use Batch.hoc in 136803

.github/workflows/nrn-modeldb-ci.yaml

@@ -63,6 +63,22 @@ jobs:
       with:
         repository: ${{ inputs.repo || github.repository }}
 
+    - name: parse versions from PR
+      run: |
+        set -x
+        parse_neuron_v1=$(gh pr view $PR_URL --json body -q '.body | capture("(?<nrnV1>NEURON_V1=[^\r\n]*)")')
+        if [ ! -z "$parse_neuron_v1" ]; then
+          echo "$(echo $parse_neuron_v1 | jq -r .nrnV1)" >> $GITHUB_ENV
+        fi
+        parse_neuron_v2=$(gh pr view $PR_URL --json body -q '.body | capture("(?<nrnV2>NEURON_V2=[^\r\n]*)")')
+        if [ ! -z "$parse_neuron_v2" ]; then
+          echo "$(echo $parse_neuron_v2 | jq -r .nrnV2)" >> $GITHUB_ENV
+        fi
+      if: github.event_name == 'pull_request' && inputs.repo == ''
+      env:
+        PR_URL: ${{ github.event.pull_request.html_url }}
+        GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+
     - name: check for Azure drop NEURON_V1 -> ${{ env.NEURON_V1 }}
       if: startsWith(env.NEURON_V1, 'https://dev.azure.com/neuronsimulator/')
       run: |
@@ -152,6 +168,7 @@ jobs:
 
     - name: Run Models with NEURON V1 -> ${{ env.NEURON_V1 }}
       run: |
+        set -x
         # Install NEURON V1
         if [[ -d "${DROP_DIR_V1}" ]]; then
           python -m pip install --user --find-links ${DROP_DIR_V1} neuron-nightly

modeldb/modeldb-run.yaml

@@ -1584,9 +1584,12 @@
     curate_patterns:
     - pattern: '^(setup|run|finish)time\s+=\s+([0-9.]+)$'
       repl: '\1time = %masked%'
+    script:
+    - sed -i '/wopen/d' Batch.hoc
+    - if $(nrniv --version | grep "VERSION 8" -q) ; then sed -i '/runit/a  nrnunit_use_legacy(1)' Batch.hoc; fi
+    - cp Batch.hoc mosinit.hoc
     run:
     - run()
-    - verify_graph_()
     model_dir: mod_files
 143719:
     script:

modeldb/modelrun.py

@@ -455,7 +455,8 @@ def _run_models(self, model_runs):
         pool = multiprocessing.Pool()
 
         processed_models = pool.imap_unordered(run_model, model_runs)
-        for model in ProgressBar.iter(processed_models, self.nof_models):
+        for i, model in enumerate(ProgressBar.iter(processed_models, self.nof_models)):
+            self.logger.info(f"[{i} / {self.nof_models}] Running model '{model.id}'")
             self.run_logs[model.id] = {}
             self.run_logs[model.id]["logs"] = model.logs
             if self._gout: