Heat Flow Data

reheatfunq.data

The reheatfunq.data module contains a function to load data from the New Global Heat Flow (NGHF) data set of Lucazeau [L2019]. The NGHF data set can be downloaded from the paper’s supporting information S02. The function read_nghf() can be used as follows:

from reheatfunq.data import read_nghf

nghf_file = 'path/to/NGHF.csv'

nghf_lon, nghf_lat, nghf_hf, nghf_quality, nghf_yr, \
nghf_type, nghf_max_depth, nghf_uncertainty, indexmap \
   = read_nghf(nghf_file)

The Jupyter notebook jupyter/REHEATFUNQ/01-Load-and-filter-NGHF.ipynb illustrates how this function was used in the derivation of the REHEATFUNQ model.


read_nghf(f)

This function reads the NGHF data base.

Parameters:

f (str | pathlib.Path) – The file path to the NGHF.csv file of Lucazeau [L2019].

Returns:

  • nghf_lon (list) – Longitudes of the data points. This field is mandatory.

  • nghf_lat (list) – Latitudes of the data points. This field is mandatory.

  • nghf_hf (list) – Heat flow of the data points in \(\mathrm{mWm}^{-2}\). This field is mandatory.

  • nghf_quality (list) – Quality of the data points. If defined, one of ‘A’, ‘B’, ‘C’, ‘D’ or ‘Z’.

  • nghf_yr (list) – Measurement year of the data points. This field is mandatory.

  • nghf_type (list) – Whether the heat flow data point is continental or oceanic. If defined, one of ‘land’ or ‘ocean’. This field is mandatory.

  • nghf_max_depth (list) – Maximum depth used in estimating the temperature gradient. If the field is empty, returns -9999999.

  • nghf_uncertainty (list) – Relative uncertainty of the data points. Returns 0.1 for ‘A’ quality, 0.2 for ‘B’ quality, 0.3 for ‘C’ quality, and infinity otherwise.

  • indexmap (dict) – Maps indices within the returned arrays to lines in the NGHF.csv table. If i was an index within the returned arrays, then j = indexmap[i] is the row within the NGHF table.