User guide# Getting started Introduction to pynapple Importing pynapple Instantiating pynapple objects nap.Tsd: 1-dimensional time serie nap.TsdFrame: 2-dimensional time series nap.TsdTensor: n-dimensionals time series nap.IntervalSet: intervals nap.Ts: timestamps nap.TsGroup: group of timestamps Interaction between pynapple objects Time support : attribute of time series Restricting time series to epochs Numpy & pynapple Slicing objects Slicing time series and intervals Slicing TsGroup Core functions Binning : counting events Thresholding Time-bin averaging of data Loading data Loading NWB Overview of advanced analysis Input-output Input-output & lazy-loading NWB Saving as NPZ Memory map Numpy memory map Zarr Navigating a dataset JSON sidecar file Core methods Interaction with numpy Initialization Attributes Slicing Arithmetic Array operations Concatenating Spliting Modifying Sorting Core methods Time series method restrict count bin_average interpolate value_from threshold Mapping between TsGroup and Tsd Parameterizing a raster Special slicing : TsdFrame 1. If not column labels are passed 2. If column labels are passed as integers 3. If column labels are passed as strings 4. If column labels are mixed type Interval sets methods Interaction between epochs union intersect set_diff split Drop intervals drop_short_intervals drop_long_intervals merge_close_intervals Metadata Adding metadata at initialization TsGroup IntervalSet TsdFrame Adding metadata after initialization set_info Using dictionary-like keys (square brackets) Using attribute assignment Accessing metadata Overwriting metadata Allowed data types Using metadata to slice objects High-level analysis Correlograms of discrete events Autocorrelograms Cross-correlograms Event-correlograms Tuning curves from epochs from timestamps activity 1-dimension tuning curves 2-dimension tuning curves from continuous activity 1-dimension tuning curves 2-dimension tuning curves Decoding 1-dimensional decoding 2-dimensional decoding Perievent Peri-Event Time Histogram (PETH) Raster plot Event trigger average Peri-Event continuous time series Randomization Shift timestamps Shuffle timestamp intervals Jitter timestamps Resample timestamps Power spectral density Generating a signal Computing power spectral density (PSD) Computing mean PSD Wavelet decomposion Generating a Dummy Signal Visualizing the Morlet Wavelets Parametrizing the wavelets Continuous wavelet transform Effect of gaussian_width Effect of window_length Effect of L1 vs L2 normalization Filtering time series Basics Inspecting frequency fesponses of a filter Performances
