Chronobiology module¶

Main module providing classes and methods to collect, generate, calculate and plot circadian cycles data.

Classes

`DBQuery`	Class to access InfluxDB 1.x and select records from it.
`CycleAnalyzer`	Class to calculate and plot circadian cycles data.

Methods

`generate_data`	Generate random input data.
`generate_night`	Generate a random `is_night` array.

class chronobiology.chronobiology.DBQuery(database, username, password, host='localhost', port=8086)¶

Class to access InfluxDB 1.x and select records from it.

Parameters

database (str) – Name of the database.
username (str) – Name of user.
password (str) – User password.
host (str, optional) – IP adress, defaults to localhost.
port (int, optional) – Connection port, defaults to 8086.

Methods

`get_measurements`	Get list of all measurments (series) in the database.
`get_tags`	Get all tags (tag names) in a series.
`get_fields`	Get all fields in a series.
`get_keys`	Get list of all tag values for a given tag in a series.
`get_data`	Get data (records) for specified fields/tags in a series.

get_measurements()¶

Get list of all measurments (series) in the database.

Return type: list[str]
Returns: List of all measurement names in the database.

get_tags(series)¶

Get all tags (tag names) in a series.

Parameters: series (str) – Name of the series.
Return type: list[str]
Returns: List of all tag names in the series.

Note

Returns an empty list if the query does not return any vaues, for example, if there are no tags in the series or if there is no series with the given name.

get_fields(series, return_types=False)¶

Get all fields in a series.

Parameters

series (str) – Name of the series.
return_types (bool, optional) – Indicates if field types should be returned, defaults to False.

Return type

list[str]|(list[str], list[str])

Returns

If return_type == False: List of field names.
If return_type == True: Field names and the corresponding InfluxDB field types as a pair of lists of strings. The possible types are: integer, float, string and boolean.

Note

Returns an empty list if the query does not return any vaues, for example, if there are no tags in the series or if there is no series with the given name.

get_keys(series, tag)¶

Get list of all tag values for a given tag in a series.

Parameters

series (str) – Name of the series.
tag (str) – Name of the tag.

Return type

list[str]

Returns

List of all values of the tag in the series.

Note

Returns an empty list if the query does not return any vaues, for example, if there are no tags in the series or if there is no series with the given name.

get_data(series, fields, keys=None, start=None, stop=None, local_tz=False)¶

Get data (records) for specified fields/tags in a series.

Parameters

series (str) – Name of the series.
fields (str|list[str]|tuple[str]|set[str]|dict[str: str|type]) –
Name(s) of fields/tags in the series. This parameter is treated differently depending on it’s type:

str
Treated as a single field/tag name to return. If fields = '*' then all fields and tags are returned.

list[str], tuple[str] or set[str]
Treated as a collection of field/tag names to return.

dict[str: str|type]
The keys are treated as field/tag names, and the values are treated as numpy types (or names of numpy types) of the corresponding keys.

The output is converted from InfluxDB types to the types specified in the dictionary.

Use None as a field type to enable type autodetection and/or avoid type conversion for that field.
keys (None|dict[str: obj], optional) –
Dictionary providing rules to select records with specific field/tag values, defaults to None.

If None then selected records are not filtered. Otherwise the dictionary is treated as follows:

Key
Name of the filtered field/tag.

Values
Value(s) of the corresponding field/tag to be selected.

Each value can be a scalar or a collection of all values to be selected (list, tuple or set)
start (None|str|int|datetime, optional) –
Inclusive lower time boundary for the returned data, defaults to None.

None indicates no lower boundary.

str is interpreted as a timestring.

int is interpreted as a Unix timestamp.

datetime is used as is.
stop (None|str|int|datetime, optional) –
Exclusive upper time boundary for the returned data, defaults to None.

None indicates no upper boundary.

str is interpreted as a timestring.

int is interpreted as a Unix timestamp.

datetime is used as is.
local_tz (bool, optional) – Indicates whether local or UTC time is used in the code, defaults to False (UTC).

