v0.20.8 fix bugs

.dockerignore

@@ -1,5 +1,7 @@
 **/soil_output
 .*
+**/.*
 **/__pycache__
 __pycache__
 *.pyc
+**/backup

.gitignore

@@ -9,3 +9,4 @@ docs/_build*
 build/*
 dist/*
 prof
+backup

CHANGELOG.md

@@ -3,6 +3,34 @@ All notable changes to this project will be documented in this file.
 
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [UNRELEASED]
+
+## [0.20.8]
+### Changed
+* Tsih bumped to version 0.1.8
+### Fixed
+* Mentions to `id` in docs. It should be `state_id` now.
+* Fixed bug: environment agents were not being added to the simulation
+
+## [0.20.7]
+### Changed
+* Creating a `time.When` from another `time.When` does not nest them anymore (it returns the argument)
+### Fixed
+* Bug with time.NEVER/time.INFINITY
+## [0.20.6]
+### Fixed
+* Agents now return `time.INFINITY` when dead, instead of 'inf'
+* `soil.__init__` does not re-export built-in time (change in `soil.simulation`. It used to create subtle import conflicts when importing soil.time.
+* Parallel simulations were broken because lambdas cannot be pickled properly, which is needed for multiprocessing.
+### Changed
+* Some internal simulation methods do not accept `*args` anymore, to avoid ambiguity and bugs.
+## [0.20.5]
+### Changed
+* Defaults are now set in the agent __init__, not in the environment. This decouples both classes a bit more, and it is more intuitive 
+## [0.20.4]
+### Added
+* Agents can now be given any kwargs, which will be used to set their state
+* Environments have a default logger `self.logger` and a log method, just like agents
 ## [0.20.3]
 ### Fixed
 * Default state values are now deepcopied again.

