From 52ff9f63679ba7a5f769bea7539461de8f58b9d8 Mon Sep 17 00:00:00 2001
From: Ben Gyori <ben.gyori@gmail.com>
Date: Thu, 22 Jun 2023 22:25:02 -0400
Subject: [PATCH 01/11] Update SIDARTHE

---
 .../ensemble/BIOMD0000000955_askenet.json     | 693 ++++++++++++++++++
 .../BIOMD0000000955_template_model.json       | 201 ++++-
 notebooks/ensemble/ensemble.ipynb             |  58 +-
 3 files changed, 920 insertions(+), 32 deletions(-)
 create mode 100644 notebooks/ensemble/BIOMD0000000955_askenet.json

diff --git a/notebooks/ensemble/BIOMD0000000955_askenet.json b/notebooks/ensemble/BIOMD0000000955_askenet.json
new file mode 100644
index 000000000..a1b78f78d
--- /dev/null
+++ b/notebooks/ensemble/BIOMD0000000955_askenet.json
@@ -0,0 +1,693 @@
+{
+ "name": "Giordano2020 - SIDARTHE model of COVID-19 spread in Italy",
+ "schema": "https://raw.githubusercontent.com/DARPA-ASKEM/Model-Representations/petrinet_v0.5/petrinet/petrinet_schema.json",
+ "schema_name": "petrinet",
+ "description": "Giordano2020 - SIDARTHE model of COVID-19 spread in Italy",
+ "model_version": "0.1",
+ "properties": {},
+ "model": {
+  "states": [
+   {
+    "id": "Susceptible",
+    "name": "Susceptible",
+    "grounding": {
+     "identifiers": {
+      "ido": "0000514"
+     },
+     "modifiers": {}
+    },
+    "units": {
+     "expression": "1",
+     "expression_mathml": "<cn>1</cn>"
+    }
+   },
+   {
+    "id": "Diagnosed",
+    "name": "Diagnosed",
+    "grounding": {
+     "identifiers": {
+      "ido": "0000511"
+     },
+     "modifiers": {
+      "diagnosis": "ncit:C15220"
+     }
+    },
+    "units": {
+     "expression": "1",
+     "expression_mathml": "<cn>1</cn>"
+    }
+   },
+   {
+    "id": "Infected",
+    "name": "Infected",
+    "grounding": {
+     "identifiers": {
+      "ido": "0000511"
+     },
+     "modifiers": {}
+    },
+    "units": {
+     "expression": "1",
+     "expression_mathml": "<cn>1</cn>"
+    }
+   },
+   {
+    "id": "Ailing",
+    "name": "Ailing",
+    "grounding": {
+     "identifiers": {
+      "ido": "0000511"
+     },
+     "modifiers": {
+      "disease_severity": "ncit:C25269",
+      "diagnosis": "ncit:C113725"
+     }
+    },
+    "units": {
+     "expression": "1",
+     "expression_mathml": "<cn>1</cn>"
+    }
+   },
+   {
+    "id": "Recognized",
+    "name": "Recognized",
+    "grounding": {
+     "identifiers": {
+      "ido": "0000511"
+     },
+     "modifiers": {
+      "diagnosis": "ncit:C15220"
+     }
+    },
+    "units": {
+     "expression": "1",
+     "expression_mathml": "<cn>1</cn>"
+    }
+   },
+   {
+    "id": "Healed",
+    "name": "Healed",
+    "grounding": {
+     "identifiers": {
+      "ido": "0000592"
+     },
+     "modifiers": {}
+    },
+    "units": {
+     "expression": "1",
+     "expression_mathml": "<cn>1</cn>"
+    }
+   },
+   {
+    "id": "Threatened",
+    "name": "Threatened",
+    "grounding": {
+     "identifiers": {
+      "ido": "0000511"
+     },
+     "modifiers": {
+      "disease_severity": "ncit:C25467"
+     }
+    },
+    "units": {
+     "expression": "1",
+     "expression_mathml": "<cn>1</cn>"
+    }
+   },
+   {
+    "id": "Extinct",
+    "name": "Extinct",
+    "grounding": {
+     "identifiers": {
+      "ncit": "C28554"
+     },
+     "modifiers": {}
+    },
+    "units": {
+     "expression": "1",
+     "expression_mathml": "<cn>1</cn>"
+    }
+   }
+  ],
+  "transitions": [
+   {
+    "id": "t1",
+    "input": [
+     "Diagnosed",
+     "Susceptible"
+    ],
+    "output": [
+     "Diagnosed",
+     "Infected"
+    ],
+    "properties": {
+     "name": "t1"
+    }
+   },
+   {
+    "id": "t2",
+    "input": [
+     "Ailing",
+     "Susceptible"
+    ],
+    "output": [
+     "Ailing",
+     "Infected"
+    ],
+    "properties": {
+     "name": "t2"
+    }
+   },
+   {
+    "id": "t3",
+    "input": [
+     "Recognized",
+     "Susceptible"
+    ],
+    "output": [
+     "Recognized",
+     "Infected"
+    ],
+    "properties": {
+     "name": "t3"
+    }
+   },
+   {
+    "id": "t4",
+    "input": [
+     "Infected",
+     "Susceptible"
+    ],
+    "output": [
+     "Infected",
+     "Infected"
+    ],
+    "properties": {
+     "name": "t4"
+    }
+   },
+   {
+    "id": "t5",
+    "input": [
+     "Infected"
+    ],
+    "output": [
+     "Diagnosed"
+    ],
+    "properties": {
+     "name": "t5"
+    }
+   },
+   {
+    "id": "t6",
+    "input": [
+     "Infected"
+    ],
+    "output": [
+     "Ailing"
+    ],
+    "properties": {
+     "name": "t6"
+    }
+   },
+   {
+    "id": "t7",
+    "input": [
+     "Infected"
+    ],
+    "output": [
+     "Healed"
+    ],
+    "properties": {
+     "name": "t7"
+    }
+   },
+   {
+    "id": "t8",
+    "input": [
+     "Diagnosed"
+    ],
+    "output": [
+     "Recognized"
+    ],
+    "properties": {
+     "name": "t8"
+    }
+   },
+   {
+    "id": "t9",
+    "input": [
+     "Diagnosed"
+    ],
+    "output": [
+     "Healed"
+    ],
+    "properties": {
+     "name": "t9"
+    }
+   },
+   {
+    "id": "t10",
+    "input": [
+     "Ailing"
+    ],
+    "output": [
+     "Recognized"
+    ],
+    "properties": {
+     "name": "t10"
+    }
+   },
+   {
+    "id": "t11",
+    "input": [
+     "Ailing"
+    ],
+    "output": [
+     "Healed"
+    ],
+    "properties": {
+     "name": "t11"
+    }
+   },
+   {
+    "id": "t12",
+    "input": [
+     "Ailing"
+    ],
+    "output": [
+     "Threatened"
+    ],
+    "properties": {
+     "name": "t12"
+    }
+   },
+   {
+    "id": "t13",
+    "input": [
+     "Recognized"
+    ],
+    "output": [
+     "Threatened"
+    ],
+    "properties": {
+     "name": "t13"
+    }
+   },
+   {
+    "id": "t14",
+    "input": [
+     "Recognized"
+    ],
+    "output": [
+     "Healed"
+    ],
+    "properties": {
+     "name": "t14"
+    }
+   },
+   {
+    "id": "t15",
+    "input": [
+     "Threatened"
+    ],
+    "output": [
+     "Extinct"
+    ],
+    "properties": {
+     "name": "t15"
+    }
+   },
+   {
+    "id": "t16",
+    "input": [
+     "Threatened"
+    ],
+    "output": [
+     "Healed"
+    ],
+    "properties": {
+     "name": "t16"
+    }
+   }
+  ]
+ },
+ "semantics": {
+  "ode": {
+   "rates": [
+    {
+     "target": "t1",
+     "expression": "Diagnosed*Susceptible*beta",
+     "expression_mathml": "<apply><times/><ci>Diagnosed</ci><ci>Susceptible</ci><ci>beta</ci></apply>"
+    },
+    {
+     "target": "t2",
+     "expression": "Ailing*Susceptible*gamma",
+     "expression_mathml": "<apply><times/><ci>Ailing</ci><ci>Susceptible</ci><ci>gamma</ci></apply>"
+    },
+    {
+     "target": "t3",
+     "expression": "Recognized*Susceptible*delta",
+     "expression_mathml": "<apply><times/><ci>Recognized</ci><ci>Susceptible</ci><ci>delta</ci></apply>"
+    },
+    {
+     "target": "t4",
+     "expression": "Infected*Susceptible*alpha",
+     "expression_mathml": "<apply><times/><ci>Infected</ci><ci>Susceptible</ci><ci>alpha</ci></apply>"
+    },
+    {
+     "target": "t5",
+     "expression": "Infected*epsilon",
+     "expression_mathml": "<apply><times/><ci>Infected</ci><ci>epsilon</ci></apply>"
+    },
+    {
+     "target": "t6",
+     "expression": "Infected*zeta",
+     "expression_mathml": "<apply><times/><ci>Infected</ci><ci>zeta</ci></apply>"
+    },
+    {
+     "target": "t7",
+     "expression": "Infected*lambda",
+     "expression_mathml": "<apply><times/><ci>Infected</ci><ci>lambda</ci></apply>"
+    },
+    {
+     "target": "t8",
+     "expression": "Diagnosed*eta",
