climind.stats package
Submodules
climind.stats.paragraphs module
- climind.stats.paragraphs.anomaly_and_rank(all_datasets: List[TimeSeriesAnnual], year: int, output_list=False) str[source]
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[source]
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[source]
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, output_list=False) str[source]
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, output_list=False) str[source]
- climind.stats.paragraphs.co2_paragraph(all_datasets: List[TimeSeriesAnnual], year: int, update=False, output_list: bool = False) str | list[source]
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[source]
- climind.stats.paragraphs.compare_to_highest_anomaly_and_rank(all_datasets: List[TimeSeriesAnnual], year: int) str[source]
- climind.stats.paragraphs.dataset_name_list(all_datasets: List[TimeSeriesMonthly | TimeSeriesAnnual], year: int = None) str[source]
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[source]
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.glacier_paragraph(all_datasets: List[TimeSeriesMonthly | TimeSeriesAnnual], year: int, output_list=False) str[source]
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, output_list: bool = False) str[source]
- Parameters:
all_datasets
year
- climind.stats.paragraphs.greenland_ice_sheet(all_datasets: List[TimeSeriesAnnual], year: int) str[source]
- climind.stats.paragraphs.greenland_ice_sheet_monthly(all_datasets: List[TimeSeriesMonthly], year: int) str[source]
- climind.stats.paragraphs.ice_sheet_monthly_sm_grace_version(all_datasets: List[TimeSeriesMonthly], year: int) str[source]
This is the method used by Shawn Marshall to deal with the GRACE approx mid-month values.
- Parameters:
all_datasets (List[TimeSeriesMonthly]) – List of datasets to be processed.
year (int) – Year of focus
- Return type:
str
- climind.stats.paragraphs.long_term_trend_paragraph(all_datasets: List[TimeSeriesMonthly], year: int, output_list=False) str[source]
- climind.stats.paragraphs.marine_heatwave_and_cold_spell_paragraph(all_datasets: List[TimeSeriesAnnual], year: int) str[source]
- climind.stats.paragraphs.max_monthly_value(all_datasets: List[TimeSeriesMonthly], year: int) str[source]
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.ordinal(n)
- climind.stats.paragraphs.pre_industrial_estimate(all_datasets: List[TimeSeriesAnnual], _) str[source]
Write a short paragraph estimating the difference between the modern baseline 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[source]
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.utils module
- climind.stats.utils.get_latitudes(resolution)[source]
Generate a “latitude” array running from -90 + half the resolution to 90 - half the resolution.
- Parameters:
resolution (float) – Resolution of the grid
- Return type:
ndarray
- climind.stats.utils.get_n_years_from_n_months(n_months)[source]
For a given number of months, count the number of full years
- Parameters:
n_months (int) – Number of months to convert to whole years
- Returns:
Number of whole years
- Return type:
int
- climind.stats.utils.monthly_to_annual_array(monthly_means)[source]
Calculate 12-month averages from a monthly array of shape (n_months, 3). For use in the IPCC averaging method
- Parameters:
monthly_means (ndarray(n_months, 3))
- Returns:
Returns the annual averages
- Return type:
ndarray(n_years, 3)
- climind.stats.utils.record_margin_table_by_year(datasets, match_year: int, years_to_show: int = 50) str[source]
- climind.stats.utils.record_margins(datasets: List[TimeSeriesMonthly | TimeSeriesAnnual], match_year: int, title: str, output_dir: Path)[source]
- climind.stats.utils.rolling_average(input_array, window_length)[source]
Calculate a rolling average of specified window_length
- Parameters:
input_array (ndarray) – input array for which rolling averages are to be calculated
window_length (int) – length of rolling average window
- Return type:
ndarray
- climind.stats.utils.run_the_numbers(datasets: List[TimeSeriesMonthly | TimeSeriesAnnual], match_year: int, title: str, output_dir: Path, ipcc_unc: bool = True)[source]
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