denspp.offline.template.pipeline_plot#

Module Contents#

Functions#

plot_frames_feature

Plotting the detected spike frame activity of used transient data

plot_transient_input_spikes

Plotting results of end-to-end signal processor with plotting the signal input and clustered spike events

plot_transient_highlight_spikes

Plotting the detected spike activity from transient data (highlighted, noise in gray)

plot_mea_transient_total

Plotting the transient signals of the transient numpy signal with electrode information Args: mea_data: Transient numpy array with neural signal [row, colomn, transient] mapping: Numpy array with electrode mapping information fs_used: Sampling rate of the signal [Hz] path2save: Path for saving the figures do_global_limit: Doing a global y-range setting do_show: Show the plots Returns: None

API#

denspp.offline.template.pipeline_plot.plot_frames_feature(signals: dict, no_electrode: int, take_feat_dim: list = (0, 1), path: str = '', show_plot: bool = False) → None[source]#

Plotting the detected spike frame activity of used transient data

Parameters:

  • signals – class containing the rawdata and processed data from class PipelineSignal

  • no_electrode – number of electrodes

  • take_feat_dim – List with dimension selection for plotting the 2d feature space

  • path – Path to save the figures

  • show_plot – If true, show plot

Returns:

None

denspp.offline.template.pipeline_plot.plot_transient_input_spikes(signals: dict, no_electrode: int, path: str = '', time_cut: list = (), show_plot: bool = False) → None[source]#

Plotting results of end-to-end signal processor with plotting the signal input and clustered spike events

Parameters:

  • signals – class containing the rawdata and processed data from class PipelineSignal

  • no_electrode – number of electrodes

  • path – Path to save the figures

  • time_cut – Time cut

  • show_plot – If true, show plot

Returns:

None

denspp.offline.template.pipeline_plot.plot_transient_highlight_spikes(signals: dict, no_electrode: int, path: str = '', time_cut: list = (), show_noise: bool = False, show_plot: bool = False) → None[source]#

Plotting the detected spike activity from transient data (highlighted, noise in gray)

Parameters:

  • signals – class containing the rawdata and processed data from class PipelineSignal

  • no_electrode – number of electrodes

  • path – Path to save the figures

  • time_cut – List for only specified range

  • show_noise – If true, show noise (otherwise flat line)

  • show_plot – If true, show plot

Returns:

None

denspp.offline.template.pipeline_plot.plot_mea_transient_total(mea_data: numpy.ndarray, mapping: numpy.ndarray, fs_used: float, path2save: str = '', do_global_limit: bool = False, do_show: bool = False) → None[source]#

Plotting the transient signals of the transient numpy signal with electrode information Args: mea_data: Transient numpy array with neural signal [row, colomn, transient] mapping: Numpy array with electrode mapping information fs_used: Sampling rate of the signal [Hz] path2save: Path for saving the figures do_global_limit: Doing a global y-range setting do_show: Show the plots Returns: None