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Quickstart of SuPy

This quickstart demonstrates the essential and simplest workflow of supy in SUEWS simulation:

  1. load input files
  2. run simulation
  3. examine results

More advanced use of supy are available in the tutorials

Before start, we need to load the following necessary packages.

In [1]:
import matplotlib.pyplot as plt
import supy as sp
import pandas as pd
import numpy as np
from pathlib import Path
get_ipython().run_line_magic('matplotlib', 'inline')

# produce high-quality figures, which can also be set as one of ['svg', 'pdf', 'retina', 'png']
# 'svg' produces high quality vector figures
%config InlineBackend.figure_format = 'svg'

Load input files

For existing SUEWS users:

First, a path to SUEWS RunControl.nml should be specified, which will direct supy to locate input files.

In [2]:
path_runcontrol = Path('../sample_run') / 'RunControl.nml'
In [3]:
df_state_init = sp.init_supy(path_runcontrol)

A sample df_state_init looks below (note that .T is used here to a nicer tableform view):

In [4]:
grid 98
var ind_dim
aerodynamicresistancemethod 0 2
evapmethod 0 2
emissionsmethod 0 2
netradiationmethod 0 3
roughlenheatmethod 0 2
roughlenmommethod 0 2
smdmethod 0 0
stabilitymethod 0 3
storageheatmethod 0 1
waterusemethod 0 0

Following the convention of SUEWS, supy loads meteorological forcing (met-forcing) files at the grid level.

In [5]:
grid = df_state_init.index[0]
df_forcing = sp.load_forcing_grid(path_runcontrol, grid)

For new users to SUEWS/SuPy:

To ease the input file preparation, a helper function load_SampleData is provided to get the sample input for SuPy simulations

In [6]:
df_state_init, df_forcing = sp.load_SampleData()

Overview of SuPy input


df_state_init includes model Initial state consisting of:

  • surface characteristics (e.g., albedo, emissivity, land cover fractions, etc.; full details refer to SUEWS documentation)
  • model configurations (e.g., stability; full details refer to SUEWS documentation)

Detailed description of variables in df_state_init refers to SuPy input

Surface land cover fraction information in the sample input dataset:

In [30]:
var bldgh dectreeh evetreeh
ind_dim 0 0 0
98 22.0 13.1 13.1
In [7]:
var sfr
ind_dim (0,) (1,) (2,) (3,) (4,) (5,) (6,)
98 0.43 0.38 0.001 0.019 0.029 0.001 0.14


df_forcing includes meteorological and other external forcing information.

Detailed description of variables in df_forcing refers to SuPy input.

Below is an overview of forcing variables of the sample data set used in the following simulations.

In [28]:
list_var_forcing = [
dict_var_label = {
    'kdown': 'Incoming Solar\n Radiation ($ \mathrm{W \ m^{-2}}$)',
    'Tair': 'Air Temperature ($^{\circ}}$C)',
    'RH': r'Relative Humidity (%)',
    'pres': 'Air Pressure (hPa)',
    'rain': 'Rainfall (mm)',
    'U': 'Wind Speed (m $\mathrm{s^{-1}}$)'
df_plot_forcing_x = df_forcing.loc[:, list_var_forcing].copy().shift(
df_plot_forcing = df_plot_forcing_x.resample('1h').mean()
df_plot_forcing['rain'] = df_plot_forcing_x['rain'].resample('1h').sum()

axes = df_plot_forcing.plot(
    figsize=(8, 12),
fig = axes[0].figure
fig.autofmt_xdate(bottom=0.2, rotation=0, ha='center')
for ax, var in zip(axes, list_var_forcing):

Run simulations

Once met-forcing (via df_forcing) and initial conditions (via df_state_init) are loaded in, we call sp.run_supy to conduct a SUEWS simulation, which will return two pandas DataFrames: df_output and df_state.

