pyneuroml.analysis package#

pyneuroml.analysis.analyse_spiketime_vs_dt(nml2_file, target, duration, simulator, cell_v_path, dts, verbose=False, spike_threshold_mV=0, show_plot_already=True, save_figure_to=None, num_of_last_spikes=None)#
pyneuroml.analysis.generate_current_vs_frequency_curve(nml2_file: str, cell_id: str, start_amp_nA: float = -0.1, end_amp_nA: float = 0.1, step_nA: float = 0.01, custom_amps_nA: List[float] = [], analysis_duration: float = 1000, analysis_delay: float = 0, pre_zero_pulse: float = 0, post_zero_pulse: float = 0, dt: float = 0.05, temperature: str = '32degC', spike_threshold_mV: float = 0.0, plot_voltage_traces: bool = False, plot_if: bool = True, plot_iv: bool = False, xlim_if: List[float] | None = None, ylim_if: List[float] | None = None, xlim_iv: List[float] | None = None, ylim_iv: List[float] | None = None, label_xaxis: bool = True, label_yaxis: bool = True, show_volts_label: bool = True, grid: bool = True, font_size: int = 12, if_iv_color: str = 'k', linewidth: str = '1', bottom_left_spines_only: bool = False, show_plot_already: bool = True, save_voltage_traces_to: str | None = None, save_if_figure_to: str | None = None, save_iv_figure_to: str | None = None, save_if_data_to: str | None = None, save_iv_data_to: str | None = None, simulator: str = 'jNeuroML', num_processors: int = 1, include_included: bool = True, title_above_plot: bool = False, return_axes: bool = False, verbose: bool = False, segment_id: str | None = None, fraction_along: float | None = None)#

Generate current vs firing rate frequency curves for provided cell.

It runs a number of simulations of the cell with different input currents, and generates the following metrics/graphs:

  • sub-threshold potentials for all currents

  • F-I curve for the cell

  • membrane potential traces for each stimulus

Using the method arguments, these graphs and the data they are generated from may be enabled/disabled/saved.

When the I-F curve plotting is enabled, it also notes the spiking threshold current value in a file. Note that this value is simply the lowest input stimulus current at which spiking was detected, so should be taken as an approximate value. It does not, for example, implement a bisection based method to find the accurate spiking threshold current. This is also true for the I-F curves: the resolution is constrained by the values of the stimulus currents.

The various plotting related arguments to this method are passed on to pyneuroml.plot.generate_plot()

Parameters:
  • nml2_file (str) – name of NeuroML file containing cell definition

  • cell_id (str) – id of cell to analyse

  • start_amp_nA (float) – min current to use for analysis

  • end_amp_nA (float) – max current to use for analysis

  • step_nA (float) – step value to use to generate analysis currents between start_amp_nA and end_amp_nA

  • custom_amps_nA – list of currents in nA to use. Note that this overrides the list created using start_amp_nA, end_amp_nA, and step_nA) :type custom_amps_nA: list(float)

  • analysis_duration (float) – duration of analysis

  • analysis_delay (float) – delay period before analysis begins

  • pre_zero_pulse (float) – duration of pre-zero pulse

  • post_zero_pulse (float) – duration of post-zero pulse

  • dt (float) – integration time step

  • temperature (str) – temperature to use for analysis

  • spike_threshold_mV (float) – spike threshold potential

  • plot_voltage_traces (bool) – toggle plotting of voltage traces

  • plot_if (bool) – toggle whether to plot I-F graphs

  • plot_iv (bool) – toggle whether to plot I-V graphs

  • xlim_if ([min, max]) – x-limits of I-F curve

  • ylim_if ([min, max]) – y limits of I-F curve

  • xlim_iv ([min, max]) – x limits of I-V curve

  • ylim_iv ([min, max]) – y limits of I-V curve

  • label_xaxis (str) – label for x axis

  • label_yaxis (str) – label for y axis

  • show_volts_label (bool) – toggle whether voltage traces should have corresponding current values labelled in the plot

