Release update to make the code more flexible.

.github/workflows/test_contact_probability.yml

@@ -12,6 +12,7 @@ on: [push]
 #permissions:
 #  contents: read
 #
+
 jobs:
   build:
 
@@ -51,13 +52,13 @@ jobs:
       # Make sure the @v0.7.0 matches the current version of the action
       - uses: webfactory/ssh-agent@v0.7.0
         with:
-          ssh-private-key: ${{ secrets.PAT_TOKEN }}
+          ssh-private-key: ${{ secrets.PAT_TOKEN }} -C "git@github.com:fredcfreitas/contacts-python"
 #          github_token: ${{ secrets.PAT_TOKEN }}
 
       - name: Creating the Jupyter Notebook
         run: |
           py2nb src/contact_probability.py
-      - name: Commiting generated files # 
+      - name: Committing generated files # 
         run: |
           git config --local user.email "github-actions[bot]@users.noreply.github.com"
           git config --local user.name "github-actions[bot]"
@@ -69,9 +70,8 @@ jobs:
         uses: ad-m/github-push-action@master
         with:
           ssh: true
-          ssh-private-key: ${{ secrets.PAT_TOKEN }}
-#          github_token: ${{ secrets.GITHUB_TOKEN }}
+          ssh-private-key: ${{ secrets.PAT_TOKEN }} -C "git@github.com:fredcfreitas/contacts-python"
           ssh_key: ${{ secrets.PAT_TOKEN }}
-          github_token: ${{ secrets.PAT_TOKEN }}
+#          github_token: ${{ secrets.PAT_TOKEN }}
           branch: ${{ github.ref }}
           force: true

README.md

@@ -1,7 +1,10 @@
 # contacts-python  
 [![Test contact_probability.py](https://github.com/fredcfreitas/contacts-python/actions/workflows/test_contact_probability.yml/badge.svg?branch=release&event=push)](https://github.com/fredcfreitas/contacts-python/actions/workflows/test_contact_probability.yml)  
 Created by Frederico Campos Freitas  
+
 This is a set of scripts to evaluate the number of contacts and/or contact probability in a GROMACS-SMOG simulation. Some features were implemented to fit our needs, so feel free to make any changes in order to tailor the scripts to your needs.  
+
 It relies on mdtraj and numpy libraries, so take a look at the requirements.  
+
 You may find some useful information in the scripts' comments.  
 Contact us if you need some help: fredcfreitas(at)gmail.com  

src/contact_probability.ipynb

@@ -15,7 +15,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#!/usr/bin/env python3\n",
     "# usage: python3 contact_probability.py #MODEL(AA or CA) #PDB_BASE #XTC_FILE \\\n",
     "# #file.cont_FILE #OUTPUT\n",
     "#\n",
@@ -231,7 +230,25 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.0"
+  }
+ },
  "nbformat": 4,
- "nbformat_minor": 5
+ "nbformat_minor": 2
 }

src/contacts_chunk.py

@@ -36,15 +36,15 @@ def evaluate_r_initial(contacts, model="AA", precision=np.double):
     """
     contacts = np.asarray(contacts, dtype=precision)
     if model == "CA":
-        r_initial = np.power(np.divide(np.multiply(contacts[:, 4], 1.2), \
-                                                   contacts[:, 3]), \
+        r_initial = np.power(np.divide(np.multiply(contacts[:, -1], 1.2), \
+                                                   contacts[:, -2]), \
                              np.divide(1, 2))
     elif model == "AA":
-        r_initial = np.power(np.divide(np.multiply(contacts[:, 4], 2), \
-                                                   contacts[:, 3]),\
+        r_initial = np.power(np.divide(np.multiply(contacts[:, -1], 2), \
+                                                   contacts[:, -2]),\
                              np.divide(1, 6))
     else:
-        print("You have not provided an unimplemented model.")
+        print("You have not provided a model.")
         sys.exit()
     return r_initial