def recalculate(self):
if self.func is not None and self.data_range is not None:
newarray = self.func(self.data_range.x_range.low,
self.data_range.x_range.high,
self.data_range.y_range.low,
self.data_range.y_range.high)
ImageData.set_data(self, newarray)
else:
self._data = array([], dtype=float)
def recalculate(self):
if self.func is not None and self.data_range is not None:
newarray = self.func(
self.data_range.x_range.low,
self.data_range.x_range.high,
self.data_range.y_range.low,
self.data_range.y_range.high,
)
ImageData.set_data(self, newarray)
else:
self._data = array([], dtype=float)
def set_pict(self):
pd = self.plot_data
if not pd:
return
try:
imgd = ImageData.fromfile(self.pict)._data[::-1]
except:
imgd = ImageData()
imgd.set_data(255*ones((2,2,3),dtype='uint8'))
imgd = imgd._data
pd.set_data("image",imgd)
def set_pict(self):
pd = self.plot_data
if not pd:
return
try:
imgd = ImageData.fromfile(self.pict)._data[::-1]
except:
imgd = ImageData()
imgd.set_data(255 * ones((2, 2, 3), dtype='uint8'))
imgd = imgd._data
pd.set_data("image", imgd)
def _corr_plot_default(self):
diag = self.covar.diagonal()
corr = self.covar / np.sqrt(np.outer(diag, diag))
N = len(diag)
value_range = DataRange1D(low=-1, high=1)
color_mapper = cmap(range=value_range)
index = GridDataSource()
value = ImageData()
mapper = GridMapper(range=DataRange2D(index),
y_low_pos=1.0,
y_high_pos=0.0)
index.set_data(xdata=np.arange(-0.5, N), ydata=np.arange(-0.5, N))
value.set_data(np.flipud(corr))
self.corr_data = value
cmap_plot = CMapImagePlot(index=index,
index_mapper=mapper,
value=value,
value_mapper=color_mapper,
padding=(40, 40, 100, 40))
yaxis = PlotAxis(
cmap_plot,
orientation='left',
tick_interval=1,
tick_label_formatter=lambda x: self.header[int(N - 1 - x)],
tick_generator=ShowAllTickGenerator(positions=np.arange(N)))
xaxis = PlotAxis(
cmap_plot,
orientation='top',
tick_interval=1,
tick_label_formatter=lambda x: self.header[int(x)],
tick_label_alignment='edge',
tick_generator=ShowAllTickGenerator(positions=np.arange(N)))
cmap_plot.overlays.append(yaxis)
cmap_plot.overlays.append(xaxis)
colorbar = ColorBar(
index_mapper=LinearMapper(range=cmap_plot.value_range),
plot=cmap_plot,
orientation='v',
resizable='v',
width=10,
padding=(40, 5, 100, 40))
container = HPlotContainer(bgcolor='transparent')
container.add(cmap_plot)
container.add(colorbar)
return container
class ImageDataTestCase(UnittestTools, unittest.TestCase):
def setUp(self):
self.myarray = arange(15).reshape(5, 3, 1)
self.data_source = ImageData(data=self.myarray)
def test_init_defaults(self):
data_source = ImageData()
assert_array_equal(data_source.data, [])
# this isn't right -
#self.assertEqual(data_source.value_dimension, "scalar")
#self.assertEqual(data_source.image_dimension, "image")
def test_basic_setup(self):
assert_array_equal(self.myarray, self.data_source.data)
#self.assertEqual(self.data_source.value_dimension, "scalar")
self.assertFalse(self.data_source.is_masked())
def test_set_data(self):
new_array = arange(0, 30, 2).reshape(5, 3, 1)
with self.assertTraitChanges(self.data_source, 'data_changed',
count=1):
self.data_source.set_data(new_array)
assert_array_equal(new_array, self.data_source.data)
self.assertEqual(self.data_source.get_bounds(), (0, 28))
def test_get_data(self):
assert_array_equal(self.myarray, self.data_source.get_data())
