smif.convert package

Submodules

smif.convert.area module

Handles conversion between the sets of regions used in the SosModel

class smif.convert.area.NamedShape(name, shape)

Bases: tuple

__getnewargs__()

Return self as a plain tuple. Used by copy and pickle.

static __new__(_cls, name, shape)

Create new instance of NamedShape(name, shape)

__repr__()

Return a nicely formatted representation string

name

Alias for field number 0

shape

Alias for field number 1

class smif.convert.area.RegionRegister

Bases: object

Holds the sets of regions used by the SectorModels and provides conversion between data values relating to compatible sets of regions.

convert(data, from_set_name, to_set_name)

Convert a list of data points for a given set of regions to another set of regions.

Parameters:

• data (dict) –
• from_set_name (str) –
• to_set_name (str) –

get_regions_in_set(set_name)

Return regions for a given set

region_set_names

Names of registered region sets

Returns:sets Return type:list of str

register(region_set)

Register a set of regions as a source/target for conversion

class smif.convert.area.RegionSet(set_name, fiona_shape_iter)

Bases: object

Hold a set of regions, spatially indexed for ease of lookup when constructing conversion matrices.

Parameters:

• set_name (str) – Name to use as identifier for this set of regions
• fiona_shape_iter (iterable) – Iterable (probably a list or a reader handle) of fiona feature records e.g. the ‘features’ entry of a GeoJSON collection

intersection(bounds)

Return the subset of regions intersecting with a bounding box

smif.convert.area.proportion_of_a_intersecting_b(shape_a, shape_b)

Calculate the proportion of shape a that intersects with shape b

smif.convert.interval module

Handles conversion between the set of time intervals used in the SosModel

There are three main classes, which are currently rather intertwined. Interval represents an individual definition of a period within a year. This is specified using the ISO8601 period syntax and exposes methods which use the isodate library to parse this into an internal hourly representation of the period.

TimeIntervalRegister holds the definitions of time-interval sets specified for the sector models at the SosModel level. This class exposes one public method, add_interval_set() which allows the SosModel to add an interval definition from a model configuration to the register.

TimeSeries is used to encapsulate any data associated with a time interval definition set, and handles conversion from the current time interval resolution to a target time interval definition held in the register.

Quantities

Quantities are associated with a duration, period or interval. For example 120 GWh of electricity generated during each week of February.:

Week 1: 120 GW
Week 2: 120 GW
Week 3: 120 GW
Week 4: 120 GW

Other examples of quantities:

• greenhouse gas emissions
• demands for infrastructure services
• materials use
• counts of cars past a junction
• costs of investments, operation and maintenance

Upscale: Divide

To convert to a higher temporal resolution, the values need to be apportioned across the new time scale. In the above example, the 120 GWh of electricity would be divided over the days of February to produce a daily time series of generation. For example:

1st Feb: 17 GWh
2nd Feb: 17 GWh
3rd Feb: 17 GWh
...

Downscale: Sum

To resample weekly values to a lower temporal resolution, the values would need to be accumulated. A monthly total would be:

Feb: 480 GWh

Remapping

Remapping quantities, as is required in the conversion from energy demand (hourly values over a year) to energy supply (hourly values for one week for each of four seasons) requires additional averaging operations. The quantities are averaged over the many-to-one relationship of hours to time-slices, so that the seasonal-hourly timeslices in the model approximate the hourly profiles found across the particular seasons in the year. For example:

hour 1: 20 GWh
hour 2: 15 GWh
hour 3: 10 GWh
...
hour 8592: 16 GWh
hour 8593: 12 GWh
hour 8594: 21 GWh
...
hour 8760: 43 GWh

To:

season 1 hour 1: 20+16+.../4 GWh # Denominator number hours in sample
season 1 hour 2: 15+12+.../4 GWh
season 1 hour 3: 10+21+.../4 GWh
...

Prices

Unlike quantities, prices are associated with a point in time. For example a spot price of £870/GWh. An average price can be associated with a duration, but even then, we are just assigning a price to any point in time within a range of times.

Upscale: Fill

Given a timeseries of monthly spot prices, converting these to a daily price can be done by a fill operation. E.g. copying the monthly price to each day.

From:

Feb: £870/GWh

To:

1st Feb: £870/GWh
2nd Feb: £870/GWh
...

Downscale: Average

On the other hand, going down scale, such as from daily prices to a monthly price requires use of an averaging function. From:

1st Feb: £870/GWh
2nd Feb: £870/GWh
...

