def convert_polygons_to_centroids(V):
"""Convert polygon vector data to point vector data
Input
V: Vector layer with polygon data
Output
Vector layer with point data and the same attributes as V
"""
msg = 'Input data %s must be polygon vector data' % V
verify(V.is_polygon_data, msg)
geometry = V.get_geometry()
N = len(V)
# Calculate points for each polygon
centroids = []
for i in range(N):
c = calculate_polygon_centroid(geometry[i])
centroids.append(c)
# Create new point vector layer with same attributes and return
V = Vector(data=V.get_data(),
projection=V.get_projection(),
geometry=centroids,
name='%s_centroid_data' % V.get_name(),
keywords=V.get_keywords())
return V
def bboxlist2string(bbox, decimals=6):
"""Convert bounding box list to comma separated string
Input
bbox: List of coordinates of the form [W, S, E, N]
Output
bbox_string: Format 'W,S,E,N' - each will have 6 decimal points
"""
msg = 'Got string %s, but expected bounding box as a list' % str(bbox)
verify(not isinstance(bbox, basestring), msg)
try:
bbox = list(bbox)
except:
msg = 'Could not coerce bbox %s into a list' % str(bbox)
raise BoundingBoxError(msg)
msg = ('Bounding box must have 4 coordinates [W, S, E, N]. '
'I got %s' % str(bbox))
verify(len(bbox) == 4, msg)
for x in bbox:
try:
float(x)
except ValueError, e:
msg = ('Bounding box %s contained non-numeric entry %s, '
'original error was "%s".' % (bbox, x, e))
raise AssertionError(msg)
def interpolate(self, X, attribute_name=None):
"""Interpolate values of this raster layer to other layer
Input
X: Layer object defining target
attribute_name: Optional name of interpolated layer.
If attribute_name is None,
the name of self is used.
Output
Y: Layer object with values of this raster layer interpolated to
geometry of input layer X
Note: If target geometry is polygon, data will be interpolated to
its centroids and the output is a point data set.
"""
if X.is_raster:
if self.get_geotransform() != X.get_geotransform():
# Need interpolation between grids
msg = 'Intergrid interpolation not yet implemented'
raise Exception(msg)
else:
# Rasters are aligned, no need to interpolate
return self
else:
# Interpolate this raster layer to geometry of X
msg = ('Name must be either a string or None. I got %s'
% (str(type(X)))[1:-1])
verify(attribute_name is None
or isinstance(attribute_name, basestring), msg)
return interpolate_raster_vector(self, X,
attribute_name=attribute_name)
def convert_line_to_points(V, delta):
"""Convert line vector data to point vector data
Input
V: Vector layer with line data
delta: Incremental step to find the points
Output
Vector layer with point data and the same attributes as V
"""
msg = 'Input data %s must be line vector data' % V
verify(V.is_line_data, msg)
geometry = V.get_geometry()
data = V.get_data()
N = len(V)
# Calculate centroids for each polygon
points = []
new_data = []
for i in range(N):
c = points_along_line(geometry[i], delta)
# We need to create a data entry for each point.
new_data.extend([data[i] for thing in c])
points.extend(c)
# Create new point vector layer with same attributes and return
V = Vector(data=new_data,
projection=V.get_projection(),
geometry=points,
name='%s_point_data' % V.get_name(),
keywords=V.get_keywords())
return V
def bboxstring2list(bbox_string):
"""Convert bounding box string to list
Input
bbox_string: String of bounding box coordinates of the form 'W,S,E,N'
Output
bbox: List of floating point numbers with format [W, S, E, N]
"""
msg = ('Bounding box must be a string with coordinates following the '
'format 105.592,-7.809,110.159,-5.647\n'
'Instead I got %s of type %s.' % (str(bbox_string),
type(bbox_string)))
verify(isinstance(bbox_string, basestring), msg)
fields = bbox_string.split(',')
msg = ('Bounding box string must have 4 coordinates in the form '
'"W,S,E,N". I got bbox == "%s"' % bbox_string)
verify(len(fields) == 4, msg)
for x in fields:
try:
float(x)
except ValueError, e:
msg = ('Bounding box %s contained non-numeric entry %s, '
'original error was "%s".' % (bbox_string, x, e))
raise AssertionError(msg)
def dom2object(node):
"""Convert DOM representation to XML_object hierarchy.
"""
value = []
textnode_encountered = None
for n in node.childNodes:
if n.nodeType == 3:
# Child is a text element - omit the dom tag #text and
# go straight to the text value.
# Note - only the last text value will be recorded
msg = 'Text element has child nodes - this shouldn\'t happen'
verify(len(n.childNodes) == 0, msg)
x = n.nodeValue.strip()
if len(x) == 0:
# Skip empty text children
continue
textnode_encountered = value = x
else:
# XML element
if textnode_encountered is not None:
msg = 'A text node was followed by a non-text tag. This is not allowed.\n'
msg += 'Offending text node: "%s" ' %str(textnode_encountered)
msg += 'was followed by node named: "<%s>"' %str(n.nodeName)
raise Exception, msg
value.append(dom2object(n))
# Deal with empty elements
if len(value) == 0: value = ''
if node.nodeType == 9:
# Root node (document)
tag = None
else:
# Normal XML node
tag = node.nodeName
X = XML_element(tag=tag,
value=value)
return X
def sigab2bnpb(E, target_attribute='VCLASS'):
"""Map SIGAB point data to BNPB vulnerability classes
Input
E: Vector object representing the OSM data
target_attribute: Optional name of the attribute containing
the mapped vulnerability class. Default
value is 'VCLASS'
Output:
Vector object like E, but with one new attribute (e.g. 'VCLASS')
representing the vulnerability class used in the guidelines
"""
# Input check
required = ['Struktur_B', 'Lantai', 'Atap', 'Dinding', 'Tingkat']
actual = E.get_attribute_names()
msg = ('Input data to sigab2bnpb must have attributes %s. '
'It has %s' % (str(required), str(actual)))
for attribute in required:
verify(attribute in actual, msg)
# Start mapping
N = len(E)
attributes = E.get_data()
for i in range(N):
levels = E.get_data('Tingkat', i).lower()
structure = E.get_data('Struktur_B', i).lower()
roof_type = E.get_data('Atap', i).lower()
wall_type = E.get_data('Dinding', i).lower()
floor_type = E.get_data('Lantai', i).lower()
if levels == 'none' or structure == 'none':
vulnerability_class = 'URM'
elif structure.startswith('beton') or structure.startswith('kayu'):
vulnerability_class = 'RM'
else:
if int(levels) >= 2:
vulnerability_class = 'RM'
else:
vulnerability_class = 'URM'
# Store new attribute value
attributes[i][target_attribute] = vulnerability_class
# Create new vector instance and return
V = Vector(data=attributes,
projection=E.get_projection(),
geometry=E.get_geometry(),
name=E.get_name() + ' mapped to BNPB vulnerability classes',
keywords=E.get_keywords())
return V
def get_geometry(self):
"""Return longitudes and latitudes (the axes) for grid.
