def cmap_plot(self, z):
from chaco.array_plot_data import ArrayPlotData
from chaco.plot import Plot
from chaco.default_colormaps import color_map_name_dict
pd = ArrayPlotData()
pd.set_data("cmapdata", z)
p = Plot(pd, padding=0)
p.img_plot("cmapdata", xbounds=(-25, 25), ybounds=(-25, 25), colormap=color_map_name_dict["hot"])
self.add(p)
return pd
def setup_images(self, n, wh):
self.container._components = []
self.plotdata = plotdata = ArrayPlotData()
for i in range(n):
plot = Plot(plotdata, padding=0, default_origin="top left")
name = 'imagedata{:03d}'.format(i)
plotdata.set_data(name, ones(wh))
plot.img_plot(name, colormap=hot)
self.container.add(plot)
self.container.request_redraw()
def setup_images(self, n, wh):
self.container._components = []
self.plotdata = plotdata = ArrayPlotData()
for i in range(n):
plot = Plot(plotdata, padding=0, default_origin="top left")
name = 'imagedata{:03d}'.format(i)
plotdata.set_data(name, ones(wh))
plot.img_plot(name, colormap=hot)
self.container.add(plot)
self.container.request_redraw()
def cmap_plot(self, z):
from chaco.array_plot_data import ArrayPlotData
from chaco.plot import Plot
from chaco.default_colormaps import color_map_name_dict
pd = ArrayPlotData()
pd.set_data('cmapdata', z)
p = Plot(pd, padding=0)
p.img_plot('cmapdata',
xbounds=(-25, 25),
ybounds=(-25, 25),
colormap=color_map_name_dict['hot'])
self.add(p)
return pd
def _matrix_plot_container_default(self):
matrix = np.nan_to_num(self.matrix)
width = matrix.shape[0]
# create a plot data object and give it this data
plot_data = ArrayPlotData()
plot_data.set_data("values", matrix)
# create the plot
plot = Plot(plot_data, origin=self.origin)
img_plot = plot.img_plot("values",
interpolation='nearest',
xbounds=(0, width),
ybounds=(0, width),
colormap=self._create_colormap())[0]
#### fix axes
self._remove_grid_and_axes(plot)
axis = self._create_increment_one_axis(plot, 0.5, width, 'bottom')
self._add_value_axis(plot, axis)
axis = self._create_increment_one_axis(
plot,
0.5,
width,
'left',
ticks=[str(i) for i in range(width - 1, -1, -1)])
self._add_index_axis(plot, axis)
#### tweak plot attributes
self._set_title(plot)
plot.aspect_ratio = 1.
# padding [left, right, up, down]
plot.padding = [0, 0, 25, 25]
# create the colorbar, handing in the appropriate range and colormap
colormap = img_plot.color_mapper
colorbar = ColorBar(index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=img_plot,
orientation='v',
resizable='v',
width=20,
padding=[0, 20, 0, 0])
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = plot.padding_bottom
# create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(use_backbuffer=True)
container.add(plot)
container.add(colorbar)
container.bgcolor = 0xFFFFFF
self.decorate_plot(container, self.matrix)
return container
def __init__(self):
super(SLM_UI, self).__init__()
filename = os.path.join(os.path.dirname(__file__), 'slm3995_at1064_P8.lut')
slm.lut(filename)
self.focus = 0
self.spherical = 0
self.a = 1
x = arange(512)
self.x, self.y = meshgrid(x, x)
self.r2 = (self.x - 256.)**2 + (self.y - 256.)**2
self.r4 = self.r2**2
self.plotdata = ArrayPlotData(x=self.x, y=self.y)
self.calculate()
plot = Plot(self.plotdata)
plot.img_plot('series1', colormap=jet)
self.plot = plot
def _matrix_plot_container_default(self):
matrix = np.nan_to_num(self.matrix)
width = matrix.shape[0]
# create a plot data object and give it this data
plot_data = ArrayPlotData()
plot_data.set_data("values", matrix)
# create the plot
plot = Plot(plot_data, origin=self.origin)
img_plot = plot.img_plot("values",
interpolation='nearest',
xbounds=(0, width),
ybounds=(0, width),
colormap=self._create_colormap())[0]
#### fix axes
self._remove_grid_and_axes(plot)
axis = self._create_increment_one_axis(plot, 0.5, width, 'bottom')
self._add_value_axis(plot, axis)
axis = self._create_increment_one_axis(
plot, 0.5, width, 'left',
ticks=[str(i) for i in range(width-1, -1, -1)])
self._add_index_axis(plot, axis)
#### tweak plot attributes
self._set_title(plot)
plot.aspect_ratio = 1.
