Source code for holoviews.plotting.bokeh.util

import itertools, inspect, re, time
from distutils.version import LooseVersion
from collections import defaultdict
import datetime as dt

import numpy as np

    from matplotlib import colors
    import as cm
except ImportError:
    cm, colors = None, None

import param
import bokeh

bokeh_version = LooseVersion(bokeh.__version__)  # noqa

from bokeh.core.enums import Palette
from bokeh.core.json_encoder import serialize_json # noqa (API import)
from import value
from bokeh.document import Document
from bokeh.layouts import WidgetBox, Row, Column
from bokeh.models import Model, HasProps, ToolbarBox, FactorRange, Range1d, Plot, Spacer, CustomJS
from bokeh.models.widgets import DataTable, Tabs, Div
from bokeh.plotting import Figure

    from bkcharts import Chart
    Chart = type(None) # Create stub for isinstance check

from ...core.options import abbreviated_exception
from ...core.overlay import Overlay
from ...core.util import basestring, unique_array, callable_name, pd, dt64_to_dt
from ...core.spaces import get_nested_dmaps, DynamicMap

from ..util import dim_axis_label, rgb2hex

# Conversion between matplotlib and bokeh markers
markers = {'s': {'marker': 'square'},
           'd': {'marker': 'diamond'},
           '^': {'marker': 'triangle', 'angle': 0},
           '>': {'marker': 'triangle', 'angle': -np.pi/2},
           'v': {'marker': 'triangle', 'angle': np.pi},
           '<': {'marker': 'triangle', 'angle': np.pi/2},
           '1': {'marker': 'triangle', 'angle': 0},
           '2': {'marker': 'triangle', 'angle': -np.pi/2},
           '3': {'marker': 'triangle', 'angle': np.pi},
           '4': {'marker': 'triangle', 'angle': np.pi/2}}

