Add util functions to list recordables in a NeuroML model

.github/workflows/ci.yml

@@ -23,12 +23,13 @@ jobs:
     - name: Install package
       run: |
         python -m pip install --upgrade pip
-        pip install flake8
+        pip install flake8 pytest
         pip install .
-    - name: Lint with flake8
-      run: |
-        flake8 . --count --exit-zero --show-source --max-line-length=127 --statistics
     - name: Run tests
       run: |
+        pytest .
         pynml -h
         ./test-ghactions.sh
+    - name: Lint with flake8
+      run: |
+        flake8 . --count --exit-zero --show-source --max-line-length=127 --statistics

examples/Vm_plot.py

@@ -7,12 +7,16 @@
 '''
 
 import sys
+import logging
 
 from pyneuroml import pynml
 from pyneuroml.lems.LEMSSimulation import LEMSSimulation
 import neuroml as nml
 
 
+logger = logging.getLogger(__name__)
+
+
 def generate_Vm_vs_time_plot(nml2_file,
                              cell_id,
                              inj_amp_nA=80,
@@ -26,8 +30,8 @@ def generate_Vm_vs_time_plot(nml2_file,
                              include_included=True):
 
     ref = "Test"
-    pynml.print_comment("Generating Vm(mV) vs Time(ms) plot for cell %s in %s using %s (Inj %snA / %sms dur after %sms delay)" %
-                        (cell_id, nml2_file, simulator, inj_amp_nA, inj_dur_ms, delay_ms))
+    logger.info("Generating Vm(mV) vs Time(ms) plot for cell %s in %s using %s (Inj %snA / %sms dur after %sms delay)" %
+                (cell_id, nml2_file, simulator, inj_amp_nA, inj_dur_ms, delay_ms))
 
     sim_id = 'Vm_%s' % ref
     duration = sim_dur_ms

pyneuroml/__init__.py

@@ -7,9 +7,9 @@
 # Define a logger for the package
 logging.basicConfig(format="pyNeuroML >>> %(asctime)s - %(name)s - %(levelname)s - %(message)s", level=logging.WARN)
 logger = logging.getLogger(__name__)
-logger.setLevel(logging.WARN)
+logger.setLevel(logging.INFO)
 ch = logging.StreamHandler()
-ch.setLevel(logging.DEBUG)
+ch.setLevel(logging.INFO)
 formatter = logging.Formatter('pyNeuroML >>> %(name)s - %(levelname)s - %(message)s')
 ch.setFormatter(formatter)
 logger.addHandler(ch)

pyneuroml/analysis/ChannelDensityPlot.py

@@ -6,14 +6,18 @@
 
 import os
 import math
+import logging
 
 import pprint
 
-from pyneuroml.pynml import get_value_in_si, print_comment, read_neuroml2_file
+from pyneuroml.pynml import get_value_in_si, read_neuroml2_file
 from pyneuroml.analysis.NML2ChannelAnalysis import get_ion_color
 from neuroml import Cell, Cell2CaPools
 
 
+logger = logging.getLogger(__name__)
+
+
 pp = pprint.PrettyPrinter(depth=6)
 
 height = 18
@@ -105,7 +109,7 @@ def generate_channel_density_plots(nml2_file, text_densities=False, passives_ere
     for cell in cell_elements:
         info = {}
         all_info[cell.id] = info
-        print_comment("Extracting channel density info from %s" % cell.id)
+        logger.info("Extracting channel density info from %s" % cell.id)
         sb = ''
         ions = {}
         maxes = {}
@@ -131,7 +135,7 @@ def generate_channel_density_plots(nml2_file, text_densities=False, passives_ere
 
         for cd in cds:
             dens_si = get_value_in_si(cd.cond_density)
-            print_comment("cd: %s, ion_channel: %s, ion: %s, density: %s (SI: %s)" % (cd.id, cd.ion_channel, cd.ion, cd.cond_density, dens_si))
+            logger.info("cd: %s, ion_channel: %s, ion: %s, density: %s (SI: %s)" % (cd.id, cd.ion_channel, cd.ion, cd.cond_density, dens_si))
 
