Modules Details

pyfiber.behavior module

The pyfiber.behavior module define two classes: Behavior and MultiBehavior, that extracts behavioral events from raw files. They possess a number of methods that are useful when dealing with complex experimental paradigm. Intervals corresponding to periods between specified timestamps can be automatically calculated by defining them in the configuration file.

class pyfiber.behavior.Behavior(filepath, filetype=None, **kwargs)

Class extracting and analyzing behavioral data.

Parameters:

  • filepath (str) – filepath (by default must be a .dat file)

  • filetype (str) – must be set to user-defined format (by default takes the value of ‘BEHAVIOR_FILE_TYPE’ in the configuration file

  • kwargs – arguments passed to _utils.PyFiber, the parent class (ex: verbosity=False)

Variables:

  • filetype – input filetype (takes the default value defined in the config file)

  • filepath – data file filepath

  • df – pandas.DataFrame containing the extracted data

  • start – first timestamp of the data file, if the input is a csv and no start is provided, it takes the value 0.0

  • end – last timestamp of the data file, if the input is a csv file, there should be an ‘end’ column with a float value

  • <other> – all generated timestamps and intervals get added as instance attribute (and can be refered to their name as strings in most functions)

property raw

Output the unprocessed data file.

property total

Return the count for all extracted event (i.e. if events a,b and c are added, return the total count of a,b and c.)

property data

Return the count for all extracted event for all calculated intervals. (i.e. if events a,b and c, and intervals A, B, C are added, return the count of a,b and c for each of the A, B, C intervals.)

property description
_extract(family, subtype, column, value)

Extract timestamps for counters from Imetronic file.

Parameters:

  • family (int) – data family (see Imetronic nomenclature)

  • subtype (int) – data subtype

  • column (str) – additional column precising event of interest

  • value (int) – value needed in column to extract it

Returns:

data corresponding to input parameters

Return type:

pandas.DataFrame

_create_imetronic_attributes()

Extract event specified in the configuration file, in the imetronic_events section.

This function gets called during instance initialization if the filetype is ‘IMETRONIC’ (see configuration file).

_compute_attributes()

Create rule-based intervals and events as specified in the configuration file (in sections basic_intervals and custom)

_translate(obj)

Translate strings into corresponding arrays.

Parameters:

obj (str) – name of the interval/event

Equivalent to a lookup in self.__dict__ with the addition of a call to the behavior.set_non function if the ~ sign is prepended to the string.

_internal_selection(obj)

Transform string into corresponding data.

Parameters:

obj (Any) – either the event/interval name or its data

Returns:

object data as a list

Return type:

list

_graph(ax, obj, label=None, color=None, demo=True, unit='min', x_lim='default', alpha=1)

Internal function for plotting on a single axis.

Parameters:

  • ax – axis on which to plot

  • obj – element to plot

  • label – figure label

  • color – plot color

  • demo – use the predifined style defined in the configuration file

  • unit – units for the x-axis (can be 'ms', 's', 'min', 'h')

  • x_lim – x-axis limits, by default from start to end

  • alpha – graph transparency

_debug_interinj()

Return interinfusion time (between 2 consecutive pump activations).

Used to eliminate recording containing short interinfusion time.

figure(obj, figsize='default', h=0.8, hspace=0, label_list=None, color_list=None, save=False, title=None, save_dpi=600, save_format=['png', 'pdf'], **kwargs)

Plot data.

Parameters:

  • obj (Events or Intervals, str, dict) – data to plot

  • save – default is False, filepath (without file extension)

  • save_dpi – dpi if figure is saved

  • save_format – file extension for saving

Returns:

matplotlib.pyplot.figure containing a eventplots or axvspan or a combination of both.

Main plotting function, calling self._plot for each axis, called itself by self.summary

summary(demo=True, **kwargs)

Return a predifined graphical summary of main events and intervals.

Parameters:

  • demo (bool) – Demo mode (True). Provides a user defined summary of intervals and events. If False plots everything.

  • kwargs – Plotting arguments, passed to self.figure and self._plot.

The data and the formating provided by the demo option can be configured in pyfiber.yaml.

timestamps(events, interval='all', length=False, intersection=[], exclude=[], user_output=False)

Selects timestamps based on conditions.

Parameters:

  • events (str or Events) – name or array of timestamps

  • interval (str, List[str], List[Intervals], Intervals) – interval or intervals in which the timestamps must be contained, can be 'all'

  • intersection (List[str], List[Intervals]) – intersection of intervals in which the timestamps must be contained

  • exclude (List[str], List[Intervals]) – interval or intervals in which the timestamps must not be contained

  • user_output (bool) – if True, returns a tuple with all the user input data (for plotting purposes)

  • length (Union[float, int, bool]) – distance from the start of the intervals defined in interval to be considered.

Returns:

Timestamps or tuple with timestamps and considered intervals (depending on user_output)

Return type:

Events or tuple

export_timestamps(events, interval='all', length=False, intersection=[], exclude=[], to_csv=True, graph=True, filename='default', start_TTL1=False, **kwargs)

User API for visualizing timestamp selection.

Parameters:

  • events (str or Events) – name or array of timestamps

  • interval (str, List[str], List[Intervals], Intervals) – interval or intervals in which the timestamps must be contained, can be 'all'

  • intersection (List[str], List[Intervals]) – intersection of intervals in which the timestamps must be contained

  • exclude (List[str], List[Intervals]) – interval or intervals in which the timestamps must not be contained

  • length (Union[float, int, bool]) –

Wrapper for the self.timestamps function, with added graphical representation and csv output.

events(recorded=False, recorded_name='TTL1_ON', window=(0, 0))

Retrieve list of events.

Parameters:

  • recorded (bool) – only output events that are recorded(default value = False)

  • recorded_name (bool) – name of the attribute storing recorded intervals (default 'TTL1_ON')

  • window (tuple) – perievent analysis window

Returns:

dictionnary with all events

Return type:

dict

Note

Optionally only those which can be used in a perievent analysis (ie during the recording period and taking into account a perievent window).

intervals(recorded=False, window=(0, 0))

Retrieve list of intervals.

Parameters:

  • recorded (bool) – only output events that are recorded(default value = False)

  • recorded_name (bool) – name of the attribute storing recorded intervals (default 'TTL1_ON')

  • window – perievent analysis window

Returns:

dictionnary of all intervals

Return type:

dict

Note

Optionally only those which can be used in a perievent analysis (ie during the recording period and taking into account a perievent window).

what_data(plot=True, figsize=(20, 40))

Return dataframe summarizing the IMETRONIC dat file.

Parameters:

  • plot (bool) – return plotted data by ID tuple

  • figsize – plot size (default=(20,40), see matplotlib documentation)

get(name)

Extract dataframe section (IMETRONIC format).

Parameters:

name (str or tuple) – name or ID of IMETRONIC data family

Returns:

pandas.DataFrame corresponding to selected counter name (‘name’) or tuple (‘idtuple’).

movement(figsize=(20, 10), cmap='seismic', save=False, save_dpi=600, save_format=['png', 'pdf'])

Show number of crossings as a heatmap.

Parameters:

  • figsize (Tuple[int, int]) – size of plot

  • cmap (str) – color map (see matplotlib)

  • save – default is False, filepath (without file extension)

