def __init__(self, filename):
self.id = uuid.uuid1()
self.filename = filename
self._reader = vtk.vtkNetCDFCFReader() #get test data
self._reader.SphericalCoordinatesOff()
self._reader.SetOutputTypeToImage()
self._reader.ReplaceFillValueWithNanOn()
self._reader.SetFileName(filename)
self._reader.UpdateInformation()
#!/usr/bin/env python
import vtk
from vtk.test import Testing
from vtk.util.misc import vtkGetDataRoot
VTK_DATA_ROOT = vtkGetDataRoot()
# This test checks netCDF reader. It uses the COARDS convention.
renWin = vtk.vtkRenderWindow()
renWin.SetSize(400, 400)
#############################################################################
# Case 1: Image type.
# Open the file.
reader_image = vtk.vtkNetCDFCFReader()
reader_image.SetFileName("" + str(VTK_DATA_ROOT) + "/Data/tos_O1_2001-2002.nc")
reader_image.SetOutputTypeToImage()
# Set the arrays we want to load.
reader_image.UpdateMetaData()
reader_image.SetVariableArrayStatus("tos", 1)
reader_image.SphericalCoordinatesOff()
aa_image = vtk.vtkAssignAttribute()
aa_image.SetInputConnection(reader_image.GetOutputPort())
aa_image.Assign("tos", "SCALARS", "POINT_DATA")
thresh_image = vtk.vtkThreshold()
thresh_image.SetInputConnection(aa_image.GetOutputPort())
thresh_image.ThresholdByLower(10000)
surface_image = vtk.vtkDataSetSurfaceFilter()
surface_image.SetInputConnection(thresh_image.GetOutputPort())
mapper_image = vtk.vtkPolyDataMapper()
mapper_image.SetInputConnection(surface_image.GetOutputPort())
mapper_image.SetScalarRange(270, 310)
actor_image = vtk.vtkActor()
import vtk
vtk.vtkObject.GlobalWarningDisplayOff() # Disable the VTK warning window
'''<Reader>'''
reader = vtk.vtkNetCDFCFReader()
reader.SetFileName('files/etopo11.nc')
reader.SetSphericalCoordinates(0)
reader.Update()
varName = reader.GetOutput().GetPointData().GetArrayName(0)
rangeStart, rangeEnd = reader.GetOutput().GetPointData().GetArray(0).GetRange()
'''<Filter>'''
reader.GetOutput().GetPointData().SetActiveScalars(varName) # 'Active' Scalar 설정
warp = vtk.vtkWarpScalar()
warp.SetInputConnection(reader.GetOutputPort())
warp.SetNormal(0, 0, 1)
warp.SetScaleFactor(0.0002)
'''<Mapper>'''
mapper = vtk.vtkDataSetMapper()
mapper.SetInputConnection(warp.GetOutputPort())
mapper.SetScalarRange(rangeStart, rangeEnd)
'''<Actor>'''
actor = vtk.vtkActor()
actor.SetMapper(mapper)
'''<Renderer>'''
renderer = vtk.vtkRenderer()
renderer.AddActor(actor)
renderer.SetBackground(1, 1, 1)
#!/usr/bin/env python
import vtk
from vtk.test import Testing
from vtk.util.misc import vtkGetDataRoot
VTK_DATA_ROOT = vtkGetDataRoot()
# This test checks netCDF CF reader for reading unstructured (p-sided) cells.
renWin = vtk.vtkRenderWindow()
renWin.SetSize(400,200)