Return type

dict[str: np.array]

Returns

Dictionary constructed as follows:

Key: Field/tag name.
Value: Numpy array of the corresponding field/tag values.

class chronobiology.chronobiology.CycleAnalyzer(timestamps, activity=None, night=None, step='1m', start=None, stop=None, descr='', max_gap='1m', min_duration='1m', min_activity=1, min_data_points=1)¶

Class to calculate and plot circadian cycles data.

Variables

start (np.datetime64) – Adjusted lower time boundary (inclusive) of data included in analysis. Due to start time adjustment it might differ from 00:00:00 but it’s guaranteed that self.start belongs to the same day (date) as the start argument passed during the class initialization (or that it falls in the same day as the first timestamp in the timestamps argument if start is None).
stop (np.datetime64) – Adjusted upper time boundary (exclusive) of data included in analysis. Due to start time adjustion it might differ from 00:00:00 but it’s guaranteed that self.stop is no less than the stop argument passed during the class initialization and that self.stop - self.start is equal to the whole number of days.
step (np.timedelta64) – Same as the constructor argument.
steps_per_day (int) – Numbers of steps (bins) in a day.
descr (str) – Same as the constructor argument.
total_days (int) – Total number of days (including filtered out) between the start and stop timestamps.
days (int) – Number of days not filtered out due to low activity.
vartype – Mask calculated from data and min_data_points. Array of length equal to total_days. Holds True for the days that weren’t filtered out due to low activity and False for those that were.
min_data_points (int) – Same as the constructor argument.
max_gap (np.timedelta64) – Same as the constructor argument, but converted to np.timedelta64[m] (minute precision).
min_duration (np.timedelta64) – Same as the constructor argument, but converted to np.timedelta64[m] (minute precision).
min_activity (int) – Same as the constructor argument.

Parameters

timestamps (np.array[np.timedelta64]) – Timestamps of activity records.
activity (None|np.array[int|float], optional) – Activity events (weights) associated with each timestamp. If not specified then it’s assumed that activity = 1 for each timestamp.
night (None|np.array[bool], optional) –
Boolean array denoting timestamps associated with night. The i-th timestamp is associated with night if night[i] = True and with day otherwise. If not specified then it’s assumed that night = True for each timestamp.

Note

When binning, if there are conflicting values for night in a bin (timestamps with both night = True and night = False fall into the same bin) then night = True is written for that bin (i.e. True values override False values in the same bin).
step (str|int|timedelta, optional) – Discretization step, defaults to '1m'. All data is discretized by binning it into bins of this size.
start (None|str|int|datetime, optional) – Lower time boundary (inclusive) for processed records, defaults to None. None indicates no lower boundary.
stop (None|str|int|datetime, optional) – Upper time boundary (exclusive) for processed records, defaults to None. None indicates no upper boundary.
descr (str, optional) – Textual description appended to the end of plot headers, defaults to ''. Used to specify the source of data on the plots.
max_gap (str|int|timedelta, optional) – Maximal gap between two consecutive activity events, defaults to '1m'. Consecutive events with distance between them larger than max_gap belong to different activity bouts.
min_duration (str|int|timedelta, optional) – Minimal duration of activity bout, defaults to '1m'. Activity bouts with shorter duration (defined as the time difference between the last activity event in a bout and the first one) are discarded.
min_activity (int, optional) – Minimal value of (binned) activity, defaults to 1. Bins with activity less than min_activity are discarded during activity bout calculations. For example, if self.step = '5 min' and min_activity = 6 then the data is binned into 5 minute bins, and during the activity bout calculation, bins with activity < 6 are treated as if there is no activity within their intervals.
min_data_points (int, optional) – Minimum number of data points (records) in a day, defaults to 1. Days with fewer data points (records) are filtered out.

Properties

`timestamps`	Timestamps of activity records.
`activity`	Activity events associated with each timestamp.
`night`	Boolean array denoting the timestamps associated with night.
`bouts`	Precalculated bouts to be used in class methods when `bouts=True` is passed to them.