  • save_dpi – dpi if figure is saved

  • save_format – file extension for saving

BEHAVIOR_FILE_TYPE = 'IMETRONIC'
basic_intervals = {'HLED': [['ON_OFF', 'both', ['hled_on', 'hled_off']], 'Houselight on (interval)'], 'LED1': [['ON_OFF', 'both', ['led1_on', 'led1_off']], 'CS light on (interval)'], 'LED2': [['ON_OFF', 'both', ['led2_on', 'led2_off']], 'Drug light on (interval)'], 'TTL1': [['ON_OFF', 'both', ['ttl1_on', 'ttl1_off']], 'TTL1 sending (interval)']}
behavior_time_ratio = 1000
csv_events = {'TTL1': [['timestamp', 'DI/O-1', 'Time(s)'], 'TTL1'], 'TTL2': [['timestamp', 'DI/O-2', 'Time(s)'], 'TTL2'], 'TTL3': [['timestamp', 'DI/O-3', 'Time(s)'], 'TTL3'], 'TTL4': [['timestamp', 'DI/O-4', 'Time(s)'], 'TTL4']}
custom = {'DARK': [['INTERSECTION', ['HLED_OFF', 'LED1_OFF', 'LED2_OFF']], 'No lights on'], 'DNI': [['NEAR_EVENT', 'DARK', 'inj1', 5], 'No lights on, close to an injection'], 'DRP': [['NEAR_EVENT', 'HLEDOFF_NODARK', 'led2_on', 1], 'Period with HLED off and near a led2 switch on'], 'DRP_INJ': [['CONTAINS', 'DRP', 'inj1'], 'Drug period that is close to an injection'], 'DRP_LONG': [['DURATION', 'DRP', '>', 600], 'Drug periods longer than 600 seconds'], 'DRUG': [['UNION', ['DRP_LONG', 'DRP_INJ']], 'Drug periods'], 'DRUG_NOTO': [['INTERSECTION', ['DRUG', '~TIMEOUT']], 'Drug period without timeouts'], 'D_n': [['GENERATIVE', 'DRUG'], 'N-th drug period'], 'HLEDOFF_NODARK': [['INTERSECTION', ['HLED_OFF', '~NOTO_DARK']], 'HLED off, excluding non-timeout-dark periods'], 'ND_first': [['NEAR_EVENT', 'HLED_ON', 'switch_1', 1], 'First ND period (excluding the first one in case of NDpre paradigm'], 'ND_n': [['GENERATIVE', 'HLED_ON'], 'No drug period (HLED on)'], 'NOTO_DARK': [['INTERSECTION', ['DARK', '~TIMEOUT']], 'No lights on, not a timeout'], 'TIMEOUT': [['UNION', ['LED1_ON', 'TO_DARK']], 'CS light on + subsequent dark period'], 'TO_DARK': [['DURATION', 'DNI', '<', 45], 'No lights on, close to an injection, shorter than 45s'], 'hled_on_end': [['boundary', 'end', 'HLED_ON'], 'Ends of each no drug periods'], 'hled_on_start': [['boundary', 'start', 'HLED_ON'], 'Starts of each no drug periods'], 'inj_first': [['indexed', 'inj1', 1], 'First injection'], 'noto_dark_end': [['boundary', 'end', 'NOTO_DARK'], "End of dark period (that isn't a timeout)"], 'np1_ND_first': [['iselement', 'np1', 'ND_first'], 'Active nosepokes during the first ND period'], 'np1_NDfirst_1': [['indexed', 'np1_ND_first', 1], 'First active nosepoke of first ND period'], 'np1_n': [['generative2', ['D_n', 'np_effective'], 5], 'N-th effective nosepoke inside each drug period'], 'np2_ND_first': [['iselement', 'np2', 'ND_first'], 'Inactive nosepokes during the first ND period'], 'np2_NDfirst_1': [['indexed', 'np2_ND_first', 1], 'First inactive nosepoke of first ND period'], 'np_HF_NOTO': [['iselement', 'np1', 'DRUG_NOTO'], 'Active nosepoke not during HLED ON and not during full timeout'], 'np_effective': [['iselement', 'np_HF_NOTO', 'LED2_ON'], 'Active nosepoke that is part of the FR series'], 'npt': [['combination', ['np1', 'np2']], 'All nosepokes (active and inactive)'], 'npt_ND_first': [['iselement', 'npt', 'ND_first'], 'Nosepokes during the first ND period'], 'npt_NDfirst_1': [['indexed', 'npt_ND_first', 1], 'First nosepoke of first ND period'], 'rec_start': [['indexed', 'ttl1_on', 1], 'Start of the recording'], 'switch_1': [['indexed', 'switch_between', 1], 'First of the switches between ND and D that are between t+100s and t+3400s'], 'switch_1_D': [['indexed', 'switch_between_D', 1], 'No to-d First of the switches between ND and D that are between t+100s and t+3400s'], 'switch_between': [['timerestricted', 'switch_dto_nd', [100, 3400]], 'Switch between ND and D that are between t+100s and t+3400s'], 'switch_between_D': [['timerestricted', 'switch_d_nd', [100, 3400]], 'No to-d Switch between ND and D that are between t+100s and t+3400s'], 'switch_d_nd': [['iselement', 'hled_on_start', 'DRUG_NOTO'], 'Switch from drug to no drug (ie excluding timeout to drug)'], 'switch_dark_d1': [['iselement', 'noto_dark_end', 'LED2_ON'], 'Switch from dark to D1'], 'switch_dto_nd': [['combination', ['switch_d_nd', 'switch_to_nd']], 'Switch of either drug/timeout to no drug'], 'switch_nd1': [['boundary', 'end', 'ND_first'], 'First ND-D switch.'], 'switch_nd_d': [['iselement', 'hled_on_end', 'LED2_ON'], 'Switch from ND to D'], 'switch_to_nd': [['iselement', 'hled_on_start', 'TIMEOUT'], 'Switch from timeout to no drug']}
elements = {'DARK': [False, 'DARK', 'k'], 'HLED_OFF': [True, 'Drug Period', '#069AF3'], 'HLED_ON': [True, 'No Drug Period', 'gold'], 'LED1_OFF': [False, 'LED1_OFF', ['darkgray', 'darkgrey']], 'LED1_ON': [True, 'Conditioned Stimulus', 'goldenrod'], 'LED2_OFF': [False, 'LED2_OFF', 'slategrey'], 'LED2_ON': [True, ' Drug Light On', 'slategrey'], 'NOTO_DARK': [False, 'NOTO_DARK', 'black'], 'TIMEOUT': [True, 'Time outs', 'k'], 'TO_DARK': [False, 'TO_DARK', 'olivedrab'], 'TTL1_ON': [True, 'Fiber Photometry Recordings', 'g'], 'all': [True, 'Session', 'b'], 'inj1': [True, 'Injections', 'k'], 'np1': [True, 'Active Nosepokes', 'r'], 'np2': [True, 'Inactive Nosepokes', 'b'], 'switch_d_nd': [False, 'Switch from D to ND', 'r'], 'switch_nd_d': [False, 'Switch from ND to D', 'b'], 'switch_to_nd': [False, 'Switch from TO to ND', 'r']}
imetronic_events = {'hled_off': [['conditional', [1, 1], ['_P', 0]], 'houselight off'], 'hled_on': [['conditional', [1, 1], ['_P', 1]], 'houselight on'], 'inj1': [['conditional', [6, 1], ['_L', 1]], 'Injection'], 'led1_off': [['conditional', [1, 2], ['_P', 0]], 'CS light off'], 'led1_on': [['conditional', [1, 2], ['_P', 1]], 'CS light on'], 'led2_off': [['conditional', [1, 3], ['_P', 0]], 'Drug light off'], 'led2_on': [['conditional', [1, 3], ['_P', 1]], 'Drug light on'], 'np1': [['conditional', [3, 1], ['_V', 1]], 'Active Nosepoke'], 'np2': [['conditional', [3, 2], ['_V', 1]], 'Inactive nosepoke'], 'ttl1_off': [['conditional', [15, 1], ['_L', 0]], 'Recording TTL off'], 'ttl1_on': [['conditional', [15, 1], ['_L', 1]], 'Recording TTL on'], 'x_coordinates': [['simple', [9, 1], '_X'], 'X coordinates'], 'xy_timestamps': [['simple', [9, 1], 'TIME'], 'Timestamps for coordinates'], 'y_coordinates': [['simple', [9, 1], '_Y'], 'Y coordinates']}
class pyfiber.behavior.MultiBehavior(folder, fileformat='dat', **kwargs)

Class for multiple behavioral file analysis

Parameters:

  • folder (str) – folder containing data files (the explorer recurse inside subfolders as well)

  • fileformat (str) – format of files to be included (default is ‘dat’)

  • kwargs – keyword arguments passed to pyfiber.Behavior instances

Variables:

  • behavior_time_ratio – conversion from file unit to seconds, default 1000 (inherited from _utils.PyFiber which directly reads it from the configuration file)

  • folder – same as folder parameter

  • fileformat – same as fileformat parameter

  • paths – list of all found filepaths

  • filepath – list of all filepaths (this is directly extracted from each member of sessions)

  • names – list of all animal names (by default same as filepath)

  • number – number of sessions

  • sessions – dictionnary containing all sessions as pyfiber.Behavior instances.

  • df – list of behavioral data dataframes (see pyfiber.Behavior(*).df )

  • start – start for all files, as a list (see pyfiber.Behavior(*).start)

  • end – end for all files, as a list (see pyfiber.Behavior(*).end )

  • <other> – all created attributed and intervals, as a pandas.DataFrame with filenames as index.

_savgol(data, window=10, polyorder=3, nosmoothing=False)
_cnt(attribute)

Return the number of events per second for a defined event for all sessions:

Parameters:

attribute (str) – event name

Returns:

Dictionnary with ‘filename’ and array with the number of events for each second

Return type:

dict

count(attribute)

Return the number of events per second for a defined event for all sessions:

Parameters:

attribute (str) – event name

Returns:

Data frame containing with number of events for each second for each animal

Return type:

pandas.DataFrame

Note

Similar to private method self._cnt but return the data as a dataframe.

cumul(attribute, plot=True, figsize=(20, 15), save=False, save_dpi=600, save_format=['png', 'pdf'], legend=False, **kwargs)

Return the cumulative sum of a given event for all animals.

Parameters:

  • attribute (str) – event name

  • plot (bool) – return a plot

  • figsize (Tuple[int, int]) – figure size (passed to matplolib via a DataFrame)

  • save – default is False, filepath (without file extension)

  • save_dpi – dpi if figure is saved

  • save_format – file extension for saving

  • legend – show session names as labels

Kwargs:

keyword arguments passed to the plot function of a pandas.DataFrame

Returns:

Data Frame containing the cumulative sum, a plot (optional)

Return type:

pandas.DataFrame

show_rate(attribute, interval='HLED_ON', binsize=120, percentiles=[15, 50, 85], figsize=(20, 10), interval_alpha=0.3, save=False, save_dpi=600, save_format=['png', 'pdf'], legend=False, **kwargs)

Show rate for all session for any given event.

Parameters:

  • attribute (str) – event name

  • interval (str or bool) – interval shown on the graph (optional)

  • binsize (int) – binsize in seconds

  • percentiles (list) – optional plot with percentiles

  • figsize (tuple) – figure size

  • interval_alpha (float) – transparency value for the interval plot

  • save (bool) – default is False, filepath (without file extension)

  • save_dpi – dpi if figure is saved

  • save_format – file extension for saving

  • legend (bool) – show session names as labels

Rtype interval_alpha:

float

summary(title=True, **kwargs)

Output the session summary for all sessions:

Parameters:

  • kwargs – keyword arguments passed to pyfiber.Behavior(*).summary

  • title (Union[bool, list]) –

Note

See documentation for pyfiber.Behavior

pyfiber.behavior.Events

alias of ndarray

pyfiber.behavior.select_interval_by_duration(interval, condition)

Select interval based on their duration.

Parameters:

  • interval (Intervals) – interval as a list of tuples

  • condition (list) – user inputed condition (e.g. ['<',25])

  • type – list

Returns:

Filtered intervals

Return type:

Intervals

This function selects intervals by their duration, the conditional argument is a list containing the operator and a float (it mainly reads from the configuration file).

pyfiber.behavior.generate_interval(on, off, end)

Compute intervals based on a two lists of right and left limits. Discards redundant values (i.e. if two consecutive right limits are provided).

Parameters:

  • on (Events) – left limits of desired intervals.

  • off (Events) – right limits of desired intervals.

  • end (float) –

Returns:

computed intervals

Return type:

Intervals

# This function is primarly used internally by the Behavior class, but can be directly invoked
>> on  = [0., 5., 20., 40]   
>> off = [10., 11., 30., 50]
# 5 and 11 are redundant and should thus be eliminated
>> end = 60
# The end should be inputed, if the last command is off, the interval can thus end at the end of the experiment
>> pyfiber.behavior.generate_interval(on,off,end)
[(0.0, 10.0), (20.0, 30.0), (40.0, 50.0)]

Warning

This function is based on the metaphor of a light switch, hence the names ‘on’ and ‘off’. However, a number of intervals can be computed with the same principle. Importantly, it assumes that the state is ‘off’ at the beginning of the experiment. If the interval of interest is by default on at the beginning of the experiment, the complementary interval should be computed and its complement (the interval of interest) itself obtained with the behavior.set_non fonction.

pyfiber.behavior.interval_is_close_to(intervals, events, nearness, closed='right')

Return a list of intervals each near of at least one event from the specified event array.

Parameters:

  • intervals (Intervals) – evaluated intervals

  • events (Events) – events of which intervals must be near

  • nearness (float) – maximum distance from interval to events (in seconds)

  • closed (str) – see pandas.IntervalArray for details

Returns:

selected intervals

Return type:

Intervals

This function is used to calculate some of the custom events. For example, if the user wishes to isolate timestamps of particular events only if they happen near a period where a certain light is on.

pyfiber.behavior.element_of(event_array, intervals, is_element=True, boolean=False)

Return events that are elements (or are not) of specified intervals.

Parameters:

  • event_array (Events) – input list of event timestamps

  • intervals (Intervals) – intervals that should (or shouldn’t) include events

  • is_element (bool) – True if intervals should be in intervals, False if they should’t

  • boolean (bool) – if True, the function return type is a boolean, else it is the list of timestamps that pass the condition

Returns:

Either the timestamps or a boolean

Return type:

Events or bool

pyfiber.behavior.set_union(*sets)

Find union of any number of Intervals.

Parameters:

sets (Intervals) – intervals to be united.

Returns:

Union of all inputed Intervals

Return type:

Intervals

pyfiber.behavior.set_non(A, end, start=0)

Returns the complement of a collection of sets.

Parameters:

  • A (Intervals) – Intervals of which to compute the complement

  • end (float) – End of experiment

  • start (float) – start of experiment, default value is 0

Returns:

Complement of A

Return type:

Intervals

pyfiber.fiber module

This module mainly defines the Fiber Class. Most of the function are tied to the class in order to take advantage of attributes to set default values. However, important functions such as the fiber.normalize_signal and fiber.detect_peaks are defined outside the class as they can be useful on their own.

class pyfiber.fiber.Fiber(filepath, name='default', ID=None, alignment=0, filetype=None, **kwargs)

Extract and analyse fiberphotometry data.