Return two vectors (longitudes and latitudes) corresponding to
grid. The values are offset by half a pixel size to correspond to
pixel registration.
I.e. If the grid origin (top left corner) is (105, 10) and the
resolution is 1 degrees in each direction, then the vectors will
take the form
longitudes = [100.5, 101.5, ..., 109.5]
latitudes = [0.5, 1.5, ..., 9.5]
"""
# Get parameters for axes
g = self.get_geotransform()
lon_ul = float(g[0]) # Longitude of upper left corner
lat_ul = float(g[3]) # Latitude of upper left corner
dx = float(g[1]) # Longitudinal resolution
dy = - float(g[5]) # Latitudinal resolution (always(?) negative)
nx = self.columns
ny = self.rows
verify(dx > 0)
verify(dy > 0)
# Coordinates of lower left corner
lon_ll = lon_ul
lat_ll = lat_ul - ny * dy
# Coordinates of upper right corner
lon_ur = lon_ul + nx * dx
# Define pixel centers along each directions
# This is to achieve pixel registration rather
# than gridline registration
dx2 = dx / 2
dy2 = dy / 2
# Define longitudes and latitudes for each axes
x = numpy.linspace(lon_ll + dx2,
lon_ur - dx2, nx)
y = numpy.linspace(lat_ll + dy2,
lat_ul - dy2, ny)
# Return
return x, y
def write_to_file(self, filename):
"""Save raster data to file
Input
filename: filename with extension .tif
"""
# Check file format
basename, extension = os.path.splitext(filename)
msg = ('Invalid file type for file %s. Only extension '
'tif allowed.' % filename)
verify(extension in ['.tif', '.asc'], msg)
format = DRIVER_MAP[extension]
# Get raster data
A = self.get_data()
# Get Dimensions. Note numpy and Gdal swap order
N, M = A.shape
# Create empty file.
# FIXME (Ole): It appears that this is created as single
# precision even though Float64 is specified
# - see issue #17
driver = gdal.GetDriverByName(format)
fid = driver.Create(filename, M, N, 1, gdal.GDT_Float64)
if fid is None:
msg = ('Gdal could not create filename %s using '
'format %s' % (filename, format))
raise Exception(msg)
# Write metada
fid.SetProjection(str(self.projection))
fid.SetGeoTransform(self.geotransform)
# Write data
fid.GetRasterBand(1).WriteArray(A)
# Write keywords if any
write_keywords(self.keywords, basename + '.keywords')
def interpolate_raster_vector(R, V, attribute_name=None):
"""Interpolate from raster layer to vector data
Input
R: Raster data set (grid)
V: Vector data set (points or polygons)
attribute_name: Name for new attribute.
If None (default) the name of R is used
Output
I: Vector data set; points located as V with values interpolated from R
Note: If target geometry is polygon, data will be interpolated to
its centroids and the output is a point data set.
"""
# Input checks
verify(R.is_raster)
verify(V.is_vector)
if V.is_polygon_data:
# Use centroids, in case of polygons
P = convert_polygons_to_centroids(V)
else:
P = V
# Interpolate from raster to point data
R = interpolate_raster_vector_points(R, P,
attribute_name=attribute_name)
if V.is_polygon_data:
# In case of polygon data, restore the polygon geometry
# Do this setting the geometry of the returned set to
# that of the original polygon
R = Vector(data=R.get_data(),
projection=R.get_projection(),
geometry=V.get_geometry(),
name=R.get_name())
# Return interpolated vector layer
return R
def get_bins(self, N=10, quantiles=False):
"""Get N values between the min and the max occurred in this dataset.
Return sorted list of length N+1 where the first element is min and
the last is max. Intermediate values depend on the keyword quantiles:
If quantiles is True, they represent boundaries between quantiles.
If quantiles is False, they represent equidistant interval boundaries.
"""
min, max = self.get_extrema()
levels = []
if quantiles is False:
# Linear intervals
d = (max - min) / N
for i in range(N):
levels.append(min + i * d)
else:
# Quantiles
# FIXME (Ole): Not 100% sure about this algorithm,
# but it is close enough
A = self.get_data(nan=True).flat[:]
mask = numpy.logical_not(numpy.isnan(A)) # Omit NaN's
A = A.compress(mask)
A.sort()
verify(len(A) == A.shape[0])
d = float(len(A) + 0.5) / N
for i in range(N):
levels.append(A[int(i * d)])
levels.append(max)
return levels
def get_data(self, attribute=None, index=None, copy=False):
"""Get vector attributes
Data is returned as a list where each entry is a dictionary of
attributes for one feature. Entries in get_geometry() and
get_data() are related as 1-to-1
If optional argument attribute is specified and a valid name,
then the list of values for that attribute is returned.
If optional argument index is specified on the that value will
be returned. Any value of index is ignored if attribute is None.
If optional argument copy is True and all attributes are requested,
a copy will be returned. Otherwise a pointer to the data is returned.