def test_get_data_no_data(self):
data_source = ImageData()
self.assertIsNone(data_source.get_data())
def test_get_data_transposed(self):
myarray = arange(15).reshape(5, 3, 1)
data_source = ImageData(data=myarray, transposed=True)
assert_array_equal(swapaxes(myarray, 0, 1), data_source.get_data())
def test_get_data_mask(self):
# XXX this is probably not the right thing
with self.assertRaises(NotImplementedError):
data, mask = self.data_source.get_data_mask()
def test_get_data_mask_no_data(self):
data_source = ImageData()
# XXX this is probably not the right thing
with self.assertRaises(NotImplementedError):
data, mask = data_source.get_data_mask()
def test_bounds(self):
bounds = self.data_source.get_bounds()
self.assertEqual(bounds, (0, 14))
@unittest.skip('test_bounds_empty() fails in this case')
def test_bounds_empty(self):
data_source = ImageData()
bounds = data_source.get_bounds()
self.assertEqual(bounds, (0, 0))
def test_data_size(self):
self.assertEqual(15, self.data_source.get_size())
def test_data_size_no_data(self):
data_source = ImageData()
self.assertEqual(0, data_source.get_size())
def test_get_width(self):
self.assertEqual(3, self.data_source.get_width())
def test_get_width_transposed(self):
myarray = arange(15).reshape(5, 3)
data_source = ImageData(data=myarray, transposed=True)
self.assertEqual(5, data_source.get_width())
def test_get_height(self):
self.assertEqual(5, self.data_source.get_height())
def test_get_height_transposed(self):
myarray = arange(15).reshape(5, 3, 1)
data_source = ImageData(data=myarray, transposed=True)
self.assertEqual(3, data_source.get_height())
def test_array_bounds(self):
self.assertEqual(((0, 3), (0, 5)), self.data_source.get_array_bounds())
def test_array_bounds_transposed(self):
myarray = arange(15).reshape(5, 3, 1)
data_source = ImageData(data=myarray, transposed=True)
self.assertEqual(((0, 5), (0, 3)), data_source.get_array_bounds())
def test_fromfile_png_rgb(self):
# basic smoke test - assume that kiva.image does the right thing
path = os.path.join(data_dir, 'PngSuite', 'basn2c08.png')
data_source = ImageData.fromfile(path)
self.assertEqual(data_source.value_depth, 3)
def test_fromfile_png_rgba(self):
# basic smoke test - assume that kiva.image does the right thing
path = os.path.join(data_dir, 'PngSuite', 'basi6a08.png')
data_source = ImageData.fromfile(path)
self.assertEqual(data_source.value_depth, 4)
def test_metadata(self):
self.assertEqual(self.data_source.metadata, {
'annotations': [],
'selections': []
})
def test_metadata_changed(self):
with self.assertTraitChanges(self.data_source,
'metadata_changed',
count=1):
self.data_source.metadata = {'new_metadata': True}
def test_metadata_items_changed(self):
with self.assertTraitChanges(self.data_source,
'metadata_changed',
count=1):
self.data_source.metadata['new_metadata'] = True
def _myTIC_default( self ):
# create an interesting scalar field for the image plot
# Eggholder function
limitF = 500.0
xA = linspace(-limitF, limitF, 600)
yA = linspace(-limitF, limitF, 600)
( xMG,yMG ) = meshgrid( xA,yA )
zMG = -(yMG + 47) * sin( sqrt(abs(yMG + xMG/2 + 47 )))
zMG = zMG - xMG * sin( sqrt(abs(xMG - (yMG + 47))))
# Create an ArrayPlotData object and give it this data
myAPD = ArrayPlotData()
myAPD.set_data( "Z", zMG )
myAPD.set_data( "X",xA )
myAPD.set_data( "Y",yA )
# Create the plot
myTP = Plot( myAPD )