Methods

`activity_bouts`	Calculate activity bouts using provided parameters.
`update_bouts`	Re-calculate and update `self.bouts` using provided parameters.
`filter_inactive`	Re-filter days based on a new minimal daily number of data points.
`plot_actogram`	Plot double actogram with right half shifted by 1 day.
`periodogram`	Calculate periodogram for the range of periods.
`plot_periodogram`	Calculate and plot `periodogram()` for the range of periods.
`light_activity`	Calculate light phase activity.
`plot_light_activity`	Calculate and plot `light_activity()` for each day.
`interdaily_stability`	Calculate interdaily stability.
`intradaily_variability`	Calculate intradaily variability.
`plot_intradaily_variability`	Calculate and plot `intradaily_variability()` for each day.
`relative_amplitude`	Calculate relative amplitude.
`plot_relative_amplitude`	Calculate and plot `relative_amplitude()` for each day.
`daily_bouts`	Calculate daily activity bout statistics.
`plot_daily_bouts`	Calculate and plot `daily_bouts()` statistics.
`plot_bout_histogram`	Plot histogram of `activity_bouts()` duration distribution.
`activity_onset`	Calculate activity onset for each day.
`plot_activity_onset`	Calculate and plot `activity_onset()` for each day.

Note

Most methods can base their analysis on either number of activity events or precalculated activity bouts.

Note

Methods accepting timespan parameters (e.g. start, min_duration and all other parameters of type str|int|timedelta) can treet them differently depending on their type:

str parameters are parsed like timedelta strings (e.g. '03:43:40'),

int parameters are treated as the number of nanoseconds,

timedelta parameters are treated as they are.

property timestamps¶

Timestamps of activity records.

Type: np.array[np.datetime64]
Returns: Bin starts (discrete time points). Days with number of data points less than min_data_points are not included.

property activity¶

Activity events associated with each timestamp.

Type: np.array[int|float]
Returns: Activity values for each bin. Days with the number of data points less than min_data_points are not included.

Activity value for each bin is equal to the sum of activity values for input data point that fall into this bin.

property night¶

Boolean array denoting the timestamps associated with night.

The i-th timestamp is associated with night if night[i] = True and with day otherwise.

Type: np.array[bool]
Returns: Filtered night values for each bin. Days with the number of data points less than min_data_points are not included.

property bouts¶

Precalculated bouts to be used in class methods when bouts=True is passed to them.

Type: np.array[int]
Returns: Bouts list.

activity_bouts(max_gap=None, min_duration=None, min_activity=None)¶

Calculate activity bouts using provided parameters.

Discretization step is taken from the constructor (self.step).

Warning

Do not update self.bouts manually, call update_bouts() instead.

Parameters

max_gap (None|str|int|timedelta, optional) – Overrides self.max_gap if specified.
min_duration (None|str|int|timedelta, optional) – Overrides self.min_duration if specified.
min_activity (None|int, optional) – Overrides self.min_activity if specified.

Return type

np.array[int]

Returns

Array holding 1 for steps that belong to some activity bout and 0 otherwise. Consecutive 1’s in bouts belong to the same bout.

update_bouts(max_gap=None, min_duration=None, min_activity=None)¶

Re-calculate and update self.bouts using provided parameters.

Also updates max_gap, min_duration and min_activity if they are not None.

Discretization step is taken from class initialization time (self.step).

Parameters

max_gap (None|str|int|timedelta, optional) – Overrides self.max_gap if specified.
min_duration (None|str|int|timedelta, optional) – Overrides self.min_duration if specified.
min_activity (None|int, optional) – Overrides self.min_activity if specified.

filter_inactive(min_data_points=1)¶

Re-filter days based on a new minimal daily number of data points.

Parameters: min_data_points (int, optional) – Minimum number of data points (records) in a day, defaults to 1. Days with fewer data points (records) are filtered out.

plot_actogram(step=None, bouts=False, log=False, activity_onset='step', percentile=20, N='6h', M='6h', filename=None, width=1000, height=100, dpi=100)¶

Plot double actogram with right half shifted by 1 day.

