Consolidate logging (and other minor tweaks)

.mypy.ini

@@ -0,0 +1,2 @@
+[mypy]
+ignore_missing_imports = True

examples/Vm_plot.py

@@ -12,8 +12,6 @@
 from pyneuroml.lems.LEMSSimulation import LEMSSimulation
 import neuroml as nml
 
-from pyneuroml.pynml import print_comment_v
-
 
 def generate_Vm_vs_time_plot(nml2_file,
                              cell_id,
@@ -28,8 +26,8 @@ def generate_Vm_vs_time_plot(nml2_file,
                              include_included=True):
 
     ref = "Test"
-    print_comment_v("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))
+    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))
 
     sim_id = 'Vm_%s' % ref
     duration = sim_dur_ms

pyneuroml/__init__.py

@@ -1,5 +1,15 @@
-
+import logging
 
 __version__ = '0.5.13'
 
 JNEUROML_VERSION = '0.11.0'
+
+# 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)
+ch = logging.StreamHandler()
+ch.setLevel(logging.DEBUG)
+formatter = logging.Formatter('pyNeuroML >>> %(name)s - %(levelname)s - %(message)s')
+ch.setFormatter(formatter)
+logger.addHandler(ch)

pyneuroml/analysis/ChannelDensityPlot.py

@@ -9,7 +9,7 @@
 
 import pprint
 
-from pyneuroml.pynml import get_value_in_si, print_comment_v, read_neuroml2_file
+from pyneuroml.pynml import get_value_in_si, print_comment, read_neuroml2_file
 from pyneuroml.analysis.NML2ChannelAnalysis import get_ion_color
 from neuroml import Cell, Cell2CaPools
 
@@ -105,7 +105,7 @@ 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_v("Extracting channel density info from %s" % cell.id)
+        print_comment("Extracting channel density info from %s" % cell.id)
         sb = ''
         ions = {}
         maxes = {}
@@ -131,7 +131,7 @@ 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_v("cd: %s, ion_channel: %s, ion: %s, density: %s (SI: %s)" % (cd.id, cd.ion_channel, cd.ion, cd.cond_density, dens_si))
+            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))
 
             ions[cd.ion_channel] = cd.ion
             erev_V = get_value_in_si(cd.erev) if hasattr(cd, 'erev') else None
@@ -217,7 +217,7 @@ def generate_channel_density_plots(nml2_file, text_densities=False, passives_ere
         sf = open(svg_file, 'w')
         sf.write(sb)
         sf.close()
-        print_comment_v("Written to %s" % os.path.abspath(svg_file))
+        print_comment("Written to %s" % os.path.abspath(svg_file))
 
         pp.pprint(all_info)
 

pyneuroml/analysis/ChannelHelper.py

@@ -1,59 +1,59 @@
 from math import exp
-from pyneuroml.pynml import get_value_in_si, print_comment_v
+from pyneuroml.pynml import get_value_in_si, print_comment
 
 
 def evaluate_HHExpLinearRate(rate, midpoint, scale, v):
     '''
-    Helper for putting values into HHExpLinearRate,
-    see also https://www.neuroml.org/NeuroML2CoreTypes/Channels.html#HHExpLinearRate
+    Helper for putting values into HHExpLinearRate, see also
+    https://docs.neuroml.org/Userdocs/Schemas/Channels.html#hhexplinearrate
     '''
     rate_si = get_value_in_si(rate)
     midpoint_si = get_value_in_si(midpoint)
     scale_si = get_value_in_si(scale)
     v_si = get_value_in_si(v)
 
-    print_comment_v('Evaluating: rate * ((v - (midpoint))/scale) / ( 1 - exp(-1 * (v - (midpoint)) / scale)) ')
-    print_comment_v('            %s * ((v - (%s))/%s) / ( 1 - exp(-1 * (v - (%s)) / %s))  for v = %s' % (rate, midpoint, scale, midpoint, scale, v))
-    print_comment_v('            %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_v('            <... type="HHExpLinearRate" rate="%s" midpoint="%s" scale="%s" />' % (rate, midpoint, scale))
+    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))
     r = rate_si * ((v_si - (midpoint_si)) / scale_si) / (1 - exp(-(v_si - (midpoint_si)) / scale_si))
-    print_comment_v('   = %s per_s' % r)
-    print_comment_v('   = %s per_ms' % (r / 1000.))
+    print_comment('   = %s per_s' % r)
+    print_comment('   = %s per_ms' % (r / 1000.))
 
 
 def evaluate_HHSigmoidRate(rate, midpoint, scale, v):
     '''
-    Helper for putting values into HHSigmoidRate,
-    see also https://www.neuroml.org/NeuroML2CoreTypes/Channels.html#HHSigmoidRate
+    Helper for putting values into HHSigmoidRate, see also
+    https://docs.neuroml.org/Userdocs/Schemas/Channels.html#hhsigmoidrate
     '''
     rate_si = get_value_in_si(rate)
     midpoint_si = get_value_in_si(midpoint)
     scale_si = get_value_in_si(scale)
     v_si = get_value_in_si(v)
 
