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# -*- coding: utf-8 -*-
# -*- coding: utf-8 -*-
import json
from .categories import *
from .shared import ALWAYS_CHANGED_FLAG, DreamStateFile
from .dreamtypes import *
_laboratory_state = DreamStateFile("laboratory")
class DreamLaboratory:
NODE_NAME = "Laboratory"
ICON = "🧪"
@classmethod
def INPUT_TYPES(cls):
return {
"required": SharedTypes.frame_counter | {
"key": ("STRING", {"default": "Random value " + str(random.randint(0, 1000000))}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"renew_policy": (["every frame", "first frame"],),
"min_value": ("FLOAT", {"default": 0.0}),
"max_value": ("FLOAT", {"default": 1.0}),
"mode": (["random uniform", "random bell", "ladder", "random walk"],),
},
"optional": {
"step_size": ("FLOAT", {"default": 0.1}),
},
}
CATEGORY = NodeCategories.UTILS
RETURN_TYPES = ("FLOAT", "INT", LogEntry.ID)
RETURN_NAMES = ("FLOAT", "INT", "log_entry")
FUNCTION = "result"
@classmethod
def IS_CHANGED(cls, *values):
return ALWAYS_CHANGED_FLAG
def _generate(self, seed, last_value, min_value, max_value, mode, step_size):
rnd = random.Random()
rnd.seed(seed)
def jsonify(v: float):
return json.loads(json.dumps(v))
if mode == "random uniform":
return jsonify(self._mode_uniform(rnd, last_value, min_value, max_value, step_size))
elif mode == "random bell":
return jsonify(self._mode_bell(rnd, last_value, min_value, max_value, step_size))
elif mode == "ladder":
return jsonify(self._mode_ladder(rnd, last_value, min_value, max_value, step_size))
else:
return jsonify(self._mode_walk(rnd, last_value, min_value, max_value, step_size))
def _mode_uniform(self, rnd: random.Random, last_value: float, min_value: float, max_value: float, step_size):
return rnd.random() * (max_value - min_value) + min_value
def _mode_bell(self, rnd: random.Random, last_value: float, min_value: float, max_value: float, step_size):
s = 0.0
for i in range(3):
s += rnd.random() * (max_value - min_value) + min_value
return s / 3.0
def _mode_ladder(self, rnd: random.Random, last_value: float, min_value: float, max_value: float, step_size):
if last_value is None:
last_value = min_value - step_size
next_value = last_value + step_size
if next_value > max_value:
d = abs(max_value - min_value)
next_value = (next_value - min_value) % d + min_value
return next_value
def _mode_walk(self, rnd: random.Random, last_value: float, min_value: float, max_value: float, step_size):
if last_value is None:
last_value = (max_value - min_value) * 0.5
if rnd.random() >= 0.5:
return min(max_value, last_value + step_size)
else:
return max(min_value, last_value - step_size)
def result(self, key, frame_counter: FrameCounter, seed, renew_policy, min_value, max_value, mode, **values):
if min_value > max_value:
t = max_value
max_value = min_value
min_value = t
step_size = values.get("step_size", abs(max_value - min_value) * 0.1)
last_value = _laboratory_state.get_section("values").get(key, None)
if (last_value is None) or (renew_policy == "every frame") or frame_counter.is_first_frame:
v = _laboratory_state.get_section("values") \
.update(key, 0, lambda old: self._generate(seed, last_value, min_value, max_value, mode, step_size))
return v, round(v), LogEntry.new(
"Laboratory generated new value for '{}': {} ({})".format(key, v, round(v)))
else:
return last_value, round(last_value), LogEntry.new("Laboratory reused value for '{}': {} ({})"
.format(key, last_value, round(last_value)))
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