#############################################################################
# Case 1: Spherical coordinates off.
# Open the file.
reader_cartesian = vtk.vtkNetCDFCFReader()
reader_cartesian.SetFileName("" + str(VTK_DATA_ROOT) + "/Data/sampleGenGrid3.nc")
# Set the arrays we want to load.
reader_cartesian.UpdateMetaData()
reader_cartesian.SetVariableArrayStatus("sample",1)
reader_cartesian.SphericalCoordinatesOff()
# Assign the field to scalars.
aa_cartesian = vtk.vtkAssignAttribute()
aa_cartesian.SetInputConnection(reader_cartesian.GetOutputPort())
aa_cartesian.Assign("sample","SCALARS","CELL_DATA")
# Extract a surface that we can render.
surface_cartesian = vtk.vtkDataSetSurfaceFilter()
surface_cartesian.SetInputConnection(aa_cartesian.GetOutputPort())
mapper_cartesian = vtk.vtkPolyDataMapper()
mapper_cartesian.SetInputConnection(surface_cartesian.GetOutputPort())
mapper_cartesian.SetScalarRange(100,2500)
actor_cartesian = vtk.vtkActor()
actor_cartesian.SetMapper(mapper_cartesian)
ren_cartesian = vtk.vtkRenderer()
def serveVTKGeoJSON(self, datasetString):
'''
Deliver a geojson encoded serialized vtkpolydata file and render it
over the canonical cpipe scene.
'''
if vtkOK == False:
return """<html><head></head><body>VTK python bindings are not loadable, be sure VTK is installed on the server and its PATHS are set appropriately.</body><html>"""
ss = vtk.vtkNetCDFCFReader() #get test data
ss.SphericalCoordinatesOff()
ss.SetOutputTypeToImage()
datadir = cherrypy.request.app.config['/data']['tools.staticdir.dir']
datadir = os.path.join(datadir, 'assets')
datafile = os.path.join(datadir, 'clt.nc')
ss.SetFileName(datafile)
sf = vtk.vtkDataSetSurfaceFilter() #convert to polydata
sf.SetInputConnection(ss.GetOutputPort())
cf = vtk.vtkContourFilter() #add some attributes
cf.SetInputConnection(sf.GetOutputPort())
cf.SetInputArrayToProcess(0,0,0,"vtkDataObject::FIELD_ASSOCIATION_POINTS", "clt")
cf.SetNumberOfContours(10)
sf.Update()
drange = sf.GetOutput().GetPointData().GetArray(0).GetRange()
for x in range(0,10):
cf.SetValue(x,x*0.1*(drange[1]-drange[0])+drange[0])
cf.ComputeScalarsOn()
ef = vtk.vtkExtractEdges() #make lines to test
ef.SetInputConnection(sf.GetOutputPort())
gf = vtk.vtkGlyph3D() #make verts to test
pts = vtk.vtkPoints()
pts.InsertNextPoint(0,0,0)
verts = vtk.vtkCellArray()
avert = vtk.vtkVertex()
avert.GetPointIds().SetId(0, 0)
verts.InsertNextCell(avert)
onevertglyph = vtk.vtkPolyData()
onevertglyph.SetPoints(pts)
onevertglyph.SetVerts(verts)
gf.SetSourceData(onevertglyph)
gf.SetInputConnection(sf.GetOutputPort())
if datasetString == "points":
toshow = gf
elif datasetString == "lines":
toshow = ef
elif datasetString == "contour":
toshow = cf
else:
toshow = sf
gw = vtk.vtkGeoJSONWriter()
gw.SetInputConnection(toshow.GetOutputPort())
gw.SetScalarFormat(2);
if True:
gw.SetFileName("/Source/CPIPES/buildogs/deploy/dataset.gj")
gw.Write()
f = file("/Source/CPIPES/buildogs/deploy/dataset.gj")
gj = str(f.readlines())
else:
gw.WriteToOutputStringOn()
gw.Write()
gj = "['"+str(gw.RegisterAndGetOutputString()).replace('\n','')+"']"
res = ("""
<html>
<head>
<script type="text/javascript" src="/common/js/gl-matrix.js"></script>
<script type="text/javascript" src="/lib/geoweb.min.js"></script>
<script type="text/javascript" src="//ajax.googleapis.com/ajax/libs/jquery/1.9.0/jquery.min.js"></script>
<script type="text/javascript">
function makedata() {
var datasetString = %(gjfile)s.join('\\n');
var data = new ogs.vgl.geojsonReader().getPrimitives(datasetString);
