def createSpecimen(self):
stake = messup_stake(V2dList.from_dalmatian_string(choice(self.pool["stakes"]), sep=","))
spaghetti = randint(1, 2) is 1
fxWeight = FractionList.from_string(self.pool["fx-weights"]).choice()
deltas = V2dList.from_dalmatian_list(self.fractionList.signed_sample_list(len(stake), 2))
tweaks = self.fractionList.signed_sample_list(len(stake), 5)
points = stake + (deltas*fxWeight)
product = ProductionGame(chainlength = len(points) -1)
product.set_constants("ZMLCQST").set_vars("IJK")
product.init_with_random_rules(levels = 2, keyrules = self.pool["rules"])
product.produce()
product_obj = product.to_obj()
brush = toBrush(product.core_chain(), points, fxWeight, tweaks, spaghetti)
frequency_info = ", ".join(["{}:{}".format(k,v) for k, v in brush.action_frequency().items()])
ruleInfo = ", ".join([r["s"] + "->" + r["r"] for r in product_obj["rules"]])
summary = "Brush based on a stake of {} points, a weight of {} and the following rules {} starting with {} resulting in frequency {}".format(len(stake), fxWeight, ruleInfo , product_obj["start"], frequency_info)
return {
"id": self.incId(),
"fx-weight": str(fxWeight),
"stake": stake.to_dalmatian_list(),
"spaghetti": spaghetti,
"deltas": deltas.to_dalmatian_list(),
"tweaks": tweaks,
"points": points.to_dalmatian_list(),
"product": product_obj,
"brush": brush.to_dalmatian_string(),
"brush-svg": brush.to_svg_string(BRUSH_WIDTH),
"brush-cartesian": brush.to_core_cartesian_string(BRUSH_WIDTH),
"summary": summary,
"tags": ""
}
def test_create(self):
product = ProductionGame(chainlength = 60)
product.set_constants("LQC").set_vars("IJK")
product.init_with_random_rules(levels = 2, keyrules = ["L", "QQ", "C"])
product.produce()
result = product.to_obj()
self.assertEqual(len(result["rules"]), 3)
self.assertGreaterEqual(len(result["chain"]), len(result["core-chain"]))
self.assertGreaterEqual(len(result["core-chain"]), 60)
def test_breeding(self):
obj1 = {
"constants": "ABC",
"variables": "IJ",
"start": "AIAI",
"rules": [
{
"s": "I",
"r": "BCJJ"
},
{
"s": "J",
"r": "ABII"
}
]
}
obj2 = {
"constants": "AD",
"variables": "JK",
"start": "DJJD",
"rules": [
{
"s": "J",
"r": "AJJA"
},
{
"s": "K",
"r": "DKDK"
}
]
}
product = ProductionGame.from_crossover(obj1, obj2, 30)
product.produce()
expected_rules = [
{
"s": "I",
"r": "BCJJ"
},
{
"s": "J",
"r": "ABIA"
},
{
"s": "K",
"r": "DKDK"
}
]
self.assertEqual(product.to_obj()["constants"], "ABCD")
self.assertEqual(product.to_obj()["variables"], "IJK")
self.assertEqual(product.to_obj()["start"], "DIAD")
self.assertEqual(product.to_obj()["rules"], expected_rules)
def test_convert(self):
product = ProductionGame(chainlength = 60)
product.set_constants("LQC").set_vars("IJK")
product.init_with_random_rules(levels = 2, keyrules = ["L", "QQ", "C"])
product.produce()
self.assertEqual(ProductionGame.from_obj(product.to_obj()), product)
def mutation_specimen(self, specimen):
# Create L-System
product = ProductionGame.from_obj(specimen["product"])
product.produce()
product_obj = product.to_obj()
# Convert chain to brushstokes
tortugaconfig = TortugaConfig().set_magnitude_page_ratio(
self.init.magnitude_page_ratio)
tortugaconfig.set_scale_magnitude_ratio_string(
self.init.scale_magnitude_ratio)
tortugaconfig.set_brushstoke_angle_offset(
self.init.brushstoke_angle_offset)
tortugaconfig.set_xy(self.init.xy)
angles = mutate_fraction(specimen["angles"], self.pool.angles)
magnitudes = mutate_fraction(specimen["magnitudes"],
self.pool.magnitudes)
tortugaconfig.set_angles_string(angles)
tortugaconfig.set_magnitudes_string(magnitudes)
tortugaconfig.set_brush_ids(self.init.brushids)
tortugaconfig.set_chain(product.chain)
brushstokes = TortugaProducer(tortugaconfig).produce()
bstats = V2dList([bs.xy for bs in brushstokes])
# Create stencil aka DalmatianMedia
stencil = DalmatianMedia(DlmtHeaders().set_brush_page_ratio(
self.init.brush_page_ratio))
stencil.add_view_string(
"view i:1 lang en xy 0 0 width 1 height 1 flags o tags all but [ ] -> everything"
)
stencil.add_tag_description_string(
"tag i:1 lang en same-as [] -> default")
for brush in self.init.brushes:
stencil.add_brush_string(brush)
stencil.set_brushstrokes(brushstokes)
allbr = stencil.page_brushstroke_list_for_view_string(
"view i:1 lang en xy 0 0 width 1 height 1 flags o tags all but [ ] -> everything"
)
fitbr = stencil.page_brushstroke_list_for_view_string(
"view i:1 lang en xy 0 0 width 1 height 1 flags O tags all but [ ] -> everything"
)
fitness = Fraction(len(fitbr), len(allbr))
ruleInfo = ", ".join(
[r["s"] + "->" + r["r"] for r in product_obj["rules"]])
correlation = bstats.get_correlation()
medianpoint = bstats.get_median_range(8)
if correlation > 0.9 or correlation < -0.9:
print("C", end="")
return None
if float(fitness) < 0.8:
print("F", end="")
return None
if medianpoint.x < self.init.min_median_range.x:
print("X", end="")
return None
if medianpoint.y < self.init.min_median_range.y:
print("Y", end="")
return None
summary = "Stencil manually selected from carefully crafted generations using a Lindenmayer system approach based on angles [ {} ], magnitudes [ {} ] and the rules {} starting with {} resulting in {} brushstokes with a visibility of {:.2%}, correlation of {} and a median range of {}".format(
angles, magnitudes, ruleInfo, product_obj["start"],
len(brushstokes), float(fitness), correlation,
medianpoint.to_float_string())
return {
"id": self.inc_id(),
"product": product_obj,
"angles": angles,
"magnitudes": magnitudes,
"stencil": stencil.to_obj(),
"summary": summary,
"tags": ""
}