Add slight delay in generate network when extra_lems_components present

examples/LEMS_SimExample3.xml

@@ -2,7 +2,7 @@
 
  <!-- 
 
- This LEMS file has been automatically generated using PyNeuroML v1.2.3 (libNeuroML v0.5.8)
+ This LEMS file has been automatically generated using PyNeuroML v1.2.13 (libNeuroML v0.5.9)
 
  -->
 

examples/LEMS_SimExample4.xml

@@ -2,7 +2,7 @@
 
  <!-- 
 
- This LEMS file has been automatically generated using PyNeuroML v1.2.3 (libNeuroML v0.5.8)
+ This LEMS file has been automatically generated using PyNeuroML v1.2.13 (libNeuroML v0.5.9)
 
  -->
 

examples/test_files/.test.ex4.jnmleden.omt

@@ -14,7 +14,7 @@ experiments:
  spike detection:
  method: threshold
  threshold: -64
- tolerance: 0.0002527679474217274
+ tolerance: 0.00025278058645101804
 
  v1:
  observables:
@@ -26,4 +26,4 @@ experiments:
  spike detection:
  method: threshold
  threshold: -69
- tolerance: 0.0002327287423609406
+ tolerance: 0.00023269200151868833

neuromllite/MatrixHandler.py

@@ -11,16 +11,20 @@
 from neuromllite.NetworkGenerator import _get_rng_for_network
 
 import numpy as np
+import os
 
 
 class MatrixHandler(ConnectivityHandler):
  colormaps_used = []
 
  weight_arrays_to_show = {}
+ weight_array_figures = {}
 
- def __init__(self, level=10, nl_network=None):
+ def __init__(self, level=10, nl_network=None, show_already=True, save_figs_to_dir=None):
  self.nl_network = nl_network
  self.level = level
+ self.show_already = show_already
+ self.save_figs_to_dir=save_figs_to_dir
 
  self.rng, seed = _get_rng_for_network(self.nl_network)
 
@@ -34,11 +38,6 @@ def print_settings(self):
  print_v("* level: %s" % self.level)
  print_v("* is_cell_level: %s" % self.is_cell_level())
  print_v("* CUTOFF_INH_SYN_MV: %s" % self.CUTOFF_INH_SYN_MV)
- # print_v('* include_inputs: %s'%self.include_inputs)
- # print_v('* scale_by_post_pop_size: %s'%self.scale_by_post_pop_size)
- # print_v('* scale_by_post_pop_cond: %s'%self.scale_by_post_pop_cond)
- # print_v('* min_weight_to_show: %s'%self.min_weight_to_show)
- # print_v('* min_weight_to_show: %s'%self.min_weight_to_show)
  print_v("*")
  print_v("* Used values: ")
  print_v("* colormaps_used: %s" % self.colormaps_used)
@@ -68,6 +67,9 @@ def finalise_document(self):
  for v in self.proj_post_pops.values():
  all_pops.append(v)
 
+ self.weight_matrices_generated = {}
+ self.weight_matrices_saved = {}
+
  for pop in all_pops:
  if self.is_cell_level():
  for i in range(self.pop_sizes[pop]):
@@ -376,10 +378,22 @@ def finalise_document(self):
  if proj_type in cbar_labels:
  cbar.set_label(cbar_labels[proj_type])
 
+ if self.save_figs_to_dir:
+ safe = proj_type.replace(' ','_').replace('(','-').replace(')','-').replace('*','_').replace('/','_')
+ save_figure_to = os.path.join(self.save_figs_to_dir, '%s_%s.png'%(self.network_id, safe))
+ plt.savefig(save_figure_to, bbox_inches="tight")
+ print_v("Saved image to %s of plot: %s" % (save_figure_to, title))
+ self.weight_array_figures[title] = save_figure_to
+
+
+
  print_v("Generating matrix for: %s" % self.network_id)
+
+
  self.print_settings()
 
- plt.show()
+ if self.show_already:
+ plt.show()
 
  def handle_population(
  self,
@@ -535,3 +549,31 @@ def finalise_projection(
 
  def finalise_input_source(self, inputListId):
  pass
+
+if __name__ == "__main__":
+
+ from neuromllite.utils import load_network_json
+
+ tests = ['/Users/padraig/neuroConstruct/osb/cerebral_cortex/networks/del-Molino2017/NeuroML/delMolinoEtAl_low_baseline.json',
+ '/Users/padraig/NeuroMLlite/examples/Example12_MultiComp.json']
+
+ for test in tests:
+
+ network = load_network_json(test)
+
+ from neuromllite.NetworkGenerator import generate_network
+
+ level = 1
+ handler = MatrixHandler(level, 
+ nl_network=network, 
+ show_already=True,
+ save_figs_to_dir='.')
+
+ generate_network(
+ network, handler, always_include_props=True, base_dir=os.path.dirname(test)
+ )
+
+ print_v("Done with MatrixHandler...")
+
+ for w in handler.weight_arrays_to_show:
+ print('%s:\n%s'%(w, handler.weight_arrays_to_show[w]))

neuromllite/NetworkGenerator.py

@@ -11,6 +11,7 @@
 import numpy as np
 import os
 import random
+import time
 
 import lems.api as lems # from pylems
 
@@ -913,6 +914,7 @@ def generate_neuroml2_from_network(
 
  if len(extra_lems_components.components) > 0:
  extra_lems_components.export_to_file(extra_lems_file)
+ time.sleep(0.5)
 
  if validate and format == "xml":
  from pyneuroml import pynml