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"""
Creates a shapefile with a given root name using data from given
X, Y, and value arrays (curvilinear-type data). The shapes are
quadrilaterals in the X,Y-plane derived from the X and Y arrays;
i.e., vertices are the (i,j), (i,j+1), (i+1,j+1), and (i+1,j)
elements of the X and Y coordinates. The value associated with
each quadrilateral comes from the value array; i.e., the (i,j)
element of the value for the previously mentioned quadrilateral.
Quadrilaterals associated with missing values are omitted from
the shapefile.
"""
from __future__ import print_function
from past.builtins import xrange
import shapefile
import pyferret
import pyferret.fershp
def ferret_init(efid):
"""
Initialization for the shapefile_writexyval PyEF
"""
retdict = { "numargs": 6,
"descript": "Writes a shapefile of XY quadrilaterals from the curvilinear data arrays.",
"restype": pyferret.FLOAT_ARRAY,
"axes": ( pyferret.AXIS_ABSTRACT,
pyferret.AXIS_DOES_NOT_EXIST,
pyferret.AXIS_DOES_NOT_EXIST,
pyferret.AXIS_DOES_NOT_EXIST,
pyferret.AXIS_DOES_NOT_EXIST,
pyferret.AXIS_DOES_NOT_EXIST, ),
"argnames": ( "SHAPEFILE", "GRIDX", "GRIDY", "VALUE", "VALNAME", "MAPPRJ"),
"argdescripts": ( "Shapefile name (any extension given is ignored)",
"X values (longitudes) for the quad. grid; must be 2D on X and Y axes",
"Y values (latitudes) for the quad. grid; must be 2D on X and Y axes",
"Shape values; must be 2D on X and Y axes",
"Name for the shape value",
"Common name or WKT description of map projection; " \
"if blank, WGS 84 is used", ),
"argtypes": ( pyferret.STRING_ONEVAL,
pyferret.FLOAT_ARRAY,
pyferret.FLOAT_ARRAY,
pyferret.FLOAT_ARRAY,
pyferret.STRING_ONEVAL,
pyferret.STRING_ONEVAL, ),
"influences": ( (False, False, False, False, False, False),
(False, False, False, False, False, False),
(False, False, False, False, False, False),
(False, False, False, False, False, False),
(False, False, False, False, False, False),
(False, False, False, False, False, False), ),
}
return retdict
def ferret_result_limits(efid):
"""
Abstract axis limits for the shapefile_writexyval PyEF
"""
return ( (1, 1), None, None, None, None, None, )
def ferret_compute(efid, result, resbdf, inputs, inpbdfs):
"""
Create the shapefile named in inputs[0] using the grid X coordinates given
in inputs[1], grid Y coordinates given in inputs[2], and shape values given
in inputs[3]. The X,Y coordinates are used for the quadrilaterals vertices
and must have an additional value along each dimension. The value [i,j]
is used for the quadrilateral with diagonal corners [i, j] and [i+1, j+1].
Quadrilateral associated with missing values are omitted from the shapefile.
The field name for the value in the shapefile given in inputs[4]. Either a
common name or a WKT description of the map projection for the coordinates
should be given in inputs[5]. If blank, WGS 84 is used. If successful,
fills result (which might as well be a 1x1x1x1 array) with zeros. If a
problem occurs, an error will be raised.