Variables:

  • data – dataframe with all data from data file

  • recordings – all separate recording

  • number_of_recordings – number of separate recordings (see note below)

  • peaks – dictionnary containing a data frame with peak data (timestamps, dF/F, zscore) for all recordings

  • rate (sampling) – mean sampling rate accross recordings

  • full_time – array containing all timestamps from the data file

  • rec_intervals – time intervals for each recording

  • rec_length – duration of each recording (as a list)

  • df – raw data as a pandas.DataFrame

  • ncl – nomenclature for reading columns, defined in the config file

  • columns – name of the retrieved columns (i.e. time, signal, control)

  • raw_columns – name of the original columns

  • ID – see parameters

  • alignment – see parameters

  • filepath – see parameters

  • filetype – see parameters

  • name – see parameters

  • FIBER_FILE_TYPE – (from _utils.PyFiber) system file type (default: DORIC_CSV)

  • split_recordings – (from _utils.PyFiber) see note below

  • split_treshold – (from _utils.PyFiber) treshold for splitting data into recording (e.g. 10 is 10 times the mean intersample distance)

  • min_sample_per_rec – (from _utils.PyFiber) minimun number of samples for a split to be considered a recordings (ignores one sample recordings that are bugs)

  • trim_recording – (from _utils.PyFiber) number of seconds automatically removed from recording start (to avoid the LED artifact)

  • perievent_window – (from _utils.PyFiber) default perievent analysis window

  • default_norm – (from _utils.PyFiber) default normalization method (‘F’ or ‘Z’, see detail in pyfiber.fiber.normalize_signal)

  • peak_window – (from _utils.PyFiber) length in seconds of the peak detection window

  • peak_distance – (from _utils.PyFiber) minimum distance between peaks (to avoid false positives), in milliseconds

  • peak_zscore – (from _utils.PyFiber) full or bins choose if post normalization standardization should be done on the full recordings or for each window

  • peak_baseline_MAD – (from _utils.PyFiber) first treshold for peak analysis

  • peak_peak_MAD – (from _utils.PyFiber) second treshold for peak analysis

Parameters:

  • filepath (str) – fiber photometry data file path

  • name (str) – session name (default is filepath without the extension)

  • ID (hashable) – animal identifier

  • alignment (float) – optional shift in the timestamps

  • filetype (str) – data file type, defaults to Doric

Note

Alignment can be used if the exact time of recording start is provided by a behavioral system. It simply shift every timestamps of the fiber file accordingly.

Note

In the fiber photometry data file, locations where the timestamps are discontinuous define breakpoints where the data is split into recordings. To change this behavior, simply add split_recordings = False at instantiation, or modify the configuration file :

>> import pyfiber
>> f = pyfiber.Fiber('filepath.csv', split_recordings = False)

# ~/.pyfiber/pyfiber.yaml
[...]
FIBER:
  split_recordings: False
[...]
_find_rec(timestamp)

Find recording number corresponding to inputed timestamp.

Parameters:

timestamp (float) – timestamp of which to find the recording number if any

Return type:

list

_read_file(filepath, alignment=0)

Read file and align the timestamps if specified.

Parameters:

  • filepath – the data file path

  • alignment – offset in seconds to align fiber datafile to behavioral data

Return type:

DataFrame

_extract_data()

Extract raw fiber data from Doric system.

Return type:

DataFrame

_split_recordings()

Cut at timestamp jumps.

The jumps are defined by a step greater than N times the mean sample space (defined in pyfiber.yaml).

Return type:

dict

plot(which='all', method='default', figsize=(20, 20), raw_label=['Ca-dependant', 'isosbestic'], norm_label='Normalized signal', xlabel='Time (s)', ylabel_raw='Signal (mV)', ylabel_norm='Signal (%)', title='Recording', hspace=0.5)

Plot fiber data.

Parameters:

  • which (Union[str, int, List[int]]) – which recording should be plotted ; default is ‘all’

  • method (str) – normalization method (see fiber.normalize_signal for details)

  • figsize (tuple) – figure size

  • raw_label (list) – plotting labels for the non normalized data

  • norm_label (str) – plotting labels for the normalized data

  • xlabel (str) – label for the x-axis

  • ylabel_raw (str) – y-axis label for non-normalized data

  • ylabel_norm (str) – y-axis label for normalized data

  • title (str) – plot title

  • hspace (float) – horizontal space between graphs

Return type:

None

to_csv(recordings='all', auto=True, columns=None, column_names=None, prefix='default')

Export data to csv.

Parameters:

  • recording – defines which recording to export (default is all)

  • auto (bool) – automatically export signal and isosbestic separately with sampling_rate

  • columns (Optional[List[str]]) – export specific columns with their timestamps

  • columns_names – change default name for columns in outputted csv (the list must correspond to the column list)

  • prefix (str) – filename prefix (default is ‘<raw filename><recording number><data column name>’’)

  • recordings (Union[str, int, List[int]]) –

  • column_names (Optional[List[str]]) –

Return type:

None

FIBER_FILE_TYPE = 'DORIC'
default_norm = 'F'
get(column, recording=1, add_time=False, as_df=False)

Extracts a data array from a specific column of a recording.

Parameters:

  • column (str) – dataframe column to extract (list of columns: self.columns)

  • add_time (bool) – if True return a 2D array (stack of time and signal)

  • as_df (bool) – if True return data as a data frame (add_time will automatically be set to True)

  • recording (int) –

Return type:

Union[DataFrame, ndarray]

Note

This function is a shortcut to <obj>.recordings[<rec_number>][<'column_name'>].to_numpy() with multiple queries (time) as an option.

min_sample_per_rec = 5
peak_baseline_MAD = 2
peak_distance = '50ms'
peak_peak_MAD = 3
peak_window = 10
peak_zscore = 'full'
perievent_window = [1.0, 1.0]
split_recordings = True
split_treshold = 10
trim_recording = 1.0
norm(rec=1, method='default', add_time=True)

Normalize data with specified method.

Parameters:

  • rec (int) – recording number

  • method (str) – normalization method (see :ref`note <n>`)

  • add_time (bool) – include timestamps with output

Return type:

ndarray

Note

This method is a wrapper of the fiber.normalize_signal function, see its documentation for more details.

_detect_peaks(rec=False, arr=[], time=[], signal=[], window='default', distance='default', plot=True, figsize=(30, 10), zscore='full', bMAD='default', pMAD='default', **kwargs)

Detect peaks in a normalized fiberphotometry signal.

API for fiber.detect_peaks, with class default. :param arr: numpy array with timestamps in first column and signal values in second column :param time: timestamps :param signal: fiber photometry signal :param window: size of each succesive bins were peaks are computed (default is 10s) :param distance: minimum distance between successive peaks (in milliseconds), see note :param plot: output a plot :param figsize: figure size :param zscore: defines if standardisation should occur at the bin level or the whole recording level :param bMAD: baseline treshold (all timepoint below bMAD times the MAD are removed) :param pMAD: peak treshold (peaks have to be at least pMAD times higher than the computed baseline) :param kwargs: keyword argument passed to scipy.signal.find_peaks, see note

Note

The final peak detection is handled by scipy.signal.find_peaks. By default, distance, provided in string format in milliseconds (ex: ‘50ms’) is converted in a number of samples based on the provided timestamp array. Additionnally, any keyword argument catched by kwargs is passed to find_peaks so more precise criteria can be used.

Parameters:

  • window (Union[str, float]) –

  • distance (str) –

  • plot (bool) –

  • figsize (tuple) –

  • zscore (str) –

  • bMAD (Union[str, float]) –

  • pMAD (Union[str, float]) –

Return type:

DataFrame

plot_transients(value='zscore', figsize=(20, 20), rec='all', colors='k', alpha=0.3, save=False, save_dpi=600, save_format=['png', 'pdf'], **kwargs)

Show graphical representation of detected transients with their amplitude.

Parameters:

  • value (str) – show either Z-scores ('zscore) or \(\frac{\Delta F}{F_0}\) ('dF/F')

  • figsize (tuple) – figure size

  • rec (Union[str, int]) – recordings to plot, default is all

  • colors (str) – bar colors

  • alpha (float) – transparency

  • kwargs – keyworg arguments passed to matplotlib

  • save – default is False, filepath (without file extension)

  • save_dpi – dpi if figure is saved

  • save_format – file extension for saving

Return type:

None

peakFA(a, b)

Return peak analysis: frequency and amplitude results for given interval.

Parameters:

  • a (float) – left limit of the interval

  • b (float) – right limit of the interval

Returns:

Mean frequency, mean peak amplitude, max peak amplitude, full peak data frame (all peaks from the interval)

Return type:

dict

pyfiber.fiber.normalize_signal(signal, control, method)

Normalize the signal using a control channel.

Parameters:

  • signal (np.ndarray) – calcium-dependant signal

  • control (np.ndarray) – isosbestic signal

  • method (str) – normalization method

Return type:

ndarray

Note

Two normalization method can be used. 'F' is the standard \(\frac{\Delta F}{F_0}\). 'Z' is the z-score difference of signal and control.

pyfiber.analysis module

class pyfiber.analysis.Session(behavior, fiber, ID=None, **kwargs)

Create object containing both fiber recordings and behavioral files.

Parameters:
classmethod from_folder(folderpath, fiber_marker='.csv', behavior_marker='.dat', **kwargs)

Create session object from folder.

Parameters:

  • folderpath (str) – folder path

  • fiber_marker (str) – string present only in fiber data file (default is '.csv')

  • behavior_marker (str) – string present only in fiber data file (default is '.dat')

  • kwargs – keyword arguments passed to __init__

_sample(time_array, event_time, window)

Get indices of a timeseries sample of the recording.

Parameters:

  • time_array (ndarray) – an array of timestamp

  • event_time (float) – event of interest

  • window (tuple) – time window left and right of the event of interest

Returns:

indices for the start, event and end of the sample, relative to the input array

Return type:

tuple

_recorded_timestamps(events, window, **kwargs)

Return analyzable timestamps (i.e. both recorded and not overlapping the perievent window on the edges).

Parameters:

  • events (Union[str, List[str]]) – name of events of interest

  • window (tuple) – perievent analysis window

  • kwargs – keyword arguments passed on to pyfiber.Behavior.timestamps

Returns:

array of timestamps corresponding to input and occuring during recorded intervals

Return type:

ndarray

events(**kwargs)

See pyfiber.Behavior.events.

Parameters:

  • recorded (bool) – only output events that are recorded(default value = False)

  • recorded_name (bool) – name of the attribute storing recorded intervals (default 'TTL1_ON')

  • window – perievent analysis window

Returns:

dictionnary with all events

Return type:

dict

intervals(**kwargs)

See pyfiber.Behavior.intervals

Parameters:

  • recorded (bool) – only output events that are recorded(default value = False)

  • recorded_name (bool) – name of the attribute storing recorded intervals (default 'TTL1_ON')

  • window – perievent analysis window

Returns:

dictionnary of all intervals

Return type:

dict

analyze(event_time, window='default', norm='default')

Return Analysis object, related to defined perievent window.

