def _create_plot_component():
# Create a scalar field to contour
# uses a randomly sampled, non-uniform grid
xs = uniform(-2*pi, 2*pi, 600)
xs.sort()
ys = uniform(-1.5*pi, 1.5*pi, 300)
ys.sort()
x, y = meshgrid(xs,ys)
z = tanh(x*y/6)*cosh(exp(-y**2)*x/3)
z = x*y
# mask out a region with nan values
mask = ((abs(x-5) <= 1) & (abs(y-2) <= 2))
z[mask] = nan
# Create a plot data object and give it this data
pd = ArrayPlotData()
pd.set_data("imagedata", z)
# Create a contour polygon plot of the data
plot = Plot(pd, default_origin="bottom left")
plot.contour_plot("imagedata",
type="poly",
poly_cmap=jet,
xbounds=x,
ybounds=y)
# Create a contour line plot for the data, too
plot.contour_plot("imagedata",
type="line",
xbounds=x,
ybounds=y)
# Tweak some of the plot properties
plot.title = "My First Contour Plot"
plot.padding = 50
plot.bg_color = "white"
plot.fill_padding = True
# Attach some tools to the plot
plot.tools.append(PanTool(plot))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
return plot
def test_bounds_2d_case():
# test for bug: contour and image plots should support the case where
# xbounds and ybounds are 2d arrays resulting from meshgrids
xs = np.linspace(-10,10,200)
ys = np.linspace(-10,10,400)
x, y = np.meshgrid(xs,ys)
z = x + y
plotdata = ArrayPlotData()
plotdata.set_data("z", z)
plot = Plot(plotdata)
plot.contour_plot("z", xbounds=x, ybounds=y)
# try to display it, that's when the exception is raised
with store_exceptions_on_all_threads():
pv = PlotViewer(plot=plot)
pv.edit_traits()
def test_bounds_2d_case():
# test for bug: contour and image plots should support the case where
# xbounds and ybounds are 2d arrays resulting from meshgrids
xs = np.linspace(-10, 10, 200)
ys = np.linspace(-10, 10, 400)
x, y = np.meshgrid(xs, ys)
z = x + y
plotdata = ArrayPlotData()
plotdata.set_data("z", z)
plot = Plot(plotdata)
plot.contour_plot("z", xbounds=x, ybounds=y)
# try to display it, that's when the exception is raised
with store_exceptions_on_all_threads():
pv = PlotViewer(plot=plot)
pv.edit_traits()
def _create_plot_component():
# Create a scalar field to contour
# uses a randomly sampled, non-uniform grid
xs = uniform(-2 * pi, 2 * pi, 600)
xs.sort()
ys = uniform(-1.5 * pi, 1.5 * pi, 300)
ys.sort()
x, y = meshgrid(xs, ys)
z = tanh(x * y / 6) * cosh(exp(-y**2) * x / 3)
z = x * y
# mask out a region with nan values
mask = ((abs(x - 5) <= 1) & (abs(y - 2) <= 2))
z[mask] = nan
# Create a plot data object and give it this data
pd = ArrayPlotData()
pd.set_data("imagedata", z)
# Create a contour polygon plot of the data
plot = Plot(pd, default_origin="bottom left")
plot.contour_plot("imagedata",
type="poly",
poly_cmap=jet,
xbounds=x,
ybounds=y)
# Create a contour line plot for the data, too
plot.contour_plot("imagedata", type="line", xbounds=x, ybounds=y)
# Tweak some of the plot properties
plot.title = "My First Contour Plot"
plot.padding = 50
plot.bg_color = "white"
plot.fill_padding = True
# Attach some tools to the plot
plot.tools.append(PanTool(plot))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
return plot
def contour_plot_from_data(self, anim_data, array_names, style_dict=None):
""" Returns the Container of contour plot corresponding to the
array data in anim_data w.r.t. arraynames
Parameters
----------
anim_data: AnimationData
Animation data to plot as a contour plot.
array_names: 4-tuple of strings
Attr names in anim_data which will be (x,y,z, levels) arrays for
the contour plot.
style_dict: dict
Dictionary of extra stylings to set, currently allowing up to one
level of nesting of attributes (with '' key meaning no nesting).
