def __init__(self, y, a):
"""
The mean squared error cost function.
Should be used as the last layer for a network.
"""
# Call the base class' constructor.
Layer.__init__(self, [y, a])
def __init__(self,
axis=None,
momentum=None,
epsilon=None,
center=None,
scale=None,
beta_initializer=None,
gamma_initializer=None,
moving_mean_initializer=None,
moving_variance_initializer=None,
beta_regularizer=None,
gamma_regularizer=None,
beta_constraint=None,
gamma_constraint=None):
Layer.__init__(self)
self.axis = axis
self.momentum = momentum
self.epsilon = epsilon
self.center = center
self.scale = scale
self.beta_initializer = beta_initializer
self.gamma_initializer = gamma_initializer
self.moving_mean_initializer = moving_mean_initializer
self.moving_variance_initializer = moving_variance_initializer
self.beta_regularizer = beta_regularizer
self.gamma_regularizer = gamma_regularizer
self.beta_constraint = beta_constraint
self.gamma_constraint = gamma_constraint
self.name = 'BatchNormalization'
def __init__(self, units, activation, recurrent_activation, use_bias,
kernel_initializer, recurrent_initializer, bias_initializer,
kernel_regularizer, recurrent_regularizer, bias_regularizer,
activity_regularizer, kernel_constraint, recurrent_constraint,
bias_constraint, dropout, recurrent_dropout, implementation,
return_sequences, return_state, go_backwards, stateful,
unroll):
Layer.__init__(self)
KernelBiasSupport.__init__(self, use_bias, kernel_initializer,
bias_initializer, kernel_regularizer,
bias_regularizer, activity_regularizer,
kernel_constraint, bias_constraint)
self.units = units
self.activation = activation
self.recurrent_activation = recurrent_activation
self.recurrent_initializer = recurrent_initializer
self.recurrent_regularizer = recurrent_regularizer
self.dropout = dropout
self.recurrent_dropout = recurrent_dropout
self.implementation = implementation
self.return_sequencies = return_sequences
self.return_state = return_state
self.go_backwards = go_backwards
self.stateful = stateful
self.unroll = unroll
self.recurrent_constraint = recurrent_constraint
def __init__(self, d={}, name='image', verbose=False):
self.name = name
Layer.__init__(self, d, verbose)
self.i = Layer.arg(d)
self.threshold = self.attr('threshold', 200)
self.imgs = {}
self.mod = None
def __init__(self, shape=None, batch_shape=None, dtype=None, sparse=None, tensor=None):
Layer.__init__(self)
self.shape = shape
self.batch_shape = batch_shape
self.dtype = dtype
self.sparse = sparse
self.tensor = tensor
self.name = 'Input'
def __init__(self, units=None, activation=None, use_bias=True, kernel_initializer=None,
bias_initializer=None, kernel_regularizer=None, bias_regularizer=None,
activity_regularizer=None, kernel_constraint=None, bias_constraint=None):
Layer.__init__(self)
KernelBiasSupport.__init__(self, use_bias, kernel_initializer, bias_initializer, kernel_regularizer,
bias_regularizer, activity_regularizer, kernel_constraint, bias_constraint)
self.units = units
self.activation = activation
self.name = 'Dense'
def __init__(self, d, verbose=False):
Layer.__init__(self, d, verbose)
assert self.attr('box')
self.attr('units')
self.outline = Layer.arg(d)
self.radius = self.attr('corner-radius', 0)
self.width = self.attr('line-width', 1)
self.fillColor = self.attr('fill-color', None)
assert self.radius==0 or self.width < self.radius
def __init__(self, name, pWidth, pHeight, inputWidth, inputHeight, channel,
prev):
self._poolInfo = [pWidth, pHeight]
self._inputInfo = [inputWidth, inputHeight, channel]
self._calcWindows()
windowSize = self._windowNum[0] * self._windowNum[1]
Layer.__init__(self, name, windowSize * self._inputInfo[CHANNEL], prev)
assert prev != None
assert prev._size == inputWidth * inputHeight * channel
def __init__(self, shape, input_shape, activation=tf.nn.tanh):
self.shape = shape
self.input_shape = input_shape
self.activation = (lambda x: x) if activation is None else activation
Layer.__init__(self, True, [
{'name': 'b1', 'type': 'b', 'shape': self.shape[-1]},
{'name': 'b2', 'type': 'b', 'shape': self.shape[-2]},
{'name': 'w', 'type': 'w', 'shape': self.shape}
])
def __init__(self, d, verbose=False):
Layer.__init__(self, d, verbose)
self.color = Layer.arg(d)
self.r1 = self.attr('radius', 2)
self.r2 = self.attr('distance', 5) + self.r1
self.grad = self.attr('grad', True)
self.opacity = int(255 * min(self.attr('opacity', 100.0), 100.0) /
100.0)
assert self.attr('box')
self.attr('units')
def __init__(self,
data=None,
projection=None,
geotransform=None,
name=None,
keywords=None,
style_info=None):
"""Initialise object with either data or filename
NOTE: Doc strings in constructor are not harvested and exposed in
online documentation. Hence the details are specified in the
class docstring.
