def _generate_functions(net_id):
functions = ""
if len(Global._objects['functions']) == 0:
return functions
for name, func in Global._objects['functions']:
functions += LatexParser._process_functions(func) + "\n"
return functions_template % {'parameters': functions, 'firstfunction': "\hdr{1}{G}{Functions}\\\\ \\hline"}
def _generate_functions(net_id):
functions = ""
if len(Global._objects['functions']) == 0:
return functions
for name, func in Global._objects['functions']:
functions += LatexParser._process_functions(func) + "\n"
return functions_template % {'parameters': functions, 'firstfunction': "\hdr{1}{G}{Functions}\\\\ \\hline"}
def _generate_summary(net_id):
txt = """
# Structure of the network
"""
# General information
backend = 'default'
if Global.config['paradigm'] == 'cuda':
backend = "CUDA"
elif Global.config['paradigm'] == "openmp" and Global.config['num_threads'] > 1:
backend = "OpenMP"
txt +="""
* ANNarchy %(version)s using the %(backend)s backend.
* Numerical step size: %(dt)s ms.
""" % {'version': ANNarchy.__release__, 'backend': backend, 'dt': Global.config['dt']}
# Populations
if len(Global._network[net_id]['populations']) > 0:
headers = ["Population", "Size", "Neuron type"]
populations = []
for pop in Global._network[net_id]['populations']:
# Find a name for the neuron
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
populations.append([
pop.name,
pop.geometry if len(pop.geometry)>1 else pop.size,
neuron_name])
txt += """
## Populations
"""
txt += _make_table(headers, populations)
# Projections
if len(Global._network[net_id]['projections']) > 0 :
headers = ["Source", "Destination", "Target", "Synapse type", "Pattern"]
projections = []
for proj in Global._network[net_id]['projections']:
# Find a name for the synapse
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
projections.append([
proj.pre.name,
proj.post.name,
LatexParser._format_list(proj.target, ' / '),
synapse_name,
proj.connector_description
])
txt += """
## Projections
"""
txt += _make_table(headers, projections)
# Monitors
if len(Global._network[net_id]['monitors']) > 0:
headers = ["Object", "Variables", "Period"]
monitors = []
for monitor in Global._network[net_id]['monitors']:
monitors.append([
monitor.object.name + (" (subset)" if isinstance(monitor.object, PopulationView) else ""),
LatexParser._format_list(monitor.variables, ', '),
monitor.period
])
txt += """
## Monitors
"""
txt += _make_table(headers, monitors)
# Functions
if len(Global._objects['functions']) > 0 :
txt += """## Functions
"""
for name, func in Global._objects['functions']:
txt += LatexParser._process_functions(func, begin="$$", end="$$\n\n")
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_synapse_models(net_id):
firstsynapse = ""
synapses = ""
firstsynapse = "\\hdr{2}{E}{Synapse Models}\\\\ \\hline"
synapse_tpl = """
\\noindent
\\begin{tabularx}{\\linewidth}{|p{0.15\\linewidth}|X|}\\hline
%(firstsynapse)s
\\textbf{Name} & %(name)s \\\\ \\hline
\\textbf{Type} & %(description)s\\\\ \\hline
%(psp)s
%(variables)s
%(preevent)s
%(postevent)s
%(functions)s
\\end{tabularx}
\\vspace{2ex}
"""
for idx, synapse in enumerate(Global._objects['synapses']):
# Do not document default synapses
if synapse.name == "-":
continue
# Find a name for the synapse
if synapse.name in Synapse._default_names.values(): # name not set
synapse_name = "Synapse " + str(synapse._rk_synapses_type)