Parameters:

  • event_time (float) – event of interest timestamp

  • window (Union[str, tuple]) – perievent analysis window

  • norm (str) – normalization method

Returns:

object containing all the analysis data and methods to display them

Return type:

object

Note

The normalization parameter correspond the process of taking into account the

update_window(new_window)

Change perievent window.

plot(what='events')

Plot either events or intervals.

Tests if they happen within recording timeframe.

BEHAVIOR_FILE_TYPE = 'IMETRONIC'
FIBER_FILE_TYPE = 'DORIC'
basic_intervals = {'HLED': [['ON_OFF', 'both', ['hled_on', 'hled_off']], 'Houselight on (interval)'], 'LED1': [['ON_OFF', 'both', ['led1_on', 'led1_off']], 'CS light on (interval)'], 'LED2': [['ON_OFF', 'both', ['led2_on', 'led2_off']], 'Drug light on (interval)'], 'TTL1': [['ON_OFF', 'both', ['ttl1_on', 'ttl1_off']], 'TTL1 sending (interval)']}
behavior_time_ratio = 1000
csv_events = {'TTL1': [['timestamp', 'DI/O-1', 'Time(s)'], 'TTL1'], 'TTL2': [['timestamp', 'DI/O-2', 'Time(s)'], 'TTL2'], 'TTL3': [['timestamp', 'DI/O-3', 'Time(s)'], 'TTL3'], 'TTL4': [['timestamp', 'DI/O-4', 'Time(s)'], 'TTL4']}
custom = {'DARK': [['INTERSECTION', ['HLED_OFF', 'LED1_OFF', 'LED2_OFF']], 'No lights on'], 'DNI': [['NEAR_EVENT', 'DARK', 'inj1', 5], 'No lights on, close to an injection'], 'DRP': [['NEAR_EVENT', 'HLEDOFF_NODARK', 'led2_on', 1], 'Period with HLED off and near a led2 switch on'], 'DRP_INJ': [['CONTAINS', 'DRP', 'inj1'], 'Drug period that is close to an injection'], 'DRP_LONG': [['DURATION', 'DRP', '>', 600], 'Drug periods longer than 600 seconds'], 'DRUG': [['UNION', ['DRP_LONG', 'DRP_INJ']], 'Drug periods'], 'DRUG_NOTO': [['INTERSECTION', ['DRUG', '~TIMEOUT']], 'Drug period without timeouts'], 'D_n': [['GENERATIVE', 'DRUG'], 'N-th drug period'], 'HLEDOFF_NODARK': [['INTERSECTION', ['HLED_OFF', '~NOTO_DARK']], 'HLED off, excluding non-timeout-dark periods'], 'ND_first': [['NEAR_EVENT', 'HLED_ON', 'switch_1', 1], 'First ND period (excluding the first one in case of NDpre paradigm'], 'ND_n': [['GENERATIVE', 'HLED_ON'], 'No drug period (HLED on)'], 'NOTO_DARK': [['INTERSECTION', ['DARK', '~TIMEOUT']], 'No lights on, not a timeout'], 'TIMEOUT': [['UNION', ['LED1_ON', 'TO_DARK']], 'CS light on + subsequent dark period'], 'TO_DARK': [['DURATION', 'DNI', '<', 45], 'No lights on, close to an injection, shorter than 45s'], 'hled_on_end': [['boundary', 'end', 'HLED_ON'], 'Ends of each no drug periods'], 'hled_on_start': [['boundary', 'start', 'HLED_ON'], 'Starts of each no drug periods'], 'inj_first': [['indexed', 'inj1', 1], 'First injection'], 'noto_dark_end': [['boundary', 'end', 'NOTO_DARK'], "End of dark period (that isn't a timeout)"], 'np1_ND_first': [['iselement', 'np1', 'ND_first'], 'Active nosepokes during the first ND period'], 'np1_NDfirst_1': [['indexed', 'np1_ND_first', 1], 'First active nosepoke of first ND period'], 'np1_n': [['generative2', ['D_n', 'np_effective'], 5], 'N-th effective nosepoke inside each drug period'], 'np2_ND_first': [['iselement', 'np2', 'ND_first'], 'Inactive nosepokes during the first ND period'], 'np2_NDfirst_1': [['indexed', 'np2_ND_first', 1], 'First inactive nosepoke of first ND period'], 'np_HF_NOTO': [['iselement', 'np1', 'DRUG_NOTO'], 'Active nosepoke not during HLED ON and not during full timeout'], 'np_effective': [['iselement', 'np_HF_NOTO', 'LED2_ON'], 'Active nosepoke that is part of the FR series'], 'npt': [['combination', ['np1', 'np2']], 'All nosepokes (active and inactive)'], 'npt_ND_first': [['iselement', 'npt', 'ND_first'], 'Nosepokes during the first ND period'], 'npt_NDfirst_1': [['indexed', 'npt_ND_first', 1], 'First nosepoke of first ND period'], 'rec_start': [['indexed', 'ttl1_on', 1], 'Start of the recording'], 'switch_1': [['indexed', 'switch_between', 1], 'First of the switches between ND and D that are between t+100s and t+3400s'], 'switch_1_D': [['indexed', 'switch_between_D', 1], 'No to-d First of the switches between ND and D that are between t+100s and t+3400s'], 'switch_between': [['timerestricted', 'switch_dto_nd', [100, 3400]], 'Switch between ND and D that are between t+100s and t+3400s'], 'switch_between_D': [['timerestricted', 'switch_d_nd', [100, 3400]], 'No to-d Switch between ND and D that are between t+100s and t+3400s'], 'switch_d_nd': [['iselement', 'hled_on_start', 'DRUG_NOTO'], 'Switch from drug to no drug (ie excluding timeout to drug)'], 'switch_dark_d1': [['iselement', 'noto_dark_end', 'LED2_ON'], 'Switch from dark to D1'], 'switch_dto_nd': [['combination', ['switch_d_nd', 'switch_to_nd']], 'Switch of either drug/timeout to no drug'], 'switch_nd1': [['boundary', 'end', 'ND_first'], 'First ND-D switch.'], 'switch_nd_d': [['iselement', 'hled_on_end', 'LED2_ON'], 'Switch from ND to D'], 'switch_to_nd': [['iselement', 'hled_on_start', 'TIMEOUT'], 'Switch from timeout to no drug']}
default_norm = 'F'
elements = {'DARK': [False, 'DARK', 'k'], 'HLED_OFF': [True, 'Drug Period', '#069AF3'], 'HLED_ON': [True, 'No Drug Period', 'gold'], 'LED1_OFF': [False, 'LED1_OFF', ['darkgray', 'darkgrey']], 'LED1_ON': [True, 'Conditioned Stimulus', 'goldenrod'], 'LED2_OFF': [False, 'LED2_OFF', 'slategrey'], 'LED2_ON': [True, ' Drug Light On', 'slategrey'], 'NOTO_DARK': [False, 'NOTO_DARK', 'black'], 'TIMEOUT': [True, 'Time outs', 'k'], 'TO_DARK': [False, 'TO_DARK', 'olivedrab'], 'TTL1_ON': [True, 'Fiber Photometry Recordings', 'g'], 'all': [True, 'Session', 'b'], 'inj1': [True, 'Injections', 'k'], 'np1': [True, 'Active Nosepokes', 'r'], 'np2': [True, 'Inactive Nosepokes', 'b'], 'switch_d_nd': [False, 'Switch from D to ND', 'r'], 'switch_nd_d': [False, 'Switch from ND to D', 'b'], 'switch_to_nd': [False, 'Switch from TO to ND', 'r']}
imetronic_events = {'hled_off': [['conditional', [1, 1], ['_P', 0]], 'houselight off'], 'hled_on': [['conditional', [1, 1], ['_P', 1]], 'houselight on'], 'inj1': [['conditional', [6, 1], ['_L', 1]], 'Injection'], 'led1_off': [['conditional', [1, 2], ['_P', 0]], 'CS light off'], 'led1_on': [['conditional', [1, 2], ['_P', 1]], 'CS light on'], 'led2_off': [['conditional', [1, 3], ['_P', 0]], 'Drug light off'], 'led2_on': [['conditional', [1, 3], ['_P', 1]], 'Drug light on'], 'np1': [['conditional', [3, 1], ['_V', 1]], 'Active Nosepoke'], 'np2': [['conditional', [3, 2], ['_V', 1]], 'Inactive nosepoke'], 'ttl1_off': [['conditional', [15, 1], ['_L', 0]], 'Recording TTL off'], 'ttl1_on': [['conditional', [15, 1], ['_L', 1]], 'Recording TTL on'], 'x_coordinates': [['simple', [9, 1], '_X'], 'X coordinates'], 'xy_timestamps': [['simple', [9, 1], 'TIME'], 'Timestamps for coordinates'], 'y_coordinates': [['simple', [9, 1], '_Y'], 'Y coordinates']}
min_sample_per_rec = 5
peak_baseline_MAD = 2
peak_distance = '50ms'
peak_peak_MAD = 3
peak_window = 10
peak_zscore = 'full'
perievent_window = [1.0, 1.0]
split_recordings = True
split_treshold = 10
trim_recording = 1.0
class pyfiber.analysis.MultiSession(folder, debug=0.8, **kwargs)

Group analyses or multiple events for single subject.

Parameters:

folder – path of the main folder containing session folders

Variables:

  • folder – path of the folder

  • sessions – dictionnary containing Session instances for each session

  • removed – list of removed file (if debug is set and a problem was detected)

  • names – subfolder nmaes, ideally reflecting session names

  • details – extracted session details from folder names (if set in the configuration file)

Input file structure
# folder and filenames can vary
main_folder/
├── session_1/
│   ├── behavior_1.dat
│   └── fiber_1.csv
├── session_2/
│   ├── behavior_2.dat
│   └── fiber_2.csv
├── session_3/
│   ├── behavior_3.dat
│   └── fiber_3.csv
├── session_4/
│   ├── behavior_4.dat
│   └── fiber_4.csv
...
_import_folder(folder)
_getID()
show_rates(**kwargs)

Show rates for specified events.

analyze(events, window='default', norm='default', sessions='all', save=False, save_dpi=600, save_format=['png', 'pdf'], **kwargs)

Analyze specific events.

events : ex: ‘np1’ window=’default’ : peri-event window (default is specified in config.yaml) norm=’default’ : normalization method used

compare_behavior(attribute, save=False, save_dpi=600, save_format=['png', 'pdf'])

Compare behavior (input should be event name)

Parameters:

  • save – default is False, filepath (without file extension)

