def _generate_projections(net_id, gather_subprojections):
txt = ""
proj_tpl = """
%(pre)s & %(post)s & %(target)s & %(synapse)s &
%(description)s \\\\ \\hline
"""
if gather_subprojections:
projections = []
for proj in Global._network[net_id]['projections']:
for existing_proj in projections:
if proj.pre.name == existing_proj.pre.name and proj.post.name == existing_proj.post.name and proj.target == existing_proj.target : # TODO
break
else:
projections.append(proj)
else:
projections = Global._network[net_id]['projections']
for proj in projections:
# Find a name for the synapse
if proj.synapse_type.name in Synapse._default_names.values(): # name not set
synapse_name = "Synapse " + str(proj.synapse_type._rk_synapses_type)
else:
synapse_name = proj.synapse_type.name
txt += proj_tpl % { 'pre': LatexParser.pop_name(proj.pre.name),
'post': LatexParser.pop_name(proj.post.name),
'target': LatexParser._format_list(proj.target, ' / '),
'synapse': synapse_name,
'description': proj.connector_description}
return connectivity_template % {'projections_description': txt}
def _generate_projections(net_id, gather_subprojections):
txt = ""
proj_tpl = """
%(pre)s & %(post)s & %(target)s & %(synapse)s &
%(description)s \\\\ \\hline
"""
if gather_subprojections:
projections = []
for proj in Global._network[net_id]['projections']:
for existing_proj in projections:
if proj.pre.name == existing_proj.pre.name and proj.post.name == existing_proj.post.name and proj.target == existing_proj.target : # TODO
break
else:
projections.append(proj)
else:
projections = Global._network[net_id]['projections']
for proj in projections:
# Find a name for the synapse
if proj.synapse_type.name in Synapse._default_names.values(): # name not set
synapse_name = "Synapse " + str(proj.synapse_type._rk_synapses_type)
else:
synapse_name = proj.synapse_type.name
txt += proj_tpl % { 'pre': LatexParser.pop_name(proj.pre.name),
'post': LatexParser.pop_name(proj.post.name),
'target': LatexParser._format_list(proj.target, ' / '),
'synapse': synapse_name,
'description': proj.connector_description}
return connectivity_template % {'projections_description': txt}
def _generate_projection_parameters(net_id, gather_subprojections):
txt = ""
proj_tpl = """
%(name)s & $%(param)s$ & %(value)s \\\\ \\hline
"""
if gather_subprojections:
projections = []
for proj in Global._network[net_id]['projections']:
for existing_proj in projections:
if proj.pre.name == existing_proj.pre.name and proj.post.name == existing_proj.post.name and proj.target == existing_proj.target: # TODO
break
else:
projections.append(proj)
else:
projections = Global._network[net_id]['projections']
first = True
for rk, proj in enumerate(projections):
parameters = ""
for idx, param in enumerate(proj.parameters):
if param == 'w':
continue
if idx == 0:
proj_name = "%(pre)s $\\rightarrow$ %(post)s with target %(target)s" % {
'pre': LatexParser.pop_name(proj.pre.name),
'post': LatexParser.pop_name(proj.post.name),
'target': LatexParser._format_list(proj.target, ' / ')
}
else:
proj_name = ""
val = proj.init[param]
if isinstance(val, (list, np.ndarray)):
val = "$[" + str(np.min(val)) + ", " + str(np.max(val)) + "]$"
parameters += proj_tpl % {
'name': proj_name,
'param': LatexParser._latexify_name(param, []),
'value': val
}
if parameters != "":
txt += projparameters_template % {
'parameters':
parameters,
'firstprojection':
"\hdr{3}{H}{Projection parameters}\\\\ \\hline"
if first else ""
}
first = False
return txt
def _generate_populations(net_id):
def format_size(pop):
size = str(pop.size)
if pop.dimension >1:
size += ' ('
for d in range(pop.dimension):
size += str(pop.geometry[d]) + '*'
size = size.rsplit('*', 1)[0] + ')'
return size
txt = ""
pop_tpl = """
%(pop_name)s & %(neuron_type)s & $N_{\\text{%(pop_name)s}}$ = %(size)s \\\\ \\hline
"""
for pop in Global._network[net_id]['populations']:
# Find a name for the neuron
if pop.neuron_type.name in Neuron._default_names.values(): # name not set
