init weather_station repo
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docs/mp_optimize_demo.md
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docs/mp_optimize_demo.md
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# Micropython程序优化实例
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这个优化例子来自 Damien 在 pycomau 上的演讲使用MicroPython高效快速编程。
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首先我们看下面的程序，它在循环中翻转LED，然后通过运行的时间和翻转次数，计算出每秒翻转的频率。
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```py
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from machine import Pin
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import time
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led = Pin('A13')
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N = 200000
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t0 = time.ticks_us()
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for i in range(N):
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led.on()
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led.off()
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t1 = time.ticks_us()
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dt = time.ticks_diff(t1, t0)
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fmt = '{:5.3f} sec, {:6.3f} usec/blink : {:8.2f} kblink/sec'
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print(fmt.format(dt * 1e-6, dt / N, N / dt * 1e3))
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```
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我们将这段代码保存为文件led1.py，然后import led1执行。在pybv10或者pyboardCN上结果是：
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> 3.381 sec, 16.905 usec/blink : 59.16 kblink/sec
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在 MicroPython程序优化原则 中，提到尽量在程序中执行功能，不要在主程序中运行，因此可以将LED翻转放在函数中执行。
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```py
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from machine import Pin
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import time
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led = Pin('A13')
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N = 200000
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def blink_simple(n):
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for i in range(n):
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led.on()
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led.off()
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def time_it(f, n):
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t0 = time.ticks_us()
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f(n)
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t1 = time.ticks_us()
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dt = time.ticks_diff(t1, t0)
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fmt = '{:5.3f} sec, {:6.3f} usec/blink : {:8.2f} kblink/sec'
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print(fmt.format(dt * 1e-6, dt / n, n / dt * 1e3))
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time_it(blink_simple, N)
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```
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运行后的结果是：
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> 2.902 sec, 14.509 usec/blink : 68.92 kblink/sec
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可以看到，我们没有做什么实质的改到，就明显提高了速度。
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循环是最消耗运行时间的，我们对循环中led.on()和led.off()两个动作进行优化，将它们预先载入内存，而无需循环中每次载入。
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```
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from machine import Pin
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import time
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led = Pin('A13')
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N = 200000
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def blink_simple(n):
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on = led.on
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off = led.off
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for i in range(n):
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on()
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off()
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def time_it(f, n):
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t0 = time.ticks_us()
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f(n)
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t1 = time.ticks_us()
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dt = time.ticks_diff(t1, t0)
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fmt = '{:5.3f} sec, {:6.3f} usec/blink : {:8.2f} kblink/sec'
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print(fmt.format(dt * 1e-6, dt / n, n / dt * 1e3))
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time_it(blink_simple, N)
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```
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运行结果是
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> 1.617 sec, 8.086 usec/blink : 123.68 kblink/sec
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速度提高了将近一倍。
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进一步将循环中对 range(n) 也进行优化
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```py
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from machine import Pin
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import time
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led = Pin('A13')
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N = 200000
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def blink_simple(n):
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on = led.on
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off = led.off
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r = range(n)
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for i in r:
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on()
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off()
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def time_it(f, n):
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t0 = time.ticks_us()
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f(n)
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t1 = time.ticks_us()
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dt = time.ticks_diff(t1, t0)
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fmt = '{:5.3f} sec, {:6.3f} usec/blink : {:8.2f} kblink/sec'
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print(fmt.format(dt * 1e-6, dt / n, n / dt * 1e3))
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time_it(blink_simple, N)
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```
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运行结果是
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> 1.121 sec, 5.607 usec/blink : 178.35 kblink/sec
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效果非常明显。
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进一步对循环中的操作优化，减少循环次数
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```py
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from machine import Pin
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import time
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led = Pin('A13')
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N = 200000
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def blink_simple(n):
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n //= 8
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on = led.on
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off = led.off
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r = range(n)
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for i in r:
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on()
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off()
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on()
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off()
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on()
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off()
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on()
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off()
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on()
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off()
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on()
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off()
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on()
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off()
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on()
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off()
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def time_it(f, n):
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t0 = time.ticks_us()
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f(n)
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t1 = time.ticks_us()
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dt = time.ticks_diff(t1, t0)
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fmt = '{:5.3f} sec, {:6.3f} usec/blink : {:8.2f} kblink/sec'
