
Source Project: neuropythy Author: noahbenson File: core.py License: GNU Affero General Public License v3.0 | 5 votes |
def denormalize(data): ''' denormalize(data) yield a denormalized version of the given JSON-friendly normalized data. This is the inverse of the normalize(obj) function. The normalize and denormalize functions use the reserved keyword '__type__' along with the <obj>.normalize() and <class>.denormalize(data) functions to manage types of objects that are not JSON-compatible. Please see help(normalize) for more details. ''' if data is None: return None elif pimms.is_scalar(data, ('number', 'bool', 'string', 'unicode')): return data elif pimms.is_map(data): # see if it's a non-native map if normalize.type_key in data: (mdl,cls) = data[normalize.type_key] if mdl is None: if cls == 'ellipsis': return Ellipsis elif cls == 'complex': return np.array(data['re']) + 1j*np.array(data['im']) elif cls == 'set': return set(denormalize(data['elements'])) elif cls == 'sparse_matrix': return sps.csr_matrix((data['vals'], (data['rows'],data['cols'])), shape=data['shape']) else: raise ValueError('unrecognized builtin denormalize class: %s' % cls) else: cls = getattr(importlib.import_module(mdl), cls) d = {k:denormalize(v) for (k,v) in six.iteritems(data) if k != normalize.type_key} return cls.denormalize(d) else: return {k:denormalize(v) for (k,v) in six.iteritems(data)} # native map else: # must be a list of some type if not hasattr(data, '__iter__'): msg = 'denormalize does not recognized object %s with type %s' % (data, type(data)) raise ValueError(msg) # lists of primitives need not be changed if pimms.is_array(data, ('number', 'bool', 'string', 'unicode')): return data return [denormalize(x) for x in data]
Example #30
Source Project: neuropythy Author: noahbenson File: core.py License: GNU Affero General Public License v3.0 | 5 votes |
def dataframe_except(df, *cols, **filters): ''' dataframe_except(df, k1=v1, k2=v2...) yields df after selecting all the columns in which the given keys (k1, k2, etc.) have been selected such that the associated columns in the dataframe contain only the rows whose cells match the given values. dataframe_except(df, col1, col2...) selects all columns except for the given columns. dataframe_except(df, col1, col2..., k1=v1, k2=v2...) selects on both conditions. The dataframe_except() function is identical to the dataframe_select() function with the single difference being that the column names provided to dataframe_except() are dropped from the result while column names passed to dataframe_select() are kept. If a value is a tuple/list of 2 elements, then it is considered a range where cells must fall between the values. If value is a tuple/list of more than 2 elements or is a set of any length then it is a list of values, any one of which can match the cell. ''' ii = np.ones(len(df), dtype='bool') for (k,v) in six.iteritems(filters): vals = df[k].values if pimms.is_set(v): jj = np.isin(vals, list(v)) elif pimms.is_vector(v) and len(v) == 2: jj = (v[0] <= vals) & (vals < v[1]) elif pimms.is_vector(v): jj = np.isin(vals, list(v)) else: jj = (vals == v) ii = np.logical_and(ii, jj) if len(ii) != np.sum(ii): df = df.loc[ii] if len(cols) > 0: df = df.drop(list(cols), axis=1, inplace=False) return df