  • save_dpi – dpi if figure is saved

  • save_format – file extension for saving

BEHAVIOR_FILE_TYPE = 'IMETRONIC'
FIBER_FILE_TYPE = 'DORIC'
basic_intervals = {'HLED': [['ON_OFF', 'both', ['hled_on', 'hled_off']], 'Houselight on (interval)'], 'LED1': [['ON_OFF', 'both', ['led1_on', 'led1_off']], 'CS light on (interval)'], 'LED2': [['ON_OFF', 'both', ['led2_on', 'led2_off']], 'Drug light on (interval)'], 'TTL1': [['ON_OFF', 'both', ['ttl1_on', 'ttl1_off']], 'TTL1 sending (interval)']}
behavior_time_ratio = 1000
csv_events = {'TTL1': [['timestamp', 'DI/O-1', 'Time(s)'], 'TTL1'], 'TTL2': [['timestamp', 'DI/O-2', 'Time(s)'], 'TTL2'], 'TTL3': [['timestamp', 'DI/O-3', 'Time(s)'], 'TTL3'], 'TTL4': [['timestamp', 'DI/O-4', 'Time(s)'], 'TTL4']}
custom = {'DARK': [['INTERSECTION', ['HLED_OFF', 'LED1_OFF', 'LED2_OFF']], 'No lights on'], 'DNI': [['NEAR_EVENT', 'DARK', 'inj1', 5], 'No lights on, close to an injection'], 'DRP': [['NEAR_EVENT', 'HLEDOFF_NODARK', 'led2_on', 1], 'Period with HLED off and near a led2 switch on'], 'DRP_INJ': [['CONTAINS', 'DRP', 'inj1'], 'Drug period that is close to an injection'], 'DRP_LONG': [['DURATION', 'DRP', '>', 600], 'Drug periods longer than 600 seconds'], 'DRUG': [['UNION', ['DRP_LONG', 'DRP_INJ']], 'Drug periods'], 'DRUG_NOTO': [['INTERSECTION', ['DRUG', '~TIMEOUT']], 'Drug period without timeouts'], 'D_n': [['GENERATIVE', 'DRUG'], 'N-th drug period'], 'HLEDOFF_NODARK': [['INTERSECTION', ['HLED_OFF', '~NOTO_DARK']], 'HLED off, excluding non-timeout-dark periods'], 'ND_first': [['NEAR_EVENT', 'HLED_ON', 'switch_1', 1], 'First ND period (excluding the first one in case of NDpre paradigm'], 'ND_n': [['GENERATIVE', 'HLED_ON'], 'No drug period (HLED on)'], 'NOTO_DARK': [['INTERSECTION', ['DARK', '~TIMEOUT']], 'No lights on, not a timeout'], 'TIMEOUT': [['UNION', ['LED1_ON', 'TO_DARK']], 'CS light on + subsequent dark period'], 'TO_DARK': [['DURATION', 'DNI', '<', 45], 'No lights on, close to an injection, shorter than 45s'], 'hled_on_end': [['boundary', 'end', 'HLED_ON'], 'Ends of each no drug periods'], 'hled_on_start': [['boundary', 'start', 'HLED_ON'], 'Starts of each no drug periods'], 'inj_first': [['indexed', 'inj1', 1], 'First injection'], 'noto_dark_end': [['boundary', 'end', 'NOTO_DARK'], "End of dark period (that isn't a timeout)"], 'np1_ND_first': [['iselement', 'np1', 'ND_first'], 'Active nosepokes during the first ND period'], 'np1_NDfirst_1': [['indexed', 'np1_ND_first', 1], 'First active nosepoke of first ND period'], 'np1_n': [['generative2', ['D_n', 'np_effective'], 5], 'N-th effective nosepoke inside each drug period'], 'np2_ND_first': [['iselement', 'np2', 'ND_first'], 'Inactive nosepokes during the first ND period'], 'np2_NDfirst_1': [['indexed', 'np2_ND_first', 1], 'First inactive nosepoke of first ND period'], 'np_HF_NOTO': [['iselement', 'np1', 'DRUG_NOTO'], 'Active nosepoke not during HLED ON and not during full timeout'], 'np_effective': [['iselement', 'np_HF_NOTO', 'LED2_ON'], 'Active nosepoke that is part of the FR series'], 'npt': [['combination', ['np1', 'np2']], 'All nosepokes (active and inactive)'], 'npt_ND_first': [['iselement', 'npt', 'ND_first'], 'Nosepokes during the first ND period'], 'npt_NDfirst_1': [['indexed', 'npt_ND_first', 1], 'First nosepoke of first ND period'], 'rec_start': [['indexed', 'ttl1_on', 1], 'Start of the recording'], 'switch_1': [['indexed', 'switch_between', 1], 'First of the switches between ND and D that are between t+100s and t+3400s'], 'switch_1_D': [['indexed', 'switch_between_D', 1], 'No to-d First of the switches between ND and D that are between t+100s and t+3400s'], 'switch_between': [['timerestricted', 'switch_dto_nd', [100, 3400]], 'Switch between ND and D that are between t+100s and t+3400s'], 'switch_between_D': [['timerestricted', 'switch_d_nd', [100, 3400]], 'No to-d Switch between ND and D that are between t+100s and t+3400s'], 'switch_d_nd': [['iselement', 'hled_on_start', 'DRUG_NOTO'], 'Switch from drug to no drug (ie excluding timeout to drug)'], 'switch_dark_d1': [['iselement', 'noto_dark_end', 'LED2_ON'], 'Switch from dark to D1'], 'switch_dto_nd': [['combination', ['switch_d_nd', 'switch_to_nd']], 'Switch of either drug/timeout to no drug'], 'switch_nd1': [['boundary', 'end', 'ND_first'], 'First ND-D switch.'], 'switch_nd_d': [['iselement', 'hled_on_end', 'LED2_ON'], 'Switch from ND to D'], 'switch_to_nd': [['iselement', 'hled_on_start', 'TIMEOUT'], 'Switch from timeout to no drug']}
default_norm = 'F'
elements = {'DARK': [False, 'DARK', 'k'], 'HLED_OFF': [True, 'Drug Period', '#069AF3'], 'HLED_ON': [True, 'No Drug Period', 'gold'], 'LED1_OFF': [False, 'LED1_OFF', ['darkgray', 'darkgrey']], 'LED1_ON': [True, 'Conditioned Stimulus', 'goldenrod'], 'LED2_OFF': [False, 'LED2_OFF', 'slategrey'], 'LED2_ON': [True, ' Drug Light On', 'slategrey'], 'NOTO_DARK': [False, 'NOTO_DARK', 'black'], 'TIMEOUT': [True, 'Time outs', 'k'], 'TO_DARK': [False, 'TO_DARK', 'olivedrab'], 'TTL1_ON': [True, 'Fiber Photometry Recordings', 'g'], 'all': [True, 'Session', 'b'], 'inj1': [True, 'Injections', 'k'], 'np1': [True, 'Active Nosepokes', 'r'], 'np2': [True, 'Inactive Nosepokes', 'b'], 'switch_d_nd': [False, 'Switch from D to ND', 'r'], 'switch_nd_d': [False, 'Switch from ND to D', 'b'], 'switch_to_nd': [False, 'Switch from TO to ND', 'r']}
imetronic_events = {'hled_off': [['conditional', [1, 1], ['_P', 0]], 'houselight off'], 'hled_on': [['conditional', [1, 1], ['_P', 1]], 'houselight on'], 'inj1': [['conditional', [6, 1], ['_L', 1]], 'Injection'], 'led1_off': [['conditional', [1, 2], ['_P', 0]], 'CS light off'], 'led1_on': [['conditional', [1, 2], ['_P', 1]], 'CS light on'], 'led2_off': [['conditional', [1, 3], ['_P', 0]], 'Drug light off'], 'led2_on': [['conditional', [1, 3], ['_P', 1]], 'Drug light on'], 'np1': [['conditional', [3, 1], ['_V', 1]], 'Active Nosepoke'], 'np2': [['conditional', [3, 2], ['_V', 1]], 'Inactive nosepoke'], 'ttl1_off': [['conditional', [15, 1], ['_L', 0]], 'Recording TTL off'], 'ttl1_on': [['conditional', [15, 1], ['_L', 1]], 'Recording TTL on'], 'x_coordinates': [['simple', [9, 1], '_X'], 'X coordinates'], 'xy_timestamps': [['simple', [9, 1], 'TIME'], 'Timestamps for coordinates'], 'y_coordinates': [['simple', [9, 1], '_Y'], 'Y coordinates']}
min_sample_per_rec = 5
peak_baseline_MAD = 2
peak_distance = '50ms'
peak_peak_MAD = 3
peak_window = 10
peak_zscore = 'full'
perievent_window = [1.0, 1.0]
split_recordings = True
split_treshold = 10
trim_recording = 1.0
class pyfiber.analysis.Analysis(ID)

Instantiated by using Session method analyze.

Contains all automatically retrieved and computed about a single peri-event analysis.

Variables:

  • event_time – timestamps of the analyzed event

  • window – perievent window

  • rec_number – recording number in the Fiber instance data

  • recordingdata – 2D-array with time and normalized signal (full recording)

  • rawdata – 2D-array with time, raw signal and control (full recording)

  • normalisation – normalization method used

  • raw_signal – raw signal (peri-event)

  • raw_control – raw control (peri-event)

  • data – 2D-array with time and normalized signal (peri-event)

  • signal – normalized signal (peri-event)

  • preevent – normalized signal (pre-event)

  • postevent – normalized signal (post-event)

  • sampling_rate – mean sampling rate (peri-event)

  • time – timestamps (peri-event)

  • pre_time – timestamps (pre-event)

  • post_time – timestamps (post-event)

  • pre_raw_sig – raw signal (pre-event)

  • post_raw_sig – raw signal (post-event)

  • post_raw_ctrl – raw control (post-event)

  • pre_raw_AUC – raw signal area under curve (pre-event)

  • post_raw_AUC – raw signal area under curve (post-event)

  • zscores – Z-scores with pre-event as baseline (peri-event)

  • pre_zscores – Z-scores with pre-event as baseline (pre-event)

  • post_zscores – Z-scores with pre-event as baseline (post-event)

  • rob_zscores – robust Z-scores with pre-event as baseline (peri-event)

  • pre_Rzscores – robust Z-scores with pre-event as baseline (pre-event)

  • post_Rzscores – robust Z-scores with pre-event as baseline (post-event)

  • preAVG_dF – average normalized signal (pre-event)

  • postAVG_dF – average normalized signal (post-event)

  • preAVG_Z – average Z-scores (pre-event)

  • postAVG_Z – average Z-scores (post-event)

  • preAVG_RZ – average robust Z-scores (pre-event)

  • postAVG_RZ – average robust Z-scores (post-event)

  • preAUC – = normalized signal area under curve (pre-event)

  • postAUC – = normalized signal area under curve (post-event)

  • preZ_AUC – = Z-scores area under curve (pre-event)

  • postZ_AUC – = Z-scores area under curve (post-event)

  • preRZ_AUC – = robust Z-scores area under curve (pre-event)

  • postRZ_AUC – = robust Z-scores area under curve (post-event)

  • post_peak_avg_Z – post-event average transient Z-scores

  • post_peak_avg_dFF – post-event average transient amplitude

  • post_peak_frequency – post-event transient frequency

  • post_peak_max_Z – post-event maximum Z-scores of transients

  • post_peak_max_dFF – post-event maximum amplitude of transients

  • pre_peak_avg_Z – pre-event average transient Z-scores

  • pre_peak_avg_dFF – pre-event average transient amplitud

  • pre_peak_frequency – pre-event transient frequency

  • pre_peak_max_Z – pre-event maximum Z-scores of transients

  • pre_peak_max_dFF – pre-event maximum amplitude of transients

_savgol(data, window=10, polyorder=3, nosmoothing=False)
plot(data, ylabel=None, xlabel='time', plot_title=None, figsize=(20, 10), event=True, event_label='event', linewidth=2, smth_method='savgol', smooth=True, smth_window='default', show_non_smoothed=True, xlim=None, ylim=None, save=None, save_dpi=600, save_format=['png', 'pdf'])

Visualize data.