neuron_name = "Neuron " + str(pop.neuron_type._rk_neurons_type)
else:
neuron_name = pop.neuron_type.name
txt += pop_tpl % {
'pop_name': LatexParser.pop_name(pop.name),
'neuron_type': neuron_name,
'size': format_size(pop)}
return populations_template % {'populations_description': txt}
def _generate_populations(net_id):
def format_size(pop):
size = str(pop.size)
if pop.dimension >1:
size += ' ('
for d in range(pop.dimension):
size += str(pop.geometry[d]) + '*'
size = size.rsplit('*', 1)[0] + ')'
return size
txt = ""
pop_tpl = """
%(pop_name)s & %(neuron_type)s & $N_{\\text{%(pop_name)s}}$ = %(size)s \\\\ \\hline
"""
for pop in Global._network[net_id]['populations']:
# Find a name for the neuron
if pop.neuron_type.name in Neuron._default_names.values(): # name not set
neuron_name = "Neuron " + str(pop.neuron_type._rk_neurons_type)
else:
neuron_name = pop.neuron_type.name
txt += pop_tpl % {
'pop_name': LatexParser.pop_name(pop.name),
'neuron_type': neuron_name,
'size': format_size(pop)}
return populations_template % {'populations_description': txt}
def _generate_population_parameters(net_id):
txt = ""
pop_tpl = """
%(name)s & $%(param)s$ & %(value)s \\\\ \\hline
"""
for rk, pop in enumerate(Global._network[net_id]['populations']):
parameters = ""
for idx, param in enumerate(pop.parameters):
val = pop.init[param]
if isinstance(val, (list, np.ndarray)):
val = "$[" + str(np.array(val).min()) + ", " + str(
np.array(val).max()) + "]$"
parameters += pop_tpl % {
'name': LatexParser.pop_name(pop.name) if idx == 0 else "",
'param': LatexParser._latexify_name(param, []),
'value': val
}
txt += popparameters_template % {
'parameters':
parameters,
'firstpopulation':
"\hdr{3}{H}{Population parameters}\\\\ \\hline" if rk == 0 else ""
}
return txt
def _generate_summary(net_id):
"part A"
population_names = str(len(Global._network[net_id]['populations'])) + ': '
connectivity = ""
neuron_models = ""
synapse_models = ""
# List the names of all populations
for pop in Global._network[net_id]['populations']:
# population name
population_names += LatexParser.pop_name(pop.name) + ", "
population_names = population_names[:-2] # suppress the last ,
# List all BOLD recordings
measurements = ""
# List all neuron types
neuron_model_names = []
for neur in Global._objects['neurons']:
# bold models sorted in measurements
if isinstance(neur, BoldModel):
if neur._model_instantiated:
measurements += neur.name + ', '
# neuron model
else:
neuron_model_names.append(neur.name)
for neur in list(set(neuron_model_names)):
neuron_models += neur + ', '
# suppress the last ,
measurements = measurements[:-2]
neuron_models = neuron_models[:-2]
list_connectivity = []
list_synapse_models = []
for proj in Global._network[net_id]['projections']:
list_connectivity.append(proj.connector_name)
if not proj.synapse_type.name in list(Synapse._default_names.values()) + ['-']:
list_synapse_models.append(proj.synapse_type.name)
for con in list(set(list_connectivity)):
connectivity += con + ', '
for syn in list(set(list_synapse_models)):
synapse_models += syn + ', '
connectivity = connectivity[:-2]
synapse_models = synapse_models[:-2] # suppress the last ,
# Write the summary
txt = summary_template % {
'population_names' : population_names,
'connectivity' : connectivity,
'neuron_models' : neuron_models,
'synapse_models' : synapse_models,
'measurements': measurements
}
return txt
def _generate_projection_parameters(net_id, gather_subprojections):
txt = ""
proj_tpl = """
%(name)s & $%(param)s$ & %(value)s \\\\ \\hline
"""
if gather_subprojections:
projections = []
for proj in Global._network[net_id]['projections']:
for existing_proj in projections:
if proj.pre.name == existing_proj.pre.name and proj.post.name == existing_proj.post.name and proj.target == existing_proj.target : # TODO
break
else:
projections.append(proj)
else:
projections = Global._network[net_id]['projections']
first = True
for rk, proj in enumerate(projections):
parameters = ""
