def quickrun_get_membership():
# This is for AlphaCluster and should be cleaner
# Should easily support the MAIN_CLUSTER_THRESHOLD option
data = get_carina()
apy.QUIET = False
apy.ORPHAN_TOLERANCE = 150
apy.ALPHA_STEP = 0.97
apy.PERSISTENCE_THRESHOLD = 3
apy.MAIN_CLUSTER_THRESHOLD = 51
apy.initialize(data)
a_x = apy.recurse()
colors, color_list, recs, base_width, lim = apy.dendrogram(a_x)
lim_alpha_lo, lim_alpha_hi = lim
plt.figure()
ax = plt.subplot(122)
for i, r_list in enumerate(recs):
for r in r_list:
r.set_facecolor(color_list[i])
ax.add_artist(r)
ax.set_xlim([-0.05 * base_width, 1.05 * base_width])
ax.set_ylim([lim_alpha_lo * .9, lim_alpha_hi * 1.1])
ax.set_yscale("log")
ax.set_xlabel("# triangles")
ax.set_ylabel("$\\alpha$")
ax.invert_yaxis()
ax = plt.subplot(121)
for c, ps in colors.items():
x, y = zip(*ps)
plt.scatter(x, y, color=c, alpha=0.8, s=1)
ax.invert_xaxis()
ax.set_xlabel("RA")
ax.set_ylabel("Dec")
plt.show()
def test_simplex_node_sort():
data = get_carina()[:100, :]
apy.QUIET = False
apy.ORPHAN_TOLERANCE = 150
apy.ALPHA_STEP = 0.97
apy.PERSISTENCE_THRESHOLD = 3
apy.MAIN_CLUSTER_THRESHOLD = 51
apy.initialize(data)
ts = apy.KEY.simplices()
for t in sorted(ts):
print(t.circumradius)
def quickrun_mean_vps():
data = get_carina()
apy.QUIET = False
apy.ORPHAN_TOLERANCE = 100
apy.ALPHA_STEP = 0.97
apy.PERSISTENCE_THRESHOLD = 1
apy.MAIN_CLUSTER_THRESHOLD = 51
apy.initialize(data)
a_x = apy.recurse()
stack = [a_x]
mean_vpss = []
while stack:
a = stack.pop()
mean_vpss.append((a.alpha_range, a.mean_vps))
stack += a.subclusters
colors, color_list, recs, base_width, lim = apy.dendrogram(a_x)
lim_alpha_lo, lim_alpha_hi = lim
plt.figure()
ax = plt.subplot(223)
for i, r_list in enumerate(recs):
for r in r_list:
r.set_facecolor(color_list[i])
ax.add_artist(r)
ax.set_xlim([-0.05 * base_width, 1.05 * base_width])
ax.set_ylim([lim_alpha_lo * .9, lim_alpha_hi * 1.1])
ax.set_yscale("log")
ax.set_xlabel("# triangles")
ax.set_ylabel("$\\alpha$")
ax.invert_yaxis()
ax = plt.subplot(221)
for c, ps in colors.items():
x, y = zip(*ps)
plt.scatter(x, y, color=c, alpha=0.8, s=1)
# ax.invert_xaxis()
ax.invert_yaxis()
# ax.set_xlabel("RA")
# ax.set_ylabel("Dec")
ax.set_xlabel("x")
ax.set_ylabel("y")
ax = plt.subplot(122)
print()
coeff = (1 + np.sin(np.pi / 6)) * np.cos(np.pi / 6)
for m, c in zip(mean_vpss, color_list):
a_r, m_vps = m
normed_vps = [abs(x) for x, a in zip(m_vps, a_r[:-1])]
plt.plot(a_r[:-1], normed_vps, '-', color=c)
ax.set_xlabel("$\\alpha$")
ax.set_ylabel("Mean Volume per Simplex, normed to equilateral")
ax.set_xscale("log")
ax.set_yscale("log")
# ax.set_xlim([lim_alpha_lo * .9, lim_alpha_hi * 1.1])
# ax.set_ylim([.01, 100])
ax.invert_xaxis()
plt.show()
def profiler():
data = get_carina()
apy.QUIET = False
apy.ORPHAN_TOLERANCE = 150
apy.ALPHA_STEP = 0.95
apy.PERSISTENCE_THRESHOLD = 3
apy.MAIN_CLUSTER_THRESHOLD = 51
apy.GAP_THRESHOLD = 1
apy.initialize(data)
import cProfile
import re
cProfile.run("apy.recurse()")
def test_compare_mst_alpha():
data = get_carina()