  • grid (bool) – toggle whether grid should be shown in plot

  • font_size (int) – font size for plot

  • if_iv_color (str) – color to use for I-F and I-V plots

  • linewidth (str) – line width for plotting

  • bottom_left_spines_only

  • show_plot_already (bool) – toggle whether generated plots should be shown

  • save_voltage_traces_to (str) – file to save membrane potential traces to

  • save_if_figure_to (str) – file to save I-F plot figure to

  • save_iv_figure_to (str) – file to save I-V plot figure to

  • save_if_data_to (str) – file to save I-F plot data to

  • save_iv_data_to (str) – file to save I-V plot data to

  • simulator (str) – simulator to use

  • num_processors (int) – number of processors to use for analysis This option is only used with NetPyNE which can use MPI for parallelising simulations. For other simulators, this is unused.

  • include_included (bool) – include included files

  • title_above_plot (str) – title to show above the plot

  • return_axes (bool) – toggle whether plotting axis should be returned. This is useful if one wants to overlay more graphs in the same plot.

  • segment_id (str) – segment id to attach input to

  • fraction_along (float) – fraction along on segment to attach to

  • verbose (bool) – toggle verbosity

pyneuroml.analysis.NML2ChannelAnalysis module#

pyneuroml.analysis.NML2ChannelAnalysis.compute_iv_curve(channel, a, results, grid=True)#
pyneuroml.analysis.NML2ChannelAnalysis.generate_lems_channel_analyser(channel_file: str, channel: str, min_target_voltage: float, step_target_voltage: float, max_target_voltage: float, clamp_delay: float, clamp_duration: float, clamp_base_voltage: float, duration: float, erev: float, gates: List[str], temperature: float, ca_conc: float, iv_curve: bool, scale_dt: float = 1, dat_suffix: str = '', verbose: bool = True)#

Generate analysis simulation LEMS file.

Parameters:
  • channel_file (str) – path to channel file

  • channel (string) – name of channel

  • min_target_voltage (float) – magnitude of minimum target voltage in mV

  • max_target_voltage (float) – magnitude of maximum target voltage in mV

  • clamp_delay (float) – magnitude of delay for clamp in ms

  • clamp_duration – magnitude of duration of clamp in ms

  • clamp_base_voltage (float) – magnitude of base voltage of clamp in mV

  • duration (float) – magnitude of duration of simulation in ms

  • erev (float) – magnitude of reversal potential in mV

  • gates (list of str) – list of gates

  • temperature (float) – magnitude of temperature in degC

  • ca_conc (float) – magnitude of calcium concentration in mM

  • iv_curve (bool) – toggle whether to plot iv curve

  • scale_dt (float) – magnitude to scale dt by in ms

  • dat_suffix (str) – suffix of data file

  • verbose (bool) – toggle verbosity

pyneuroml.analysis.NML2ChannelAnalysis.get_channel_gates(channel: IonChannel | IonChannelHH) List[GateHHUndetermined | GateHHRates | GateHHTauInf | GateHHInstantaneous]#

Get gates in a channel

Parameters:

channel (neuroml.IonChannel, neuroml.IonChannelHH) – channel object

Returns:

list of gates

Return type:

list

pyneuroml.analysis.NML2ChannelAnalysis.get_channels_from_channel_file(channel_file: str) List[IonChannelHH | IonChannel]#

Get IonChannelHH and IonChannel instances from a NeuroML channel file.

Parameters:

channel_file (str) – complete path to a channel file

Returns:

list of channels

Return type:

list

pyneuroml.analysis.NML2ChannelAnalysis.get_conductance_expression(channel: IonChannel | IonChannelHH) str#

Get expression of conductance in channel.

Parameters:

channel (neuroml.IonChannel, neuroml.IonChannelHH) – channel object

Returns:

expression for conductance of channel.