else:
synapse_name = synapse.name
# Generate the code for the equations
psp, variables, pre_desc, post_desc = LatexParser._process_synapse_equations(synapse)
eqs = ""
for var in variables:
eqs += """
\\begin{dmath*}
%(eq)s
\\end{dmath*}
""" % {'eq': var['latex']}
variables_eqs = """
%(eqs)s
\\\\ \\hline
""" % {'eqs': eqs}
# Synaptic variables
variables = """
\\textbf{Equations} & %(variables)s
\\\\ \\hline""" % {'variables':eqs} if eqs != "" else ""
# PSP
if psp != "":
psp_code = """
\\textbf{PSP} & \\begin{dmath*}
%(psp)s
\\end{dmath*}
\\\\ \\hline""" % {'psp': psp}
else:
psp_code = ""
# Spiking neurons have extra fields for the event-driven
if synapse.type == 'spike':
if len(pre_desc) > 0:
txt_pre = ""
for l in pre_desc:
txt_pre += """
\\begin{dmath*}
%(eq)s
\\end{dmath*}
""" % {'eq': l}
preevent = """
\\textbf{Pre-synaptic event} &
%(preevent)s
\\\\ \\hline
""" % {'preevent': txt_pre}
else:
preevent = ""
if len(post_desc) > 0:
txt_post = ""
for l in post_desc:
txt_post += """
\\begin{dmath*}
%(eq)s
\\end{dmath*}
""" % {'eq': l}
postevent = """
\\textbf{Post-synaptic event} &
%(postevent)s
\\\\ \\hline
""" % {'postevent': txt_post}
else:
postevent = ""
else:
preevent = ""
postevent = ""
# Possible functions
functions = ""
if not synapse.functions == None:
functions = """
\\textbf{Functions} &
%(functions)s
\\\\ \\hline
""" % {'functions': LatexParser._process_functions(synapse.functions)}
# Build the dictionary
desc = {
'name': synapse_name,
'description': synapse.short_description,
'firstsynapse': firstsynapse if idx == 0 else "",
'variables': variables,
'psp': psp_code,
'preevent': preevent,
'postevent': postevent,
'functions': functions
}
# Generate the code
synapses += synapse_tpl % desc
return synapses
def _generate_neuron_models(net_id):
neurons = ""
firstneuron = "\\hdr{2}{D}{Neuron Models}\\\\ \\hline"
neuron_tpl = """
\\noindent
\\begin{tabularx}{\\linewidth}{|p{0.15\\linewidth}|X|}\\hline
%(firstneuron)s
\\textbf{Name} & %(name)s \\\\ \\hline
\\textbf{Type} & %(description)s\\\\ \\hline
\\textbf{%(equation_type)s} &
%(variables)s
%(spike)s
%(functions)s
\\end{tabularx}
\\vspace{2ex}
"""
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
# Generate the code for the equations
variables, spike_condition, spike_reset = LatexParser._process_neuron_equations(neuron)
eqs = ""
for var in variables:
eqs += """
\\begin{dmath*}
%(eq)s
\\end{dmath*}
""" % {'eq': var['latex']}
variables_eqs = """
%(eqs)s
\\\\ \\hline
""" % {'eqs': eqs}
# Spiking neurons have an extra field for the spike condition
spike_extra = ""
if neuron.type == 'spike':
spike_code = "If $" + spike_condition + "$ or $t \leq t^* + t_{\\text{refractory}}$:"
# Reset
spike_code += """
\\begin{enumerate}
\\item Emit a spike at time $t^*$"""
for var in spike_reset:
spike_code += """
\\item $""" + var + "$"
spike_code += """
\\end{enumerate}"""
spike_extra = """
\\textbf{Spiking} &
%(spike)s
\\\\ \\hline
""" % {'spike': spike_code}
# Possible function
functions = ""
if not neuron.functions == None:
functions = """
\\textbf{Functions} &
%(functions)s
\\\\ \\hline
""" % {'functions': LatexParser._process_functions(neuron.functions)}
# Build the dictionary
desc = {
'name': neuron_name,
'description': neuron.short_description,
'firstneuron': firstneuron if idx ==0 else "",
'variables': variables_eqs,
'spike': spike_extra,
