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

  2. Please report issues with the manual on the GitHub Issues.

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

6.1.3. API reference# Top-level Functions#

init_supy(path_init[, force_reload, check_input])

Initialise supy by loading initial model states.

load_forcing_grid(path_runcontrol, grid[, ...])

Load forcing data for a specific grid included in the index of df_state_init.

run_supy(df_forcing, df_state_init[, ...])

Perform supy simulation.

save_supy(df_output, df_state_final[, ...])

Save SuPy run results to files


Load sample data for quickly starting a demo run.

show_version([mode, as_json])

print SuPy and supy_driver version information. Utility Functions# ERA-5 Data Downloader#

download_era5(lat_x, lon_x, start, end, ...)

Generate ERA-5 cdsapi-based requests and download data for area of interests.

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

Generate SUEWS forcing files using ERA-5 data. Typical Meteorological Year#

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

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


Read in epw file as a DataFrame Gap Filling#

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

Fill all gaps in a time series using data from neighbouring divisions of 'freq' 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). 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. WRF-SUEWS#


Extract reclassification info from path_nml as a DataFrame.

plot_reclassification(path_nml[, path_save, ...])

Produce Sankey Diagram to visualise the reclassification specified in path_nml Plotting#

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 Roughness Calculation#

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) Command-Line Tools#