class DataTablesHTMLJinjaTableBlock(HTMLJinjaTableBlock):
resource_deps = [
Css("css/jquery-dataTables"),
JScript("jquery"),
JScript("jquery-dataTables"),
JScript("jquery-dataTables-impl")
]
def __init__(self,
df,
formatters=None,
use_default_formatters=False,
paging=True,
searching=True,
info=True,
**kwargs):
if formatters is None and use_default_formatters is False:
formatters = [
DataTablesCSSClass(paging, searching, info), fmt_fontsize_12,
fmt_table_center, fmt_align_cells, fmt_align_header_index,
fmt_header_index_bold, fmt_page_break, fmt_decimals_2
]
super(DataTablesHTMLJinjaTableBlock,
self).__init__(df, formatters, use_default_formatters, **kwargs)
def _write_plot(self, container, container_id, id_gen, static_output):
"""
Write out the chart construction machinery.
"""
js_timer_var_name = "_ins_timer_" + next(id_gen)
# Plumbing to make sure script rendering waits until the container element is ready
stream = StringIO()
chart_cfg = self._chart_cfg.override(Chart(render_to=container_id))
if static_output:
# Chart load wait handles for static output.
stream.write("registerWaitHandle('%s');" % container_id)
overrides = [
Chart(
Events(load=Expr("function(){setLoaded('%s');}" %
container_id))),
Exporting(enabled=False),
Navigator(enabled=False),
Scrollbar(enabled=False),
PlotOptions(
Series(enable_mouse_tracking=False,
shadow=False,
animation=False)),
RangeSelector(enabled=False)
]
chart_cfg = chart_cfg.override_many(*overrides)
stream.write(("var %s=setInterval(function(){"
"var container=document.getElementById('%s');"
"if(container){clearInterval(%s);") %
(js_timer_var_name, container_id, js_timer_var_name))
# Write out the chart script into a separate buffer before running it through
# the encoding/compression
chart_buf = StringIO()
chart_buf.write("var cfg=")
self._write_dict(chart_buf, chart_cfg)
chart_buf.write(";")
chart_buf.write("var chart = new Highcharts." + self._chart_cls +
"(cfg);")
self._write_plot_postprocess(chart_buf)
JScript.write_compressed(stream, chart_buf.getvalue())
stream.write("}},10);")
js_elem(container, stream.getvalue())
def __init__(self, contents, **kwargs):
"""
Writes out the content as raw text or HTML.
:param contents: Bokeh plotting figure.
:param kwargs: Optional styling arguments. The `style` keyword argument has special
meaning in that it allows styling to be grouped as one argument.
It is also useful in case a styling parameter name clashes with a standard
block parameter.
"""
self.resource_deps = [
JScript(script_string=s, name='bokeh_js')
for s in JSResources().js_raw
]
self.resource_deps += [
Css(css_string=s, name='bokeh_css') for s in CSSResources().css_raw
]
super(BokehPlotBlock, self).__init__(**kwargs)
if not isinstance(contents, BokehFigure):
raise ValueError(
"Expected bokeh.plotting.figure.Figure type but got %s",
type(contents))
script, div = components(contents)
self._contents = script + div
def __init__(self, contents, plotly_kwargs=None, **kwargs):
"""
Writes out the content as raw text or HTML.
:param contents: Plotly graphics object figure.
:param plotly_kwargs: Kwargs that are passed to plotly plot function.
:param kwargs: Optional styling arguments. The `style` keyword argument has special
meaning in that it allows styling to be grouped as one argument.
It is also useful in case a styling parameter name clashes with a standard
block parameter.
"""
self.resource_deps = [
JScript(script_string=po.offline.get_plotlyjs(), name='plotly')
]
super(PlotlyPlotBlock, self).__init__(**kwargs)
if not isinstance(contents, PlotlyFigure):
raise ValueError(
"Expected plotly.graph_objs.graph_objs.Figure type but got %s",
type(contents))
plotly_kwargs = plotly_kwargs or {}
self._contents = po.plot(contents,
include_plotlyjs=True,
output_type='div',
**plotly_kwargs)
from six.moves import range
import sys
if sys.version_info > (3, ):
long = int
# Sets of plots based on dimensionality
_univariate_plots = {
"area", "areaspline", "column", "flags", "line", "scatter", "spline",
"pie", "gauge", "funnel"
}
_range_plots = {
"arearange", "areasplinerange", "candlestick", "columnrange", "ohlc"
}
highstock_jscript = JScript("highstock")
highcharts_more_jscript = JScript("highcharts-more")
highcharts_3d_jscript = JScript("highcharts-3d")
highcharts_heatmap_jscript = JScript("heatmap")
highcharts_funnel_jscript = JScript("funnel")
highcharts_exporting_jscript = JScript("exporting")
highcharts_exporting_csv_jscript = JScript("export-csv")
highcharts_pybloqs_jscript = JScript("highcharts-pybloqs")
register_interactive(highstock_jscript, highcharts_more_jscript,
highcharts_3d_jscript, highcharts_heatmap_jscript,
highcharts_funnel_jscript, highcharts_exporting_jscript,
highcharts_exporting_csv_jscript,
highcharts_pybloqs_jscript)
class Plot(BaseBlock):
"""
A composable chart. When the input data is a list of plot objects, they will be merged into a single
figure.