examples/newsspread/NewsSpread.ipynb

@@ -2,13 +2,12 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-11-08T16:22:30.732107Z",
      "start_time": "2017-11-08T17:22:30.059855+01:00"
-    },
+    }
-    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -28,24 +27,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-11-08T16:22:35.580593Z",
      "start_time": "2017-11-08T17:22:35.542745+01:00"
     }
    },
-   "outputs": [
+   "outputs": [],
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Populating the interactive namespace from numpy and matplotlib\n"
-     ]
-    }
-   ],
    "source": [
-    "%pylab inline\n",
+    "%matplotlib inline\n",
     "\n",
     "from soil import *"
    ]
@@ -66,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-11-08T16:22:37.242327Z",
@@ -86,7 +77,7 @@
       "  prob_neighbor_spread: 0.0\r\n",
       "  prob_tv_spread: 0.01\r\n",
       "interval: 1\r\n",
-      "max_time: 30\r\n",
+      "max_time: 300\r\n",
       "name: Sim_all_dumb\r\n",
       "network_agents:\r\n",
       "- agent_type: DumbViewer\r\n",
@@ -110,7 +101,7 @@
       "  prob_neighbor_spread: 0.0\r\n",
       "  prob_tv_spread: 0.01\r\n",
       "interval: 1\r\n",
-      "max_time: 30\r\n",
+      "max_time: 300\r\n",
       "name: Sim_half_herd\r\n",
       "network_agents:\r\n",
       "- agent_type: DumbViewer\r\n",
@@ -142,18 +133,18 @@
       "  prob_neighbor_spread: 0.0\r\n",
       "  prob_tv_spread: 0.01\r\n",
       "interval: 1\r\n",
-      "max_time: 30\r\n",
+      "max_time: 300\r\n",
       "name: Sim_all_herd\r\n",
       "network_agents:\r\n",
       "- agent_type: HerdViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
-      "    id: neutral\r\n",
+      "    state_id: neutral\r\n",
       "  weight: 1\r\n",
       "- agent_type: HerdViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
-      "    id: neutral\r\n",
+      "    state_id: neutral\r\n",
       "  weight: 1\r\n",
       "network_params:\r\n",
       "  generator: barabasi_albert_graph\r\n",
@@ -169,13 +160,13 @@
       "  prob_tv_spread: 0.01\r\n",
       "  prob_neighbor_cure: 0.1\r\n",
       "interval: 1\r\n",
-      "max_time: 30\r\n",
+      "max_time: 300\r\n",
       "name: Sim_wise_herd\r\n",
       "network_agents:\r\n",
       "- agent_type: HerdViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
-      "    id: neutral\r\n",
+      "    state_id: neutral\r\n",
       "  weight: 1\r\n",
       "- agent_type: WiseViewer\r\n",
       "  state:\r\n",
@@ -195,13 +186,13 @@
       "  prob_tv_spread: 0.01\r\n",
       "  prob_neighbor_cure: 0.1\r\n",
       "interval: 1\r\n",
-      "max_time: 30\r\n",
+      "max_time: 300\r\n",
       "name: Sim_all_wise\r\n",
       "network_agents:\r\n",
       "- agent_type: WiseViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
-      "    id: neutral\r\n",
+      "    state_id: neutral\r\n",
       "  weight: 1\r\n",
       "- agent_type: WiseViewer\r\n",
       "  state:\r\n",
@@ -225,7 +216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-11-08T18:07:46.781745Z",
@@ -233,7 +224,24 @@
     },
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "No objects to concatenate",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m evodumb \u001b[38;5;241m=\u001b[39m \u001b[43manalysis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_data\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msoil_output/Sim_all_dumb/\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprocess\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43manalysis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_count\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mid\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m;\n",
+      "File \u001b[0;32m/mnt/data/home/j/git/lab.gsi/soil/soil/soil/analysis.py:14\u001b[0m, in \u001b[0;36mread_data\u001b[0;34m(group, *args, **kwargs)\u001b[0m\n\u001b[1;32m     12\u001b[0m iterable \u001b[38;5;241m=\u001b[39m _read_data(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m group:\n\u001b[0;32m---> 14\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgroup_trials\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterable\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     16\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(iterable)\n",
+      "File \u001b[0;32m/mnt/data/home/j/git/lab.gsi/soil/soil/soil/analysis.py:201\u001b[0m, in \u001b[0;36mgroup_trials\u001b[0;34m(trials, aggfunc)\u001b[0m\n\u001b[1;32m    199\u001b[0m trials \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(trials)\n\u001b[1;32m    200\u001b[0m trials \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mmap\u001b[39m(\u001b[38;5;28;01mlambda\u001b[39;00m x: x[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mtuple\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m x, trials))\n\u001b[0;32m--> 201\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconcat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrials\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mgroupby(level\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\u001b[38;5;241m.\u001b[39magg(aggfunc)\u001b[38;5;241m.\u001b[39mreorder_levels([\u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m1\u001b[39m] ,axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
+      "File \u001b[0;32m/mnt/data/home/j/git/lab.gsi/soil/soil/.env-v0.20/lib/python3.8/site-packages/pandas/util/_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    325\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[1;32m    326\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    327\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[1;32m    328\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m    329\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[1;32m    330\u001b[0m     )\n\u001b[0;32m--> 331\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/mnt/data/home/j/git/lab.gsi/soil/soil/.env-v0.20/lib/python3.8/site-packages/pandas/core/reshape/concat.py:368\u001b[0m, in \u001b[0;36mconcat\u001b[0;34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001b[0m\n\u001b[1;32m    146\u001b[0m \u001b[38;5;129m@deprecate_nonkeyword_arguments\u001b[39m(version\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, allowed_args\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobjs\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m    147\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mconcat\u001b[39m(\n\u001b[1;32m    148\u001b[0m     objs: Iterable[NDFrame] \u001b[38;5;241m|\u001b[39m Mapping[HashableT, NDFrame],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    157\u001b[0m     copy: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m    158\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame \u001b[38;5;241m|\u001b[39m Series:\n\u001b[1;32m    159\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    160\u001b[0m \u001b[38;5;124;03m    Concatenate pandas objects along a particular axis.\u001b[39;00m\n\u001b[1;32m    161\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    366\u001b[0m \u001b[38;5;124;03m    1   3   4\u001b[39;00m\n\u001b[1;32m    367\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 368\u001b[0m     op \u001b[38;5;241m=\u001b[39m \u001b[43m_Concatenator\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    369\u001b[0m \u001b[43m        \u001b[49m\u001b[43mobjs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    370\u001b[0m \u001b[43m        \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    371\u001b[0m \u001b[43m        \u001b[49m\u001b[43mignore_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    372\u001b[0m \u001b[43m        \u001b[49m\u001b[43mjoin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    373\u001b[0m \u001b[43m        \u001b[49m\u001b[43mkeys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    374\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlevels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    375\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnames\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnames\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    376\u001b[0m \u001b[43m        \u001b[49m\u001b[43mverify_integrity\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverify_integrity\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    377\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    378\u001b[0m \u001b[43m        \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    379\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    381\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m op\u001b[38;5;241m.\u001b[39mget_result()\n",
+      "File \u001b[0;32m/mnt/data/home/j/git/lab.gsi/soil/soil/.env-v0.20/lib/python3.8/site-packages/pandas/core/reshape/concat.py:425\u001b[0m, in \u001b[0;36m_Concatenator.__init__\u001b[0;34m(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)\u001b[0m\n\u001b[1;32m    422\u001b[0m     objs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(objs)\n\u001b[1;32m    424\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(objs) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 425\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo objects to concatenate\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    427\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m keys \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    428\u001b[0m     objs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(com\u001b[38;5;241m.\u001b[39mnot_none(\u001b[38;5;241m*\u001b[39mobjs))\n",
+      "\u001b[0;31mValueError\u001b[0m: No objects to concatenate"
+     ]
+    }
+   ],
    "source": [
     "evodumb = analysis.read_data('soil_output/Sim_all_dumb/', process=analysis.get_count, group=True, keys=['id']);"
    ]
@@ -721,9 +729,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "venv-soil",
    "language": "python",
-   "name": "python3"
+   "name": "venv-soil"
   },
   "language_info": {
    "codemirror_mode": {
@@ -735,7 +743,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.2"
+   "version": "3.8.10"
   },
   "toc": {
    "colors": {

