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spatio_flux/processes/comets.py

@@ -1,13 +1,11 @@
 """
 COMETS composite made of dFBAs and diffusion-advection processes.
 """
-
 from process_bigraph import Composite
 from bigraph_viz import plot_bigraph
 from spatio_flux import core
 from spatio_flux.viz.plot import plot_time_series, plot_species_distributions_to_gif
 
-
 # TODO -- need to do this to register???
 from spatio_flux.processes.dfba import get_spatial_dfba_state
 from spatio_flux.processes.diffusion_advection import get_diffusion_advection_spec
@@ -78,7 +76,7 @@ def run_comets(
     # plot timeseries
     plot_time_series(
         comets_results,
-        coordinates=[(0, 0), (5, 5)],
+        coordinates=[(0, 0), (n_bins[0]-1, n_bins[1]-1)],
         out_dir='out',
         filename='comets_timeseries.png',
     )

spatio_flux/processes/dfba.py

@@ -4,7 +4,6 @@
 
 Process for a pluggable dFBA simulation.
 """
-
 import numpy as np
 import warnings
 import cobra
@@ -234,6 +233,45 @@ def get_spatial_dfba_state(
     }
 
 
+def run_dfba_single(
+        total_time=60,
+        mol_ids=None,
+):
+    single_dfba_config = {
+        'dfba': get_single_dfba_spec(path=['fields']),
+        'fields': {
+            'glucose': 10,
+            'acetate': 0,
+            'biomass': 0.1
+        }
+    }
+
+    # make the simulation
+    sim = Composite({
+        'state': single_dfba_config,
+        'emitter': {'mode': 'all'}
+    }, core=core)
+
+    # save the document
+    sim.save(filename='single_dfba.json', outdir='out')
+
+    # simulate
+    print('Simulating...')
+    sim.update({}, total_time)
+
+    # gather results
+    dfba_results = sim.gather_results()
+
+    print('Plotting results...')
+    # plot timeseries
+    plot_time_series(
+        dfba_results,
+        # coordinates=[(0, 0), (1, 1), (2, 2)],
+        out_dir='out',
+        filename='dfba_single_timeseries.png',
+    )
+
+
 def run_dfba_spatial(
         total_time=60,
         n_bins=(3, 3),  # TODO -- why can't do (5, 10)??
@@ -271,7 +309,6 @@ def run_dfba_spatial(
 
     # gather results
     dfba_results = sim.gather_results()
-    # print(dfba_results)
 
     print('Plotting results...')
     # plot timeseries
@@ -293,4 +330,5 @@ def run_dfba_spatial(
 
 
 if __name__ == '__main__':
+    run_dfba_single()
     run_dfba_spatial(n_bins=(8,8))

spatio_flux/processes/diffusion_advection.py

@@ -4,7 +4,6 @@
 
 This process is a simple 2D diffusion-advection process. It takes a 2D field as input and returns a 2D field as output.
 """
-
 import numpy as np
 from scipy.ndimage import convolve
 from process_bigraph import Process, Composite
@@ -122,9 +121,6 @@ def diffusion_delta(self, state, interval, diffusion_coeff, advection_coeff):
             # Update the current state
             updated_state += (laplacian + advective_flux_x + advective_flux_y) * dt
 
-            # # Ensure non-negativity
-            # current_states[species] = np.maximum(updated_state, 0)
-
             # Update time
             t += dt
 
@@ -199,9 +195,6 @@ def get_diffusion_advection_state(
             'acetate': 0,
             'biomass': 0.1
         }
-    # initial_fields = {
-    #     mol_id: np.random.uniform(low=0, high=initial_max[mol_id], size=n_bins[1], n_bins[0]))
-    #     for mol_id in mol_ids}
     initial_fields = {
         mol_id: np.random.uniform(low=0, high=initial_max[mol_id], size=n_bins)
         for mol_id in mol_ids}
@@ -267,7 +260,6 @@ def run_diffusion_process(
 
     # gather results
     diffadv_results = sim.gather_results()
-    # print(diffadv_results)
 
     print('Plotting results...')
     # plot 2d video

spatio_flux/processes/particle_comets.py

@@ -1,38 +1,49 @@
 """
 Particle-COMETS composite made of dFBAs, diffusion-advection, and particle processes.
 """
-
 from process_bigraph import Composite
 from bigraph_viz import plot_bigraph
 from spatio_flux import core
 from spatio_flux.viz.plot import plot_time_series, plot_species_distributions_with_particles_to_gif
 