In [9]:
df_output, df_state_final = sp.run_supy(df_forcing, df_state_init)


df_output is an ensemble output collection of major SUEWS output groups, including:

  • SUEWS: the essential SUEWS output variables
  • DailyState: variables of daily state information
  • snow: snow output variables (effective when snowuse = 1 set in df_state_init)

Detailed description of variables in df_output refers to SuPy output

In [10]:
Index(['SUEWS', 'snow', 'DailyState'], dtype='object', name='group')


df_state_final is a DataFrame for holding:

  1. all model states if save_state is set to True when calling sp.run_supy and supy may run significantly slower for a large simulation;
  2. or, only the final state if save_state is set to False (the default setting) in which mode supy has a similar performance as the standalone compiled SUEWS executable.

Entries in df_state_final have the same data structure as df_state_init and can thus be used for other SUEWS simulations staring at the timestamp as in df_state_final.

Detailed description of variables in df_state_final refers to SuPy output

In [11]:
grid 98
datetime 2012-01-01 00:05:00 2013-01-01 00:05:00
var ind_dim
aerodynamicresistancemethod 0 2.0 2.0
ah_min (0,) 15.0 15.0
(1,) 15.0 15.0
ah_slope_cooling (0,) 2.7 2.7
(1,) 2.7 2.7

Examine results

Thanks to the functionality inherited from pandas and other packages under the PyData stack, compared with the standard SUEWS simulation workflow, supy enables more convenient examination of SUEWS results by statistics calculation, resampling, plotting (and many more).

Ouptut structure

df_output is organised with MultiIndex (grid,timestamp) and (group,varaible) as index and columns, respectively.

In [12]:
group SUEWS ... DailyState
var Kdown Kup Ldown Lup Tsurf QN QF QS QH QE ... DensSnow_Paved DensSnow_Bldgs DensSnow_EveTr DensSnow_DecTr DensSnow_Grass DensSnow_BSoil DensSnow_Water a1 a2 a3
grid datetime
98 2012-01-01 00:05:00 0.153333 0.018279 344.310184 371.986259 11.775615 -27.541021 40.574001 -46.53243 62.420064 3.576493 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2012-01-01 00:10:00 0.153333 0.018279 344.310184 371.986259 11.775615 -27.541021 39.724283 -46.53243 61.654096 3.492744 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2012-01-01 00:15:00 0.153333 0.018279 344.310184 371.986259 11.775615 -27.541021 38.874566 -46.53243 60.885968 3.411154 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2012-01-01 00:20:00 0.153333 0.018279 344.310184 371.986259 11.775615 -27.541021 38.024849 -46.53243 60.115745 3.331660 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2012-01-01 00:25:00 0.153333 0.018279 344.310184 371.986259 11.775615 -27.541021 37.175131 -46.53243 59.343488 3.254200 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

5 rows × 218 columns

Here we demonstrate several typical scenarios for SUEWS results examination.

The essential SUEWS output collection is extracted as a separate variable for easier processing in the following sections. More advanced slicing techniques are available in pandas documentation.

In [13]:
df_output_suews = df_output['SUEWS']

Statistics Calculation

We can use .describe() method for a quick overview of the key surface energy balance budgets.

In [14]:
df_output_suews.loc[:, ['QN', 'QS', 'QH', 'QE', 'QF']].describe()
count 105408.000000 105408.000000 105408.000000 105408.000000 105408.000000
mean 42.574626 -2.128683 101.666596 22.880054 79.047033
std 134.685026 83.616791 64.426005 28.535854 31.237533
min -84.389073 -81.747551 -44.370665 -0.649114 26.333882
25% -41.096052 -54.493597 52.976865 1.918672 50.066249
50% -24.869018 -43.957916 82.984095 13.057008 82.896007
75% 77.405862 20.563903 140.933003 31.672138 104.858241
max 689.067820 387.412149 338.167114 272.755143 160.076122


Basic example

Plotting is very straightforward via the .plot method bounded with pandas.DataFrame. Note the usage of loc for to slices of the output DataFrame.

In [15]:
# a dict for better display variable names
dict_var_disp = {
    'QN': '$Q^*$',
    'QS': r'$\Delta Q_S$',
    'QE': '$Q_E$',
    'QH': '$Q_H$',
    'QF': '$Q_F$',
    'Kdown': r'$K_{\downarrow}$',
    'Kup': r'$K_{\uparrow}$',
    'Ldown': r'$L_{\downarrow}$',
    'Lup': r'$L_{\uparrow}$',
    'Rain': '$P$',
    'Irr': '$I$',
    'Evap': '$E$',
    'RO': '$R$',
    'TotCh': '$\Delta S$',

Quick look at the simulation results:

In [16]:
ax_output = df_output_suews\
    .loc['2012 6 1':'2012 6 7',
         ['QN', 'QS', 'QE', 'QH', 'QF']]\
ax_output.set_ylabel('Flux ($ \mathrm{W \ m^{-2}}$)')
<matplotlib.legend.Legend at 0x1263f4f98>

More examples

Below is a more complete example for examination of urban energy balance over the whole summer (June to August).

