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

import matplotlib.pyplot as plt
import supy as sp
import pandas as pd
import numpy as np
from pathlib import Path

%matplotlib inline

/opt/homebrew/Caskroom/mambaforge/base/envs/supy/lib/python3.9/site-packages/pandas/core/reshape/merge.py:916: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)
  key_col = Index(lvals).where(~mask_left, rvals)

sp.show_version()

SuPy versions
-------------
supy: 2022.9.19.dev1-dirty
supy_driver: 2021a15

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.

path_runcontrol = Path('../sample_run') / 'RunControl.nml'

df_state_init = sp.init_supy(path_runcontrol)

2022-09-20 23:04:30,116 - SuPy - INFO - All cache cleared.

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

df_state_init.filter(like='method').T

	grid	1
var	ind_dim
aerodynamicresistancemethod	0	2
basetmethod	0	1
evapmethod	0	2
diagmethod	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.

grid = df_state_init.index[0]
df_forcing = sp.load_forcing_grid(path_runcontrol, grid)
# by default, two years of forcing data are included;
# to save running time for demonstration, we only use one year in this demo
df_forcing=df_forcing.loc['2012'].iloc[1:]

2022-09-20 23:04:30,731 - SuPy - INFO - All cache cleared.

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

df_state_init, df_forcing = sp.load_SampleData()
grid = df_state_init.index[0]
# by default, two years of forcing data are included;
# to save running time for demonstration, we only use one year in this demo
df_forcing=df_forcing.loc['2012'].iloc[1:]

2022-09-20 23:04:34,398 - SuPy - INFO - All cache cleared.

Overview of SuPy input

df_state_init

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:

df_state_init.loc[:,['bldgh','evetreeh','dectreeh']]

var	bldgh	evetreeh	dectreeh
ind_dim	0	0	0
grid
1	22.0	13.1	13.1

df_state_init.filter(like='sfr_surf')

var	sfr_surf
ind_dim	(0,)	(1,)	(2,)	(3,)	(4,)	(5,)	(6,)
grid
1	0.43	0.38	0.0	0.02	0.03	0.0	0.14

df_forcing

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.

list_var_forcing = [
    "kdown",
    "Tair",
    "RH",
    "pres",
    "U",
    "rain",
]
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(-1).dropna(how="any")
)
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(subplots=True, figsize=(8, 12), legend=False,)
fig = axes[0].figure
fig.tight_layout()
fig.autofmt_xdate(bottom=0.2, rotation=0, ha="center")
for ax, var in zip(axes, list_var_forcing):
    _ = ax.set_ylabel(dict_var_label[var])

Modification of SuPy input

Given pandas.DataFrame is the core data structure of SuPy, all operations, including modification, output, demonstration, etc., on SuPy inputs (df_state_init and df_forcing) can be done using pandas-based functions/methods.

Specifically, for modification, the following operations are essential:

locating data

Data can be located in two ways, namely: 1. by name via `.loc <http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#selection-by-label>`__; 2. by position via `.iloc <http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#selection-by-position>`__.

# view the surface fraction variable: `sfr`
df_state_init.loc[:,'sfr_surf']

ind_dim	(0,)	(1,)	(2,)	(3,)	(4,)	(5,)	(6,)
grid
1	0.43	0.38	0.0	0.02	0.03	0.0	0.14

# view the second row of `df_forcing`, which is a pandas Series
df_forcing.iloc[1]

iy       2012.000000
id          1.000000
it          0.000000
imin       10.000000
qn       -999.000000
qh       -999.000000
qe       -999.000000
qs       -999.000000
qf       -999.000000
U           5.176667
RH         86.195000
Tair       11.620000
pres     1001.833333
rain        0.000000
kdown       0.173333
snow     -999.000000
ldown    -999.000000
fcld     -999.000000
Wuh         0.000000
xsmd     -999.000000
lai      -999.000000
kdiff    -999.000000
kdir     -999.000000
wdir     -999.000000
isec        0.000000
Name: 2012-01-01 00:10:00, dtype: float64

# view a particular position of `df_forcing`, which is a value
df_forcing.iloc[8,9]

4.78

setting new values

Setting new values is very straightforward: after locating the variables/data to modify, just set the new values accordingly:

# modify surface fractions
df_state_init.loc[:,'sfr_surf']=[.1,.1,.2,.3,.25,.05,0]
# check the updated values
df_state_init.loc[:,'sfr_surf']

ind_dim	(0,)	(1,)	(2,)	(3,)	(4,)	(5,)	(6,)
grid
1	0.1	0.1	0.2	0.3	0.25	0.05	0

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.

df_output, df_state_final = sp.run_supy(df_forcing, df_state_init)