# padding [left, right, up, down]
plot.padding = [0, 0, 25, 25]
# create the colorbar, handing in the appropriate range and colormap
colormap = img_plot.color_mapper
colorbar = ColorBar(index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=img_plot,
orientation='v',
resizable='v',
width=20,
padding=[0, 20, 0, 0])
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = plot.padding_bottom
# create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(use_backbuffer=True)
container.add(plot)
container.add(colorbar)
container.bgcolor = 0xFFFFFF
self.decorate_plot(container, self.matrix)
return container
def _load_image(self, path):
self.container = self._container_factory()
im = Image.open(path)
# oim = array(im)
im = im.convert('L')
odim = ndim = array(im)
pd = ArrayPlotData()
pd.set_data('img', odim)
plot = Plot(data=pd, padding=[30, 5, 5, 30], default_origin='top left')
img_plot = plot.img_plot('img',
colormap=color_map_name_dict[self.colormap_name_1]
)[0]
self.add_inspector(img_plot)
self.add_tools(img_plot)
self.oplot = plot
self.container.add(self.oplot)
# self.container.add(self.plot)
self.container.request_redraw()
def _plot_container_default(self):
data = self.posterior
nannotations, nclasses = data.shape
# create a plot data object
plot_data = ArrayPlotData()
plot_data.set_data("values", data)
# create the plot
plot = Plot(plot_data, origin=self.origin)
img_plot = plot.img_plot("values",
interpolation='nearest',
xbounds=(0, nclasses),
ybounds=(0, nannotations),
colormap=self._create_colormap())[0]
ndisp = 55
img_plot.y_mapper.range.high = ndisp
img_plot.y_mapper.domain_limits=((0, nannotations))
self._set_title(plot)
plot.padding_top = 80
# create x axis for labels
label_axis = self._create_increment_one_axis(plot, 0.5, nclasses, 'top')
label_axis.title = 'classes'
self._add_index_axis(plot, label_axis)
plot.y_axis.title = 'items'
# tweak plot aspect
goal_aspect_ratio = 2.0
plot_width = (goal_aspect_ratio * self.plot_height
* nclasses / ndisp)
self.plot_width = min(max(plot_width, 200), 400)
plot.aspect_ratio = self.plot_width / self.plot_height
# add colorbar
colormap = img_plot.color_mapper
colorbar = ColorBar(index_mapper = LinearMapper(range=colormap.range),
color_mapper = colormap,
plot = img_plot,
orientation = 'v',
resizable = '',
width = 15,
height = 250)
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = int(self.plot_height - colorbar.height -
plot.padding_top)
colorbar.padding_left = 0
colorbar.padding_right = 30
# create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(use_backbuffer=True)
container.add(plot)
container.add(colorbar)
container.bgcolor = 0xFFFFFF # light gray: 0xEEEEEE
# add pan tools
img_plot.tools.append(PanTool(img_plot, constrain=True,
constrain_direction="y", speed=7.))
self.decorate_plot(container, self.posterior)
self.plot_posterior = plot
return container
def _plot_container_default(self):
data = self.posterior
nannotations, nclasses = data.shape
# create a plot data object
plot_data = ArrayPlotData()
plot_data.set_data("values", data)
# create the plot
plot = Plot(plot_data, origin=self.origin)
img_plot = plot.img_plot("values",
interpolation='nearest',
xbounds=(0, nclasses),
ybounds=(0, nannotations),
colormap=self._create_colormap())[0]
ndisp = 55
img_plot.y_mapper.range.high = ndisp
img_plot.y_mapper.domain_limits = ((0, nannotations))
self._set_title(plot)
plot.padding_top = 80
# create x axis for labels
label_axis = self._create_increment_one_axis(plot, 0.5, nclasses,
'top')
label_axis.title = 'classes'
self._add_index_axis(plot, label_axis)
plot.y_axis.title = 'items'
# tweak plot aspect
goal_aspect_ratio = 2.0
plot_width = (goal_aspect_ratio * self.plot_height * nclasses / ndisp)
self.plot_width = min(max(plot_width, 200), 400)
plot.aspect_ratio = self.plot_width / self.plot_height
# add colorbar
colormap = img_plot.color_mapper
colorbar = ColorBar(index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=img_plot,
orientation='v',
resizable='',
width=15,
height=250)
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = int(self.plot_height - colorbar.height -
plot.padding_top)
colorbar.padding_left = 0
colorbar.padding_right = 30
# create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(use_backbuffer=True)
container.add(plot)
container.add(colorbar)
container.bgcolor = 0xFFFFFF # light gray: 0xEEEEEE
# add pan tools
img_plot.tools.append(
PanTool(img_plot,
constrain=True,
constrain_direction="y",
speed=7.))
