def test_rasterValuesAtPoints(self):
# We need to make a .tif to test this function.
self.make_me_a_tif()
# Get the range of the tif
tifRange=su.getRasterExtent('PointData_TestData.tif')
# Now try to get some point values -- note they will be rounded to the
# nearest cell
xA=numpy.array([0., 10.3, 50.9, 100.])+tifRange[0]+0.5
yA=numpy.array([0., 20.1, 75.1, 100.])+tifRange[2]+0.5
z_predicted=numpy.round(xA)+numpy.round(yA)-tifRange[0]-tifRange[2]-1.0
InDat=numpy.vstack([xA,yA]).transpose()
z_fitted=su.rasterValuesAtPoints(InDat, rasterFile='PointData_TestData.tif')
try:
assert(numpy.allclose(z_fitted,z_predicted))
except:
raise Exception, 'Error could be in rasterValuesAtPoints or in Make_Geotif'
# Try with bilinear interpolation
z_fitted=su.rasterValuesAtPoints(InDat, rasterFile='PointData_TestData.tif',
interpolation='bilinear')
z_predicted = xA + yA - tifRange[0] - tifRange[2] - 1.0
try:
assert(numpy.allclose(z_fitted,z_predicted))
except:
raise Exception, 'Error could be in rasterValuesAtPoints or in Make_Geotif'
return
def test_rasterValuesAtPoints(self):
# We need to make a .tif to test this function.
self.make_me_a_tif()
# Get the range of the tif
tifRange = su.getRasterExtent('PointData_TestData.tif')
# Now try to get some point values -- note they will be rounded to the
# nearest cell
xA = numpy.array([0., 10.3, 50.9, 100.])+tifRange[0]+0.5
yA = numpy.array([0., 20.1, 75.1, 100.])+tifRange[2]+0.5
z_predicted = numpy.round(xA)+numpy.round(yA) - \
tifRange[0]-tifRange[2]-1.0
InDat = numpy.vstack([xA, yA]).transpose()
z_fitted = su.rasterValuesAtPoints(
InDat, rasterFile='PointData_TestData.tif')
try:
assert(numpy.allclose(z_fitted, z_predicted))
except:
raise Exception(
'Error could be in rasterValuesAtPoints or in Make_Geotif')
# Try with bilinear interpolation
z_fitted = su.rasterValuesAtPoints(InDat, rasterFile='PointData_TestData.tif',
interpolation='bilinear')
z_predicted = xA + yA - tifRange[0] - tifRange[2] - 1.0
try:
assert(numpy.allclose(z_fitted, z_predicted))
except:
raise Exception(
'Error could be in rasterValuesAtPoints or in Make_Geotif')
return
def test_getRasterExtent(self):
self.make_me_a_tif()
extentOut=su.getRasterExtent('PointData_TestData.tif')
assert(numpy.allclose(extentOut[0], 307000.-0.5))
assert(numpy.allclose(extentOut[1], 307100.+0.5))
assert(numpy.allclose(extentOut[2], 6193000.-0.5))
assert(numpy.allclose(extentOut[3], 6193100.+0.5))
extentOut=su.getRasterExtent('PointData_TestData.tif',asPolygon=True)
assert(numpy.allclose(extentOut[0][0], 307000.-0.5))
assert(numpy.allclose(extentOut[3][0], 307000.-0.5))
assert(numpy.allclose(extentOut[1][0], 307100.+0.5))
assert(numpy.allclose(extentOut[2][0], 307100.+0.5))
assert(numpy.allclose(extentOut[0][1], 6193000.-0.5))
assert(numpy.allclose(extentOut[1][1], 6193000.-0.5))
assert(numpy.allclose(extentOut[2][1], 6193100.+0.5))
assert(numpy.allclose(extentOut[3][1], 6193100.+0.5))
def test_getRasterExtent(self):
self.make_me_a_tif()
extentOut = su.getRasterExtent('PointData_TestData.tif')
assert(numpy.allclose(extentOut[0], 307000.-0.5))
assert(numpy.allclose(extentOut[1], 307100.+0.5))
assert(numpy.allclose(extentOut[2], 6193000.-0.5))
assert(numpy.allclose(extentOut[3], 6193100.+0.5))
extentOut = su.getRasterExtent(
'PointData_TestData.tif', asPolygon=True)
assert(numpy.allclose(extentOut[0][0], 307000.-0.5))
assert(numpy.allclose(extentOut[3][0], 307000.-0.5))
assert(numpy.allclose(extentOut[1][0], 307100.+0.5))
assert(numpy.allclose(extentOut[2][0], 307100.+0.5))
assert(numpy.allclose(extentOut[0][1], 6193000.-0.5))
assert(numpy.allclose(extentOut[1][1], 6193000.-0.5))
assert(numpy.allclose(extentOut[2][1], 6193100.+0.5))
assert(numpy.allclose(extentOut[3][1], 6193100.+0.5))
def F(x, y):
"""This is the function returned by composite_quantity_setting_function
It can be passed to set_quantity
"""
