def translate_query(self, q):
if type(q) == str:
q = literal_eval(q)
assert type(q) == dict
# be sure all params have a value in the new query
query = {}
for param in self.fs.parameter_list.keys():
default = [self.fs.get_parameter(param)["default"]]
query[param] = q.get(param, default)
layers = self.qt.translateQuery(query)
# convert layers to image data
layer = layers[0]
img = vtk.vtkImageData()
dims = [0, 0, 0]
if layer.hasColorArray():
ncolors = layer.getColorArray()
# now, the numpy array's shape matches the 2D image
dims[0] = ncolors.shape[1]
dims[1] = ncolors.shape[0]
img.SetDimensions(dims[0], dims[1], 1)
ncolors = ncolors.reshape((dims[0]*dims[1], -1))
vcolors = dsa.numpyTovtkDataArray(ncolors, "Colors")
img.GetPointData().SetScalars(vcolors)
nluminance = layer.getLuminance()
if nluminance is not None:
nluminance = nluminance.reshape((dims[0]*dims[1], -1))
vluminance = dsa.numpyTovtkDataArray(nluminance, "Luminance")
img.GetPointData().AddArray(vluminance)
return [img]
def coprocess(time, timeStep, grid, attributes):
global coProcessor
import vtk
from paraview.modules import vtkPVCatalyst as catalyst
import paraview
from paraview.vtk.util import numpy_support
dataDescription = catalyst.vtkCPDataDescription()
dataDescription.SetTimeData(time, timeStep)
dataDescription.AddInput("input")
if coProcessor.RequestDataDescription(dataDescription):
import fedatastructures
imageData = vtk.vtkImageData()
imageData.SetExtent(grid.XStartPoint, grid.XEndPoint, 0,
grid.NumberOfYPoints - 1, 0,
grid.NumberOfZPoints - 1)
imageData.SetSpacing(grid.Spacing)
velocity = numpy_support.numpy_to_vtk(attributes.Velocity)
velocity.SetName("velocity")
imageData.GetPointData().AddArray(velocity)
pressure = numpy_support.numpy_to_vtk(attributes.Pressure)
pressure.SetName("pressure")
imageData.GetCellData().AddArray(pressure)
dataDescription.GetInputDescriptionByName("input").SetGrid(imageData)
dataDescription.GetInputDescriptionByName("input").SetWholeExtent(
0, grid.NumberOfGlobalXPoints - 1, 0, grid.NumberOfYPoints - 1, 0,
grid.NumberOfZPoints - 1)
coProcessor.CoProcess(dataDescription)
def computeAveragesAndBuildImgData():
imgDims = [1440, 720, 10]
imgData = vtk.vtkImageData()
imgData.SetDimensions(imgDims[0] + 1, imgDims[1] + 1, imgDims[2] + 1)
imgData.SetSpacing(1, 1, 1)
imgData.SetOrigin(0, 0, 0)
imgData.SetExtent(0, imgDims[0], 0, imgDims[1], 0, imgDims[2])
imgData.AllocateScalars(vtk.VTK_FLOAT, 1)
flattenedArrays = []
for fileName in fileNames:
fpath = os.path.join(basepath, fileName)
print('processing %s' % fpath)
year = fileName.split('_')[-1][:-4]
dataset = gdal.Open(fpath)
sumArray = ma.zeros((dataset.RasterYSize, dataset.RasterXSize))
total = 0
count = 0
numBands = dataset.RasterCount
for bandId in range(numBands):
band = ma.masked_outside(dataset.GetRasterBand(bandId + 1).ReadAsArray(), VALUE_RANGE[0], VALUE_RANGE[1])
sumArray += band
sumArray /= numBands
total = ma.sum(ma.sum(sumArray))
count = sumArray.count()
minCell = ma.min(sumArray)
maxCell = ma.max(sumArray)
imgDims = [dataset.RasterXSize, dataset.RasterYSize]
# flattenedArrays.append(np.ndarray.flatten(sumArray[::-1,:], 0).astype(np.dtype(np.float32)))
flattenedArrays.append(np.ndarray.flatten(sumArray[::-1,:], 0))
allYearsAvgs = np.ma.concatenate(flattenedArrays)
cellData = imgData.GetCellData()
dataArray = numpy_support.numpy_to_vtk(np.ndarray.flatten(allYearsAvgs, 0), deep = 1)
dataArray.SetName('Annual Avg Temp')
cellData.SetScalars(dataArray)
imgWriter = vtkIOXML.vtkXMLImageDataWriter()
imgWriter.SetFileName('/data/scott/Documents/tasmax.vti')
imgWriter.SetInputData(imgData)
imgWriter.Write()
print('\nFinished writing image data\n')
def computeAveragesUsingNumpy():
for fileName in fileNames:
fpath = os.path.join(basepath, fileName)
print('processing %s' % fpath)
year = fileName.split('_')[-1][:-4]
dataset = gdal.Open(fpath)
sumArray = ma.zeros((dataset.RasterYSize, dataset.RasterXSize))
total = 0
count = 0
numBands = dataset.RasterCount
for bandId in range(numBands):