self.decorate_plot(container, self.posterior)
self.plot_posterior = plot
return container
def create_plot(self):
if hasattr(self.value, 'shadows'):
color_gen = color_generator()
shadowcolors = {}
for shadow in self.value.shadows:
shadowcolors[shadow] = color_gen.next()
container_class = {
'h': HPlotContainer,
'v': VPlotContainer
}[self.orientation]
container = container_class(spacing=15,
padding=15,
bgcolor='transparent')
container.fill_padding = True
container.bgcolor = (236 / 255.0, 233 / 255.0, 216 / 255.0)
if self.show_all:
self.plot_items = self.value.keys()
if len(self.plot_items) > 0:
plot_configs = []
for (plot_num, var_name) in enumerate(self.plot_items):
if not (isinstance(self.value[var_name], ndarray) and \
len(self.value[var_name].shape) == 1):
continue
plot_configs.append(
PlotConfig(x=var_name + '_index',
y=var_name,
type='Line',
number=plot_num))
self.plot_configs = plot_configs
if len(self.plot_configs) > 0:
number_to_plots = {}
for plot_config in self.plot_configs:
plotlist = number_to_plots.get(plot_config.number, [])
plotlist.append(plot_config)
number_to_plots[plot_config.number] = plotlist
keys = number_to_plots.keys()
keys.sort()
container_list = [number_to_plots[number] for number in keys]
for plot_group in container_list:
context_adapter = PlotDataContextAdapter(context=self.value)
plot = Plot(context_adapter)
plot.padding = 15
plot.padding_left = 35
plot.padding_bottom = 30
plot.spacing = 15
plot.border_visible = True
for plot_item in plot_group:
if len(self.value[plot_item.y].shape) == 2:
color_range = DataRange1D(
low=min(self.value[plot_item.y]),
high=max(self.value[plot_item.y]))
plot.img_plot(plot_item.y,
colormap=gray(color_range),
name=plot_item.y)
else:
plot_type = {
'Line': 'line',
'Scatter': 'scatter'
}[plot_item.type]
plot.plot(
(plot_item.x, plot_item.y),
name=plot_item.x + " , " + plot_item.y,
color=(.7, .7, .7),
type=plot_type,
)
if plot.index_axis.title != '':
plot.index_axis.title = plot.index_axis.title + ', ' + plot_item.x
else:
plot.index_axis.title = plot_item.x
if plot.value_axis.title != '':
plot.value_axis.title = plot.value_axis.title + ', ' + plot_item.y
else:
plot.value_axis.title = plot_item.y
if self.view_shadows and hasattr(
self.value, 'shadows'):
self.generate_shadow_plots(plot, shadowcolors,
plot_item, plot_type)
plot.tools.append(PanTool(plot))
container.add(plot)
self.plot = container
def create_plot(self):
if hasattr(self.value, 'shadows'):
color_gen = color_generator()
shadowcolors = {}
for shadow in self.value.shadows:
shadowcolors[shadow] = color_gen.next()
container_class = {'h' : HPlotContainer, 'v' : VPlotContainer}[self.orientation]
container = container_class(spacing=15, padding=15, bgcolor = 'transparent')
container.fill_padding = True
container.bgcolor=(236/255.0, 233/255.0, 216/255.0)
if self.show_all:
self.plot_items = self.value.keys()
if len(self.plot_items)>0:
plot_configs = []
for (plot_num, var_name) in enumerate(self.plot_items):
if not (isinstance(self.value[var_name], ndarray) and \
len(self.value[var_name].shape) == 1):
continue
plot_configs.append(PlotConfig(x=var_name + '_index',
y=var_name,
type='Line',
number=plot_num))
self.plot_configs = plot_configs
if len(self.plot_configs)>0:
number_to_plots = {}
for plot_config in self.plot_configs:
plotlist = number_to_plots.get(plot_config.number, [])
plotlist.append(plot_config)
number_to_plots[plot_config.number] = plotlist
keys = number_to_plots.keys()
keys.sort()
container_list = [number_to_plots[number] for number in keys]
for plot_group in container_list:
context_adapter = PlotDataContextAdapter(context=self.value)
plot = Plot(context_adapter)
plot.padding = 15
plot.padding_left=35
plot.padding_bottom = 30
plot.spacing=15
plot.border_visible = True
for plot_item in plot_group:
if len(self.value[plot_item.y].shape) == 2:
color_range = DataRange1D(low=min(self.value[plot_item.y]),
high=max(self.value[plot_item.y]))
plot.img_plot(plot_item.y, colormap=gray(color_range),
name=plot_item.y)
else:
plot_type = {'Line':'line', 'Scatter':'scatter'}[plot_item.type]
plot.plot((plot_item.x, plot_item.y),
name=plot_item.x + " , " + plot_item.y,
color=(.7, .7, .7),
type=plot_type,)
if plot.index_axis.title != '':
plot.index_axis.title = plot.index_axis.title + ', ' + plot_item.x
else:
plot.index_axis.title = plot_item.x
if plot.value_axis.title != '':
plot.value_axis.title = plot.value_axis.title + ', ' + plot_item.y
else:
plot.value_axis.title = plot_item.y
if self.view_shadows and hasattr(self.value, 'shadows'):
self.generate_shadow_plots(plot, shadowcolors, plot_item, plot_type)
plot.tools.append(PanTool(plot))
container.add(plot)
self.plot = container
