def draw_clusters(self,
N,
cluster_size,
hard_radius=None,
separation=0,
margin=None):
"""Draws N clusters at random locations, using minimum separation
and a margin. If separation > 0, less than N features may be drawn."""
if hard_radius is None:
hard_radius = self.hard_radius
if margin is None:
margin = self.hard_radius
if margin is None:
margin = 0
pos = gen_random_locations(self.shape, N, margin)
if separation > 0:
pos = drop_close(pos, separation)
if self.ndim == 2:
angles = np.random.uniform(0, 2 * np.pi, N)
elif self.ndim == 3:
angles = np.random.uniform(0, 2 * np.pi, (N, 3))
for p, a in zip(pos, angles):
self.draw_cluster(cluster_size, p, a, hard_radius)
return pos
def gen_nonoverlapping_locations(shape, count, separation, margin=0):
""" Generates `count` number of positions within `shape`, that have minimum
distance `separation` from each other. The number of positions returned may
be lower than `count`, because positions too close to each other will be
deleted. If a `margin` is given, positions will be inside this margin.
Margin may be tuple-valued.
"""
positions = gen_random_locations(shape, count, margin)
return drop_close(positions, separation)
def gen_nonoverlapping_locations(shape, count, separation, margin=0):
""" Generates `count` number of positions within `shape`, that have minimum
distance `separation` from each other. The number of positions returned may
be lower than `count`, because positions too close to each other will be
deleted. If a `margin` is given, positions will be inside this margin.
Margin may be tuple-valued.
"""
positions = gen_random_locations(shape, count, margin)
return drop_close(positions, separation)
def draw_features(self, N, separation=0, margin=None):
"""Draws N features at random locations, using minimum separation
and a margin. If separation > 0, less than N features may be drawn."""
if margin is None:
margin = self.hard_radius
if margin is None:
margin = 0
pos = gen_random_locations(self.shape, N, margin)
if separation > 0:
pos = drop_close(pos, separation)
for p in pos:
self.draw_feature(p)
return pos
def draw_features(self, N, separation=0, margin=None):
"""Draws N features at random locations, using minimum separation
and a margin. If separation > 0, less than N features may be drawn."""
if margin is None:
margin = self.hard_radius
if margin is None:
margin = 0
pos = gen_random_locations(self.shape, N, margin)
if separation > 0:
pos = drop_close(pos, separation)
for p in pos:
self.draw_feature(p)
return pos
def draw_clusters(self, N, cluster_size, hard_radius=None, separation=0,
margin=None):
"""Draws N clusters at random locations, using minimum separation
and a margin. If separation > 0, less than N features may be drawn."""
if hard_radius is None:
hard_radius = self.hard_radius
if margin is None:
margin = self.hard_radius
if margin is None:
margin = 0
pos = gen_random_locations(self.shape, N, margin)
if separation > 0:
pos = drop_close(pos, separation)
if self.ndim == 2:
angles = np.random.uniform(0, 2*np.pi, N)
elif self.ndim == 3:
angles = np.random.uniform(0, 2*np.pi, (N, 3))
for p, a in zip(pos, angles):
self.draw_cluster(cluster_size, p, a, hard_radius)
return pos