"""
shapefile_name = inputs[0]
grid_xs = inputs[1]
grid_ys = inputs[2]
grid_vals = inputs[3]
missing_val = inpbdfs[3]
field_name = inputs[4].strip()
if not field_name:
field_name = "VALUE"
map_projection = inputs[5]
# Verify the shapes are as expected
if (grid_vals.shape[2] != 1) or (grid_vals.shape[3] != 1) or \
(grid_vals.shape[4] != 1) or (grid_vals.shape[5] != 1):
raise ValueError("The Z, T, E, and F axes of VALUE must be undefined or singleton axes")
exp_shape = ( grid_vals.shape[0] + 1, grid_vals.shape[1] + 1, 1, 1, 1, 1 )
if (grid_xs.shape != exp_shape) or (grid_ys.shape != exp_shape):
raise ValueError('GRIDX and GRIDY must have one more value along both X and Y axes compared to VALUE')
# Create polygons with a single field value
sfwriter = shapefile.Writer(shapefile.POLYGON)
sfwriter.field(field_name, "N", 20, 7)
# Add the shapes with their values
shape_written = False
for j in xrange(grid_vals.shape[1]):
for i in xrange(grid_vals.shape[0]):
if grid_vals[i, j, 0, 0, 0, 0] != missing_val:
shape_written = True
pyferret.fershp.addquadxyvalues(sfwriter,
(grid_xs[i, j, 0, 0, 0, 0], grid_ys[i, j, 0, 0, 0, 0]),
(grid_xs[i, j+1, 0, 0, 0, 0], grid_ys[i, j+1, 0, 0, 0, 0]),
(grid_xs[i+1, j+1, 0, 0, 0, 0], grid_ys[i+1, j+1, 0, 0, 0, 0]),
(grid_xs[i+1, j, 0, 0, 0, 0], grid_ys[i+1, j, 0, 0, 0, 0]),
None, [ float(grid_vals[i, j, 0, 0, 0, 0]) ])
if not shape_written:
raise ValueError("All values are missing values")
sfwriter.save(shapefile_name)
# Create the .prj file from the map projection common name or the WKT description
pyferret.fershp.createprjfile(map_projection, shapefile_name)
result[:, :, :, :, :, :] = 0
#
# The following is only for testing this module from the command line
#
if __name__ == "__main__":
import numpy
import os
shapefilename = "tripolar"
fieldname = "AREA"
wgs84_descript = 'GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433]]'
# Real world longitudes and latitudes of tripolar coordinates X=80W:60E:10 + 100E:120W:10,Y=45N:90N:5
geolon_c = numpy.array([
[ -100.0,-100.0,-100.0,-100.0,-100.0,-100.0,-100.0,-100.0, 80.0, 80.0,
80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0,
80.0, 80.0, 260.0, 260.0, 260.0, 260.0, 260.0, 260.0, 260.0, 260.0, ],
[ -92.1, -87.7, -82.4, -75.7, -66.7, -53.8, -34.9, -10.0, 14.9, 33.8,
46.7, 55.7, 62.4, 67.7, 72.1, 87.9, 92.3, 97.6, 104.3, 113.3,
126.2, 145.1, 170.0, 194.9, 213.8, 226.7, 235.7, 242.4, 247.7, 252.1, ],
[ -86.0, -78.5, -70.2, -60.9, -50.2, -38.1, -24.5, -10.0, 4.5, 18.1,
30.2, 40.9, 50.2, 58.5, 66.0, 94.0, 101.5, 109.8, 119.1, 129.8,
141.9, 155.5, 170.0, 184.5, 198.1, 210.2, 220.9, 230.2, 238.5, 246.0, ],
[ -82.3, -73.1, -63.6, -53.7, -43.3, -32.5, -21.4, -10.0, 1.4, 12.5,
23.3, 33.7, 43.6, 53.1, 62.3, 97.7, 106.9, 116.4, 126.3, 136.7,
147.5, 158.6, 170.0, 181.4, 192.5, 203.3, 213.7, 223.6, 233.1, 242.3, ],
