Merge branch 'main' of https://github.com/freiburgermsu/ModelSEEDpy

examples/Community Modeling/MSSteadyCom/MSCommunity_thermo.ipynb

@@ -103,32 +103,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 5,
    "metadata": {
-    "scrolled": true,
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "cobrakbase 0.3.1\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# import kbase\n",
     "import os\n",
-    "local_cobrakbase_path = os.path.join('C:', 'Users', 'Andrew Freiburger','Documents','Argonne','cobrakbase')\n",
+    "# local_cobrakbase_path = os.path.join('C:', 'Users', 'Andrew Freiburger','Documents','Argonne','cobrakbase')\n",
+    "local_cobrakbase_path = os.path.join('..', '..', '..', '..', 'cobrakbase')\n",
     "os.environ[\"HOME\"] = local_cobrakbase_path\n",
     "\n",
     "import cobrakbase\n",
-    "with open(\"C:/Users/Andrew Freiburger/Documents/Argonne/kbase_token.txt\") as token_file:\n",
+    "# with open(\"C:/Users/Andrew Freiburger/Documents/Argonne/kbase_token.txt\") as token_file:\n",
+    "os.system(\"pwd\")\n",
+    "with open(\"../../../../kbase_token.txt\") as token_file:\n",
     "    kbase = cobrakbase.KBaseAPI(token_file.readline())\n",
     "\n",
     "model = kbase.get_from_ws(\"CMM_iAH991V2_iML1515.kb\",40576)\n",
-    "media = kbase.get_from_ws(\"Btheta_Ecoli_minimal_media\",40576)\n",
+    "# media = kbase.get_from_ws(\"Btheta_Ecoli_minimal_media\",40576)\n",
     "\n",
     "%matplotlib inline"
    ]
@@ -178,7 +172,7 @@
    ],
    "source": [
     "model = kbase.get_from_ws(\"CMM_iAH991V2_iML1515.kb\",40576)\n",
-    "media = kbase.get_from_ws(\"Btheta_Ecoli_minimal_media\",40576)\n",
+    "# media = kbase.get_from_ws(\"Btheta_Ecoli_minimal_media\",40576)\n",
     "\n",
     "# simulate and visualize the model\n",
     "from modelseedpy import MSCommunity\n",
@@ -623,7 +617,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -637,7 +631,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,