isSet = numpy.zeros(len(x)) # 0/1 - record if each point has been set
quantityVal = x * 0 + numpy.nan # Function return value
# Record points which evaluated to nan on their first preference
# dataset.
was_ever_nan = (x * 0).astype(int)
lpf = len(poly_fun_pairs)
if (lpf <= 0):
raise Exception('Must have at least 1 fun-poly-pair')
# Make an array of 'transformed' spatial coordinates, for checking
# polygon inclusion
xll = domain.geo_reference.xllcorner
yll = domain.geo_reference.yllcorner
xy_array_trans = numpy.vstack([x + xll, y + yll]).transpose()
# Check that none of the pi polygons [except perhaps the last] is 'All'
for i in range(lpf - 1):
if (poly_fun_pairs[i][0] == 'All'):
# This is only ok if all the othe poly_fun_pairs are None
remaining_poly_fun_pairs_are_None = \
[poly_fun_pairs[j][0] is None for j in range(i+1,lpf)]
if (not all(remaining_poly_fun_pairs_are_None)):
raise Exception('Can only have the last polygon = All')
# Main Loop
# Apply the fi inside the pi
for i in range(lpf):
fi = poly_fun_pairs[i][1] # The function
pi = poly_fun_pairs[i][0] # The polygon
# Quick exit
if (pi is None):
continue
###################################################################
# Get indices fInds of points in polygon pi which are not already
# set
###################################################################
if (pi == 'All'):
# Get all unset points
fInside = (1 - isSet)
fInds = (fInside == 1).nonzero()[0]
else:
if (pi == 'Extent'):
# Here fi MUST be a gdal-compatible raster
if (not (type(fi) == str)):
msg = ' pi = "Extent" can only be used when fi is a' +\
' raster file name'
raise Exception(msg)
if (not os.path.exists(fi)):
msg = 'fi ' + str(fi) + ' is supposed to be a ' +\
' raster filename, but it could not be found'
raise Exception(msg)
# Then we get the extent from the raster itself
pi_path = su.getRasterExtent(fi, asPolygon=True)
if verbose:
print 'Extracting extent from raster: ', fi
print 'Extent: ', pi_path
elif ((type(pi) == str) and os.path.isfile(pi)):
# pi is a file
pi_path = su.read_polygon(pi)
else:
# pi is the actual polygon data
pi_path = pi
# Get the insides of unset points inside pi_path
notSet = (isSet == 0.).nonzero()[0]
fInds = inside_polygon(xy_array_trans[notSet, :], pi_path)
fInds = notSet[fInds]
if len(fInds) == 0:
# No points found, move on
continue
###################################################################
# Evaluate fi at the points inside pi
###################################################################
# We use various tricks to infer whether fi is a function,
# a constant, a file (raster or csv), or an array
if (hasattr(fi, '__call__')):
# fi is a function
quantityVal[fInds] = fi(x[fInds], y[fInds])
elif isinstance(fi, (int, long, float)):
# fi is a numerical constant
quantityVal[fInds] = fi * 1.0
elif (type(fi) is str and os.path.exists(fi)):
# fi is a file which is assumed to be
# a gdal-compatible raster OR an x,y,z elevation file
if os.path.splitext(fi)[1] in ['.txt', '.csv']:
fi_array = su.read_csv_optional_header(fi)
# Check the results
if fi_array.shape[1] is not 3:
print 'Treated input file ' + fi +\
' as xyz array with an optional header'
msg = 'Array should have 3 columns -- x,y,value'
raise Exception(msg)
newfi = make_nearestNeighbour_quantity_function(
fi_array,
domain,
k_nearest_neighbours=default_k_nearest_neighbours)
quantityVal[fInds] = newfi(x[fInds], y[fInds])
else:
# Treating input file as a raster
newfi = quantityRasterFun(
domain, fi, interpolation=default_raster_interpolation)
quantityVal[fInds] = newfi(x[fInds], y[fInds])
elif (type(fi) is numpy.ndarray):
if fi.shape[1] is not 3:
msg = 'Array should have 3 columns -- x,y,value'
raise Exception(msg)
newfi = make_nearestNeighbour_quantity_function(
fi,
domain,
k_nearest_neighbours=default_k_nearest_neighbours)
quantityVal[fInds] = newfi(x[fInds], y[fInds])
else:
print 'Error with function from'
print fi
msg = 'Cannot make function from type ' + str(type(fi))
raise Exception, msg
###################################################################
# Check for nan values
###################################################################
#nan_flag = (quantityVal[fInds] != quantityVal[fInds])
nan_flag = 1 * numpy.isnan(quantityVal[fInds])
nan_inds = nan_flag.nonzero()[0]
was_ever_nan[fInds[nan_inds]] = 1
if len(nan_inds) > 0:
if nan_treatment == 'exception':
msg = 'nan values generated by the poly_fun_pair at '\
'index ' + str(i) + ' '\
'in composite_quantity_setting_function. ' + \
'To allow these values to be set by later ' + \
'poly_fun pairs, pass the argument ' + \
'nan_treatment="fall_through" ' + \
'to composite_quantity_setting_function'
raise Exception(msg)
elif nan_treatment == 'fall_through':
msg = 'WARNING: nan values generated by the ' + \
'poly_fun_pair at index ' + str(i) + ' '\
'in composite_quantity_setting_function. ' + \
'They will be passed to later poly_fun_pairs'
if verbose: print msg
not_nan_inds = (1 - nan_flag).nonzero()[0]
if len(not_nan_inds) > 0:
fInds = fInds[not_nan_inds]
else:
# All values are nan
msg = '( Actually all the values were nan - ' + \
'Are you sure they should be? Possible error?)'
