def _initialize_plot(self, plotcontainer):
# Create the data source
ppd = PandasPlotData(self.dataset)
plotcontainer.data = ppd
if self.facet_layout is None:
[g.plot(plotcontainer, self.aes) for g in self.geoms]
else:
# Use the PandasPlotData to create a list of faceted data sources
facet_pds = ppd.facet(self.facet_layout.factors)
container = None
if self.facet_layout.ftype == "grid":
# Determine the shape by looking at the first faceted datasource.
# Reaching into the facet_pds is sort of gorpy; need to think
# of a better interface for PandasPlotData.
levels = facet_pds[0]._groupby.grouper.levels
grid_shape = (len(levels[0]), len(levels[1]))
print "Factors:", self.facet_layout.factors
print "Grid of shape:", grid_shape
print "Levels:", levels[0], levels[1]
container = chaco.GridContainer(padding=20, fill_padding=True,
bgcolor="lightgray", use_backbuffer=False,
shape=grid_shape, spacing=(10,10))
pd_dict = dict((pd.group_key, pd) for pd in facet_pds)
factors = self.facet_layout.factors
title = factors[0] + "=%d, " + factors[1] + "=%d"
for i in levels[0]:
for j in levels[1]:
if (i,j) in pd_dict:
plot = chaco.Plot(pd_dict[(i,j)], title=title%(i,j),
padding=15)
plot.index_range.tight_bounds = False
plot.value_range.tight_bounds = False
[g.plot(plot, self.aes) for g in self.geoms]
else:
plot = chaco.OverlayPlotContainer(bgcolor="lightgray")
container.add(plot)
elif self.facet_layout.ftype == "wrap":
# This is not really wrapping, instead just using a horizontal
# plot container.
container = chaco.HPlotContainer(padding=40, fill_padding=True,
bgcolor="lightgray", use_backbuffer=True, spacing=20)
self.window.set_container(container)
container.request_redraw()
def to_html(self, notebook=False):
""" Returns HTML representing the plot. Does not include any headers
or javascript dependencies, etc.
If **notebook** is True, then does the right things for producing
output that can be embedded in IPython notebook.
"""
import mpl
client = mpl.PlotClient()
if notebook:
client.notebooksources()
# Create the data source
ppd = PandasPlotData(self.dataset)
if self.facet_layout is None:
datasource = client.make_source(**dict(
(dataname, ppd.get_data(dataname))
for dataname in ppd.list_data()))
for g in self.geoms:
p = g.plot(client, datasource, self.aes)
if notebook:
return p
else:
return client.htmldump()
else:
# Use the PandasPlotData to create a list of faceted data sources
facet_pds = ppd.facet(self.facet_layout.factors)
container = None
if self.facet_layout.ftype == "grid":
# Determine the shape by looking at the first faceted datasource.
# Reaching into the facet_pds is sort of gorpy; need to think
# of a better interface for PandasPlotData.
levels = facet_pds[0]._groupby.grouper.levels
grid_shape = (len(levels[0]), len(levels[1]))
plots = []
pd_dict = dict((pd.group_key, pd) for pd in facet_pds)
factors = self.facet_layout.factors
title = factors[0] + "=%d, " + factors[1] + "=%d"
for i in levels[0]:
plotrow = []
for j in levels[1]:
if (i, j) in pd_dict:
pd = pd_dict[(i, j)]
datasource = client.make_source(**dict(
(dataname, pd.get_data(dataname))
for dataname in pd.list_data()))
plot = self.geoms[0].plot(client, datasource,
self.aes)
if len(self.geoms) > 1:
[
g.plot(client, datasource, self.aes)
for g in self.geoms[1:]
]
else:
# Create a new empty plot area
plot = client._newxyplot()
plotrow.append(plot)
client.figure()
plots.append(plotrow)
container = client.grid(plots)
elif self.facet_layout.ftype == "wrap":
pass
if notebook:
return container
else:
return container.htmldump()
def to_html(self, notebook=False):
""" Returns HTML representing the plot. Does not include any headers
or javascript dependencies, etc.
If **notebook** is True, then does the right things for producing
output that can be embedded in IPython notebook.