examples/rabbits/rabbits.yml

@@ -1,7 +1,7 @@
 ---
 load_module: rabbit_agents
 name: rabbits_example
-max_time: 100
+max_time: 1000
 interval: 1
 seed: MySeed
 agent_type: rabbit_agents.RabbitModel

requirements.txt

@@ -6,4 +6,4 @@ pandas>=0.23
 SALib>=1.3
 Jinja2
 Mesa>=0.8
-tsih>=0.1.5
+tsih>=0.1.9

soil/VERSION

@@ -1 +1 @@
-0.20.3
+0.20.8

soil/agents/__init__.py

@@ -35,7 +35,9 @@ class BaseAgent(Agent):
                  unique_id,
                  model,
                  name=None,
-                 interval=None):
+                 interval=None,
+                 **kwargs
+    ):
         # Check for REQUIRED arguments
         # Initialize agent parameters
         if isinstance(unique_id, Agent):
@@ -52,6 +54,12 @@ class BaseAgent(Agent):
 
         if hasattr(self, 'level'):
             self.logger.setLevel(self.level)
+        for (k, v) in self.defaults.items():
+            if not hasattr(self, k) or getattr(self, k) is None:
+                setattr(self, k, deepcopy(v))
+
+        for (k, v) in kwargs.items():
+            setattr(self, k, v)
 
 
     # TODO: refactor to clean up mesa compatibility
@@ -137,6 +145,7 @@ class BaseAgent(Agent):
         self.alive = False
         if remove:
             self.remove_node(self.id)
+        return time.NEVER
 
     def step(self):
         if not self.alive:
@@ -272,7 +281,7 @@ def default_state(func):
 
 class MetaFSM(type):
     def __init__(cls, name, bases, nmspc):
-        super(MetaFSM, cls).__init__(name, bases, nmspc)
+        super().__init__(name, bases, nmspc)
         states = {}
         # Re-use states from inherited classes
         default_state = None
@@ -305,18 +314,16 @@ class FSM(NetworkAgent, metaclass=MetaFSM):
 
     def step(self):
         self.debug(f'Agent {self.unique_id} @ state {self.state_id}')
-        try:
+        interval = super().step()
-            interval = super().step()
-        except DeadAgent:
-            return time.When('inf')
         if 'id' not in self.state:
-            # if 'id' in self.state:
-            #     self.set_state(self.state['id'])
             if self.default_state:
                 self.set_state(self.default_state.id)
             else:
                 raise Exception('{} has no valid state id or default state'.format(self))
-        return self.states[self.state_id](self) or interval
+        interval = self.states[self.state_id](self) or interval
+        if not self.alive:
+            return time.NEVER
+        return interval
 
     def set_state(self, state):
         if hasattr(state, 'id'):
@@ -475,6 +482,7 @@ def _definition_to_dict(definition, size=None, default_state=None):
     distro = sorted([item for item in definition if 'weight' in item])
 
     ix = 0
+
     def init_agent(item, id=ix):
         while id in agents:
             id += 1

soil/analysis.py

@@ -112,7 +112,7 @@ def get_types(df):
     Get the value type for every key stored in a raw history dataframe.
     '''
     dtypes = df.groupby(by=['key'])['value_type'].unique()
-    return {k:v[0] for k,v in dtypes.iteritems()}
+    return {k:v[0] for k,v in dtypes.items()}
 
 
 def process_one(df, *keys, columns=['key', 'agent_id'], values='value',
@@ -146,7 +146,7 @@ def get_count(df, *keys):
     counts = pd.DataFrame()
     for key in df.columns.levels[0]:
         g = df[[key]].apply(pd.Series.value_counts, axis=1).fillna(0)
-        for value, series in g.iteritems():
+        for value, series in g.items():
             counts[key, value] = series
     counts.columns = pd.MultiIndex.from_tuples(counts.columns)
     return counts

soil/environment.py

@@ -5,6 +5,7 @@ import math
 import random
 import yaml
 import tempfile
+import logging
 import pandas as pd
 from time import time as current_time
 from copy import deepcopy
@@ -101,6 +102,8 @@ class Environment(Model):
             environment_agents = agents._convert_agent_types(distro)
         self.environment_agents = environment_agents
 