"""
resource_deps = [JScript(m) for m in HIGHCHARTS_ALL]
def __init__(self, data, *args, **kwargs):
"""
Create a chart or composite chart from the supplied data.
:param data: List, tuple, pandas.Series/DataFrame to use as chart data.
In case `data` is a list of Plot objects, a composite chart will be constructed.
:param chart_cls: The chart class to use. Available values are "Chart" or "StockChart".
StockCharts have extra features like a navigator pane and special handling
for timestamps. A sensible default will be chosen based on the data by default.
:param flatten: When set to True, the data will be flattened and categories will be
extracted from labelled data automatically.
Useful when creating plots where both axes need category labels (e.g. heatmaps).
:param args: Chart level configuration. Axes definitions will be applied to subplots
that do not have custom axes. Plots option sets supplied here will
be used by default for any subplots that do not specify their own.
:param kwargs: Optional styling arguments. The `style` keyword argument has special
meaning in that it allows styling to be grouped as one argument.
It is also useful in case a styling parameter name clashes with a standard
block parameter.
"""
chart_cls = kwargs.pop("chart_cls", self._choose_chart_class(data))
flatten = kwargs.pop("flatten", False)
switch_zy = kwargs.pop("switch_zy", False)
super(Plot, self).__init__(**kwargs)
chart_cfg, plot_cfg = self._parse_args(args)
# In case we got a list/tuple of Plots, create a composite chart.
if isinstance(data,
(list, tuple)) and isinstance(data[0], (Plot, NDFrame)):
# Parse out any axis defaults
axis_defaults = {}
for ax_name in ["x_axis", "y_axis"]:
axis_cfg = chart_cfg.pop(ax_name, None)
if axis_cfg is not None:
axis_defaults[ax_name] = axis_cfg
axes = Cfg(x_axis=[], y_axis=[])
chart_series = []
for plot_data in data:
# Convert data to a plot in case the it was not an actual plot instance
# (but a pandas object for example).
if not isinstance(plot_data, Plot):
plot_data = Plot(plot_data)
# If no chart class was defined yet, choose one based on the data.
if chart_cls is None:
chart_cls = plot_data._chart_cls
# Extract the chart configuration.
subchart_cfg = plot_data._chart_cfg
# Extract the series from the subchart config
subchart_series = subchart_cfg.series
# Check for axis definitions.
def _set_axis(axis_name):
# Get a custom axis, or use/create a default one.
axis = subchart_cfg.setdefault(
axis_name, None) or axis_defaults.setdefault(
axis_name, Cfg())
if isinstance(axis, (list, tuple)):
if len(axis) != 1:
raise ValueError(
"There can be at most one sub-chart axis definition for each axis."
)
axis = axis[0]
# Get the index of this axis from the list of known axes. Optionally, add a new entry.
axis_coll = axes[axis_name]
try:
axis_idx = axis_coll.index(axis)
except ValueError:
axis_coll.append(axis)
axis_idx = len(axis_coll) - 1
for series_cfg in subchart_series:
series_cfg[axis_name] = axis_idx
_set_axis("x_axis")
_set_axis("y_axis")
