WIP different small python scripts to generate corresponding images
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.
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242
example.py
242
example.py
@@ -2,7 +2,7 @@ import math
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import matplotlib.pyplot as plt
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import numpy as np
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import squidpy as sq
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# import squidpy as sq
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from graph_tool.all import *
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from src import centrality
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@@ -29,6 +29,7 @@ def mibitof():
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adata = sq.datasets.mibitof()
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return adata
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def random_graph(n=5000, seed=None):
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"""
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Uniformly random point cloud generation.
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@@ -96,207 +97,60 @@ def spatial_graph(adata):
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weight[e] = math.sqrt(sum(map(abs, pos[e.source()].a - pos[e.target()].a)))**2
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return g, weight
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def merfish_example():
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# generate spatial graph from a given dataset
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g, weight = spatial_graph(merfish().obsm['spatial'])
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g = GraphView(g)
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x_spatial = []
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for v in g.vertices():
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x_spatial.append(g.vp["pos"][v][0])
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points, seed = random_graph()
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g, weight = spatial_graph(points)
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g = GraphView(g)
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# calculate centrality values
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vp = closeness(g, weight=weight)
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vp.a = np.nan_to_num(vp.a) # correct floating point values
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# calculate centrality values
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vp = closeness(g, weight=weight)
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vp.a = np.nan_to_num(vp.a) # correct floating point values
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# ep.a = np.nan_to_num(ep.a) # correct floating point values
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# normalization
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min_val, max_val = vp.a.min(), vp.a.max()
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vp.a = (vp.a - min_val) / (max_val - min_val)
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# normalization
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# min_val, max_val = vp.a.min(), vp.a.max()
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# vp.a = (vp.a - min_val) / (max_val - min_val)
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# calculate convex hull
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convex_hull = centrality.convex_hull(g)
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# calculate convex hull
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convex_hull = centrality.convex_hull(g)
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# plot graph with convex_hull
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fig = plt.figure(figsize=(15, 5))
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ax0, ax1 = fig.subplots(1, 2)
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plot.graph_plot(fig, ax0, g, vp, convex_hull, f"Merfish\nCloseness")
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# plot graph with convex_hull
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fig = plt.figure(figsize=(15, 5))
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ax0, ax1 = fig.subplots(1, 2)
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plot.graph_plot(fig, ax0, g, vp, convex_hull, f"Random Graph (seed: {seed})")
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# generate model based on convex hull and associated centrality values
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quantification = plot.quantification_data(g, vp, convex_hull)
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# generate model based on convex hull and associated centrality values
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quantification = plot.quantification_data(g, vp, convex_hull)
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# optimize model's piece-wise linear function
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d = quantification[:, 0]
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C = quantification[:, 1]
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m_opt, c0_opt, b_opt, aic_opt = fitting.fit_piece_wise_linear(d, C)
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# optimize model's piece-wise linear function
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d = quantification[:, 0]
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C = quantification[:, 1]
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m_opt, c0_opt, b_opt, aic_opt = fitting.fit_piece_wise_linear(d, C)
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# TODO
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# should this be part of the plotting function itself, it should not be necessary for me to do this
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d_curve = np.linspace(min(d), max(d), 500)
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C_curve = np.piecewise(
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d_curve,
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[d_curve <= b_opt, d_curve > b_opt],
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[lambda x: m_opt * x + c0_opt, lambda x: m_opt * b_opt + c0_opt]
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)
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# plot model containing modeled piece-wise linear function
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plot.quantification_plot(ax1, quantification, d_curve, C_curve, 'Models', aic_opt)
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# TODO
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# should this be part of the plotting function itself, it should not be necessary for me to do this