if verbose: print msg
continue
else:
msg = 'Found nan values in ' + \
'composite_quantity_setting_function but ' + \
'nan_treatment is not a recognized value'
raise Exception(msg)
# Record that the points have been set
isSet[fInds] = 1
# Enforce clip_range
if clip_range is not None:
lower_bound = clip_range[i][0]
upper_bound = clip_range[i][1]
quantityVal[fInds] = numpy.maximum(quantityVal[fInds],
lower_bound)
quantityVal[fInds] = numpy.minimum(quantityVal[fInds],
upper_bound)
# End of loop
# Find points which were nan on their first preference dataset + are
# inside nan_interpolation_region_polygon. Then reinterpolate their
# values from the other x,y, quantityVal points.
if (nan_interpolation_region_polygon is not None) &\
(was_ever_nan.sum() > 0):
if nan_interpolation_region_polygon == 'All':
points_to_reinterpolate = was_ever_nan.nonzero()[0]
else:
# nan_interpolation_region_polygon contains information on 1 or
# more polygons
# Inside those polygons, we need to re-interpolate points which
# first evaluted to na
possible_points_to_reint = was_ever_nan.nonzero()[0]
points_to_reinterpolate = numpy.array([]).astype(int)
for i in range(len(nan_interpolation_region_polygon)):
nan_pi = nan_interpolation_region_polygon[i]
# Ensure nan_pi = list of x,y points making a polygon
if (type(nan_pi) == str):
nan_pi = su.read_polygon(nan_pi)
points_in_nan_pi = inside_polygon(
xy_array_trans[possible_points_to_reint, :], nan_pi)
if len(points_in_nan_pi) > 0:
points_to_reinterpolate = numpy.hstack([
points_to_reinterpolate,
possible_points_to_reint[points_in_nan_pi]
])
if verbose:
print 'Re-interpolating ', len(points_to_reinterpolate),\
' points which were nan under their',\
' first-preference and are inside the',\
' nan_interpolation_region_polygon'
if len(points_to_reinterpolate) > 0:
msg = 'WARNING: nan interpolation is being applied. This ',\
'should be done in serial prior to distributing the ',\
'domain, as there is no parallel communication ',\
'implemented yet [so parallel results might depend on ',\
'the number of processes]'
if verbose:
print msg
# Find the interpolation points = points not needing reinterpolation
ip = x * 0 + 1
ip[points_to_reinterpolate] = 0
number_of_ip = ip.sum()
ip = ip.nonzero()[0]
# Check that none of the ip points has an nan value
nan_ip = (quantityVal[ip] != quantityVal[ip]).nonzero()[0]
if len(nan_ip) > 0:
print 'There are ', len(nan_ip), ' points outside the ',\
'nan_interpolation_region_polygon have nan values.'
print 'The user should ensure this does not happen.'
