Tutorals and code modified

RuleGenerator.ipynb

@@ -7,7 +7,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from microberx.RuleGenerator import RuleGenerator\n",
+    "from MicrobeRX.RuleGenerator import RuleGenerator\n",
     "\n",
     "from pprint import pprint\n",
     "\n",
@@ -19,7 +19,7 @@
    "id": "870a13f8-057c-480e-aa45-66e85b0fccee",
    "metadata": {},
    "source": [
-    "## MetaNetX"
+    "## Reaction Data"
    ]
   },
   {
@@ -267,9 +267,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Chem_3",
+   "display_name": "MicrobeRX",
    "language": "python",
-   "name": "chem_3"
+   "name": "microberx"
   },
   "language_info": {
    "codemirror_mode": {
@@ -281,7 +281,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.13"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

package/MicrobeRX/Predictor/Predictor.py

@@ -36,7 +36,7 @@ def run_prediction(self, query:Chem.Mol,name:str='metabolite'):
                     tmp_table['product'] = self.rules_table['product'][index]
                     tmp_table['num_atoms'] = self.rules_table.num_atoms[index]
                     tmp_table['reacting_atoms_efficiency'] = round(self.rules_table.num_atoms[index]/self.query_atoms_num,3)
-                    self.predicted_metabolites=pd.concat([predicted_metabolites,tmp_table],ignore_index=True)
+                    self.predicted_metabolites=pd.concat([self.predicted_metabolites,tmp_table],ignore_index=True)
             #except Exception:
                 #pass
 

package/setup.py

@@ -7,7 +7,7 @@
 with codecs.open(os.path.join(here, "README.md"), encoding="utf-8") as fh:
     long_description = "\n" + fh.read()
 
-VERSION = 'v0.1.3'
+VERSION = 'v0.1.4'
 DESCRIPTION = 'MicrobeRX is A tool for enzymatic reaction-based metabolite prediction in the gut microbiome.'
 #LONG_DESCRIPTION = ''