def _process_variables(variables, neuron=True):
eqs = ""
for var in variables:
# Min value
if 'min' in var['bounds'].keys():
min_val = ", minimum: " + str(var['bounds']['min'])
else:
min_val =""
# Max value
if 'max' in var['bounds'].keys():
max_val = ", maximum: " + str(var['bounds']['max'])
else:
max_val =""
# Method
if var['ode']:
method = ", " + find_method(var) + " numerical method"
else:
method = ""
eqs += """
* Variable %(name)s : %(locality)s, initial value: %(init)s%(min)s%(max)s%(method)s
$$
%(code)s
$$
""" % { 'name': "$" + LatexParser._latexify_name(var['name'], []) + "$",
'code': var['latex'],
'locality': _adapt_locality_neuron(var['locality']) if neuron else _adapt_locality_synapse(var['locality']),
'init': var['init'],
'min': min_val,
'max': max_val,
'method': method}
return eqs
def _process_variables(variables, neuron=True):
eqs = ""
for var in variables:
# Min value
if 'min' in var['bounds'].keys():
min_val = ", minimum: " + str(var['bounds']['min'])
else:
min_val =""
# Max value
if 'max' in var['bounds'].keys():
max_val = ", maximum: " + str(var['bounds']['max'])
else:
max_val =""
# Method
if var['ode']:
method = ", " + find_method(var) + " numerical method"
else:
method = ""
eqs += """
* Variable %(name)s : %(locality)s, initial value: %(init)s%(min)s%(max)s%(method)s
$$
%(code)s
$$
""" % { 'name': "$" + LatexParser._latexify_name(var['name'], []) + "$",
'code': var['latex'],
'locality': _adapt_locality_neuron(var['locality']) if neuron else _adapt_locality_synapse(var['locality']),
'init': var['init'],
'min': min_val,
'max': max_val,
'method': method}
return eqs
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_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_constants(net_id):
cst_tpl = """
& $%(param)s$ & %(value)s \\\\ \\hline
"""
parameters = ""
if len(Global._objects['constants']) == 0:
return ""
for constant in Global._objects['constants']:
parameters += cst_tpl % {'param': LatexParser._latexify_name(constant.name, []), 'value': constant.value}
txt = constants_template % {'parameters': parameters}
return txt
def _generate_constants(net_id):
cst_tpl = """
& $%(param)s$ & %(value)s \\\\ \\hline
"""
parameters = ""
if len(Global._objects['constants']) == 0:
return ""
for constant in Global._objects['constants']:
parameters += cst_tpl % {'param': LatexParser._latexify_name(constant.name, []), 'value': constant.value}
txt = constants_template % {'parameters': parameters}
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_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_synapse_models(net_id):
txt = """
# Synapse models
"""
for idx, synapse in enumerate(Global._objects['synapses']):
# Do not document default synapses
if synapse.name == "-":
continue
# Find a name for the synapse
synapse_name = "Synapse " + str(synapse._rk_synapses_type) if synapse.name in Synapse._default_names.values() else synapse.name
# Description
description = synapse.short_description
if description == None:
description = "Spiking synapse." if synapse.type == 'spike' else 'Rate-coded synapse'
# Parameters
parameters = extract_parameters(synapse.parameters, synapse.extra_values)
parameters_list = [
["$" + LatexParser._latexify_name(param['name'], []) + "$", param['init'], _adapt_locality_synapse(param['locality']), param['ctype']]
for param in parameters]
parameters_headers = ["Name", "Default value", "Locality", "Type"]
parameters_table = _make_table(parameters_headers, parameters_list)
if len(parameters) == 0:
parameters_table = "$$\\varnothing$$"
# Generate the code for the equations
psp, variables, pre_desc, post_desc = LatexParser._process_synapse_equations(synapse)
eqs = _process_variables(variables, neuron = False)
# PSP
if synapse.type == "rate":
psp = """
**Weighted sum:**
$$%(transmission)s$$
""" % {'transmission': psp}
elif synapse.type == "spike" and psp != "":
psp = """
**Continuous transmission:**
$$%(transmission)s$$
""" % {'transmission': psp}
else:
psp = ""
# Pre- and post-events
if synapse.type == "spike":
if len(pre_desc) > 0:
eqs += """
**Pre-synaptic event at $t_\\text{pre} + d$:**
"""
for pre in pre_desc:
eqs += "$$"+pre+"$$\n"
if len(post_desc) > 0:
eqs += """
**Post-synaptic event at $t_\\text{post}$:**
"""
for post in post_desc:
eqs += "$$"+post+"$$\n"
# Finalize the template
txt += synapse_tpl % { 'name': synapse_name,
'description': description,
'psp': psp,
'parameters': parameters_table,
'eqs': eqs}
return txt
def _generate_neuron_models(net_id):
txt = """
# Neuron models
"""
for idx, neuron in enumerate(Global._objects['neurons']):
# Name
if neuron.name in Neuron._default_names.values(): # name not set
neuron_name = "Neuron " + str(neuron._rk_neurons_type)
else:
neuron_name = neuron.name
# Description
description = neuron.short_description
if description == None:
description = "Spiking neuron." if neuron.type == 'spike' else 'Rate-coded neuron'