print 'The points have the following coordinates:'
print xy_array_trans[ip[nan_ip], :]
msg = "There are nan points outside of " +\
"nan_interpolation_region_polygon, even after all " +\
"fall-through's"
raise Exception(msg)
if (number_of_ip < default_k_nearest_neighbours):
raise Exception('Too few non-nan points to interpolate from')
# Make function for re-interpolation. Note this requires
# x,y,z in georeferenced coordinates, whereas x,y are ANUGA
# coordinates
reinterp_F = make_nearestNeighbour_quantity_function(
numpy.vstack([
xy_array_trans[ip, 0], xy_array_trans[ip, 1],
quantityVal[ip]
]).transpose(),
domain,
k_nearest_neighbours=default_k_nearest_neighbours)
# re-interpolate
quantityVal[points_to_reinterpolate] = reinterp_F(
x[points_to_reinterpolate], y[points_to_reinterpolate])
isSet[points_to_reinterpolate] = 1
# Check there are no remaining nan values
if (min(isSet) != 1):
print 'Some points remain as nan, which is not allowed'
unset_inds = (isSet != 1).nonzero()[0]
lui = min(5, len(unset_inds))
print 'There are ', len(unset_inds), ' such points'
print 'Here are a few:'
for i in range(lui):
print x[unset_inds[i]] + xll, y[unset_inds[i]] + yll
raise Exception('It seems the input data needs to be fixed')
return quantityVal
def F(x,y):
"""This is the function returned by composite_quantity_setting_function
It can be passed to set_quantity
"""
isSet = numpy.zeros(len(x)) # 0/1 - record if each point has been set
quantityVal = x*0 + numpy.nan # Function return value
# Record points which evaluated to nan on their first preference
# dataset.
was_ever_nan = (x*0).astype(int)
lpf = len(poly_fun_pairs)
if(lpf <= 0):
raise Exception('Must have at least 1 fun-poly-pair')
# Make an array of 'transformed' spatial coordinates, for checking
# polygon inclusion
xll = domain.geo_reference.xllcorner
yll = domain.geo_reference.yllcorner
xy_array_trans = numpy.vstack([x+xll,y+yll]).transpose()
# Check that none of the pi polygons [except perhaps the last] is 'All'
for i in range(lpf-1):
if(poly_fun_pairs[i][0]=='All'):
# This is only ok if all the othe poly_fun_pairs are None
remaining_poly_fun_pairs_are_None = \
[poly_fun_pairs[j][0] is None for j in range(i+1,lpf)]
if(not all(remaining_poly_fun_pairs_are_None)):
raise Exception('Can only have the last polygon = All')
# Main Loop
# Apply the fi inside the pi
for i in range(lpf):
fi = poly_fun_pairs[i][1] # The function
pi = poly_fun_pairs[i][0] # The polygon
# Quick exit
if(pi is None):
continue
###################################################################
# Get indices fInds of points in polygon pi which are not already
# set
###################################################################
if(pi == 'All'):
# Get all unset points
fInside = (1-isSet)
fInds = (fInside==1).nonzero()[0]
else:
if(pi == 'Extent'):
# Here fi MUST be a gdal-compatible raster
if(not (type(fi) == str)):
msg = ' pi = "Extent" can only be used when fi is a' +\
' raster file name'
raise Exception(msg)
if(not os.path.exists(fi)):
msg = 'fi ' + str(fi) + ' is supposed to be a ' +\
' raster filename, but it could not be found'
raise Exception(msg)
# Then we get the extent from the raster itself
pi_path = su.getRasterExtent(fi,asPolygon=True)
if verbose:
print 'Extracting extent from raster: ', fi
print 'Extent: ', pi_path
elif( (type(pi) == str) and os.path.isfile(pi) ):
# pi is a file
pi_path = su.read_polygon(pi)
else:
# pi is the actual polygon data
pi_path = pi
# Get the insides of unset points inside pi_path
notSet = (isSet==0.).nonzero()[0]
fInds = inside_polygon(xy_array_trans[notSet,:], pi_path)
fInds = notSet[fInds]
if len(fInds) == 0:
# No points found, move on
continue
###################################################################
# Evaluate fi at the points inside pi
###################################################################
# We use various tricks to infer whether fi is a function,
# a constant, a file (raster or csv), or an array
if(hasattr(fi,'__call__')):
# fi is a function
quantityVal[fInds] = fi(x[fInds], y[fInds])
elif isinstance(fi, (int, long, float)):
# fi is a numerical constant
quantityVal[fInds] = fi*1.0
elif ( type(fi) is str and os.path.exists(fi)):
# fi is a file which is assumed to be
# a gdal-compatible raster OR an x,y,z elevation file
if os.path.splitext(fi)[1] in ['.txt', '.csv']:
fi_array = su.read_csv_optional_header(fi)
# Check the results
if fi_array.shape[1] is not 3:
print 'Treated input file ' + fi +\
' as xyz array with an optional header'
