simply_nwb package

Subpackages

Submodules

simply_nwb.simple_nwb module

class simply_nwb.simple_nwb.SimpleNWB

Bases: _TIFMixin, _MP4Mixin, _LabjackMixin, _BlackrockMixin, _PergMixin, _EyetrackingMixin, object

static add_to_processing_module(nwbfile: NWBFile, data: Any, module_name: str | None = None, module_description: str | None = None) → NWBFile

Add data to a processing module, automatically creating it if it doesn’t already exist

Parameters:

  • nwbfile – NWBFile to add the data to

  • module_name – Name of the processing module to create or use

  • module_description – Description of the module, if not provided will be auto generated

  • data – Data to be added to the processing module

Returns:

NWBFile

static create_nwb(session_description: str, session_start_time: ~datetime.datetime, experimenter: [<class 'str'>], lab: str, experiment_description: str, institution: str, identifier: str | None = None, subject: ~pynwb.file.Subject | None = None, session_id: str | None = None, keywords: list[str] | None = None, related_publications: str | None = None) → NWBFile

Create a new nwbfile from the given params. More infor here https://pynwb.readthedocs.io/en/stable/pynwb.file.html#pynwb.file.NWBFile

Parameters:

  • session_description – description of the session

  • session_start_time – start date and time of recording session

  • experimenter – name of experimenter, list of form [“Lastname, Firstname”]

  • lab – name of lab

  • experiment_description – experiment description

  • institution – institution

  • identifier – Optional identifier for the file, if not supplied will be generated

  • subject – Optional pynwb.file.Subject object for metadata

  • session_id – Optional lab-specific session id, if not supplied will be generated

  • keywords – Optional list of keywords e.g [“keyword1”, “keyword2”, …]

  • related_publications – Optional related publications in a list of the DOI, URL, PMID etc [“DOI:1234/asdf”]

Returns:

NWBFile

static inspect_filename(nwbfilename: str) → list[Any]

Inspect the given NWBFile from filename

Parameters:

nwbfilename – filename to the NWB

Returns:

List of issues with the file, if empty, inspection passed

static inspect_obj(nwbfile: NWBFile) → list[Any]

Inspect the given NWBFile object

Parameters:

nwbfile – NWBFile object

Returns:

List of issues with the file, if empty, inspection passed

static processing_add_dataframe(nwbfile: NWBFile, processed_name: str, processed_description: str, data: DataFrame) → NWBFile

Add a processed pandas Dataframe into the NWB that doesn’t fit in any other part of the NWB. MAKE SURE YOU CANNOT ADD IT ELSEWHERE BEFORE USING THIS FUNC!

Parameters:

  • nwbfile – NWBFile to add data to

  • processed_name – Name of the processing module

  • processed_description – description of the processed data

  • data – Pandas Dataframe data to add

Returns:

NWBFile

static processing_add_dict(nwbfile: NWBFile, processed_name: str, data_dict: dict, processed_description: str, processing_module_name: str | None = None, uneven_columns: bool = False) → NWBFile

Add a processed dict into the NWB that doesn’t fit in any other part of the NWB. MAKE SURE YOU CANNOT ADD IT ELSEWHERE BEFORE USING THIS FUNC!

Parameters:

  • nwbfile – NWBFile to add data to

  • processing_module_name – Name of the processing module to add the data to. If not set will default to ‘misc’

  • processed_name – Name of the dynamic table

  • processed_description – description of the processed data

  • data_dict – dict data to add

  • uneven_columns – Set this to True if the keys of the dict have different lengths

Returns:

NWBFile

static test_nwb() → NWBFile

Generate a dummy nwb object to test with

Returns:

test nwb object

static two_photon_add_data(nwbfile: NWBFile, device_name: str, device_description: str, device_manufacturer: str, optical_channel_description: str, optical_channel_emission_lambda: float, imaging_name: str, imaging_rate: float, excitation_lambda: float, indicator: str, location: str, grid_spacing: list[float], grid_spacing_unit: str, origin_coords: list[float], origin_coords_unit: str, two_photon_unit: str, two_photon_rate: float, photon_series_name: str, image_data: Any) → NWBFile

Add images from a two photon microscope to an NWB with metadata. Load TIF images easily with the simply_nwb.acquisition.tools.tif module

Parameters:

  • nwbfile – NWBFile to add the data to

  • device_name – Name of the microscope e.g. MyMicroscope1

  • device_description – Description of the microscope

  • device_manufacturer – Manufacturer of the microscope

  • optical_channel_description – Description of the optical channel, electrode name

  • optical_channel_emission_lambda – Emission wavelength for optical channel, in nm

  • imaging_name – Name of this imaging dataset e.g ‘my_images’

  • imaging_rate – Rate at which images were acquired, in Hz

  • excitation_lambda – Excitation wavelength in nm

  • indicator – Indicator, e.g. GFP

  • location – Location, e.g. V1

  • grid_spacing – Spacing of the grids used e.g. [0.1, 0.1]

  • grid_spacing_unit – Unit of the grid spacing e.g. ‘meters’

  • origin_coords – Coords of the origin e.g. [0.1, 0.2] or for 3d data [0.1, 0.2, 0.3]

  • origin_coords_unit – Unit of the origin coords

  • two_photon_unit – Unit for the photon microscope, e.g. ‘normalized amplitude’

  • two_photon_rate – two photon sampling rate in Hz

  • photon_series_name – Name of the two photon series for storage

  • image_data – Numpy array of the data in shape (samples, xres, yres, channels) if only one channel, can omit

Returns:

NWBFile

static write(nwbfile: NWBFile, filename: str, verify_on_write: bool | None = True) → NWBFile

Write the give NWBFile object to file

Parameters:

  • nwbfile – NWBFile object to write

  • filename – path to file to write, WILL OVERWRITE!