"""
plot = Plot(self.plot_data)
x_name, y_name, z_name, levels_name = array_names
xs = getattr(anim_data, x_name)
xbounds = (min(xs), max(xs))
ys = getattr(anim_data, y_name)
ybounds = (min(ys), max(ys))
levels = getattr(anim_data, levels_name).tolist()
kwargs = style_dict.get('') if '' in style_dict else {}
# Override the colormap's range with the full range of the data
if style_dict is BOUND_CONC_PLOT_STYLE_DICT:
data_max = self.active_anim_data.beadboundZ.max()
else:
data_max = self.active_anim_data.columnliqZ.max()
contour_plot = plot.contour_plot(z_name,
xbounds=xbounds,
ybounds=ybounds,
levels=levels,
**kwargs)[0]
# Overwrite the colormapper range to be set by the full data, not just
# the 1 time slice currently plotted:
contour_plot.color_mapper.range.set_bounds(EPS, data_max)
contour_plot.color_mapper.updated = True
# Adjust plot style for any remaining attrs
for attr_name, attr_styles in style_dict.items():
if attr_name == '':
continue
attr = getattr(plot, attr_name)
for attr_attr_name, attr_attr_val in attr_styles.items():
setattr(attr, attr_attr_name, attr_attr_val)
return plot
def _create_plot_component():
# Create a scalar field to contour
xs = linspace(-2 * pi, 2 * pi, 600)
ys = linspace(-1.5 * pi, 1.5 * pi, 300)
x, y = meshgrid(xs, ys)
z = tanh(x * y / 6) * cosh(exp(-y**2) * x / 3)
z = x * y
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("imagedata", z)
# Create a contour polygon plot of the data
plot = Plot(pd, default_origin="top left")
plot.contour_plot("imagedata",
type="poly",
poly_cmap=jet,
xbounds=(xs[0], xs[-1]),
ybounds=(ys[0], ys[-1]))
# Create a contour line plot for the data, too
plot.contour_plot("imagedata",
type="line",
xbounds=(xs[0], xs[-1]),
ybounds=(ys[0], ys[-1]))
# Tweak some of the plot properties
plot.title = "My First Contour Plot"
plot.padding = 50
plot.bg_color = "white"
plot.fill_padding = True
# Attach some tools to the plot
plot.tools.append(PanTool(plot))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
return plot
def _create_plot_component():
# Create img data to colormap
fname = "/Users/maye/Data/hirise/PSP_003092_0985/PSP_003092_0985_RED.cal.norm.map.equ.mos.cub"
hirise = mars.HiRISE(fname)
win1 = mars.Window(mars.Point(1792*2,5888*2),width=512)
hirise.read_window(win1)
hirise.normalize()
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("imagedata", hirise.data)
# Create the left plot, a colormap and simple contours
lplot = Plot(pd)
lplot.img_plot("imagedata",
name="cm_plot",
# xbounds=x_extents,
# ybounds=y_extents,
origin='top left',
colormap=gmt_drywet)
# lplot.contour_plot("imagedata",
# type="line",
# # xbounds=x_extents,
# # ybounds=y_extents,
# )
# Tweak some of the plot properties
lplot.title = "Colormap and contours"
lplot.padding = 20
lplot.bg_color = "white"
lplot.fill_padding = True
# Add some tools to the plot
zoom = ZoomTool(lplot, tool_mode="box", always_on=False)
lplot.overlays.append(zoom)
lplot.tools.append(PanTool(lplot, constrain_key="shift"))
# # Right now, some of the tools are a little invasive, and we need the
# # actual CMapImage object to give to them
# cm_plot = lplot.plots["cm_plot"][0]
#
# # Create the colorbar, handing in the appropriate range and colormap
# colormap = cm_plot.color_mapper
# colorbar = ColorBar(index_mapper=LinearMapper(range=colormap.range),
# color_mapper=colormap,
# plot=cm_plot,
# orientation='v',
# resizable='v',
# width=30,
# padding=20)
# colorbar.padding_top = lplot.padding_top
# colorbar.padding_bottom = lplot.padding_bottom
# Create the left plot, contours of varying color and width
rplot = Plot(pd, range2d=lplot.range2d)