-    print_comment_v('Evaluating: rate / (1 + exp(-1 * (v - midpoint)/scale))  ')
-    print_comment_v('            %s / ( 1 + exp(-1 * (v - (%s)) / %s))  for v = %s' % (rate, midpoint, scale, v))
-    print_comment_v('            %s / ( 1 + exp(-1 * (%s - (%s)) / %s)) ' % (rate_si, v_si, midpoint_si, scale_si))
-    print_comment_v('            <... type="HHSigmoidRate" rate="%s" midpoint="%s" scale="%s" />' % (rate, midpoint, scale))
+    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))
     r = rate_si / (1 + exp(-1 * (v_si - midpoint_si) / scale_si))
-    print_comment_v('   = %s per_s' % r)
-    print_comment_v('   = %s per_ms' % (r / 1000.))
+    print_comment('   = %s per_s' % r)
+    print_comment('   = %s per_ms' % (r / 1000.))
 
 
 def evaluate_HHExpRate(rate, midpoint, scale, v):
     '''
-        Helper for putting values into HHExpRate,
-        see also https://www.neuroml.org/NeuroML2CoreTypes/Channels.html#HHExpRate
+        Helper for putting values into HHExpRate, see also
+        https://docs.neuroml.org/Userdocs/Schemas/Channels.html#hhexprate
     '''
     rate_si = get_value_in_si(rate)
     midpoint_si = get_value_in_si(midpoint)
     scale_si = get_value_in_si(scale)
     v_si = get_value_in_si(v)
 
-    print_comment_v('Evaluating: rate * exp( (v - midpoint) / scale) ')
-    print_comment_v('            %s * exp( (v - (%s)) / %s)  for v = %s' % (rate, midpoint, scale, v))
-    print_comment_v('            %s * exp( (%s - (%s)) / %s) ' % (rate_si, v_si, midpoint_si, scale_si))
-    print_comment_v('            <... type="HHExpRate" rate="%s" midpoint="%s" scale="%s" />' % (rate, midpoint, scale))
+    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))
     r = rate_si * exp((v_si - midpoint_si) / scale_si)
-    print_comment_v('   = %s per_s' % r)
-    print_comment_v('   = %s per_ms' % (r / 1000.))
+    print_comment('   = %s per_s' % r)
+    print_comment('   = %s per_ms' % (r / 1000.))

pyneuroml/analysis/NML2ChannelAnalysis.py

@@ -10,13 +10,16 @@
 import argparse
 import pprint
 import re
+import logging
 
 import airspeed
 import matplotlib.pyplot as plt
 
 from pyneuroml.pynml import run_lems_with_jneuroml, print_comment, \
-    print_comment_v, read_neuroml2_file
+    read_neuroml2_file
 
+
+logger = logging.getLogger(__name__)
 pp = pprint.PrettyPrinter(depth=4)
 
 OUTPUT_DIR = os.getcwd()
@@ -255,7 +258,7 @@ def generate_lems_channel_analyser(channel_file, channel, min_target_voltage,
 
     print_comment(("Generating LEMS file to investigate %s in %s, %smV->%smV, "
                    "%sdegC") % (channel, channel_file, min_target_voltage,
-                                max_target_voltage, temperature), verbose)
+                                max_target_voltage, temperature))
 
     target_voltages = []
     v = min_target_voltage
@@ -331,7 +334,7 @@ def get_includes_from_channel_file(channel_file):
 def process_channel_file(channel_file, a):
     # Get name of channel mechanism to test
     if a.v:
-        print_comment_v("Going to test channel from file: " + channel_file)
+        logger.info("Going to test channel from file: " + channel_file)
 
     if not os.path.isfile(channel_file):
         raise IOError("File could not be found: %s!\n" % channel_file)
@@ -341,7 +344,7 @@ def process_channel_file(channel_file, a):
     channels_info = []
     for channel in channels:
         if len(get_channel_gates(channel)) == 0:
-            print_comment_v("Skipping %s in %s as it has no channels (probably passive conductance)" % (channel.id, channel_file))
+            logger.warning("Skipping %s in %s as it has no channels (probably passive conductance)" % (channel.id, channel_file))
         else:
             new_lems_file = make_lems_file(channel, a)
             if not a.norun:
@@ -397,7 +400,7 @@ def make_lems_file(channel, a):
     lf.write(lems_content)
     lf.close()
     if a.v:
-        print_comment_v("Written generated LEMS file to %s\n" % new_lems_file)
+        logger.info("Written generated LEMS file to %s\n" % new_lems_file)
     return new_lems_file
 
 
@@ -519,7 +522,7 @@ def compute_iv_curve(channel, a, results, grid=True):
             t_start = a.clamp_delay / 1000.0
             t_steady_end = end_time_ms / 1000.0
 