"""
if hasattr(self, 'data'):
if attribute is None:
if copy:
return copy_module.deepcopy(self.data)
else:
return self.data
else:
msg = ('Specified attribute %s does not exist in '
'vector layer %s. Valid names are %s'
'' % (attribute, self, self.data[0].keys()))
verify(attribute in self.data[0], msg)
if index is None:
# Return all values for specified attribute
return [x[attribute] for x in self.data]
else:
# Return value for specified attribute and index
msg = ('Specified index must be either None or '
'an integer. I got %s' % index)
verify(type(index) == type(0), msg)
msg = ('Specified index must lie within the bounds '
'of vector layer %s which is [%i, %i]'
'' % (self, 0, len(self) - 1))
verify(0 <= index < len(self), msg)
return self.data[index][attribute]
else:
msg = 'Vector data instance does not have any attributes'
raise Exception(msg)
def get_topN(self, attribute, N=10):
"""Get top N features
Input
attribute: The name of attribute where values are sought
N: How many
Output
layer: New vector layer with selected features
"""
# FIXME (Ole): Maybe generalise this to arbitrary expressions
# Input checks
msg = ('Specfied attribute must be a string. '
'I got %s' % (type(attribute)))
verify(isinstance(attribute, basestring), msg)
msg = 'Specified attribute was empty'
verify(attribute != '', msg)
msg = 'N must be a positive number. I got %i' % N
verify(N > 0, msg)
# Create list of values for specified attribute
values = self.get_data(attribute)
# Sort and select using Schwarzian transform
A = zip(values, self.data, self.geometry)
A.sort()
# Pick top N and unpack
_, data, geometry = zip(*A[-N:])
# Create new Vector instance and return
return Vector(data=data,
projection=self.get_projection(),
geometry=geometry)
def __init__(self, name='', projection=None,
keywords=None, style_info=None):
"""Common constructor for all types of layers
See docstrings for class Raster and class Vector for details.
"""
# Name
msg = ('Specified name must be a string. '
'I got %s with type %s' % (name, str(type(name))[1:-1]))
verify(isinstance(name, basestring), msg)
self.name = name
# Projection
self.projection = Projection(projection)
# Keywords
if keywords is None:
self.keywords = {}
else:
msg = ('Specified keywords must be either None or a '
'dictionary. I got %s' % keywords)
verify(isinstance(keywords, dict), msg)
self.keywords = keywords
# Style info
if style_info is None:
self.style_info = {}
else:
msg = ('Specified style_info must be either None or a '
'dictionary. I got %s' % style_info)
verify(isinstance(style_info, dict), msg)
self.style_info = style_info
# Defaults
self.filename = None
self.data = None
def get_data(self, nan=True, scaling=None, copy=False):
"""Get raster data as numeric array
Input
nan: Optional flag controlling handling of missing values.
If nan is True (default), nodata values will be replaced
with numpy.nan
If keyword nan has a numeric value, nodata values will
be replaced by that value. E.g. to set missing values to 0,
do get_data(nan=0.0)
scaling: Optional flag controlling if data is to be scaled
if it has been resampled. Admissible values are
False: data is retrieved without modification.
True: Data is rescaled based on the squared ratio between
its current and native resolution. This is typically
required if raster data represents a density
such as population per km^2
None: The behaviour will depend on the keyword
"population" associated with the layer. If
it is "density", scaling will be applied
otherwise not. This is the default.
scalar value: If scaling takes a numerical scalar value,
that will be use to scale the data
copy (optional): If present and True return copy
NOTE: Scaling does not currently work with projected layers.
See issue #123
"""
if hasattr(self, 'data') and self.data is not None:
if copy:
A = copy_module.deepcopy(self.data)
else:
A = self.data
verify(A.shape[0] == self.rows and A.shape[1] == self.columns)
else:
# Read from raster file
A = self.band.ReadAsArray()
# Convert to double precision (issue #75)
A = numpy.array(A, dtype=numpy.float64)
# Self check
M, N = A.shape
msg = ('Dimensions of raster array do not match those of '
'raster file %s' % self.filename)
verify(M == self.rows, msg)
verify(N == self.columns, msg)
# Handle no data value
if nan is False:
pass
else:
if nan is True:
NAN = numpy.nan
else:
NAN = nan
# Replace NODATA_VALUE with NaN
nodata = self.get_nodata_value()
NaN = numpy.ones(A.shape, A.dtype) * NAN
A = numpy.where(A == nodata, NaN, A)
# Take care of possible scaling
if scaling is None:
# Redefine scaling from density keyword if possible
kw = self.get_keywords()
if 'datatype' in kw and kw['datatype'].lower() == 'density':
scaling = True
else:
scaling = False
if scaling is False:
# No change
sigma = 1
elif scaling is True:
# Calculate scaling based on resolution change
actual_res = self.get_resolution(isotropic=True)
native_res = self.get_resolution(isotropic=True, native=True)
#print
#print 'Actual res', actual_res
#print 'Native res', native_res
sigma = (actual_res / native_res) ** 2
#print 'Scaling', sigma
else:
# See if scaling can work as a scalar value
try:
sigma = float(scaling)
except Exception, e:
msg = ('Keyword scaling "%s" could not be converted to a '
'number. It must be either True, False, None or a '
'number: %s' % (scaling, str(e)))
raise Exception(msg)
def check_data_integrity(layer_objects):
"""Check list of layer objects
Input
layer_objects: List of InaSAFE layer instances
Output
Nothing
Raises
Exceptions for a range of errors
This function checks that
* Layers have correct keywords
* That they have the same georeferences
"""
# Link to documentation
manpage = ('http://risiko_dev.readthedocs.org/en/latest/usage/'
'plugins/development.html')
instructions = ('Please add keywords as <keyword>:<value> pairs '
' in the .keywords file. For more information '
'please read the sections on impact functions '
'and keywords in the manual: %s' % manpage)
# Set default values for projection and geotransform.
# Enforce DEFAULT (WGS84).
# Choosing 'None' will use value of first layer.
reference_projection = Projection(DEFAULT_PROJECTION)
geotransform = None
coordinates = None
for layer in layer_objects:
# Check that critical keywords exist and are non empty
keywords = layer.get_keywords()
for kw in REQUIRED_KEYWORDS:
msg = ('Layer %s did not have required keyword "%s". '
'%s' % (layer.name, kw, instructions))
verify(kw in keywords, msg)
val = keywords[kw]
msg = ('No value found for keyword "%s" in layer %s. '
'%s' % (kw, layer.name, instructions))
verify(val, msg)
# Ensure that projection is consistent across all layers
if reference_projection is None:
reference_projection = layer.projection
else:
msg = ('Projections in input layer %s is not as expected:\n'
'projection: %s\n'
'default: %s'
'' % (layer, layer.projection, reference_projection))
verify(reference_projection == layer.projection, msg)
# Ensure that geotransform and dimensions is consistent across
# all *raster* layers
if layer.is_raster:
if geotransform is None:
geotransform = layer.get_geotransform()
else:
msg = ('Geotransforms in input raster layers are different: '
'%s %s' % (geotransform, layer.get_geotransform()))
verify(numpy.allclose(geotransform,
layer.get_geotransform(),
rtol=1.0e-12), msg)
# In case of vector layers, we just check that they are non-empty
# FIXME (Ole): Not good as nasty error is raised in cases where
# there are no buildings in the hazard area. Need to be more graceful
# See e.g. shakemap dated 20120227190230
if layer.is_vector:
msg = ('There are no vector data features. '
'Perhaps zoom out or pan to the study area '
'and try again')
verify(len(layer) > 0, msg)