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d_curve = np.linspace(min(d), max(d), 500)
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C_curve = np.piecewise(
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d_curve,
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[d_curve <= b_opt, d_curve > b_opt],
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[lambda x: m_opt * x + c0_opt, lambda x: m_opt * b_opt + c0_opt]
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)
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# plot model containing modeled piece-wise linear function
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plot.quantification_plot(ax1, quantification, d_curve, C_curve, 'Closeness', aic_opt)
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# linear regression model
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m_reg, c_reg, aic_reg = fitting.fit_linear_regression(d, C)
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# # linear regression model
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# m_reg, c0_reg, b_reg, aic_reg = fitting.fit_cut(d, C)
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# print(f"m_reg = {m_reg}")
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x = np.linspace(min(d), max(d), 500)
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y = m_reg * x + c_reg
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ax1.plot(x, y, color='k', linewidth=1, label=f"Simple Linear Regression | AIC: {aic_reg}")
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ax1.legend()
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# # TODO
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# # should this be part of the plotting function itself, it should not be necessary for me to do this
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# d_curve = np.linspace(min(d), max(d), 500)
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# C_curve = np.piecewise(
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# d_curve,
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# [d_curve <= b_reg, d_curve > b_reg],
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# [lambda x: m_reg * x + c0_reg, lambda x: m_reg * b_reg + c0_reg]
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# )
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# ax1.plot(d_curve, C_curve, color='k', linewidth=1, label=f"Top Cut | AIC: {aic_reg}")
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# ax1.legend()
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fig.savefig(f"Merfish_closeness.svg", format='svg')
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for i in range(1, 6):
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points, seed = random_graph()
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g, weight = spatial_graph(points)
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g = GraphView(g)
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x_spatial = []
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for v in g.vertices():
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x_spatial.append(g.vp["pos"][v][0])
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# calculate centrality values
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vp = closeness(g, weight=weight)
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vp.a = np.nan_to_num(vp.a) # correct floating point values
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# ep.a = np.nan_to_num(ep.a) # correct floating point values
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# normalization
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min_val, max_val = vp.a.min(), vp.a.max()
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vp.a = (vp.a - min_val) / (max_val - min_val)
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# calculate convex hull
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convex_hull = centrality.convex_hull(g)
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# plot graph with convex_hull
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fig = plt.figure(figsize=(15, 5))
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ax0, ax1 = fig.subplots(1, 2)
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plot.graph_plot(fig, ax0, g, vp, convex_hull, f"Random Graph (seed: {seed})\nCloseness")
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# generate model based on convex hull and associated centrality values
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quantification = plot.quantification_data(g, vp, convex_hull)
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# optimize model's piece-wise linear function
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d = quantification[:, 0]
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C = quantification[:, 1]
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m_opt, c0_opt, b_opt, aic_opt = fitting.fit_piece_wise_linear(d, C)
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# TODO
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# should this be part of the plotting function itself, it should not be necessary for me to do this
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d_curve = np.linspace(min(d), max(d), 500)
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C_curve = np.piecewise(
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d_curve,
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[d_curve <= b_opt, d_curve > b_opt],
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[lambda x: m_opt * x + c0_opt, lambda x: m_opt * b_opt + c0_opt]
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)
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# plot model containing modeled piece-wise linear function
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plot.quantification_plot(ax1, quantification, d_curve, C_curve, 'Models', aic_opt)
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# linear regression model
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m_reg, c_reg, aic_reg = fitting.fit_linear_regression(d, C)
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x = np.linspace(min(d), max(d), 500)
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y = m_reg * x + c_reg
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ax1.plot(x, y, color='k', linewidth=1, label=f"Simple Linear Regression | AIC: {aic_reg}")
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ax1.legend()
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fig.savefig(f"uniform_random_point_clouds/{i}_closeness.svg", format='svg')
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# ---------------------------------------------------------------------------------------------
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# calculate centrality values
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vp, ep = betweenness(g, weight=weight)
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vp.a = np.nan_to_num(vp.a) # correct floating point values