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print(fmt.format(dt * 1e-6, dt / n, n / dt * 1e3))
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time_it(blink_simple, N)
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```
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速度又有明显提升。
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> 0.913 sec, 4.563 usec/blink : 219.16 kblink/sec
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根据MicroPython的优化功能，可以将程序声明为native code（本地代码），它使用CPU的操作码（opcode），而不是字节码（bytecode）
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```py
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from machine import Pin
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import time
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led = Pin('A13')
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N = 200000
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@micropython.native
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def blink_simple(n):
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n //= 8
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on = led.on
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off = led.off
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r = range(n)
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for i in r:
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on()
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off()
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on()
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off()
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on()
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off()
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on()
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off()
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on()
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off()
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on()
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off()
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on()
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off()
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on()
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off()
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def time_it(f, n):
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t0 = time.ticks_us()
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f(n)
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t1 = time.ticks_us()
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dt = time.ticks_diff(t1, t0)
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fmt = '{:5.3f} sec, {:6.3f} usec/blink : {:8.2f} kblink/sec'
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print(fmt.format(dt * 1e-6, dt / n, n / dt * 1e3))
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time_it(blink_simple, N)
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```
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结果如下
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> 0.704 sec, 3.521 usec/blink : 284.00 kblink/sec
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除了native，还可以使用viper code模式，它进一步提升了整数计算和位操作性能
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```py
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from machine import Pin
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import time, stm
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led = Pin('A13')
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N = 200000
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@micropython.viper
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def blink_simple(n:int):
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n //= 8
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p = ptr16(stm.GPIOB + stm.GPIO_BSRR)
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for i in range(n):
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p[0] = 1 << 4
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p[1] = 1 << 4
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p[0] = 1 << 4
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p[1] = 1 << 4
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p[0] = 1 << 4
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p[1] = 1 << 4
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p[0] = 1 << 4
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p[1] = 1 << 4
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p[0] = 1 << 4
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p[1] = 1 << 4
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p[0] = 1 << 4
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p[1] = 1 << 4
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p[0] = 1 << 4
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p[1] = 1 << 4
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p[0] = 1 << 4
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p[1] = 1 << 4
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def time_it(f, n):
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t0 = time.ticks_us()
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f(n)
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t1 = time.ticks_us()
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dt = time.ticks_diff(t1, t0)
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fmt = '{:5.3f} sec, {:6.3f} usec/blink : {:8.2f} kblink/sec'
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print(fmt.format(dt * 1e-6, dt / n, n / dt * 1e3))
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time_it(blink_simple, N)
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```
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运行结果的确是大幅提升了性能
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> 0.016 sec, 0.078 usec/blink : 12879.13 kblink/sec
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最终我们还可以通过嵌入汇编方式，最大限度提升性能
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```py
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from machine import Pin
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import time, stm
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led = Pin('A13')
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N = 200000
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@micropython.asm_thumb
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def blink_simple(r0):
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lsr(r0, r0, 3)
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movwt(r1, stm.GPIOB + stm.GPIO_BSRR)
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mov(r2, 1 << 4)
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label(loop)
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strh(r2, [r1, 0])
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strh(r2, [r1, 2])
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strh(r2, [r1, 0])
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strh(r2, [r1, 2])
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strh(r2, [r1, 0])
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strh(r2, [r1, 2])
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strh(r2, [r1, 0])
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strh(r2, [r1, 2])
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strh(r2, [r1, 0])
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strh(r2, [r1, 2])
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strh(r2, [r1, 0])
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strh(r2, [r1, 2])
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strh(r2, [r1, 0])
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strh(r2, [r1, 2])
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strh(r2, [r1, 0])
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strh(r2, [r1, 2])
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sub(r0, 1)
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bne(loop)
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def time_it(f, n):
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t0 = time.ticks_us()
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f(n)
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t1 = time.ticks_us()
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dt = time.ticks_diff(t1, t0)
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fmt = '{:5.3f} sec, {:6.3f} usec/blink : {:8.2f} kblink/sec'
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print(fmt.format(dt * 1e-6, dt / n, n / dt * 1e3))
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time_it(blink_simple, N)
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```
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运行结果是
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> 0.007 sec, 0.037 usec/blink : 27322.40 kblink/sec
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这个结果已经非常接近极限了。
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从前面的优化顺序，可以看到我们并没有大幅修改程序，就可以极高程序的性能。实际使用中，大家可以灵活选择，提高程序的性能。
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