To:

Feb: £870/GWh

Development Notes

• We could use numpy.convolve() to compare time intervals as hourly arrays before adding them to the set of intervals

class smif.convert.interval.Interval(name, list_of_intervals, base_year=2010)

Bases: object

A time interval

Parameters:

• id (str) – The unique name of the Interval
• list_of_intervals (str) – A list of tuples of valid ISO8601 duration definition string denoting the time elapsed from the beginning of the year to the (beginning, end) of the interval
• base_year (int, default=2010) – The reference year used for conversion to a datetime tuple

Example

>>> a = Interval('id', ('PT0H', 'PT1H'))
>>> a.interval = ('PT1H', 'PT2H')
>>> repr(a)
"Interval('id', [('PT0H', 'PT1H'), ('PT1H', 'PT2H')], base_year=2010)"
>>> str(a)
"Interval 'id' starts at hour 0 and ends at hour 1"

baseyear

The reference year

end

The end hour of the interval(s)

Returns:A list of integers, representing the hour from the beginning of the year associated with the end of each of the intervals Return type:list

interval

The list of intervals

Setter appends a tuple or list of intervals to the list of intervals

name

start

The start hour of the interval(s)

Returns:A list of integers, representing the hour from the beginning of the year associated with the start of each of the intervals Return type:list

to_hourly_array()

Converts a list of intervals to a boolean array of hours

to_hours()

Return a list of tuples of the intervals in terms of hours

Returns:A list of tuples of the start and end hours of the year of the interval Return type:list

class smif.convert.interval.TimeIntervalRegister(base_year=2010)

Bases: object

Holds the set of time-intervals used by the SectorModels

Parameters:base_year (int, default=2010) – Set the year which is used as a reference by all time interval sets and repeated for each future year

convert(timeseries, from_interval, to_interval)

Convert some data to a time_interval type

Parameters:

• timeseries (TimeSeries) – The timeseries to convert from from_interval to to_interval
• from_interval (str) – The unique identifier of a interval type which matches the timeseries
• to_interval (str) – The unique identifier of a registered interval type

Returns:

A dictionary with keys name and value, where the entries for key are the name of the target time interval, and the values are the resampled timeseries values.

Return type:

dict

get_intervals_in_set(set_name)

Parameters:set_name (str) – The unique identifying name of the interval definitions Returns:Returns a collection of the intervals in the order in which they were defined Return type:collections.OrderedDict

interval_set_names

A list of the interval set names contained in the register

Returns: Return type:list

register(intervals, set_name)

Add a time-interval definition to the set of intervals types

Detects duplicate references to the same annual-hours by performing a convolution of the two one-dimensional arrays of time-intervals.

Parameters:

• intervals (list) – Time intervals required as a list of dicts, with required keys start, end and name
• set_name (str) – A unique identifier for the set of time intervals

class smif.convert.interval.TimeSeries(data)

Bases: object

A series of values associated with an interval definition

Parameters:data (list) – A list of dicts, each entry containing ‘name’ and ‘value’ keys

hourly_values

The timeseries resampled to hourly values

Module contents

In this module, we implement the conversion across space and time

The SpaceTimeConvertor is instantiated with data to convert, and the names of the four source and destination spatio-temporal resolutions.

The convert() method returns a new list of smif.SpaceTimeValue namedtuples for passing to a sector model.

class smif.convert.SpaceTimeConvertor(data, from_spatial, to_spatial, from_temporal, to_temporal, region_register, interval_register)

Bases: object

Handles the conversion of time and space for a list of values

Parameters:

• data (list) – A list of smif.SpaceTimeValue
• from_spatial (str) – The name of the spatial resolution of the data
• to_spatial (str) – The name of the required spatial resolution
• from_temporal (str) – The name of the temporal resolution of the data
• to_temporal (str) – The name of the required temproal resolution
• region_register (smif.convert.area.RegionRegister) – A fully populated register of the models’ regions
• interval_register (smif.convert.interval.TimeIntervalRegister) – A fully populated register of the models’ intervals

Notes

Future development requires using a data object which allows multiple views upon the values across the three dimensions of time, space and units. This will then allow more efficient conversion across any one of these dimensions while holding the others constant. One option could be collections.ChainMap.

convert()

Convert the data according to the parameters passed to the SpaceTimeConvertor

Returns:A list of smif.SpaceTimeValue Return type:list

data_by_region

data_regions