'functions': functions,
'equation_type': "Subthreshold dynamics" if neuron.type == 'spike' else 'Equations'
}
# Generate the code depending on the neuron position
neurons += neuron_tpl % desc
return neurons
def _generate_synapse_models(net_id):
firstsynapse = ""
synapses = ""
firstsynapse = "\\hdr{2}{E}{Synapse Models}\\\\ \\hline"
synapse_tpl = """
\\noindent
\\begin{tabularx}{\\linewidth}{|p{0.15\\linewidth}|X|}\\hline
%(firstsynapse)s
\\textbf{Name} & %(name)s \\\\ \\hline
\\textbf{Type} & %(description)s\\\\ \\hline
%(psp)s
%(variables)s
%(preevent)s
%(postevent)s
%(functions)s
\\end{tabularx}
\\vspace{2ex}
"""
for idx, synapse in enumerate(Global._objects['synapses']):
# Do not document default synapses
if synapse.name == "-":
continue
# Find a name for the synapse
if synapse.name in Synapse._default_names.values(): # name not set
synapse_name = "Synapse " + str(synapse._rk_synapses_type)
else:
synapse_name = synapse.name
# Generate the code for the equations
psp, variables, pre_desc, post_desc = LatexParser._process_synapse_equations(synapse)
eqs = ""
for var in variables:
eqs += """
\\begin{dmath*}
%(eq)s
\\end{dmath*}
""" % {'eq': var['latex']}
variables_eqs = """
%(eqs)s
\\\\ \\hline
""" % {'eqs': eqs}
# Synaptic variables
variables = """
\\textbf{Equations} & %(variables)s
\\\\ \\hline""" % {'variables':eqs} if eqs != "" else ""
# PSP
if psp != "":
psp_code = """
\\textbf{PSP} & \\begin{dmath*}
%(psp)s
\\end{dmath*}
\\\\ \\hline""" % {'psp': psp}
else:
psp_code = ""
# Spiking neurons have extra fields for the event-driven
if synapse.type == 'spike':
if len(pre_desc) > 0:
txt_pre = ""
for l in pre_desc:
txt_pre += """
\\begin{dmath*}
%(eq)s
\\end{dmath*}
""" % {'eq': l}
preevent = """
\\textbf{Pre-synaptic event} &
%(preevent)s
\\\\ \\hline
""" % {'preevent': txt_pre}
else:
preevent = ""
if len(post_desc) > 0:
txt_post = ""
for l in post_desc:
txt_post += """
\\begin{dmath*}
%(eq)s
\\end{dmath*}
""" % {'eq': l}
postevent = """
\\textbf{Post-synaptic event} &
%(postevent)s
\\\\ \\hline
""" % {'postevent': txt_post}
else:
postevent = ""
else:
preevent = ""
postevent = ""
# Possible functions
functions = ""
if not synapse.functions == None:
functions = """
\\textbf{Functions} &
%(functions)s
\\\\ \\hline
""" % {'functions': LatexParser._process_functions(synapse.functions)}
# Build the dictionary
desc = {
'name': synapse_name,
'description': synapse.short_description,
'firstsynapse': firstsynapse if idx == 0 else "",
'variables': variables,
'psp': psp_code,
'preevent': preevent,
'postevent': postevent,
'functions': functions
}
# Generate the code
synapses += synapse_tpl % desc
return synapses
def _generate_neuron_models(net_id):
neurons = ""
firstneuron = "\\hdr{2}{D}{Neuron Models}\\\\ \\hline"
neuron_tpl = """
\\noindent
\\begin{tabularx}{\\linewidth}{|p{0.15\\linewidth}|X|}\\hline
%(firstneuron)s
\\textbf{Name} & %(name)s \\\\ \\hline
\\textbf{Type} & %(description)s\\\\ \\hline
\\textbf{%(equation_type)s} &
%(variables)s
%(spike)s
%(functions)s
\\end{tabularx}
\\vspace{2ex}
"""
first_neuron = True
for neuron in Global._objects['neurons']:
# skip bold models
if isinstance(neuron, BoldModel):
continue
# 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
# Generate the code for the equations
variables, spike_condition, spike_reset = LatexParser._process_neuron_equations(neuron)
eqs = ""