for idx, param in enumerate(proj.parameters):
if param == 'w':
continue
if idx == 0:
proj_name = "%(pre)s $\\rightarrow$ %(post)s with target %(target)s" % {
'pre': LatexParser.pop_name(proj.pre.name),
'post': LatexParser.pop_name(proj.post.name),
'target': LatexParser._format_list(proj.target, ' / ')}
else:
proj_name = ""
val = proj.init[param]
if isinstance(val, (list, np.ndarray)):
val = "$[" + str(np.min(val)) + ", " + str(np.max(val)) + "]$"
parameters += proj_tpl % {'name': proj_name, 'param': LatexParser._latexify_name(param, []), 'value': val}
if parameters != "":
txt += projparameters_template % {'parameters': parameters, 'firstprojection': "\hdr{3}{H}{Projection parameters}\\\\ \\hline" if first else ""}
first = False
return txt
def _generate_population_parameters(net_id):
txt = ""
pop_tpl = """
%(name)s & $%(param)s$ & %(value)s \\\\ \\hline
"""
for rk, pop in enumerate(Global._network[net_id]['populations']):
parameters = ""
for idx, param in enumerate(pop.parameters):
val = pop.init[param]
if isinstance(val, (list, np.ndarray)):
val = "$[" + str(np.array(val).min()) + ", " + str(np.array(val).max()) + "]$"
parameters += pop_tpl % {'name': LatexParser.pop_name(pop.name) if idx==0 else "", 'param': LatexParser._latexify_name(param, []), 'value': val}
txt += popparameters_template % {'parameters': parameters, 'firstpopulation': "\hdr{3}{H}{Population parameters}\\\\ \\hline" if rk==0 else ""}
return txt
def _generate_summary(net_id):
"part A"
population_names = str(len(Global._network[net_id]['populations'])) + ': '
connectivity = ""
neuron_models = ""
synapse_models = ""
# List the names of all populations
for pop in Global._network[net_id]['populations']:
# population name
population_names += LatexParser.pop_name(pop.name) + ", "
population_names = population_names[:-2] # suppress the last ,
# List all neuron types
neuron_model_names = []
for neur in Global._objects['neurons']:
neuron_model_names.append(neur.name)
for neur in list(set(neuron_model_names)):
neuron_models += neur + ', '
neuron_models = neuron_models[:-2] # suppress the last ,
list_connectivity = []
list_synapse_models = []
for proj in Global._network[net_id]['projections']:
list_connectivity.append(proj.connector_name)
if not proj.synapse_type.name in list(Synapse._default_names.values()) + ['-']:
list_synapse_models.append(proj.synapse_type.name)
for con in list(set(list_connectivity)):
connectivity += con + ', '
for syn in list(set(list_synapse_models)):
synapse_models += syn + ', '
connectivity = connectivity[:-2]
synapse_models = synapse_models[:-2] # suppress the last ,
# Write the summary
txt = summary_template % {
'population_names' : population_names,
'connectivity' : connectivity,
'neuron_models' : neuron_models,
'synapse_models' : synapse_models
}
return txt
def _generate_parameters(net_id, gather_subprojections):
txt = """
# Parameters
"""
# Constants
if len(Global._objects['constants']) > 0:
txt += """
## Constants
"""
constants_list = [
["$" + LatexParser._latexify_name(constant.name, []) + "$", constant.value]
for constant in Global._objects['constants']]
constants_headers = ["Name", "Value"]
txt += _make_table(constants_headers, constants_list)
# Population parameters
txt += """
## Population parameters
"""
parameters_list = []
for rk, pop in enumerate(Global._network[net_id]['populations']):
neuron_name = "Neuron " + str(pop.neuron_type._rk_neurons_type) if pop.neuron_type.name in Neuron._default_names.values() \
else pop.neuron_type.name
for idx, param in enumerate(pop.parameters):
val = pop.init[param]
if isinstance(val, (list, np.ndarray)):
val = "$[" + str(np.array(val).min()) + ", " + str(np.array(val).max()) + "]$"
parameters_list.append(
[ LatexParser.pop_name(pop.name) if idx==0 else "",
neuron_name if idx==0 else "",
"$" + LatexParser._latexify_name(param, []) + "$",
val ] )
population_headers = ["Population", "Neuron type", "Name", "Value"]
txt += _make_table(population_headers, parameters_list)