# Check that arrays are aligned.
refname = None
for layer in layer_objects:
if layer.is_raster:
if refname is None:
refname = layer.get_name()
M = layer.rows
N = layer.columns
msg = ('Rasters are not aligned!\n'
'Raster %s has %i rows but raster %s has %i rows\n'
'Refer to issue #102' % (layer.get_name(),
layer.rows,
refname, M))
verify(layer.rows == M, msg)
msg = ('Rasters are not aligned!\n'
'Raster %s has %i columns but raster %s has %i columns\n'
'Refer to issue #102' % (layer.get_name(),
layer.columns,
refname, N))
verify(layer.columns == N, msg)
raise Exception(msg)
if native:
keywords = self.get_keywords()
if 'resolution' in keywords:
resolution = keywords['resolution']
try:
res = float(resolution)
except:
# Assume resolution is a string of the form:
# (0.00045228819716044, 0.00045228819716044)
msg = ('Unknown format for resolution keyword: %s'
% resolution)
verify((resolution.startswith('(') and
resolution.endswith(')')), msg)
dx, dy = [float(s) for s in resolution[1:-1].split(',')]
if not isotropic:
res = (dx, dy)
else:
msg = ('Resolution of layer "%s" was not isotropic: '
'[dx, dy] == %s' % (self.get_name(), res))
verify(numpy.allclose(dx, dy,
rtol=1.0e-12, atol=1.0e-12), msg)
res = dx
else:
if not isotropic:
res = (res, res)
# Return either 2-tuple or scale depending on isotropic
def calculate_impact(layers, impact_fcn,
comment=''):
"""Calculate impact levels as a function of list of input layers
Input
layers: List of Raster and Vector layer objects to be used for analysis
impact_fcn: Function of the form f(layers)
comment:
Output
filename of resulting impact layer (GML). Comment is embedded as
metadata. Filename is generated from input data and date.
Note
The admissible file types are tif and asc/prj for raster and
gml or shp for vector data
Assumptions
1. All layers are in WGS84 geographic coordinates
2. Layers are equipped with metadata such as names and categories
"""
# Input checks
check_data_integrity(layers)
# Get an instance of the passed impact_fcn
impact_function = impact_fcn()
# Pass input layers to plugin
F = impact_function.run(layers)
msg = 'Impact function %s returned None' % str(impact_function)
verify(F is not None, msg)
# Write result and return filename
if F.is_raster:
extension = '.tif'
# use default style for raster
else:
extension = '.shp'
# use default style for vector
output_filename = unique_filename(suffix=extension)
F.filename = output_filename
F.write_to_file(output_filename)
# Establish default name (layer1 X layer1 x impact_function)
if not F.get_name():
default_name = ''
for layer in layers:
default_name += layer.name + ' X '
if hasattr(impact_function, 'plugin_name'):
default_name += impact_function.plugin_name
else:
# Strip trailing 'X'
default_name = default_name[:-2]
F.set_name(default_name)
# FIXME (Ole): If we need to save style as defined by the impact_function
# this is the place
# Return layer object
return F
def interpolate_raster_vector_points(R, V, attribute_name=None):
"""Interpolate from raster layer to point data
Input
R: Raster data set (grid)
V: Vector data set (points)
attribute_name: Name for new attribute.
If None (default) the name of layer R is used
Output
I: Vector data set; points located as V with values interpolated from R
"""
msg = ('There are no data points to interpolate to. Perhaps zoom out '
'and try again')
verify(len(V) > 0, msg)
# Input checks
verify(R.is_raster)
verify(V.is_vector)
verify(V.is_point_data)
# Get raster data and corresponding x and y axes
A = R.get_data(nan=True)
longitudes, latitudes = R.get_geometry()
verify(len(longitudes) == A.shape[1])
verify(len(latitudes) == A.shape[0])
# Get vector point geometry as Nx2 array
coordinates = numpy.array(V.get_geometry(),
dtype='d',
copy=False)
# Get original attributes
attributes = V.get_data()
# Create new attribute and interpolate
N = len(V)
if attribute_name is None:
attribute_name = R.get_name()
try:
values = interpolate_raster(longitudes, latitudes, A,
coordinates, mode='linear')
except Exception, e:
msg = (_('Could not interpolate from raster layer %(raster)s to '
'vector layer %(vector)s. Error message: %(error)s')
% {'raster': R.get_name(),
'vector': V.get_name(),
'error': str(e)})
raise Exception(msg)
def check_bbox_string(bbox_string):
"""Check that bbox string is valid
"""
msg = 'Expected bbox as a string with format "W,S,E,N"'
verify(isinstance(bbox_string, basestring), msg)
