Yves Biener
7581966c88
The final API will be derived from these scripts into a different repository, which then only holds the corresponding functions that provide the corresponding functionalities described in the associated master thesis.
62 lines
2.0 KiB
Python
62 lines
2.0 KiB
Python
import math
|
|
|
|
import matplotlib.pyplot as plt
|
|
import numpy as np
|
|
from graph_tool.all import *
|
|
|
|
from src import centrality
|
|
from src import plot
|
|
from src import fitting
|
|
|
|
def random_graph(n=5000, seed=None):
|
|
"""
|
|
Uniformly random point cloud generation.
|
|
`n` [int] Number of points to generate. Default 5000 seems like a good starting point in point density and corresponding runtime for the subsequent calculations.
|
|
@return [numpy.ndarray] Array of shape(n, 2) containing the coordinates for each point of the generated point cloud.
|
|
"""
|
|
if seed is None:
|
|
import secrets
|
|
seed = secrets.randbits(128)
|
|
rng = np.random.default_rng(seed=seed)
|
|
return rng.random((n, 2)), seed
|
|
|
|
|
|
def spatial_graph(adata):
|
|
g, pos = graph_tool.generation.triangulation(adata, type="delaunay")
|
|
g.vp["pos"] = pos
|
|
weight = g.new_edge_property("double")
|
|
for e in g.edges():
|
|
weight[e] = math.sqrt(sum(map(abs, pos[e.source()].a - pos[e.target()].a)))**2
|
|
return g, weight
|
|
|
|
|
|
points, seed = random_graph()
|
|
g, weight = spatial_graph(points)
|
|
g = GraphView(g)
|
|
|
|
# calculate centrality values
|
|
vp = closeness(g, weight=weight)
|
|
vp.a = np.nan_to_num(vp.a) # correct floating point values
|
|
# ep.a = np.nan_to_num(ep.a) # correct floating point values
|
|
|
|
# calculate convex hull
|
|
convex_hull = centrality.convex_hull(g)
|
|
|
|
# plot graph with convex_hull
|
|
fig = plt.figure(figsize=(15, 5))
|
|
ax0, ax1 = fig.subplots(1, 2)
|
|
plot.graph_plot(fig, ax0, g, vp, convex_hull, f"Closeness without prediction")
|
|
|
|
# generate model based on convex hull and associated centrality values
|
|
quantification = plot.quantification_data(g, vp, convex_hull)
|
|
|
|
# optimize model's piece-wise linear function
|
|
d = quantification[:, 0]
|
|
C = quantification[:, 1]
|
|
m_opt, c0_opt, b_opt, aic_opt = fitting.fit_piece_wise_linear(d, C)
|
|
|
|
vp = centrality.correct(g, vp, m_opt, c0_opt, b_opt)
|
|
plot.graph_plot(fig, ax1, g, vp, convex_hull, f"Closeness with model prediction")
|
|
|
|
fig.savefig(f"model_prediction_comparison.svg", format='svg')
|