Add openbis connection

aiidalab_widgets_base/elns.py

@@ -141,10 +141,39 @@ def _observe_node(self, _=None):
                 )
                 info = q.all(flat=True)[0]
             except IndexError:
-                info = {}
+                structures, _ = self.get_all_structures_and_geoopts(self.node)
+                info = structures[-1].base.extras.all["eln"]
 
         self.eln.set_sample_config(**info)
 
+    def get_all_structures_and_geoopts(self, node):
+        """Get all atomistic models that led to the one used in the simulation"""
+        current_node = node
+        all_structures = []
+        all_geoopts = []
+
+        while current_node is not None:
+            if isinstance(current_node, orm.StructureData):
+                all_structures.append(current_node)
+                current_node = current_node.creator
+
+            elif isinstance(current_node, orm.CalcJobNode):
+                current_node = current_node.caller
+
+            elif isinstance(current_node, orm.CalcFunctionNode):
+                current_node = current_node.inputs.source_structure
+
+            elif isinstance(current_node, orm.WorkChainNode):
+                if "GeoOpt" in current_node.label:
+                    all_geoopts.append(current_node)
+                    current_node = current_node.inputs.structure
+                elif "ORBITALS" in current_node.label or "STM" in current_node.label:
+                    current_node = current_node.inputs.structure
+                else:
+                    current_node = current_node.caller
+
+        return all_structures, all_geoopts
+
     def send_to_eln(self, _=None):
         if self.eln and self.eln.is_connected:
             self.message.value = f"\u29d7 Sending data to {self.eln.eln_instance}..."

aiidalab_widgets_base/nodes.py

@@ -36,6 +36,13 @@
         "parameter_name": "structure_uuid",
         "description": "Optimize atomic positions and/or unit cell employing Quantum ESPRESSO. Quantum ESPRESSO is preferable for small structures with no cell dimensions larger than 15 Å. Additionally, you can choose to compute electronic properties of the material such as band structure and density of states.",
     },
+    {
+        "name": "surfaces",
+        "calculation_type": "geo_opt",
+        "notebook": "submit_geometry_optimization.ipynb",
+        "parameter_name": "structure_uuid",
+        "description": "Optimize atomic positions and/or unit cell employing CP2K. CP2K is preferable for large structures with cell dimensions larger than 15 Å.",
+    },
     {
         "name": "aiidalab-lsmo",
         "calculation_type": "geo_opt",

notebooks/eln_import.ipynb

@@ -45,7 +45,13 @@
     "from aiidalab_widgets_base import AiidaNodeViewWidget, OpenAiidaNodeInAppWidget, ElnImportWidget\n",
     "import urllib.parse as urlparse\n",
     "from aiidalab_widgets_base import viewer\n",
-    "from traitlets import dlink"
+    "from traitlets import dlink\n",
+    "\n",
+    "import json\n",
+    "from pyld import jsonld\n",
+    "\n",
+    "import sys\n",
+    "import os"
    ]
   },
   {
@@ -55,29 +61,82 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "#sys.path.append(os.path.dirname(\"/home/jovyan/aiida-openbis/Notebooks/Metadata_Schemas_LinkML/\"))\n",
+    "# from materialMLinfo import Molecule, slots\n",
+    "# def extract_molecule_data_with_linkml(molecule_jsonld):\n",
+    "#     expanded = jsonld.expand(molecule_jsonld)\n",
+    "#     molecule_data = {}\n",
+    "#     for obj in jsonld_object:\n",
+    "#         if '@type' in obj and str(Molecule.class_class_uri) in obj['@type']:\n",
+    "#             molecule_data['name'] = obj.get(str(slots.name.uri), [None])[0]['@value']\n",
+    "#             molecule_data['smiles'] = obj.get(str(slots.smiles.uri), [None])[0]['@value']\n",
+    "#     return molecule_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Extract data from the converted JSON-LD\n",
+    "def extract_molecule_data(jsonld_object, context):\n",
+    "    expanded = jsonld.expand(molecule_jsonld)\n",
+    "    compacted = jsonld.compact(expanded, context)\n",
+    "    return {\"name\": compacted[\"name\"], \"smiles\": compacted[\"smiles\"]}\n",
+    "\n",
+    "def read_json(filepath: str) -> dict:\n",
+    "    return json.load(open(filepath, \"r\"))\n",
+    "\n",
     "url = urlparse.urlsplit(jupyter_notebook_url)\n",
     "parsed_url = urlparse.parse_qs(url.query)\n",
     "params = {key:value[0] for key, value in parsed_url.items()}\n",
-    "eln_widget = ElnImportWidget(**params)"
+    "molecule_jsonld = json.loads(params[\"molecule_info\"])\n",
+    "\n",
+    "molecule_info_valid = False\n",
+    "\n",
+    "jsonld_context_filename = os.path.join(\n",
+    "    os.sep, \n",
+    "    \"home\", \n",
+    "    \"jovyan\", \n",
+    "    \"aiida-openbis\", \n",
+    "    \"Notebooks\", \n",
+    "    \"Metadata_Schemas_LinkML\",\n",
+    "    \"materialMLinfoContext.jsonld\"\n",
+    ")\n",
+    "\n",
+    "\n",
+    "aiida_context = read_json(jsonld_context_filename)\n",
+    "\n",
+    "try:\n",
+    "    molecule_data = extract_molecule_data(molecule_jsonld, aiida_context)\n",
+    "    params[\"molecule_info\"] = json.dumps(molecule_data)\n",
+    "    eln_widget = ElnImportWidget(path_to_root=\"../../\", **params)\n",
+    "    molecule_info_valid = True\n",
+    "except ValueError as e:\n",
+    "    print(e)\n",
+    "\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5",
+   "id": "6",
    "metadata": {},
    "outputs": [],
    "source": [
-    "object_displayed = AiidaNodeViewWidget()\n",
-    "open_in_app = OpenAiidaNodeInAppWidget()\n",
+    "if molecule_info_valid:\n",
+    "    object_displayed = AiidaNodeViewWidget()\n",
+    "    open_in_app = OpenAiidaNodeInAppWidget(path_to_root=\"../../\")\n",
     "\n",
-    "_ = dlink((eln_widget, 'node'), (object_displayed, 'node'))\n",
-    "_ = dlink((eln_widget, 'node'), (open_in_app, 'node'))"
+    "    _ = dlink((eln_widget, 'node'), (object_displayed, 'node'))\n",
+    "    _ = dlink((eln_widget, 'node'), (open_in_app, 'node'))"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "6",
+   "id": "7",
    "metadata": {},
    "source": [
     "## Selected object:"
@@ -86,17 +145,18 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7",
+   "id": "8",
    "metadata": {},
    "outputs": [],
    "source": [
-    "display(object_displayed)\n",
-    "display(eln_widget)"
+    "if molecule_info_valid:\n",
+    "    display(object_displayed)\n",
+    "    display(eln_widget)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "8",
+   "id": "9",
    "metadata": {},
    "source": [
     "## What's next?"
@@ -105,11 +165,12 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9",
+   "id": "10",
    "metadata": {},
    "outputs": [],
    "source": [
-    "display(open_in_app)"
+    "if molecule_info_valid:\n",
+    "    display(open_in_app)"
    ]
   }
  ],