# Use checks from string to list conversion
# FIXME (Ole): Would be better to separate the checks from the conversion
# and use those checks directly.
minx, miny, maxx, maxy = bboxstring2list(bbox_string)
# Check semantic integrity
msg = ('Western border %.5f of bounding box %s was out of range '
'for longitudes ([-180:180])' % (minx, bbox_string))
verify(-180 <= minx <= 180, msg)
msg = ('Eastern border %.5f of bounding box %s was out of range '
'for longitudes ([-180:180])' % (maxx, bbox_string))
verify(-180 <= maxx <= 180, msg)
msg = ('Southern border %.5f of bounding box %s was out of range '
'for latitudes ([-90:90])' % (miny, bbox_string))
verify(-90 <= miny <= 90, msg)
msg = ('Northern border %.5f of bounding box %s was out of range '
'for latitudes ([-90:90])' % (maxy, bbox_string))
verify(-90 <= maxy <= 90, msg)
msg = ('Western border %.5f was greater than or equal to eastern border '
'%.5f of bounding box %s' % (minx, maxx, bbox_string))
verify(minx < maxx, msg)
msg = ('Southern border %.5f was greater than or equal to northern border '
'%.5f of bounding box %s' % (miny, maxy, bbox_string))
verify(miny < maxy, msg)
def run(self, layers):
"""Risk plugin for Padang building survey
"""
# Extract data
H = get_hazard_layer(layers) # Ground shaking
E = get_exposure_layer(layers) # Building locations
datatype = E.get_keywords()['datatype']
vclass_tag = 'ITB_Class'
if datatype.lower() == 'osm':
# Map from OSM attributes to the ITB building classes
# Emap = osm2itb(E)
print 'osm2itb has not been implemented'
elif datatype.lower() == 'sigab':
# Emap = sigabitb(E)
print 'sigab2itb has not been implemented'
elif datatype.lower() == 'itb':
Emap = E
# Interpolate hazard level to building locations
Hi = H.interpolate(Emap, attribute_name='MMI')
# Extract relevant numerical data
coordinates = Emap.get_geometry()
shaking = Hi.get_data()
N = len(shaking)
# List attributes to carry forward to result layer
attributes = Emap.get_attribute_names()
# Calculate building damage
count50 = 0
count25 = 0
count10 = 0
count0 = 0
building_damage = []
for i in range(N):
mmi = float(shaking[i]['MMI'])
building_class = Emap.get_data(vclass_tag, i)
building_type = str(building_class)
damage_params = vul_curves[building_type]
beta = damage_params['beta']
median = damage_params['median']
msg = 'Invalid parameter value for ' + building_type
verify(beta + median > 0.0, msg)
percent_damage = lognormal_cdf(mmi,
median=median,
sigma=beta) * 100
# Collect shake level and calculated damage
result_dict = {self.target_field: percent_damage,
'MMI': mmi}
# Carry all orginal attributes forward
for key in attributes:
result_dict[key] = Emap.get_data(key, i)
# Record result for this feature
building_damage.append(result_dict)
# Debugging
#if percent_damage > 0.01:
# print mmi, percent_damage
# Calculate statistics
if percent_damage < 10:
count0 += 1
if 10 <= percent_damage < 33:
count10 += 1
if 33 <= percent_damage < 66:
count25 += 1
if 66 <= percent_damage:
count50 += 1
# fid.close()
# Create report
Hname = H.get_name()
Ename = E.get_name()
impact_summary = ('<b>In case of "%s" the estimated impact to '
'"%s" '
'is:</b><br><br><p>' % (Hname, Ename))
impact_summary += ('<table border="0" width="320px">'
' <tr><th><b>%s</b></th><th><b>%s</b></th></th>'
' <tr></tr>'
' <tr><td>%s:</td><td>%i</td></tr>'
' <tr><td>%s (<10%%):</td><td>%i</td></tr>'
' <tr><td>%s (10-33%%):</td><td>%i</td></tr>'
' <tr><td>%s (33-66%%):</td><td>%i</td></tr>'
' <tr><td>%s (66-100%%):</td><td>%i</td></tr>'
'</table></font>' % (_('Buildings'), _('Total'),
_('All'), N,
_('No damage'), count0,
_('Low damage'), count10,
_('Medium damage'), count25,
_('High damage'), count50))
impact_summary += '<br>' # Blank separation row
impact_summary += '<b>' + _('Assumption') + ':</b><br>'
# This is the proper text:
#_('Levels of impact are defined by post 2009 '
# 'Padang earthquake survey conducted by Geoscience '
# 'Australia and Institute of Teknologi Bandung.'))
#_('Unreinforced masonry is assumed where no '
# 'structural information is available.'))
impact_summary += _('Levels of impact are defined by post 2009 '
'Padang earthquake survey conducted by Geoscience '
'Australia and Institute of Teknologi Bandung.')
impact_summary += _('Unreinforced masonry is assumed where no '
'structural information is available.')
# Create style
style_classes = [dict(label=_('No damage'), min=0, max=10,
colour='#00ff00', transparency=1),
dict(label=_('Low damage'), min=10, max=33,
colour='#ffff00', transparency=1),
dict(label=_('Medium damage'), min=33, max=66,
colour='#ffaa00', transparency=1),
dict(label=_('High damage'), min=66, max=100,
colour='#ff0000', transparency=1)]
style_info = dict(target_field=self.target_field,
style_classes=style_classes)
# Create vector layer and return
V = Vector(data=building_damage,
projection=E.get_projection(),
geometry=coordinates,
name='Estimated pct damage',
keywords={'impact_summary': impact_summary},
style_info=style_info)
return V
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 interpolate(self, X, name=None, attribute_name=None):
"""Interpolate values of this vector layer to other layer
Input
X: Layer object defining target
name: Optional name of returned interpolated layer
attribute_name: Optional attribute name to use.
If None, all attributes are used.
FIXME (Ole): Single attribute not tested well yet
and not implemented for lines
Output
Y: Layer object with values of this vector layer interpolated to
geometry of input layer X
"""
msg = 'Input to Vector.interpolate must be a vector layer instance'
verify(X.is_vector, msg)
msg = ('Projections must be the same: I got %s and %s'
% (self.projection, X.projection))
verify(self.projection == X.projection, msg)
msg = ('Vector layer to interpolate from must be polygon geometry. '
'I got OGR geometry type %s'
% geometrytype2string(self.geometry_type))
verify(self.is_polygon_data, msg)
# FIXME (Ole): Organise this the same way it is done with rasters
original_geometry = X.get_geometry() # Geometry for returned data
if X.is_polygon_data:
# Use centroids, in case of polygons
X = convert_polygons_to_centroids(X)
elif X.is_line_data:
# Clip lines to polygon and return centroids
# FIXME (Ole): Need to separate this out, but identify what is
# common with points and lines
#
#X.write_to_file('line_data.shp')
#self.write_to_file('poly_data.shp')
# Extract line features
lines = X.get_geometry()
line_attributes = X.get_data()
N = len(X)
verify(len(lines) == N)
verify(len(line_attributes) == N)
# Extract polygon features
polygons = self.get_geometry()
poly_attributes = self.get_data()
verify(len(polygons) == len(poly_attributes))
# Data structure for resulting line segments
clipped_geometry = []
clipped_attributes = []