# contains a dict of default colormaps and their functions. We have to
# pass the colormapper the data range of interest to set up the private
# attributes
default_colormaps.color_map_name_dict
# the colormap method needs the range of the image data that we want to
# plot. We first put the image data (zMG) into an ImageData object. We
# then use DataRange1D on the ImageData instance to produce a DataRange1D
# instance describing the ImageData data. Finally, we feed the DataRange1D
# instance into the colormapper to produce a working colormapper.
myID = ImageData( )
myID.set_data( zMG )
myDR1D = DataRange1D( myID )
# pick a colormap
myColorMapperFn = default_colormaps.color_map_name_dict['copper']
# choose one or more modifications to the colormap function
#myColorMapperFn = default_colormaps.reverse( myColorMapperFn )
#myColorMapperFn = default_colormaps.center( myColorMapperFn,500 )
#myColorMapperFn = default_colormaps.fix( myColorMapperFn,(-500,500) )
# finally, build the colormapper function
myColorMapper = myColorMapperFn( myDR1D )
# add the image plot to this plot object
# specify the colormap explicitly
myTP.img_plot(
"Z",
xbounds = (xA[0],xA[-1]),
ybounds = (yA[0],yA[-1]),
colormap = myColorMapper,
)
# add the title and padding around the plot
myTP.title = "Eggholder Function"
myTP.padding = 50
# grids, fonts, etc
myTP.x_axis.title = "X"
myTP.y_axis.title = "Y"
# generate a ColorBar. pulls its colormapper from the myTP Plot object
myTCB = ColorBar(
plot = myTP,
index_mapper = LinearMapper( range = myTP.color_mapper.range ),
orientation = 'v',
resizable = 'v',
width = 40,
padding = 30,
)
# set the padding of the ColorBar to match the padding of the plot
myTCB.padding_top = myTP.padding_top
myTCB.padding_bottom = myTP.padding_bottom
# range of the colormapper. Changes the min/max values that are mapped
# to the ends of the color range. Try +/-2000 for poor contrast and +/-200 for
# saturated. Asymmetrical values work as well.
#myTP.color_mapper.range.low_setting = 0
#myTP.color_mapper.range.high_setting = 1000
# build up a single container for the colorbar and the image
myHPC = HPlotContainer( use_backbuffer = True )
myHPC.add( myTP )
myHPC.add( myTCB )
return( myHPC )
class ImageDataTestCase(UnittestTools, unittest.TestCase):
def setUp(self):
self.myarray = arange(15).reshape(5, 3, 1)
self.data_source = ImageData(data=self.myarray)
def test_init_defaults(self):
data_source = ImageData()
assert_array_equal(data_source.data, [])
# this isn't right -
#self.assertEqual(data_source.value_dimension, "scalar")
#self.assertEqual(data_source.image_dimension, "image")
def test_basic_setup(self):
assert_array_equal(self.myarray, self.data_source.data)
#self.assertEqual(self.data_source.value_dimension, "scalar")
self.assertFalse(self.data_source.is_masked())
def test_set_data(self):
new_array = arange(0, 30, 2).reshape(5, 3, 1)
with self.assertTraitChanges(self.data_source, 'data_changed', count=1):
self.data_source.set_data(new_array)
assert_array_equal(new_array, self.data_source.data)
self.assertEqual(self.data_source.get_bounds(), (0, 28))
def test_get_data(self):
assert_array_equal(self.myarray, self.data_source.get_data())
def test_get_data_no_data(self):
data_source = ImageData()
self.assertIsNone(data_source.get_data())
def test_get_data_transposed(self):
myarray = arange(15).reshape(5, 3, 1)
data_source = ImageData(data=myarray, transposed=True)
assert_array_equal(swapaxes(myarray, 0, 1), data_source.get_data())
def test_get_data_mask(self):
# XXX this is probably not the right thing
with self.assertRaises(NotImplementedError):
data, mask = self.data_source.get_data_mask()
def test_get_data_mask_no_data(self):
data_source = ImageData()
# XXX this is probably not the right thing
with self.assertRaises(NotImplementedError):
data, mask = data_source.get_data_mask()
def test_bounds(self):
bounds = self.data_source.get_bounds()
self.assertEqual(bounds, (0, 14))
@unittest.skip('test_bounds_empty() fails in this case')
def test_bounds_empty(self):
data_source = ImageData()
bounds = data_source.get_bounds()
self.assertEqual(bounds, (0, 0))
def test_data_size(self):
self.assertEqual(15, self.data_source.get_size())