# Parameters
parameters = extract_parameters(neuron.parameters, neuron.extra_values)
parameters_list = [
["$" + LatexParser._latexify_name(param['name'], []) + "$", param['init'],
_adapt_locality_neuron(param['locality']), param['ctype']]
for param in parameters]
parameters_headers = ["Name", "Default value", "Locality", "Type"]
parameters_table = _make_table(parameters_headers, parameters_list)
if len(parameters) == 0:
parameters_table = "$$\\varnothing$$"
# Generate the code for the equations
variables, spike_condition, spike_reset = LatexParser._process_neuron_equations(neuron)
eqs = _process_variables(variables, neuron=True)
# Spiking neurons
if neuron.type == 'spike':
reset_txt = "* Emit a spike a time $t$.\n"
for r in spike_reset:
reset_txt += "* $" + r + "$\n"
eqs += """
**Spike emission:**
if $%(condition)s$ :
%(reset)s
""" % {'condition': spike_condition, 'reset': reset_txt}
# Possible function
if not neuron.functions == None:
eqs += """
**Functions**
%(functions)s
""" % {'functions': LatexParser._process_functions(neuron.functions, begin="$$", end="$$\n\n")}
# Finalize the template
txt += neuron_tpl % { 'name': neuron_name,
'description': description,
'parameters': parameters_table,
'eqs': eqs}
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_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_synapse_models(net_id):
txt = """
# Synapse models
"""
for idx, synapse in enumerate(Global._objects['synapses']):
# Do not document default synapses
if synapse.name == "-":
continue
# Find a name for the synapse
synapse_name = "Synapse " + str(synapse._rk_synapses_type) if synapse.name in Synapse._default_names.values() else synapse.name
# Description
description = synapse.short_description
if description == None:
description = "Spiking synapse." if synapse.type == 'spike' else 'Rate-coded synapse'
# Parameters
parameters = extract_parameters(synapse.parameters, synapse.extra_values)
parameters_list = [
["$" + LatexParser._latexify_name(param['name'], []) + "$", param['init'], _adapt_locality_synapse(param['locality']), param['ctype']]
for param in parameters]
parameters_headers = ["Name", "Default value", "Locality", "Type"]
parameters_table = _make_table(parameters_headers, parameters_list)
if len(parameters) == 0:
parameters_table = "$$\\varnothing$$"
# Generate the code for the equations
psp, variables, pre_desc, post_desc = LatexParser._process_synapse_equations(synapse)
eqs = _process_variables(variables, neuron = False)
# PSP
if synapse.type == "rate":
psp = """
**Weighted sum:**
$$%(transmission)s$$
""" % {'transmission': psp}
elif synapse.type == "spike" and psp != "":
psp = """
**Continuous transmission:**
$$%(transmission)s$$
""" % {'transmission': psp}
else:
psp = ""
# Pre- and post-events
if synapse.type == "spike":
if len(pre_desc) > 0:
eqs += """
**Pre-synaptic event at $t_\\text{pre} + d$:**
"""
for pre in pre_desc:
eqs += "$$"+pre+"$$\n"
if len(post_desc) > 0:
eqs += """
**Post-synaptic event at $t_\\text{post}$:**
"""
for post in post_desc:
eqs += "$$"+post+"$$\n"
# Finalize the template
txt += synapse_tpl % { 'name': synapse_name,
'description': description,
'psp': psp,
'parameters': parameters_table,
'eqs': eqs}
return txt
def _generate_neuron_models(net_id):
txt = """
# Neuron models
"""
for idx, neuron in enumerate(Global._objects['neurons']):
# Name
if neuron.name in Neuron._default_names.values(): # name not set
neuron_name = "Neuron " + str(neuron._rk_neurons_type)
else:
neuron_name = neuron.name
# Description
description = neuron.short_description
if description == None:
description = "Spiking neuron." if neuron.type == 'spike' else 'Rate-coded neuron'
# Parameters
parameters = extract_parameters(neuron.parameters, neuron.extra_values)
parameters_list = [
["$" + LatexParser._latexify_name(param['name'], []) + "$", param['init'],
_adapt_locality_neuron(param['locality']), param['ctype']]
for param in parameters]
parameters_headers = ["Name", "Default value", "Locality", "Type"]
parameters_table = _make_table(parameters_headers, parameters_list)
if len(parameters) == 0:
parameters_table = "$$\\varnothing$$"
# Generate the code for the equations
variables, spike_condition, spike_reset = LatexParser._process_neuron_equations(neuron)
eqs = _process_variables(variables, neuron=True)
# Spiking neurons
if neuron.type == 'spike':
reset_txt = "* Emit a spike a time $t$.\n"
for r in spike_reset:
reset_txt += "* $" + r + "$\n"
eqs += """
**Spike emission:**
if $%(condition)s$ :
%(reset)s
""" % {'condition': spike_condition, 'reset': reset_txt}
# Possible function
if not neuron.functions == None:
eqs += """
**Functions**
%(functions)s
""" % {'functions': LatexParser._process_functions(neuron.functions, begin="$$", end="$$\n\n")}
# Finalize the template
txt += neuron_tpl % { 'name': neuron_name,
'description': description,
'parameters': parameters_table,
'eqs': eqs}
return txt