Night intervals for each day are also plotted on actogram as well as activity onset for each day.

Parameters

step (None|str|int|timedelta, optional) – Bin size (the number of bars in actogram is equal to [24 hours] / step), defaults to None. None stands for self.max_gap.
bouts (bool, optional) – Indicates that the calculation is based on the number of activity bouts rather then activity events, defualts to False.
log (bool, optional) – Indicates whether the log of activity is plotted, defaults to False. Has no effect if bouts = True.
activity_onset (None|False|str, optional) – Shape of the convolution kernel to use for activity onset calculation, defaults to 'step'. To turn the activity onset display off pass None, False or string 'none' (case-insensitive). Otherwise it must be one of the strings recognized by self.activity_onset().
percentile (int, optional) – Percentile of the daily non-zero activity to use as a threshold for activity onset calculation, defaults to 20. Activity lower than that is treated as a zero activity.
N (str|int|timedelta, optional) – Length of the period of negative values (daytime, left part) in the convolution kernel for activity onset calculation, defaults to '6h'.
M (str|int|timedelta, optional) – Length of the period of positive values (nighttime, right part) in the convolution kernel for activity onset calculation, defaults to '6h'.
filename (None|str|PathLike, optional) – Name of the file where the graph is saved. If not specified then the graph is displayed on the screen.
width (int, optional) – Plot width in pixels, defaults to 1000.
height (int, optional) – Plot height in pixels, defaults to 100.
dpi (int, optional) – Plot resolution, defaults to 100.

periodogram(step=None, min_period='16h', max_period='32h', bouts=False)¶

Calculate periodogram for the range of periods.

If periods, powers = periodogram() then periods[powers.argmax()] returns the period with the maximal periodogram power.

Parameters

step (None|str|int|timedelta, optional) – Step size for the generating periods. The first period has a length of min_period, the second period has a length of min_period + step and so on. None stands for self.max_gap.
min_period (str|int|timedelta, optional) – Minimal period length, inclusive, defaults to '16h'.
max_period (str|int|timedelta, optional) – Maximal period length, inclusive, defaults to '32h'.
bouts (bool, optional) – Indicates that the calculation is based on the number of activity bouts rather then activity events, defualts to False.

Return type

(np.array[np.timedelta64], np.array[float])

Returns

(periods, powers) where

periods is periods in the range from min_period to max_period (inclusive).

powers is the corresponding periodogram powers for each period.

plot_periodogram(step=None, min_period='16h', max_period='32h', bouts=False, filename=None, width=1000, height=600, dpi=100)¶

Calculate and plot periodogram() for the range of periods.

The plot also includes an indication of the period with the maximal periodogram power.

Parameters

step (None|str|int|timedelta, optional) – Step size for the generating periods. The first period has a length of min_period, the second period has a length of min_period + step and so on. None stands for self.max_gap.
min_period (str|int|timedelta, optional) – Minimal period length, inclusive, defaults to '16h'.
max_period (str|int|timedelta, optional) – Maximal period length, inclusive, defaults to '32h'.
bouts (bool, optional) – Indicates that the calculation is based on the number of activity bouts rather then activity events, defualts to False.
filename (None|str|PathLike, optional) – Name of the file where the graph is saved. If not specified then the graph is displayed on the screen.
width (int, optional) – Plot width in pixels, defaults to 1000.
height (int, optional) – Plot height in pixels, defaults to 600.
dpi (int, optional) – Plot resolution, defaults to 100.

light_activity(bouts=False, auc=False)¶

Calculate light phase activity.

Parameters

bouts (bool, optional) – Indicates that the calculation is based on the number of activity bouts rather then activity events, defualts to False.
auc (bool, optional) – Flag to return AUC, defaults to False.