  • verify_on_write – Verify that most fields wrote correctly and the file didn’t corrupt

Returns:

NWBFile

simply_nwb.util module

simply_nwb.util.age_str_from_birthday(birthday_str: str) → str

Create a ISO-8601 Period date string from a birthday. Interprets most date formats correctly, to ensure correct behavior, format ‘MM-DD-YYYY’ is recommended

Parameters:

birthday_str – String of birthday

simply_nwb.util.compare_nwbfiles(nwbfile1: NWBFile, nwbfile2: NWBFile, name1: str, name2: str)

Compare two nwbfiles, checking that they contain the same things, only check a handful of fields TODO? This is useful when writing a file, want to ensure all our fields that successfully wrote are read in correctly. If there is a difference, will throw a ValueError

Parameters:

  • nwbfile1 – NWBFile 1 to compare

  • nwbfile2 – NWBFile 2 to compare

  • name1 – String name of NWBFile 1, used in error message only

  • name2 – String name of NWBFile 2, used in error message only

simply_nwb.util.create_mouse_subject(subject_id: str, birthday_str: str, strain: str, sex: str, desc: str) → Subject

Create a Subject object, simple wrapper

Parameters:

  • subject_id – ID to use to uniquely identify a mouse

  • birthday_str – String of birthday of the mouse

  • strain – Strain of the mouse

  • sex – Mouse sex

  • desc – Description of the mouse

simply_nwb.util.date_to_mouse_age(date_str: str | None) → str | None

Convert a date into an ISO-8601 period time value

Parameters:

date_str – string of the date, should be pendulum parsable

Returns:

string like ‘P90D’

simply_nwb.util.dict_to_dyn_tables(dict_data: dict, table_name: str, description: str, multiple_objs: bool = True) → DynamicTable

Util function to transform a python dict into a DynamicTable object If keys are not the same length, set multiple_objs=True

Parameters:

  • dict_data – Dict to add

  • table_name – name of the table

  • description – description of the table

  • multiple_objs – set to true if the columns are uneven

Returns:

DynamicTable

simply_nwb.util.inspect_nwb_file(filename: str) → list[Any]

Return the inspection list of a given NWB file

Parameters:

filename – filename of the NWB to inspect

Returns:

list of inspection objects for the given NWB, if empty, no issues found

simply_nwb.util.inspect_nwb_obj(obj: NWBFile) → list[Any]

Return the inspection list of a given NWB object

Parameters:

obj – NWBFile object to inspect

Returns:

list of inspection objects, if empty no issues were found

simply_nwb.util.is_camel_case(string: str, do_print: bool = True) → bool

Check if the given string is in CamelCase

Parameters:

  • string – string to check

  • do_print – if False, will not print out anything

Returns:

bool if str is CamelCase

simply_nwb.util.is_filesystem_safe(string: str) → bool

Generic check function for if a string is filesystem safe, limits to a-z A-Z 0-9 ‘_’ ‘-’

Parameters:

string – String to check

Returns:

True if the given string matches the regex

simply_nwb.util.is_snake_case(string: str, do_print: bool = True) → bool

Checks if the given string is snake_case

Parameters:

  • string – string to check if is snake_case

  • do_print – if False, will not print anything

Returns:

bool of if the string is snake_case

simply_nwb.util.nwb_write(nwb_obj: NWBFile, filename: str, verify: bool)

Write an NWB object to a file on the local filesystem, and verify the contents were written correctly and the file isn’t corrupted

Parameters:

  • nwb_obj – pynwb.file.NWBFile object

  • filename – path of a local file, doesn’t need to exist

  • verify – Verify that most fields wrote correctly and the file didn’t corrupt

Returns:

None

simply_nwb.util.panda_df_to_dyn_table(pd_df: DataFrame, table_name: str, description: str) → DynamicTable

Util function to transform a pandas DataFrame into a DynamicTable object

Parameters:

  • pd_df – Pandas DataFrame

  • table_name – name of the table

  • description – description of the table

Returns:

DynamicTable

simply_nwb.util.panda_df_to_list_of_timeseries(pd_df: DataFrame, measured_unit_list: list[str], start_time: float, sampling_rate: float, description: str, series_name_prefix: str = '', comments: str | None = None) → list[TimeSeries]

Turns a panda dataframe into a list of TimeSeries objects

Parameters:

  • pd_df – dataframe to transform

  • measured_unit_list – list of units for each column of the dataframe

  • series_name_prefix – optional series prefix

  • start_time – time the data started for each timeseries

  • sampling_rate – sampling rate in Hz

  • description – description of this dataframe

  • comments – optional comments

Returns:

simply_nwb.util.warn_on_name_format(name_value: str, context_str: str = '') → bool

Send a warning if the name format isn’t in ‘snake_case’

Parameters:

  • name_value – value to check

  • context_str – Extra string to put in warning message

Returns:

True if passes, False otherwise

Module contents