In [17]:
# energy balance
ax_output = df_output_suews.loc[grid]\
    .loc['2012 6':'2012 8', ['QN', 'QS', 'QE', 'QH', 'QF']]\
        figsize=(10, 3),
        title='Surface Energy Balance',
ax_output.set_ylabel('Flux ($ \mathrm{W \ m^{-2}}$)')
<matplotlib.legend.Legend at 0x12642e8d0>


The suggested runtime/simulation frequency of SUEWS is 300 s, which usually results a large output and may be over-weighted for storage and analysis. Also, you may feel apparent slowdown in producing the above figure as a large amount of data were used for the plotting. To slim down the result size for analysis and output, we can resample the default output very easily.

In [18]:
rsmp_1d = df_output_suews.loc[grid].resample('1d')
# daily mean values
df_1d_mean = rsmp_1d.mean()
# daily sum values
df_1d_sum = rsmp_1d.sum()

We can then re-examine the above energy balance at hourly scale and plotting will be significantly faster.

In [19]:
# energy balance
ax_output = df_1d_mean\
    .loc[:, ['QN', 'QS', 'QE', 'QH', 'QF']]\
            figsize=(10, 3),
            title='Surface Energy Balance',
ax_output.set_ylabel('Flux ($ \mathrm{W \ m^{-2}}$)')
<matplotlib.legend.Legend at 0x126dccb70>

Then we use the hourly results for other analyses.

In [20]:
# radiation balance
ax_output = df_1d_mean\
    .loc[:, ['QN', 'Kdown', 'Kup', 'Ldown', 'Lup']]\
        figsize=(10, 3),
        title='Radiation Balance',
ax_output.set_ylabel('Flux ($ \mathrm{W \ m^{-2}}$)')
<matplotlib.legend.Legend at 0x1272d6358>
In [21]:
# water balance
ax_output = df_1d_sum\
    .loc[:, ['Rain', 'Irr', 'Evap', 'RO', 'TotCh']]\
        figsize=(10, 3),
        title='Surface Water Balance',
ax_output.set_ylabel('Water amount (mm)')
<matplotlib.legend.Legend at 0x127610668>

Get an overview of partitioning in energy and water balance at monthly scales:

In [22]:
# get a monthly Resampler
rsmp_1M = df_plot\
    .resample('1M', kind='period')
# mean values
df_1M_mean = rsmp_1M.mean()
# sum values
df_1M_sum = rsmp_1M.sum()

In [23]:
# month names
name_mon = [x.strftime('%b') for x in rsmp_1M.groups]
# create subplots showing two panels together
fig, axes = plt.subplots(2, 1, sharex=True)
# surface energy balance
    .loc[:, ['QN', 'QS', 'QE', 'QH', 'QF']]\
        ax=axes[0],  # specify the axis for plotting
        figsize=(10, 6),  # specify figure size
        title='Surface Energy Balance',
# surface water balance
    .loc[:, ['Rain', 'Irr', 'Evap', 'RO', 'TotCh']]\
        ax=axes[1],  # specify the axis for plotting
        title='Surface Water Balance',

# annotations
axes[0].set_ylabel('Mean Flux ($ \mathrm{W \ m^{-2}}$)')
axes[1].set_ylabel('Total Water Amount (mm)')
axes[1].xaxis.set_ticklabels(name_mon, rotation=0)
<matplotlib.legend.Legend at 0x127add320>


The resampled output can be outputed for a smaller file.

In [24]:

For a justified format, we use the to_string for better format controlling and write the formatted string out to a file.

In [25]:
str_out = df_1d_mean.to_string(
with open('suews_sample.txt', 'w') as file_out:
    print(str_out, file=file_out)