# Add the subplot series to the figure series.
chart_series.extend(subchart_series)
chart_cfg = chart_cfg.override(axes)
chart_cfg.series = chart_series
else:
if flatten:
data, chart_cfg = self._flatten_data(data,
chart_cfg,
switch_zy=switch_zy)
chart_cfg.series = self._construct_plot_series(data, plot_cfg)
chart_cfg = self._set_chart_defaults(chart_cfg, chart_cls)
self._chart_cfg = chart_cfg
self._chart_cls = chart_cls
def inherit(self, *args):
self._chart_cfg = self._chart_cfg.inherit_many(*args)
@staticmethod
def _flatten_data(data, chart_cfg, switch_zy=False):
plot_axes_def = [(0, XAxis), (1, YAxis)]
# Inject categories into the axis definitions of the plot
if isinstance(data, NDFrame):
for i, plot_axis in plot_axes_def[:data.ndim]:
categories = data.axes[i]
# Skip numeric indices
if not categories.is_numeric():
chart_cfg = chart_cfg.inherit_many(
plot_axis(categories=list(categories)))
data = [
list(index) + [value]
for index, value in list(np.ndenumerate(data))
]
if switch_zy:
for i in range(len(data)):
tmp = data[i][-1]
data[i][-1] = data[i][-2]
data[i][-2] = tmp
return data, chart_cfg
@staticmethod
def _set_chart_defaults(chart_cfg, chart_cls):
if chart_cls == "StockChart":
chart_cfg = chart_cfg.inherit_many(
Chart(zoom_type="x"),
PlotOptions(Series(States(Hover(enabled=False, halo=False)))))
else:
chart_cfg = chart_cfg.inherit_many(Chart(zoom_type="xy"))
# Stockcharts forces the Y Axis to be on the right side by default and it
# does not honor overrides set globally in JS. Fix it here.
if "y_axis" not in chart_cfg:
return chart_cfg.inherit(YAxis(opposite=False))
else:
base_cfg = Cfg(opposite=False)
# In case we have a single y axis, just set the defaults on it
if isinstance(chart_cfg.y_axis, Cfg):
chart_cfg.y_axis = chart_cfg.y_axis.inherit(base_cfg)
else:
y_axes = chart_cfg.y_axis
# Set the default on all y axes
for i in range(len(y_axes)):
y_axes[i] = y_axes[i].inherit(base_cfg)
return chart_cfg
@staticmethod
def _parse_args(args, allowed_cfg_types=(Cfg, )):
"""
Parse the supplied argument list into a configuration object.
:param args: Argument list.
:param allowed_cfg_types: The listed configuration object types will be allowed.
:return: Tuple of (chart configuration object, plot configuration object OR None)
"""
plot_cfg = None
configs = Cfg()
for arg in args:
# Construct an instance in case the config object is passed in as a type
if isinstance(arg, type):
arg = arg()
if isinstance(arg, _PlotOpts):
if plot_cfg:
plot_cfg = plot_cfg.inherit(arg)
else:
plot_cfg = arg
elif isinstance(arg, allowed_cfg_types):
configs = configs.inherit(arg)
else:
raise ValueError(
"%s is not recognized as a plot or chart configuration object"
% arg)
return configs, plot_cfg
@staticmethod
def _construct_plot_series(data, plot_cfg=None):
"""
Construct a list of series configurations.
:param data: The data to bundle into chart series.
:param plot_cfg: Optional. Existing plot configuration. If unspecified, a sensible
default will be chosen based on the data type.
:return: List of chart series.
"""
def _decompose_l1(cfg):
return [
cfg.override_many(data=value).inherit_many(name=key)
for key, value in data.iteritems()
]
def _decompose_l2(cfg):
component_series = []
for k1, v1 in iteritems(data):
for k2, v2 in iteritems(v1):
component_series.append(
cfg.override_many(data=v2).inherit_many(
name="%s - %s" % (k1, k2)))
return component_series
def _wrap(cfg, name):
cfg = cfg.override(Cfg(data=data))
if "name" not in cfg and name is not None:
cfg.name = name
return [cfg]
data_labelled = True
# Construct a series configuration, with optionally choosing a default plot configuration as well
if isinstance(data, pd.Series):
plot_cfg = plot_cfg or Line()
series = _wrap(plot_cfg, data.name)
elif isinstance(data, pd.DataFrame):
plot_cfg = plot_cfg or Line()
if plot_cfg.type in _univariate_plots:
series = _decompose_l1(plot_cfg)
else:
series = _wrap(plot_cfg, None)
else:
plot_cfg = plot_cfg or Line()
series = _wrap(plot_cfg, getattr(data, "name", None))
data_labelled = False
plot_cfg.check_array_shape(data, data_labelled)
return series
@staticmethod
def _choose_chart_class(data):
"""
Tries to guess the appropriate chart class based on the data.