apy.QUIET = False
apy.ORPHAN_TOLERANCE = 100
apy.ALPHA_STEP = 0.97
apy.PERSISTENCE_THRESHOLD = 3
apy.MAIN_CLUSTER_THRESHOLD = 51
apy.initialize(data)
a_x = apy.recurse()
colors, color_list, recs, base_width, lim = apy.dendrogram(a_x)
lim_alpha_lo, lim_alpha_hi = lim
plt.figure()
ax_dend = plt.subplot(122)
for i, r_list in enumerate(recs):
for r in r_list:
r.set_facecolor(color_list[i])
ax_dend.add_artist(r)
ax_dend.set_xlim([-0.05 * base_width, 1.05 * base_width])
ax_dend.set_ylim([lim_alpha_lo * .9, lim_alpha_hi * 1.1])
ax_dend.set_yscale("log")
ax_dend.set_xlabel("# triangles")
ax_dend.set_ylabel("$\\alpha$")
ax_dend.invert_yaxis()
ax_points = plt.subplot(121)
for c, ps in colors.items():
x, y = zip(*ps)
plt.scatter(x, y, color=c, alpha=0.8, s=1)
ax_points.invert_xaxis()
ax_points.set_xlabel("RA")
ax_points.set_ylabel("Dec")
print("Halfway..")
tri = apy.KEY.delaunay
min_sp_tree = mstcluster.prepare_mst_simple(tri)
cutoff_alpha = mstcluster.get_cdf_cutoff(min_sp_tree)
clusters = mstcluster.reduce_mst_clusters(min_sp_tree)
for c in clusters:
if len(c) >= apy.ORPHAN_TOLERANCE / 2.:
coords = np.array([tri.points[i, :] for i in c])
hull = ConvexHull(coords)
# noinspection PyUnresolvedReferences
for simplex in hull.simplices:
plt.plot(coords[simplex, 0],
coords[simplex, 1],
'-',
color='r',
lw=2)
ax_dend.plot([0, base_width], [cutoff_alpha, cutoff_alpha], '--', 'r')
plt.show()
def quickrun_get_membership_3d():
# This is for AlphaCluster and should be cleaner
# Should easily support the MAIN_CLUSTER_THRESHOLD option
data = get_ScoOB()
apy.QUIET = False
apy.ORPHAN_TOLERANCE = 150
apy.ALPHA_STEP = 0.97
apy.PERSISTENCE_THRESHOLD = 3
apy.MAIN_CLUSTER_THRESHOLD = 51
apy.initialize(data)
a_x = apy.recurse()
colors, color_list, recs, base_width, lim = apy.dendrogram(a_x)
lim_alpha_lo, lim_alpha_hi = lim
plt.figure()
ax = plt.subplot2grid((2, 4), (1, 3))
for i, r_list in enumerate(recs):
for r in r_list:
r.set_facecolor(color_list[i])
ax.add_artist(r)
ax.set_xlim([-0.05 * base_width, 1.05 * base_width])
ax.set_ylim([lim_alpha_lo * .9, lim_alpha_hi * 1.1])
ax.set_yscale("log")
ax.set_xlabel("# triangles")
ax.set_ylabel("$\\alpha$")
ax.invert_yaxis()
ax = plt.subplot2grid((2, 4), (0, 0),
colspan=3,
rowspan=2,
projection='3d')
print()
for c, ps in colors.items():
x, y, z = zip(*ps)
ax.plot(x,
y,
zs=z,
marker='.',
color=c,
alpha=0.8,
linestyle='None',
markersize=1)
# alpha_of_interest = 0.484 #a_x.alpha_range[int(len(a_x.alpha_range)/2)]
# faces = apy.alpha_surfaces(a_x, alpha_of_interest)
# for f in faces:
# ax.add_collection3d(f)
ax.set_xlabel("$\Delta$RA off center")
ax.set_ylabel("$\Delta$Dec off center")
ax.set_zlabel("radial distance (kpc)")
plt.show()
def test_cayley_menger_high_d():
n = 4
data = get_data_n_dim(n)[:30, :]
apy.QUIET = False
apy.ORPHAN_TOLERANCE = 150
apy.ALPHA_STEP = 0.97
apy.PERSISTENCE_THRESHOLD = 3
apy.MAIN_CLUSTER_THRESHOLD = 51
apy.initialize(data)
v, r = zip(*[(s.volume, s.circumradius) for s in apy.KEY.simplices()])
sr = np.array(sorted(list(r)))
plt.plot(r, v, '.')