-            print_comment_v('Looking at holding voltage %s V, and currents between times %s s and %s s' % (voltage, t_start, t_steady_end))
+            logger.info('Looking at holding voltage %s V, and currents between times %s s and %s s' % (voltage, t_start, t_steady_end))
 
             for index in range(len(results['t'])):
                 i = results[r][index]
@@ -621,7 +624,7 @@ def make_html_file(info):
     lf = open(new_html_file, 'w')
     lf.write(merged)
     lf.close()
-    print_comment_v('Written HTML info to: %s' % new_html_file)
+    print_comment('Written HTML info to: %s' % new_html_file)
 
 
 def make_md_file(info):
@@ -631,7 +634,7 @@ def make_md_file(info):
     lf = open(new_md_file, 'w')
     lf.write(merged)
     lf.close()
-    print_comment_v('Written Markdown info to: %s' % new_md_file)
+    print_comment('Written Markdown info to: %s' % new_md_file)
 
 
 def build_namespace(a=None, **kwargs):
@@ -704,7 +707,7 @@ 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_v("\nAnalysing channels from files: %s\n" % channel_files)
+    print_comment("\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

@@ -1,11 +1,14 @@
+import math
+import os
+import logging
+
 from pyneuroml import pynml
 from pyneuroml.lems.LEMSSimulation import LEMSSimulation
+from pyneuroml.lems import generate_lems_file_for_neuroml
 import neuroml as nml
 
-from pyneuroml.pynml import print_comment_v, print_comment
-from pyneuroml.lems import generate_lems_file_for_neuroml
-import math
-import os
+
+logger = logging.getLogger(__name__)
 
 
 def generate_current_vs_frequency_curve(
@@ -51,7 +54,7 @@ def generate_current_vs_frequency_curve(
     verbose=False
 ):
 
-    print_comment("Running generate_current_vs_frequency_curve() on %s (%s)" % (nml2_file, os.path.abspath(nml2_file)), verbose)
+    logger.info("Running generate_current_vs_frequency_curve() on %s (%s)" % (nml2_file, os.path.abspath(nml2_file)))
     from pyelectro.analysis import max_min
     from pyelectro.analysis import mean_spike_frequency
     import numpy as np
@@ -78,8 +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)
 
-    print_comment_v("Generating an IF curve for cell %s in %s using %s %s" %
-                    (cell_id, nml2_file, simulator, stim_info))
+    pynml.print_comment("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,
@@ -131,7 +134,7 @@ def generate_current_vs_frequency_curve(
 
     lems_file_name = ls.save_to_file()
 
-    print_comment("Written LEMS file %s (%s)" % (lems_file_name, os.path.abspath(lems_file_name)), verbose)
+    pynml.print_comment("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,
@@ -158,7 +161,7 @@ def generate_current_vs_frequency_curve(
     else:
         raise Exception("Sorry, cannot yet run current vs frequency analysis using simulator %s" % simulator)
 
-    print_comment("Completed run in simulator %s (results: %s)" % (simulator, results.keys()), verbose)
+    pynml.print_comment("Completed run in simulator %s (results: %s)" % (simulator, results.keys()))
 
     # print(results.keys())
     times_results = []
@@ -186,7 +189,7 @@ def generate_current_vs_frequency_curve(
             freq = 1000 * count / float(analysis_duration)
 
         mean_freq = mean_spike_frequency(spike_times)
-        # print("--- %s nA, spike times: %s, mean_spike_frequency: %f, freq (%fms -> %fms): %f"%(stims[i],spike_times, mean_freq, analysis_delay, analysis_duration+analysis_delay, freq))
+        logger.debug("--- %s nA, spike times: %s, mean_spike_frequency: %f, freq (%fms -> %fms): %f" % (stims[i], spike_times, mean_freq, analysis_delay, analysis_duration + analysis_delay, freq))
         if_results[stims[i]] = freq
 
         if freq == 0:
@@ -324,8 +327,7 @@ def analyse_spiketime_vs_dt(nml2_file,
     dts = list(np.sort(dts))
 
     for dt in dts:
-        if verbose:
-            print_comment_v(" == Generating simulation for dt = %s ms" % dt)
+        logger.info(" == Generating simulation for dt = %s ms" % dt)
         ref = str("Sim_dt_%s" % dt).replace('.', '_')
         lems_file_name = "LEMS_%s.xml" % ref
         generate_lems_file_for_neuroml(ref,
@@ -344,7 +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)
 
-        print("Results reloaded: %s" % results.keys())
+        pynml.print_comment("Results reloaded: %s" % results.keys())
         all_results[dt] = results
 
     xs = []

pyneuroml/lems/LEMSSimulation.py

@@ -165,7 +165,7 @@ 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), True)
+        print_comment("Written LEMS Simulation %s to file: %s" % (self.lems_info['sim_id'], file_name))
 
         return file_name