var geoms = data.geoms;
var mapper = new ogs.vgl.mapper();
mapper.setGeometryData(geoms[0]);
""" +
self.gjShader +
"""
var actor = new ogs.vgl.actor();
actor.setMapper(mapper);
actor.setMaterial(mat);
return actor;
}
</script>
<script type="text/javascript">
function main() {
var mapOptions = {
zoom : 1,
center : ogs.geo.latlng(0.0, 0.0),
source : '/data/assets/land_shallow_topo_2048.png'
};
var myMap = ogs.geo.map(document.getElementById("glcanvas"), mapOptions);
var planeLayer = ogs.geo.featureLayer({
"opacity" : 1,
"showAttribution" : 1,
"visible" : 1
},
makedata()
);
myMap.addLayer(planeLayer);
}
</script>
<link rel="stylesheet" href="http://code.jquery.com/ui/1.10.1/themes/base/jquery-ui.css" />
<script src="http://code.jquery.com/jquery-1.9.1.js"></script>
<script src="http://code.jquery.com/ui/1.10.1/jquery-ui.js"></script>
</head>
<body onload="main()">
<canvas id="glcanvas" width="800" height="600"></canvas>
</body>
</html>
""") % {'gjfile' :gj}
return res
#!/usr/bin/env python
import vtk
from vtk.util.misc import vtkGetDataRoot
VTK_DATA_ROOT = vtkGetDataRoot()
# This test checks netCDF reader. It uses the COARDS convention.
# Open the file.
reader = vtk.vtkNetCDFCFReader()
reader.SetFileName("" + str(VTK_DATA_ROOT) + "/Data/tos_O1_2001-2002.nc")
# Set the arrays we want to load.
reader.UpdateMetaData()
reader.SetVariableArrayStatus("tos",1)
reader.SetSphericalCoordinates(0)
aa = vtk.vtkAssignAttribute()
aa.SetInputConnection(reader.GetOutputPort())
aa.Assign("tos","SCALARS","POINT_DATA")
thresh = vtk.vtkThreshold()
thresh.SetInputConnection(aa.GetOutputPort())
thresh.ThresholdByLower(10000)
surface = vtk.vtkDataSetSurfaceFilter()
surface.SetInputConnection(thresh.GetOutputPort())
mapper = vtk.vtkPolyDataMapper()
mapper.SetInputConnection(surface.GetOutputPort())
mapper.SetScalarRange(270,310)
actor = vtk.vtkActor()
actor.SetMapper(mapper)
ren = vtk.vtkRenderer()
ren.AddActor(actor)
renWin = vtk.vtkRenderWindow()
renWin.SetSize(200,200)
renWin.AddRenderer(ren)
#!/usr/bin/env python
import vtk
from vtk.test import Testing
from vtk.util.misc import vtkGetDataRoot
VTK_DATA_ROOT = vtkGetDataRoot()
# This test checks netCDF CF reader for reading unstructured (p-sided) cells.
renWin = vtk.vtkRenderWindow()
renWin.SetSize(400, 200)
#############################################################################
# Case 1: Spherical coordinates off.
# Open the file.
reader_cartesian = vtk.vtkNetCDFCFReader()
reader_cartesian.SetFileName("" + str(VTK_DATA_ROOT) +
"/Data/sampleGenGrid3.nc")
# Set the arrays we want to load.
reader_cartesian.UpdateMetaData()
reader_cartesian.SetVariableArrayStatus("sample", 1)
reader_cartesian.SphericalCoordinatesOff()
# Assign the field to scalars.
aa_cartesian = vtk.vtkAssignAttribute()
aa_cartesian.SetInputConnection(reader_cartesian.GetOutputPort())
aa_cartesian.Assign("sample", "SCALARS", "CELL_DATA")
# Extract a surface that we can render.
surface_cartesian = vtk.vtkDataSetSurfaceFilter()
surface_cartesian.SetInputConnection(aa_cartesian.GetOutputPort())
mapper_cartesian = vtk.vtkPolyDataMapper()
mapper_cartesian.SetInputConnection(surface_cartesian.GetOutputPort())
mapper_cartesian.SetScalarRange(100, 2500)
actor_cartesian = vtk.vtkActor()
actor_cartesian.SetMapper(mapper_cartesian)
#!/usr/bin/env python
import vtk
from vtk.test import Testing
from vtk.util.misc import vtkGetDataRoot
VTK_DATA_ROOT = vtkGetDataRoot()
# This test checks netCDF reader. It uses the COARDS convention.
renWin = vtk.vtkRenderWindow()
renWin.SetSize(400,400)