Return type:

str

pyneuroml.analysis.NML2ChannelAnalysis.get_includes_from_channel_file(channel_file: str) List[IncludeType]#

Get includes from a NeuroML channel file

Parameters:

channel_file (str) – complete path to a channel file

Returns:

list of includes

Return type:

list

pyneuroml.analysis.NML2ChannelAnalysis.make_html_file(info)#
pyneuroml.analysis.NML2ChannelAnalysis.make_iv_curve_fig(a, grid=True)#
pyneuroml.analysis.NML2ChannelAnalysis.make_lems_file(channel, a)#
pyneuroml.analysis.NML2ChannelAnalysis.make_md_file(info)#
pyneuroml.analysis.NML2ChannelAnalysis.make_overview_dir()#
pyneuroml.analysis.NML2ChannelAnalysis.merge_with_template(model: Dict[Any, Any], templfile: str) str#

Merge model information with airspeed template.

Parameters:
  • model (dict) – model information

  • templfile (string) – name of airspeed template file

Returns:

merged template string

Return type:

str

pyneuroml.analysis.NML2ChannelAnalysis.plot_channel(channel, a, results, iv_data=None, grid=True)#
pyneuroml.analysis.NML2ChannelAnalysis.plot_iv_curve(a, hold_v, i, *plt_args, **plt_kwargs)#

A single IV curve

pyneuroml.analysis.NML2ChannelAnalysis.plot_iv_curve_vm(channel, a, hold_v, times, currents, grid=True)#
pyneuroml.analysis.NML2ChannelAnalysis.plot_iv_curves(channel, a, iv_data, grid=True)#
pyneuroml.analysis.NML2ChannelAnalysis.plot_kinetics(channel, a, results, grid=True)#
pyneuroml.analysis.NML2ChannelAnalysis.plot_steady_state(channel, a, results, grid=True)#
pyneuroml.analysis.NML2ChannelAnalysis.process_channel_file(channel_file: str, a) List[Any]#

Process the channel file.

Parameters:
  • channel_file (str) – complete path to channel file

  • a (argparse.Namsepace) – argparse.Namespace instance holding all arguments

Returns:

list of channel information

Return type:

list

Raises:

IOError – if channel file could not be found

pyneuroml.analysis.NML2ChannelAnalysis.run(a=None, **kwargs)#
pyneuroml.analysis.NML2ChannelAnalysis.run_lems_file(lems_file, verbose)#
pyneuroml.analysis.NML2ChannelAnalysis.save_fig(name)#

pyneuroml.analysis.ChannelDensityPlot module#

pyneuroml.analysis.ChannelDensityPlot.add_text(row, text)#
pyneuroml.analysis.ChannelDensityPlot.channel_density_plotter_cli(args=None)#
pyneuroml.analysis.ChannelDensityPlot.channel_density_plotter_process_args()#

Parse command-line arguments.

Returns:

None

pyneuroml.analysis.ChannelDensityPlot.channel_density_plotter_runner(a=None, **kwargs)#
pyneuroml.analysis.ChannelDensityPlot.format_float(dens)#
pyneuroml.analysis.ChannelDensityPlot.generate_channel_density_plots(nml2_file, text_densities=False, passives_erevs=False, target_directory=None)#
pyneuroml.analysis.ChannelDensityPlot.get_channel_densities(nml_cell: Cell) Dict[str, List[ChannelDensity | ChannelDensityGHK | ChannelDensityGHK2 | ChannelDensityVShift | ChannelDensityNernst | ChannelDensityNernstCa2 | ChannelDensityNonUniform | ChannelDensityNonUniformGHK | ChannelDensityNonUniformNernst]]#

Get channel densities from a NeuroML Cell.

Parameters:

nml_cell (neuroml.Cell) – a NeuroML cell object

Returns:

ordered dictionary of channel densities on cell with the ion channel id as the key, and list of channel density objects as the value

pyneuroml.analysis.ChannelDensityPlot.get_conductance_density_for_segments(cell: Cell, channel_density: ChannelDensity | ChannelDensityGHK | ChannelDensityGHK2 | ChannelDensityVShift | ChannelDensityNernst | ChannelDensityNernstCa2 | ChannelDensityNonUniform | ChannelDensityNonUniformGHK | ChannelDensityNonUniformNernst, seg_ids: str | List[str] | None = None) Dict[int, float]#

Get conductance density for provided segments to be able to generate a morphology plot.