+     "expression_mathml": "<apply><times/><ci>Diagnosed</ci><ci>eta</ci></apply>"
+    },
+    {
+     "target": "t9",
+     "expression": "Diagnosed*rho",
+     "expression_mathml": "<apply><times/><ci>Diagnosed</ci><ci>rho</ci></apply>"
+    },
+    {
+     "target": "t10",
+     "expression": "Ailing*theta",
+     "expression_mathml": "<apply><times/><ci>Ailing</ci><ci>theta</ci></apply>"
+    },
+    {
+     "target": "t11",
+     "expression": "Ailing*kappa",
+     "expression_mathml": "<apply><times/><ci>Ailing</ci><ci>kappa</ci></apply>"
+    },
+    {
+     "target": "t12",
+     "expression": "Ailing*mu",
+     "expression_mathml": "<apply><times/><ci>Ailing</ci><ci>mu</ci></apply>"
+    },
+    {
+     "target": "t13",
+     "expression": "Recognized*nu",
+     "expression_mathml": "<apply><times/><ci>Recognized</ci><ci>nu</ci></apply>"
+    },
+    {
+     "target": "t14",
+     "expression": "Recognized*xi",
+     "expression_mathml": "<apply><times/><ci>Recognized</ci><ci>xi</ci></apply>"
+    },
+    {
+     "target": "t15",
+     "expression": "Threatened*tau",
+     "expression_mathml": "<apply><times/><ci>Threatened</ci><ci>tau</ci></apply>"
+    },
+    {
+     "target": "t16",
+     "expression": "Threatened*sigma",
+     "expression_mathml": "<apply><times/><ci>Threatened</ci><ci>sigma</ci></apply>"
+    }
+   ],
+   "initials": [
+    {
+     "target": "Susceptible",
+     "expression": "0.999996300000000",
+     "expression_mathml": "<cn>0.99999629999999995</cn>"
+    },
+    {
+     "target": "Diagnosed",
+     "expression": "3.33333333000000e-7",
+     "expression_mathml": "<cn>3.33333333e-7</cn>"
+    },
+    {
+     "target": "Infected",
+     "expression": "3.33333333000000e-6",
+     "expression_mathml": "<cn>3.3333333299999999e-6</cn>"
+    },
+    {
+     "target": "Ailing",
+     "expression": "1.66666666000000e-8",
+     "expression_mathml": "<cn>1.6666666599999999e-8</cn>"
+    },
+    {
+     "target": "Recognized",
+     "expression": "3.33333333000000e-8",
+     "expression_mathml": "<cn>3.33333333e-8</cn>"
+    },
+    {
+     "target": "Healed",
+     "expression": "0.0",
+     "expression_mathml": "<cn>0.0</cn>"
+    },
+    {
+     "target": "Threatened",
+     "expression": "0.0",
+     "expression_mathml": "<cn>0.0</cn>"
+    },
+    {
+     "target": "Extinct",
+     "expression": "0.0",
+     "expression_mathml": "<cn>0.0</cn>"
+    }
+   ],
+   "parameters": [
+    {
+     "id": "beta",
+     "value": 0.011,
+     "distribution": {
+      "type": "StandardUniform1",
+      "parameters": {
+       "minimum": 0.008799999999999999,
+       "maximum": 0.0132
+      }
+     }
+    },
+    {
+     "id": "gamma",
+     "value": 0.456,
+     "distribution": {
+      "type": "StandardUniform1",
+      "parameters": {
+       "minimum": 0.3648,
+       "maximum": 0.5472
+      }
+     }
+    },
+    {
+     "id": "delta",
+     "value": 0.011,
+     "distribution": {
+      "type": "StandardUniform1",
+      "parameters": {
+       "minimum": 0.008799999999999999,
+       "maximum": 0.0132
+      }
+     }
+    },
+    {
+     "id": "alpha",
+     "value": 0.57,
+     "distribution": {
+      "type": "StandardUniform1",
+      "parameters": {
+       "minimum": 0.45599999999999996,
+       "maximum": 0.6839999999999999
+      }
+     }
+    },
+    {
+     "id": "epsilon",
+     "value": 0.171,
+     "distribution": {
+      "type": "StandardUniform1",
+      "parameters": {
+       "minimum": 0.1368,
+       "maximum": 0.20520000000000002
+      }
+     }
+    },
+    {
+     "id": "zeta",
+     "value": 0.125,
+     "distribution": {
+      "type": "StandardUniform1",
+      "parameters": {
+       "minimum": 0.1,
+       "maximum": 0.15
+      }
+     }
+    },
+    {
+     "id": "lambda",
+     "value": 0.034,
+     "distribution": {
+      "type": "StandardUniform1",
+      "parameters": {
+       "minimum": 0.027200000000000002,
+       "maximum": 0.0408
+      }
+     }
+    },
+    {
+     "id": "eta",
+     "value": 0.125,
+     "distribution": {
+      "type": "StandardUniform1",
+      "parameters": {
+       "minimum": 0.1,
+       "maximum": 0.15
+      }
+     }
+    },
+    {
+     "id": "rho",
+     "value": 0.034,
+     "distribution": {
+      "type": "StandardUniform1",
+      "parameters": {
+       "minimum": 0.027200000000000002,
+       "maximum": 0.0408
+      }
+     }
+    },
+    {
+     "id": "theta",
+     "value": 0.371,
+     "distribution": {
+      "type": "StandardUniform1",
+      "parameters": {
+       "minimum": 0.2968,
+       "maximum": 0.4452
+      }
+     }
+    },
+    {
+     "id": "kappa",
+     "value": 0.017,
+     "distribution": {
+      "type": "StandardUniform1",
+      "parameters": {
+       "minimum": 0.013600000000000001,
+       "maximum": 0.0204
+      }
+     }
+    },
+    {
+     "id": "mu",
+     "value": 0.017,
+     "distribution": {
+      "type": "StandardUniform1",
+      "parameters": {
+       "minimum": 0.013600000000000001,
+       "maximum": 0.0204
+      }
+     }
+    },
+    {
+     "id": "nu",
+     "value": 0.027,
+     "distribution": {
+      "type": "StandardUniform1",
+      "parameters": {
+       "minimum": 0.0216,
+       "maximum": 0.0324
+      }
+     }
+    },
+    {
+     "id": "xi",
+     "value": 0.017,
+     "distribution": {
+      "type": "StandardUniform1",
+      "parameters": {
+       "minimum": 0.013600000000000001,
+       "maximum": 0.0204
+      }
+     }
+    },
+    {
+     "id": "tau",
+     "value": 0.01,
+     "distribution": {
+      "type": "StandardUniform1",
+      "parameters": {
+       "minimum": 0.008,
+       "maximum": 0.012
+      }
+     }
+    },
+    {
+     "id": "sigma",
+     "value": 0.017,
+     "distribution": {
+      "type": "StandardUniform1",
+      "parameters": {
+       "minimum": 0.013600000000000001,
+       "maximum": 0.0204
+      }
+     }
+    }
+   ],
+   "observables": [
+    {
+     "id": "Cases",
+     "name": "Cases",
+     "expression": "Diagnosed + Recognized + Threatened",
+     "expression_mathml": "<apply><plus/><ci>Diagnosed</ci><ci>Recognized</ci><ci>Threatened</ci></apply>"
+    },
+    {
+     "id": "Hospitalizations",
+     "name": "Hospitalizations",
+     "expression": "Recognized + Threatened",
+     "expression_mathml": "<apply><plus/><ci>Recognized</ci><ci>Threatened</ci></apply>"
+    },
+    {
+     "id": "Deaths",
+     "name": "Deaths",
+     "expression": "Extinct",
+     "expression_mathml": "<ci>Extinct</ci>"
+    }
+   ],
+   "time": {
+    "id": "t",
+    "units": {
+     "expression": "day",
+     "expression_mathml": "<ci>day</ci>"
+    }
+   }
+  }
+ },
+ "metadata": {
+  "annotations": {
+   "license": "CC0",
+   "authors": [],
+   "references": [
+    "pubmed:32322102"
+   ],
+   "time_scale": null,
+   "time_start": null,
+   "time_end": null,
+   "locations": [],
+   "pathogens": [
+    "ncbitaxon:2697049"
+   ],
+   "diseases": [
+    "doid:0080600"
+   ],
+   "hosts": [
+    "ncbitaxon:9606"
+   ],
+   "model_types": [
+    "mamo:0000028"
+   ]
+  }
+ }
+}
\ No newline at end of file
diff --git a/notebooks/ensemble/BIOMD0000000955_template_model.json b/notebooks/ensemble/BIOMD0000000955_template_model.json
index 95dd018ba..1ec403d82 100644
--- a/notebooks/ensemble/BIOMD0000000955_template_model.json
+++ b/notebooks/ensemble/BIOMD0000000955_template_model.json
@@ -11,6 +11,9 @@
     },
     "context": {
      "diagnosis": "ncit:C15220"
+    },
+    "units": {
+     "expression": "1"