band = ma.masked_outside(
dataset.GetRasterBand(bandId + 1).ReadAsArray(),
VALUE_RANGE[0], VALUE_RANGE[1])
sumArray += band
sumArray /= numBands
total = ma.sum(ma.sum(sumArray))
count = sumArray.count()
minCell = ma.min(sumArray)
maxCell = ma.max(sumArray)
imgDims = [dataset.RasterXSize, dataset.RasterYSize]
print(' finished computing averge for %s, writing image data' % year)
imgData = vtk.vtkImageData()
imgData.SetDimensions(imgDims[0], imgDims[1], 0)
imgData.SetSpacing(1, 1, 1)
imgData.SetOrigin(0, 0, 0)
imgData.SetExtent(0, imgDims[0] - 1, 0, imgDims[1] - 1, 0, 0)
imgData.AllocateScalars(vtk.VTK_FLOAT, 1)
pointData = imgData.GetPointData()
dataArray = numpy_support.numpy_to_vtk(np.ndarray.flatten(
sumArray[::-1, :], 0).astype(np.dtype(np.int32)),
deep=1)
dataArray.SetName('Annual Avg Temp')
pointData.SetScalars(dataArray)
imgWriter = vtkIOXML.vtkXMLImageDataWriter()
imgWriter.SetFileName('tasmax_%s.vti' % (year))
imgWriter.SetInputData(imgData)
imgWriter.Write()
print(' finished writing image data for %s' % year)
def layer2img(layer):
"""converts a layer to vtkImageData."""
if not layer:
return None
img = vtk.vtkImageData()
dims = [0, 0, 0]
if layer.hasValueArray():
nvalues = layer.getValueArray()
# now, the numpy array's shape matches the 2D image
dims[0] = nvalues.shape[1]
dims[1] = nvalues.shape[0]
img.SetDimensions(dims[0], dims[1], 1)
nvalues = nvalues.reshape(dims[0]*dims[1])
vvalues = dsa.numpyTovtkDataArray(nvalues, "Values")
img.GetPointData().SetScalars(vvalues)
elif layer.hasColorArray():
ncolors = layer.getColorArray()
# now, the numpy array's shape matches the 2D image
dims[0] = ncolors.shape[1]
dims[1] = ncolors.shape[0]
img.SetDimensions(dims[0], dims[1], 1)
ncolors = ncolors.reshape((dims[0]*dims[1],-1))
vcolors = dsa.numpyTovtkDataArray(ncolors, "Colors")
img.GetPointData().SetScalars(vcolors)
ndepth = layer.getDepth()
if ndepth is None:
raise RuntimeError("Missing 'depth'")
ndepth = ndepth.reshape(dims[0]*dims[1])
vdepth = dsa.numpyTovtkDataArray(ndepth, "Depth");
img.GetPointData().AddArray(vdepth)
nluminance = layer.getLuminance()
if nluminance is not None:
nluminance = nluminance.reshape((dims[0]*dims[1], -1))
vluminance = dsa.numpyTovtkDataArray(nluminance, "Luminance")
img.GetPointData().AddArray(vluminance)
#from paraview.vtk.vtkIOLegacy import vtkDataSetWriter
#writer = vtkDataSetWriter()
#writer.SetInputDataObject(img)
#writer.SetFileName("/tmp/layer.vtk")
#writer.Update()
#del writer
return img
def layer2img(layer):
"""converts a layer to vtkImageData."""
if not layer:
return None
img = vtk.vtkImageData()
dims = [0, 0, 0]
if layer.hasValueArray():
nvalues = layer.getValueArray()
# now, the numpy array's shape matches the 2D image
dims[0] = nvalues.shape[1]
dims[1] = nvalues.shape[0]
img.SetDimensions(dims[0], dims[1], 1)
nvalues = nvalues.reshape(dims[0]*dims[1])
vvalues = dsa.numpyTovtkDataArray(nvalues, "Values")
img.GetPointData().SetScalars(vvalues)
elif layer.hasColorArray():
ncolors = layer.getColorArray()
# now, the numpy array's shape matches the 2D image
dims[0] = ncolors.shape[1]
dims[1] = ncolors.shape[0]
img.SetDimensions(dims[0], dims[1], 1)
ncolors = ncolors.reshape((dims[0]*dims[1], -1))
vcolors = dsa.numpyTovtkDataArray(ncolors, "Colors")
img.GetPointData().SetScalars(vcolors)
ndepth = layer.getDepth()
if ndepth is None:
raise RuntimeError("Missing 'depth'")
ndepth = ndepth.reshape(dims[0]*dims[1])
vdepth = dsa.numpyTovtkDataArray(ndepth, "Depth")
img.GetPointData().AddArray(vdepth)
nluminance = layer.getLuminance()
if nluminance is not None:
nluminance = nluminance.reshape((dims[0]*dims[1], -1))
vluminance = dsa.numpyTovtkDataArray(nluminance, "Luminance")
img.GetPointData().AddArray(vluminance)
# from paraview.vtk.vtkIOLegacy import vtkDataSetWriter
# writer = vtkDataSetWriter()
# writer.SetInputDataObject(img)
# writer.SetFileName("/tmp/layer.vtk")
# writer.Update()
# del writer
return img
def numpy_to_image(numpy_array):
"""Convert a numpy 2D or 3D array to a vtkImageData object.