"""
# Invoke common layer constructor
Layer.__init__(self,
name=name,
projection=projection,
keywords=keywords,
style_info=style_info)
# Input checks
if data is None:
# Instantiate empty object
self.geotransform = None
self.rows = self.columns = 0
return
# Initialisation
if isinstance(data, basestring):
self.read_from_file(data)
elif isinstance(data, QgsRasterLayer):
self.read_from_qgis_native(data)
else:
# Assume that data is provided as a numpy array
# with extra keyword arguments supplying metadata
self.data = numpy.array(data, dtype='d', copy=False)
proj4 = self.get_projection(proj4=True)
if 'longlat' in proj4 and 'WGS84' in proj4:
# This is only implemented for geographic coordinates
# Omit check for projected coordinate systems
check_geotransform(geotransform)
self.geotransform = geotransform
self.rows = data.shape[0]
self.columns = data.shape[1]
self.number_of_bands = 1
# We assume internal numpy layers are using nan correctly
# FIXME (Ole): If read from file is refactored to load the data
# this should be taken care of there
self.nodata_value = numpy.nan
def __init__(self, shape):
self.shape = shape
Layer.__init__(self, False, [{
'name': '_marginal_init',
'type': 'b',
'shape': shape[1:]
}])
# Define state placeholder
self.marginal = tf.placeholder(tf.float32, [None, self.shape[-1]])
self._marginal = self.marginal
def __init__(self, name, prev):
assert prev != None
Layer.__init__(self, name, prev._size, prev)
self._average = np.zeros((prev._size, 1))
self._variance = np.zeros((prev._size, 1))
self._iter = 0
self._isTraining = False
self._beta = np.zeros((prev._size, 1))
self._beta.fill(1)
self._gamma = np.zeros((prev._size, 1))
self._gamma.fill(1)
def __init__(self, input_dim=None, output_dim=None, embeddings_initializer=None,
embeddings_regularizer=None, activity_regularizer=None, embeddings_constraint=None,
mask_zero=None, input_length=None):
Layer.__init__(self)
self.input_dim = input_dim
self.output_dim = output_dim
self.embedding_initializer = embeddings_initializer
self.embedding_regularizer = embeddings_regularizer
self.activity_regularizer = activity_regularizer
self.embedding_constraint = embeddings_constraint
self.mask_zero = mask_zero
self.input_length = input_length
self.name = 'Embedding'
def __init__(self, data=None, projection=None, geotransform=None,
name=None, keywords=None, style_info=None):
"""Initialise object with either data or filename
NOTE: Doc strings in constructor are not harvested and exposed in
online documentation. Hence the details are specified in the
class docstring.