-
 # TODO -- need to do this to register???
 from spatio_flux.processes.dfba import DynamicFBA, get_spatial_dfba_state
 from spatio_flux.processes.diffusion_advection import DiffusionAdvection, get_diffusion_advection_spec
 from spatio_flux.processes.particles import Particles, get_particles_spec, get_particles_state
 
 
 default_config = {
-    'total_time': 10.0,
+    'total_time': 100.0,
     # environment size
     'bounds': (10.0, 20.0),
     'n_bins': (8, 16),
     # set fields
     'mol_ids': ['glucose', 'acetate', 'biomass', 'detritus'],
     'field_diffusion_rate': 1e-1,
     'field_advection_rate': (0, 0),
-    'initial_min_max': {'glucose': (10, 10), 'acetate': (0, 0), 'biomass': (0, 0.1), 'detritus': (0, 0)},
+    'initial_min_max': {
+        'glucose': (10, 10),
+        'acetate': (0, 0),
+        'biomass': (0, 0.1),
+        'detritus': (0, 0)
+    },
     # set particles
     'n_particles': 10,
     'particle_diffusion_rate': 1e-1,
     'particle_advection_rate': (0, -0.1),
     'particle_add_probability': 0.3,
     'particle_boundary_to_add': ['top'],
     'field_interactions': {
-        'biomass': {'vmax': 0.1, 'Km': 1.0, 'interaction_type': 'uptake'},
-        'detritus': {'vmax': -0.1, 'Km': 1.0, 'interaction_type': 'secretion'},
+        'biomass': {
+            'vmax': 0.1,
+            'Km': 1.0,
+            'interaction_type': 'uptake'
+        },
+        'detritus': {
+            'vmax': -0.1,
+            'Km': 1.0,
+            'interaction_type': 'secretion'
+        },
     },
 }
 

spatio_flux/processes/particles.py

@@ -4,7 +4,6 @@
 
 A process for simulating the motion of particles in a 2D environment.
 """
-
 import uuid
 import numpy as np
 from process_bigraph import Process, Composite
@@ -239,13 +238,13 @@ def check_boundary_hit(self, new_x_position, new_y_position):
 
     def get_boundary_position(self, boundary):
         if boundary == 'left':
-            return (self.env_size[0][0], np.random.uniform(*self.env_size[1]))
+            return self.env_size[0][0], np.random.uniform(*self.env_size[1])
         elif boundary == 'right':
-            return (self.env_size[0][1], np.random.uniform(*self.env_size[1]))
+            return self.env_size[0][1], np.random.uniform(*self.env_size[1])
         elif boundary == 'top':
-            return (np.random.uniform(*self.env_size[0]), self.env_size[1][1])
+            return np.random.uniform(*self.env_size[0]), self.env_size[1][1]
         elif boundary == 'bottom':
-            return (np.random.uniform(*self.env_size[0]), self.env_size[1][0])
+            return np.random.uniform(*self.env_size[0]), self.env_size[1][0]
 
 
 core.register_process('Particles', Particles)
@@ -300,7 +299,7 @@ def get_particles_state(
         }
     if initial_min_max is None:
         initial_min_max = {
-            'biomass': (0, 0.1),
+            'biomass': (0.1, 0.2),
             'detritus': (0, 0),
         }
 
@@ -329,7 +328,7 @@ def get_particles_state(
 
 
 def run_particles(
-        total_time=20,  # Total frames
+        total_time=100,  # Total frames
         bounds=(10.0, 20.0),  # Bounds of the environment
         n_bins=(20, 40),  # Number of bins in the x and y directions
         n_particles=20,
@@ -372,9 +371,8 @@ def run_particles(
     # gather results
     particles_results = sim.gather_results()
     emitter_results = particles_results[('emitter',)]
-
+    # resort results
     particles_history = [p['particles'] for p in emitter_results]
-    # print(particles_history)
 
     print('Plotting...')
     # plot particles

spatio_flux/viz/plot.py

@@ -14,6 +14,8 @@
 
 def sort_results(results):
     emitter_results = results[('emitter',)]
+    if emitter_results[0] is None:
+        return
     sorted_results = {'fields': {
         key: [] for key in emitter_results[0]['fields'].keys()
     }, 'time': []}
@@ -45,7 +47,7 @@ def plot_time_series(
     :param coordinates: List of coordinates (indices) to plot.
                         Example: [(0, 0), (1, 2)]
     """
-    coordinates = coordinates or [(0, 0)]
+    # coordinates = coordinates or [(0, 0)]
     field_names = field_names or ['glucose', 'acetate', 'biomass']
     sorted_results = sort_results(results)
     time = sorted_results['time']
@@ -56,13 +58,15 @@ def plot_time_series(
     for field_name in field_names:
         if field_name in sorted_results['fields']:
             field_data = sorted_results['fields'][field_name]
-
-            for coord in coordinates:
-                x, y = coord
-                time_series = [field_data[t][x, y] for t in range(len(time))]
-                ax.plot(time, time_series, label=f'{field_name} at {coord}')
-                # plot log scale on y axis
-                # ax.set_yscale('log')
+            if coordinates is None:
+                ax.plot(time, field_data, label=field_name)
+            else:
+                for coord in coordinates:
+                    x, y = coord
+                    time_series = [field_data[t][x, y] for t in range(len(time))]
+                    ax.plot(time, time_series, label=f'{field_name} at {coord}')
+                    # plot log scale on y axis
+                    # ax.set_yscale('log')
         else:
             print(f"Field '{field_name}' not found in results['fields']")