def pid(setpoint, ylist, previous_e, integral, Kp, Ki, Kd):
""""PID controller to smoothly go to target(setpoint)"""
# get measured value
measured_value = lr.findy(lr.xlist(ylist)[-1] + 1, lr.xlist(ylist), ylist)
# reset previous error
previous_error = previous_e
error = abs(setpoint - measured_value)
# set newintegral
newintegral = integral + error
# I and D
integral = (integral + error) / 0.1
derivative = (error - previous_error) / 0.1
# output and tuning
output = (Kp * error) + (Ki * integral) + (Kd * derivative)
return output, measured_value, error, newintegral
def difference(data, change, maxchange):
""""returns te amount time to change"""
# receive data
target = data[0][0]
time_list = data[1][0][-6:]
# make prediction for bedtime.
predicted_bt = lr.findy(
lr.xlist(time_list)[-1] + 1, lr.xlist(time_list), time_list)
# Calculate bedtime offset to bedtime target.
diff = target - predicted_bt
bt_change = change * diff
bt_change = abs(bt_change)
# restrict to not go over the maxchange
if bt_change > maxchange:
bt_change = maxchange
return predicted_bt, bt_change, maxchange
def draw_pid_grapth(target, pid_data, errors, error_dict):
""""draw graph"""
# make x tiks
x = lr.xlist(pid_data)
# draw graph
plt.plot(x, pid_data, label="with PID")
plt.plot(x, errors, label="no PID")
plt.axhline(y=target, linewidth=1, color='k')
plt.scatter(list(error_dict.keys()),
list(error_dict.values()),
color='black')
# make labels
plt.xlabel("Time in days")
plt.ylabel("diviation from target")
plt.legend(loc='upper left')
plt.show()
def integral(data):
""""returns te amount time to change"""
# set n for number of squars in riemann sum
n = 100
# receive data
ylist = data[1][0]
# calculate delta x, the 7 stands for the start of the PID influance
deltax = len(ylist) - 0 / n
#integral:
i = 0
intgrl = 0
while i < len(ylist[7:]):
# get y
y = lr.findy(i, lr.xlist(ylist[7:]), ylist[7:]) - ylist[6]
# calculate integral
area = y * deltax
intgrl += area
i += deltax
return intgrl
def drawgrapth(profile, altered_profile):
""""draw graph"""
# make x tiks
x = lr.xlist(altered_profile[1][0])
# make y tiks
altered_data = altered_profile[1][0]
random_data = cl.lst_strip_day(profile[1][0])
# make Ys ready for graph
altered_data = cl.lst_strip_day(altered_data)
random_data = cl.lst_strip_day(random_data)
# draw graph
plt.plot(x, altered_data, label="altered times")
plt.plot(x, random_data, 'ro', label="unaltered times")
plt.yticks(np.arange(0, 24, step=1))
plt.xticks(np.arange(0, 61, step=7))
# make labels
plt.xlabel("Time in days")
plt.ylabel("time of day")
plt.legend(loc='upper left')
plt.show()