     }
    },
    "subject": {
@@ -19,7 +22,10 @@
     "identifiers": {
      "ido": "0000514"
     },
-    "context": {}
+    "context": {},
+    "units": {
+     "expression": "1"
+    }
    },
    "outcome": {
     "name": "Infected",
@@ -27,7 +33,10 @@
     "identifiers": {
      "ido": "0000511"
     },
-    "context": {}
+    "context": {},
+    "units": {
+     "expression": "1"
+    }
    },
    "provenance": []
   },
@@ -43,6 +52,9 @@
     "context": {
      "disease_severity": "ncit:C25269",
      "diagnosis": "ncit:C113725"
+    },
+    "units": {
+     "expression": "1"
     }
    },
    "subject": {
@@ -51,7 +63,10 @@
     "identifiers": {
      "ido": "0000514"
     },
-    "context": {}
+    "context": {},
+    "units": {
+     "expression": "1"
+    }
    },
    "outcome": {
     "name": "Infected",
@@ -59,7 +74,10 @@
     "identifiers": {
      "ido": "0000511"
     },
-    "context": {}
+    "context": {},
+    "units": {
+     "expression": "1"
+    }
    },
    "provenance": []
   },
@@ -74,6 +92,9 @@
     },
     "context": {
      "diagnosis": "ncit:C15220"
+    },
+    "units": {
+     "expression": "1"
     }
    },
    "subject": {
@@ -82,7 +103,10 @@
     "identifiers": {
      "ido": "0000514"
     },
-    "context": {}
+    "context": {},
+    "units": {
+     "expression": "1"
+    }
    },
    "outcome": {
     "name": "Infected",
@@ -90,7 +114,10 @@
     "identifiers": {
      "ido": "0000511"
     },
-    "context": {}
+    "context": {},
+    "units": {
+     "expression": "1"
+    }
    },
    "provenance": []
   },
@@ -103,7 +130,10 @@
     "identifiers": {
      "ido": "0000511"
     },
-    "context": {}
+    "context": {},
+    "units": {
+     "expression": "1"
+    }
    },
    "subject": {
     "name": "Susceptible",
@@ -111,7 +141,10 @@
     "identifiers": {
      "ido": "0000514"
     },
-    "context": {}
+    "context": {},
+    "units": {
+     "expression": "1"
+    }
    },
    "outcome": {
     "name": "Infected",
@@ -119,7 +152,10 @@
     "identifiers": {
      "ido": "0000511"
     },
-    "context": {}
+    "context": {},
+    "units": {
+     "expression": "1"
+    }
    },
    "provenance": []
   },
@@ -132,7 +168,10 @@
     "identifiers": {
      "ido": "0000511"
     },
-    "context": {}
+    "context": {},
+    "units": {
+     "expression": "1"
+    }
    },
    "outcome": {
     "name": "Diagnosed",
@@ -142,6 +181,9 @@
     },
     "context": {
      "diagnosis": "ncit:C15220"
+    },
+    "units": {
+     "expression": "1"
     }
    },
    "provenance": []
@@ -155,7 +197,10 @@
     "identifiers": {
      "ido": "0000511"
     },
-    "context": {}
+    "context": {},
+    "units": {
+     "expression": "1"
+    }
    },
    "outcome": {
     "name": "Ailing",
@@ -166,6 +211,9 @@
     "context": {
      "disease_severity": "ncit:C25269",
      "diagnosis": "ncit:C113725"
+    },
+    "units": {
+     "expression": "1"
     }
    },
    "provenance": []
@@ -179,7 +227,10 @@
     "identifiers": {
      "ido": "0000511"
     },
-    "context": {}
+    "context": {},
+    "units": {
+     "expression": "1"
+    }
    },
    "outcome": {
     "name": "Healed",
@@ -187,7 +238,10 @@
     "identifiers": {
      "ido": "0000592"
     },
-    "context": {}
+    "context": {},
+    "units": {
+     "expression": "1"
+    }
    },
    "provenance": []
   },
@@ -202,6 +256,9 @@
     },
     "context": {
      "diagnosis": "ncit:C15220"
+    },
+    "units": {
+     "expression": "1"
     }
    },
    "outcome": {
@@ -212,6 +269,9 @@
     },
     "context": {
      "diagnosis": "ncit:C15220"
+    },
+    "units": {
+     "expression": "1"
     }
    },
    "provenance": []
@@ -227,6 +287,9 @@
     },
     "context": {
      "diagnosis": "ncit:C15220"
+    },
+    "units": {
+     "expression": "1"
     }
    },
    "outcome": {
@@ -235,7 +298,10 @@
     "identifiers": {
      "ido": "0000592"
     },
-    "context": {}
+    "context": {},
+    "units": {
+     "expression": "1"
+    }
    },
    "provenance": []
   },
@@ -251,6 +317,9 @@
     "context": {
      "disease_severity": "ncit:C25269",
      "diagnosis": "ncit:C113725"
+    },
+    "units": {
+     "expression": "1"
     }
    },
    "outcome": {
@@ -261,6 +330,9 @@
     },
     "context": {
      "diagnosis": "ncit:C15220"
+    },
+    "units": {
+     "expression": "1"
     }
    },
    "provenance": []
@@ -277,6 +349,9 @@
     "context": {
      "disease_severity": "ncit:C25269",
      "diagnosis": "ncit:C113725"
+    },
+    "units": {
+     "expression": "1"
     }
    },
    "outcome": {
@@ -285,7 +360,10 @@
     "identifiers": {
      "ido": "0000592"
     },
-    "context": {}
+    "context": {},
+    "units": {
+     "expression": "1"
+    }
    },
    "provenance": []
   },
@@ -301,6 +379,9 @@
     "context": {
      "disease_severity": "ncit:C25269",
      "diagnosis": "ncit:C113725"
+    },
+    "units": {
+     "expression": "1"
     }
    },
    "outcome": {
@@ -311,6 +392,9 @@
     },
     "context": {
      "disease_severity": "ncit:C25467"
+    },
+    "units": {
+     "expression": "1"
     }
    },
    "provenance": []
@@ -326,6 +410,9 @@
     },
     "context": {
      "diagnosis": "ncit:C15220"
+    },
+    "units": {
+     "expression": "1"
     }
    },
    "outcome": {
@@ -336,6 +423,9 @@
     },
     "context": {
      "disease_severity": "ncit:C25467"
+    },
+    "units": {
+     "expression": "1"
     }
    },
    "provenance": []
@@ -351,6 +441,9 @@
     },
     "context": {
      "diagnosis": "ncit:C15220"
+    },
+    "units": {
+     "expression": "1"
     }
    },
    "outcome": {
@@ -359,7 +452,10 @@
     "identifiers": {
      "ido": "0000592"
     },
-    "context": {}
+    "context": {},
+    "units": {
+     "expression": "1"
+    }
    },
    "provenance": []
   },
@@ -374,6 +470,9 @@
     },
     "context": {
      "disease_severity": "ncit:C25467"
+    },
+    "units": {
+     "expression": "1"
     }
    },
    "outcome": {
@@ -382,7 +481,10 @@
     "identifiers": {
      "ncit": "C28554"
     },
-    "context": {}
+    "context": {},
+    "units": {
+     "expression": "1"
+    }
    },
    "provenance": []
   },
@@ -397,6 +499,9 @@
     },
     "context": {
      "disease_severity": "ncit:C25467"
+    },
+    "units": {
+     "expression": "1"
     }
    },
    "outcome": {
@@ -405,7 +510,10 @@
     "identifiers": {
      "ido": "0000592"
     },
-    "context": {}
+    "context": {},
+    "units": {
+     "expression": "1"
+    }
    },
    "provenance": []
   }
@@ -416,6 +524,7 @@
    "description": "alpha",
    "identifiers": {},
    "context": {},
+   "units": null,
    "value": 0.57,
    "distribution": {
     "type": "StandardUniform1",
@@ -430,6 +539,7 @@
    "description": "beta",
    "identifiers": {},
    "context": {},
+   "units": null,
    "value": 0.011,
    "distribution": {
     "type": "StandardUniform1",
@@ -444,6 +554,7 @@
    "description": "gamma",
    "identifiers": {},
    "context": {},
+   "units": null,
    "value": 0.456,
    "distribution": {
     "type": "StandardUniform1",
@@ -458,6 +569,7 @@
    "description": "delta",
    "identifiers": {},
    "context": {},
+   "units": null,
    "value": 0.011,
    "distribution": {
     "type": "StandardUniform1",
@@ -472,6 +584,7 @@
    "description": "epsilon",
    "identifiers": {},
    "context": {},
+   "units": null,
    "value": 0.171,
    "distribution": {
     "type": "StandardUniform1",
@@ -486,6 +599,7 @@
    "description": "theta",
    "identifiers": {},