Parameters:

  • save – default is False, filepath (without file extension)

  • save_dpi – dpi if figure is saved

  • save_format – file extension for saving

By default smoothes data with Savitski Golay filter (window size 250ms).

_possible_data()
smooth(data, method='savgol', window='default', polyorder=3, add_time=True)

Return smoothed data.

Possible methods:

  • Savitsky-Golay filter

  • rolling average.

class pyfiber.analysis.MultiAnalysis

Contains results of multiple analysis of similar peri-event intervals.

Variables:

  • exclude_list – optional list of excluded sessions in the analysis

  • nb_of_points – mean number of data points for all sessions (used to interpolate each session data)

  • WINDOW – peri-event window

  • dict – dictionnary containing a list of Analysis objects for each session

  • key – corresponding session for each event in each session

  • event_id – unique event identifier (session name + event time)

  • epoch – interpolated epochs for each event in each session

  • EPOCH – reference epoch (based on nb_of_points and WINDOW)

  • interpolated_raw_control – interpolated raw control for each event in each session

  • RAW_CONTROL – mean raw control

  • interpolated_raw_signal – interpolated raw signal for each event in each session

  • RAW_SIGNAL – mean raw signal

  • interpolated_rob_zscores – interpolated robust Z-scores for each event in each session

  • ROB_ZSCORES – mean robust Z-scores

  • interpolated_signal – interpolated normalized signal for each event in each session

  • SIGNAL – mean normalized signal

  • interpolated_time – interpolated time for each event in each session

  • TIME – mean time for all recordings (automatically generated)

  • interpolated_zscores – interpolated Z-scores for each event in each session

  • ZSCORES – mean Z-scores

  • interpolated_epoch – reference timestamps,

  • event_name – queried event (defined in config file)

  • event_time – timestamps of the analyzed event for each event in each session

  • window – perievent windows for each event in each session

  • rec_number – recording number in the Fiber instance data for each event in each session

  • recordingdata – list of 2D-arrays with time and normalized signal (full recording) for each event in each session

  • rawdata – list of 2D-arrays with time, raw signal and control (full recording) for each event in each session

  • normalisation – normalization method used for each event in each session

  • raw_signal – raw signal (peri-event) for each event in each session

  • raw_control – raw control (peri-event) for each event in each session

  • data – 2D-array with time and normalized signal (peri-event) for each event in each session

  • signal – normalized signal (peri-event) for each event in each session

  • preevent – normalized signal (pre-event) for each event in each session

  • postevent – normalized signal (post-event) for each event in each session

  • sampling_rate – mean sampling rate (peri-event) for each event in each session

  • time – timestamps (peri-event) for each event in each session

  • pre_time – timestamps (pre-event) for each event in each session

  • post_time – timestamps (post-event) for each event in each session

  • pre_raw_sig – raw signal (pre-event) for each event in each session

  • post_raw_sig – raw signal (post-event) for each event in each session

  • post_raw_ctrl – raw control (post-event) for each event in each session

  • pre_raw_AUC – raw signal area under curve (pre-event) for each event in each session

  • post_raw_AUC – raw signal area under curve (post-event) for each event in each session

  • zscores – Z-scores with pre-event as baseline (peri-event) for each event in each session

  • pre_zscores – Z-scores with pre-event as baseline (pre-event) for each event in each session

  • post_zscores – Z-scores with pre-event as baseline (post-event) for each event in each session

  • rob_zscores – robust Z-scores with pre-event as baseline (peri-event) for each event in each session

  • pre_Rzscores – robust Z-scores with pre-event as baseline (pre-event) for each event in each session

  • post_Rzscores – robust Z-scores with pre-event as baseline (post-event) for each event in each session

  • preAVG_dF – average normalized signal (pre-event) for each event in each session

  • postAVG_dF – average normalized signal (post-event) for each event in each session

  • preAVG_Z – average Z-scores (pre-event) for each event in each session

  • postAVG_Z – average Z-scores (post-event) for each event in each session

  • preAVG_RZ – average robust Z-scores (pre-event) for each event in each session

  • postAVG_RZ – average robust Z-scores (post-event) for each event in each session

  • preAUC – normalized signal area under curve (pre-event) for each event in each session

  • postAUC – normalized signal area under curve (post-event) for each event in each session

  • preZ_AUC – Z-scores area under curve (pre-event) for each event in each session

  • postZ_AUC – Z-scores area under curve (post-event) for each event in each session

  • preRZ_AUC – robust Z-scores area under curve (pre-event) for each event in each session

  • postRZ_AUC – robust Z-scores area under curve (post-event) for each event in each session

  • post_peak_avg_Z – post-event average transient Z-scores

  • post_peak_avg_dFF – post-event average transient amplitude

  • post_peak_frequency – post-event transient frequency

  • post_peak_max_Z – post-event maximum Z-scores of transients

  • post_peak_max_dFF – post-event maximum amplitude of transients

  • pre_peak_avg_Z – pre-event average transient Z-scores

  • pre_peak_avg_dFF – pre-event average transient amplitud

  • pre_peak_frequency – pre-event transient frequency

  • pre_peak_max_Z – pre-event maximum Z-scores of transients

  • pre_peak_max_dFF – pre-event maximum amplitude of transients

property data
property full_data
BEHAVIOR_FILE_TYPE = 'IMETRONIC'
FIBER_FILE_TYPE = 'DORIC'
basic_intervals = {'HLED': [['ON_OFF', 'both', ['hled_on', 'hled_off']], 'Houselight on (interval)'], 'LED1': [['ON_OFF', 'both', ['led1_on', 'led1_off']], 'CS light on (interval)'], 'LED2': [['ON_OFF', 'both', ['led2_on', 'led2_off']], 'Drug light on (interval)'], 'TTL1': [['ON_OFF', 'both', ['ttl1_on', 'ttl1_off']], 'TTL1 sending (interval)']}
behavior_time_ratio = 1000
csv_events = {'TTL1': [['timestamp', 'DI/O-1', 'Time(s)'], 'TTL1'], 'TTL2': [['timestamp', 'DI/O-2', 'Time(s)'], 'TTL2'], 'TTL3': [['timestamp', 'DI/O-3', 'Time(s)'], 'TTL3'], 'TTL4': [['timestamp', 'DI/O-4', 'Time(s)'], 'TTL4']}
custom = {'DARK': [['INTERSECTION', ['HLED_OFF', 'LED1_OFF', 'LED2_OFF']], 'No lights on'], 'DNI': [['NEAR_EVENT', 'DARK', 'inj1', 5], 'No lights on, close to an injection'], 'DRP': [['NEAR_EVENT', 'HLEDOFF_NODARK', 'led2_on', 1], 'Period with HLED off and near a led2 switch on'], 'DRP_INJ': [['CONTAINS', 'DRP', 'inj1'], 'Drug period that is close to an injection'], 'DRP_LONG': [['DURATION', 'DRP', '>', 600], 'Drug periods longer than 600 seconds'], 'DRUG': [['UNION', ['DRP_LONG', 'DRP_INJ']], 'Drug periods'], 'DRUG_NOTO': [['INTERSECTION', ['DRUG', '~TIMEOUT']], 'Drug period without timeouts'], 'D_n': [['GENERATIVE', 'DRUG'], 'N-th drug period'], 'HLEDOFF_NODARK': [['INTERSECTION', ['HLED_OFF', '~NOTO_DARK']], 'HLED off, excluding non-timeout-dark periods'], 'ND_first': [['NEAR_EVENT', 'HLED_ON', 'switch_1', 1], 'First ND period (excluding the first one in case of NDpre paradigm'], 'ND_n': [['GENERATIVE', 'HLED_ON'], 'No drug period (HLED on)'], 'NOTO_DARK': [['INTERSECTION', ['DARK', '~TIMEOUT']], 'No lights on, not a timeout'], 'TIMEOUT': [['UNION', ['LED1_ON', 'TO_DARK']], 'CS light on + subsequent dark period'], 'TO_DARK': [['DURATION', 'DNI', '<', 45], 'No lights on, close to an injection, shorter than 45s'], 'hled_on_end': [['boundary', 'end', 'HLED_ON'], 'Ends of each no drug periods'], 'hled_on_start': [['boundary', 'start', 'HLED_ON'], 'Starts of each no drug periods'], 'inj_first': [['indexed', 'inj1', 1], 'First injection'], 'noto_dark_end': [['boundary', 'end', 'NOTO_DARK'], "End of dark period (that isn't a timeout)"], 'np1_ND_first': [['iselement', 'np1', 'ND_first'], 'Active nosepokes during the first ND period'], 'np1_NDfirst_1': [['indexed', 'np1_ND_first', 1], 'First active nosepoke of first ND period'], 'np1_n': [['generative2', ['D_n', 'np_effective'], 5], 'N-th effective nosepoke inside each drug period'], 'np2_ND_first': [['iselement', 'np2', 'ND_first'], 'Inactive nosepokes during the first ND period'], 'np2_NDfirst_1': [['indexed', 'np2_ND_first', 1], 'First inactive nosepoke of first ND period'], 'np_HF_NOTO': [['iselement', 'np1', 'DRUG_NOTO'], 'Active nosepoke not during HLED ON and not during full timeout'], 'np_effective': [['iselement', 'np_HF_NOTO', 'LED2_ON'], 'Active nosepoke that is part of the FR series'], 'npt': [['combination', ['np1', 'np2']], 'All nosepokes (active and inactive)'], 'npt_ND_first': [['iselement', 'npt', 'ND_first'], 'Nosepokes during the first ND period'], 'npt_NDfirst_1': [['indexed', 'npt_ND_first', 1], 'First nosepoke of first ND period'], 'rec_start': [['indexed', 'ttl1_on', 1], 'Start of the recording'], 'switch_1': [['indexed', 'switch_between', 1], 'First of the switches between ND and D that are between t+100s and t+3400s'], 'switch_1_D': [['indexed', 'switch_between_D', 1], 'No to-d First of the switches between ND and D that are between t+100s and t+3400s'], 'switch_between': [['timerestricted', 'switch_dto_nd', [100, 3400]], 'Switch between ND and D that are between t+100s and t+3400s'], 'switch_between_D': [['timerestricted', 'switch_d_nd', [100, 3400]], 'No to-d Switch between ND and D that are between t+100s and t+3400s'], 'switch_d_nd': [['iselement', 'hled_on_start', 'DRUG_NOTO'], 'Switch from drug to no drug (ie excluding timeout to drug)'], 'switch_dark_d1': [['iselement', 'noto_dark_end', 'LED2_ON'], 'Switch from dark to D1'], 'switch_dto_nd': [['combination', ['switch_d_nd', 'switch_to_nd']], 'Switch of either drug/timeout to no drug'], 'switch_nd1': [['boundary', 'end', 'ND_first'], 'First ND-D switch.'], 'switch_nd_d': [['iselement', 'hled_on_end', 'LED2_ON'], 'Switch from ND to D'], 'switch_to_nd': [['iselement', 'hled_on_start', 'TIMEOUT'], 'Switch from timeout to no drug']}
default_norm = 'F'
elements = {'DARK': [False, 'DARK', 'k'], 'HLED_OFF': [True, 'Drug Period', '#069AF3'], 'HLED_ON': [True, 'No Drug Period', 'gold'], 'LED1_OFF': [False, 'LED1_OFF', ['darkgray', 'darkgrey']], 'LED1_ON': [True, 'Conditioned Stimulus', 'goldenrod'], 'LED2_OFF': [False, 'LED2_OFF', 'slategrey'], 'LED2_ON': [True, ' Drug Light On', 'slategrey'], 'NOTO_DARK': [False, 'NOTO_DARK', 'black'], 'TIMEOUT': [True, 'Time outs', 'k'], 'TO_DARK': [False, 'TO_DARK', 'olivedrab'], 'TTL1_ON': [True, 'Fiber Photometry Recordings', 'g'], 'all': [True, 'Session', 'b'], 'inj1': [True, 'Injections', 'k'], 'np1': [True, 'Active Nosepokes', 'r'], 'np2': [True, 'Inactive Nosepokes', 'b'], 'switch_d_nd': [False, 'Switch from D to ND', 'r'], 'switch_nd_d': [False, 'Switch from ND to D', 'b'], 'switch_to_nd': [False, 'Switch from TO to ND', 'r']}
imetronic_events = {'hled_off': [['conditional', [1, 1], ['_P', 0]], 'houselight off'], 'hled_on': [['conditional', [1, 1], ['_P', 1]], 'houselight on'], 'inj1': [['conditional', [6, 1], ['_L', 1]], 'Injection'], 'led1_off': [['conditional', [1, 2], ['_P', 0]], 'CS light off'], 'led1_on': [['conditional', [1, 2], ['_P', 1]], 'CS light on'], 'led2_off': [['conditional', [1, 3], ['_P', 0]], 'Drug light off'], 'led2_on': [['conditional', [1, 3], ['_P', 1]], 'Drug light on'], 'np1': [['conditional', [3, 1], ['_V', 1]], 'Active Nosepoke'], 'np2': [['conditional', [3, 2], ['_V', 1]], 'Inactive nosepoke'], 'ttl1_off': [['conditional', [15, 1], ['_L', 0]], 'Recording TTL off'], 'ttl1_on': [['conditional', [15, 1], ['_L', 1]], 'Recording TTL on'], 'x_coordinates': [['simple', [9, 1], '_X'], 'X coordinates'], 'xy_timestamps': [['simple', [9, 1], 'TIME'], 'Timestamps for coordinates'], 'y_coordinates': [['simple', [9, 1], '_Y'], 'Y coordinates']}
min_sample_per_rec = 5
peak_baseline_MAD = 2
peak_distance = '50ms'
peak_peak_MAD = 3
peak_window = 10
peak_zscore = 'full'
perievent_window = [1.0, 1.0]
possible_data()

