Source code for holoviews.plotting.mpl.util

import re
import inspect
import warnings

import numpy as np
from matplotlib import ticker
from matplotlib.transforms import Bbox, TransformedBbox, Affine2D

from ...core.util import basestring
from ...element import Raster, RGB

[docs]def wrap_formatter(formatter): """ Wraps formatting function or string in appropriate matplotlib formatter type. """ if isinstance(formatter, ticker.Formatter): return formatter elif callable(formatter): args = [arg for arg in inspect.getargspec(formatter).args if arg != 'self'] wrapped = formatter if len(args) == 1: def wrapped(val, pos=None): return formatter(val) return ticker.FuncFormatter(wrapped) elif isinstance(formatter, basestring): if re.findall(r"\{(\w+)\}", formatter): return ticker.StrMethodFormatter(formatter) else: return ticker.FormatStrFormatter(formatter)
def unpack_adjoints(ratios): new_ratios = {} offset = 0 for k, (num, ratios) in sorted(ratios.items()): unpacked = [[] for _ in range(num)] for r in ratios: nr = len(r) for i in range(num): unpacked[i].append(r[i] if i < nr else np.nan) for i, r in enumerate(unpacked): new_ratios[k+i+offset] = r offset += num-1 return new_ratios def normalize_ratios(ratios): normalized = {} for i, v in enumerate(zip(*ratios.values())): arr = np.array(v) normalized[i] = arr/float(np.nanmax(arr)) return normalized def compute_ratios(ratios, normalized=True): unpacked = unpack_adjoints(ratios) with warnings.catch_warnings(): warnings.filterwarnings('ignore', r'All-NaN (slice|axis) encountered') if normalized: unpacked = normalize_ratios(unpacked) sorted_ratios = sorted(unpacked.items()) return np.nanmax(np.vstack([v for _, v in sorted_ratios]), axis=0)
[docs]def axis_overlap(ax1, ax2): """ Tests whether two axes overlap vertically """ b1, t1 = ax1.get_position().intervaly b2, t2 = ax2.get_position().intervaly return t1 > b2 and b1 < t2
[docs]def resolve_rows(rows): """ Recursively iterate over lists of axes merging them by their vertical overlap leaving a list of rows. """ merged_rows = [] for row in rows: overlap = False for mrow in merged_rows: if any(axis_overlap(ax1, ax2) for ax1 in row for ax2 in mrow): mrow += row overlap = True break if not overlap: merged_rows.append(row) if rows == merged_rows: return rows else: return resolve_rows(merged_rows)
[docs]def fix_aspect(fig, nrows, ncols, title=None, extra_artists=[], vspace=0.2, hspace=0.2): """ Calculate heights and widths of axes and adjust the size of the figure to match the aspect. """ fig.canvas.draw() w, h = fig.get_size_inches() # Compute maximum height and width of each row and columns rows = resolve_rows([[ax] for ax in fig.axes]) rs, cs = len(rows), max([len(r) for r in rows]) heights = [[] for i in range(cs)] widths = [[] for i in range(rs)] for r, row in enumerate(rows): for c, ax in enumerate(row): bbox = ax.get_tightbbox(fig.canvas.get_renderer()) heights[c].append(bbox.height) widths[r].append(bbox.width) height = (max([sum(c) for c in heights])) + nrows*vspace*fig.dpi width = (max([sum(r) for r in widths])) + ncols*hspace*fig.dpi # Compute aspect and set new size (in inches) aspect = height/width offset = 0 if title and title.get_text(): offset = title.get_window_extent().height/fig.dpi fig.set_size_inches(w, (w*aspect)+offset) # Redraw and adjust title position if defined fig.canvas.draw() if title and title.get_text(): extra_artists = [a for a in extra_artists if a is not title] bbox = get_tight_bbox(fig, extra_artists) top = bbox.intervaly[1] if title and title.get_text(): title.set_y((top/(w*aspect)))
[docs]def get_tight_bbox(fig, bbox_extra_artists=[], pad=None): """ Compute a tight bounding box around all the artists in the figure. """ renderer = fig.canvas.get_renderer() bbox_inches = fig.get_tightbbox(renderer) bbox_artists = bbox_extra_artists[:] bbox_artists += fig.get_default_bbox_extra_artists() bbox_filtered = [] for a in bbox_artists: bbox = a.get_window_extent(renderer) if isinstance(bbox, tuple): continue if a.get_clip_on(): clip_box = a.get_clip_box() if clip_box is not None: bbox = Bbox.intersection(bbox, clip_box) clip_path = a.get_clip_path() if clip_path is not None and bbox is not None: clip_path = clip_path.get_fully_transformed_path() bbox = Bbox.intersection(bbox, clip_path.get_extents()) if bbox is not None and (bbox.width != 0 or bbox.height != 0): bbox_filtered.append(bbox) if bbox_filtered: _bbox = Bbox.union(bbox_filtered) trans = Affine2D().scale(1.0 / fig.dpi) bbox_extra = TransformedBbox(_bbox, trans) bbox_inches = Bbox.union([bbox_inches, bbox_extra]) return bbox_inches.padded(pad) if pad else bbox_inches
[docs]def get_raster_array(image): """ Return the array data from any Raster or Image type """ if isinstance(image, RGB): rgb = image.rgb data = np.dstack([np.flipud(rgb.dimension_values(d, flat=False)) for d in rgb.vdims]) else: data = image.dimension_values(2, flat=False) if type(image) is Raster: data = data.T else: data = np.flipud(data) return data