numpy_array
2D or 3D numpy array containing image data
return
vtkImageData with the numpy_array content
"""
try:
import numpy
except:
paraview.print_error("Error: Cannot import numpy")
shape = numpy_array.shape
if len(shape) < 2:
raise Exception('numpy array must have dimensionality of at least 2')
h, w = shape[0], shape[1]
c = 1
if len(shape) == 3:
c = shape[2]
# Reshape 2D image to 1D array suitable for conversion to a
# vtkArray with numpy_support.numpy_to_vtk()
linear_array = numpy.reshape(numpy_array, (w*h, c))
try:
from paraview.vtk.util import numpy_support
except:
paraview.print_error("Error: Cannot import vtk.util.numpy_support")
vtk_array = numpy_support.numpy_to_vtk(linear_array)
image = vtk.vtkImageData()
image.SetDimensions(w, h, 1)
image.AllocateScalars(vtk_array.GetDataType(), 4)
image.GetPointData().GetScalars().DeepCopy(vtk_array)
return image
def processFile(directory, filename):
# junk = raw_input('blah')
fpath = os.path.join(directory, filename)
print('Processing %s' % filename)
year = filename.split('_')[-1][:-4]
dataset = gdal.Open(fpath)
numBands = dataset.RasterCount
imgDims = [dataset.RasterXSize, dataset.RasterYSize]
for bandId in range(numBands):
# for bandId in [0, 89, 179, 269]:
imgData = vtk.vtkImageData()
imgData.SetDimensions(imgDims[0], imgDims[1], 0)
imgData.SetSpacing(1, 1, 1)
imgData.SetOrigin(0, 0, 0)
imgData.SetExtent(0, imgDims[0] - 1, 0, imgDims[1] - 1, 0, 0)
imgData.AllocateScalars(vtk.VTK_FLOAT, 1)
band = dataset.GetRasterBand(bandId + 1).ReadAsArray()
pointData = imgData.GetPointData()
dataArray = numpy_support.numpy_to_vtk(np.ndarray.flatten(
band[::-1, :], 0).astype(np.dtype(np.int32)),
deep=1)
dataArray.SetName('Daily Max')
pointData.SetScalars(dataArray)
imgWriter = vtkIOXML.vtkXMLImageDataWriter()
outputFileName = '%s_%d.vti' % (filename, (bandId + 1))
imgWriter.SetFileName(outputFileName)
imgWriter.SetInputData(imgData)
imgWriter.Write()
print(' wrote to %s' % outputFileName)
def processFile(fileName):
# junk = raw_input('blah')
print('Processing %s' % fileName)
year = fileName.split('_')[-1][:-4]
dataset = gdal.Open(fileName)
numBands = dataset.RasterCount
imgDims = [dataset.RasterXSize, dataset.RasterYSize]
imgData = vtk.vtkImageData()
imgData.SetDimensions(imgDims[0], imgDims[1], 0)
imgData.SetSpacing(1, 1, 1)
imgData.SetOrigin(0, 0, 0)
imgData.SetExtent(0, imgDims[0] - 1, 0, imgDims[1] - 1, 0, 0)
pointData = imgData.GetPointData()
# for bandId in range(numBands):
for bandId in [0, 89, 179, 269]:
print(' band %d' % (bandId + 1))
band = dataset.GetRasterBand(bandId + 1).ReadAsArray()
dataArray = numpy_support.numpy_to_vtk(np.ndarray.flatten(
band[::-1, :], 0).astype(np.dtype(np.int32)),
deep=1)
dataArray.SetName('Daily Max (%d)' % (bandId + 1))
pointData.AddArray(dataArray)
pointData.SetActiveScalars('Daily Max (0)')
imgWriter = vtkIOXML.vtkXMLImageDataWriter()
imgWriter.SetFileName('tasmax_%s.vti' % (year))
imgWriter.SetInputData(imgData)
imgWriter.Write()