Return type

(np.array[float], float)|(np.array[float], float, float)

Returns

(daily_values, total) or (daily_values, total, auc_val) if auc = True where

daily_values is the relative amplitude for each day;

total is the relative amplitude calculated from the data for all days;

auc_val is the area under the curve (integral).

plot_light_activity(bouts=False, filename=None, width=1000, height=600, dpi=100)¶

Calculate and plot light_activity() for each day.

The plot also includes total value based on data for all days.

Parameters

bouts (bool, optional) – Indicates that the calculation is based on the number of activity bouts rather then activity events, defualts to False.
filename (None|str|PathLike, optional) – Name of the file where the graph is saved. If not specified then the graph is displayed on the screen.
width (int, optional) – Plot width in pixels, defaults to 1000.
height (int, optional) – Plot height in pixels, defaults to 600.
dpi (int, optional) – Plot resolution, defaults to 100.

interdaily_stability(step='1h', bouts=False)¶

Calculate interdaily stability.

Note

Small step values lead to high interdaily variability even with stable activity patterns.

Parameters

step (None|str|int|timedelta, optional) – Data discretization step used for calculations, defaults to None. None stands for self.max_gap.
bouts (bool, optional) – Indicates that the calculation is based on the number of activity bouts rather then activity events, defualts to False.

Return type

float

Returns

Number from 0 to 1 (inclusive).

intradaily_variability(step='1h', bouts=False, auc=False)¶

Calculate intradaily variability.

Note

Small step values lead to high interdaily variability even with stable activity patterns.

Parameters

step (None|str|int|timedelta, optional) – Data discretization step used for calculations, defaults to None. None stands for self.max_gap.
bouts (bool, optional) – Indicates that the calculation is based on the number of activity bouts rather then activity events, defualts to False.
auc (bool, optional) – Flag to return AUC, defaults to False.

Return type

(np.array[float], float)|(np.array[float], float, float)

Returns

(daily_values, total) or (daily_values, total, auc_val) if auc = True where

daily_values is the relative amplitude for each day;

total is the relative amplitude calculated from the data for all days;

auc_val is the area under the curve (integral).

plot_intradaily_variability(step='1h', bouts=False, filename=None, width=1000, height=600, dpi=100)¶

Calculate and plot intradaily_variability() for each day.

The plot also includes the total value based on data for all days.

Parameters

step (None|str|int|timedelta, optional) – Data discretization step used for calculations, defaults to None. None stands for self.max_gap.
bouts (bool, optional) – Indicates that the calculation is based on the number of activity bouts rather then activity events, defualts to False.
filename (None|str|PathLike, optional) – Name of the file where the graph is saved. If not specified then the graph is displayed on the screen.
width (int, optional) – Plot width in pixels, defaults to 1000.
height (int, optional) – Plot height in pixels, defaults to 600.
dpi (int, optional) – Plot resolution, defaults to 100.

relative_amplitude(most_active='10h', least_active='5h', bouts=False, auc=False)¶

Calculate relative amplitude.

Parameters

most_active (str|int|timedelta, optional) – Length of the most active period.
least_active (str|int|timedelta, optional) – Length of the least active period.
bouts (bool, optional) – Indicates that the calculation is based on the number of activity bouts rather then activity events, defualts to False.

Return type

(np.array[float], float)|(np.array[float], float, float)

Returns

(daily_values, total) or (daily_values, total, auc_val) if auc = True where

daily_values is the relative amplitude for each day;

total is the relative amplitude calculated from the data for all days;

auc_val is the area under the curve (integral).

plot_relative_amplitude(most_active='10h', least_active='5h', bouts=False, filename=None, width=1000, height=600, dpi=100)¶

Calculate and plot relative_amplitude() for each day.

The plot also includes the total value based on data for all days.

