test.py

import numpy as np
import cloudpickle
import matplotlib.pyplot as plt

Po=np.linspace(200,8000,10000)
Isonzo=np.linspace(0,1500,10000)
Timavo=np.linspace(8,150,10000)
variables=["N1p","N3n","S","T","Chla"]
depth={"O m":0, "10 m":1, "30 m":2, "60 m":3, "100 m":4}






# --- CONFIGURE YOUR CHOICES ---

depth_list = [0,10,30,60,100]
variables_list=["N1p","N3n","S","T","Chla"]
days_list=[0,1,2,3,4]

depth_dict={
    0:0,
    10:1,
    30:2,
    60:3,
    100:4

}

# --- WIDGETS ---
days_ctx = 0
variables_ctx  = 0
depth_ctx  =  0

po= value=Po[-1]/2
isonzo = Isonzo[-1]/2
timavo = value=Timavo[-1]/2

# --- MODEL PREDICTION (placeholder) ---
def model_predict(days, variable, depth, po, isonzo, timavo):
    tmp=np.load("{}_{}.npy".format(variable,depth))
    with open("model_{}_{}.pkl".format(variable,depth_dict[depth]), 'rb') as f:
        model = cloudpickle.load(f)
        output=model(tmp.reshape(1,-1), isonzo.reshape(1,-1), po.reshape(1,-1), timavo.reshape(1,-1), days)
    return output

# Compute
Z = model_predict(days_ctx, variables[variables_ctx], depth_ctx, po, isonzo, timavo)


# --- PLOT ---
fig, ax = plt.subplots()
ax.imshow(Z, origin='lower', aspect='auto')

# --- DISPLAY ---
plt.show()