+        self.logger = utils.logger.getChild(self.name)
+
     @property
     def now(self):
         if self.schedule:
@@ -121,7 +124,8 @@ class Environment(Model):
     def environment_agents(self, environment_agents):
         self._environment_agents = environment_agents
 
-        self._env_agents = agents._definition_to_dict(definition=environment_agents)
+        for (ix, agent) in enumerate(self._environment_agents):
+            self.init_agent(len(self.G) + ix, agent_definitions=environment_agents, with_node=False)
 
     @property
     def network_agents(self):
@@ -136,15 +140,19 @@ class Environment(Model):
         for ix in self.G.nodes():
             self.init_agent(ix, agent_definitions=network_agents)
 
-    def init_agent(self, agent_id, agent_definitions):
+    def init_agent(self, agent_id, agent_definitions, with_node=True):
-        node = self.G.nodes[agent_id]
         init = False
-        state = dict(node)
+
+        state = {}
+        if with_node:
+            node = self.G.nodes[agent_id]
+            state = dict(node)
+        state.update(self.states.get(agent_id, {}))
 
         agent_type = None
-        if 'agent_type' in self.states.get(agent_id, {}):
+        if 'agent_type' in state:
-            agent_type = self.states[agent_id]['agent_type']
+            agent_type = state['agent_type']
-        elif 'agent_type' in node:
+        elif with_node and 'agent_type' in node:
             agent_type = node['agent_type']
         elif 'agent_type' in self.default_state:
             agent_type = self.default_state['agent_type']
@@ -154,15 +162,16 @@ class Environment(Model):
         elif agent_definitions:
             agent_type, state = agents._agent_from_definition(agent_definitions, unique_id=agent_id)
         else:
-            serialization.logger.debug('Skipping node {}'.format(agent_id))
+            serialization.logger.debug('Skipping agent {}'.format(agent_id))
             return
-        return self.set_agent(agent_id, agent_type, state)
+        return self.set_agent(agent_id, agent_type, state, with_node=with_node)
 
-    def set_agent(self, agent_id, agent_type, state=None):
+    def set_agent(self, agent_id, agent_type, state=None, with_node=True):
-        node = self.G.nodes[agent_id]
         defstate = deepcopy(self.default_state) or {}
         defstate.update(self.states.get(agent_id, {}))
-        defstate.update(node.get('state', {}))
+        if with_node:
+            node = self.G.nodes[agent_id]
+            defstate.update(node.get('state', {}))
         if state:
             defstate.update(state)
         a = None
@@ -172,14 +181,11 @@ class Environment(Model):
                            unique_id=agent_id
             )
 
-        for (k, v) in getattr(a, 'defaults', {}).items():
-            if not hasattr(a, k) or getattr(a, k) is None:
-                setattr(a, k, deepcopy(v))
-
         for (k, v) in state.items():
             setattr(a, k, v)
 
-        node['agent'] = a
+        if with_node:
+            node['agent'] = a
         self.schedule.add(a)
         return a
 
@@ -198,6 +204,18 @@ class Environment(Model):
         start = start or self.now
         return self.G.add_edge(agent1, agent2, **attrs)
 
+    def log(self, message, *args, level=logging.INFO, **kwargs):
+        if not self.logger.isEnabledFor(level):
+            return
+        message = message + " ".join(str(i) for i in args)
+        message = " @{:>3}: {}".format(self.now, message)
+        for k, v in kwargs:
+            message += " {k}={v} ".format(k, v)
+        extra = {}
+        extra['now'] = self.now
+        extra['unique_id'] = self.name
+        return self.logger.log(level, message, extra=extra)
+
     def step(self):
         super().step()
         self.schedule.step()

soil/simulation.py

@@ -1,11 +1,12 @@
 import os
-import time
 import importlib
 import sys
 import yaml
 import traceback
 import logging
 import networkx as nx
+
+from time import strftime
 from networkx.readwrite import json_graph
 from multiprocessing import Pool
 from functools import partial
@@ -98,7 +99,7 @@ class Simulation:
         self.network_params = network_params
         self.name = name or 'Unnamed'
         self.seed = str(seed or name)
-        self._id = '{}_{}'.format(self.name, time.strftime("%Y-%m-%d_%H.%M.%S"))
+        self._id = '{}_{}'.format(self.name, strftime("%Y-%m-%d_%H.%M.%S"))
         self.group = group or ''
         self.num_trials = num_trials
         self.max_time = max_time
@@ -142,10 +143,10 @@ class Simulation:
         '''Run the simulation and return the list of resulting environments'''
         return list(self.run_gen(*args, **kwargs))
 