rplot.contour_plot("imagedata",
type="line",
# xbounds=x_extents,
# ybounds=y_extents,
bgcolor="black",
levels=15,
styles="solid",
widths=list(linspace(4.0, 0.1, 15)),
colors=gmt_drywet)
# Add some tools to the plot
zoom = ZoomTool(rplot, tool_mode="box", always_on=False)
rplot.overlays.append(zoom)
rplot.tools.append(PanTool(rplot, constrain_key="shift"))
# Tweak some of the plot properties
rplot.title = "Varying contour lines"
rplot.padding = 20
rplot.bg_color = "white"
rplot.fill_padding = True
# Create a container and add our plots
container = HPlotContainer(padding=40, fill_padding=True,
bgcolor = "white", use_backbuffer=True)
container.add(colorbar)
container.add(lplot)
container.add(rplot)
return container
def _create_plot_component():
# Create a scalar field to colormap
x_extents = (-2 * pi, 2 * pi)
y_extents = (-1.5 * pi, 1.5 * pi)
xs = linspace(-2 * pi, 2 * pi, 200)
ys = linspace(-1.5 * pi, 1.5 * pi, 100)
x, y = meshgrid(xs, ys)
zs = sin(log(abs((x+1)**4)+0.05))*cos(y)*1.1*(-y) + \
sin(((x+1)**2 + y**2)/4)
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("imagedata", zs)
# Create the left plot, a colormap and simple contours
lplot = Plot(pd)
lplot.img_plot("imagedata",
name="cm_plot",
xbounds=x_extents,
ybounds=y_extents,
colormap=gmt_drywet)
lplot.contour_plot("imagedata",
type="line",
xbounds=x_extents,
ybounds=y_extents)
# Tweak some of the plot properties
lplot.title = "Colormap and contours"
lplot.padding = 20
lplot.bg_color = "white"
lplot.fill_padding = True
# Add some tools to the plot
zoom = ZoomTool(lplot, tool_mode="box", always_on=False)
lplot.overlays.append(zoom)
lplot.tools.append(PanTool(lplot, constrain_key="shift"))
# Right now, some of the tools are a little invasive, and we need the
# actual CMapImage object to give to them
cm_plot = lplot.plots["cm_plot"][0]
# Create the colorbar, handing in the appropriate range and colormap
colormap = cm_plot.color_mapper
colorbar = ColorBar(index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=cm_plot,
orientation='v',
resizable='v',
width=30,
padding=20)
colorbar.padding_top = lplot.padding_top
colorbar.padding_bottom = lplot.padding_bottom
# Create the left plot, contours of varying color and width
rplot = Plot(pd, range2d=lplot.range2d)
rplot.contour_plot("imagedata",
type="line",
xbounds=x_extents,
ybounds=y_extents,
bgcolor="black",
levels=15,
styles="solid",
widths=list(linspace(4.0, 0.1, 15)),
colors=gmt_drywet)
# Add some tools to the plot
zoom = ZoomTool(rplot, tool_mode="box", always_on=False)
rplot.overlays.append(zoom)
rplot.tools.append(PanTool(rplot, constrain_key="shift"))
# Tweak some of the plot properties
rplot.title = "Varying contour lines"
rplot.padding = 20
rplot.bg_color = "white"
rplot.fill_padding = True
# Create a container and add our plots
container = HPlotContainer(padding=40,
fill_padding=True,
bgcolor="white",
use_backbuffer=True)
container.add(colorbar)
container.add(lplot)
container.add(rplot)
return container
def _create_plot_component():
# Create a scalar field to colormap
x_extents = (-2*pi, 2*pi)
y_extents = (-1.5*pi, 1.5*pi)
xs = linspace(-2*pi, 2*pi, 200)
ys = linspace(-1.5*pi, 1.5*pi, 100)
x, y = meshgrid(xs,ys)
zs = sin(log(abs((x+1)**4)+0.05))*cos(y)*1.1*(-y) + \
sin(((x+1)**2 + y**2)/4)
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("imagedata", zs)
# Create the left plot, a colormap and simple contours
lplot = Plot(pd)