plt.plot(sr, sr**n)
plt.xscale('log')
plt.yscale('log')
plt.xlabel("Circumradius")
plt.ylabel("Volume")
plt.legend()
plt.show()
def test_cayley_menger():
a = np.array([[0, 0], [1, 0], [0, 1]], dtype=np.float64)
v, r = apy.cayley_menger_vr(a)
print("Volume", v, "Circumradius", r)
data = get_carina()[:100, :]
apy.QUIET = False
apy.ORPHAN_TOLERANCE = 150
apy.ALPHA_STEP = 0.97
apy.PERSISTENCE_THRESHOLD = 3
apy.MAIN_CLUSTER_THRESHOLD = 51
apy.initialize(data)
v, r = zip(*[(s.volume, s.circumradius) for s in apy.KEY.simplices()])
plt.plot(r, v, '.')
sr = np.array(sorted(list(r)))
coeff = (1 + np.sin(np.pi / 6)) * np.cos(np.pi / 6)
plt.plot(sr, (sr**2) * coeff,
label="Equilateral triangle volume: upper bound on $v(r)$")
plt.xscale('log')
plt.yscale('log')
plt.xlabel("Circumradius")
plt.ylabel("Volume")
plt.legend()
plt.show()
import numpy as np import alphax_utils as apy import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D import sys import timeit def test_something(): srcfile = "../../PyAlpha_drafting/test_data/Ssets/s1.txt" points = np.genfromtxt(srcfile) return points data = test_something() apy.initialize(data) apy.KEY.alpha_step = 0.95 apy.KEY.orphan_tolerance = 80 apy.recurse()
def test_boundary_3d():
# This is for AlphaCluster and should be cleaner
# Should easily support the MAIN_CLUSTER_THRESHOLD option
data = get_gaia_data()
apy.QUIET = False
apy.ORPHAN_TOLERANCE = 50
apy.ALPHA_STEP = .97
apy.PERSISTENCE_THRESHOLD = 3
apy.GAP_THRESHOLD = 1
apy.MAIN_CLUSTER_THRESHOLD = 51
apy.initialize(data)
a_x = apy.recurse()
colors, color_list, recs, base_width, lim = apy.dendrogram(a_x)
lim_alpha_lo, lim_alpha_hi = lim
plt.figure()
ax = plt.subplot2grid((2, 4), (1, 3))
for i, r_list in enumerate(recs):
for r in r_list:
r.set_facecolor(color_list[i])
ax.add_artist(r)
ax.set_xlim([-0.05 * base_width, 1.05 * base_width])
ax.set_ylim([lim_alpha_lo * .9, lim_alpha_hi * 1.1])
ax.set_yscale("log")
ax.set_xlabel("# triangles")
ax.set_ylabel("$\\alpha$")
ax.invert_yaxis()
ax = plt.subplot2grid((2, 4), (0, 0),
colspan=3,
rowspan=2,
projection='3d')
print()
stack = [a_x]
while stack:
a = stack.pop()
for i, b_list in enumerate(a.boundary_range):
if a.alpha_range[i] > 2.1:
continue
# We used to check if len(b_list) > 1 but I think that's counterproductive
# b_list is the list of gap (set(), frozenset()) tuples for a given alpha
if b_list:
for b in b_list:
# b is a tuple of (set(), frozenset())
# it represents a given gap at a given alpha
# the set (index 0) gives the boundary edges (SimplexEdge)
# the frozenset (index 1) gives the boundary volume elements (SimplexNode)
verts = []
for s in b[0]:
# add triangles to triangulation
verts.append(s.coord_array())
tri = Poly3DCollection(verts, linewidths=1)
tri.set_alpha(0.2)
tri.set_facecolor('k')