msg = 'Array should have 3 columns -- x,y,value'
raise Exception(msg)
newfi = make_nearestNeighbour_quantity_function(
fi_array, domain,
k_nearest_neighbours = default_k_nearest_neighbours)
quantityVal[fInds] = newfi(x[fInds], y[fInds])
else:
# Treating input file as a raster
newfi = quantityRasterFun(domain, fi,
interpolation = default_raster_interpolation)
quantityVal[fInds] = newfi(x[fInds], y[fInds])
elif(type(fi) is numpy.ndarray):
if fi.shape[1] is not 3:
msg = 'Array should have 3 columns -- x,y,value'
raise Exception(msg)
newfi = make_nearestNeighbour_quantity_function(fi, domain,
k_nearest_neighbours = default_k_nearest_neighbours)
quantityVal[fInds] = newfi(x[fInds], y[fInds])
else:
print 'Error with function from'
print fi
msg='Cannot make function from type ' + str(type(fi))
raise Exception, msg
###################################################################
# Check for nan values
###################################################################
#nan_flag = (quantityVal[fInds] != quantityVal[fInds])
nan_flag = 1*numpy.isnan(quantityVal[fInds])
nan_inds = nan_flag.nonzero()[0]
was_ever_nan[fInds[nan_inds]] = 1
if len(nan_inds)>0:
if nan_treatment == 'exception':
msg = 'nan values generated by the poly_fun_pair at '\
'index ' + str(i) + ' '\
'in composite_quantity_setting_function. ' + \
'To allow these values to be set by later ' + \
'poly_fun pairs, pass the argument ' + \
'nan_treatment="fall_through" ' + \
'to composite_quantity_setting_function'
raise Exception(msg)
elif nan_treatment == 'fall_through':
msg = 'WARNING: nan values generated by the ' + \
'poly_fun_pair at index ' + str(i) + ' '\
'in composite_quantity_setting_function. ' + \
'They will be passed to later poly_fun_pairs'
if verbose: print msg
not_nan_inds = (1-nan_flag).nonzero()[0]
if len(not_nan_inds)>0:
fInds = fInds[not_nan_inds]
else:
# All values are nan
msg = '( Actually all the values were nan - ' + \
'Are you sure they should be? Possible error?)'
if verbose: print msg
continue
else:
msg = 'Found nan values in ' + \
'composite_quantity_setting_function but ' + \
'nan_treatment is not a recognized value'
raise Exception(msg)
# Record that the points have been set
isSet[fInds] = 1
# Enforce clip_range
if clip_range is not None:
lower_bound = clip_range[i][0]
upper_bound = clip_range[i][1]
quantityVal[fInds] = numpy.maximum(
quantityVal[fInds], lower_bound)
quantityVal[fInds] = numpy.minimum(
quantityVal[fInds], upper_bound)
# End of loop
# Find points which were nan on their first preference dataset + are
# inside nan_interpolation_region_polygon. Then reinterpolate their
# values from the other x,y, quantityVal points.
if (nan_interpolation_region_polygon is not None) &\
(was_ever_nan.sum() > 0):
if nan_interpolation_region_polygon == 'All':
points_to_reinterpolate = was_ever_nan.nonzero()[0]
else:
# nan_interpolation_region_polygon contains information on 1 or
# more polygons
# Inside those polygons, we need to re-interpolate points which
# first evaluted to na
possible_points_to_reint = was_ever_nan.nonzero()[0]
points_to_reinterpolate = numpy.array([]).astype(int)
for i in range(len(nan_interpolation_region_polygon)):
nan_pi = nan_interpolation_region_polygon[i]
# Ensure nan_pi = list of x,y points making a polygon
if(type(nan_pi) == str):
nan_pi = su.read_polygon(nan_pi)
points_in_nan_pi = inside_polygon(
xy_array_trans[possible_points_to_reint,:],
nan_pi)
if len(points_in_nan_pi)>0:
points_to_reinterpolate = numpy.hstack(
[points_to_reinterpolate,
possible_points_to_reint[points_in_nan_pi]])
if verbose:
print 'Re-interpolating ', len(points_to_reinterpolate),\
' points which were nan under their',\
' first-preference and are inside the',\
' nan_interpolation_region_polygon'
if len(points_to_reinterpolate) > 0:
msg = 'WARNING: nan interpolation is being applied. This ',\
'should be done in serial prior to distributing the ',\
'domain, as there is no parallel communication ',\
'implemented yet [so parallel results might depend on ',\
'the number of processes]'
if verbose:
print msg
# Find the interpolation points = points not needing reinterpolation
ip = x*0 + 1
ip[points_to_reinterpolate] = 0
number_of_ip = ip.sum()
ip = ip.nonzero()[0]
# Check that none of the ip points has an nan value
nan_ip = (quantityVal[ip] != quantityVal[ip]).nonzero()[0]
if len(nan_ip) > 0:
print 'There are ', len(nan_ip), ' points outside the ',\
'nan_interpolation_region_polygon have nan values.'
print 'The user should ensure this does not happen.'