"""
# Invoke common layer constructor
Layer.__init__(self,
name=name,
projection=projection,
keywords=keywords,
style_info=style_info)
# Input checks
if data is None:
# Instantiate empty object
self.geotransform = None
self.rows = self.columns = 0
return
# Initialisation
if isinstance(data, basestring):
self.read_from_file(data)
elif isinstance(data, QgsRasterLayer):
self.read_from_qgis_native(data)
else:
# Assume that data is provided as a numpy array
# with extra keyword arguments supplying metadata
self.data = numpy.array(data, dtype='d', copy=False)
proj4 = self.get_projection(proj4=True)
if 'longlat' in proj4 and 'WGS84' in proj4:
# This is only implemented for geographic coordinates
# Omit check for projected coordinate systems
check_geotransform(geotransform)
self.geotransform = geotransform
self.rows = data.shape[0]
self.columns = data.shape[1]
self.number_of_bands = 1
# We assume internal numpy layers are using nan correctly
# FIXME (Ole): If read from file is refactored to load the data
# this should be taken care of there
self.nodata_value = numpy.nan
def __init__(self, name, size, prev):
Layer.__init__(self, name, size, prev)
assert prev != None
# randomly initiate weight and bias
self._w = np.random.uniform(0,
2.0 / prev._size,
size=(size, prev._size))
self._b = np.zeros((size, 1))
self._dLdw = np.zeros((size, prev._size))
self._dLdb = np.zeros((size, 1))
self._Vdw = np.zeros((size, prev._size))
self._Vdb = np.zeros((size, 1))
self._Sdw = np.zeros((size, prev._size))
self._Sdb = np.zeros((size, 1))
def __init__(self, filters=None, kernel_size=None, strides=1, padding='valid',
data_format='channels_last', dilation_rate=1, activation=None, use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None,
kernel_constraint=None, bias_constraint=None):
Layer.__init__(self)
KernelBiasSupport.__init__(self, use_bias, kernel_initializer, bias_initializer, kernel_regularizer,
bias_regularizer, activity_regularizer, kernel_constraint, bias_constraint)
self.strides = strides
self.activation = activation
self.filters = filters
self.kernel_size = kernel_size
self.padding = padding
self.data_format = data_format
self.dilation_rate = dilation_rate
def __init__(self, K, N):
Layer.__init__(self)
# input number of neuron
self.K_ = K
# output number of neuron
self.N_ = N
self.bias_term_ = None
self.bias_multiplier_ = None
self.transpose_ = False
self.W = Blob()
self.b = Blob()
self.blobs_.append(self.W)
self.blobs_.append(self.b)
def __init__(self, index, data_mgr, config_topography, size_total,
ocean_altitude_relative, border_start_relative):
Layer.__init__(self, "topography", index, data_mgr)
self.requires_layers = []
# parameters:
self.height = config_topography.getfloat("height")
self.scale = config_topography.getfloat("scale")
self.ocean_altitude = ocean_altitude_relative * self.height
self.border_start_relative = border_start_relative
#
self.perlin_seed = 9601
self.perlin_levels = [] # [(scale,amplitude), ...]
# components:
self.perlin = None
# internal variables:
self._size_half = int(self.data_mgr.size_total / 2.0) * self.scale
self._border_start = self.border_start_relative * self._size_half
def __init__(self, name, depth, kWidth, kHeight, stride, prev, inputWidth,
inputHeight, inputChannel):
self._kernalInfo = [kWidth, kHeight, depth, stride]
self._inputInfo = [inputWidth, inputHeight, inputChannel]
self._calcWindows()
assert prev != None
assert prev._size == inputWidth * inputHeight * inputChannel
Layer.__init__(
self, name,
self._windowNum[0] * self._windowNum[1] * self._kernalInfo[DEPTH],
prev)
self._kernal = np.random.normal(
size=(self._kernalInfo[WIDTH] * self._kernalInfo[HEIGHT] *
self._inputInfo[CHANNEL], self._kernalInfo[DEPTH]))
self._bias = np.random.normal(size=(1, self._kernalInfo[DEPTH]))
self._dLdKernal = np.zeros(self._kernal.shape)
self._dLdb = np.zeros(self._bias.shape)
def __init__(self, hh, ww, fout, pad, stride):
Layer.__init__(self)
self.kernel_shape_ = None
self.stride_ = None
self.pad_ = None
self.dilation_ = None
self.conv_input_shape_ = None
self.col_buffer_shape_ = None
self.output_shape_ = None
self.bottom_shape_ = None
self.num_spatial_axes_ = None
self.bottom_dim_ = None
self.top_dim_ = None
self.channel_axis_ = None
self.num_ = None
self.channels_ = None
self.group_ = None
self.out_spatial_dim_ = None
self.weight_offset_ = None
self.num_output_ = None
self.bias_term_ = None
self.is_1x1_ = None
self.force_nd_im2col_ = None
#self.N = N
#self.C = C
self.hh = hh
self.ww = ww
self.fout = fout
self.pad = pad
self.stride = stride
self.W = Blob()
self.b = Blob()
self.blobs_.append(self.W)
self.blobs_.append(self.b)
def __init__(self, shape):
self.shape = shape
self.input_shape = shape[0]