#############################################################################
# Case 1: Image type.
# Open the file.
reader_image = vtk.vtkNetCDFCFReader()
reader_image.SetFileName("" + str(VTK_DATA_ROOT) + "/Data/tos_O1_2001-2002.nc")
reader_image.SetOutputTypeToImage()
# Set the arrays we want to load.
reader_image.UpdateMetaData()
reader_image.SetVariableArrayStatus("tos",1)
reader_image.SphericalCoordinatesOff()
aa_image = vtk.vtkAssignAttribute()
aa_image.SetInputConnection(reader_image.GetOutputPort())
aa_image.Assign("tos","SCALARS","POINT_DATA")
thresh_image = vtk.vtkThreshold()
thresh_image.SetInputConnection(aa_image.GetOutputPort())
thresh_image.ThresholdByLower(10000)
surface_image = vtk.vtkDataSetSurfaceFilter()
surface_image.SetInputConnection(thresh_image.GetOutputPort())
mapper_image = vtk.vtkPolyDataMapper()
mapper_image.SetInputConnection(surface_image.GetOutputPort())
mapper_image.SetScalarRange(270,310)
actor_image = vtk.vtkActor()
def read(filename, vars, rqstTime):
''' Read a file or files from a directory given a wild-card expression
'''
# @todo Reading a single file of netcdf cf convention now
#cherrypy.log("vtkread " + filename + " " + vars + " " + str(time))
reader = vtk.vtkNetCDFCFReader() #get test data
reader.SphericalCoordinatesOff()
reader.SetOutputTypeToImage()
reader.ReplaceFillValueWithNanOn()
reader.SetFileName(filename)
reader.UpdateInformation()
#obtain temporal information
rawTimes = reader.GetOutputInformation(0).Get(vtk.vtkStreamingDemandDrivenPipeline.TIME_STEPS())
tunits = reader.GetTimeUnits()
converters = attrib_to_converters(tunits)
# pick particular timestep
if rqstTime is not None and rawTimes is not None:
utcconverter = attrib_to_converters("days since 1970-0-0")
abs_request_time = utcconverter[0](float(rqstTime)/(1000*60*60*24))
local_request_time = converters[5](abs_request_time)
# For now clamp to time range
if float(local_request_time) < rawTimes[0]:
local_request_time = rawTimes[0]
elif float(local_request_time) > rawTimes[-1]:
local_request_time = rawTimes[-1]
sddp = reader.GetExecutive()
sddp.SetUpdateTimeStep(0, local_request_time)
# enable only chosen array(s)
narrays = reader.GetNumberOfVariableArrays()
for x in range(0,narrays):
arrayname = reader.GetVariableArrayName(x)
if arrayname in vars:
#cherrypy.log("Enable " + arrayname)
reader.SetVariableArrayStatus(arrayname, 1)
else:
#cherrypy.log("Disable " + arrayname)
reader.SetVariableArrayStatus(arrayname, 0)
# wrap around to get the implicit cell
extent = reader.GetOutputInformation(0).Get(vtk.vtkStreamingDemandDrivenPipeline.WHOLE_EXTENT())
pad = vtk.vtkImageWrapPad()
reader.Update()
data = reader.GetOutput()
da = data.GetPointData().GetArray(0).GetName();
data.GetPointData().SetActiveScalars(da)
pad.SetInputData(data)
pad.SetOutputWholeExtent(extent[0], extent[1]+1,
extent[2], extent[3],
extent[4], extent[5]);
# Convert to polydata
sf = vtk.vtkDataSetSurfaceFilter()
sf.SetInputConnection(pad.GetOutputPort())