lplot.img_plot("imagedata",
name="cm_plot",
xbounds=x_extents,
ybounds=y_extents,
colormap=gmt_drywet)
lplot.contour_plot("imagedata",
type="line",
xbounds=x_extents,
ybounds=y_extents)
# Tweak some of the plot properties
lplot.title = "Colormap and contours"
lplot.padding = 20
lplot.bg_color = "white"
lplot.fill_padding = True
# Add some tools to the plot
zoom = ZoomTool(lplot, tool_mode="box", always_on=False)
lplot.overlays.append(zoom)
lplot.tools.append(PanTool(lplot, constrain_key="shift"))
# Right now, some of the tools are a little invasive, and we need the
# actual CMapImage object to give to them
cm_plot = lplot.plots["cm_plot"][0]
# Create the colorbar, handing in the appropriate range and colormap
colormap = cm_plot.color_mapper
colorbar = ColorBar(index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=cm_plot,
orientation='v',
resizable='v',
width=30,
padding=20)
colorbar.padding_top = lplot.padding_top
colorbar.padding_bottom = lplot.padding_bottom
# Create the left plot, contours of varying color and width
rplot = Plot(pd, range2d=lplot.range2d)
rplot.contour_plot("imagedata",
type="line",
xbounds=x_extents,
ybounds=y_extents,
bgcolor="black",
levels=15,
styles="solid",
widths=list(linspace(4.0, 0.1, 15)),
colors=gmt_drywet)
# Add some tools to the plot
zoom = ZoomTool(rplot, tool_mode="box", always_on=False)
rplot.overlays.append(zoom)
rplot.tools.append(PanTool(rplot, constrain_key="shift"))
# Tweak some of the plot properties
rplot.title = "Varying contour lines"
rplot.padding = 20
rplot.bg_color = "white"
rplot.fill_padding = True
# Create a container and add our plots
container = HPlotContainer(padding=40, fill_padding=True,
bgcolor = "white", use_backbuffer=True)
container.add(colorbar)
container.add(lplot)
container.add(rplot)
return container
def __init__(self, *args, **kwargs):
super(Demo, self).__init__(*args, **kwargs)
from imread import imread
imarray = imread(find_resource('imageAlignment', '../images/GIRLS-IN-SPACE.jpg',
'../images/GIRLS-IN-SPACE.jpg', return_path=True))
self.pd = ArrayPlotData(imagedata=imarray)
#self.pd.x_axis.orientation = "top"
self.plot = HPlotContainer()
titles = ["I KEEP DANCE", "ING ON MY OWN"]
self._load()
i = 0
for plc in [Plot, Plot]:
xs = linspace(0, 334*pi, 333)
ys = linspace(0, 334*pi, 333)
x, y = meshgrid(xs,ys)
z = tanh(x*y/6)*cosh(exp(-y**2)*x/3)
z = x*y
_pd = ArrayPlotData()
_pd.set_data("drawdata", z)
_pd.set_data("imagedata", self.pd.get_data('imagedata'))
plc = Plot(_pd,
title="render_style = hold",
padding=50, border_visible=True, overlay_border=True)
self.plot.add(plc)
plc.img_plot("imagedata",
alpha=0.95)
# Create a contour polygon plot of the data
plc.contour_plot("drawdata",
type="poly",
poly_cmap=jet,
xbounds=(0, 499),
ybounds=(0, 582),
alpha=0.35)
# Create a contour line plot for the data, too
plc.contour_plot("drawdata",
type="line",
xbounds=(0, 499),
ybounds=(0, 582),
alpha=0.35)
# Create a plot data obect and give it this data
plc.legend.visible = True
plc.title = titles[i]
i += 1
#plc.plot(("index", "y0"), name="j_0", color="red", render_style="hold")
#plc.padding = 50
#plc.padding_top = 75
plc.tools.append(PanTool(plc))
zoom = ZoomTool(component=plc, tool_mode="box", always_on=False)
plc.overlays.append(zoom)
# Tweak some of the plot properties
plc.padding = 50
#zoom = ZoomTool(component=plot1, tool_mode="box", always_on=False)
#plot1.overlays.append(zoom)
# Attach some tools to the plot
#attach_tools(plc)
plc.bg_color = None
plc.fill_padding = True