python - 创建一个函数来获取 s 形曲线的值

Question

我想为连接到家庭助理的一些飞利浦 Hue 灯创建一个模拟日出的 pyhton 脚本。

我想要实现的是遵循亮度和开尔文值的 sigmoid / s 形曲线。

我希望亮度从 1 到 100 (%)，开尔文值从 2500 到 4000。

我当前的脚本以线性方式执行此操作：

#import time
def sunrise(entity_id, minutes, updatesecs=10, startbrightness=1, endbrightness=100, startkelvin=2500, endkelvin=4000):
    # Set current brightness and kelvin to the staring values
    currentbrightness=startbrightness
    currentkelvin=startkelvin
    # Calculate the needed iterations for the while loop
    numberofiterations=minutes*60/updatesecs
    kelvinincreasebyiteration=(endkelvin-startkelvin)/numberofiterations
    i=0
    while(i<=numberofiterations):
        # Set new brightness value
        currentbrightness = currentbrightness+endbrightness/numberofiterations
        currentkelvin = currentkelvin+kelvinincreasebyiteration
        if currentbrightness <= endbrightness:
            #print(round(currentbrightness)) # This value will be used for setting the brightness
            #print(round(currentkelvin))
            service_data = {"entity_id": entity_id, "kelvin": currentkelvin, "brightness_pct": currentbrightness, "transition": updatesecs-1}
            hass.services.call("light", "turn_on", service_data, False)

            time.sleep(updatesecs)
        else:
            break

entity_id = data.get("entity_id")
minutes = data.get("minutes")
updatesecs = data.get("updatesecs")

sunrise(entity_id,minutes,updatesecs)

任何使用 s 形值而不是线性设置亮度/开尔文的想法都值得赞赏。

Answer 1

您可以简单地迭代最终的df并使用亮度和开尔文值，每个间隔睡一分钟左右并调用您的api。

import numpy as np
import seaborn as sns
import pandas as pd
import math
import matplotlib.pyplot as plt

def sigmoid(x):  
    return math.exp(-np.logaddexp(0, -x))

# You could change 60 to some other interval if you want
t = [(i,sigmoid(x)) for i,x in enumerate(np.linspace(-10,10,60))]
# df of time interval and y value
df = pd.DataFrame(t)


df.columns = ['time','sig']

# multiply sig by 100 to scale up to a percent for brightness
df['brightness'] = (df['sig'] * 100).astype(int)+1

# Scale sig values to 2500,4000 for kelvin
a = df.sig.values
df['kelvin'] = np.interp(a, (a.min(), a.max()), (2500, 4000)).astype(int)


fig, (ax1, ax2) = plt.subplots(ncols=2, sharey=False)
sns.lineplot(data=df,x='time',y='brightness', ax=ax1)
sns.lineplot(data=df,x='time',y='kelvin', ax=ax2)