tri.set_edgecolor('k')
ax.add_collection3d(tri)
stack += a.subclusters
for c, ps in colors.items():
x, y, z = zip(*ps)
plt.plot(x, y, zs=z, marker='.', color=c, alpha=0.8, linestyle='None')
ax.invert_xaxis()
ax.set_zlim([290, 310])
ax.set_xlabel("$\Delta$RA off center")
ax.set_ylabel("$\Delta$Dec off center")
ax.set_zlabel("radial distance (kpc)")
plt.show()
def test_boundary():
# This is for AlphaCluster and should be cleaner
# Should easily support the MAIN_CLUSTER_THRESHOLD option
data = get_carina()
apy.QUIET = False
apy.ORPHAN_TOLERANCE = 150
apy.ALPHA_STEP = .97 #0.97
apy.PERSISTENCE_THRESHOLD = 1
apy.GAP_THRESHOLD = 15
apy.MAIN_CLUSTER_THRESHOLD = 51
apy.initialize(data)
a_x = apy.recurse()
colors, color_list, recs, base_width, lim = apy.dendrogram(a_x)
lim_alpha_lo, lim_alpha_hi = lim
plt.figure()
ax = plt.subplot2grid((6, 6), (3, 4), colspan=2, rowspan=3)
for i, r_list in enumerate(recs):
for r in r_list:
r.set_facecolor(color_list[i])
ax.add_artist(r)
ax.set_xlim([-0.05 * base_width, 1.05 * base_width])
ax.set_ylim([lim_alpha_lo * .9, lim_alpha_hi * 1.1])
ax.set_yscale("log")
ax.set_xlabel("# triangles")
ax.set_ylabel("$\\alpha$")
ax.invert_yaxis()
ax = plt.subplot2grid((6, 6), (0, 0), colspan=4, rowspan=5)
print()
stack = [a_x]
while stack:
a = stack.pop()
for b_list in a.boundary_range:
if b_list and len(b_list) > 1:
# cool_colors = cycle(['k', 'b', 'g', 'y', 'orange', 'navy', 'cyan'])
for b in b_list:
# clr = next(cool_colors)
for s in b[1]:
ax.add_artist(
Polygon(s.coord_array(),
alpha=0.1,
facecolor='k',
edgecolor=None))
# x, y = [], []
# for p in e:
# x.append(p[0]), y.append(p[1])
# if clr == 'r':
# plt.plot(x, y, '--', color=clr)
# else:
# plt.plot(x, y, '-', color=clr)
for c, ps in colors.items():
x, y = zip(*ps)
plt.scatter(x, y, color=c, alpha=0.8, s=1)
ax.invert_xaxis()
ax.set_xlabel("RA")
ax.set_ylabel("Dec")
# ax = plt.subplot(223)
# plt.scatter(apy.KEY.delaunay.points[:, 0], apy.KEY.delaunay.points[:, 1], color='k', alpha=0.6, s=1)
# ax.invert_xaxis()
# ax.set_xlabel("RA")
# ax.set_ylabel("Dec")
plt.show()
elif prefix == "STEP":
apy.ALPHA_STEP = n
elif prefix == "PT":
apy.PERSISTENCE_THRESHOLD = n
elif prefix == "MCT":
apy.MAIN_CLUSTER_THRESHOLD = n
return a_x
scoOB_pickle_local = "../PyAlpha_drafting/test_data/ScoOB_AX.pkl"
# This is for AlphaCluster and should be cleaner
# Should easily support the MAIN_CLUSTER_THRESHOLD option
a_x = get_pickle(scoOB_pickle_local)
ax = scoOB_plt(speedplot(a_x, alpha_of_interest=0.484))
plt.show()
"""
data = get_ScoOB()
apy.QUIET = False
apy.ORPHAN_TOLERANCE = 150
apy.ALPHA_STEP = 0.97
apy.PERSISTENCE_THRESHOLD = 1
apy.MAIN_CLUSTER_THRESHOLD = 51
apy.initialize(data)
a_x = apy.recurse()
scoOB_plt(speedplot(a_x, alpha_of_interest=0.484))
plt.show()
"""