def color(self):
"""Render cell's genome as hue/sat, cell's energy as value."""
if self._legacy_coloring:
red, green, blue = GenomeColor.modular(self.main.rule >> 1, 360)
else:
red, green, blue = GenomeColor.positional(self.main.rule,
self.main.rule.bit_width)
is_live = (self.main.rule > 0) * (self.main.energy < 255)
energy = (255 - self.main.energy) * is_live
red = xmath.int(red * energy)
green = xmath.int(green * energy)
blue = xmath.int(blue * energy)
return (red, green, blue, )
def emit(self):
"""Apply ConcervedLife dynamics."""
self.main.new_energy = 1 * self.main.energy
# calculate number of full neighbors
num_full = core.IntegerVariable()
for i in range(len(self.buffers)):
is_full = self.neighbors[i].main.energy >= self.meta.full_treshold
num_full += is_full
# decide, do cell need to spread an energy
me_full = self.main.energy >= self.meta.full_treshold
me_empty = self.main.energy < self.meta.full_treshold
me_dying = xmath.int(self.main.rule.is_sustained(num_full)) == 0
me_dying = xmath.int(me_dying)
me_birthing = self.main.rule.is_born(num_full)
has_free_energy = self.main.energy > self.meta.full_treshold
has_free_energy = xmath.int(has_free_energy)
need_spread_full = 1 * me_full * (has_free_energy | me_dying)
need_spread_empty = 1 * me_empty * (xmath.int(me_birthing) == 0)
need_spread = need_spread_full + need_spread_empty
# search the direction to spread energy
denergy = core.IntegerVariable()
energy_passed = core.IntegerVariable()
energy_passed *= 1
gate = core.IntegerVariable()
gate += xmath.int(self.main.rng.uniform * 8)
gate_final = core.IntegerVariable()
treshold = self.meta.full_treshold
for i in range(len(self.buffers) * 3):
i_valid = (i >= gate) * (i < gate + len(self.buffers) * 2)
is_birthing = self.neighbors[i % 8].main.birthing * (i < 8 + gate)
is_empty = self.neighbors[i % 8].main.energy < treshold
is_empty = is_empty * (i >= 8 + gate)
is_fit = is_empty + is_birthing
denergy *= 0
denergy += xmath.min(self.main.new_energy, self.meta.death_speed)
denergy *= need_spread * is_fit * (energy_passed == 0)
denergy *= i_valid
gate_final *= (energy_passed != 0)
gate_final += (i % 8) * (energy_passed == 0)
energy_passed += denergy
# spread the energy in chosen direction
for i in range(len(self.buffers)):
gate_fit = i == gate_final
self.buffers[i].energy = energy_passed * gate_fit
self.buffers[i].rule = self.main.rule * gate_fit
self.main.new_energy -= energy_passed
def genome_crossover(state, num_genes, *genomes, max_genes=None,
mutation_prob=0, rng_name="rng"):
"""
Crossover given genomes in stochastic way.
:param state:
A container holding model's properties.
:param num_genes:
Genome length, assuming all genomes has the same number of genes.
:param genomes:
A list of genomes (integers) to crossover
:param max_genes:
Upper limit for '1' genes in the resulting genome.
:param mutation_prob:
Probability of a single gene's mutation.
:param rng_name:
Name of ``RandomProperty``.
:returns: Single integer, a resulting genome.
"""
max_genes = max_genes or num_genes
gene_choose = core.IntegerVariable()
new_genome = core.IntegerVariable()
num_genomes = core.IntegerVariable()
num_active = core.IntegerVariable()
new_gene = core.IntegerVariable()
rand_val = getattr(state, rng_name).uniform
start_gene = core.IntegerVariable()
start_gene *= 0
start_gene += xmath.int(rand_val * num_genes) % len(genomes)
for gene in range(num_genes):
gene_choose *= 0
num_genomes *= 0
gene = (gene + start_gene) % num_genes
for genome in genomes:
gene_choose += ((genome >> gene) & 1 & (genome > 0)) << num_genomes
num_genomes += (genome > 0)
rand_val = getattr(state, rng_name).uniform
winner_gene = xmath.int(rand_val * num_genomes)
new_gene *= 0
new_gene += ((gene_choose >> winner_gene) & 1)
num_active += new_gene
new_gene *= num_active <= max_genes
is_mutated = 0
if mutation_prob > 0:
is_mutated = getattr(state, rng_name).uniform < mutation_prob
is_mutated = is_mutated * (gene_choose > 0)
new_genome += (new_gene ^ is_mutated) << gene
return new_genome
def emit(self):
"""
Implement the logic of emit phase.
Each cell is a Poisson process that fires an energy in the
gate direction, when Poisson event occurs.