Parameters

most_active (str|int|timedelta, optional) – Length of the most active period.
least_active (str|int|timedelta, optional) – Length of the least active period.
bouts (bool, optional) – Indicates that the calculation is based on the number of activity bouts rather then activity events, defualts to False.
filename (None|str|PathLike, optional) – Name of the file where the graph is saved. If not specified then the graph is displayed on the screen.
width (int, optional) – Plot width in pixels, defaults to 1000.
height (int, optional) – Plot height in pixels, defaults to 600.
dpi (int, optional) – Plot resolution, defaults to 100.

daily_bouts(max_gap=None, min_duration=None, min_activity=None)¶

Calculate daily activity bout statistics.

At first it calculates activity bouts based on provided parameters and then for each day, it calculates the number of activity bouts and the average activity bout duration.

Parameters

max_gap (None|str|int|timedelta, optional) – Overrides self.max_gap if specified.
min_duration (None|str|int|timedelta, optional) – Overrides self.min_duration if specified.
min_activity (None|int, optional) – Overrides self.min_activity if specified.

Return type

(np.array[int], np.array[float])

Returns

(bout_counts, bout_durations) where

bout_counts is the number of activity bouts in each day;

bout_durations is the average activity bout durations for each day in minutes.

plot_daily_bouts(max_gap=None, min_duration=None, min_activity=None, filename=None, width=1000, height=600, dpi=100)¶

Calculate and plot daily_bouts() statistics.

Bout counts are plotted as a bar plot vs left y-axis and average bout durations are plotted as a line plot vs right y-axis.

Parameters

max_gap (None|str|int|timedelta, optional) – Overrides self.max_gap if specified.
min_duration (None|str|int|timedelta, optional) – Overrides self.min_duration if specified.
min_activity (None|int, optional) – Overrides self.min_activity if specified.
filename (None|str|PathLike, optional) – Name of the file where the graph is saved. If not specified then the graph is displayed on the screen.
width (int, optional) – Plot width in pixels, defaults to 1000.
height (int, optional) – Plot height in pixels, defaults to 600.
dpi (int, optional) – Plot resolution, defaults to 100.

plot_bout_histogram(max_gap=None, min_duration=None, min_activity=None, bins=50, filename=None, width=1000, height=600, dpi=100)¶

Plot histogram of activity_bouts() duration distribution.

Parameters

max_gap (None|str|int|timedelta, optional) – Overrides self.max_gap if specified.
min_duration (None|str|int|timedelta, optional) – Overrides self.min_duration if specified.
min_activity (None|int, optional) – Overrides self.min_activity if specified.
bins (int|np.array[int], optional) – Number of bins or array of bin edges.
filename (None|str|PathLike, optional) – Name of the file where the graph is saved. If not specified then the graph is displayed on the screen.
width (int, optional) – Plot width in pixels, defaults to 1000.
height (int, optional) – Plot height in pixels, defaults to 600.
dpi (int, optional) – Plot resolution, defaults to 100.

activity_onset(step=None, percentile=20, N='6h', M='6h', bouts=False, mode='step')¶

Calculate activity onset for each day.

Activity onset for a given day is calculated as follows:

Split the day into intervals of a uniform length (discretize it with some step).

Denote all points where activity is less than the percentile of non-zero activity as being inactive.

Set activity values of all inactive points to -1 and all other points to 1.

Apply the convolution kernel with the left part being negative and the right part being positive to the activity values. This will smoothly summarize the activity values.

Find the point with the maximal summarized value. This will be the activity onset.

Parameters

step (None|str|int|timedelta, optional) – Data discretization step used for calculations, defaults to None. None stands for self.max_gap.
percentile (int, optional) – Percentile of the daily non-zero activity to use as a threshold for activity onset calculation. Activity lower than that is treated as a zero activity.
N (str|int|timedelta, optional) – Length of the period of negative values (daytime, left part) in the convolution kernel for activity onset calculation.
M (str|int|timedelta, optional) – Length of the period of positive values (nighttime, right part) in the convolution kernel for activity onset calculation.
bouts (bool, optional) – Indicates that the calculation is based on the number of activity bouts rather then activity events, defualts to False.
mode (str, optional) –
Defines the shape of the convolution kernel. Each kernel has a discontinuity where left and righ parts connect (the values around the discontinuity are -1 and 1 for the left and right parts respectively). For kernels other than step the left part is decreasing from 0 to -1 and the right part is decreasing from 1 to 0, i.e. the parts of the kernel further from the discontinuity are given less weight.

