solarwindpy.solar_activity.base

Base classes for solar activity indicators.

Classes

ActivityIndicator()
Base()	Abstract base class providing a logger interface.
DataLoader(key, url)
ID(key)	Container for identifying a particular data product.
IndicatorExtrema(*args, **kwargs)	Base class for objects describing indicator extrema.

class Base

Bases: ABC

Abstract base class providing a logger interface.

property logger

logging.Logger attached to the instance.

class ID(key: str)

Bases: Base

Container for identifying a particular data product.

__init__(key: str) → None

Instantiate the identifier and set the corresponding URL.

Parameters:

key (str) – Key that maps to a URL fragment in _trans_url.

property key

property url

URL specifying location from which data was downloaded.

set_key(key)

Set the identifier key and construct the download URL.

property logger

logging.Logger attached to the instance.

class DataLoader(key, url)

Bases: Base

__init__(key, url)

Initialize a data loader.

Parameters:

• key (str) – Unique data identifier, typically from something like SIDC_ID.

• url (str) – Full download URL for the data source.

abstract property data_path

abstractmethod static convert_nans(data)

abstractmethod download_data()

abstractmethod load_data()

property logger

logging.Logger attached to the instance.

property data

property key

property url

property ctime

property age

set_key(key)

set_url(new)

get_data_ctime()

Determine when the current data set was created.

Returns:

Creation time inferred from the file name, or 1970-01-01 if no prior data are found.

Return type:

pandas.Timestamp

get_data_age()

maybe_update_stale_data()

Download new data if the existing cache is stale.

class ActivityIndicator

Bases: Base

property id

property loader

property data

Shortcut to self.loader.data.

property extrema

property norm_by

property interpolated

set_id(new)

abstractmethod interpolate_data(source_data, target_index)

Interpolate source_data onto target_index.

Parameters:

• source_data (pandas.Series or pandas.DataFrame) – Data with a DatetimeIndex to interpolate.

• target_index (pandas.DatetimeIndex) – Target time axis for the interpolation.

Returns:

Data interpolated onto target_index.

Return type:

pandas.DataFrame

abstract property normalized

abstractmethod set_extrema()

abstractmethod run_normalization()

Normalize the indicator within each solar cycle.

Parameters:

norm_by ({{"max", "zscore", "feature-scale"}}) – Normalization algorithm to apply.

Returns:

Normalized values indexed by time.

Return type:

pandas.Series

property logger

logging.Logger attached to the instance.

class IndicatorExtrema(*args, **kwargs)

Bases: Base

Base class for objects describing indicator extrema.

__init__(*args, **kwargs)

property data

property cycle_intervals

pd.Interval for rising and falling edges and full cycle.

property extrema_bands

Bands of time (\(\Delta t\)) about indicator extrema.

Parameters:

dt (str or pandas.Timedelta) – Window half-width used in calculate_extrema_bands().

abstractmethod load_or_set_data()

calculate_intervals()

Compute rising, falling, and full-cycle time intervals.

Notes

The rising edge comes before the falling edge in time, i.e. it’s Min N followed by Max N. Also calculate intervals for a full SSN cycle.

cut_spec_by_interval(epoch, kind=None, tk_cycles=None)

Assign epochs to solar-cycle intervals.

Parameters:

• epoch (pandas.Series or pandas.DatetimeIndex) – Data to cut.

• kind (str, optional) –

  If provided, restricts the cut to a subset of interval types.

  Key	Description
  None	Cut by all available options.
  ”Cycle”	Cut by solar cycle
  ”Rise”	Cut by rising edge
  ”Fall”	Cut by falling edge
  ”Edges”	Cut by [“Fall”, “Rise”]. Exclusive option.

  Note that "Edges" is exclusive and will specify ["Fall", "Rise"] alone.

• tk_cycles (list or slice, optional) – If not None, a selector used to choose target solar cycles.

Returns:

Series of pandas.Interval objects labeling each epoch.

Return type:

pandas.Series

calculate_extrema_bands(dt='365d')

Return time windows around indicator extrema.

Parameters:

dtstr or pandas.Timedelta, optional

Half-width of the window around each extremum. Defaults to "365d".

Returns:

pandas.DataFrame

Min and Max intervals for each cycle.

cut_about_extrema_bands(epoch, tk_cycles=None, kind=None)

Bin epochs relative to extrema bands.

Computed with calculate_extrema_bands().

Parameters:

• epoch (pandas.DatetimeIndex) – Times to classify.

• tk_cycles (slice, optional) – Subset of cycles to use when cutting.

• kind ({{"Min", "Max"}}, optional) – Restrict the classification to minima or maxima.

Returns:

A series of intervals and a mapped series of the form "N-Min" or "N-Max".

Return type:

tuple[pandas.Series, pandas.Series]

property logger

logging.Logger attached to the instance.