[ -80.5, -70.6, -60.7, -50.7, -40.6, -30.5, -20.3, -10.0, 0.3, 10.5,
20.6, 30.7, 40.7, 50.6, 60.5, 99.5, 109.4, 119.3, 129.3, 139.4,
149.5, 159.7, 170.0, 180.3, 190.5, 200.6, 210.7, 220.7, 230.6, 240.5, ],
[ -80.0, -70.0, -60.0, -50.0, -40.0, -30.0, -20.0, -10.0, 0.0, 10.0,
20.0, 30.0, 40.0, 50.0, 60.0, 100.0, 110.0, 120.0, 130.0, 140.0,
150.0, 160.0, 170.0, 180.0, 190.0, 200.0, 210.0, 220.0, 230.0, 240.0, ],
[ -80.0, -70.0, -60.0, -50.0, -40.0, -30.0, -20.0, -10.0, 0.0, 10.0,
20.0, 30.0, 40.0, 50.0, 60.0, 100.0, 110.0, 120.0, 130.0, 140.0,
150.0, 160.0, 170.0, 180.0, 190.0, 200.0, 210.0, 220.0, 230.0, 240.0, ],
[ -80.0, -70.0, -60.0, -50.0, -40.0, -30.0, -20.0, -10.0, 0.0, 10.0,
20.0, 30.0, 40.0, 50.0, 60.0, 100.0, 110.0, 120.0, 130.0, 140.0,
150.0, 160.0, 170.0, 180.0, 190.0, 200.0, 210.0, 220.0, 230.0, 240.0, ],
[ -80.0, -70.0, -60.0, -50.0, -40.0, -30.0, -20.0, -10.0, 0.0, 10.0,
20.0, 30.0, 40.0, 50.0, 60.0, 100.0, 110.0, 120.0, 130.0, 140.0,
150.0, 160.0, 170.0, 180.0, 190.0, 200.0, 210.0, 220.0, 230.0, 240.0, ],
[ -80.0, -70.0, -60.0, -50.0, -40.0, -30.0, -20.0, -10.0, 0.0, 10.0,
20.0, 30.0, 40.0, 50.0, 60.0, 100.0, 110.0, 120.0, 130.0, 140.0,
150.0, 160.0, 170.0, 180.0, 190.0, 200.0, 210.0, 220.0, 230.0, 240.0, ],
], dtype=numpy.float64)
geolon_c = geolon_c.T[:, :, numpy.newaxis, numpy.newaxis, numpy.newaxis, numpy.newaxis]
geolat_c = numpy.array([
[ 72.35, 75.41, 78.20, 80.77, 83.20, 85.52, 87.78, 90.00, 87.78, 85.52,
83.20, 80.77, 78.20, 75.41, 72.35, 72.35, 75.41, 78.20, 80.77, 83.20,
85.52, 87.78, 90.00, 87.78, 85.52, 83.20, 80.77, 78.20, 75.41, 72.35, ],
[ 71.85, 74.69, 77.25, 79.54, 81.58, 83.30, 84.53, 85.00, 84.53, 83.30,
81.58, 79.54, 77.25, 74.69, 71.85, 71.85, 74.69, 77.25, 79.54, 81.58,
83.30, 84.53, 85.00, 84.53, 83.30, 81.58, 79.54, 77.25, 74.69, 71.85, ],
[ 70.51, 72.81, 74.83, 76.56, 77.99, 79.08, 79.76, 80.00, 79.76, 79.08,
77.99, 76.56, 74.83, 72.81, 70.51, 70.51, 72.81, 74.83, 76.56, 77.99,
79.08, 79.76, 80.00, 79.76, 79.08, 77.99, 76.56, 74.83, 72.81, 70.51, ],
[ 68.71, 70.29, 71.67, 72.83, 73.76, 74.44, 74.86, 75.00, 74.86, 74.44,
73.76, 72.83, 71.67, 70.29, 68.71, 68.71, 70.29, 71.67, 72.83, 73.76,
74.44, 74.86, 75.00, 74.86, 74.44, 73.76, 72.83, 71.67, 70.29, 68.71, ],
[ 66.80, 67.60, 68.30, 68.90, 69.37, 69.72, 69.93, 70.00, 69.93, 69.72,
69.37, 68.90, 68.30, 67.60, 66.80, 66.80, 67.60, 68.30, 68.90, 69.37,
69.72, 69.93, 70.00, 69.93, 69.72, 69.37, 68.90, 68.30, 67.60, 66.80, ],
[ 65.00, 65.00, 65.00, 65.00, 65.00, 65.00, 65.00, 65.00, 65.00, 65.00,
65.00, 65.00, 65.00, 65.00, 65.00, 65.00, 65.00, 65.00, 65.00, 65.00,
65.00, 65.00, 65.00, 65.00, 65.00, 65.00, 65.00, 65.00, 65.00, 65.00, ],
[ 60.00, 60.00, 60.00, 60.00, 60.00, 60.00, 60.00, 60.00, 60.00, 60.00,