# Clip line lines to polygons
for i, polygon in enumerate(polygons):
for j, line in enumerate(lines):
inside, outside = clip_line_by_polygon(line, polygon)
# Create new attributes
# FIXME (Ole): Not done single specified polygon
# attribute
inside_attributes = {}
outside_attributes = {}
for key in line_attributes[j]:
inside_attributes[key] = line_attributes[j][key]
outside_attributes[key] = line_attributes[j][key]
for key in poly_attributes[i]:
inside_attributes[key] = poly_attributes[i][key]
outside_attributes[key] = None
# Always create default attribute flagging if segment was
# inside any of the polygons
inside_attributes[DEFAULT_ATTRIBUTE] = True
outside_attributes[DEFAULT_ATTRIBUTE] = False
# Assign new attribute set to clipped lines
for segment in inside:
clipped_geometry.append(segment)
clipped_attributes.append(inside_attributes)
for segment in outside:
clipped_geometry.append(segment)
clipped_attributes.append(outside_attributes)
# Create new Vector instance and return
V = Vector(data=clipped_attributes,
projection=X.get_projection(),
geometry=clipped_geometry,
geometry_type='line')
#V.write_to_file('clipped_and_tagged.shp')
return V
# The following applies only to Polygon-Point interpolation
msg = ('Vector layer to interpolate to must be point geometry. '
'I got OGR geometry type %s'
% geometrytype2string(X.geometry_type))
verify(X.is_point_data, msg)
msg = ('Name must be either a string or None. I got %s'
% (str(type(X)))[1:-1])
verify(name is None or isinstance(name, basestring), msg)
msg = ('Attribute must be either a string or None. I got %s'
% (str(type(X)))[1:-1])
verify(attribute_name is None or
isinstance(attribute_name, basestring), msg)
attribute_names = self.get_attribute_names()
if attribute_name is not None:
msg = ('Requested attribute "%s" did not exist in %s'
% (attribute_name, attribute_names))
verify(attribute_name in attribute_names, msg)
#----------------
# Start algorithm
#----------------
# Extract point features
points = ensure_numeric(X.get_geometry())
attributes = X.get_data()
N = len(X)
# Extract polygon features
geom = self.get_geometry()
data = self.get_data()
verify(len(geom) == len(data))
# Augment point features with empty attributes from polygon
for a in attributes:
if attribute_name is None:
# Use all attributes
for key in attribute_names:
a[key] = None
else:
# Use only requested attribute
# FIXME (Ole): Test for this is not finished
a[attribute_name] = None
# Always create default attribute flagging if point was
# inside any of the polygons
a[DEFAULT_ATTRIBUTE] = None
# Traverse polygons and assign attributes to points that fall inside
for i, polygon in enumerate(geom):
if attribute_name is None:
# Use all attributes
poly_attr = data[i]
else:
# Use only requested attribute
poly_attr = {attribute_name: data[i][attribute_name]}
# Assign default attribute to indicate points inside
poly_attr[DEFAULT_ATTRIBUTE] = True
# Clip data points by polygons and add polygon attributes
indices = inside_polygon(points, polygon)
for k in indices:
for key in poly_attr:
# Assign attributes from polygon to points
attributes[k][key] = poly_attr[key]
# Create new Vector instance and return
V = Vector(data=attributes,
projection=X.get_projection(),
geometry=original_geometry,
name=X.get_name())
return V
def sigab2padang(E):
"""Map SIGAB attributes to Padang vulnerability classes
Input
E: Vector object representing the SIGAB data
Output:
Vector object like E, but with one new attribute ('VCLASS')
representing the vulnerability class used in the Padang dataset
"""
# Input check
required = ['Struktur_B', 'Lantai', 'Atap', 'Dinding', 'Tingkat']
actual = E.get_attribute_names()
msg = ('Input data to sigab2bnpb must have attributes %s. '
'It has %s' % (str(required), str(actual)))
for attribute in required:
verify(attribute in actual, msg)
# Start mapping
N = len(E)
attributes = E.get_data()
for i in range(N):
levels = E.get_data('Tingkat', i).lower()
structure = E.get_data('Struktur_B', i).lower()
roof_type = E.get_data('Atap', i).lower()
wall_type = E.get_data('Dinding', i).lower()
floor_type = E.get_data('Lantai', i).lower()
if levels == 'none' or structure == 'none':
vulnerability_class = 2
else:
if int(levels) >= 2:
vulnerability_class = 7 # RC low
else:
# Low
if structure in ['beton bertulang']:
vulnerability_class = 6 # Concrete shear
elif structure.startswith('rangka'):
vulnerability_class = 8 # Confined
elif 'kayu' in structure or 'wood' in structure:
vulnerability_class = 9 # Wood
else:
vulnerability_class = 2 # URM
# Store new attribute value
attributes[i]['VCLASS'] = vulnerability_class
# Selfcheck for use with osm_080811.shp
if E.get_name() == 'osm_080811':
if levels > 0:
msg = ('Got %s expected %s. levels = %f, structure = %s'
% (vulnerability_class,
attributes[i]['TestBLDGCl'],
levels,
structure))
verify(numpy.allclose(attributes[i]['TestBLDGCl'],
vulnerability_class), msg)
# Create new vector instance and return
V = Vector(data=attributes,
projection=E.get_projection(),
geometry=E.get_geometry(),
name=E.get_name() + ' mapped to Padang vulnerability classes',
keywords=E.get_keywords())
return V
def osm2bnpb(E, target_attribute='VCLASS'):
"""Map OSM attributes to BNPB vulnerability classes
This maps attributes collected in the OpenStreetMap exposure data
(data.kompetisiosm.org) to 2 vulnerability classes identified by
BNPB in Kajian Risiko Gempabumi VERS 1.0, 2011. They are
URM: Unreinforced Masonry and RM: Reinforced Masonry
Input
E: Vector object representing the OSM data
target_attribute: Optional name of the attribute containing
the mapped vulnerability class. Default
value is 'VCLASS'
Output:
Vector object like E, but with one new attribute (e.g. 'VCLASS')
representing the vulnerability class used in the guidelines
"""
# Input check
required = ['levels', 'structure']
actual = E.get_attribute_names()
msg = ('Input data to osm2bnpb must have attributes %s. '
'It has %s' % (str(required), str(actual)))
for attribute in required:
verify(attribute in actual, msg)
# Start mapping
N = len(E)
attributes = E.get_data()
count = 0
for i in range(N):
levels = E.get_data('levels', i)
structure = E.get_data('structure', i)
if levels is None or structure is None:
vulnerability_class = 'URM'
count += 1
else:
# Map string variable levels to integer
if levels.endswith('+'):
levels = 100
try:
levels = int(levels)
except:
# E.g. 'ILP jalan'
vulnerability_class = 'URM'
count += 1
else:
# Start mapping depending on levels
if levels >= 4:
# High
vulnerability_class = 'RM'
elif 1 <= levels < 4:
# Low
if structure in ['reinforced_masonry', 'confined_masonry']:
vulnerability_class = 'RM'
elif 'kayu' in structure or 'wood' in structure:
vulnerability_class = 'RM'
else:
vulnerability_class = 'URM'
elif numpy.allclose(levels, 0):