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# ep.a = np.nan_to_num(ep.a) # correct floating point values
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# normalization
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min_val, max_val = vp.a.min(), vp.a.max()
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vp.a = (vp.a - min_val) / (max_val - min_val)
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# calculate convex hull
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convex_hull = centrality.convex_hull(g)
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# plot graph with convex_hull
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fig = plt.figure(figsize=(15, 5))
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ax0, ax1 = fig.subplots(1, 2)
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plot.graph_plot(fig, ax0, g, vp, convex_hull, f"Random Graph (seed: {seed})\nBetweenness")
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# generate model based on convex hull and associated centrality values
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quantification = plot.quantification_data(g, vp, convex_hull)
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# optimize model's piece-wise linear function
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d = quantification[:, 0]
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C = quantification[:, 1]
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m_opt, c0_opt, b_opt, aic_opt = fitting.fit_piece_wise_linear(d, C)
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# TODO
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# should this be part of the plotting function itself, it should not be necessary for me to do this
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d_curve = np.linspace(min(d), max(d), 500)
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C_curve = np.piecewise(
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d_curve,
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[d_curve <= b_opt, d_curve > b_opt],
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[lambda x: m_opt * x + c0_opt, lambda x: m_opt * b_opt + c0_opt]
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)
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# plot model containing modeled piece-wise linear function
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plot.quantification_plot(ax1, quantification, d_curve, C_curve, 'Models', aic_opt)
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# linear regression model
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m_reg, c_reg, aic_reg = fitting.fit_linear_regression(d, C)
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x = np.linspace(min(d), max(d), 500)
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y = m_reg * x + c_reg
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ax1.plot(x, y, color='k', linewidth=1, label=f"Simple Linear Regression | AIC: {aic_reg}")
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ax1.legend()
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fig.savefig(f"uniform_random_point_clouds/{i}_betweenness.svg", format='svg')
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# ---------------------------------------------------------------------------------------------
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# calculate centrality values
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vp = pagerank(g, weight=weight)
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vp.a = np.nan_to_num(vp.a) # correct floating point values
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# ep.a = np.nan_to_num(ep.a) # correct floating point values
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# normalization
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min_val, max_val = vp.a.min(), vp.a.max()
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vp.a = (vp.a - min_val) / (max_val - min_val)
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# calculate convex hull
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convex_hull = centrality.convex_hull(g)
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# plot graph with convex_hull
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fig = plt.figure(figsize=(15, 5))
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ax0, ax1 = fig.subplots(1, 2)
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plot.graph_plot(fig, ax0, g, vp, convex_hull, f"Random Graph (seed: {seed})\nPageRank")
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# generate model based on convex hull and associated centrality values
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quantification = plot.quantification_data(g, vp, convex_hull)
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# optimize model's piece-wise linear function
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d = quantification[:, 0]
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C = quantification[:, 1]
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m_opt, c0_opt, b_opt, aic_opt = fitting.fit_piece_wise_linear(d, C)
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# TODO
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# should this be part of the plotting function itself, it should not be necessary for me to do this
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d_curve = np.linspace(min(d), max(d), 500)
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C_curve = np.piecewise(
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d_curve,
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[d_curve <= b_opt, d_curve > b_opt],
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[lambda x: m_opt * x + c0_opt, lambda x: m_opt * b_opt + c0_opt]
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)
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# plot model containing modeled piece-wise linear function
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plot.quantification_plot(ax1, quantification, d_curve, C_curve, 'Models', aic_opt)
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# linear regression model
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m_reg, c_reg, aic_reg = fitting.fit_linear_regression(d, C)
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x = np.linspace(min(d), max(d), 500)
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y = m_reg * x + c_reg
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ax1.plot(x, y, color='k', linewidth=1, label=f"Simple Linear Regression | AIC: {aic_reg}")
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ax1.legend()
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fig.savefig(f"uniform_random_point_clouds/{i}_pagerank.svg", format='svg')
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fig.savefig(f"model_closeness_5000_fitted.svg", format='svg')
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