60.00, 60.00, 60.00, 60.00, 60.00, 60.00, 60.00, 60.00, 60.00, 60.00,
60.00, 60.00, 60.00, 60.00, 60.00, 60.00, 60.00, 60.00, 60.00, 60.00, ],
[ 55.00, 55.00, 55.00, 55.00, 55.00, 55.00, 55.00, 55.00, 55.00, 55.00,
55.00, 55.00, 55.00, 55.00, 55.00, 55.00, 55.00, 55.00, 55.00, 55.00,
55.00, 55.00, 55.00, 55.00, 55.00, 55.00, 55.00, 55.00, 55.00, 55.00, ],
[ 50.00, 50.00, 50.00, 50.00, 50.00, 50.00, 50.00, 50.00, 50.00, 50.00,
50.00, 50.00, 50.00, 50.00, 50.00, 50.00, 50.00, 50.00, 50.00, 50.00,
50.00, 50.00, 50.00, 50.00, 50.00, 50.00, 50.00, 50.00, 50.00, 50.00, ],
[ 45.00, 45.00, 45.00, 45.00, 45.00, 45.00, 45.00, 45.00, 45.00, 45.00,
45.00, 45.00, 45.00, 45.00, 45.00, 45.00, 45.00, 45.00, 45.00, 45.00,
45.00, 45.00, 45.00, 45.00, 45.00, 45.00, 45.00, 45.00, 45.00, 45.00, ],
], dtype=numpy.float64)
geolat_c = geolat_c.T[:, :, numpy.newaxis, numpy.newaxis, numpy.newaxis, numpy.newaxis]
# Make the value an approximate sphere surface area (in square degrees) of the quadrilateral
vals = geolon_c[:-1, :-1] * geolat_c[:-1, 1:]
vals -= geolon_c[:-1, 1:] * geolat_c[:-1, :-1]
vals += geolon_c[:-1, 1:] * geolat_c[ 1:, 1:]
vals -= geolon_c[ 1:, 1:] * geolat_c[:-1, 1:]
vals += geolon_c[ 1:, 1:] * geolat_c[ 1:, :-1]
vals -= geolon_c[ 1:, :-1] * geolat_c[ 1:, 1:]
vals += geolon_c[ 1:, :-1] * geolat_c[:-1, :-1]
vals -= geolon_c[:-1, :-1] * geolat_c[ 1:, :-1]
vals = 0.5 * numpy.fabs(vals)
vals *= numpy.cos( 0.25 * numpy.deg2rad(geolat_c[:-1, :-1] + \
geolat_c[:-1, 1:] + \
geolat_c[ 1:, 1:] + \
geolat_c[ 1:, :-1]) )
# Assign the value of the rectangles between 60E and 100E with the missing value
resbdf = numpy.array([-99999.0], dtype=numpy.float64)
inpbdfs = numpy.array([-88888.0, -77777.0, -66666.0, -55555.0, -44444.0, -33333.0], dtype=numpy.float64)
vals[14,:,0,0] = inpbdfs[3]
# Make sure these calls do not generate errors
info = ferret_init(0)
del info
limits = ferret_result_limits(0)
del limits
# Create the shapefile
result = numpy.ones((1,1,1,1,1,1), dtype=numpy.float64)
ferret_compute(0, result, resbdf, (shapefilename, geolon_c, geolat_c, vals, fieldname, ""), inpbdfs)
# Read the shapefile back in and check
sfreader = shapefile.Reader(shapefilename)
shapes = sfreader.shapes()
records = sfreader.records()
explen = (vals.shape[0] - 1) * vals.shape[1]
if len(shapes) != explen:
raise ValueError("Expected %d shapes; found %d" % (explen, len(shapes)))
if len(records) != explen:
raise ValueError("Expected %d records; found %d" % (explen, len(records)))
# Create the expected arrays of shape coordinates and values
exppoints = []
expvals = []
for j in xrange(vals.shape[1]):
for i in xrange(vals.shape[0]):
if vals[i, j, 0, 0, 0, 0] != inpbdfs[3]:
exppoints.append( numpy.array([ [ geolon_c[i, j, 0, 0, 0, 0],
geolat_c[i, j, 0, 0, 0, 0] ],
[ geolon_c[i+1, j, 0, 0, 0, 0],
geolat_c[i+1, j, 0, 0, 0, 0] ],