The possible kernels are:

'step' – function with uniform left and right parts.

'linear' – linear function of the relative distance to the discontinuity.

'quadratic' – quadratic function (square root) of the relative distance to the discontinuity.

'sine' – function proportional to a shifted sine (cosine) of the relative distance to the discontinuity.

Return type

np.array[np.datetime64]

Returns

Activity onsets for each day.

Note

The returned array contains timestamps (absolute time values of an activity onset), not relative shifts from the start of the day.

plot_activity_onset(step=None, percentile=20, N='6h', M='6h', bouts=False, mode='step', filename=None, width=1000, height=600, dpi=100)¶

Calculate and plot activity_onset() for each day.

Activity onsets for each day are plotted as shifts (in hours) from the start of the day (the maximal possible value is 24).

Parameters

step (None|str|int|timedelta, optional) – Data discretization step used for calculations, defaults to None. None stands for self.max_gap.
percentile (int, optional) – Percentile of the daily non-zero activity to use as a threshold for activity onset calculation. Activity lower than that is treated as a zero activity.
N (str|int|timedelta, optional) – Length of the period of negative values (daytime, left part) in the convolution kernel for activity onset calculation.
M (str|int|timedelta, optional) – Length of the period of positive values (nighttime, right part) in the convolution kernel for activity onset calculation.
bouts (bool, optional) – Indicates that the calculation is based on the number of activity bouts rather then activity events, defualts to False.
mode (str, optional) –
Defines the shape of the convolution kernel. Each kernel has a discontinuity where left and righ parts connect (the values around the discontinuity are -1 and 1 for the left and right parts respectively). For kernels other than step the left part is decreasing from 0 to -1 and the right part is decreasing from 1 to 0, i.e. the parts of the kernel further from the discontinuity are given less weight.

The possible kernels are:

'step' – function with uniform left and right parts.

'linear' – linear function of the relative distance to the discontinuity.

'quadratic' – quadratic function (square root) of the relative distance to the discontinuity.

'sine' – function proportional to a shifted sine (cosine) of the relative distance to the discontinuity.
filename (None|str|PathLike, optional) – Name of the file where the graph is saved. If not specified then the graph is displayed on the screen.
width (int, optional) – Plot width in pixels, defaults to 1000.
height (int, optional) – Plot height in pixels, defaults to 600.
dpi (int, optional) – Plot resolution, defaults to 100.

chronobiology.chronobiology.generate_data(points_per_day=100, days=10, activity_period='24h', night_period='24h', bg_ratio=0.2, multiactivity=False)¶

Generate random input data.

Parameters

points_per_day (int) – Number of measurement points per day, defaults to 100.
days (int) – Series length in days, defaults to 10.
activity_period (str|int|timedelta) – Activity period length, defaults to '24h'.
night_period (str|int|timedelta) – Period of night, defaults to '24h'.
bg_ratio (float) – Backgraoud activity ratio, defaults to 0.2.
multiactivity (bool) – Multiactivity, defaults to False.

Return type

dict[str: np.array[np.datetime64]|np.array[float]|np.array[bool]]

Returns

Dictionary containing arrays 'time', 'value' and 'is_night' ready to be used as input for the CycleAnalyzer constructor.

Usage example

>>> data = generate_data()
>>> ca = CycleAnalyzer(data['time'], data['value'], data['is_night'])

chronobiology.chronobiology.generate_night(timeseries, night_period='24h')¶

Generate a random is_night array.

Parameters

timeseries (np.array[np.datetime64]) – Timestamps of measurements.
night_period (str|int|timedelta, optional) – Period of night, defualts to '24h'.

Return type

np.array[bool]

Returns

Array denoting whether night (True) or day (False) is associated with a corresponding measurement.