Note

1. This page was generated from docs/source/tutorial/quick-start.ipynb. Interactive online version: Slideshow:

2. Need help? Please let us know in the UMEP Community.
3. A good understanding of SUEWS is a prerequisite to the proper use of SuPy.

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

sp.show_version()

SuPy versions
-------------
supy: 2020.7.1dev
supy_driver: 2020b1

=================
SYSTEM DEPENDENCY

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.3.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 19.5.0
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.0.3
numpy            : 1.17.5
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 19.3.1
setuptools       : 45.1.0.post20200119
Cython           : None
pytest           : 5.3.1
hypothesis       : None
sphinx           : 3.1.1
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : 4.5.0
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.10.3
IPython          : 7.11.1
pandas_datareader: None
bs4              : 4.8.2
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.5.0
matplotlib       : 3.1.2
numexpr          : 2.7.1
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
pytest           : 5.3.1
pyxlsb           : None
s3fs             : None
scipy            : 1.4.1
sqlalchemy       : None
tables           : 3.6.1
tabulate         : 0.8.6
xarray           : 0.14.1
xlrd             : None
xlwt             : None
xlsxwriter       : None
numba            : 0.46.0

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)

2020-07-05 22:59:45,696 - 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
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:]

2020-07-05 22:59:47,526 - 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:]

2020-07-05 22:59:50,754 - 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	dectreeh	evetreeh
ind_dim	0	0	0
grid
1	22.0	13.1	13.1

df_state_init.filter(like='sfr')

var	sfr
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']

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

ind_dim	(0,)	(1,)	(2,)	(3,)	(4,)	(5,)	(6,)
grid
1	0.1	0.1	0.2	0.3	0.25	0.05	0.0

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)

2020-07-05 22:59:56,659 - SuPy - INFO - ====================
2020-07-05 22:59:56,660 - SuPy - INFO - Simulation period:
2020-07-05 22:59:56,660 - SuPy - INFO -   Start: 2012-01-01 00:05:00
2020-07-05 22:59:56,661 - SuPy - INFO -   End: 2012-12-31 23:55:00
2020-07-05 22:59:56,662 - SuPy - INFO -
2020-07-05 22:59:56,662 - SuPy - INFO - No. of grids: 1
2020-07-05 22:59:56,663 - SuPy - INFO - SuPy is running in serial mode
2020-07-05 23:00:15,586 - SuPy - INFO - Execution time: 18.9 s
2020-07-05 23:00:15,587 - 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', 'SOLWEIG', '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	0.021459	344.179805	371.680316	11.607207	-27.345303	40.574001	-5.886447	15.276915	-7.777741	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	2012-01-01 00:10:00	0.173333	0.046164	344.190048	372.637243	11.620000	-28.320026	39.724283	-1.013319	-22.518257	-81.748807	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	2012-01-01 00:15:00	0.170000	0.045271	344.200308	372.715137	11.635000	-28.390100	38.874566	-1.001900	-23.450672	-82.273388	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	2012-01-01 00:20:00	0.166667	0.044378	344.210586	372.793044	11.650000	-28.460168	38.024849	-0.989860	-24.350304	-82.818868	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	2012-01-01 00:25:00	0.163333	0.043485	344.220882	372.870963	11.665000	-28.530232	37.175131	-0.977988	-25.191350	-83.410146	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

5 rows × 371 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	39.375516	5.729435	66.614072	46.798096	79.024549
std	131.952334	48.981924	71.535234	70.441795	31.231867
min	-86.331686	-75.287258	-98.890985	-84.805997	26.327536
25%	-42.635690	-27.871115	20.680393	0.960748	50.058031
50%	-26.001734	-7.830453	48.672443	14.846743	82.883410
75%	73.479667	18.009734	91.152469	65.817674	104.812507
max	679.848644	237.932439	480.602696	532.281922	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()

<matplotlib.axes._subplots.AxesSubplot at 0x7fb081241128>

<matplotlib.axes._subplots.AxesSubplot at 0x7fb0942eb5f8>

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	0.021459	344.179805	371.680316	11.607207	-27.345303	40.574001	-5.886447	15.276915	-7.777741	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	2012-01-01 00:10:00	0.173333	0.046164	344.190048	372.637243	11.620000	-28.320026	39.724283	-1.013319	-22.518257	-81.748807	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	2012-01-01 00:15:00	0.170000	0.045271	344.200308	372.715137	11.635000	-28.390100	38.874566	-1.001900	-23.450672	-82.273388	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	2012-01-01 00:20:00	0.166667	0.044378	344.210586	372.793044	11.650000	-28.460168	38.024849	-0.989860	-24.350304	-82.818868	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	2012-01-01 00:25:00	0.163333	0.043485	344.220882	372.870963	11.665000	-28.530232	37.175131	-0.977988	-25.191350	-83.410146	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
	2012-12-31 23:35:00	0.000000	0.000000	330.263407	363.676342	10.140000	-33.412935	53.348682	-4.399144	2.559974	21.774918	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	2012-12-31 23:40:00	0.000000	0.000000	330.263407	363.676342	10.140000	-33.412935	52.422737	-4.397669	2.178582	21.228889	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	2012-12-31 23:45:00	0.000000	0.000000	330.263407	363.676342	10.140000	-33.412935	51.496792	-4.395831	1.797190	20.682498	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	2012-12-31 23:50:00	0.000000	0.000000	330.263407	363.676342	10.140000	-33.412935	50.570847	-4.393681	1.436708	20.114885	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	2012-12-31 23:55:00	0.000000	0.000000	330.263407	363.676342	10.140000	-33.412935	46.174492	-4.391264	-0.234230	17.387051	...	100.0	100.0	100.0	100.0	100.0	100.0	449.702073	0.36935	0.3242	8.0995

105407 rows × 371 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_snow_60.txt
1_2012_RSL_60.txt
1_2012_SOLWEIG_60.txt
df_state.csv