             ions[cd.ion_channel] = cd.ion
             erev_V = get_value_in_si(cd.erev) if hasattr(cd, 'erev') else None
@@ -217,7 +221,7 @@ def generate_channel_density_plots(nml2_file, text_densities=False, passives_ere
         sf = open(svg_file, 'w')
         sf.write(sb)
         sf.close()
-        print_comment("Written to %s" % os.path.abspath(svg_file))
+        logger.info("Written to %s" % os.path.abspath(svg_file))
 
         pp.pprint(all_info)
 

pyneuroml/analysis/ChannelHelper.py

@@ -1,5 +1,9 @@
 from math import exp
-from pyneuroml.pynml import get_value_in_si, print_comment
+from pyneuroml.pynml import get_value_in_si
+import logging
+
+
+logger = logging.getLogger(__name__)
 
 
 def evaluate_HHExpLinearRate(rate, midpoint, scale, v):
@@ -12,13 +16,13 @@ def evaluate_HHExpLinearRate(rate, midpoint, scale, v):
     scale_si = get_value_in_si(scale)
     v_si = get_value_in_si(v)
 
-    print_comment('Evaluating: rate * ((v - (midpoint))/scale) / ( 1 - exp(-1 * (v - (midpoint)) / scale)) ')
-    print_comment('            %s * ((v - (%s))/%s) / ( 1 - exp(-1 * (v - (%s)) / %s))  for v = %s' % (rate, midpoint, scale, midpoint, scale, v))
-    print_comment('            %s * ((%s - (%s))/%s) / ( 1 - exp(-1 * (%s - (%s)) / %s)) ' % (rate_si, v_si, midpoint_si, scale_si, v_si, midpoint_si, scale_si))
-    print_comment('            <... type="HHExpLinearRate" rate="%s" midpoint="%s" scale="%s" />' % (rate, midpoint, scale))
+    logger.info('Evaluating: rate * ((v - (midpoint))/scale) / ( 1 - exp(-1 * (v - (midpoint)) / scale)) ')
+    logger.info('            %s * ((v - (%s))/%s) / ( 1 - exp(-1 * (v - (%s)) / %s))  for v = %s' % (rate, midpoint, scale, midpoint, scale, v))
+    logger.info('            %s * ((%s - (%s))/%s) / ( 1 - exp(-1 * (%s - (%s)) / %s)) ' % (rate_si, v_si, midpoint_si, scale_si, v_si, midpoint_si, scale_si))
+    logger.info('            <... type="HHExpLinearRate" rate="%s" midpoint="%s" scale="%s" />' % (rate, midpoint, scale))
     r = rate_si * ((v_si - (midpoint_si)) / scale_si) / (1 - exp(-(v_si - (midpoint_si)) / scale_si))
-    print_comment('   = %s per_s' % r)
-    print_comment('   = %s per_ms' % (r / 1000.))
+    logger.info('   = %s per_s' % r)
+    logger.info('   = %s per_ms' % (r / 1000.))
 
 
 def evaluate_HHSigmoidRate(rate, midpoint, scale, v):
@@ -31,13 +35,13 @@ def evaluate_HHSigmoidRate(rate, midpoint, scale, v):
     scale_si = get_value_in_si(scale)
     v_si = get_value_in_si(v)
 
-    print_comment('Evaluating: rate / (1 + exp(-1 * (v - midpoint)/scale))  ')
-    print_comment('            %s / ( 1 + exp(-1 * (v - (%s)) / %s))  for v = %s' % (rate, midpoint, scale, v))
-    print_comment('            %s / ( 1 + exp(-1 * (%s - (%s)) / %s)) ' % (rate_si, v_si, midpoint_si, scale_si))
-    print_comment('            <... type="HHSigmoidRate" rate="%s" midpoint="%s" scale="%s" />' % (rate, midpoint, scale))
+    logger.info('Evaluating: rate / (1 + exp(-1 * (v - midpoint)/scale))  ')
+    logger.info('            %s / ( 1 + exp(-1 * (v - (%s)) / %s))  for v = %s' % (rate, midpoint, scale, v))
+    logger.info('            %s / ( 1 + exp(-1 * (%s - (%s)) / %s)) ' % (rate_si, v_si, midpoint_si, scale_si))
+    logger.info('            <... type="HHSigmoidRate" rate="%s" midpoint="%s" scale="%s" />' % (rate, midpoint, scale))
     r = rate_si / (1 + exp(-1 * (v_si - midpoint_si) / scale_si))
-    print_comment('   = %s per_s' % r)
-    print_comment('   = %s per_ms' % (r / 1000.))
+    logger.info('   = %s per_s' % r)
+    logger.info('   = %s per_ms' % (r / 1000.))
 
