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 _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 _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
