corrected green house sim with buffer switching dynamics and added ad…

…apted low fidelity sim to include temperature control

sim_transfer/sims/dynamics_models.py

@@ -997,11 +997,13 @@ def _greenhouse_dynamics_hf(self, x, u, params: GreenHouseParams):
         g_f = (params.f1 - params.f2 * d_p) * (params.qg ** ((t_g - params.Tg) / 10.0))
         g_l = g_f * params.v1 * jnp.exp(params.v2 * (t_g - params.v3))
         b = self.buffer_switching_func(mb, params.b1)
-        buff_1 = b * (params.f * g_f * mf + params.v * g_l * ml / params.z)
+        buff_1 = self.buffer_switching_func(params.f * g_f * mf + params.v * g_l * ml / params.z, params.b1)
+        # buff_1 = b * (params.f * g_f * mf + params.v * g_l * ml / params.z)
         factor = (params.qr ** ((t_g - params.Tg) / 10.0))
         rf = params.MF * factor
         rl = params.ML * factor
-        buff_2 = b * (rf * mf + rl * ml / params.z)
+        buff_2 = self.buffer_switching_func(rf * mf + rl * ml / params.z, params.b1)
+        # buff_2 = b * (rf * mf + rl * ml / params.z)
         dmb_dt = self.get_crop_photosynthesis(x, u, params) - buff_1 - buff_2
         h = self.get_harvest_coefficient(x, u, params)
         hf, hl = h * params.yf, h * params.yl
@@ -1029,6 +1031,7 @@ def _greenhouse_dynamics_hf(self, x, u, params: GreenHouseParams):
         return dx_dt
 
     def _greenhouse_dynamics_lf(self, x, u, params: GreenHouseParams):
+        # C, C, C, m, g/m^3, kg/m^-3
         t_g, t_p, t_s, c_i, v_i = x[0], x[1], x[2], x[3], x[4]
         # g/m^-2, g/m^-2, g/m^-2, []
         mb, mf, ml, d_p = x[self.greenhouse_state_dim], x[self.greenhouse_state_dim + 1], \
@@ -1043,8 +1046,9 @@ def _greenhouse_dynamics_lf(self, x, u, params: GreenHouseParams):
                 + params.psi
         k_v = params.rho_a * params.cp_a * phi_v
 
+        alpha = params.nu * jnp.sqrt(params.tau)
         dt_g_dt = (k_v + params.kr) * (t_o - t_g) + params.ks * (t_s - t_g) \
-                  + G * params.eta
+                  + G * params.eta + alpha * (t_h - t_g)
         dt_g_dt = dt_g_dt / params.cg
 
         dt_p_dt = jnp.zeros_like(dt_g_dt)
@@ -1062,11 +1066,13 @@ def _greenhouse_dynamics_lf(self, x, u, params: GreenHouseParams):
         g_f = (params.f1 - params.f2 * d_p) * (params.qg ** ((t_g - params.Tg) / 10.0))
         g_l = g_f * params.v1 * jnp.exp(params.v2 * (t_g - params.v3))
         b = self.buffer_switching_func(mb, params.b1)
-        buff_1 = b * (params.f * g_f * mf + params.v * g_l * ml / params.z)
+        buff_1 = self.buffer_switching_func(params.f * g_f * mf + params.v * g_l * ml / params.z, params.b1)
+        # buff_1 = b * (params.f * g_f * mf + params.v * g_l * ml / params.z)
         factor = (params.qr ** ((t_g - params.Tg) / 10.0))
         rf = params.MF * factor
         rl = params.ML * factor
-        buff_2 = b * (rf * mf + rl * ml / params.z)
+        buff_2 = self.buffer_switching_func(rf * mf + rl * ml / params.z, params.b1)
+        # buff_2 = b * (rf * mf + rl * ml / params.z)
         dmb_dt = self.get_crop_photosynthesis(x, u, params) - buff_1 - buff_2
         h = self.get_harvest_coefficient(x, u, params)
         hf, hl = h * params.yf, h * params.yl

sim_transfer/sims/simulators.py

@@ -1158,8 +1158,8 @@ def _sample_x_data(self, rng_key: jax.random.PRNGKey, num_samples_train: int, nu
 class GreenHouseSim(FunctionSimulator):
     param_ratio = 0.4
 