-    def _run_sync_or_async(self, parallel=False, *args, **kwargs):
+    def _run_sync_or_async(self, parallel=False, **kwargs):
         if parallel and not os.environ.get('SENPY_DEBUG', None):
             p = Pool()
-            func = lambda x: self.run_trial_exceptions(trial_id=x, *args, **kwargs)
+            func = partial(self.run_trial_exceptions, **kwargs)
             for i in p.imap_unordered(func, range(self.num_trials)):
                 if isinstance(i, Exception):
                     logger.error('Trial failed:\n\t%s', i.message)
@@ -154,10 +155,9 @@ class Simulation:
         else:
             for i in range(self.num_trials):
                 yield self.run_trial(trial_id=i,
-                                     *args,
                                      **kwargs)
 
-    def run_gen(self, *args, parallel=False, dry_run=False,
+    def run_gen(self, parallel=False, dry_run=False,
                 exporters=[default, ], stats=[], outdir=None, exporter_params={},
                 stats_params={}, log_level=None,
                 **kwargs):
@@ -183,8 +183,7 @@ class Simulation:
 
             for exporter in exporters:
                 exporter.start()
-            for env in self._run_sync_or_async(*args,
+            for env in self._run_sync_or_async(parallel=parallel,
-                                               parallel=parallel,
                                                log_level=log_level,
                                                **kwargs):
 

soil/stats.py

@@ -52,7 +52,7 @@ class distribution(Stats):
             except TypeError:
                 pass
 
-            for name, count in t.value_counts().iteritems():
+            for name, count in t.value_counts().items():
                 if a not in stats['count']:
                     stats['count'][a] = {}
                 stats['count'][a][name] = count
@@ -68,10 +68,10 @@ class distribution(Stats):
         mean = {}
 
         if self.means:
-            res = dfm.groupby(by=['key']).agg(['mean', 'std', 'count', 'median', 'max', 'min'])
+            res = dfm.drop('metric', axis=1).groupby(by=['key']).agg(['mean', 'std', 'count', 'median', 'max', 'min'])
             mean = res['value'].to_dict()
         if self.counts:
-            res = dfc.groupby(by=['key', 'value']).agg(['mean', 'std', 'count', 'median', 'max', 'min'])
+            res = dfc.drop('metric', axis=1).groupby(by=['key', 'value']).agg(['mean', 'std', 'count', 'median', 'max', 'min'])
             for k,v in res['count'].to_dict().items():
                 if k not in count:
                     count[k] = {}

soil/time.py

@@ -10,13 +10,17 @@ INFINITY = float('inf')
 
 class When:
     def __init__(self, time):
+        if isinstance(time, When):
+            return time
         self._time = time
 
     def abs(self, time):
         return self._time
 
+NEVER = When(INFINITY)
 
-class Delta:
+
+class Delta(When):
     def __init__(self, delta):
         self._delta = delta
 
@@ -58,7 +62,8 @@ class TimedActivation(BaseScheduler):
             (when, agent_id) = heappop(self._queue)
             logger.debug(f'Stepping agent {agent_id}')
 
-            when = (self._agents[agent_id].step() or Delta(1)).abs(self.time)
+            returned = self._agents[agent_id].step()
+            when = (returned or Delta(1)).abs(self.time)
             if when < self.time:
                 raise Exception("Cannot schedule an agent for a time in the past ({} < {})".format(when, self.time))
 

tests/test_agents.py

@@ -0,0 +1,22 @@
+from unittest import TestCase
+import pytest
+
+from soil import agents, environment
+from soil import time as stime
+
+class Dead(agents.FSM):
+    @agents.default_state
+    @agents.state
+    def only(self):
+        self.die()
+
+class TestMain(TestCase):
+    def test_die_raises_exception(self):
+        d = Dead(unique_id=0, model=environment.Environment())
+        d.step()
+        with pytest.raises(agents.DeadAgent):
+            d.step()
+
+    def test_die_returns_infinity(self):
+        d = Dead(unique_id=0, model=environment.Environment())
+        assert d.step().abs(0) == stime.INFINITY