Return dictionnary of possible data to plot

split_recordings = True
split_treshold = 10
trim_recording = 1.0
exclude(list_of_sessions)
update(nb_of_points='default')

Recalculate mean values for all relevant values (perievent data). “nb of points” is the desired number of points for the new aligned data. Default is mean number of points of the data.

plot(data='signal', smooth_data=True, smooth_mean=True, data_window=500, mean_window=500, label=None, save=False, save_dpi=600, save_format=['png', 'pdf'], **kwargs)

Visualize specified data.

Parameters:

  • save – default is False, filepath (without file extension)

  • save_dpi – dpi if figure is saved

  • save_format – file extension for saving

pyfiber._utils module

class pyfiber._utils.PyFiber(**kwargs)

Bases: object

Parent object for Behavioral, Fiber and Analysis objects.

Variables:

CFG – dictionnary containing the whole 'pyfiber.yaml' file content

Parameters:

  • verbose – if False, activates silent mode (the log is still available in self.log)

  • kwargs – add additional attribute or modify config option at runtine

FOLDER = '/home/docs/.pyfiber'
_config_path = '/home/docs/.pyfiber/pyfiber.yaml'
_sample_config = '/home/docs/checkouts/readthedocs.org/user_builds/pyfiber/checkouts/latest/src/pyfiber/pyfiber.yaml'
CFG
property log

Show the history of most of the operation (extraction, normalization, errors, …) for all classes derived of _utils.PyFiber. Users can optionnaly add information to the log, by assigning the new information:

In [10]: obj.log
'15:29:36 --- did something'
'15:29:37 --- did something else'
In [11]: obj.log = "new entry"
In [12]: obj.log
'15:29:36 --- did something'
'15:29:37 --- did something else'
'15:35:10 --- new entry'
property help
property _help
property info
_list(anything)

Convert user input into list if not already.

