target-py 0.1.1

Python version of the air temperature response to green/blue infrastructure evaluation tool (TARGET)

Target

Target stands for The Air-temperature Response to Green/blue-infrastructure Evaluation Tool. Check out the publication.

Installation

pip install target-py

Usage

Option 1 - via Python

from target_py import Target
from target_py import generate_example
import os

# generate example input data your home directory
home_path = os.path.expanduser("~")
generate_example(
    path=home_path,        # where to generate the example
    site_name="my_site",   # the examples site name
    run_name="my_run",     # the simulation's run name
    obs=False,             # do not generate observation to sample data for later validation
    grid=False,             # generate a non-grid example
    empty=False            # do not generate an empty example
)

# creating a model instance and loading configuration 
conf_path = os.path.join(home_path, "my_site","config.ini")
tar = Target(
  conf_path,            # passing the simulation's config file
  progress=True         # show progress bars
  )
tar.load_config()

# run simulation
tar.run_simulation(
    save_csv=True,      # save model results in csv format
)

# save parameters and config used for simulation
tar.save_simulation_parameters()

Option 2 - via Terminal

Get the command line interface's help:

python -m target_py --help

Two possible tasks are available run and gen. You might want to check out their help sections:

python -m target_py run --help
python -m target_py gen --help

Generate an example:

python -m target_py gen -p "your-desired-path" --site my_site --run my_run_name

You can also generate an 'empty' example, which will only create the folder structure, configuration file and parameters file:

python -m target_py gen -p "your-desired-path" --empty --site my_new_site --run my_run_name

Run the model:

• pass configuration file -c
• showing a progress bar during simulation -p
• saving output in csv files --save-csv

python -m target_py run -c "path-to-ini-file"  -p --save-csv

Tips - Parameters

You can write formulas in the parameters.json. These will be parsed and calculated using the math module of python. List values can not be used for formulas!

In order to write a formula, the entry must start with "formula_". Functions from the python math module must be referred to using the module. For Example math.sqrt() not sqrt(). Dependencies to other parameters can be formulated with a dollar sign $. Getting the value K for SoilW = saturated soil layer benath water can be accessed like this $K_soilW. An underscore _ signifies a nested value. (Nested as in two keywords.)

A example for a valid formula would be:

formula_math.sqrt(2.*$K_soilW/(2.*math.pi / 86400.)*$cp)

Notes

The target air temp modules use "control files" to the run simulations.

In the control file the user must define:

work_dir - directory where input and output is stored
para_json_path - path to parameters filer
site name - string that creates folders for different sites
run name - string for name of run
inpt_met_file - file name of input meteorological file
inpt_lc_file - file name of input land cover file
date_fmt - format of datetime in input met files
timestep - number of minutes for each timestep (30 min is recommended)
date1A - start date for simulation (should be a minimum of 24 hours prior to date1)
date1 - start date for validation period
date2 - end of model run

The other fields in the control file are for setting up model validation and generating output plots. The plotting scripts are not yet setup for general use. However, if you run either of the two example control files they should work.

There are example control files for two different model runs

Input files:

Each run requires input Meteorology file and input land cover file in the provided example:

• Input meteorology should be placed in ./site_name/input/MET/
• Input land cover should be placed in ./site_name/input/LC/

File names for each input file are defined the in control file.

Met forcing file must have following fields (in any order):

datetime (format for this can be set in control file):
Ta (air temperature in C)
RH (relative humidity)
WS (wind speed in ms-1)
P (pressure in hPa) Kd (incoming shortwave Wm-2)

(optional) Ld (incoming longwave Wm-2) - can be modelled if this data is unavailable, by including "mod_Ld=Y" in the config file.

The land cover data must have the fraction of each land cover type and building heights and street widths (m) (keep headers the same as example). The “FID” is the identifier for each point – this can be stations numbers (as in the Mawson_stations eg) or grid cells. In the case of the Mawson grid, the FID values correspond to a grid shape file (.shp), so output can easily mapped to a GIS grid.

Outputs:

At the moment, the code dumps the output as a numpy array (*.npy) in ./site name/output/run name.npy

This can be read with numpy.load

The array has the following cols:

ID – same as FID Ws – wind speed Ta – canyon air temperature
Ts_horz – surface temperature
Tmrt mean radiant temperature UTCI - universal thermal comfort index (Celsius degrees) UTCI_cat – universal thermal comfort index (category) date – datetime object

You can index the array to desired ID and/or times and then output the variables. I'm sure there is a better way to this... but that is that at the moment.

The LC shp file must have following fields:

FID (object id) roof (percent)
road (percent)
watr (percent)
conc (percent) Veg (percent)
dry (percent)
irr (percent)
H (house height)
W (canyon width) - strongly recommended to include, otherwise the model can calculate it as the grid resolution * (1 - roof fraction)

Important

W (canyon width) may not be 0!