# A few buildings exist with 0 levels.
# In general, we should be assigning here the most
# frequent building in the area which could be defined
# by admin boundaries.
vulnerability_class = 'URM'
else:
msg = 'Unknown number of levels: %s' % levels
raise Exception(msg)
# Store new attribute value
attributes[i][target_attribute] = vulnerability_class
#print 'Got %i without levels or structure (out of %i total)' % (count, N)
# Create new vector instance and return
V = Vector(data=attributes,
projection=E.get_projection(),
geometry=E.get_geometry(),
name=E.get_name() + ' mapped to BNPB vulnerability classes',
keywords=E.get_keywords())
return V
def run(layers):
"""Risk plugin for earthquake fatalities
Input
layers: List of layers expected to contain
H: Raster layer of flood depth
P: Raster layer of population data on the same grid as H
"""
# Depth above which people are regarded affected [m]
threshold = 0.1
thresholds = [0.1, 0.2, 0.3, 0.5, 0.8, 1.0]
# Identify hazard and exposure layers
inundation = get_hazard_layer(layers) # Flood inundation [m]
# Get population and gender ratio
population = gender_ratio = None
for layer in get_exposure_layers(layers):
keywords = layer.get_keywords()
if 'datatype' not in keywords:
population = layer
else:
datatype = keywords['datatype']
if 'ratio' not in datatype:
population = layer
else:
# if 'female' in datatype and 'ratio' in datatype:
gender_ratio_unit = keywords['unit']
msg = ('Unit for gender ratio must be either '
'"percent" or "ratio"')
if gender_ratio_unit not in ['percent', 'ratio']:
raise Exception(msg)
gender_ratio = layer
msg = 'No population layer was found in: %s' % str(layers)
verify(population is not None, msg)
# Extract data as numeric arrays
D = inundation.get_data(nan=0.0) # Depth
# Calculate impact as population exposed to depths > threshold
if population.get_resolution(native=True, isotropic=True) < 0.0005:
# Keep this for backwards compatibility just a little while
# This uses the original custom population set and
# serves as a reference
P = population.get_data(nan=0.0) # Population density
pixel_area = 2500
I = numpy.where(D > threshold, P, 0) / 100000.0 * pixel_area
else:
# This is the new generic way of scaling (issue #168 and #172)
P = population.get_data(nan=0.0, scaling=True)
I = numpy.where(D > threshold, P, 0)
if gender_ratio is not None:
# Extract gender ratio at each pixel (as ratio)
G = gender_ratio.get_data(nan=0.0)
if gender_ratio_unit == 'percent':
G /= 100
# Calculate breakdown
P_female = P * G
P_male = P - P_female
I_female = I * G
I_male = I - I_female
# Generate text with result for this study
total = str(int(sum(P.flat) / 1000))
count = str(int(sum(I.flat) / 1000))
# Create report
impact_summary = ('<table border="0" width="320px">'
' <tr><td><b>%s:</b></td>'
'<td align="right"><b>%s</b></td></tr>'
% ('Jumlah Penduduk', total))
if gender_ratio is not None:
total_female = str(int(sum(P_female.flat) / 1000))
total_male = str(int(sum(P_male.flat) / 1000))
impact_summary += (' <tr><td>%s:</td>'
'<td align="right">%s</td></tr>'
% (' - Wanita', total_female))
impact_summary += (' <tr><td>%s:</td>'
'<td align="right">%s</td></tr>'
% (' - Pria', total_male))
impact_summary += '<tr><td> </td></tr>' # Blank row
impact_summary += (' <tr><td><b>%s:</b></td>'
'<td align="right"><b>%s</b></td></tr>'
% ('Perkiraan Jumlah Terdampak (> %.1fm)' % threshold,
count))
if gender_ratio is not None:
affected_female = str(int(sum(I_female.flat) / 1000))
affected_male = str(int(sum(I_male.flat) / 1000))
impact_summary += (' <tr><td>%s:</td>'
'<td align="right">%s</td></tr>'
% (' - Wanita', affected_female))
impact_summary += (' <tr><td>%s:</td>'
'<td align="right">%s</td></tr>'
% (' - Pria', affected_male))
impact_summary += '</table>'
impact_summary += '<br>' # Blank separation row
impact_summary += 'Catatan: Semua nomor x 1000'
# Create raster object and return
R = Raster(I,
projection=inundation.get_projection(),
geotransform=inundation.get_geotransform(),
name='People affected',
keywords={'impact_summary': impact_summary})
return R
def osm2padang(E):
"""Map OSM attributes to Padang vulnerability classes
This maps attributes collected in the OpenStreetMap exposure data
(data.kompetisiosm.org) to 9 vulnerability classes identified by
Geoscience Australia and ITB in the post 2009 Padang earthquake
survey (http://trove.nla.gov.au/work/38470066).
The mapping was developed by Abigail Baca, GFDRR.
Input
E: Vector object representing the OSM data
Output:
Vector object like E, but with one new attribute ('VCLASS')
representing the vulnerability class used in the Padang dataset
Algorithm
1. Class the "levels" field into height bands where 1-3 = low,
4-10 = mid, >10 = high
2. Where height band = mid then building type = 4
"RC medium rise Frame with Masonry in-fill walls"
3. Where height band = high then building type = 6
"Concrete Shear wall high rise* Hazus C2H"
4. Where height band = low and structure = (plastered or
reinforced_masonry) then building type = 7
"RC low rise Frame with Masonry in-fill walls"
5. Where height band = low and structure = confined_masonry then
building type = 8 "Confined Masonry"
6. Where height band = low and structure = unreinforced_masonry then
building type = 2 "URM with Metal Roof"
"""
# Input check
required = ['levels', 'structure']
actual = E.get_attribute_names()
msg = ('Input data to osm2padang must have attributes %s. '
'It has %s' % (str(required), str(actual)))
for attribute in required:
verify(attribute in actual, msg)
# Start mapping
N = len(E)
attributes = E.get_data()
count = 0
for i in range(N):
levels = E.get_data('levels', i)
structure = E.get_data('structure', i)
if levels is None or structure is None:
vulnerability_class = 2
count += 1
else:
# Map string variable levels to integer
if levels.endswith('+'):
levels = 100
try:
levels = int(levels)
except:
# E.g. 'ILP jalan'
vulnerability_class = 2
count += 1
else:
# Start mapping depending on levels
if levels >= 10:
# High
vulnerability_class = 6 # Concrete shear
elif 4 <= levels < 10:
# Mid
vulnerability_class = 4 # RC mid
elif 1 <= levels < 4:
# Low
if structure in ['plastered',
'reinforced masonry',
'reinforced_masonry']:
vulnerability_class = 7 # RC low
elif structure == 'confined_masonry':
vulnerability_class = 8 # Confined
elif 'kayu' in structure or 'wood' in structure:
vulnerability_class = 9 # Wood
else:
vulnerability_class = 2 # URM
elif numpy.allclose(levels, 0):
