import matplotlib.pyplot as plt from matplotlib.collections import LineCollection # container for line segments from matplotlib.text import Text, Annotation import matplotlib.cm as cm # colour mappings import random
It was found, also, that the shape of the brain, at the foundation-point, was of the same general form or shape as the earth on which we dwell; that is to say, an oblate spheroid, whence, by experiment, it was deduced that such section of a figure, oblately spheroidal, was also the very best form of a magic mirror. Such a figure having two mathematically true and absolutely certain foci, so that a stream of magnetism being thrown upon one focus slid along the surface and returned to the centre of the other focus, from the centre of the fore-brain, thus completing a magnetic circuit and rendering the portion of brain in the line of contact exceedingly active, by reason of its increased magnetic play and motion of the brain-particles there situate.
EdgeList = list[np.ndarray] # list of 2xdim arrays def get_cuts(N: Net) -> EdgeList: return np.concatenate([N.tope.meta[2][i]["cuts"] for i in range(len(N. ↪facets))]) def get_edges(N: Net) -> EdgeList: # apply to unfolded Net edges = [] for i, vertices in N.facets.items(): facet_template = N.tope.get_face(i) # has correct indices edges.extend((vertices[list(e)] for e in facet_template.faces[1])) return edges import itertools Net2d = None # new format of Net def iter_edges(N: Net2d) -> Iterable[np.ndarray[2,2]]: # apply to unfolded 2d␣ ↪Net return N.iter_faces_as_vertices(dim=1) FacetLabels = list[tuple[str, np.ndarray]] # label, position def get_facet_labels(N: Net) -> FacetLabels: labels = [] for i, vertices in N.facets.items(): labels.append((N.tope.meta[N.tope.dim-1][i]["index"], vertices. ↪mean(axis=0))) return labels def iter_facet_labels(N: Net2d, key: str) -> Iterable[str]: return zip(N.iter_meta(dim=2, key="index"), map(N.cells.values(), lambda x:␣ ↪x.vertices.mean(axis=0)))