    "context": {},
+   "units": null,
    "value": 0.371,
    "distribution": {
     "type": "StandardUniform1",
@@ -500,6 +614,7 @@
    "description": "zeta",
    "identifiers": {},
    "context": {},
+   "units": null,
    "value": 0.125,
    "distribution": {
     "type": "StandardUniform1",
@@ -514,6 +629,7 @@
    "description": "eta",
    "identifiers": {},
    "context": {},
+   "units": null,
    "value": 0.125,
    "distribution": {
     "type": "StandardUniform1",
@@ -528,6 +644,7 @@
    "description": "mu",
    "identifiers": {},
    "context": {},
+   "units": null,
    "value": 0.017,
    "distribution": {
     "type": "StandardUniform1",
@@ -542,6 +659,7 @@
    "description": "nu",
    "identifiers": {},
    "context": {},
+   "units": null,
    "value": 0.027,
    "distribution": {
     "type": "StandardUniform1",
@@ -556,6 +674,7 @@
    "description": "tau",
    "identifiers": {},
    "context": {},
+   "units": null,
    "value": 0.01,
    "distribution": {
     "type": "StandardUniform1",
@@ -570,6 +689,7 @@
    "description": "lambda",
    "identifiers": {},
    "context": {},
+   "units": null,
    "value": 0.034,
    "distribution": {
     "type": "StandardUniform1",
@@ -584,6 +704,7 @@
    "description": "kappa",
    "identifiers": {},
    "context": {},
+   "units": null,
    "value": 0.017,
    "distribution": {
     "type": "StandardUniform1",
@@ -598,6 +719,7 @@
    "description": "rho",
    "identifiers": {},
    "context": {},
+   "units": null,
    "value": 0.034,
    "distribution": {
     "type": "StandardUniform1",
@@ -612,6 +734,7 @@
    "description": "sigma",
    "identifiers": {},
    "context": {},
+   "units": null,
    "value": 0.017,
    "distribution": {
     "type": "StandardUniform1",
@@ -626,6 +749,7 @@
    "description": "xi",
    "identifiers": {},
    "context": {},
+   "units": null,
    "value": 0.017,
    "distribution": {
     "type": "StandardUniform1",
@@ -640,6 +764,7 @@
    "description": "epsilon_modifier",
    "identifiers": {},
    "context": {},
+   "units": null,
    "value": 1.0,
    "distribution": {
     "type": "StandardUniform1",
@@ -654,6 +779,7 @@
    "description": "alpha_modifier",
    "identifiers": {},
    "context": {},
+   "units": null,
    "value": 1.0,
    "distribution": {
     "type": "StandardUniform1",
@@ -672,7 +798,8 @@
     "identifiers": {
      "ido": "0000514"
     },
-    "context": {}
+    "context": {},
+    "units": null
    },
    "value": 0.9999963
   },
@@ -683,7 +810,10 @@
     "identifiers": {
      "ido": "0000511"
     },
-    "context": {}
+    "context": {},
+    "units": {
+     "expression": "1"
+    }
    },
    "value": 3.33333333e-06
   },
@@ -696,6 +826,9 @@
     },
     "context": {
      "diagnosis": "ncit:C15220"
+    },
+    "units": {
+     "expression": "1"
     }
    },
    "value": 3.33333333e-07
@@ -710,6 +843,9 @@
     "context": {
      "disease_severity": "ncit:C25269",
      "diagnosis": "ncit:C113725"
+    },
+    "units": {
+     "expression": "1"
     }
    },
    "value": 1.66666666e-08
@@ -723,6 +859,9 @@
     },
     "context": {
      "diagnosis": "ncit:C15220"
+    },
+    "units": {
+     "expression": "1"
     }
    },
    "value": 3.33333333e-08
@@ -736,6 +875,9 @@
     },
     "context": {
      "disease_severity": "ncit:C25467"
+    },
+    "units": {
+     "expression": "1"
     }
    },
    "value": 0.0
@@ -747,7 +889,10 @@
     "identifiers": {
      "ido": "0000592"
     },
-    "context": {}
+    "context": {},
+    "units": {
+     "expression": "1"
+    }
    },
    "value": 0.0
   },
@@ -758,7 +903,10 @@
     "identifiers": {
      "ncit": "C28554"
     },
-    "context": {}
+    "context": {},
+    "units": {
+     "expression": "1"
+    }
    },
    "value": 0.0
   }
@@ -773,6 +921,7 @@
    "context": {
     "diagnosed": "ncit:C113725"
    },
+   "units": null,
    "expression": "Diagnosed + Recognized + Threatened"
   },
   "Hospitalizations": {
@@ -784,6 +933,7 @@
    "context": {
     "hospitalization": "ncit:C25179"
    },
+   "units": null,
    "expression": "Recognized + Threatened"
   },
   "Deaths": {
@@ -793,6 +943,7 @@
     "ncit": "C28554"
    },
    "context": {},
+   "units": null,
    "expression": "Extinct"
   }
  },
@@ -820,5 +971,11 @@
   "model_types": [
    "mamo:0000028"
   ]
+ },
+ "time": {
+  "name": "t",
+  "units": {
+   "expression": "day"
+  }
  }
 }
\ No newline at end of file
diff --git a/notebooks/ensemble/ensemble.ipynb b/notebooks/ensemble/ensemble.ipynb
index dba298266..0f0e735fb 100644
--- a/notebooks/ensemble/ensemble.ipynb
+++ b/notebooks/ensemble/ensemble.ipynb
@@ -58,7 +58,7 @@
     "from mira.metamodel.search import find_models_with_grounding\n",
     "from mira.metamodel.comparison import get_dkg_refinement_closure\n",
     "from mira.modeling import Model\n",
-    "from mira.modeling.petri import PetriNetModel\n",
+    "from mira.modeling.askenet.petrinet import AskeNetPetriNetModel\n",
     "from mira.metamodel.ops import simplify_rate_laws"
    ]
   },
@@ -84,7 +84,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|████████████████████████████████████████████████████████████████| 33/33 [00:07<00:00,  4.19it/s]\n"
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 33/33 [00:10<00:00,  3.24it/s]\n"
      ]
     }
    ],
@@ -839,7 +839,19 @@
    "execution_count": 23,
    "id": "3bb98476",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'PetriNetModel' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[23], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m pm \u001b[38;5;241m=\u001b[39m \u001b[43mPetriNetModel\u001b[49m(Model(model))\n\u001b[1;32m      2\u001b[0m pm\u001b[38;5;241m.\u001b[39mto_json_file(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBIOMD0000000983_miranet.json\u001b[39m\u001b[38;5;124m'\u001b[39m, indent\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'PetriNetModel' is not defined"
+     ]
+    }
+   ],
    "source": [
     "pm = PetriNetModel(Model(model))\n",
     "pm.to_json_file('BIOMD0000000983_miranet.json', indent=1)"
@@ -872,7 +884,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -915,6 +927,30 @@
     "    add_distribution(v)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "b146d858",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mira.metamodel import Unit, Time\n",
+    "import sympy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "be0b6315",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for template in model.templates:\n",
+    "    for concept in template.get_concepts():\n",
+    "        concept.units = Unit(expression=sympy.parse_expr('1'))\n",
+    "model.time = Time(name='t', units=Unit(expression=sympy.parse_expr('day')))"
+   ]
+  },
   {
    "attachments": {
     "image.png": {
@@ -930,7 +966,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 35,
    "id": "f0c25d9e",
    "metadata": {},
    "outputs": [],
@@ -944,14 +980,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 36,
    "id": "aabee5d5",
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [],
    "source": [
     "model_to_json_file(model, 'BIOMD0000000955_template_model.json')\n",
-    "pm = PetriNetModel(Model(model))\n",
-    "pm.to_json_file('BIOMD0000000955_miranet.json', indent=1)"
+    "pm = AskeNetPetriNetModel(Model(model))\n",
+    "pm.to_json_file('BIOMD0000000955_askenet.json', indent=1)"
    ]
   },
   {
@@ -1145,7 +1183,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.11"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