 
 def evaluate_HHExpRate(rate, midpoint, scale, v):
@@ -50,10 +54,10 @@ def evaluate_HHExpRate(rate, midpoint, scale, v):
     scale_si = get_value_in_si(scale)
     v_si = get_value_in_si(v)
 
-    print_comment('Evaluating: rate * exp( (v - midpoint) / scale) ')
-    print_comment('            %s * exp( (v - (%s)) / %s)  for v = %s' % (rate, midpoint, scale, v))
-    print_comment('            %s * exp( (%s - (%s)) / %s) ' % (rate_si, v_si, midpoint_si, scale_si))
-    print_comment('            <... type="HHExpRate" rate="%s" midpoint="%s" scale="%s" />' % (rate, midpoint, scale))
+    logger.info('Evaluating: rate * exp( (v - midpoint) / scale) ')
+    logger.info('            %s * exp( (v - (%s)) / %s)  for v = %s' % (rate, midpoint, scale, v))
+    logger.info('            %s * exp( (%s - (%s)) / %s) ' % (rate_si, v_si, midpoint_si, scale_si))
+    logger.info('            <... type="HHExpRate" rate="%s" midpoint="%s" scale="%s" />' % (rate, midpoint, scale))
     r = rate_si * exp((v_si - midpoint_si) / scale_si)
-    print_comment('   = %s per_s' % r)
-    print_comment('   = %s per_ms' % (r / 1000.))
+    logger.info('   = %s per_s' % r)
+    logger.info('   = %s per_ms' % (r / 1000.))

pyneuroml/analysis/NML2ChannelAnalysis.py

@@ -15,8 +15,7 @@
 import airspeed
 import matplotlib.pyplot as plt
 
-from pyneuroml.pynml import run_lems_with_jneuroml, print_comment, \
-    read_neuroml2_file
+from pyneuroml.pynml import run_lems_with_jneuroml, read_neuroml2_file
 
 
 logger = logging.getLogger(__name__)
@@ -256,9 +255,9 @@ def generate_lems_channel_analyser(channel_file, channel, min_target_voltage,
                                    gates, temperature, ca_conc, iv_curve, scale_dt=1,
                                    dat_suffix='', verbose=True):
 
-    print_comment(("Generating LEMS file to investigate %s in %s, %smV->%smV, "
-                   "%sdegC") % (channel, channel_file, min_target_voltage,
-                                max_target_voltage, temperature))
+    logger.info(("Generating LEMS file to investigate %s in %s, %smV->%smV, "
+                 "%sdegC") % (channel, channel_file, min_target_voltage,
+                              max_target_voltage, temperature))
 
     target_voltages = []
     v = min_target_voltage
@@ -624,7 +623,7 @@ def make_html_file(info):
     lf = open(new_html_file, 'w')
     lf.write(merged)
     lf.close()
-    print_comment('Written HTML info to: %s' % new_html_file)
+    logger.info('Written HTML info to: %s' % new_html_file)
 
 
 def make_md_file(info):
@@ -634,7 +633,7 @@ def make_md_file(info):
     lf = open(new_md_file, 'w')
     lf.write(merged)
     lf.close()
-    print_comment('Written Markdown info to: %s' % new_md_file)
+    logger.info('Written Markdown info to: %s' % new_md_file)
 
 
 def build_namespace(a=None, **kwargs):
@@ -707,7 +706,7 @@ def run(a=None, **kwargs):
                 other_chan_files.append(channel_file)
 
     channel_files = na_chan_files + k_chan_files + ca_chan_files + other_chan_files
-    print_comment("\nAnalysing channels from files: %s\n" % channel_files)
+    logger.info("\nAnalysing channels from files: %s\n" % channel_files)
 
     for channel_file in channel_files:
         channels_info = process_channel_file(channel_file, a)

pyneuroml/analysis/__init__.py

@@ -81,8 +81,8 @@ def generate_current_vs_frequency_curve(
 
         stim_info = '(%snA->%snA; %s steps of %snA; %sms)' % (start_amp_nA, end_amp_nA, len(stims), step_nA, total_duration)
 