2022-09-20 23:04:39,021 - SuPy - INFO - ====================
2022-09-20 23:04:39,021 - SuPy - INFO - Simulation period:
2022-09-20 23:04:39,022 - SuPy - INFO -   Start: 2012-01-01 00:05:00
2022-09-20 23:04:39,022 - SuPy - INFO -   End: 2012-12-31 23:55:00
2022-09-20 23:04:39,023 - SuPy - INFO -
2022-09-20 23:04:39,023 - SuPy - INFO - No. of grids: 1
2022-09-20 23:04:39,024 - SuPy - INFO - SuPy is running in serial mode
2022-09-20 23:04:49,343 - SuPy - INFO - Execution time: 10.3 s
2022-09-20 23:04:49,344 - SuPy - INFO - ====================

df_output

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

df_output.columns.levels[0]

Index(['SUEWS', 'snow', 'RSL', 'BEERS', 'debug', 'SPARTACUS', 'ESTMExt',
       'DailyState'],
      dtype='object', name='group')

df_state_final

df_state_final is a DataFrame for holding:

1. all model states if save_state is set to True when calling sp.run_supy (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 starting at the timestamp as in df_state_final.

Detailed description of variables in df_state_final refers to SuPy output

df_state_final.T.head()

	datetime	2012-01-01 00:05:00	2013-01-01 00:00:00
	grid	1	1
var	ind_dim
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
ah_slope_heating	(0,)	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.

df_output.head()

	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
1	2012-01-01 00:05:00	0.176667	0.02332	344.179805	371.582645	11.607452	-27.249493	40.574001	-6.382243	19.664156	0.042594	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	2012-01-01 00:10:00	0.173333	0.02288	344.190048	371.657938	11.622405	-27.317436	39.724283	-6.228797	18.593922	0.041722	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	2012-01-01 00:15:00	0.170000	0.02244	344.200308	371.733243	11.637359	-27.385375	38.874566	-6.082788	17.531131	0.040849	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	2012-01-01 00:20:00	0.166667	0.02200	344.210586	371.808562	11.652312	-27.453309	38.024849	-5.943907	16.475472	0.039975	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	2012-01-01 00:25:00	0.163333	0.02156	344.220882	371.883893	11.667265	-27.521237	37.175131	-5.811855	15.426648	0.039101	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

5 rows × 924 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.

df_output_suews = df_output['SUEWS']

Statistics Calculation

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

df_output_suews.loc[:, ['QN', 'QS', 'QH', 'QE', 'QF']].describe()

var	QN	QS	QH	QE	QF
count	105407.000000	105407.000000	105407.000000	105407.000000	105407.000000
mean	39.883231	5.830107	62.666636	50.411038	79.024549
std	132.019300	49.161894	77.074237	78.484562	31.231867
min	-86.331686	-75.287258	-177.705269	0.000000	26.327536
25%	-42.499510	-27.895414	16.069451	0.676206	50.058031
50%	-25.749393	-8.183901	43.844985	14.712552	82.883410
75%	74.815479	19.121287	85.722951	69.135212	104.812507
max	679.848644	237.932439	480.795771	624.179069	160.023207

Plotting

Basic example

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

# 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:

ax_output = df_output_suews\
    .loc[grid]\
    .loc['2012 6 1':'2012 6 7',
         ['QN', 'QS', 'QE', 'QH', 'QF']]\
    .rename(columns=dict_var_disp)\
    .plot()
_ = ax_output.set_xlabel('Date')
_ = ax_output.set_ylabel('Flux ($ \mathrm{W \ m^{-2}}$)')
_ = ax_output.legend()

More examples

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

# energy balance
ax_output = (
    df_output_suews.loc[grid]
    .loc["2012 6":"2012 8", ["QN", "QS", "QE", "QH", "QF"]]
    .rename(columns=dict_var_disp)
    .plot(figsize=(10, 3), title="Surface Energy Balance",)
)
_ = ax_output.set_xlabel("Date")
_ = ax_output.set_ylabel("Flux ($ \mathrm{W \ m^{-2}}$)")
_ = ax_output.legend()

Resampling

The suggested runtime/simulation frequency of SUEWS is 300 s, which usually results in a large output and may be over-weighted for storage and analysis. Also, you may feel an 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.

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.

# energy balance
ax_output = (
    df_1d_mean.loc[:, ["QN", "QS", "QE", "QH", "QF"]]
    .rename(columns=dict_var_disp)
    .plot(figsize=(10, 3), title="Surface Energy Balance",)
)
_ = ax_output.set_xlabel("Date")
_ = ax_output.set_ylabel("Flux ($ \mathrm{W \ m^{-2}}$)")
_ = ax_output.legend()

Then we use the hourly results for other analyses.