Layer.__init__(self, True, [{
'name': 'b1',
'type': 'b',
'shape': shape[-1]
}, {
'name': 'b2',
'type': 'b',
'shape': shape[-1]
}, {
'name': 'b3',
'type': 'b',
'shape': shape[-1]
}, {
'name': 'w1',
'type': 'w',
'shape': (shape[0] + shape[-1], shape[-1])
}, {
'name': 'w2',
'type': 'w',
'shape': (shape[0] + shape[-1], shape[-1])
}, {
'name': 'w3',
'type': 'w',
'shape': (shape[0] + shape[-1], shape[-1])
}, {
'name': '_state_init',
'type': 'b',
'shape': (shape[-1], )
}])
# Define state placeholder
self.state = tf.placeholder(tf.float32, [None, self.shape[-1]])
self._state = self.state
def __init__(self, d, verbose=False):
Layer.__init__(self, d, verbose)
self.color = Layer.arg(d)
self.radius = self.attr('gauss-blur-radius', 2)
def __init__(self, batch_size):
Layer.__init__(self)
self.batch_size_ = batch_size
self.datasets_ = None
self.cur_ = 0
def __init__(self, tree, act_citation):
Layer.__init__(self, tree)
self.act_citation = act_citation
def __init__(self, d, verbose=False):
Layer.__init__(self, d, verbose)
self.color = Layer.arg(d)
def __init__(self, d, verbose=False):
Layer.__init__(self, d, verbose)
self.opacity = min(Layer.arg(d), 100.0) / 100.0
def __init__(self, d, verbose=False):
Layer.__init__(self, d, verbose)
self.level = int(Layer.arg(d))
def __init__(self, d, verbose=False):
Layer.__init__(self, d, verbose)
self.color = Layer.arg(d)
self.range = self.attr('range', [.2, .5])
def __init__(self, size):
Layer.__init__(self, "input", size, None)
def __init__(self, d, verbose=False):
Layer.__init__(self, d, verbose)
self.mode = Layer.arg(d)
def __init__(self, data=None, projection=None, geometry=None,
geometry_type=None,
name='', keywords=None, style_info=None):
"""Initialise object with either geometry or filename
Input
data: Can be either
* a filename of a vector file format known to GDAL
* List of dictionaries of fields associated with
point coordinates
* None
projection: Geospatial reference in WKT format.
Only used if geometry is provide as a numeric array,
if None, WGS84 geographic is assumed
geometry: A list of either point coordinates or polygons/lines
(see note below)
geometry_type: Desired interpretation of geometry.
Valid options are 'point', 'line', 'polygon' or
the ogr types: 1, 2, 3
If None, a geometry_type will be inferred
name: Optional name for layer.
Only used if geometry is provide as a numeric array
keywords: Optional dictionary with keywords that describe the
layer. When the layer is stored, these keywords will
be written into an associated file with extension
.keywords.
Keywords can for example be used to display text
about the layer in a web application.
style_info: Dictionary with information about how this layer
should be styled. See impact_functions/styles.py
for examples.
Notes
If data is a filename, all other arguments are ignored
as they will be inferred from the file.
The geometry type will be inferred from the dimensions of geometry.
If each entry is one set of coordinates the type will be ogr.wkbPoint,
if it is an array of coordinates the type will be ogr.wkbPolygon.
Each polygon or line feature take the form of an Nx2 array representing
vertices where line segments are joined
"""
# Invoke common layer constructor
Layer.__init__(self,
name=name,
projection=projection,
keywords=keywords,
style_info=style_info)
# Input checks
if data is None and geometry is None:
# Instantiate empty object
self.geometry_type = None
self.extent = [0, 0, 0, 0]
return
if isinstance(data, basestring):
self.read_from_file(data)
else:
# Assume that data is provided as sequences provided as
# arguments to the Vector constructor
# with extra keyword arguments supplying metadata
msg = 'Geometry must be specified'
verify(geometry is not None, msg)
msg = 'Geometry must be a sequence'
verify(is_sequence(geometry), msg)
self.geometry = geometry
self.geometry_type = get_geometry_type(geometry, geometry_type)
if data is None:
# Generate default attribute as OGR will do that anyway
# when writing
data = []
for i in range(len(geometry)):
data.append({'ID': i})
# Check data
self.data = data
if data is not None:
msg = 'Data must be a sequence'
verify(is_sequence(data), msg)
msg = ('The number of entries in geometry and data '
'must be the same')
verify(len(geometry) == len(data), msg)
def __init__(self, tree, notices):
Layer.__init__(self, tree)
self.notices = notices
def __init__(self, data=None, projection=None, geometry=None,
geometry_type=None, name=None, keywords=None,
style_info=None, sublayer=None):
"""Initialise object with either geometry or filename
Args:
* data: Can be either
* A filename of a vector file format known to GDAL.
* List of dictionaries of field names and attribute values
associated with each point coordinate.