If no segment ids are provided, provide values for all segments that the channel density is present on.

For uniform channel densities, the value is reported in SI units, but for non-uniform channel densities, for example ChannelDensityNonUniform, where the conductance density can be a function of an arbitrary variable, like distance from soma, the conductance density can be provided by an arbitrary function. In this case, the units of the conductance are not reported since the arbitrary function only provides a magnitude.

For non-uniform channel densities, we evaluate the provided expression using sympy.sympify.

Parameters:
  • cell (Cell) – a NeuroML Cell

  • seg_ids (None or str or list(str)) – segment id or list of segment ids to report, if None, report on all segments that channel density is present

  • channel_density (ChannelDensityGHK or ChannelDensityGHK2 or ChannelDensityVShift or ChannelDensityNernst or ChannelDensityNernstCa2 or ChannelDensityNonUniform or ChannelDensityNonUniformGHK or ChannelDensityNonUniformNernst,) – a channel density object

Returns:

dictionary with keys as segment ids and the conductance density for that segment as the value

New in version 1.0.10.

pyneuroml.analysis.ChannelDensityPlot.plot_channel_densities(cell: Cell, channel_density_ids: str | List[str] | None = None, ion_channels: str | List[str] | None = None, ymin: float | None = None, ymax: float | None = None, colormap_name: str = 'autumn_r', plane2d: str = 'xy', distance_plots: bool = False, show_plots_already: bool = True, morph_plot_type: str = 'constant', morph_min_width: float = 2.0, target_directory=None)#

Plot channel densities on a Cell on morphology plots.

You can either provide a list of channel densities where it’ll generate one plot per channel density. Or, you can provide a list of ions, and it’ll generate one plot per ion—adding up the conductance densities of the different channel densities for that ion. If neither are provided, plots for all ion channels on the cell are generated.

New in version 1.0.10.

Parameters:
  • cell (neuroml.Cell) – a NeuroML cell object

  • channel_density_ids (str or list(str)) – a channel density id or list of ids

  • ion_channels (str or list(str)) – an ion channel or list of ions channels

  • ymin (float or None) – min y value for plots, if None, automatically calculated

  • ymax (float or None) – max y value for plots, if None, automatically calculated

  • plane2d (str "xy" or "yz" or "zx") – plane to plot morphology plot in, passed on to the :py:function::plot_2D_cell_morphology function

  • morph_plot_type (str) – plot type for morphology plot passed on to plot_2D_cell_morphology

  • morph_min_width (float) – min width for morphology plot passed on to plot_2D_cell_morphology

  • distance_plots (bool) – also generate plots showing conductance densities at distances from the soma

  • colormap_name (str) –

    name of matplotlib colormap to use for morph plot. Note that if there’s only one overlay value, the colormap is modified to only show the max value of the colormap to indicate this.

    See: https://matplotlib.org/stable/users/explain/colors/colormaps.html

Returns:

None

pyneuroml.analysis.ChannelHelper module#

pyneuroml.analysis.ChannelHelper.evaluate_HHExpLinearRate(rate, midpoint, scale, v)#

Helper for putting values into HHExpLinearRate, see also https://docs.neuroml.org/Userdocs/Schemas/Channels.html#hhexplinearrate

pyneuroml.analysis.ChannelHelper.evaluate_HHExpRate(rate, midpoint, scale, v)#

Helper for putting values into HHExpRate, see also https://docs.neuroml.org/Userdocs/Schemas/Channels.html#hhexprate

pyneuroml.analysis.ChannelHelper.evaluate_HHSigmoidRate(rate, midpoint, scale, v)#

Helper for putting values into HHSigmoidRate, see also https://docs.neuroml.org/Userdocs/Schemas/Channels.html#hhsigmoidrate