From 23579a1a66f043f7b3e17f65d6e232f8e71f804c Mon Sep 17 00:00:00 2001
From: Ben Gyori <ben.gyori@gmail.com>
Date: Thu, 22 Jun 2023 22:26:09 -0400
Subject: [PATCH 02/11] Fix handling of mathml serialization

---
 mira/modeling/askenet/petrinet.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/mira/modeling/askenet/petrinet.py b/mira/modeling/askenet/petrinet.py
index d764e371a..469413cc1 100644
--- a/mira/modeling/askenet/petrinet.py
+++ b/mira/modeling/askenet/petrinet.py
@@ -75,7 +75,7 @@ def __init__(self, model: Model):
                 states_dict['units'] = {
                     'expression': str(var.concept.units.expression),
                     'expression_mathml': expression_to_mathml(
-                        var.concept.units.expression),
+                        var.concept.units.expression.args[0]),
                 }
 
             self.states.append(states_dict)
@@ -102,7 +102,7 @@ def __init__(self, model: Model):
                 'name': observable.observable.name,
                 'expression': str(observable.observable.expression),
                 'expression_mathml': expression_to_mathml(
-                    observable.observable.expression),
+                    observable.observable.expression.args[0]),
             }
             self.observables.append(obs_data)
 
@@ -112,7 +112,7 @@ def __init__(self, model: Model):
                 self.time['units'] = {
                     'expression': str(model.template_model.time.units.expression),
                     'expression_mathml': expression_to_mathml(
-                        model.template_model.time.units.expression),
+                        model.template_model.time.units.expression.args[0]),
                 }
         else:
             self.time = None
@@ -186,7 +186,7 @@ def __init__(self, model: Model):
                 param_dict['units'] = {
                     'expression': str(param.concept.units.expression),
                     'expression_mathml': expression_to_mathml(
-                        param.concept.units.expression),
+                        param.concept.units.expression.args[0]),
                 }
             self.parameters.append(param_dict)
 

From 9bff643d2fe006318d590083879516b4dec594fc Mon Sep 17 00:00:00 2001
From: Ben Gyori <ben.gyori@gmail.com>
Date: Mon, 26 Jun 2023 15:54:23 -0400
Subject: [PATCH 03/11] Add lookup for SBML units

---
 mira/resources/__init__.py    | 16 +++++++++++++++-
 mira/resources/sbml_units.csv | 36 +++++++++++++++++++++++++++++++++++
 2 files changed, 51 insertions(+), 1 deletion(-)
 create mode 100644 mira/resources/sbml_units.csv

diff --git a/mira/resources/__init__.py b/mira/resources/__init__.py
index 864a12f64..4ca489e03 100644
--- a/mira/resources/__init__.py
+++ b/mira/resources/__init__.py
@@ -1,4 +1,5 @@
 import os
+import csv
 
 HERE = os.path.dirname(os.path.abspath(__file__))
 
@@ -16,4 +17,17 @@ def get_resource_file(fname):
     str
         The path to the file.
     """
-    return os.path.join(HERE, fname)
\ No newline at end of file
+    return os.path.join(HERE, fname)
+
+
+def make_sbml_units():
+    import libsbml
+    module_contents = dir(libsbml)
+    unit_kinds = {var: var.split('_')[-1].lower()
+                  for var in module_contents
+                  if var.startswith("UNIT_KIND")
+                  and var != "UNIT_KIND_INVALID"}
+    unit_kinds = {getattr(libsbml, var): unit_name
+                  for var, unit_name in unit_kinds.items()}
+    with open(get_resource_file('sbml_units.csv'), 'w') as fh:
+        csv.writer(fh).writerows(sorted(unit_kinds.items()))
\ No newline at end of file
diff --git a/mira/resources/sbml_units.csv b/mira/resources/sbml_units.csv
new file mode 100644
index 000000000..53fbcab92
--- /dev/null
+++ b/mira/resources/sbml_units.csv
@@ -0,0 +1,36 @@
+0,ampere
+1,avogadro
+2,becquerel
+3,candela
+4,celsius
+5,coulomb
+6,dimensionless
+7,farad
+8,gram
+9,gray
+10,henry
+11,hertz
+12,item
+13,joule
+14,katal
+15,kelvin
+16,kilogram
+17,liter
+18,litre
+19,lumen
+20,lux
+21,meter
+22,metre
+23,mole
+24,newton
+25,ohm
+26,pascal
+27,radian
+28,second
+29,siemens
+30,sievert
+31,steradian
+32,tesla
+33,volt
+34,watt
+35,weber

From ef64001bb60aee53e569738bde91eb1014341f0f Mon Sep 17 00:00:00 2001
From: Ben Gyori <ben.gyori@gmail.com>
Date: Mon, 26 Jun 2023 16:00:59 -0400
Subject: [PATCH 04/11] Move SBML unit mappings and implement unit extraction