* None
* projection: Geospatial reference in WKT format.
Only used if geometry is provided as a numeric array,
if None, WGS84 geographic is assumed.
* geometry: A list of either point coordinates or polygons/lines
(see note below).
* geometry_type: Desired interpretation of geometry.
Valid options are 'point', 'line', 'polygon' or
the ogr types: 1, 2, 3.
If None, a geometry_type will be inferred from the data.
* name: Optional name for layer. If None, basename is used.
* keywords: Optional dictionary with keywords that describe the
layer. When the layer is stored, these keywords will
be written into an associated file with extension
'.keywords'.
Keywords can for example be used to display text about the
layer in an application.
* style_info: Dictionary with information about how this layer
should be styled. See impact_functions/styles.py
for examples.
* sublayer: str Optional sublayer (band name in the case of raster,
table name in case of sqlite etc.) to load. Only applicable
to those dataformats supporting more than one layer in the
data file.
Returns:
* An instance of class Vector.
Raises:
* Propogates any exceptions encountered.
Notes:
If data is a filename, all other arguments are ignored
as they will be inferred from the file.
The geometry type will be inferred from the dimensions of geometry.
If each entry is one set of coordinates the type will be
ogr.wkbPoint,
if it is an array of coordinates the type will be ogr.wkbPolygon.
To cast array entries as lines set geometry_type explicitly to
'line' in the call to Vector. Otherwise, they will default to
polygons.
Each polygon or line feature take the form of an Nx2 array
representing vertices where line segments are joined.
If polygons have holes, their geometry must be passed in as a
list of polygon geometry objects
(as defined in module geometry.py)
"""
# Invoke common layer constructor
Layer.__init__(self,
name=name,
projection=projection,
keywords=keywords,
style_info=style_info,
sublayer=sublayer)
# Input checks
if data is None and geometry is None:
# Instantiate empty object
self.geometry_type = None
self.extent = [0, 0, 0, 0]
return
if isinstance(data, basestring):
self.read_from_file(data)
else:
# Assume that data is provided as sequences provided as
# arguments to the Vector constructor
# with extra keyword arguments supplying metadata
msg = 'Geometry must be specified'
verify(geometry is not None, msg)
msg = 'Geometry must be a sequence'
verify(is_sequence(geometry), msg)
if len(geometry) > 0 and isinstance(geometry[0], Polygon):
self.geometry_type = ogr.wkbPolygon
self.geometry = geometry
else:
self.geometry_type = get_geometry_type(geometry, geometry_type)
# Convert to objects if input is a list of simple arrays
if self.is_polygon_data:
self.geometry = [Polygon(outer_ring=x) for x in geometry]
else:
self.geometry = geometry
if data is None:
# Generate default attribute as OGR will do that anyway
# when writing
data = []
for i in range(len(geometry)):
data.append({'ID': i})
# Check data
self.data = data
if data is not None:
msg = 'Data must be a sequence'
verify(is_sequence(data), msg)
msg = ('The number of entries in geometry and data '
'must be the same')
verify(len(geometry) == len(data), msg)
# Establish extent
if len(geometry) == 0:
# Degenerate layer
self.extent = [0, 0, 0, 0]
return
# Compute bounding box for each geometry type
minx = miny = sys.maxint
maxx = maxy = -minx
if self.is_point_data:
A = numpy.array(self.get_geometry())
minx = min(A[:, 0])
maxx = max(A[:, 0])
miny = min(A[:, 1])
maxy = max(A[:, 1])
elif self.is_line_data:
for g in self.get_geometry():
A = numpy.array(g)
minx = min(minx, min(A[:, 0]))
maxx = max(maxx, max(A[:, 0]))
miny = min(miny, min(A[:, 1]))
maxy = max(maxy, max(A[:, 1]))
elif self.is_polygon_data:
# Do outer ring only
for g in self.get_geometry(as_geometry_objects=False):
A = numpy.array(g)
minx = min(minx, min(A[:, 0]))
maxx = max(maxx, max(A[:, 0]))
miny = min(miny, min(A[:, 1]))
maxy = max(maxy, max(A[:, 1]))
self.extent = [minx, maxx, miny, maxy]
def __init__(self, data=None, projection=None, geotransform=None,
name='', keywords=None, style_info=None):
"""Initialise object with either data or filename
Input
data: Can be either
* a filename of a raster file format known to GDAL
* an MxN array of raster data
* None (FIXME (Ole): Remove this option)
projection: Geospatial reference in WKT format.
