Code does not iterate correctly - python-2.7

Question

I know there is many similar problems on this site, but unfortunately I was not able to fix my own with the existing answer.. I hope you don't mind to help me.
I have a code and am trying to append values to a list while iterating through the code. However, something goes wrong in the process and I end up with value 0.
I am not very good at debugging yet so I can't find the problem.
I made a simpler version of the code with less variables below:
`import math
from pylab import *
import numpy as np
#---------------------------INITIALIZATION-----------------------
#initializing the parameters of the model
Dt = 1
time_step = 50
#initializing the connection weights
w1 = 1
#initializing parameter values for the alogistic function
steepness_SS_a=1
speed_SS_a=1
threshold_SS_a=1
#--------------------THE FUNCTIONS---------------------------
def initialize():
global t, timesteps, WS_a, SS_a, new_WS_a, new_SS_a
#initialize model states
WS_a=1
SS_a=0
# initialize lists to update all states
new_SS_a=np.array([SS_a])
#initializing time list
t=0.
timesteps=[t]
def observe():
global t, timesteps, WS_a, SS_a, new_WS_a, new_SS_a
np.append(SS_a, new_SS_a)
timesteps.append(t)
def update():
global t, timesteps, WS_a, SS_a, new_WS_a, new_SS_a
# for each state activation value, write how to update it in the form of new_WS(a)=function(WS(a))
SS_a = SS_a + speed_SS_a * (((1/(1+math.exp(-steepness_SS_a * (w1 * WS_a - threshold_SS_a))))-(1/(1+math.exp(steepness_SS_a *threshold_SS_a))))* (1+math.exp(-steepness_SS_a*threshold_SS_a))-SS_a)
# for each state activation value now move the state value to new value
SS_a=new_SS_a
#updating the timestep
t = t + Dt
#--------------------THE PROGRAM-----------------
initialize()
while t<30.:
update()
observe()
#--------------------PLOTTING--------------------
print SS_a
plot(new_SS_a)
show()`

Answer 1

how about this:
import math
import numpy as np
from pylab import plot, show
def model():
Dt = 1
time_step = 50
#initializing the connection weights
w1 = 1
#initializing parameter values for the alogistic function
steepness_SS_a=1
speed_SS_a=1
threshold_SS_a=1
#initialize model states
WS_a=1
SS_a=0
# initialize lists to update all states
new_SS_a=np.array([SS_a])
#initializing time list
t=0.0
timesteps=[t]
while t<30.0:
## update()
# for each state activation value, write how to update it in the form of new_WS(a)=function(WS(a))
new = SS_a + speed_SS_a * (
(
(1/(1+math.exp(-steepness_SS_a * (w1 * WS_a - threshold_SS_a))))
-
(1/(1+math.exp(steepness_SS_a *threshold_SS_a)))
)
*
(1+math.exp(-steepness_SS_a*threshold_SS_a))
-
SS_a
)
new_SS_a = np.append(new_SS_a,new)
# for each state activation value now move the state value to new value
SS_a=new
#updating the timestep
t = t + Dt
##observe()
#np.append(SS_a, new_SS_a)
timesteps.append(t)
print( SS_a )
print( new_SS_a )
print( timesteps )
plot(new_SS_a)
show()
model()
the mayor change is in SS_a = SS_a + speed_SS_a *... first I save that value like new = SS_a + speed_SS_a *... then I add that value to new_SS_a with new_SS_a = np.append(new_SS_a,new) (you need to catch the result of this, otherwise you lose it) and override the old one in SS_a=new
The other thing is that I put all together because the use of globals in this fashion is a bad practice, global should be used as constants and don't be modify, if you need to change it in base to a function result, then make that function return that value and then make the change to the global constant. Also import * is another bad practice because pollute your namespace with unneeded stuff and in some cases that extra stuff can override without your knowledge some other that you need, and is worse is you use multiple of those because make the debugging harder by hiding from were it come each stuff forcing you to make extra lookup to figuring which stuff belong to each library if you don't know or don't remember, so always use explicit imports