print 'The points have the following coordinates:'
print xy_array_trans[ip[nan_ip],:]
msg = "There are nan points outside of " +\
"nan_interpolation_region_polygon, even after all " +\
"fall-through's"
raise Exception(msg)
if(number_of_ip < default_k_nearest_neighbours):
raise Exception('Too few non-nan points to interpolate from')
# Make function for re-interpolation. Note this requires
# x,y,z in georeferenced coordinates, whereas x,y are ANUGA
# coordinates
reinterp_F = make_nearestNeighbour_quantity_function(
numpy.vstack([xy_array_trans[ip,0], xy_array_trans[ip,1],
quantityVal[ip]]).transpose(),
domain,
k_nearest_neighbours = default_k_nearest_neighbours)
# re-interpolate
quantityVal[points_to_reinterpolate] = reinterp_F(
x[points_to_reinterpolate], y[points_to_reinterpolate])
isSet[points_to_reinterpolate] = 1
# Check there are no remaining nan values
if( min(isSet) != 1):
print 'Some points remain as nan, which is not allowed'
unset_inds = (isSet != 1).nonzero()[0]
lui = min(5, len(unset_inds))
print 'There are ', len(unset_inds), ' such points'
print 'Here are a few:'
for i in range(lui):
print x[unset_inds[i]] + xll, y[unset_inds[i]] + yll
raise Exception('It seems the input data needs to be fixed')
return quantityVal
def make_me_some_tifs(
sww_file,
bounding_polygon,
proj4string,
my_time_step='collected_max',
tif_output_subdir='/TIFS/',
cell_size=5.0,
k_nearest_neighbours=1,
make_highres_drape_plot=False,
elevation_raster=None,
depth_threshold=None,
clip_polygon=None,
clip_polygon_layer=None,
creation_options=['COMPRESS=DEFLATE'],
):
"""
### INPUT DATA
- swwFile -- Full path name of sww to read outputs from
- bounding_polygon -- ANUGA's bounding polygon, or another polygon to clip
the rasters to, [in ANUGA's polygon format].
- proj4string defining the coordinate system
- my_time_step -- option from util.Make_Geotif
use 'max' to plot the maxima
use [0, 5, 10] to plot the first, sixth and eleventh output time step
use 'collected_max' to read the csv outputs from
collect_max_quantities_operator
- tif_outputdir -- Write outputs to this folder inside the swwFile's
directory (MUST INCLUDE TRAILING SLASH /)
- cell_size -- Desired raster cellSize (m)
- k_nearest_neighbours -- use inverse distance weighted interpolation with this many neighbours
- make_highres_drape_plot -- True/False, Make a high-res drape plot?
- elevation_raster -- Filename of elevation raster for 'high-res-drape'
depth plot [from subtracting stage from high res topography]
- depth_threshold -- Depth threshold for high-res-drape' plot (m). If
ANUGA's triangle has depth
< depth_threshold, then depth=0 in all high-res cells in that triangle
- clipPolygon -- Polygon to clip 'high-res-drape' plot. Must be
provided if make_highres_drape_plot==True (can use bounding polygon or
another choice)
- clipPolygonLayer -- Layer for above as used by gdal (usually shapefile
name without .shp)
- creation_options -- list of gdal tif creation options
## OUTPUT
Nothing is returned, but tifs are made in tif_output_subdir inside the
swwFile directory
"""
# Convert utm_zone to proj4string
# p=Proj(proj='utm', south=(utm_zone<0.),
# zone=abs(utm_zone), ellps='WGS84')
# proj4string = p.srs
tif_output_dir = os.path.dirname(sww_file) + tif_output_subdir
# Make the geotifs
if my_time_step == 'collected_max':
# Read max quantity output files inputs
max_qfiles = glob.glob(os.path.dirname(sww_file) + '/*_UH_MAX.csv')
if len(max_qfiles) == 0:
raise Exception(
'Cannot find any files containing collected maxima')
for i in range(len(max_qfiles)):
if i == 0:
max_qs = numpy.genfromtxt(max_qfiles[i], delimiter=',')
else:
extra_data = numpy.genfromtxt(max_qfiles[i], delimiter=',')
max_qs = \
numpy.vstack([max_qs, extra_data])
# Make the geotiff's
for (i, quant) in enumerate(
['stage', 'depth', 'velocity', 'depthIntegratedVelocity']):