Only used if data is provide as a numeric array,
if None, WGS84 geographic is assumed
geotransform: GDAL geotransform (6-tuple).
(top left x, w-e pixel resolution, rotation,
top left y, rotation, n-s pixel resolution).
See e.g. http://www.gdal.org/gdal_tutorial.html
Only used if data is provide as a numeric array,
name: Optional name for layer.
Only used if data is provide as a numeric array,
keywords: Optional dictionary with keywords that describe the
layer. When the layer is stored, these keywords will
be written into an associated file with extension
.keywords.
Keywords can for example be used to display text
about the layer in a web application.
style_info: Dictionary with information about how this layer
should be styled. See impact_functions/styles.py
for examples.
Note that if data is a filename, all other arguments are ignored
as they will be inferred from the file.
"""
# Invoke common layer constructor
Layer.__init__(self,
name=name,
projection=projection,
keywords=keywords,
style_info=style_info)
# Input checks
if data is None:
# Instantiate empty object
self.geotransform = None
self.rows = self.columns = 0
return
# Initialisation
if isinstance(data, basestring):
self.read_from_file(data)
else:
# Assume that data is provided as an array
# with extra keyword arguments supplying metadata
self.data = numpy.array(data, dtype='d', copy=False)
self.geotransform = geotransform
self.rows = data.shape[0]
self.columns = data.shape[1]
self.number_of_bands = 1
def __init__(self, data=None, projection=None, geometry=None,
geometry_type=None, name=None, keywords=None,
style_info=None, sublayer=None):
"""Initialise object with either geometry or filename
NOTE: Doc strings in constructor are not harvested and exposed in
online documentation. Hence the details are specified in the
class docstring.
"""
# Invoke common layer constructor
Layer.__init__(self,
name=name,
projection=projection,
keywords=keywords,
style_info=style_info,
sublayer=sublayer)
# Input checks
if data is None and geometry is None:
# Instantiate empty object
self.geometry_type = None
self.extent = [0, 0, 0, 0]
return
if isinstance(data, basestring):
self.read_from_file(data)
else:
# Assume that data is provided as sequences provided as
# arguments to the Vector constructor
# with extra keyword arguments supplying metadata
msg = 'Geometry must be specified'
verify(geometry is not None, msg)
msg = 'Geometry must be a sequence'
verify(is_sequence(geometry), msg)
if len(geometry) > 0 and isinstance(geometry[0], Polygon):
self.geometry_type = ogr.wkbPolygon
self.geometry = geometry
else:
self.geometry_type = get_geometry_type(geometry, geometry_type)
if self.is_polygon_data:
# Convert to objects if input is a list of simple arrays
self.geometry = [Polygon(outer_ring=x) for x in geometry]
else:
# Convert to list if input is an array
if isinstance(geometry, numpy.ndarray):
self.geometry = geometry.tolist()
else:
self.geometry = geometry
if data is None:
# Generate default attribute as OGR will do that anyway
# when writing
data = []
for i in range(len(geometry)):
data.append({'ID': i})
# Check data
self.data = data
if data is not None:
msg = 'Data must be a sequence'
verify(is_sequence(data), msg)
msg = ('The number of entries in geometry and data '
'must be the same')
verify(len(geometry) == len(data), msg)
# Establish extent
if len(geometry) == 0:
# Degenerate layer
self.extent = [0, 0, 0, 0]
return
# Compute bounding box for each geometry type
minx = miny = sys.maxint
maxx = maxy = -minx
if self.is_point_data:
A = numpy.array(self.get_geometry())
minx = min(A[:, 0])
maxx = max(A[:, 0])
miny = min(A[:, 1])
maxy = max(A[:, 1])
elif self.is_line_data:
for g in self.get_geometry():
A = numpy.array(g)
minx = min(minx, min(A[:, 0]))
maxx = max(maxx, max(A[:, 0]))
miny = min(miny, min(A[:, 1]))
maxy = max(maxy, max(A[:, 1]))
elif self.is_polygon_data:
# Do outer ring only
for g in self.get_geometry(as_geometry_objects=False):
A = numpy.array(g)
minx = min(minx, min(A[:, 0]))
maxx = max(maxx, max(A[:, 0]))
miny = min(miny, min(A[:, 1]))
maxy = max(maxy, max(A[:, 1]))
self.extent = [minx, maxx, miny, maxy]
def __init__(self, d, verbose=False):
Layer.__init__(self, d, verbose)
self.angle = float(Layer.arg(d))