TemporalData

class TemporalData(temporal_data, variables, times, true_times)[source]

Class representing any sort of temporal data for a specified list of variables

Since teneto’s TemporalNetwork class requires all times to start at zero, we again have the concept of ‘true’ times as well as times offset to start at zero.

Parameters

temporal_data (ndarray) – 2D array whose columns are the variables we’re interested in and whose rows are the temporal data for these variables
variables (list of str) – List (not a numpy array) of variable names
times (ndarray) – 1D array based on true_times but with values shifted to start at zero
true_times ndarray – 1D array of time points

downsample(skip)[source]

Return a downsampled copy of the TemporalData object, by skipping ever ‘skip’ elements

Parameters: skip (int, optional) – How many times to skip: 1 out of skip is kept.
Return type: TemporalData

classmethod from_df(df, times, true_times, i_tend=None)[source]

Create a TemporalData from a DataFrame with variables as columns and timepoints as rows

Parameters

df (pandas DataFrame) – DataFrame containing the temporal data. Indices correspond to time points and columns to variables.
times (ndarray) – 1D array based on true_times but with values shifted to start at zero
true_times ndarray – 1D array of time points
i_tend (int, optional) – Index after which to cut the time series. Default: None (not cut).

Return type

TemporalData

classmethod from_dict(dict_, times, true_times, i_tend=None)[source]

Create a TemporalData from a dict with variables as keys and time series as values

Parameters

dict_ (dict) – Dictionary containing the temporal data. Keys are variables names and values are the associated time series.
times (ndarray) – 1D array based on true_times but with values shifted to start at zero
true_times ndarray – 1D array of time points
i_tend (int, optional) – Index after which to cut the time series. Default: None (not cut).

Return type

TemporalData

normalise()[source]: Return new TemporalData where each time series is normalised by its maximum

plot_relative_optima(variable, optima_type, ax=None, use_true_times=True, plot_var=True)[source]

Plot relative optima for a prticular variable

Parameters

variable (str) – The name of the variable whose optima we want to plot
optima_type ({‘minima’, ‘maxima’}) – Wether to look for minima or maxima
ax (matplotlib.Axes, optional) – Axes on which to plot
use_true_times (bool, optional) – Whether to use the ‘actual’ times or offset the times so that they start at zero
plot_var (bool, optional) – Wether to plot the times series associated to the variable.

Return type

None

plot_series(variables=None, ax=None, norm=False, add_labels=True, labels_xvals=None, use_true_times=True)[source]

Plot particular variables’ values over time

Parameters

variables (list of str) – Names of the variable whose values we want to plot
ax (matplotlib.Axes, optional) – Axes on which to plot
norm (bool, optional) – Whether or not to normalise the time series by dividing through by the max (default False)
add_labels (bool, optional) – Whether to label the variables when plotting (default True)
labels_xvals (list of float, optional) – The positions along the x-axis at which to place the variables’ labels (if using). If set to None (default), labels will be placed at regular intervals along x-axis.
use_true_times (bool, optional) – Whether to use the ‘actual’ times or offset the times so that they start at zero (default True)

Return type

None

relative_optima(variable, optima_type)[source]

Get relative optima for a particular variable

Parameters

variable (str) – Name of the variable whose optima we want
optima_type ({‘minima’, ‘maxima’}) – Type of optima to look for

Returns

optima (list_like) – Value of the variable at its optima
optima_times (list_like) – Times at which these optima occur

save_to_csv(filepath, index=False)[source]

Save temporal data to .csv file, with variables as rows and time points as columns.

Parameters

filepath (str) – Path at which to save the file.
index (bool, optional) – Whether to save the index. Default: False.

Return type

None

series(variable)[source]

Get temporal data for a particular variable

Parameters: variable (str) – The name of a variable for which there is temporal data.
Returns: Time series
Return type: np.ndarray

to_df()[source]: Convert temporal data to pandas.DataFrame format, with variables as rows and time points as columns

to_dict()[source]: Convert temporal data to dict format, with variables as keys and time series as values