# FIXME: The interpolation function is remade for every quantity,
# since only a 3 column array can be passed to Make_Geotif Could
# make it fast (do it only once) by changing Make_Geotif
tmp_arr = max_qs[:, [0, 1, i + 2]]
util.Make_Geotif(
tmp_arr,
output_quantities=[quant + '_MAX'],
CellSize=cell_size,
proj4string=proj4string,
verbose=True,
bounding_polygon=bounding_polygon,
output_dir=tif_output_dir,
creation_options=creation_options,
k_nearest_neighbours=k_nearest_neighbours,
)
# Also plot elevation + friction
# Try to reduce memory demands by only extracting first timestep
fid = NetCDFFile(sww_file)
# Make xy coordinates (note -- max_quantity_collector outputs might
# have repeated x,y at parallel ghost cells)
x_v = fid.variables['x'][:] + fid.xllcorner
y_v = fid.variables['y'][:] + fid.yllcorner
vols = fid.variables['volumes'][:]
xc = (1. / 3.) * (x_v[vols[:, 0]] + x_v[vols[:, 1]] + x_v[vols[:, 2]])
yc = (1. / 3.) * (y_v[vols[:, 0]] + y_v[vols[:, 1]] + y_v[vols[:, 2]])
for (i, quant) in enumerate(['elevation_c', 'friction_c']):
# Get the quantity if it exists
if fid.variables.has_key(quant):
quant_values = fid.variables[quant]
# If multi time-steps, only get first timestep
if (len(quant_values.shape) > 1):
quant_values = quant_values[0, :]
else:
# Set quantity to nan if it is not stored
quant_values = xc * 0. + numpy.nan
tmp_arr = numpy.vstack([xc, yc, quant_values]).transpose()
util.Make_Geotif(
tmp_arr,
output_quantities=[quant + '_INITIAL'],
CellSize=cell_size,
proj4string=proj4string,
verbose=True,
bounding_polygon=bounding_polygon,
output_dir=tif_output_dir,
creation_options=creation_options,
k_nearest_neighbours=k_nearest_neighbours,
)
else:
util.Make_Geotif(
sww_file,
myTimeStep=my_time_step,
output_quantities=[
'depth',
'stage',
'elevation',
'velocity',
'depthIntegratedVelocity',
'friction',
],
CellSize=cell_size,
proj4string=proj4string,
verbose=True,
bounding_polygon=bounding_polygon,
output_dir=tif_output_dir,
creation_options=creation_options,
k_nearest_neighbours=k_nearest_neighbours,
)
# Early finish
if not make_highres_drape_plot:
return
# Get extent of geotifs
sample_rast = glob.glob(tif_output_dir + '*.tif')[0]
raster_extent = su.getRasterExtent(sample_rast)
# Make the resampled elevation data
make_resampled_elevation(
elevation_raster,
raster_extent,
cell_size,
clip_polygon,
clip_polygon_layer,
proj4string,
tif_output_dir,
)
elevation = glob.glob(tif_output_dir + 'LIDAR_resampled*')[0]
mask = glob.glob(tif_output_dir + 'Mask_resampled*')[0]
if my_time_step == 'collected_max':
depth = glob.glob(tif_output_dir + 'PointData_depth_*')[0]
stage = glob.glob(tif_output_dir + 'PointData_stage_*')[0]
else:
depth = glob.glob(tif_output_dir + '*_depth_max.tif')[0]
stage = glob.glob(tif_output_dir + '*_stage_max.tif')[0]
# Call gdal_calc
gdal_calc_command(stage, depth, elevation, mask, depth_threshold)
return
def make_me_some_tifs(
sww_file,
bounding_polygon,
proj4string,
my_time_step='collected_max',
tif_output_subdir='/TIFS/',
cell_size=5.0,
k_nearest_neighbours=1,
make_highres_drape_plot=False,
elevation_raster=None,
depth_threshold=None,
clip_polygon=None,
clip_polygon_layer=None,
creation_options=['COMPRESS=DEFLATE'],
):
"""
### INPUT DATA
- swwFile -- Full path name of sww to read outputs from
- bounding_polygon -- ANUGA's bounding polygon, or another polygon to clip
the rasters to, [in ANUGA's polygon format].
- proj4string defining the coordinate system
- my_time_step -- option from util.Make_Geotif
use 'max' to plot the maxima
use [0, 5, 10] to plot the first, sixth and eleventh output time step
use 'collected_max' to read the csv outputs from
collect_max_quantities_operator
- tif_outputdir -- Write outputs to this folder inside the swwFile's
directory (MUST INCLUDE TRAILING SLASH /)
- cell_size -- Desired raster cellSize (m)
- k_nearest_neighbours -- use inverse distance weighted interpolation with this many neighbours
- make_highres_drape_plot -- True/False, Make a high-res drape plot?