[docs]def convert_timestamp(timestamp): """ Converts bokehJS timestamp to datetime64. """ return np.datetime64(dt.datetime.fromtimestamp(timestamp/1000.))
[docs]def rgba_tuple(rgba): """ Ensures RGB(A) tuples in the range 0-1 are scaled to 0-255. """ if isinstance(rgba, tuple): return tuple(int(c*255) if i<3 else c for i, c in enumerate(rgba)) else: return rgba
[docs]def mplcmap_to_palette(cmap, ncolors=None): """ Converts a matplotlib colormap to palette of RGB hex strings." """ if colors is None: raise ValueError("Using cmaps on objects requires matplotlib.") with abbreviated_exception(): colormap = cm.get_cmap(cmap) #choose any matplotlib colormap here if ncolors: return [rgb2hex(colormap(i)) for i in np.linspace(0, 1, ncolors)] return [rgb2hex(m) for m in colormap(np.arange(colormap.N))]
[docs]def get_cmap(cmap): """ Returns matplotlib cmap generated from bokeh palette or directly accessed from matplotlib. """ with abbreviated_exception(): rgb_vals = getattr(Palette, cmap, None) if rgb_vals: return colors.ListedColormap(rgb_vals, name=cmap) return cm.get_cmap(cmap)
[docs]def mpl_to_bokeh(properties): """ Utility to process style properties converting any matplotlib specific options to their nearest bokeh equivalent. """ new_properties = {} for k, v in properties.items(): if k == 's': new_properties['size'] = v elif k == 'marker': new_properties.update(markers.get(v, {'marker': v})) elif k == 'color' or k.endswith('_color'): with abbreviated_exception(): v = colors.ColorConverter.colors.get(v, v) if isinstance(v, tuple): with abbreviated_exception(): v = rgb2hex(v) new_properties[k] = v else: new_properties[k] = v new_properties.pop('cmap', None) return new_properties
[docs]def layout_padding(plots, renderer): """ Pads Nones in a list of lists of plots with empty plots. """ widths, heights = defaultdict(int), defaultdict(int) for r, row in enumerate(plots): for c, p in enumerate(row): if p is not None: width, height = renderer.get_size(p) widths[c] = max(widths[c], width) heights[r] = max(heights[r], height) expanded_plots = [] for r, row in enumerate(plots): expanded_plots.append([]) for c, p in enumerate(row): if p is None: p = empty_plot(widths[c], heights[r]) elif hasattr(p, 'plot_width') and p.plot_width == 0 and p.plot_height == 0: p.plot_width = widths[c] p.plot_height = heights[r] expanded_plots[r].append(p) return expanded_plots
[docs]def compute_plot_size(plot): """ Computes the size of bokeh models that make up a layout such as figures, rows, columns, widgetboxes and Plot. """ if isinstance(plot, Div): # Cannot compute size for Div return 0, 0 elif isinstance(plot, (Row, Column, ToolbarBox, WidgetBox, Tabs)): if not plot.children: return 0, 0 if isinstance(plot, Row) or (isinstance(plot, ToolbarBox) and plot.toolbar_location not in ['right', 'left']): w_agg, h_agg = (np.sum, np.max) elif isinstance(plot, Tabs): w_agg, h_agg = (np.max, np.max) else: w_agg, h_agg = (np.max, np.sum) widths, heights = zip(*[compute_plot_size(child) for child in plot.children]) width, height = w_agg(widths), h_agg(heights) elif isinstance(plot, (Figure, Chart)): width, height = plot.plot_width, plot.plot_height elif isinstance(plot, (Plot, DataTable, Spacer)): width, height = plot.width, plot.height return width, height
[docs]def empty_plot(width, height): """ Creates an empty and invisible plot of the specified size. """ x_range = Range1d(start=0, end=1) y_range = Range1d(start=0, end=1) p = Figure(plot_width=width, plot_height=height, x_range=x_range, y_range=y_range) p.xaxis.visible = False p.yaxis.visible = False p.outline_line_alpha = 0 p.grid.grid_line_alpha = 0 return p
[docs]def font_size_to_pixels(size): """ Convert a fontsize to a pixel value """ if size is None or not isinstance(size, basestring): return conversions = {'em': 16, 'pt': 16/12.} val = re.findall('\d+', size) unit = re.findall('[a-z]+', size) if (val and not unit) or (val and unit[0] == 'px'): return int(val[0]) elif val and unit[0] in conversions: return (int(int(val[0]) * conversions[unit[0]]))
def make_axis(axis, size, factors, dim, flip=False, rotation=0, label_size=None, tick_size=None, axis_height=35): factors = list(map(dim.pprint_value, factors)) nchars = np.max([len(f) for f in factors]) ranges = FactorRange(factors=factors) ranges2 = Range1d(start=0, end=1) axis_label = dim_axis_label(dim) reset = "range.setv({start: 0, end: range.factors.length})" ranges.callback = CustomJS(args=dict(range=ranges), code=reset) axis_props = {} if label_size: axis_props['axis_label_text_font_size'] = value(label_size) if tick_size: axis_props['major_label_text_font_size'] = value(tick_size) tick_px = font_size_to_pixels(tick_size) if tick_px is None: tick_px = 8 label_px = font_size_to_pixels(label_size) if label_px is None: label_px = 10 rotation = np.radians(rotation) if axis == 'x': align = 'center' # Adjust height to compensate for label rotation height = int(axis_height + np.abs(np.sin(rotation)) * ((nchars*tick_px)*0.82)) + tick_px + label_px opts = dict(x_axis_type='auto', x_axis_label=axis_label, x_range=ranges, y_range=ranges2, plot_height=height, plot_width=size) else: # Adjust width to compensate for label rotation align = 'left' if flip else 'right' width = int(axis_height + np.abs(np.cos(rotation)) * ((nchars*tick_px)*0.82)) + tick_px + label_px opts = dict(y_axis_label=axis_label, x_range=ranges2, y_range=ranges, plot_width=width, plot_height=size) p = Figure(toolbar_location=None, **opts) p.outline_line_alpha = 0 p.grid.grid_line_alpha = 0 if axis == 'x': p.yaxis.visible = False axis = p.xaxis[0] if flip: p.above = p.below p.below = [] p.xaxis[:] = p.above else: p.xaxis.visible = False axis = p.yaxis[0] if flip: p.right = p.left p.left = [] p.yaxis[:] = p.right axis.major_label_orientation = rotation axis.major_label_text_align = align axis.major_label_text_baseline = 'middle' axis.update(**axis_props) return p def convert_datetime(time): return time.astype('datetime64[s]').astype(float)*1000
[docs]def hsv_to_rgb(hsv): """ Vectorized HSV to RGB conversion, adapted from: """ h, s, v = (hsv[..., i] for i in range(3)) shape = h.shape i = np.int_(h*6.) f = h*6.-i q = f t = 1.-f i = np.ravel(i) f = np.ravel(f) i%=6 t = np.ravel(t) q = np.ravel(q) s = np.ravel(s) v = np.ravel(v) clist = (1-s*np.vstack([np.zeros_like(f),np.ones_like(f),q,t]))*v #0:v 1:p 2:q 3:t order = np.array([[0,3,1],[2,0,1],[1,0,3],[1,2,0],[3,1,0],[0,1,2]]) rgb = clist[order[i], np.arange([:,None]] return rgb.reshape(shape+(3,))
[docs]def pad_width(model, table_padding=0.85, tabs_padding=1.2): """ Computes the width of a model and sets up appropriate padding for Tabs and DataTable types. """ if isinstance(model, Row): vals = [pad_width(child) for child in model.children] width = np.max([v for v in vals if v is not None]) elif isinstance(model, Column): vals = [pad_width(child) for child in model.children] width = np.sum([v for v in vals if v is not None]) elif isinstance(model, Tabs): vals = [pad_width(t) for t in model.tabs] width = np.max([v for v in vals if v is not None]) for model in model.tabs: model.width = width width = int(tabs_padding*width) elif isinstance(model, DataTable): width = model.width model.width = int(table_padding*width) elif isinstance(model, WidgetBox): width = model.width elif model: width = model.plot_width else: width = 0 return width
[docs]def pad_plots(plots): """ Accepts a grid of bokeh plots in form of a list of lists and wraps any DataTable or Tabs in a WidgetBox with appropriate padding. Required to avoid overlap in gridplot. """ widths = [] for row in plots: row_widths = [] for p in row: width = pad_width(p) row_widths.append(width) widths.append(row_widths) plots = [[WidgetBox(p, width=w) if isinstance(p, (DataTable, Tabs)) else p for p, w in zip(row, ws)] for row, ws in zip(plots, widths)] total_width = np.max([np.sum(row) for row in widths]) return plots, total_width
[docs]def filter_toolboxes(plots): """ Filters out toolboxes out of a list of plots to be able to compose them into a larger plot. """ if isinstance(plots, list): plots = [filter_toolboxes(plot) for plot in plots] elif hasattr(plots, 'children'): plots.children = [filter_toolboxes(child) for child in plots.children if not isinstance(child, ToolbarBox)] return plots
[docs]def py2js_tickformatter(formatter, msg=''): """ Uses flexx.pyscript to compile a python tick formatter to JS code """ try: from flexx.pyscript import py2js except ImportError: param.main.warning(msg+'Ensure Flexx is installed ' '("conda install -c bokeh flexx" or ' '"pip install flexx")') return try: jscode = py2js(formatter, 'formatter') except Exception as e: error = 'Pyscript raised an error: {0}'.format(e) error = error.replace('%', '%%') param.main.warning(msg+error) return args = inspect.getargspec(formatter).args arg_define = 'var %s = tick;' % args[0] if args else '' return_js = 'return formatter();\n' jsfunc = '\n'.join([arg_define, jscode, return_js]) match ='(function \(.*\))', jsfunc ) return jsfunc[:match.start()] + 'function ()' + jsfunc[match.end():]
[docs]def get_tab_title(key, frame, overlay): """ Computes a title for bokeh tabs from the key in the overlay, the element and the containing (Nd)Overlay. """ if isinstance(overlay, Overlay): if frame is not None: title = [] if frame.label: title.append(frame.label) if != frame.params('group').default: title.append( else: title.append( else: title = key title = ' '.join(title) else: title = ' | '.join([d.pprint_value_string(k) for d, k in zip(overlay.kdims, key)]) return title