for var in variables:
eqs += """
\\begin{dmath*}
%(eq)s
\\end{dmath*}
""" % {'eq': var['latex']}
variables_eqs = """
%(eqs)s
\\\\ \\hline
""" % {'eqs': eqs}
# Spiking neurons have an extra field for the spike condition
spike_extra = ""
if neuron.type == 'spike':
spike_code = "If $" + spike_condition + "$ or $t \leq t^* + t_{\\text{refractory}}$:"
# Reset
spike_code += """
\\begin{enumerate}
\\item Emit a spike at time $t^*$"""
for var in spike_reset:
spike_code += """
\\item $""" + var + "$"
spike_code += """
\\end{enumerate}"""
spike_extra = """
\\textbf{Spiking} &
%(spike)s
\\\\ \\hline
""" % {'spike': spike_code}
# Possible function
functions = ""
if not neuron.functions == None:
functions = """
\\textbf{Functions} &
%(functions)s
\\\\ \\hline
""" % {'functions': LatexParser._process_functions(neuron.functions)}
# Build the dictionary
desc = {
'name': neuron_name,
'description': neuron.short_description,
'firstneuron': firstneuron if first_neuron else "",
'variables': variables_eqs,
'spike': spike_extra,
'functions': functions,
'equation_type': "Subthreshold dynamics" if neuron.type == 'spike' else 'Equations'
}
# Generate the code depending on the neuron position
neurons += neuron_tpl % desc
# first_neuron = False, to prevent multiple header
first_neuron = False
return neurons
def _generate_summary(net_id):
txt = """
# Structure of the network
"""
# General information
backend = 'default'
if Global.config['paradigm'] == 'cuda':
backend = "CUDA"
elif Global.config['paradigm'] == "openmp" and Global.config['num_threads'] > 1:
backend = "OpenMP"
txt +="""
* ANNarchy %(version)s using the %(backend)s backend.
* Numerical step size: %(dt)s ms.
""" % {'version': ANNarchy.__release__, 'backend': backend, 'dt': Global.config['dt']}
# Populations
if len(Global._network[net_id]['populations']) > 0:
headers = ["Population", "Size", "Neuron type"]
populations = []
for pop in Global._network[net_id]['populations']:
# Find a name for the neuron
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
populations.append([
pop.name,
pop.geometry if len(pop.geometry)>1 else pop.size,
neuron_name])
txt += """
## Populations
"""
txt += _make_table(headers, populations)
# Projections
if len(Global._network[net_id]['projections']) > 0 :
headers = ["Source", "Destination", "Target", "Synapse type", "Pattern"]
projections = []
for proj in Global._network[net_id]['projections']:
# Find a name for the synapse
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
projections.append([
proj.pre.name,
proj.post.name,
LatexParser._format_list(proj.target, ' / '),
synapse_name,
proj.connector_description
])
txt += """
## Projections
"""
txt += _make_table(headers, projections)
# Monitors
if len(Global._network[net_id]['monitors']) > 0:
headers = ["Object", "Variables", "Period"]
monitors = []
for monitor in Global._network[net_id]['monitors']:
monitors.append([
monitor.object.name + (" (subset)" if isinstance(monitor.object, PopulationView) else ""),
LatexParser._format_list(monitor.variables, ', '),
monitor.period
])
txt += """
## Monitors
"""
txt += _make_table(headers, monitors)
# Functions
if len(Global._objects['functions']) > 0 :
txt += """## Functions
"""
for name, func in Global._objects['functions']:
txt += LatexParser._process_functions(func, begin="$$", end="$$\n\n")
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