def test_data_size_no_data(self):
data_source = ImageData()
self.assertEqual(0, data_source.get_size())
def test_get_width(self):
self.assertEqual(3, self.data_source.get_width())
def test_get_width_transposed(self):
myarray = arange(15).reshape(5, 3)
data_source = ImageData(data=myarray, transposed=True)
self.assertEqual(5, data_source.get_width())
def test_get_height(self):
self.assertEqual(5, self.data_source.get_height())
def test_get_height_transposed(self):
myarray = arange(15).reshape(5, 3, 1)
data_source = ImageData(data=myarray, transposed=True)
self.assertEqual(3, data_source.get_height())
def test_array_bounds(self):
self.assertEqual(((0, 3), (0, 5)), self.data_source.get_array_bounds())
def test_array_bounds_transposed(self):
myarray = arange(15).reshape(5, 3, 1)
data_source = ImageData(data=myarray, transposed=True)
self.assertEqual(((0, 5), (0, 3)), data_source.get_array_bounds())
def test_fromfile_png_rgb(self):
# basic smoke test - assume that kiva.image does the right thing
path = os.path.join(data_dir, 'PngSuite', 'basn2c08.png')
data_source = ImageData.fromfile(path)
self.assertEqual(data_source.value_depth, 3)
def test_fromfile_png_rgba(self):
# basic smoke test - assume that kiva.image does the right thing
path = os.path.join(data_dir, 'PngSuite', 'basi6a08.png')
data_source = ImageData.fromfile(path)
self.assertEqual(data_source.value_depth, 4)
def test_metadata(self):
self.assertEqual(self.data_source.metadata,
{'annotations': [], 'selections': []})
def test_metadata_changed(self):
with self.assertTraitChanges(self.data_source, 'metadata_changed', count=1):
self.data_source.metadata = {'new_metadata': True}
def test_metadata_items_changed(self):
with self.assertTraitChanges(self.data_source, 'metadata_changed', count=1):
self.data_source.metadata['new_metadata'] = True
def _myTIC_default( self ):
# create an interesting scalar field for the image plot
# Eggholder function
limitF = 500.0
xA = linspace(-limitF, limitF, 600)
yA = linspace(-limitF, limitF, 600)
( xMG,yMG ) = meshgrid( xA,yA )
zMG = -(yMG + 47) * sin( sqrt(abs(yMG + xMG/2 + 47 )))
zMG = zMG - xMG * sin( sqrt(abs(xMG - (yMG + 47))))
# Create an ArrayPlotData object and give it this data
myAPD = ArrayPlotData()
myAPD.set_data( "Z", zMG )
myAPD.set_data( "X",xA )
myAPD.set_data( "Y",yA )
# Create the plot
self.myTP = Plot( myAPD )
# contains a dict of default colormaps and their functions. We have to
# pass the colormapper the data range of interest to set up the private
# attributes
default_colormaps.color_map_name_dict
# the colormap method needs the range of the image data that we want to
# plot. We first put the image data (zMG) into an ImageData object. We
# then use DataRange1D on the ImageData instance to produce a DataRange1D
# instance describing the ImageData data. Finally, we feed the DataRange1D
# instance into the colormapper to produce a working colormapper.
myID = ImageData( )
myID.set_data( zMG )
self.myDR1D = DataRange1D( myID )
# pick an unmodified (i.e. unreversed, no ranges) colormap and build
# the colormap functions
myColorMapperFn = default_colormaps.color_map_name_dict[self.colormapNameTE]
myColorMapper = myColorMapperFn( self.myDR1D )
# add the image plot to this plot object
# specify the colormap explicitly
self.myTP.img_plot(
"Z",
xbounds = (xA[0],xA[-1]),
ybounds = (yA[0],yA[-1]),
colormap = myColorMapper,
)
# add the title and padding around the plot
self.myTP.title = "Eggholder Function"
self.myTP.padding = 50
# grids, fonts, etc
self.myTP.x_axis.title = "X"
self.myTP.y_axis.title = "Y"
# generate a ColorBar. pulls its colormapper from the myTP Plot object
self.myTCB = ColorBar(
plot = self.myTP,
index_mapper = LinearMapper( range = self.myTP.color_mapper.range ),
orientation = 'v',
resizable = 'v',
width = 40,
padding = 30,
)
# set the padding of the ColorBar to match the padding of the plot
self.myTCB.padding_top = self.myTP.padding_top
self.myTCB.padding_bottom = self.myTP.padding_bottom
# build up a single container for the colorbar and the image
myHPC = HPlotContainer( use_backbuffer = True )
myHPC.add( self.myTP )
myHPC.add( self.myTCB )
return( myHPC )