BEHAVIOR = {'BEHAVIOR_FILE_TYPE': 'IMETRONIC', 'basic_intervals': {'HLED': [['ON_OFF', 'both', ['hled_on', 'hled_off']], 'Houselight on (interval)'], 'LED1': [['ON_OFF', 'both', ['led1_on', 'led1_off']], 'CS light on (interval)'], 'LED2': [['ON_OFF', 'both', ['led2_on', 'led2_off']], 'Drug light on (interval)'], 'TTL1': [['ON_OFF', 'both', ['ttl1_on', 'ttl1_off']], 'TTL1 sending (interval)']}, 'behavior_time_ratio': 1000, 'csv_events': {'TTL1': [['timestamp', 'DI/O-1', 'Time(s)'], 'TTL1'], 'TTL2': [['timestamp', 'DI/O-2', 'Time(s)'], 'TTL2'], 'TTL3': [['timestamp', 'DI/O-3', 'Time(s)'], 'TTL3'], 'TTL4': [['timestamp', 'DI/O-4', 'Time(s)'], 'TTL4']}, 'custom': {'DARK': [['INTERSECTION', ['HLED_OFF', 'LED1_OFF', 'LED2_OFF']], 'No lights on'], 'DNI': [['NEAR_EVENT', 'DARK', 'inj1', 5], 'No lights on, close to an injection'], 'DRP': [['NEAR_EVENT', 'HLEDOFF_NODARK', 'led2_on', 1], 'Period with HLED off and near a led2 switch on'], 'DRP_INJ': [['CONTAINS', 'DRP', 'inj1'], 'Drug period that is close to an injection'], 'DRP_LONG': [['DURATION', 'DRP', '>', 600], 'Drug periods longer than 600 seconds'], 'DRUG': [['UNION', ['DRP_LONG', 'DRP_INJ']], 'Drug periods'], 'DRUG_NOTO': [['INTERSECTION', ['DRUG', '~TIMEOUT']], 'Drug period without timeouts'], 'D_n': [['GENERATIVE', 'DRUG'], 'N-th drug period'], 'HLEDOFF_NODARK': [['INTERSECTION', ['HLED_OFF', '~NOTO_DARK']], 'HLED off, excluding non-timeout-dark periods'], 'ND_first': [['NEAR_EVENT', 'HLED_ON', 'switch_1', 1], 'First ND period (excluding the first one in case of NDpre paradigm'], 'ND_n': [['GENERATIVE', 'HLED_ON'], 'No drug period (HLED on)'], 'NOTO_DARK': [['INTERSECTION', ['DARK', '~TIMEOUT']], 'No lights on, not a timeout'], 'TIMEOUT': [['UNION', ['LED1_ON', 'TO_DARK']], 'CS light on + subsequent dark period'], 'TO_DARK': [['DURATION', 'DNI', '<', 45], 'No lights on, close to an injection, shorter than 45s'], 'hled_on_end': [['boundary', 'end', 'HLED_ON'], 'Ends of each no drug periods'], 'hled_on_start': [['boundary', 'start', 'HLED_ON'], 'Starts of each no drug periods'], 'inj_first': [['indexed', 'inj1', 1], 'First injection'], 'noto_dark_end': [['boundary', 'end', 'NOTO_DARK'], "End of dark period (that isn't a timeout)"], 'np1_ND_first': [['iselement', 'np1', 'ND_first'], 'Active nosepokes during the first ND period'], 'np1_NDfirst_1': [['indexed', 'np1_ND_first', 1], 'First active nosepoke of first ND period'], 'np1_n': [['generative2', ['D_n', 'np_effective'], 5], 'N-th effective nosepoke inside each drug period'], 'np2_ND_first': [['iselement', 'np2', 'ND_first'], 'Inactive nosepokes during the first ND period'], 'np2_NDfirst_1': [['indexed', 'np2_ND_first', 1], 'First inactive nosepoke of first ND period'], 'np_HF_NOTO': [['iselement', 'np1', 'DRUG_NOTO'], 'Active nosepoke not during HLED ON and not during full timeout'], 'np_effective': [['iselement', 'np_HF_NOTO', 'LED2_ON'], 'Active nosepoke that is part of the FR series'], 'npt': [['combination', ['np1', 'np2']], 'All nosepokes (active and inactive)'], 'npt_ND_first': [['iselement', 'npt', 'ND_first'], 'Nosepokes during the first ND period'], 'npt_NDfirst_1': [['indexed', 'npt_ND_first', 1], 'First nosepoke of first ND period'], 'rec_start': [['indexed', 'ttl1_on', 1], 'Start of the recording'], 'switch_1': [['indexed', 'switch_between', 1], 'First of the switches between ND and D that are between t+100s and t+3400s'], 'switch_1_D': [['indexed', 'switch_between_D', 1], 'No to-d First of the switches between ND and D that are between t+100s and t+3400s'], 'switch_between': [['timerestricted', 'switch_dto_nd', [100, 3400]], 'Switch between ND and D that are between t+100s and t+3400s'], 'switch_between_D': [['timerestricted', 'switch_d_nd', [100, 3400]], 'No to-d Switch between ND and D that are between t+100s and t+3400s'], 'switch_d_nd': [['iselement', 'hled_on_start', 'DRUG_NOTO'], 'Switch from drug to no drug (ie excluding timeout to drug)'], 'switch_dark_d1': [['iselement', 'noto_dark_end', 'LED2_ON'], 'Switch from dark to D1'], 'switch_dto_nd': [['combination', ['switch_d_nd', 'switch_to_nd']], 'Switch of either drug/timeout to no drug'], 'switch_nd1': [['boundary', 'end', 'ND_first'], 'First ND-D switch.'], 'switch_nd_d': [['iselement', 'hled_on_end', 'LED2_ON'], 'Switch from ND to D'], 'switch_to_nd': [['iselement', 'hled_on_start', 'TIMEOUT'], 'Switch from timeout to no drug']}, 'elements': {'DARK': [False, 'DARK', 'k'], 'HLED_OFF': [True, 'Drug Period', '#069AF3'], 'HLED_ON': [True, 'No Drug Period', 'gold'], 'LED1_OFF': [False, 'LED1_OFF', ['darkgray', 'darkgrey']], 'LED1_ON': [True, 'Conditioned Stimulus', 'goldenrod'], 'LED2_OFF': [False, 'LED2_OFF', 'slategrey'], 'LED2_ON': [True, ' Drug Light On', 'slategrey'], 'NOTO_DARK': [False, 'NOTO_DARK', 'black'], 'TIMEOUT': [True, 'Time outs', 'k'], 'TO_DARK': [False, 'TO_DARK', 'olivedrab'], 'TTL1_ON': [True, 'Fiber Photometry Recordings', 'g'], 'all': [True, 'Session', 'b'], 'inj1': [True, 'Injections', 'k'], 'np1': [True, 'Active Nosepokes', 'r'], 'np2': [True, 'Inactive Nosepokes', 'b'], 'switch_d_nd': [False, 'Switch from D to ND', 'r'], 'switch_nd_d': [False, 'Switch from ND to D', 'b'], 'switch_to_nd': [False, 'Switch from TO to ND', 'r']}, 'imetronic_events': {'hled_off': [['conditional', [1, 1], ['_P', 0]], 'houselight off'], 'hled_on': [['conditional', [1, 1], ['_P', 1]], 'houselight on'], 'inj1': [['conditional', [6, 1], ['_L', 1]], 'Injection'], 'led1_off': [['conditional', [1, 2], ['_P', 0]], 'CS light off'], 'led1_on': [['conditional', [1, 2], ['_P', 1]], 'CS light on'], 'led2_off': [['conditional', [1, 3], ['_P', 0]], 'Drug light off'], 'led2_on': [['conditional', [1, 3], ['_P', 1]], 'Drug light on'], 'np1': [['conditional', [3, 1], ['_V', 1]], 'Active Nosepoke'], 'np2': [['conditional', [3, 2], ['_V', 1]], 'Inactive nosepoke'], 'ttl1_off': [['conditional', [15, 1], ['_L', 0]], 'Recording TTL off'], 'ttl1_on': [['conditional', [15, 1], ['_L', 1]], 'Recording TTL on'], 'x_coordinates': [['simple', [9, 1], '_X'], 'X coordinates'], 'xy_timestamps': [['simple', [9, 1], 'TIME'], 'Timestamps for coordinates'], 'y_coordinates': [['simple', [9, 1], '_Y'], 'Y coordinates']}}
DORIC_CSV = {'AIn-1 - Demodulated(Lock-In)': 'signal', 'AIn-2 - Demodulated(Lock-In)': 'control', 'DI/O-1': 'TTL1', 'DI/O-2': 'TTL2', 'DI/O-3': 'TTL3', 'DI/O-4': 'TTL4', 'Time(s)': 'time'}
DORIC_HDF = {'DataAcquisition/FPConsole/Signals/Series0001/AIN01xAOUT01-LockIn/Time': 'time', 'DataAcquisition/FPConsole/Signals/Series0001/AIN01xAOUT01-LockIn/Values': 'signal', 'DataAcquisition/FPConsole/Signals/Series0001/AIN02xAOUT02-LockIn/Values': 'control', 'DataAcquisition/FPConsole/Signals/Series0001/DigitalIO/DIO01': 'TTL1', 'DataAcquisition/FPConsole/Signals/Series0001/DigitalIO/DIO02': 'TTL2', 'DataAcquisition/FPConsole/Signals/Series0001/DigitalIO/DIO03': 'TTL3', 'DataAcquisition/FPConsole/Signals/Series0001/DigitalIO/DIO04': 'TTL4'}
EXAMPLE_NOMENCLATURE = {'control_column': 'control', 'signal_column': 'signal', 'time_column': 'time', 'ttl1': 'TTL1'}
FIBER = {'FIBER_FILE_TYPE': 'DORIC', 'default_norm': 'F', 'min_sample_per_rec': 5, 'peak_baseline_MAD': 2, 'peak_distance': '50ms', 'peak_peak_MAD': 3, 'peak_window': 10, 'peak_zscore': 'full', 'perievent_window': [1.0, 1.0], 'split_recordings': True, 'split_treshold': 10, 'trim_recording': 1.0}
GENERAL = {'ID': 'Not specified', 'folder_nomenclature': {'ID': ['experiment', 'rat_number'], 'meanings': ['experiment', '', 'rat_number', 'experiment_type', 'session_number'], 'naming': '@_rat_@_@_j@', 'separator': '_'}}
ID = 'Not specified'
IMETRONIC = {'DISTRIBUTEUR': {'D1': [4, 1], 'D10': [4, 10], 'D11': [4, 11], 'D12': [4, 12], 'D2': [4, 2], 'D3': [4, 3], 'D4': [4, 4], 'D5': [4, 5], 'D6': [4, 6], 'D7': [4, 7], 'D8': [4, 8], 'D9': [4, 9]}, 'DIVERS': {'EVT': [10, 5], 'ON': [10, 1]}, 'DIVERS_CA': {'ADC': [6, 8], 'BUL': [6, 13], 'FL': [6, 10], 'INJ1': [6, 1], 'INJ2': [6, 7], 'OD': [6, 12], 'PUSH': [6, 5], 'RD': [6, 11], 'SHK': [6, 4], 'SND': [6, 2], 'SNDpP': [6, 9], 'TOP': [6, 6], 'WH': [6, 14], 'WN': [6, 3]}, 'DIVERS_NON_STOCKES': {'I': [11, 4]}, 'LEVERS': {'L1': [2, 1], 'L2': [2, 2], 'L3': [2, 3], 'L4': [2, 4], 'L5': [2, 5], 'L6': [2, 6]}, 'LICKMETER': {'LK1': [5, 1], 'LK2': [5, 2], 'LK3': [5, 3], 'LK4': [5, 4], 'LK5': [5, 5]}, 'LIGHTS': {'HLED': [1, 1], 'LED1': [1, 2], 'LED2': [1, 3], 'LED3': [1, 4], 'LED4': [1, 5], 'LED5': [1, 6], 'LED6': [1, 7], 'LED7': [1, 8]}, 'MESSAGE': {'STR1': [13, 1], 'STR2': [13, 2], 'STR3': [13, 3], 'STR4': [13, 4]}, 'NOSEPOKE': {'NP1': [3, 1], 'NP2': [3, 2], 'NP3': [3, 3], 'NP4': [3, 4], 'NP5': [3, 5]}, 'PORTE': {'G1': [7, 1], 'G10': [7, 10], 'G11': [7, 11], 'G12': [7, 12], 'G2': [7, 2], 'G3': [7, 3], 'G4': [7, 4], 'G5': [7, 5], 'G6': [7, 6], 'G7': [7, 7], 'G8': [7, 8], 'G9': [7, 9]}, 'RFID': {'I1': [12, 1], 'I2': [12, 2], 'I3': [12, 3], 'I4': [12, 4]}, 'ZONE': {'Z1': [9, 1], 'Z10': [9, 10], 'Z11': [9, 11], 'Z12': [9, 12], 'Z13': [9, 13], 'Z2': [9, 2], 'Z3': [9, 3], 'Z4': [9, 4], 'Z5': [9, 5], 'Z6': [9, 6], 'Z7': [9, 7], 'Z8': [9, 8], 'Z9': [9, 9]}}
SYSTEM = {'DORIC_CSV': {'AIn-1 - Demodulated(Lock-In)': 'signal', 'AIn-2 - Demodulated(Lock-In)': 'control', 'DI/O-1': 'TTL1', 'DI/O-2': 'TTL2', 'DI/O-3': 'TTL3', 'DI/O-4': 'TTL4', 'Time(s)': 'time'}, 'DORIC_HDF': {'DataAcquisition/FPConsole/Signals/Series0001/AIN01xAOUT01-LockIn/Time': 'time', 'DataAcquisition/FPConsole/Signals/Series0001/AIN01xAOUT01-LockIn/Values': 'signal', 'DataAcquisition/FPConsole/Signals/Series0001/AIN02xAOUT02-LockIn/Values': 'control', 'DataAcquisition/FPConsole/Signals/Series0001/DigitalIO/DIO01': 'TTL1', 'DataAcquisition/FPConsole/Signals/Series0001/DigitalIO/DIO02': 'TTL2', 'DataAcquisition/FPConsole/Signals/Series0001/DigitalIO/DIO03': 'TTL3', 'DataAcquisition/FPConsole/Signals/Series0001/DigitalIO/DIO04': 'TTL4'}, 'EXAMPLE_NOMENCLATURE': {'control_column': 'control', 'signal_column': 'signal', 'time_column': 'time', 'ttl1': 'TTL1'}, 'IMETRONIC': {'DISTRIBUTEUR': {'D1': [4, 1], 'D10': [4, 10], 'D11': [4, 11], 'D12': [4, 12], 'D2': [4, 2], 'D3': [4, 3], 'D4': [4, 4], 'D5': [4, 5], 'D6': [4, 6], 'D7': [4, 7], 'D8': [4, 8], 'D9': [4, 9]}, 'DIVERS': {'EVT': [10, 5], 'ON': [10, 1]}, 'DIVERS_CA': {'ADC': [6, 8], 'BUL': [6, 13], 'FL': [6, 10], 'INJ1': [6, 1], 'INJ2': [6, 7], 'OD': [6, 12], 'PUSH': [6, 5], 'RD': [6, 11], 'SHK': [6, 4], 'SND': [6, 2], 'SNDpP': [6, 9], 'TOP': [6, 6], 'WH': [6, 14], 'WN': [6, 3]}, 'DIVERS_NON_STOCKES': {'I': [11, 4]}, 'LEVERS': {'L1': [2, 1], 'L2': [2, 2], 'L3': [2, 3], 'L4': [2, 4], 'L5': [2, 5], 'L6': [2, 6]}, 'LICKMETER': {'LK1': [5, 1], 'LK2': [5, 2], 'LK3': [5, 3], 'LK4': [5, 4], 'LK5': [5, 5]}, 'LIGHTS': {'HLED': [1, 1], 'LED1': [1, 2], 'LED2': [1, 3], 'LED3': [1, 4], 'LED4': [1, 5], 'LED5': [1, 6], 'LED6': [1, 7], 'LED7': [1, 8]}, 'MESSAGE': {'STR1': [13, 1], 'STR2': [13, 2], 'STR3': [13, 3], 'STR4': [13, 4]}, 'NOSEPOKE': {'NP1': [3, 1], 'NP2': [3, 2], 'NP3': [3, 3], 'NP4': [3, 4], 'NP5': [3, 5]}, 'PORTE': {'G1': [7, 1], 'G10': [7, 10], 'G11': [7, 11], 'G12': [7, 12], 'G2': [7, 2], 'G3': [7, 3], 'G4': [7, 4], 'G5': [7, 5], 'G6': [7, 6], 'G7': [7, 7], 'G8': [7, 8], 'G9': [7, 9]}, 'RFID': {'I1': [12, 1], 'I2': [12, 2], 'I3': [12, 3], 'I4': [12, 4]}, 'ZONE': {'Z1': [9, 1], 'Z10': [9, 10], 'Z11': [9, 11], 'Z12': [9, 12], 'Z13': [9, 13], 'Z2': [9, 2], 'Z3': [9, 3], 'Z4': [9, 4], 'Z5': [9, 5], 'Z6': [9, 6], 'Z7': [9, 7], 'Z8': [9, 8], 'Z9': [9, 9]}}}
_savgol(data, window=10, polyorder=3, nosmoothing=False)
f = <_io.TextIOWrapper name='/home/docs/.pyfiber/pyfiber.yaml' mode='r' encoding='UTF-8'>
folder_nomenclature = {'ID': ['experiment', 'rat_number'], 'meanings': ['experiment', '', 'rat_number', 'experiment_type', 'session_number'], 'naming': '@_rat_@_@_j@', 'separator': '_'}
_print(thing)
pyfiber._utils.Events

alias of ndarray