# Projection parameters
txt += """
## Projection parameters
"""
if gather_subprojections:
projections = []
for proj in Global._network[net_id]['projections']:
for existing_proj in projections:
if proj.pre.name == existing_proj.pre.name and proj.post.name == existing_proj.post.name \
and proj.target == existing_proj.target : # TODO
break
else:
projections.append(proj)
else:
projections = Global._network[net_id]['projections']
parameters_list = []
for rk, proj in enumerate(projections):
for idx, param in enumerate(proj.parameters):
if param == 'w':
continue
if idx == 0:
proj_name = "%(pre)s $\\rightarrow$ %(post)s with target %(target)s" % {
'pre': LatexParser.pop_name(proj.pre.name),
'post': LatexParser.pop_name(proj.post.name),
'target': LatexParser._format_list(proj.target, ' / ')}
else:
proj_name = ""
synapse_name = "Synapse " + str(proj.synapse_type._rk_synapses_type) if proj.synapse_type.name in Synapse._default_names.values() \
else proj.synapse_type.name
val = proj.init[param]
if isinstance(val, (list, np.ndarray)):
val = "$[" + str(np.array(val).min()) + ", " + str(np.array(val).max()) + "]$"
parameters_list.append(
[ proj_name,
synapse_name if idx == 0 else "",
"$" + LatexParser._latexify_name(param, []) + "$",
val ] )
projection_headers = ["Projection", "Synapse type", "Name", "Value"]
txt += _make_table(projection_headers, parameters_list)
return txt
def _generate_parameters(net_id, gather_subprojections):
txt = """
# Parameters
"""
# Constants
if len(Global._objects['constants']) > 0:
txt += """
## Constants
"""
constants_list = [
["$" + LatexParser._latexify_name(constant.name, []) + "$", constant.value]
for constant in Global._objects['constants']]
constants_headers = ["Name", "Value"]
txt += _make_table(constants_headers, constants_list)
# Population parameters
txt += """
## Population parameters
"""
parameters_list = []
for rk, pop in enumerate(Global._network[net_id]['populations']):
neuron_name = "Neuron " + str(pop.neuron_type._rk_neurons_type) if pop.neuron_type.name in Neuron._default_names.values() \
else pop.neuron_type.name
for idx, param in enumerate(pop.parameters):
val = pop.init[param]
if isinstance(val, (list, np.ndarray)):
val = "$[" + str(np.array(val).min()) + ", " + str(np.array(val).max()) + "]$"
parameters_list.append(
[ LatexParser.pop_name(pop.name) if idx==0 else "",
neuron_name if idx==0 else "",
"$" + LatexParser._latexify_name(param, []) + "$",
val ] )
population_headers = ["Population", "Neuron type", "Name", "Value"]
txt += _make_table(population_headers, parameters_list)
# Projection parameters
txt += """
## Projection parameters
"""
if gather_subprojections:
projections = []
for proj in Global._network[net_id]['projections']:
for existing_proj in projections:
if proj.pre.name == existing_proj.pre.name and proj.post.name == existing_proj.post.name \
and proj.target == existing_proj.target : # TODO
break
else:
projections.append(proj)
else:
projections = Global._network[net_id]['projections']
parameters_list = []
for rk, proj in enumerate(projections):
for idx, param in enumerate(proj.parameters):
if param == 'w':
continue
if idx == 0:
proj_name = "%(pre)s $\\rightarrow$ %(post)s with target %(target)s" % {
'pre': LatexParser.pop_name(proj.pre.name),
'post': LatexParser.pop_name(proj.post.name),
'target': LatexParser._format_list(proj.target, ' / ')}
else:
proj_name = ""
synapse_name = "Synapse " + str(proj.synapse_type._rk_synapses_type) if proj.synapse_type.name in Synapse._default_names.values() \
else proj.synapse_type.name
val = proj.init[param]
if isinstance(val, (list, np.ndarray)):
val = "$[" + str(np.array(val).min()) + ", " + str(np.array(val).max()) + "]$"
parameters_list.append(
[ proj_name,
synapse_name if idx == 0 else "",
"$" + LatexParser._latexify_name(param, []) + "$",
val ] )
projection_headers = ["Projection", "Synapse type", "Name", "Value"]
txt += _make_table(projection_headers, parameters_list)
return txt