- elevation_raster -- Filename of elevation raster for 'high-res-drape'
depth plot [from subtracting stage from high res topography]
- depth_threshold -- Depth threshold for high-res-drape' plot (m). If
ANUGA's triangle has depth
< depth_threshold, then depth=0 in all high-res cells in that triangle
- clipPolygon -- Polygon to clip 'high-res-drape' plot. Must be
provided if make_highres_drape_plot==True (can use bounding polygon or
another choice)
- clipPolygonLayer -- Layer for above as used by gdal (usually shapefile
name without .shp)
- creation_options -- list of gdal tif creation options
## OUTPUT
Nothing is returned, but tifs are made in tif_output_subdir inside the
swwFile directory
"""
# Convert utm_zone to proj4string
# p=Proj(proj='utm', south=(utm_zone<0.),
# zone=abs(utm_zone), ellps='WGS84')
# proj4string = p.srs
tif_output_dir = os.path.dirname(sww_file) + tif_output_subdir
# Make the geotifs
if my_time_step == 'collected_max':
# Read max quantity output files inputs
max_qfiles = glob.glob(os.path.dirname(sww_file)
+ '/*_UH_MAX.csv')
if len(max_qfiles) == 0:
raise Exception(
'Cannot find any files containing collected maxima')
for i in range(len(max_qfiles)):
if i == 0:
max_qs = numpy.genfromtxt(max_qfiles[i], delimiter=',')
else:
extra_data = numpy.genfromtxt(max_qfiles[i], delimiter=',')
max_qs = \
numpy.vstack([max_qs, extra_data])
# Make the geotiff's
for (i, quant) in enumerate(['stage', 'depth', 'velocity',
'depthIntegratedVelocity']):
# FIXME: The interpolation function is remade for every quantity,
# since only a 3 column array can be passed to Make_Geotif Could
# make it fast (do it only once) by changing Make_Geotif
tmp_arr = max_qs[:, [0, 1, i + 2]]
util.Make_Geotif(
tmp_arr,
output_quantities=[quant + '_MAX'],
CellSize=cell_size,
proj4string=proj4string,
verbose=True,
bounding_polygon=bounding_polygon,
output_dir=tif_output_dir,
creation_options=creation_options,
k_nearest_neighbours=k_nearest_neighbours,
)
# Also plot elevation + friction
# Try to reduce memory demands by only extracting first timestep
fid = NetCDFFile(sww_file)
# Make xy coordinates (note -- max_quantity_collector outputs might
# have repeated x,y at parallel ghost cells)
x_v = fid.variables['x'][:] + fid.xllcorner
y_v = fid.variables['y'][:] + fid.yllcorner
vols = fid.variables['volumes'][:]
xc = (1. / 3.) * (x_v[vols[:, 0]] + x_v[vols[:, 1]] + x_v[vols[:, 2]])
yc = (1. / 3.) * (y_v[vols[:, 0]] + y_v[vols[:, 1]] + y_v[vols[:, 2]])
for (i, quant) in enumerate(['elevation_c', 'friction_c']):
# Get the quantity if it exists
if fid.variables.has_key(quant):
quant_values = fid.variables[quant]
# If multi time-steps, only get first timestep
if(len(quant_values.shape) > 1):
quant_values = quant_values[0, :]
else:
# Set quantity to nan if it is not stored
quant_values = xc * 0. + numpy.nan
tmp_arr = numpy.vstack([xc, yc, quant_values]).transpose()
util.Make_Geotif(
tmp_arr,
output_quantities=[quant + '_INITIAL'],
CellSize=cell_size,
proj4string=proj4string,
verbose=True,
bounding_polygon=bounding_polygon,
output_dir=tif_output_dir,
creation_options=creation_options,
k_nearest_neighbours=k_nearest_neighbours,
)
else:
util.Make_Geotif(
sww_file,
myTimeStep=my_time_step,
output_quantities=[
'depth',
'stage',
'elevation',
'velocity',
'depthIntegratedVelocity',
'friction',
],
CellSize=cell_size,
proj4string=proj4string,
verbose=True,
bounding_polygon=bounding_polygon,
output_dir=tif_output_dir,
creation_options=creation_options,
k_nearest_neighbours=k_nearest_neighbours,
)
# Early finish
if not make_highres_drape_plot:
return
# Get extent of geotifs
sample_rast = glob.glob(tif_output_dir + '*.tif')[0]
raster_extent = su.getRasterExtent(sample_rast)
# Make the resampled elevation data
make_resampled_elevation(
elevation_raster,
raster_extent,
cell_size,
clip_polygon,
clip_polygon_layer,
proj4string,
tif_output_dir,
)
elevation = glob.glob(tif_output_dir + 'LIDAR_resampled*')[0]
mask = glob.glob(tif_output_dir + 'Mask_resampled*')[0]
if my_time_step == 'collected_max':
depth = glob.glob(tif_output_dir + 'PointData_depth_*')[0]
stage = glob.glob(tif_output_dir + 'PointData_stage_*')[0]
else:
depth = glob.glob(tif_output_dir + '*_depth_max.tif')[0]
stage = glob.glob(tif_output_dir + '*_stage_max.tif')[0]
# Call gdal_calc
gdal_calc_command(stage, depth, elevation, mask, depth_threshold)
return