-    pynml.print_comment("Generating an IF curve for cell %s in %s using %s %s" %
-                        (cell_id, nml2_file, simulator, stim_info))
+    logger.info("Generating an IF curve for cell %s in %s using %s %s" %
+                      (cell_id, nml2_file, simulator, stim_info))
 
     number_cells = len(stims)
     pop = nml.Population(id="population_of_%s" % cell_id,
@@ -134,7 +134,7 @@ def generate_current_vs_frequency_curve(
 
     lems_file_name = ls.save_to_file()
 
-    pynml.print_comment("Written LEMS file %s (%s)" % (lems_file_name, os.path.abspath(lems_file_name)))
+    logger.info("Written LEMS file %s (%s)" % (lems_file_name, os.path.abspath(lems_file_name)))
 
     if simulator == "jNeuroML":
         results = pynml.run_lems_with_jneuroml(lems_file_name,
@@ -161,7 +161,7 @@ def generate_current_vs_frequency_curve(
     else:
         raise Exception("Sorry, cannot yet run current vs frequency analysis using simulator %s" % simulator)
 
-    pynml.print_comment("Completed run in simulator %s (results: %s)" % (simulator, results.keys()))
+    logger.info("Completed run in simulator %s (results: %s)" % (simulator, results.keys()))
 
     # print(results.keys())
     times_results = []
@@ -346,7 +346,7 @@ def analyse_spiketime_vs_dt(nml2_file,
         if simulator == 'jNeuroML_NEURON':
             results = pynml.run_lems_with_jneuroml_neuron(lems_file_name, nogui=True, load_saved_data=True, plot=False, verbose=verbose)
 
-        pynml.print_comment("Results reloaded: %s" % results.keys())
+        logger.info("Results reloaded: %s" % results.keys())
         all_results[dt] = results
 
     xs = []

pyneuroml/lems/LEMSSimulation.py

@@ -5,15 +5,17 @@
 
 import airspeed
 import os.path
+import logging
 
 from pyneuroml import __version__ as pynml_ver
 from neuroml import __version__ as libnml_ver
 from pyneuroml.pynml import read_neuroml2_file
 from pyneuroml.pynml import read_lems_file
-from pyneuroml.pynml import print_comment
 from pyneuroml.pynml import get_next_hex_color
 import random
 
+logger = logging.getLogger(__name__)
+
 
 class LEMSSimulation():
 
@@ -70,7 +72,7 @@ def set_report_file(self, report_file_name):
     def include_neuroml2_file(self, nml2_file_name, include_included=True, relative_to_dir='.'):
         full_path = os.path.abspath(relative_to_dir + '/' + nml2_file_name)
         base_path = os.path.dirname(full_path)
-        # print_comment_v("Including in generated LEMS file: %s (%s)"%(nml2_file_name, full_path))
+        # logger.info_v("Including in generated LEMS file: %s (%s)"%(nml2_file_name, full_path))
         if nml2_file_name not in self.lems_info['include_files']:
             self.lems_info['include_files'].append(nml2_file_name)
 
@@ -165,7 +167,7 @@ def save_to_file(self, file_name=None):
         lems_file = open(file_name, 'w')
         lems_file.write(self.to_xml())
         lems_file.close()
-        print_comment("Written LEMS Simulation %s to file: %s" % (self.lems_info['sim_id'], file_name))
+        logger.info("Written LEMS Simulation %s to file: %s" % (self.lems_info['sim_id'], file_name))
 
         return file_name
 

pyneuroml/lems/__init__.py

@@ -4,7 +4,7 @@
 import shutil
 import os
 import logging
-from pyneuroml.pynml import read_neuroml2_file, get_next_hex_color, print_comment
+from pyneuroml.pynml import read_neuroml2_file, get_next_hex_color
 import random
 import neuroml
 
