Utility Functions

Tip

  1. Need help? Please let us know in the UMEP Community.

  2. Please report issues with the manual on GitHub Issues (or use Report Issue for This Page for page-specific feedback).

  3. Please cite SUEWS with proper information from our Zenodo page.

6.3. Utility Functions#

SuPy provides a comprehensive set of utility functions for data processing, analysis, and visualisation.

6.3.1. ERA-5 Data Processing#

Functions for downloading and processing ERA-5 reanalysis data:

gen_forcing_era5(lat_x, lon_x, start, end[, ...])

Generate SUEWS forcing files using ERA-5 timeseries data.

6.3.2. Meteorological Data#

Functions for working with EPW weather files and meteorological data:

gen_epw(df_output, lat, lon[, tz, path_epw])

Generate an epw file of uTMY (urbanised Typical Meteorological Year) using SUEWS simulation results

read_epw(path_epw)

Read in epw file as a DataFrame

fill_gap_all(ser_to_fill[, freq, ...])

Fill all gaps in a time series using data from neighbouring divisions of 'freq'

6.3.3. Energy Balance Analysis#

Functions for analysing the Objective Hysteresis Model (OHM):

derive_ohm_coef(ser_QS, ser_QN)

A function to linearly fit two independant variables to a dependent one.

sim_ohm(ser_qn, a1, a2, a3)

Calculate QS using OHM (Objective Hysteresis Model).

6.3.4. Surface Conductance#

Functions for calculating and calibrating surface conductance:

cal_gs_suews(kd, ta_c, rh, pa, smd, lai, ...)

Model surface conductance/resistance using phenology and atmospheric forcing conditions.

cal_gs_obs(qh, qe, ta, rh, pa, ra)

Calculate surface conductance based on observations, notably turbulent fluxes.

calib_g(df_fc_suews, ser_ra, g_max, lai_max, ...)

Calibrate parameters for modelling surface conductance over vegetated surfaces using LMFIT.

6.3.5. Plotting and Visualisation#

Functions for visualising SUEWS results:

plot_comp(df_var[, scatter_kws, kde_kws, ...])

Produce a scatter plot with linear regression line to compare simulation results and observations.

plot_day_clm(df_var[, fig, ax, show_dif, ...])

Produce a ensemble diurnal climatologies with uncertainties shown in inter-quartile ranges.

plot_rsl(df_output[, var, fig, ax])

Produce a quick plot of RSL results

6.3.6. Roughness Calculations#

Functions for calculating roughness parameters:

cal_z0zd(ser_qh, ser_ustar, ser_ta_c, ...[, ...])

Calculates surface roughness and zero plane displacement height.

cal_neutral(ser_qh, ser_ustar, ser_ta_c, ...)

Calculates the rows associated with neutral condition (threshold=0.01)
Contents