climind.stats package

Submodules

climind.stats.paragraphs module

climind.stats.paragraphs.anomaly_and_rank(all_datasets: List[TimeSeriesAnnual], year: int) → str

Write a short paragraph, returned as a string, which gives the rank range and data value for the chosen year, as well as saying how many data sets and which datasets were used.

Parameters

all_datasets (List[TimeSeriesAnnual]) – List of datasets to be used to derive the ranks and values
year (int) – Year for which the paragraph should be generated.

Return type

str

climind.stats.paragraphs.anomaly_and_rank_plus_new_base(all_datasets: List[TimeSeriesAnnual], year: int) → str

Write a short paragraph, returned as a string, which gives the rank range and data value for the chosen year, as well as saying how many data sets and which datasets were used. Then it adds the anomalies relative to the 1961-1990 baseline, with information about the number of datasets.

Parameters

all_datasets (List[TimeSeriesAnnual]) – List of datasets to be used to derive the ranks and values
year (int) – Year for which the paragraph should be generated.

Return type

str

climind.stats.paragraphs.antarctic_ice_paragraph(all_datasets: List[TimeSeriesMonthly], year: int) → str

Generate a paragraph of some standard stats for the Antarctic sea ice: rank and value for max and min extents in the year (Feb and September).

Parameters

all_datasets (List[TimeSeriesMonthly]) – List of datasets on which the assessment will be based
year (int) – Chosen year to focus on

Returns

Paragraph of text

Return type

str

climind.stats.paragraphs.arctic_ice_paragraph(all_datasets: List[TimeSeriesMonthly], year: int) → str

Generate a paragraph of some standard stats for the Arctic sea ice: rank and value for max and min extents in the year (March and September).

Parameters

all_datasets (List[TimeSeriesMonthly]) – List of datasets on which the assessment will be based
year (int) – Chosen year to focus on

Returns

Paragraph of text

Return type

str

climind.stats.paragraphs.basic_anomaly_and_rank(all_datasets: List[TimeSeriesAnnual], year: int) → str

climind.stats.paragraphs.co2_paragraph(all_datasets: List[TimeSeriesAnnual], year: int, update=False) → str

Generate a paragraph of some standard stats for greenhouse gases

Parameters

all_datasets (List[TimeSeriesAnnual]) – List of datasets on which the assessment will be based
year (int) – Chosen year to focus on
update (bool) – If set to True treat this as an update

Returns

Paragraph of text

Return type

str

climind.stats.paragraphs.co2_paragraph_update(all_datasets: List[TimeSeriesAnnual], year: int) → str

climind.stats.paragraphs.compare_to_highest_anomaly_and_rank(all_datasets: List[TimeSeriesAnnual], year: int) → str

climind.stats.paragraphs.dataset_name_list(all_datasets: List[Union[TimeSeriesMonthly, TimeSeriesAnnual]], year: Optional[int] = None) → str

Given a list of dataset, return a comma-and-and separated list of the names.

Parameters

all_datasets (List[Union[TimeSeriesMonthly, TimeSeriesAnnual]]) – List of data sets whose names you want in a list
year (int) – If year is specified, the name list will specify to what month data are available if the year is incomplete.

Returns

A list of the dataset names separated by commas and, where appropriate, ‘and’

Return type

str

climind.stats.paragraphs.fancy_html_units(units: str) → str

Convert plain text units into html fancy units, which use subscripts and special characters to render.

Parameters: units (str) – Units to be rendered into fancy units
Returns: Units in fancy html form, or unchanged.
Return type: str

climind.stats.paragraphs.get_last_month(in_str)

climind.stats.paragraphs.glacier_paragraph(all_datasets: List[Union[TimeSeriesMonthly, TimeSeriesAnnual]], year: int) → str

Write the glacier paragraph :param all_datasets: list of data sets to be processed :type all_datasets: list[Union[TimeSeriesMonthly, TimeSeriesAnnual]] :param year: Year for which to do the evaluation :type year: int

Return type: str

climind.stats.paragraphs.global_anomaly_and_rank(all_datasets: List[TimeSeriesAnnual], year: int) → str

Parameters

all_datasets –
year –

climind.stats.paragraphs.greenland_ice_sheet(all_datasets: List[TimeSeriesAnnual], year: int) → str

climind.stats.paragraphs.greenland_ice_sheet_monthly(all_datasets: List[TimeSeriesMonthly], year: int) → str

climind.stats.paragraphs.long_term_trend_paragraph(all_datasets: List[TimeSeriesMonthly], _) → str

climind.stats.paragraphs.marine_heatwave_and_cold_spell_paragraph(all_datasets: List[TimeSeriesAnnual], year: int) → str

climind.stats.paragraphs.max_monthly_value(all_datasets: List[TimeSeriesMonthly], year: int) → str

Find the highest monthly data value within the chosen year and return a paragraph, as a string, which gives the value and rank for that month.

Parameters

all_datasets (List[TimeSeriesMonthly]) – List of datasets to be used in the evaluation
year (int) – Year to be analysed

Returns

Short paragraph of text

Return type

str

climind.stats.paragraphs.nice_list(names)

climind.stats.paragraphs.ordinal(n)

climind.stats.paragraphs.pre_industrial_estimate(all_datasets: List[TimeSeriesAnnual], _) → str

Write a short paragraph estimating the difference between the modern baselin and 1850 to 1900.

Parameters: all_datasets (List[TimeSeriesAnnual]) – List of all the data sets to be analysed
Returns: Returns a paragraph of text stating an estimate of the pre-industrial temperature from these data sets
Return type: str

climind.stats.paragraphs.rank_ranges(low: int, high: int) → str

Given an upper and lower limit on the rank, return a string which describes the range. e.g. ‘the 2nd’ or ‘between the 4th and 8th’.

Parameters

low (int) – Lower of the two ranks.
high (int) – Higher of the two ranks.

Returns

Short string which describes the range. e.g. ‘the 2nd’ or ‘between the 4th and 8th’ or similar.

Return type

str

climind.stats.paragraphs.superlative(variable)

climind.stats.utils module

climind.stats.utils.get_ranks(datasets, match_year)

climind.stats.utils.get_values(datasets, match_year)

climind.stats.utils.run_the_numbers(datasets: List[Union[TimeSeriesMonthly, TimeSeriesAnnual]], match_year: int, title: str, output_dir: Path)

Given a list of datasets calculate various statistics relating to ranking and values

Parameters

datasets (list) – List of data sets
match_year (int) – Year of interest. Stats will be calculated up to the year of interest, but note that rankings will include years after the year of interest if it is not the most recent year in the data sets
title (str) – name for the file
output_dir (Path) –

Return type

None

climind.stats.utils.table_by_year(datasets, match_year: int, years_to_show: int = 20) → str