# Error reading file?
if not sf.GetOutput():
raise IOError("Unable to load data file: " + filename)
# Convert to GeoJSON
gw = vtk.vtkGeoJSONWriter()
gw.SetInputConnection(sf.GetOutputPort())
gw.SetScalarFormat(2)
gw.WriteToOutputStringOn()
gw.Write()
gj = str(gw.RegisterAndGetOutputString()).replace('\n','')
return gj
def import_file(self, collection, filename, private=True):
"""Import metadata from a filename into the database
This method reads a filename (fullpath) for its metadata
and stores it into the specified collection of the database.
:param collection: Name of the collection to look for basename.
:type collection: str.
:param filename: Name of the file with fullpath (eg. /home/clt.nc).
:type filename: str.
:param private: Should the entry be marked as private.
:type private: bool
"""
if (not (os.path.isfile(filename) and os.path.exists(filename))):
raise Exception("File " + filename + " does not exist")
# @note Assuming that getting mongo collection everytime
# is not going to cause much performance penalty
coll = self._db[collection]
print 'Begin importing %s into database' % filename
variables = []
basename = os.path.basename(filename)
filenamesplitted = os.path.splitext(basename)
fileprefix = filenamesplitted[0]
filesuffix = filenamesplitted[1]
if self.is_exists(collection, filename):
print 'Data %s already exists' % filename
return
if filesuffix == ".nc":
# VTK is required
import vtk
reader = vtk.vtkNetCDFCFReader()
reader.SphericalCoordinatesOff()
reader.SetOutputTypeToImage()
reader.ReplaceFillValueWithNanOn()
reader.SetFileName(filename)
reader.Update()
data = reader.GetOutput()
# Obtain spatial information
bounds = data.GetBounds()
# Obtain temporal information
timeInfo = {}
times = reader.GetOutputInformation(0).Get(vtk.vtkStreamingDemandDrivenPipeline.TIME_STEPS())
timeInfo['rawTimes'] = times #time steps in raw format
tunits = reader.GetTimeUnits()
timeInfo['units'] = tunits #calendar info needed to interpret/convert times
converters = attrib_to_converters(tunits)
if converters and times:
timeInfo['numSteps'] = len(times)
nativeStart = converters[3]
timeInfo['nativeStart'] = nativeStart
stepUnits = converters[2]
timeInfo['nativeUnits'] = stepUnits
stepSize = 0
if len(times) > 1:
stepSize = times[1]-times[0]
timeInfo['nativeDelta'] = stepSize
stdTimeRange = (converters[0](times[0]), converters[0](times[-1]))
timeInfo['nativeRange'] = (times[0], times[-1])
stdTimeDelta = 0
if len(times) > 1:
stdTimeDelta = converters[0](times[1]) - converters[0](times[0])
timeInfo['stdDelta'] = stdTimeDelta
stdTimeRange = (converters[0](times[0]), converters[0](times[-1]))
timeInfo['stdTimeRange'] = stdTimeRange #first and last time as normalized integers
dateRange = (converters[1](stdTimeRange[0]), converters[1](stdTimeRange[1]))
timeInfo['dateRange'] = dateRange #first and last time in Y,M,D format
# Obtain array information
pds = data.GetPointData()
pdscount = pds.GetNumberOfArrays()
if times == None:
times = [0]
# Go through all timesteps to accumulate global min and max values
for t in times:
firstTStep = t==times[0]
arrayindex = 0
# Go through all arrays
for i in range(0, pdscount):
pdarray = pds.GetArray(i)
if not pdarray:
# Got an abstract array
continue
if firstTStep:
# Create new record for this array
variable = {}
else:
# Extend existing record
variable = variables[arrayindex]
# Tell reader to read data so that we can get info about this time step
sddp = reader.GetExecutive()
sddp.SetUpdateTimeStep(0,t)
sddp.Update()
arrayindex = arrayindex + 1
if firstTStep:
# Record unchanging meta information
variable["name"] = pdarray.GetName()
variable["dim"] = []