"""
# In case the data has an index and the first entry is a datetime type, return a stock chart
# specialized for viewing time series.
if isinstance(data, pd.Series):
if isinstance(data.index[0], (np.datetime64, datetime)):
return "StockChart"
elif isinstance(data, NDFrame):
for labels in data.axes:
if isinstance(labels[0], (np.datetime64, datetime)):
return "StockChart"
elif hasattr(data, "__getitem__"):
try:
if isinstance(data[0], Plot):
return data[0]._chart_cls
if (len(data[0]) > 1) \
and isinstance(data[0], (list, tuple)) \
and isinstance(data[0][0], (np.datetime64, datetime)):
return "StockChart"
except TypeError:
pass
return "Chart"
def _write_contents(self,
container,
actual_cfg,
id_gen,
static_output=False,
**kwargs):
plot_container = append_to(container, "div")
plot_container["id"] = plot_container_id = next(id_gen)
# Write the config to the plot target as well
self._write_container_attrs(plot_container, actual_cfg)
self._write_plot(plot_container, plot_container_id, id_gen,
static_output)
def _write_plot(self, container, container_id, id_gen, static_output):
"""
Write out the chart construction machinery.
"""
js_timer_var_name = "_ins_timer_" + next(id_gen)
# Plumbing to make sure script rendering waits until the container element is ready
stream = StringIO()
chart_cfg = self._chart_cfg.override(Chart(render_to=container_id))
if static_output:
# Chart load wait handles for static output.
stream.write("registerWaitHandle('%s');" % container_id)
overrides = [
Chart(
Events(load=Expr("function(){setLoaded('%s');}" %
container_id))),
Exporting(enabled=False),
Navigator(enabled=False),
Scrollbar(enabled=False),
PlotOptions(
Series(enable_mouse_tracking=False,
shadow=False,
animation=False)),
RangeSelector(enabled=False)
]
chart_cfg = chart_cfg.override_many(*overrides)
stream.write(("var %s=setInterval(function(){"
"var container=document.getElementById('%s');"
"if(container){clearInterval(%s);") %
(js_timer_var_name, container_id, js_timer_var_name))
# Write out the chart script into a separate buffer before running it through
# the encoding/compression
chart_buf = StringIO()
chart_buf.write("var cfg=")
self._write_dict(chart_buf, chart_cfg)
chart_buf.write(";")
chart_buf.write("var chart = new Highcharts." + self._chart_cls +
"(cfg);")
self._write_plot_postprocess(chart_buf)
JScript.write_compressed(stream, chart_buf.getvalue())
stream.write("}},10);")
js_elem(container, stream.getvalue())
def _write_plot_postprocess(self, chart_buf):
pass
def _write_value(self, stream, value):
"""
Write the supplied value to the stream.
"""
# WARNING: The bool case must come before the (int, float) since python bools are ints as well.
if isinstance(value, np.datetime64):
stream.write(str(np_dt_epoch_msec(value)))
elif isinstance(value, datetime):
stream.write(str(dt_epoch_msecs(value)))
elif isinstance(value, (bool, np.bool_)):
stream.write("true" if value else "false")
elif isinstance(value,
(int, long, float, np.int, np.float, np.number)):
if np.isnan(value):
stream.write('null')
elif np.isinf(value):
stream.write('null')
else:
stream.write(str(value))
elif isinstance(value, str):
stream.write("'" + value + "'")
elif isinstance(value, text_type):
stream.write("'" + str(value) + "'")
elif isinstance(value, dict):
self._write_dict(stream, value)
elif isinstance(value, (list, tuple, set)):
self._write_iterable(stream, value)
elif isinstance(value, pd.Series):
self._write_iterable(stream, zip(value.index, value.values))
elif isinstance(value, pd.DataFrame):
labels = value.index
values = value.values
# Merge DFrame into a single list of lists
merged = []
for i in range(len(labels)):
label = labels[i]
ndval = values[i]
if isinstance(label, tuple):
merged.append(label + tuple(ndval))
else:
merged.append([label] + list(ndval))
self._write_iterable(stream, merged)
elif isinstance(value, (np.ndarray, pd.Index)):
self._write_iterable(stream, value)
elif hasattr(value, "write_jscript"):
value.write_jscript(stream)
elif value is None:
stream.write("null")
else:
raise ValueError("Unhandled config item type " + str(type(value)))
def _write_dict(self, stream, dct):
"""
Write out a dictionary.
"""
stream.write("{")
for i, item in enumerate(iteritems(dct)):
# If this is not the first item at this level, prepend a comma
if i > 0:
stream.write(",")
key, value = item
# camelCase the key as appropriate
stream.write(camelcase(key) + ":")
self._write_value(stream, value)
stream.write("}")
def _write_iterable(self, stream, iterable):
"""
Write out an iterable.
"""
stream.write("[")
for i, item in enumerate(iterable):
# If this is not the first item at this level, prepend a comma
if i > 0:
stream.write(",")
self._write_value(stream, item)
stream.write("]")
def _to_static(self):
return ImgBlock(self)