"""
import mpl
client = mpl.PlotClient()
if notebook:
client.notebooksources()
# Create the data source
ppd = PandasPlotData(self.dataset)
if self.facet_layout is None:
datasource = client.make_source(**dict((dataname, ppd.get_data(dataname)) for dataname in ppd.list_data()))
for g in self.geoms:
p = g.plot(client, datasource, self.aes)
if notebook:
return p.notebook()
else:
return client.htmldump()
else:
# Use the PandasPlotData to create a list of faceted data sources
facet_pds = ppd.facet(self.facet_layout.factors)
container = None
if self.facet_layout.ftype == "grid":
# Determine the shape by looking at the first faceted datasource.
# Reaching into the facet_pds is sort of gorpy; need to think
# of a better interface for PandasPlotData.
levels = facet_pds[0]._groupby.grouper.levels
grid_shape = (len(levels[0]), len(levels[1]))
plots = []
pd_dict = dict((pd.group_key, pd) for pd in facet_pds)
factors = self.facet_layout.factors
title = factors[0] + "=%d, " + factors[1] + "=%d"
for i in levels[0]:
plotrow = []
for j in levels[1]:
if (i,j) in pd_dict:
pd = pd_dict[(i,j)]
datasource = client.make_source(
**dict((dataname, pd.get_data(dataname)) for dataname in pd.list_data()))
plot = self.geoms[0].plot(client, datasource, self.aes, title=title%(i,j))
if len(self.geoms) > 1:
[g.plot(client, datasource, self.aes) for g in self.geoms[1:]]
else:
# Create a new empty plot area
plot = client._newxyplot()
plotrow.append(plot)
client.figure()
plots.append(plotrow)
container = client.grid(plots)
elif self.facet_layout.ftype == "wrap":
pass
if notebook:
return container.notebook()
else:
return container.htmldump()
def to_html(self, notebook=False):
""" Returns HTML representing the plot. Does not include any headers
or javascript dependencies, etc.
If **notebook** is True, then does the right things for producing
output that can be embedded in IPython notebook.
"""
from cdxlib import mpl
client = mpl.PlotClient()
if notebook:
client.notebooksources()
# Create the data source
ppd = PandasPlotData(self.dataset)
if self.facet_layout is None:
#[g.plot(plotcontainer, self.aes) for g in self.geoms]
datasource = client.make_source(**dict((dataname, ppd.get_data(dataname)) for dataname in ppd.list_data()))
for g in self.geoms:
p = g.plot(client, datasource, self.aes)
if notebook:
return p.notebook()
else:
return client.htmldump()
else:
# Use the PandasPlotData to create a list of faceted data sources
facet_pds = ppd.facet(self.facet_layout.factors)
container = None
if self.facet_layout.ftype == "grid":
# Determine the shape by looking at the first faceted datasource.
# Reaching into the facet_pds is sort of gorpy; need to think
# of a better interface for PandasPlotData.
levels = facet_pds[0]._groupby.grouper.levels
grid_shape = (len(levels[0]), len(levels[1]))
#print "Factors:", self.facet_layout.factors
#print "Grid of shape:", grid_shape
#print "Levels:", levels[0], levels[1]
plots = []
pd_dict = dict((pd.group_key, pd) for pd in facet_pds)
factors = self.facet_layout.factors
title = factors[0] + "=%d, " + factors[1] + "=%d"
for i in levels[0]:
plotrow = []
for j in levels[1]:
if (i,j) in pd_dict:
pd = pd_dict[(i,j)]
datasource = client.make_source(
**dict((dataname, pd.get_data(dataname)) for dataname in pd.list_data()))
plot = self.geoms[0].plot(client, datasource, self.aes, title=title%(i,j))
if len(self.geoms) > 1:
[g.plot(client, datasource, self.aes) for g in self.geoms[1:]]
else:
#raise NotImplementedError("Emtpy facets currently unsupported")
print "Empty facet for", i, j
plot = client._newxyplot()
plotrow.append(plot)
client.figure()
plots.append(plotrow)
container = client.grid(plots)
elif self.facet_layout.ftype == "wrap":
# This is not really wrapping, instead just using a horizontal
# plot container.
#container = chaco.HPlotContainer(padding=40, fill_padding=True,
# bgcolor="lightgray", use_backbuffer=True, spacing=20)
pass
if notebook:
return container.notebook()
else:
return container.htmldump()