variable["tags"] = []
variable["units"] = reader.QueryArrayUnits(pdarray.GetName())
# Find min and max for each component of this array at this timestep
componentCount = pdarray.GetNumberOfComponents()
minmax = []
for j in range(0, componentCount):
minmaxJ = [0,-1]
pdarray.GetRange(minmaxJ, j)
minmax.append(minmaxJ[0])
minmax.append(minmaxJ[1])
if firstTStep:
# Remember what we learned about this new array
variable["range"] = minmax
variables.append(variable)
else:
# Extend range if necessary from this timesteps range
for j in range(0, componentCount):
if minmax[j*2+0] < variable["range"][j*2+0]:
variable["range"][j*2+0] = minmax[j*2+0]
if minmax[j*2+1] > variable["range"][j*2+1]:
variable["range"][j*2+1] = minmax[j*2+1]
# Record what we've learned in the data base
insertId = coll.insert({"name":fileprefix, "basename":filename,
"variables":variables,
"timeInfo":timeInfo,
"spatialInfo":bounds,
"private":private})
print 'Done importing %s into database' % filename
def import_directory(seff, server, database, collection, directory):
if (not (os.path.isdir(directory) and os.path.exists(directory))):
raise Exception("Directory " + directory + " does not exist")
conn = pymongo.Connection(server)
db = conn[database]
coll = db[collection]
# Collect filenames under the directory
from os import listdir
from os.path import isfile, join
files = [ f for f in listdir(directory) if isfile(join(directory,f)) ]
# Add files to the database
for filename in files:
print 'Working on %s' % filename
variables = []
basename = os.path.basename(filename)
filenamesplitted = os.path.splitext(basename)
fileprefix = filenamesplitted[0]
filesuffix = filenamesplitted[1]
if filesuffix == ".nc":
import vtk
reader = vtk.vtkNetCDFCFReader()
reader.SphericalCoordinatesOff()
reader.SetOutputTypeToImage()
reader.ReplaceFillValueWithNanOn()
reader.SetFileName(os.path.join(directory, filename))
reader.Update()
data = reader.GetOutput()
#obtain spatial information
bounds = data.GetBounds()
#obtain temporal information
timeInfo = {}
times = reader.GetOutputInformation(0).Get(vtk.vtkStreamingDemandDrivenPipeline.TIME_STEPS())
timeInfo['rawTimes'] = times #time steps in raw format
tunits = reader.GetTimeUnits()
timeInfo['units'] = tunits #calendar info needed to interpret/convert times
converters = attrib_to_converters(tunits)
stdTimeRange = None
dateRange = None
if converters and times:
stdTimeRange = (converters[0](times[0]),converters[0](times[-1]))
timeInfo['stdTimeRange'] = stdTimeRange #first and last time as normalized integers
dateRange = (converters[1](stdTimeRange[0]), converters[1](stdTimeRange[1]))
timeInfo['dateRange'] = dateRange #first and last time in Y,M,D format
print filename, "tunits:", tunits, "times: ", times, "std time range:", stdTimeRange, "dates: ", dateRange
#obtain array information
pds = data.GetPointData()
pdscount = pds.GetNumberOfArrays()
for i in range(0, pdscount):
variable = {}
pdarray = pds.GetArray(i)
if not pdarray:
# got an abstract array
continue
variable["name"] = pdarray.GetName()
variable["dim"] = []
variable["tags"] = []
variable["units"] = reader.QueryArrayUnits(pdarray.GetName())
# todo: iterate over all timesteps, default (first) timestep may not be representative
variable["time"] = []
componentCount = pdarray.GetNumberOfComponents()
minmax = []
for j in range(0, componentCount):
minmaxJ = [0,-1]
pdarray.GetRange(minmaxJ, j)
minmax.append(minmaxJ[0])
minmax.append(minmaxJ[1])
variable["range"] = minmax
variables.append(variable)
#record what we've learned
insertId = coll.insert({"name":fileprefix, "basename":basename,
"variables":variables,
"timeInfo":timeInfo,
"spatialInfo":bounds})