---
 mira/resources/__init__.py     | 13 ------------
 mira/resources/sbml_units.csv  | 36 ----------------------------------
 mira/sources/sbml/processor.py | 29 +++++++++++++++++++++++++++
 3 files changed, 29 insertions(+), 49 deletions(-)
 delete mode 100644 mira/resources/sbml_units.csv

diff --git a/mira/resources/__init__.py b/mira/resources/__init__.py
index 4ca489e03..8578a480f 100644
--- a/mira/resources/__init__.py
+++ b/mira/resources/__init__.py
@@ -18,16 +18,3 @@ def get_resource_file(fname):
         The path to the file.
     """
     return os.path.join(HERE, fname)
-
-
-def make_sbml_units():
-    import libsbml
-    module_contents = dir(libsbml)
-    unit_kinds = {var: var.split('_')[-1].lower()
-                  for var in module_contents
-                  if var.startswith("UNIT_KIND")
-                  and var != "UNIT_KIND_INVALID"}
-    unit_kinds = {getattr(libsbml, var): unit_name
-                  for var, unit_name in unit_kinds.items()}
-    with open(get_resource_file('sbml_units.csv'), 'w') as fh:
-        csv.writer(fh).writerows(sorted(unit_kinds.items()))
\ No newline at end of file
diff --git a/mira/resources/sbml_units.csv b/mira/resources/sbml_units.csv
deleted file mode 100644
index 53fbcab92..000000000
--- a/mira/resources/sbml_units.csv
+++ /dev/null
@@ -1,36 +0,0 @@
-0,ampere
-1,avogadro
-2,becquerel
-3,candela
-4,celsius
-5,coulomb
-6,dimensionless
-7,farad
-8,gram
-9,gray
-10,henry
-11,hertz
-12,item
-13,joule
-14,katal
-15,kelvin
-16,kilogram
-17,liter
-18,litre
-19,lumen
-20,lux
-21,meter
-22,metre
-23,mole
-24,newton
-25,ohm
-26,pascal
-27,radian
-28,second
-29,siemens
-30,sievert
-31,steradian
-32,tesla
-33,volt
-34,watt
-35,weber
diff --git a/mira/sources/sbml/processor.py b/mira/sources/sbml/processor.py
index d37f1d953..dcfd13a6c 100644
--- a/mira/sources/sbml/processor.py
+++ b/mira/sources/sbml/processor.py
@@ -114,6 +114,20 @@ def _lookup_concepts_filtered(species_ids) -> List[Concept]:
                 if species_id not in reporter_ids and 'cumulative' not in species_id
             ]
 
+        units = {}
+        for unit_def in self.sbml_model.unit_definitions:
+            unit_type = unit_def.id
+            full_unit_expr = sympy.Integer(1)
+            for unit in unit_def.units:
+                unit_symbol = sympy.Symbol(SBML_UNITS[unit.kind])
+                unit_expr = \
+                    ((unit.multiplier * unit_symbol) ** unit.exponent) * \
+                    (10 ** unit.scale)
+                full_unit_expr *= unit_expr
+            units[unit_type] = full_unit_expr
+        print(units)
+
+
         # Iterate thorugh all reactions and piecewise convert to templates
         templates: List[Template] = []
         # see docs on reactions
@@ -815,3 +829,18 @@ def parse_context_grounding(grounding_str):
 
 
 grounding_map = _get_grounding_map()
+
+
+def get_sbml_units():
+    import libsbml
+    module_contents = dir(libsbml)
+    unit_kinds = {var: var.split('_')[-1].lower()
+                  for var in module_contents
+                  if var.startswith("UNIT_KIND")
+                  and var != "UNIT_KIND_INVALID"}
+    unit_kinds = {getattr(libsbml, var): unit_name
+                  for var, unit_name in unit_kinds.items()}
+    return unit_kinds
+
+
+SBML_UNITS = get_sbml_units()

From ada79e34898abb578e7f1e619ffa8e3edfccfab7 Mon Sep 17 00:00:00 2001
From: Ben Gyori <ben.gyori@gmail.com>
Date: Tue, 27 Jun 2023 20:18:45 -0400
Subject: [PATCH 05/11] Implement more unit processing

---
 mira/sources/sbml/processor.py | 47 ++++++++++++++++++++++------------
 1 file changed, 30 insertions(+), 17 deletions(-)

diff --git a/mira/sources/sbml/processor.py b/mira/sources/sbml/processor.py
index dcfd13a6c..594e1059d 100644
--- a/mira/sources/sbml/processor.py
+++ b/mira/sources/sbml/processor.py
@@ -14,6 +14,7 @@
 from typing import Dict, Iterable, List, Mapping, Optional, Tuple
 
 import bioregistry
+import libsbml
 import sympy
 from lxml import etree
 from tqdm import tqdm
@@ -100,6 +101,7 @@ def __init__(self, sbml_model, model_id=None, reporter_ids=None):
         self.sbml_model = sbml_model
         self.model_id = model_id
         self.reporter_ids = reporter_ids
+        self.units = get_units(self.sbml_model.unit_definitions)
 
     def extract_model(self):
         if self.model_id is None:
@@ -114,20 +116,6 @@ def _lookup_concepts_filtered(species_ids) -> List[Concept]:
                 if species_id not in reporter_ids and 'cumulative' not in species_id
             ]
 
-        units = {}
-        for unit_def in self.sbml_model.unit_definitions:
-            unit_type = unit_def.id
-            full_unit_expr = sympy.Integer(1)
-            for unit in unit_def.units:
-                unit_symbol = sympy.Symbol(SBML_UNITS[unit.kind])
-                unit_expr = \
-                    ((unit.multiplier * unit_symbol) ** unit.exponent) * \
-                    (10 ** unit.scale)
-                full_unit_expr *= unit_expr
-            units[unit_type] = full_unit_expr
-        print(units)
-
-
         # Iterate thorugh all reactions and piecewise convert to templates
         templates: List[Template] = []
         # see docs on reactions
@@ -179,6 +167,10 @@ def _lookup_concepts_filtered(species_ids) -> List[Concept]:
             for species in self.sbml_model.species
         }
 
+        species_units = {
+            species.id: self.units[species.units] for species in self.sbml_model.species
+        }
+
         all_locals = {k: v for k, v in (list(parameter_symbols.items()) +
                                         list(compartment_symbols.items()) +
                                         list(function_lambdas.items()) +
@@ -329,6 +321,25 @@ def _lookup_concepts_filtered(species_ids) -> List[Concept]:
         return template_model
 
 
+def get_units(unit_definitions):
+    units = {}
+    for unit_def in unit_definitions:
+        unit_type = unit_def.id
+        full_unit_expr = sympy.Integer(1)
+        for unit in unit_def.units:
+            unit_symbol = sympy.Symbol(SBML_UNITS[unit.kind])
+            # We do this to avoid the spurious factors in the expression
+            if unit.multiplier != 1:
+                unit_symbol *= unit.multiplier
+            if unit.exponent != 1:
+                unit_symbol **= unit.exponent
+            if unit.scale != 0:
+                unit_symbol *= 10 ** unit.scale
+            full_unit_expr *= unit_symbol
+        units[unit_type] = full_unit_expr
+    return units
+
+
 def get_model_annotations(sbml_model) -> Annotations:
     """Get the model annotations from the SBML model."""
     et = etree.fromstring(sbml_model.getAnnotationString())
@@ -583,7 +594,7 @@ def variables_from_ast(ast_node):
     return variables_in_ast
 
 
-def _extract_concept(species, model_id=None):
+def _extract_concept(species, units=None, model_id=None):
     species_id = species.getId()
     species_name = species.getName()
     if '(' in species_name:
@@ -597,6 +608,7 @@ def _extract_concept(species, model_id=None):
             name=species_name,
             identifiers=copy.deepcopy(mapped_ids),
             context=copy.deepcopy(mapped_context),
+            units=units
         )
         return concept
     else:
@@ -610,7 +622,8 @@ def _extract_concept(species, model_id=None):
     annotation_string = species.getAnnotationString()
     if not annotation_string:
         logger.debug(f"[{model_id} species:{species_id}] had no annotations")
-        concept = Concept(name=species_name, identifiers={}, context={})
+        concept = Concept(name=species_name, identifiers={}, context={},
+                          units=units)
         return concept
 
     annotation_tree = etree.fromstring(annotation_string)
@@ -707,6 +720,7 @@ def _extract_concept(species, model_id=None):
         identifiers=identifiers,
         # TODO how to handle multiple properties? can we extend context to allow lists?
         context=context,
+        units=units,
     )
     concept = grounding_normalize(concept)
     return concept
@@ -832,7 +846,6 @@ def parse_context_grounding(grounding_str):
 
 
 def get_sbml_units():
-    import libsbml
     module_contents = dir(libsbml)
     unit_kinds = {var: var.split('_')[-1].lower()
                   for var in module_contents