[ geolon_c[i+1, j+1, 0, 0, 0, 0],
geolat_c[i+1, j+1, 0, 0, 0, 0] ],
[ geolon_c[i, j+1, 0, 0, 0, 0],
geolat_c[i, j+1, 0, 0, 0, 0] ],
[ geolon_c[i, j, 0, 0, 0, 0],
geolat_c[i, j, 0, 0, 0, 0] ] ]) )
expvals.append(vals[i, j, 0, 0, 0, 0])
# Verify these arrays - does not depend on the same ordering of the shapes
for (shape, record) in zip(shapes, records):
for k in range(len(exppoints)):
if numpy.allclose(shape.points, exppoints[k], rtol=1.0E-4):
break
else:
raise ValueError("Unexpected vertices %s" % str(shape.points))
if not numpy.allclose(record, expvals[k], rtol=1.0E-4):
raise ValueError("Expected value %s; found %s for shape.points %s" % \
(str(expvals[k]), str(record), str(shape.points)))
junk = exppoints.pop(k)
junk = expvals.pop(k)
# Verify the projection file
prjfile = file("%s.prj" % shapefilename, "r")
datalines = prjfile.readlines()
prjfile.close()
if len(datalines) != 1:
raise ValueError("Number of lines in the .prj file: expected: 1, found %d" % len(datalines))
descript = datalines[0].strip()
if descript != wgs84_descript:
raise ValueError("Description in the .prj file:\n" \
" expect: %s\n" \
" found: %s" % (wgs84_descript, descript))
# Shapefile data files no longer needed
os.remove("%s.dbf" % shapefilename)
os.remove("%s.shp" % shapefilename)
os.remove("%s.shx" % shapefilename)
os.remove("%s.prj" % shapefilename)
# Commented-out code used for further testing
testcode = """
sortedvals = numpy.sort(vals[ vals != inpbdfs[3] ])
numvals = sortedvals.shape[0]
limits = [ sortedvals[1 * numvals // 5],
sortedvals[2 * numvals // 5],
sortedvals[3 * numvals // 5],
sortedvals[4 * numvals // 5], ]
print (str( [ sortedvals[0] ] + limits + [ sortedvals[-1] ] ))
partvals = vals.copy()
partvals[ partvals >= limits[0] ] = inpbdfs[3]
ferret_compute(0, result, resbdf, (shapefilename + "_1",
geolon_c, geolat_c, partvals,
fieldname, ""), inpbdfs)
partvals = vals.copy()
partvals[ partvals < limits[0] ] = inpbdfs[3]
partvals[ partvals >= limits[1] ] = inpbdfs[3]
ferret_compute(0, result, resbdf, (shapefilename + "_2",
geolon_c, geolat_c, partvals,
fieldname, ""), inpbdfs)
partvals = vals.copy()
partvals[ partvals < limits[1] ] = inpbdfs[3]
partvals[ partvals >= limits[2] ] = inpbdfs[3]
ferret_compute(0, result, resbdf, (shapefilename + "_3",
geolon_c, geolat_c, partvals,
fieldname, ""), inpbdfs)
partvals = vals.copy()
partvals[ partvals < limits[2] ] = inpbdfs[3]
partvals[ partvals >= limits[3] ] = inpbdfs[3]
ferret_compute(0, result, resbdf, (shapefilename + "_4",
geolon_c, geolat_c, partvals,
fieldname, ""), inpbdfs)
partvals = vals.copy()
partvals[ partvals < limits[3] ] = inpbdfs[3]
ferret_compute(0, result, resbdf, (shapefilename + "_5",
geolon_c, geolat_c, partvals,
fieldname, ""), inpbdfs)
"""
print ("shapefile_writexyval: SUCCESS")
|