@@ -47,7 +47,7 @@ def generate_lems_file_for_neuroml(sim_id,
 
     file_name_full = '%s/%s' % (target_dir, lems_file_name)
 
-    print_comment('Creating LEMS file at: %s for NeuroML 2 file: %s (copy: %s)' % (file_name_full, neuroml_file, copy_neuroml))
+    logger.info('Creating LEMS file at: %s for NeuroML 2 file: %s (copy: %s)' % (file_name_full, neuroml_file, copy_neuroml))
 
     ls = LEMSSimulation(sim_id, duration, dt, target, simulation_seed=simulation_seed)
 
@@ -95,15 +95,15 @@ def generate_lems_file_for_neuroml(sim_id,
             if os.path.isfile(include.href):
                 incl_curr = include.href
 
-            print_comment(' - Including %s (located at %s; nml dir: %s), copying to %s' % (include.href, incl_curr, nml_dir, target_dir))
+            logger.info(' - Including %s (located at %s; nml dir: %s), copying to %s' % (include.href, incl_curr, nml_dir, target_dir))
 
             '''
             if not os.path.isfile("%s/%s" % (target_dir, os.path.basename(incl_curr))) and \
                not os.path.isfile("%s/%s" % (target_dir, incl_curr)) and \
                not os.path.isfile(incl_curr):
                 shutil.copy(incl_curr, target_dir)
             else:
-                print_comment("No need to copy...")'''
+                logger.info("No need to copy...")'''
 
             f1 = "%s/%s" % (target_dir, os.path.basename(incl_curr))
             f2 = "%s/%s" % (target_dir, incl_curr)
@@ -121,7 +121,7 @@ def generate_lems_file_for_neuroml(sim_id,
             if sub_doc.__class__ == neuroml.nml.nml.NeuroMLDocument:
                 for include in sub_doc.includes:
                     incl_curr = '%s/%s' % (sub_dir, include.href)
-                    print_comment(' -- Including %s located at %s' % (include.href, incl_curr))
+                    logger.info(' -- Including %s located at %s' % (include.href, incl_curr))
 
                     if not os.path.isfile("%s/%s" % (target_dir, os.path.basename(incl_curr))) and \
                        not os.path.isfile("%s/%s" % (target_dir, incl_curr)):
@@ -173,7 +173,7 @@ def generate_lems_file_for_neuroml(sim_id,
                     size = len(population.instances)
 
                 if gen_plots_for_all_v or population.id in gen_plots_for_only_populations:
-                    print_comment('Generating %i plots for %s in population %s' % (size, component, population.id))
+                    logger.info('Generating %i plots for %s in population %s' % (size, component, population.id))
 
                     disp0 = 'DispPop__%s' % population.id
                     ls.create_display(disp0, "Membrane potentials of cells in %s" % population.id, "-90", "50")
@@ -189,7 +189,7 @@ def generate_lems_file_for_neuroml(sim_id,
                             ls.add_line_to_display(disp0, "%s[%i]: v" % (population.id, i), quantity, "1mV", get_next_hex_color(my_random))
 
                 if gen_saves_for_all_v or population.id in gen_saves_for_only_populations:
-                    print_comment('Saving %i values of %s for %s in population %s' % (size, variable, component, population.id))
+                    logger.info('Saving %i values of %s for %s in population %s' % (size, variable, component, population.id))
 
                     of0 = 'Volts_file__%s' % population.id
                     ls.create_output_file(of0, "%s.%s.%s.dat" % (sim_id, population.id, variable))
@@ -206,7 +206,7 @@ def generate_lems_file_for_neuroml(sim_id,
                             quantities_saved.append(quantity)
 
                 if gen_spike_saves_for_all_somas or population.id in gen_spike_saves_for_only_populations:
-                    print_comment('Saving spikes in %i somas for %s in population %s' % (size, component, population.id))
+                    logger.info('Saving spikes in %i somas for %s in population %s' % (size, component, population.id))
 
                     eof0 = 'Spikes_file__%s' % population.id
                     ls.create_event_output_file(eof0, "%s.%s.spikes" % (sim_id, population.id), format=spike_time_format)