From 0929a4891c784e83a7d61e51ea546bb4c3a1787c Mon Sep 17 00:00:00 2001
From: Ben Gyori <ben.gyori@gmail.com>
Date: Tue, 27 Jun 2023 21:14:56 -0400
Subject: [PATCH 06/11] Refactor unit extraction and apply mappings

---
 mira/sources/sbml/processor.py | 55 ++++++++++++++++++++++++++--------
 1 file changed, 42 insertions(+), 13 deletions(-)

diff --git a/mira/sources/sbml/processor.py b/mira/sources/sbml/processor.py
index 594e1059d..ea8045771 100644
--- a/mira/sources/sbml/processor.py
+++ b/mira/sources/sbml/processor.py
@@ -322,24 +322,49 @@ def _lookup_concepts_filtered(species_ids) -> List[Concept]:
 
 
 def get_units(unit_definitions):
+    """Return units from a list of unit definition blocks."""
     units = {}
     for unit_def in unit_definitions:
-        unit_type = unit_def.id
-        full_unit_expr = sympy.Integer(1)
-        for unit in unit_def.units:
-            unit_symbol = sympy.Symbol(SBML_UNITS[unit.kind])
-            # We do this to avoid the spurious factors in the expression
-            if unit.multiplier != 1:
-                unit_symbol *= unit.multiplier
-            if unit.exponent != 1:
-                unit_symbol **= unit.exponent
-            if unit.scale != 0:
-                unit_symbol *= 10 ** unit.scale
-            full_unit_expr *= unit_symbol
-        units[unit_type] = full_unit_expr
+        units[unit_def.id] = process_unit_definition(unit_def)
+    print(units)
     return units
 
 
+unit_symbol_mappings = {
+    'item': 'person',
+    'metre': 'meter',
+    'litre': 'liter',
+}
+
+
+def process_unit_definition(unit_definition):
+    """Process a unit definition block to extract an expression."""
+    full_unit_expr = sympy.Integer(1)
+    for unit in unit_definition.units:
+        unit_symbol_str = SBML_UNITS[unit.kind]
+        # We assume person instead of item here
+        if unit_symbol_str in unit_symbol_mappings:
+            unit_symbol_str = unit_symbol_mappings[unit_symbol_str]
+        unit_symbol = sympy.Symbol(unit_symbol_str)
+        # We do this to avoid the spurious factors in the expression
+        if unit.multiplier != 1:
+            unit_symbol *= unit.multiplier
+        if unit.exponent != 1:
+            unit_symbol **= unit.exponent
+        if unit.scale != 0:
+            unit_symbol *= 10 ** unit.scale
+        full_unit_expr *= unit_symbol
+    # We apply some mappings for canonical units we want to change
+    if full_unit_expr == 86400.0 * sympy.Symbol('second'):
+        full_unit_expr = sympy.Symbol('day')
+    elif full_unit_expr == 1 / (86400.0 * sympy.Symbol('second')):
+        full_unit_expr = 1/sympy.Symbol('day')
+    elif full_unit_expr == 1 / (86400.0 * sympy.Symbol('second') *
+                                sympy.Symbol('person')):
+        full_unit_expr = 1/(sympy.Symbol('day') * sympy.Symbol('person'))
+    return full_unit_expr
+
+
 def get_model_annotations(sbml_model) -> Annotations:
     """Get the model annotations from the SBML model."""
     et = etree.fromstring(sbml_model.getAnnotationString())
@@ -846,6 +871,10 @@ def parse_context_grounding(grounding_str):
 
 
 def get_sbml_units():
+    """Build up a mapping of SBML unit kinds to their names.
+
+    This is necessary because units are given as numbers.
+    """
     module_contents = dir(libsbml)
     unit_kinds = {var: var.split('_')[-1].lower()
                   for var in module_contents

From 7c339efbd221240fa8d68c8373536e64028dbcf3 Mon Sep 17 00:00:00 2001
From: Ben Gyori <ben.gyori@gmail.com>
Date: Tue, 27 Jun 2023 21:28:20 -0400
Subject: [PATCH 07/11] Extract units for concepts

---
 mira/sources/sbml/processor.py | 27 +++++++++++++++------------
 1 file changed, 15 insertions(+), 12 deletions(-)

diff --git a/mira/sources/sbml/processor.py b/mira/sources/sbml/processor.py
index ea8045771..811d1cb48 100644
--- a/mira/sources/sbml/processor.py
+++ b/mira/sources/sbml/processor.py
@@ -108,7 +108,7 @@ def extract_model(self):
             self.model_id = get_model_id(self.sbml_model)
         model_annots = get_model_annotations(self.sbml_model)
         reporter_ids = set(self.reporter_ids or [])
-        concepts = _extract_concepts(self.sbml_model, model_id=self.model_id)
+        concepts = self._extract_concepts()
 
         def _lookup_concepts_filtered(species_ids) -> List[Concept]:
             return [
@@ -320,6 +320,20 @@ def _lookup_concepts_filtered(species_ids) -> List[Concept]:
         template_model = replace_constant_concepts(template_model)
         return template_model
 
+    def _extract_concepts(self) -> Mapping[str, Concept]:
+        """Extract concepts from an SBML model."""
+        concepts = {}
+        # see https://sbml.org/software/libsbml/5.18.0/docs/formatted/python-api/classlibsbml_1_1_species.html
+        for species in self.sbml_model.getListOfSpecies():
+            # Extract the units for the species
+            units = Unit(expression=self.units[species.units]) \
+                if species.units else None
+            concept = _extract_concept(species, model_id=self.model_id,
+                                       units=units)
+            concepts[species.getId()] = concept
+
+        return concepts
+
 
 def get_units(unit_definitions):
     """Return units from a list of unit definition blocks."""
@@ -751,17 +765,6 @@ def _extract_concept(species, units=None, model_id=None):
     return concept
 
 
-def _extract_concepts(sbml_model, *, model_id: Optional[str] = None) -> Mapping[str, Concept]:
-    """Extract concepts from an SBML model."""
-    concepts = {}
-    # see https://sbml.org/software/libsbml/5.18.0/docs/formatted/python-api/classlibsbml_1_1_species.html
-    for species in sbml_model.getListOfSpecies():
-        concept = _extract_concept(species, model_id=model_id)
-        concepts[species.getId()] = concept
-
-    return concepts
-
-
 def grounding_normalize(concept):
     # A common curation mistake in BioModels: mixing up IDO and NCIT identifiers
     for k, v in deepcopy(concept.identifiers).items():

From 49ebcd61201fcca605e1604689cc7627bd3a49b5 Mon Sep 17 00:00:00 2001
From: Ben Gyori <ben.gyori@gmail.com>
Date: Tue, 27 Jun 2023 21:58:07 -0400
Subject: [PATCH 08/11] Extract units for parameters and compartments

---
 mira/sources/sbml/processor.py | 26 +++++++++++++++++++-------
 1 file changed, 19 insertions(+), 7 deletions(-)

diff --git a/mira/sources/sbml/processor.py b/mira/sources/sbml/processor.py
index 811d1cb48..a89bb7e40 100644
--- a/mira/sources/sbml/processor.py
+++ b/mira/sources/sbml/processor.py
@@ -122,9 +122,13 @@ def _lookup_concepts_filtered(species_ids) -> List[Concept]:
         # https://sbml.org/software/libsbml/5.18.0/docs/formatted/python-api/
         # classlibsbml_1_1_reaction.html
         all_species = {species.id for species in self.sbml_model.species}
+
         all_parameters = {
-            clean_formula(parameter.id): {'value': parameter.value,
-                                          'description': parameter.name}
+            clean_formula(parameter.id): {
+                'value': parameter.value,
+                'description': parameter.name,
+                'units': self.get_object_units(parameter)
+            }
             for parameter in self.sbml_model.parameters
         }
         parameter_symbols = \
@@ -136,7 +140,8 @@ def _lookup_concepts_filtered(species_ids) -> List[Concept]:
         # Add compartment volumes as parameters
         for compartment in self.sbml_model.compartments:
             all_parameters[compartment.id] = {'value': compartment.volume,
-                                              'description': compartment.name}
+                                              'description': compartment.name,
+                                              'units': self.get_object_units(compartment)}
 