[docs]def expand_batched_style(style, opts, mapping, nvals): """ Computes styles applied to a batched plot by iterating over the supplied list of style options and expanding any options found in the supplied style dictionary returning a data and mapping defining the data that should be added to the ColumnDataSource. """ opts = sorted(opts, key=lambda x: x in ['color', 'alpha']) applied_styles = set(mapping) style_data, style_mapping = {}, {} for opt in opts: if 'color' in opt: alias = 'color' elif 'alpha' in opt: alias = 'alpha' else: alias = None if opt not in style or opt in mapping: continue elif opt == alias: if alias in applied_styles: continue elif 'line_'+alias in applied_styles: if 'fill_'+alias not in opts: continue opt = 'fill_'+alias val = style[alias] elif 'fill_'+alias in applied_styles: opt = 'line_'+alias val = style[alias] else: val = style[alias] else: val = style[opt] style_mapping[opt] = {'field': opt} applied_styles.add(opt) if 'color' in opt and isinstance(val, tuple): val = rgb2hex(val) style_data[opt] = [val]*nvals return style_data, style_mapping
[docs]def filter_batched_data(data, mapping): """ Iterates over the data and mapping for a ColumnDataSource and replaces columns with repeating values with a scalar. This is purely and optimization for scalar types. """ for k, v in list(mapping.items()): if isinstance(v, dict) and 'field' in v: if 'transform' in v: continue v = v['field'] elif not isinstance(v, basestring): continue values = data[v] try: if len(unique_array(values)) == 1: mapping[k] = values[0] del data[v] except: pass
[docs]def recursive_model_update(model, props): """ Recursively updates attributes on a model including other models. If the type of the new model matches the old model properties are simply updated, otherwise the model is replaced. """ updates = {} valid_properties = model.properties_with_values() for k, v in props.items(): if isinstance(v, Model): nested_model = getattr(model, k) if type(v) is type(nested_model): nested_props = v.properties_with_values(include_defaults=False) recursive_model_update(nested_model, nested_props) else: setattr(model, k, v) elif k in valid_properties and v != valid_properties[k]: updates[k] = v model.update(**updates)
[docs]def update_shared_sources(f): """ Context manager to ensures data sources shared between multiple plots are cleared and updated appropriately avoiding warnings and allowing empty frames on subplots. Expects a list of shared_sources and a mapping of the columns expected columns for each source in the plots handles. """ def wrapper(self, *args, **kwargs): source_cols = self.handles.get('source_cols', {}) shared_sources = self.handles.get('shared_sources', []) for source in shared_sources: ret = f(self, *args, **kwargs) for source in shared_sources: expected = source_cols[id(source)] found = [c for c in expected if c in] empty = np.full_like([found[0]], np.NaN) if found else [] patch = {c: empty for c in expected if c not in} return ret return wrapper
[docs]def categorize_array(array, dim): """ Uses a Dimension instance to convert an array of values to categorical (i.e. string) values and applies escaping for colons, which bokeh treats as a categorical suffix. """ return np.array([dim.pprint_value(x) for x in array])
[docs]class periodic(object): """ Mocks the API of periodic Thread in hv.core.util, allowing a smooth API transition on bokeh server. """ def __init__(self, document): self.document = document self.callback = None self.period = None self.count = None self.counter = None self._start_time = None self.timeout = None @property def completed(self): return self.counter is None def start(self): self._start_time = time.time() if self.document is None: raise RuntimeError('periodic was registered to be run on bokeh' 'server but no document was found.') self.document.add_periodic_callback(self._periodic_callback, self.period) def __call__(self, period, count, callback, timeout=None, block=False): if isinstance(count, int): if count < 0: raise ValueError('Count value must be positive') elif not type(count) is type(None): raise ValueError('Count value must be a positive integer or None') self.callback = callback self.period = period*1000. self.timeout = timeout self.count = count self.counter = 0 return self def _periodic_callback(self): self.callback(self.counter) self.counter += 1 if self.timeout is not None: dt = (time.time() - self._start_time) if dt > self.timeout: self.stop() if self.counter == self.count: self.stop() def stop(self): self.counter = None self.timeout = None try: self.document.remove_periodic_callback(self._periodic_callback) except ValueError: # Already stopped pass def __repr__(self): return 'periodic(%s, %s, %s)' % (self.period, self.count, callable_name(self.callback)) def __str__(self): return repr(self)
[docs]def attach_periodic(plot): """ Attaches plot refresh to all streams on the object. """ def append_refresh(dmap): for dmap in get_nested_dmaps(dmap): dmap.periodic._periodic_util = periodic(plot.document) return plot.hmap.traverse(append_refresh, [DynamicMap])
[docs]def date_to_integer(date): """ Converts datetime types to bokeh's integer format. """ if isinstance(date, np.datetime64): date = dt64_to_dt(date) elif pd and isinstance(date, pd.Timestamp): date = date.to_pydatetime() if isinstance(date, dt.datetime): dt_int = time.mktime(date.timetuple())*1000 else: raise ValueError('Datetime type not recognized') return dt_int