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