The rate of events depends on cell's energy level: more
energy, higher the rate.
The amount of energy depends on the cell's own "valency" and
its gated neighbour's "valency" (energy % 8).
When neighbour's valency is 0, cell spreads the amount of
energy, equal to its own valency. Otherwise, it spreads some
default amount of energy.
Cells are also spreading their gate values when event occurs,
so they are "syncing" with neighbours.
"""
shade = xmath.min(255, self.main.energy)
lmb = xmath.float(self.main.interval) / xmath.float(256 - shade)
prob = 1 - xmath.exp(-lmb)
fired = core.IntegerVariable()
fired += xmath.int(self.main.rng.uniform < prob)
fired *= self.main.energy > 0
denergy = xmath.min(self.default_denergy, shade)
gate = self.main.gate
mutated = core.IntegerVariable()
mutated += xmath.int(self.main.rng.uniform < 0.0001)
denergy = denergy - 1 * (denergy > 0) * mutated
energy_passed = core.IntegerVariable()
for i in range(len(self.buffers)):
valency1 = self.neighbors[i].main.energy % 8
valency2 = self.main.energy % 8
gate_fit = (i == gate)
full_transition = denergy * (valency1 != 0 | valency2 == 0)
potent_transition = valency2 * (valency1 == 0)
denergy_fin = full_transition + potent_transition
energy_passed += denergy_fin * fired * gate_fit
self.buffers[i].energy = energy_passed * fired * gate_fit
self.buffers[i].gate = (self.main.gate + 7) * fired * gate_fit
self.main.interval = (self.main.interval + 1) * (energy_passed == 0)
self.main.energy -= energy_passed * (energy_passed > 0)
self.main.gate = (self.main.gate + 1) % 8
def _get_next(self):
"""Generate and return the next RNG value."""
val_int = xmath.int(DeferredExpression(self.var_name))
val = ((val_int * 58321 + 11113)) % 65535
self.__set__(self, val)
container = inspect.currentframe().f_back.f_locals['obj']
self.bsca.deferred_write(container)
def color(self):
"""Render cell's genome as hue/sat, cell's energy as value."""
if self._legacy_coloring:
red, green, blue = GenomeColor.modular(self.main.rule >> 1, 360)
else:
red, green, blue = GenomeColor.positional(self.main.rule,
self.main.rule.bit_width)
shade = xmath.min(self.main.energy, self.meta.full_treshold)
shade = shade * 255 / self.meta.full_treshold
red = xmath.int(red * shade)
green = xmath.int(green * shade)
blue = xmath.int(blue * shade)
return (
red,
green,
blue,
)
def hsv2rgb(hue, saturation, value):
"""
Convert HSV color to RGB format.
:param hue: Hue value [0, 1]
:param saturation: Saturation value [0, 1]
:param value: Brightness value [0, 1]
:returns: tuple (red, green, blue)
"""
if isinstance(hue, DeferredExpression):
hue_f = core.FloatVariable()
hue_f += hue * 6
hue_i = core.IntegerVariable()
hue_i += xmath.int(hue_f)
hue_f -= hue_i
sat = core.FloatVariable()
sat += saturation
val = core.FloatVariable()
val += value
grad_p = core.FloatVariable()
grad_p += val * (1 - sat)
grad_q = core.FloatVariable()
grad_q += val * (1 - sat * hue_f)
grad_t = core.FloatVariable()
grad_t += val * (1 - sat * (1 - hue_f))
else:
hue_f = hue * 6
hue_i = int(hue_f)
hue_f -= hue_i
sat = saturation
val = value
grad_p = val * (1 - sat)
grad_q = val * (1 - sat * hue_f)
grad_t = val * (1 - sat * (1 - hue_f))
red = ((hue_i == 0) | (hue_i >= 5)) * val
red = red + (hue_i == 1) * grad_q
red = red + ((hue_i == 2) | (hue_i == 3)) * grad_p
red = red + (hue_i == 4) * grad_t
green = ((hue_i == 0) | (hue_i >= 6)) * grad_t
green = green + ((hue_i == 1) | (hue_i == 2)) * val
green = green + (hue_i == 3) * grad_q
green = green + ((hue_i == 4) | (hue_i == 5)) * grad_p
blue = ((hue_i == 0) | (hue_i == 1) | (hue_i >= 6)) * grad_p
blue = blue + (hue_i == 2) * grad_t
blue = blue + ((hue_i == 3) | (hue_i == 4)) * val
blue = blue + (hue_i == 5) * grad_q
return (red, green, blue)
def color(self):
"""Make some noise, now for real."""
val = xmath.int(self.main.rng.uniform * 255)
return (val, val, val)