-    def __init__(self, use_hf: bool = False):
-        self.model = GreenHouseDynamics(use_hf=use_hf)
+    def __init__(self, use_hf: bool = False, dt: float = 300):
+        self.model = GreenHouseDynamics(use_hf=use_hf, dt=dt)
         FunctionSimulator.__init__(self, input_size=self.model.x_dim + self.model.u_dim,
                                    output_size=self.model.x_dim)
         self._typical_params, self._lower_bound_params, self._upper_bound_params = None, None, None
@@ -1463,30 +1463,30 @@ def evaluate_sim(self, x: jnp.array, params: NamedTuple) -> jnp.array:
     key1, key2 = jax.random.split(jax.random.PRNGKey(435349), 2)
     key_hf, key_lf = jax.random.split(key1, 2)
 
-    # function_sim = GreenHouseSim(use_hf=True)
-    # test_p, test_p_train = function_sim.sample_params(key1)
-    # x, _ = function_sim._sample_x_data(key_hf, 64, 1)
-    # param1 = function_sim._typical_params
-    # f1 = function_sim.sample_function_vals(x, num_samples=4000, rng_key=key2)
-    # f1 = function_sim.model.transform_state(f1)
-    # import numpy as np
-    #
-    # f2 = function_sim._typical_f(x)
-    # f2 = function_sim.model.transform_state(f2)
-    # print(jnp.isnan(f1).any())
-    # print(jnp.isnan(f2).any())
-    # check = np.max(np.abs(np.asarray(f1 - function_sim.model.transform_state(x[..., : 16]))), axis=0)
-    # function_sim = GreenHouseSim(use_hf=False)
-    # test_p, test_p_train = function_sim.sample_params(key1)
-    # x, _ = function_sim._sample_x_data(key_lf, 64, 1)
-    # param1 = function_sim._typical_params
-    # f1 = function_sim.sample_function_vals(x, num_samples=4000, rng_key=key2)
-    # import numpy as np
+    function_sim = GreenHouseSim(use_hf=True)
+    test_p, test_p_train = function_sim.sample_params(key1)
+    x, _ = function_sim._sample_x_data(key_hf, 64, 1)
+    param1 = function_sim._typical_params
+    f1 = function_sim.sample_function_vals(x, num_samples=4000, rng_key=key2)
+    f1 = function_sim.model.transform_state(f1)
+    import numpy as np
     #
-    # f2 = function_sim._typical_f(x)
-    # check = np.max(np.abs(np.asarray(f1 - function_sim.model.transform_state(x[..., : 16]))), axis=0)
-    # print(jnp.isnan(f1).any())
-    # print(jnp.isnan(f2).any())
+    f2 = function_sim._typical_f(x)
+    f2 = function_sim.model.transform_state(f2)
+    print(jnp.isnan(f1).any())
+    print(jnp.isnan(f2).any())
+    check = np.max(np.abs(np.asarray(f1 - function_sim.model.transform_state(x[..., : 16]))), axis=0)
+    function_sim = GreenHouseSim(use_hf=False)
+    test_p, test_p_train = function_sim.sample_params(key1)
+    x, _ = function_sim._sample_x_data(key_lf, 64, 1)
+    param1 = function_sim._typical_params
+    f1 = function_sim.sample_function_vals(x, num_samples=4000, rng_key=key2)
+    f1 = function_sim.model.transform_state(f1)
+    f2 = function_sim._typical_f(x)
+    f2 = function_sim.model.transform_state(f2)
+    print(jnp.isnan(f1).any())
+    print(jnp.isnan(f2).any())
+    check = np.max(np.abs(np.asarray(f1 - function_sim.model.transform_state(x[..., : 16]))), axis=0)
 
     function_sim = SergioSim(5, 10, use_hf=False)
     function_sim.sample_params(key1)