Add python examples to CI (#1183)

* Add some log messages in python examples
* Add python examples to CI

.travis.yml

@@ -143,6 +143,8 @@ install:
       curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
       python$PY get-pip.py
       pip$PY --version
+      pip$PY install pandas
+      pip$PY install seaborn
     fi
   - if [[ ( "$WITH_PYTHON" == "true" ) && ( "$TRAVIS_OS_NAME" == "osx" ) ]]; then pip$PY install numpy; fi
   - |

python/example/network_ring.py

-import sys
 import arbor
 import pandas, seaborn
 from math import sqrt
@@ -109,9 +108,6 @@ print(f'{recipe}')
 
 meters.checkpoint('recipe-create', context)
 
-decomp = arbor.partition_load_balance(recipe, context)
-print(f'{decomp}')
-
 hint = arbor.partition_hint()
 hint.prefer_gpu = True
 hint.gpu_group_size = 1000
@@ -148,8 +144,12 @@ for sp in spike_recorder.spikes:
     print(' ', sp)
 
 # Plot the recorded voltages over time.
-df = pandas.DataFrame()
+print("Plotting results ...")
+df_list = []
 for gid in range(ncells):
-    for s in samplers[gid].samples(arbor.cell_member(gid,0)):
-        df=df.append({'t/ms': s.time, 'U/mV': s.value, 'Cell': f"cell {gid}"}, ignore_index=True)
+    times = [s.time  for s in samplers[gid].samples(arbor.cell_member(gid,0))]
+    volts = [s.value for s in samplers[gid].samples(arbor.cell_member(gid,0))]
+    df_list.append(pandas.DataFrame({'t/ms': times, 'U/mV': volts, 'Cell': f"cell {gid}"}))
+
+df = pandas.concat(df_list)
 seaborn.relplot(data=df, kind="line", x="t/ms", y="U/mV",hue="Cell").savefig('network_ring_result.svg')

python/example/single_cell_model.py

 import arbor
+import pandas, seaborn # You may have to pip install these.
 
 # (1) Create a morphology with a single (cylindrical) segment of length=diameter=6 μm
 tree = arbor.segment_tree()
@@ -33,7 +34,7 @@ else:
     print('no spikes')
 
 # (8) Plot the recorded voltages over time.
-import pandas, seaborn # You may have to pip install these.
+print("Plotting results ...")
 seaborn.set_theme() # Apply some styling to the plot
 df = pandas.DataFrame({'t/ms': m.traces[0].time, 'U/mV': m.traces[0].value})
 seaborn.relplot(data=df, kind="line", x="t/ms", y="U/mV").savefig('single_cell_model_result.svg')

python/example/single_cell_multi_branch.py

@@ -91,6 +91,7 @@ m.probe('voltage', '"dtips"',  10000) # at the tips of the dendrites.
 # Run simulation for 100 ms of simulated activity.
 tfinal=100
 m.run(tfinal)
+print("Simulation done.")
 
 # Print spike times.
 if len(m.spikes)>0:
@@ -101,8 +102,9 @@ else:
     print('no spikes')
 
 # Plot the recorded voltages over time.
+print("Plotting results...")
 df = pandas.DataFrame()
 for t in m.traces:
-    df=df.append( pandas.DataFrame({'t/ms': t.time, 'U/mV': t.value, 'Location': t.location, "Variable": t.variable}) )
+    df=df.append(pandas.DataFrame({'t/ms': t.time, 'U/mV': t.value, 'Location': t.location, "Variable": t.variable}) )
 
 seaborn.relplot(data=df, kind="line", x="t/ms", y="U/mV",hue="Location",col="Variable").savefig('single_cell_multi_branch_result.svg')

python/example/single_cell_swc.py

@@ -12,9 +12,16 @@ import arbor
 from arbor import mechanism as mech
 from arbor import location as loc
 import pandas, seaborn
+import sys
 
 # Load a cell morphology from an swc file.
-tree = arbor.load_swc('../../test/unit/swc/pyramidal.swc')
+# Example present here: ../../test/unit/swc/pyramidal.swc
+if len(sys.argv) < 2:
+    print("No SWC file passed to the program")
+    sys.exit(0)
+
+filename = sys.argv[1]
+tree = arbor.load_swc(filename)
 
 # Define the regions and locsets in the model.
 defs = {'soma': '(tag 1)',  # soma has tag 1 in swc files.
@@ -45,7 +52,7 @@ cell.paint('"dend"', rL=500)
 cell.place('"stim_site"', arbor.iclamp(3, 1, current=2))
 cell.place('"stim_site"', arbor.iclamp(8, 1, current=4))
 # Detect spikes at the soma with a voltage threshold of -10 mV.
-cell.place('"root"', arbor.spike_detector(-10))
+cell.place('"axon_end"', arbor.spike_detector(-10))
 
 # Have one compartment between each sample point.
 cell.compartments_on_segments()
@@ -62,6 +69,7 @@ m.probe('voltage', where='"axon_end"', frequency=50000)
 # Simulate the cell for 15 ms.
 tfinal=15
 m.run(tfinal)
+print("Simulation done.")
 
 # Print spike times.
 if len(m.spikes)>0:
@@ -72,8 +80,11 @@ else:
     print('no spikes')
 
 # Plot the recorded voltages over time.
-df = pandas.DataFrame()
+print("Plotting results ...")
+df_list = []
 for t in m.traces:
-    df=df.append( pandas.DataFrame({'t/ms': t.time, 'U/mV': t.value, 'Location': t.location, "Variable": t.variable}) )
+    df_list.append(pandas.DataFrame({'t/ms': t.time, 'U/mV': t.value, 'Location': t.location, "Variable": t.variable}))
+
+df = pandas.concat(df_list)
 
 seaborn.relplot(data=df, kind="line", x="t/ms", y="U/mV",hue="Location",col="Variable").savefig('single_cell_swc.svg')

scripts/travis/build.sh

@@ -107,9 +107,14 @@ fi
 
 if [[ "${WITH_PYTHON}" == "true" ]]; then
     progress "Building python module"
-    make pyarb -j4                                                           || error "building pyarb"
+    make pyarb -j4                                                                              || error "building pyarb"
     progress "Python unit tests"
-    python$PY $python_path/test/unit/runner.py -v2                           || error "running python unit tests (serial)"
+    python$PY $python_path/test/unit/runner.py -v2                                              || error "running python unit tests (serial)"
+    progress "Python examples"
+    python$PY $python_path/example/network_ring.py                                              || error "running python network_ring example"
+    python$PY $python_path/example/single_cell_model.py                                         || error "running python single_cell_model example"
+    python$PY $python_path/example/single_cell_multi_branch.py                                  || error "running python single_cell_multi_branch example"
+    python$PY $python_path/example/single_cell_swc.py  $base_path/test/unit/swc/pyramidal.swc   || error "running python single_cell_swc example"
     if [[ "${WITH_DISTRIBUTED}" = "mpi" ]]; then
         if [[ "$TRAVIS_OS_NAME" = "osx" ]]; then
             progress "Python distributed unit tests (MPI)"