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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, freq_s, …) |
Save SuPy run results to files |
load_SampleData() |
Load sample data for quickly starting a demo run. |
show_version() |
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[, path_epw, ratio_dif_dir]) |
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 |
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_mod(kd, ta_k, rh, pa, smd, lai, g_cst) |
Model surface conductance/resistance using phenology and atmospheric forcing conditions. |
cal_gs_obs(qh, qe, ta, rh, pa) |
Calculate surface conductance based on observations, notably turbulent fluxes. |
calib_g(df_fc_suews[, g_max, lai_max, s1, …]) |
Calibrate parameters for modelling surface conductance over vegetated surfaces using LMFIT. |
WRF-SUEWS¶
extract_reclassification(path_nml) |
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[, fig, ax]) |
Produce a scatter plot with linear regression line to compare simulation results and observations. |
plot_day_clm(df_var[, fig, ax]) |
Produce a ensemble diurnal climatologies with uncertainties shown in inter-quartile ranges. |