Source code for holoviews.plotting.plotly.images

import numpy as np
from plotly.graph_objs.layout import Image as _Image

from ...core.util import VersionError
from ...element import Tiles
from .element import ElementPlot
from .selection import PlotlyOverlaySelectionDisplay

[docs]class RGBPlot(ElementPlot): style_opts = ['opacity'] apply_ranges = True selection_display = PlotlyOverlaySelectionDisplay() _supports_geo = True
[docs] def init_graph(self, datum, options, index=0, is_geo=False, **kwargs): if is_geo: layer = dict(datum, **options) dummy_trace = { 'type': 'scattermapbox', 'lat': [None], 'lon': [None], 'mode': 'markers', 'showlegend': False } return dict(mapbox=dict(layers=[layer]), traces=[dummy_trace]) else: image = dict(datum, **options) # Create a dummy invisible scatter trace for this image. # This serves two purposes # 1. The two points placed on the corners of the image are used by the # autoscale logic to allow using the autoscale button to properly center # the image. # 2. This trace will be given a UID, and this UID will make it possible to # associate callbacks with the image element. This is needed, in particular # to support datashader dummy_trace = { 'type': 'scatter', 'x': [image['x'], image['x'] + image['sizex']], 'y': [image['y'] - image['sizey'], image['y']], 'mode': 'markers', 'marker': {'opacity': 0}, "showlegend": False, } return dict(images=[image], traces=[dummy_trace])
def get_data(self, element, ranges, style, is_geo=False, **kwargs): try: import PIL.Image except ImportError: raise VersionError("""\ Rendering RGB elements with the plotly backend requires the Pillow package""") from None img = np.flip( np.dstack([element.dimension_values(d, flat=False) for d in element.vdims]), axis=0 ) if img.dtype.kind == 'f': img = img * 255 if img.size and (img.min() < 0 or img.max() > 255): self.param.warning('Clipping input data to the valid ' 'range for RGB data ([0..1] for ' 'floats or [0..255] for integers).') img = np.clip(img, 0, 255) if != 'uint8': img = img.astype(np.uint8) if 0 in img.shape: img = np.zeros((1, 1, 3), dtype=np.uint8) if img.ndim != 3 or img.shape[2] not in (3, 4): raise ValueError(f"Unsupported image array with shape: {img.shape}") # Ensure axis inversions are handled correctly l, b, r, t = element.bounds.lbrt() if self.invert_axes: img = np.rot90(img.swapaxes(0, 1), 2) l, b, r, t = b, l, t, r if self.invert_xaxis: l, r = r, l img = np.flip(img, axis=1) if self.invert_yaxis: img = np.flip(img, axis=0) b, t = t, b if img.shape[2] == 3: pil_img = PIL.Image.fromarray(img, 'RGB') else: pil_img = PIL.Image.fromarray(img, 'RGBA') source = _Image(source=pil_img).source if is_geo: lon_left, lat_top = Tiles.easting_northing_to_lon_lat(l, t) lon_right, lat_bottom = Tiles.easting_northing_to_lon_lat(r, b) coordinates = [ [lon_left, lat_top], [lon_right, lat_top], [lon_right, lat_bottom], [lon_left, lat_bottom], ] layer = { "sourcetype": "image", "source": source, "coordinates": coordinates, "below": 'traces', } return [layer] else: return [dict(source=source, x=l, y=t, sizex=r - l, sizey=t - b, xref='x', yref='y', sizing='stretch', layer='above')]