Merge pull request #29 from scioip34/feature/replay

Feature/replay

abm/contrib/colors.py

@@ -5,6 +5,7 @@
 GREEN = (50, 150, 50)
 PURPLE = (130, 0, 130)
 GREY = (230, 230, 230)
+DARK_GREY = (210, 210, 210)
 YELLOW = (190, 175, 50)
 RED = (255, 0, 0)
 LIGHT_RED = (255, 180, 180)

abm/contrib/ifdb_params.py

@@ -14,6 +14,14 @@
 INFLUX_PSWD = "password"
 INFLUX_DB_NAME = "home"
 
+T = float(int(envconf["T"]))
+if T <= 1000:
+    write_batch_size = T
+else:
+    if T % 1000 != 0:
+        raise Exception("Simulation time (T) must be dividable by 1000 or smaller than 1000!")
+    write_batch_size = 1000
+
 # SAVE_DIR is counted from the ABM parent directory.
 SAVE_DIR = envconf.get("SAVE_ROOT_DIR", "abm/data/simulation_data")
 

abm/data/metaprotocol/experiments/exp7.py

@@ -0,0 +1,87 @@
+description_text = """
+Experiment file using the MetaRunner interfacing language to define a set of criteria for batch simulations
+
+Title:      Experiment 1
+Goal:       Understand the balance of social vs individual excitablility in fixed environment
+Defined by: mezdahun and DominikDeffner @ github
+"""
+from abm.metarunner.metarunner import Tunable, Constant, MetaProtocol, TunedPairRestrain
+
+# Defining fixed criteria for all automized simulations/experiments
+fixed_criteria = [
+    Constant("USE_IFDB_LOGGING", 1),
+    Constant("SAVE_CSV_FILES", 1),
+    Constant("WITH_VISUALIZATION", 0),  # how does the simulation speed scale with N
+    Constant("TELEPORT_TO_MIDDLE", 0),
+    Constant("GHOST_WHILE_EXPLOIT", 1),
+    Constant("PATCHWISE_SOCIAL_EXCLUSION", 1),
+    Constant("POOLING_TIME", 0),
+    Constant("MOV_EXP_VEL_MIN", 1),
+    Constant("MOV_EXP_VEL_MAX", 1),
+    Constant("MOV_REL_DES_VEL", 1),
+    Constant("SHOW_VISUAL_FIELDS", 0),
+    Constant("SHOW_VISUAL_FIELDS_RETURN", 0),
+    Constant("SHOW_VISION_RANGE", 0),
+    Constant("ENV_WIDTH", 500),
+    Constant("ENV_HEIGHT", 500),
+    Constant("VISUAL_FIELD_RESOLUTION", 1200),
+    Constant("VISUAL_EXCLUSION", 1),
+    Constant("VISION_RANGE", 1000),
+    Constant("AGENT_CONSUMPTION", 1),
+    Constant("RADIUS_AGENT", 10),
+    Constant("RADIUS_RESOURCE", 40),
+    Constant("MAX_RESOURCE_QUALITY", -1),  # so that the minimum value will be used as definite
+    Constant("MAX_RESOURCE_PER_PATCH", -1),  # so that the minimum value will be used as definite
+    Constant("MOV_EXP_TH_MIN", -0.25),
+    Constant("MOV_EXP_TH_MAX", 0.25),
+    Constant("MOV_REL_TH_MAX", 0.5),
+    Constant("CONS_STOP_RATIO", 0.1),
+    Constant("REGENERATE_PATCHES", 1),
+    Constant("DEC_FN", 0.5),
+    Constant("DEC_FR", 0.5),
+    Constant("DEC_TAU", 10),
+    Constant("DEC_BW", 0),
+    Constant("DEC_WMAX", 1),
+    Constant("DEC_BU", 0),
+    Constant("DEC_UMAX", 1),
+    Constant("DEC_GW", 0.085),
+    Constant("DEC_GU", 0.085),
+    Constant("DEC_TW", 0.5),
+    Constant("DEC_TU", 0.5)
+]
+
+# Defining decision param
+sum_resources = 3000
+num_patches = [1, 2, 3, 5, 6, 10]
+criteria_exp = [
+    Constant("N", 10),
+    Constant("AGENT_FOV", 1),  # unlimited
+    Tunable("DEC_EPSW", values_override=[0, 0.5, 0.75, 1, 2, 3]),
+    Constant("DEC_EPSU", 1),
+    Constant("MIN_RESOURCE_QUALITY", 0.25),
+    Tunable("MIN_RESOURCE_PER_PATCH", values_override=[int(sum_resources/np) for np in num_patches]),
+    Constant("DEC_SWU", 0),
+    Constant("DEC_SUW", 0),
+    Tunable("N_RESOURCES", values_override=num_patches),
+    Constant("T", 15000)
+]
+
+# Creating metaprotocol and add defined criteria
+mp = MetaProtocol(experiment_name="Experiment7", num_batches=10, parallel=True,
+                  description=description_text)
+for crit in fixed_criteria:
+    mp.add_criterion(crit)
+for crit in criteria_exp:
+    mp.add_criterion(crit)
+
+# Locking the overall resource units in environment
+constant_runits = TunedPairRestrain("N_RESOURCES", "MIN_RESOURCE_PER_PATCH", sum_resources)
+mp.add_tuned_pair(constant_runits)
+
+# Generating temporary env files with criterion combinations. Comment this out if you want to continue simulating due
+# to interruption
+mp.generate_temp_env_files()
+
+# Running the simulations
+mp.run_protocols()
+

abm/environment/rescource.py

@@ -41,7 +41,7 @@ def __init__(self, id, radius, position, env_size, color, window_pad, resc_units
         self.position = np.array(position, dtype=np.float64)  # saved
         self.center = (self.position[0] + self.radius, self.position[1] + self.radius)
         self.color = color
-        self.resc_left_color = (color[0]-20, color[1]-20, color[2]-20)
+        self.resc_left_color = colors.DARK_GREY
         self.unit_per_timestep = quality  # saved
 
         # Environment related parameters