         # Handle custom function definitions in the model
         function_lambdas = {}
@@ -310,7 +315,8 @@ def _lookup_concepts_filtered(species_ids) -> List[Concept]:
             )
 
         param_objs = {k: Parameter(name=k, value=v['value'],
-                                   description=v['description'])
+                                   description=v['description'],
+                                   units=v['units'])
                       for k, v in all_parameters.items()}
         template_model = TemplateModel(templates=templates,
                                        parameters=param_objs,
@@ -326,21 +332,25 @@ def _extract_concepts(self) -> Mapping[str, Concept]:
         # see https://sbml.org/software/libsbml/5.18.0/docs/formatted/python-api/classlibsbml_1_1_species.html
         for species in self.sbml_model.getListOfSpecies():
             # Extract the units for the species
-            units = Unit(expression=self.units[species.units]) \
-                if species.units else None
+            units = self.get_object_units(species)
             concept = _extract_concept(species, model_id=self.model_id,
                                        units=units)
             concepts[species.getId()] = concept
 
         return concepts
 
+    def get_object_units(self, object):
+        if object.units:
+            return Unit(expression=self.units[object.units])
+        else:
+            return None
+
 
 def get_units(unit_definitions):
     """Return units from a list of unit definition blocks."""
     units = {}
     for unit_def in unit_definitions:
         units[unit_def.id] = process_unit_definition(unit_def)
-    print(units)
     return units
 
 
@@ -500,6 +510,7 @@ def find_constant_concepts(template_model: TemplateModel) -> Iterable[str]:
 
 
 def replace_constant_concepts(template_model: TemplateModel):
+    """Replace concepts that are constant by parameters."""
     constant_concepts = find_constant_concepts(template_model)
     for constant_concept in constant_concepts:
         initial = template_model.initials.get(constant_concept)
@@ -509,6 +520,7 @@ def replace_constant_concepts(template_model: TemplateModel):
             initial_val = 1.0
         # Fixme, do we need more grounding (identifiers, concept)
         # for the concept here?
+        # Get the units of the concept here
         template_model.parameters[constant_concept] = \
             Parameter(name=constant_concept, value=initial_val)
         new_templates = []

From d00e3c7543b2d798ba699fc05b626f5808fddb5d Mon Sep 17 00:00:00 2001
From: Ben Gyori <ben.gyori@gmail.com>
Date: Tue, 27 Jun 2023 22:42:52 -0400
Subject: [PATCH 09/11] Aggregate parameters with units

---
 mira/metamodel/ops.py | 15 +++++++++++++--
 1 file changed, 13 insertions(+), 2 deletions(-)

diff --git a/mira/metamodel/ops.py b/mira/metamodel/ops.py
index 4b9f62750..9ad683a5d 100644
--- a/mira/metamodel/ops.py
+++ b/mira/metamodel/ops.py
@@ -273,8 +273,18 @@ def aggregate_parameters(template_model, exclude=None):
         params_for_subs = {
             k: v.value for k, v in template_model.parameters.items()
         }
+        residual_units = 1
         if not (free_symbols & set(template.get_concept_names())):
-            residual_rate_law = residual_rate_law.subs(params_for_subs)
+            # We do subtitutions one by one so that we can keep track of which
+            # parameters were used and adjust residual units accordingly
+            for k, v in params_for_subs.items():
+                starting_rate_law = residual_rate_law
+                residual_rate_law = starting_rate_law.subs({k: v})
+                # This means a substitution was made
+                if starting_rate_law != residual_rate_law:
+                    units = template_model.parameters[k].units.expression \
+                        if template_model.parameters[k].units else 1
+                    residual_units *= units
             if isinstance(residual_rate_law, (int, float)) or \
                     residual_rate_law.is_Number:
                 pvalue = float(residual_rate_law)
@@ -283,7 +293,8 @@ def aggregate_parameters(template_model, exclude=None):
                 # original distributions if the original parameters
                 # had them annotated
                 template_model.parameters[pname] = \
-                    Parameter(name=pname, value=pvalue, distribution=None)
+                    Parameter(name=pname, value=pvalue, distribution=None,
+                              units=Unit(expression=residual_units))
                 template.set_mass_action_rate_law(pname)
                 idx += 1
         # 4. If the replaced parameters disappear completely then we can remove

From fcdb2563b0616756d406f5cb3314e956620f7a7b Mon Sep 17 00:00:00 2001
From: Ben Gyori <ben.gyori@gmail.com>
Date: Tue, 27 Jun 2023 23:01:17 -0400
Subject: [PATCH 10/11] Handle dimensionless

---
 mira/sources/sbml/processor.py | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/mira/sources/sbml/processor.py b/mira/sources/sbml/processor.py
index a89bb7e40..e540755cd 100644
--- a/mira/sources/sbml/processor.py
+++ b/mira/sources/sbml/processor.py
@@ -172,10 +172,6 @@ def _lookup_concepts_filtered(species_ids) -> List[Concept]:
             for species in self.sbml_model.species
         }
 
-        species_units = {
-            species.id: self.units[species.units] for species in self.sbml_model.species
-        }
-
         all_locals = {k: v for k, v in (list(parameter_symbols.items()) +
                                         list(compartment_symbols.items()) +
                                         list(function_lambdas.items()) +
@@ -341,7 +337,10 @@ def _extract_concepts(self) -> Mapping[str, Concept]:
 
     def get_object_units(self, object):
         if object.units:
-            return Unit(expression=self.units[object.units])
+            if object.units == 'dimensionless':
+                return Unit(expression=sympy.Integer(1))
+            else:
+                return Unit(expression=self.units[object.units])
         else:
             return None
 

From 5379e5f166156131fbb0d125ec9b34ad0f2c85a0 Mon Sep 17 00:00:00 2001
From: Ben Gyori <ben.gyori@gmail.com>
Date: Wed, 28 Jun 2023 00:05:08 -0400
Subject: [PATCH 11/11] Fix unit expression replacement

---
 mira/sources/sbml/processor.py | 22 +++++++++++++++-------
 1 file changed, 15 insertions(+), 7 deletions(-)

diff --git a/mira/sources/sbml/processor.py b/mira/sources/sbml/processor.py
index e540755cd..153c3129c 100644
--- a/mira/sources/sbml/processor.py
+++ b/mira/sources/sbml/processor.py
@@ -360,6 +360,18 @@ def get_units(unit_definitions):
 }
 
 
+unit_expression_mappings = {
+    86400.0 * sympy.Symbol('second'): sympy.Symbol('day'),
+    1 / (86400.0 * sympy.Symbol('second')): 1 / sympy.Symbol('day'),
+    1 / (86400.0 * sympy.Symbol('second') * sympy.Symbol('person')):
+        1 / (sympy.Symbol('day') * sympy.Symbol('person')),
+    31536000.0 * sympy.Symbol('second'): sympy.Symbol('year'),
+    1 / (31536000.0 * sympy.Symbol('second')): 1 / sympy.Symbol('year'),
+    1 / (31536000.0 * sympy.Symbol('second') * sympy.Symbol('person')):
+        1 / (sympy.Symbol('year') * sympy.Symbol('person')),
+}
+
+
 def process_unit_definition(unit_definition):
     """Process a unit definition block to extract an expression."""
     full_unit_expr = sympy.Integer(1)
@@ -378,13 +390,9 @@ def process_unit_definition(unit_definition):
             unit_symbol *= 10 ** unit.scale
         full_unit_expr *= unit_symbol
     # We apply some mappings for canonical units we want to change
-    if full_unit_expr == 86400.0 * sympy.Symbol('second'):
-        full_unit_expr = sympy.Symbol('day')
-    elif full_unit_expr == 1 / (86400.0 * sympy.Symbol('second')):
-        full_unit_expr = 1/sympy.Symbol('day')
-    elif full_unit_expr == 1 / (86400.0 * sympy.Symbol('second') *
-                                sympy.Symbol('person')):
-        full_unit_expr = 1/(sympy.Symbol('day') * sympy.Symbol('person'))
+    for unit_expr, new_unit_expr in unit_expression_mappings.items():
+        if full_unit_expr == unit_expr:
+            full_unit_expr = new_unit_expr
     return full_unit_expr