examples/HTML_report.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "3370e326-9146-43c4-9004-dd229bad1123",
+   "id": "loaded-dakota",
    "metadata": {},
    "source": [
     "# Load a model"
@@ -11,7 +11,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "7e8d874e-4c45-43ad-a4e1-ec5a83f2c942",
+   "id": "induced-button",
    "metadata": {},
    "outputs": [
     {
@@ -42,7 +42,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "04a852cc-d5ac-4efa-813e-f83694c90aa6",
+   "id": "verified-drove",
    "metadata": {
     "tags": []
    },
@@ -55,7 +55,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "e9a4b845-bb73-459c-9b0e-3b9e9dd3d83f",
+   "id": "continuous-animal",
    "metadata": {},
    "outputs": [
     {
@@ -144,7 +144,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "349697ee-04ec-4e46-ac96-8264deecbf5e",
+   "id": "entitled-belle",
    "metadata": {},
    "outputs": [
     {
@@ -163,7 +163,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "503d83c7-f056-4bdd-8dc4-5af7c0f8450b",
+   "id": "lyric-donna",
    "metadata": {},
    "source": [
     "# Run SteadyCom"
@@ -172,7 +172,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "8b554016-2f00-4294-878b-2b1d3dc9cf58",
+   "id": "monthly-majority",
    "metadata": {
     "scrolled": true,
     "tags": []
@@ -331,7 +331,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e6be5b9d-3d6b-498a-ad02-2b9fcb7ffa5d",
+   "id": "rotary-listing",
    "metadata": {},
    "source": [
     "# Construct the HTML report"
@@ -340,7 +340,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "d049c63f-03c4-4345-a120-1f8c236c84ba",
+   "id": "annoying-physiology",
    "metadata": {},
    "outputs": [
     {
@@ -362,7 +362,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "46b790aa-280e-43cc-b43f-a709a5e1c7e7",
+   "id": "instructional-thriller",
    "metadata": {},
    "source": [
     "### Combine pandas Series into a single DataFrame"
@@ -371,7 +371,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "c2502a8b-34b3-4cec-95da-7f8f4b254069",
+   "id": "adjacent-boost",
    "metadata": {},
    "outputs": [
     {
@@ -432,7 +432,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "899e1302-287f-4fae-a697-ed69eff8cf0e",
+   "id": "genuine-forest",
    "metadata": {},
    "source": [
     "# Construct a SMETANA report"
@@ -441,7 +441,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "3c202015-42d0-46c9-a577-2c3b0e6bff78",
+   "id": "assigned-teach",
    "metadata": {
     "scrolled": true,
     "tags": []
@@ -1039,7 +1039,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "1bdbd095-9a05-4b1a-ae0d-b95ec1ed8db0",
+   "id": "nonprofit-macro",
    "metadata": {},
    "outputs": [
     {
@@ -1070,7 +1070,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "e8406924-ce43-4aa6-8a9d-726c91130349",
+   "id": "accurate-filename",
    "metadata": {},
    "outputs": [
     {
@@ -1134,7 +1134,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "40f0f0fe-ea52-4eb2-9909-f85237c43674",
+   "id": "mysterious-speed",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1149,7 +1149,7 @@
   {
    "cell_type": "code",
    "execution_count": 50,
-   "id": "c9edc46c-b53a-4e7d-8e93-1e5a90500290",
+   "id": "royal-ranch",
    "metadata": {},
    "outputs": [
     {
@@ -1317,7 +1317,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },

modelseedpy/__init__.py

@@ -40,7 +40,7 @@
 from modelseedpy.core.exceptions import *
 
 from modelseedpy.community import (
-    MSCommunity, CommunityMember, MSCompatibility, MSKineticsFBA, CommScores, CommPhitting,
+    MSCommunity, CommunityMember, MSKineticsFBA, CommScores, CommPhitting,
     MSSteadyCom, build_from_species_models, phenotypes)
 
 from modelseedpy.biochem import ModelSEEDBiochem

modelseedpy/community/__init__.py

@@ -9,7 +9,6 @@
 from modelseedpy.community.datastandardization import *
 from modelseedpy.community.mssteadycom import MSSteadyCom
 from modelseedpy.community.commphitting import CommPhitting
-from modelseedpy.community.mscompatibility import MSCompatibility
 from modelseedpy.community.commhelper import build_from_species_models, phenotypes
 from modelseedpy.community.mskineticsfba import MSKineticsFBA
 

modelseedpy/community/commhelper.py

@@ -1,10 +1,10 @@
 from modelseedpy.core.msminimalmedia import minimizeFlux_withGrowth, bioFlux_check
 from modelseedpy.core.exceptions import NoFluxError, ObjectiveError
-from modelseedpy.community.mscompatibility import MSCompatibility
 from modelseedpy.core.msmodelutl import MSModelUtil
 from modelseedpy.core.fbahelper import FBAHelper
 from cobra import Model, Reaction, Metabolite
 from cobra.medium import minimal_medium
+from commscores import GEMCompatibility
 from cobra.flux_analysis import pfba
 from collections import OrderedDict
 from optlang.symbolics import Zero
@@ -55,7 +55,7 @@ def build_from_species_models(org_models, model_id=None, name=None, abundances=N
     ------
     """
     # construct the new model
-    models = org_models if not standardize else MSCompatibility.standardize(
+    models = org_models if not standardize else GEMCompatibility.standardize(
         org_models, exchanges=True, conflicts_file_name='exchanges_conflicts.json')
     biomass_indices = []
     biomass_index = minimal_biomass_index = 2