# radiation balance
ax_output = (
    df_1d_mean.loc[:, ["QN", "Kdown", "Kup", "Ldown", "Lup"]]
    .rename(columns=dict_var_disp)
    .plot(figsize=(10, 3), title="Radiation Balance",)
)
_ = ax_output.set_xlabel("Date")
_ = ax_output.set_ylabel("Flux ($ \mathrm{W \ m^{-2}}$)")
_ = ax_output.legend()

# water balance
ax_output = (
    df_1d_sum.loc[:, ["Rain", "Irr", "Evap", "RO", "TotCh"]]
    .rename(columns=dict_var_disp)
    .plot(figsize=(10, 3), title="Surface Water Balance",)
)
_ = ax_output.set_xlabel("Date")
_ = ax_output.set_ylabel("Water amount (mm)")
_ = ax_output.legend()

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

# get a monthly Resampler
df_plot = df_output_suews.loc[grid].copy()
df_plot.index = df_plot.index.set_names("Month")
rsmp_1M = df_plot.shift(-1).dropna(how="all").resample("1M", kind="period")
# mean values
df_1M_mean = rsmp_1M.mean()
# sum values
df_1M_sum = rsmp_1M.sum()

# 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
df_1M_mean.loc[:, ["QN", "QS", "QE", "QH", "QF"]].rename(columns=dict_var_disp).plot(
    ax=axes[0],  # specify the axis for plotting
    figsize=(10, 6),  # specify figure size
    title="Surface Energy Balance",
    kind="bar",
)
# surface water balance
df_1M_sum.loc[:, ["Rain", "Irr", "Evap", "RO", "TotCh"]].rename(
    columns=dict_var_disp
).plot(
    ax=axes[1],  # specify the axis for plotting
    title="Surface Water Balance",
    kind="bar",
)

# annotations
_ = axes[0].set_ylabel("Mean Flux ($ \mathrm{W \ m^{-2}}$)")
_ = axes[0].legend()
_ = axes[1].set_xlabel("Month")
_ = axes[1].set_ylabel("Total Water Amount (mm)")
_ = axes[1].xaxis.set_ticklabels(name_mon, rotation=0)
_ = axes[1].legend()

<AxesSubplot:title={'center':'Surface Energy Balance'}, xlabel='Month'>

<AxesSubplot:title={'center':'Surface Water Balance'}, xlabel='Month'>

Output

The supy output can be saved as txt files for further analysis using supy function save_supy.

df_output

	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
1	2012-01-01 00:05:00	0.176667	0.02332	344.179805	371.582645	11.607452	-27.249493	40.574001	-6.382243	19.664156	0.042594	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	2012-01-01 00:10:00	0.173333	0.02288	344.190048	371.657938	11.622405	-27.317436	39.724283	-6.228797	18.593922	0.041722	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	2012-01-01 00:15:00	0.170000	0.02244	344.200308	371.733243	11.637359	-27.385375	38.874566	-6.082788	17.531131	0.040849	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	2012-01-01 00:20:00	0.166667	0.02200	344.210586	371.808562	11.652312	-27.453309	38.024849	-5.943907	16.475472	0.039975	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	2012-01-01 00:25:00	0.163333	0.02156	344.220882	371.883893	11.667265	-27.521237	37.175131	-5.811855	15.426648	0.039101	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
	2012-12-31 23:35:00	0.000000	0.00000	330.263407	363.676342	10.140000	-33.412935	53.348682	-4.399144	0.904146	23.430745	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	2012-12-31 23:40:00	0.000000	0.00000	330.263407	363.676342	10.140000	-33.412935	52.422737	-4.397669	0.394992	23.012479	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	2012-12-31 23:45:00	0.000000	0.00000	330.263407	363.676342	10.140000	-33.412935	51.496792	-4.395831	-0.121686	22.601374	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	2012-12-31 23:50:00	0.000000	0.00000	330.263407	363.676342	10.140000	-33.412935	50.570847	-4.393681	-0.645680	22.197273	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	2012-12-31 23:55:00	0.000000	0.00000	330.263407	363.676342	10.140000	-33.412935	46.174492	-4.391264	-2.949124	20.101945	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.36935	0.3242	8.0995

105407 rows × 924 columns

list_path_save = sp.save_supy(df_output, df_state_final)

for file_out in list_path_save:
    print(file_out.name)

1_2012_DailyState.txt
1_2012_SUEWS_60.txt
1_2012_RSL_60.txt
1_2012_BEERS_60.txt
1_2012_debug_60.txt
1_2012_ESTMExt_60.txt
df_state.csv