# A few buildings exist with 0 levels.
# In general, we should be assigning here the most
# frequent building in the area which could be defined
# by admin boundaries.
vulnerability_class = 2
else:
msg = 'Unknown number of levels: %s' % levels
raise Exception(msg)
# Store new attribute value
attributes[i]['VCLASS'] = vulnerability_class
# Selfcheck for use with osm_080811.shp
if E.get_name() == 'osm_080811':
if levels > 0:
msg = ('Got %s expected %s. levels = %f, structure = %s'
% (vulnerability_class,
attributes[i]['TestBLDGCl'],
levels,
structure))
verify(numpy.allclose(attributes[i]['TestBLDGCl'],
vulnerability_class), msg)
#print 'Got %i without levels or structure (out of %i total)' % (count, N)
# Create new vector instance and return
V = Vector(data=attributes,
projection=E.get_projection(),
geometry=E.get_geometry(),
name=E.get_name() + ' mapped to Padang vulnerability classes',
keywords=E.get_keywords())
return V
def write_to_file(self, filename):
"""Save vector data to file
Input
filename: filename with extension .shp or .gml
Note, if attribute names are longer than 10 characters they will be
truncated. This is due to limitations in the shp file driver and has
to be done here since gdal v1.7 onwards has changed its handling of
this issue: http://www.gdal.org/ogr/drv_shapefile.html
"""
# Check file format
basename, extension = os.path.splitext(filename)
msg = ('Invalid file type for file %s. Only extensions '
'shp or gml allowed.' % filename)
verify(extension == '.shp' or extension == '.gml', msg)
driver = DRIVER_MAP[extension]
# FIXME (Ole): Tempory flagging of GML issue (ticket #18)
if extension == '.gml':
msg = ('OGR GML driver does not store geospatial reference.'
'This format is disabled for the time being. See '
'https://github.com/AIFDR/riab/issues/18')
raise Exception(msg)
# Derive layername from filename (excluding preceding dirs)
layername = os.path.split(basename)[-1]
# Get vector data
geometry = self.get_geometry()
data = self.get_data()
N = len(geometry)
# Clear any previous file of this name (ogr does not overwrite)
try:
os.remove(filename)
except:
pass
# Create new file with one layer
drv = ogr.GetDriverByName(driver)
if drv is None:
msg = 'OGR driver %s not available' % driver
raise Exception(msg)
ds = drv.CreateDataSource(filename)
if ds is None:
msg = 'Creation of output file %s failed' % filename
raise Exception(msg)
lyr = ds.CreateLayer(layername,
self.projection.spatial_reference,
self.geometry_type)
if lyr is None:
msg = 'Could not create layer %s' % layername
raise Exception(msg)
# Define attributes if any
store_attributes = False
if data is not None:
if len(data) > 0:
try:
fields = data[0].keys()
except:
msg = ('Input parameter "attributes" was specified '
'but it does not contain dictionaries with '
'field information as expected. The first'
'element is %s' % data[0])
raise Exception(msg)
else:
# Establish OGR types for each element
ogrtypes = {}
for name in fields:
att = data[0][name]
py_type = type(att)
msg = ('Unknown type for storing vector '
'data: %s, %s' % (name, str(py_type)[1:-1]))
verify(py_type in TYPE_MAP, msg)
ogrtypes[name] = TYPE_MAP[py_type]
else:
msg = ('Input parameter "data" was specified '
'but appears to be empty')
raise Exception(msg)
# Create attribute fields in layer
store_attributes = True
for name in fields:
fd = ogr.FieldDefn(name, ogrtypes[name])
# FIXME (Ole): Trying to address issue #16
# But it doesn't work and
# somehow changes the values of MMI in test
#width = max(128, len(name))
#print name, width
#fd.SetWidth(width)
# Silent handling of warnings like
# Warning 6: Normalized/laundered field name:
#'CONTENTS_LOSS_AUD' to 'CONTENTS_L'
gdal.PushErrorHandler('CPLQuietErrorHandler')
if lyr.CreateField(fd) != 0:
msg = 'Could not create field %s' % name
raise Exception(msg)
# Restore error handler
gdal.PopErrorHandler()
# Store geometry
geom = ogr.Geometry(self.geometry_type)
layer_def = lyr.GetLayerDefn()
for i in range(N):
# Create new feature instance
feature = ogr.Feature(layer_def)
# Store geometry and check
if self.is_point_data:
x = float(geometry[i][0])
y = float(geometry[i][1])
geom.SetPoint_2D(0, x, y)
elif self.is_polygon_data:
wkt = array2wkt(geometry[i], geom_type='POLYGON')
geom = ogr.CreateGeometryFromWkt(wkt)
elif self.is_line_data:
wkt = array2wkt(geometry[i], geom_type='LINESTRING')
geom = ogr.CreateGeometryFromWkt(wkt)
else:
msg = 'Geometry type %s not implemented' % self.geometry_type
raise Exception(msg)
feature.SetGeometry(geom)
G = feature.GetGeometryRef()
if G is None:
msg = 'Could not create GeometryRef for file %s' % filename
raise Exception(msg)
# Store attributes
if store_attributes:
for j, name in enumerate(fields):
actual_field_name = layer_def.GetFieldDefn(j).GetNameRef()
val = data[i][name]
if type(val) == numpy.ndarray:
# A singleton of type <type 'numpy.ndarray'> works
# for gdal version 1.6 but fails for version 1.8
# in SetField with error: NotImplementedError:
# Wrong number of arguments for overloaded function
val = float(val)
elif val is None:
val = ''
feature.SetField(actual_field_name, val)
# Save this feature
if lyr.CreateFeature(feature) != 0:
msg = 'Failed to create feature %i in file %s' % (i, filename)
raise Exception(msg)
feature.Destroy()
# Write keywords if any
write_keywords(self.keywords, basename + '.keywords')
