Metadata-Version: 1.1
Name: uwg
Version: 5.3.2
Summary: The Urban Weather Generator engine for Urban Heat Island modelling
Home-page: https://github.com/ladybug-tools/uwg
Author: ladybug-tools
Author-email: UNKNOWN
License: UNKNOWN
Description-Content-Type: text/markdown
Description: # Urban Weather Generator
        
        [![Build Status](https://travis-ci.org/ladybug-tools/uwg.svg?branch=master)](https://travis-ci.org/ladybug-tools/uwg)
        
        The Urban Weather Generator (uwg) is a Python application for modeling the [urban heat island effect](https://en.wikipedia.org/wiki/Urban_heat_island). Specifically, it morphs rural [EnergyPlus weather (.epw) files](http://www.ladybug.tools/epwmap/) to reflect average conditions within the urban canyon using a range of properties including:
        
        * Building geometry (including building height, ground coverage, window:wall area, and facade:site area)
        * Building use (including program type, HVAC systems, and occupancy/equipment scheduling)
        * Cooling system heat rejection to the outdoors (for Summer)
        * Indoor heat leakage to the outdoors (for Winter)
        * Urban materials (including the thermal mass, albedo and emissivity of roads, walls, and roofs)
        * Anthropogenic heat from traffic (including traffic schedules)
        * Vegetation coverage (both trees and shrubs)
        * Atmospheric heat transfer from urban boundary and canopy layers
        
        The [original Urban Weather Generator](http://urbanmicroclimate.scripts.mit.edu/uwg.php) was developed by Bruno Bueno for [his PhD thesis at MIT](https://dspace.mit.edu/handle/1721.1/59107).  Since this time, it has been validated 3 times and has been [enhanced by Aiko Nakano](https://dspace.mit.edu/handle/1721.1/108779).  In 2016, Joseph Yang also [improved the engine and added a range of building templates](https://dspace.mit.edu/handle/1721.1/107347).
        
        This repository is a Python translation of the original [MATLAB Urban Weather Generator](https://github.com/hansukyang/UWG_Matlab).
        
        # Example
        Here is a Python example that shows how to create and run an Urban Weather Generator object. The example script is available [at resources/uwg_example.py](https://github.com/ladybug-tools/uwg/blob/master/resources/uwg_example.py). Run it through your command prompt in the main uwg directory with the following: ```python -m resources.uwg_example```
        
        ```python
        from uwg import uwg
        
        # Define the .epw, .uwg filenames to create an uwg object.
        # uwg will look for the .epw file in the uwg/resources/epw folder,
        # and the .uwg file in the uwg/resources/parameters folder.
        epw_filename = "SGP_Singapore.486980_IWEC.epw"      # .epw file name
        param_filename = "initialize_singapore.uwg"         # .uwg file name
        
        # Initialize the UWG object and run the simulation
        uwg_ = uwg(epw_filename, param_filename)
        uwg_.run()
        ```
        
        Here is the sample .uwg file used in the simulation above. The .uwg file is a a required input where the local building, urban, and geographic features are defined. These features are then used in the simulation to morph the .epw file. This file is available [at resources/initialize_singapore.uwg](https://github.com/ladybug-tools/uwg/blob/master/resources/initialize_singapore.uwg).
        
        ```
        # =================================================
        # REQUIRED PARAMETERS
        # =================================================
        
        # Urban characteristics
        bldHeight,10,     # average building height (m)
        bldDensity,0.5,   # urban area building plan density (0-1)
        verToHor,0.8,     # urban area vertical to horizontal ratio
        h_mix,1,           # fraction of building HVAC waste heat set to the street canyon [as opposed to the roof]
        charLength,1000,  # dimension of a square that encompasses the whole neighborhood [aka. characteristic length] (m)
        albRoad,0.1,      # road albedo (0 - 1)
        dRoad,0.5,        # road pavement thickness (m)
        kRoad,1,          # road pavement conductivity (W/m K)
        cRoad,1600000,    # road volumetric heat capacity (J/m^3 K)
        sensAnth,20,      # non-building sensible heat at street level [aka. heat from cars, pedestrians, street cooking, etc. ] (W/m^2)
        latAnth,2,        # non-building latent heat (W/m^2) (currently not used)
        
        # Climate Zone (Eg. City)   Zone number
        # 1A(Miami)                     1
        # 2A(Houston)                   2
        # 2B(Phoenix)                   3
        # 3A(Atlanta)                   4
        # 3B-CA(Los Angeles)            5
        # 3B(Las Vegas)                 6
        # 3C(San Francisco)             7
        # 4A(Baltimore)                 8
        # 4B(Albuquerque)               9
        # 4C(Seattle)                   10
        # 5A(Chicago)                   11
        # 5B(Boulder)                   12
        # 6A(Minneapolis)               13
        # 6B(Helena)                    14
        # 7(Duluth)                     15
        # 8(Fairbanks)                  16
        
        zone,1,
        
        # Vegetation parameters
        vegCover,0.2,     # Fraction of the urban ground covered in grass/shrubs only (0-1)
        treeCoverage,0.1, # Fraction of the urban ground covered in trees (0-1)
        vegStart,4,       # The month in which vegetation starts to evapotranspire (leaves are out)
        vegEnd,10,        # The month in which vegetation stops evapotranspiring (leaves fall)
        albVeg,0.25,      # Vegetation albedo
        latGrss,0.4,      # Fraction of the heat absorbed by grass that is latent (goes to evaporating water)
        latTree,0.6,      # Fraction of the heat absorbed by trees that is latent (goes to evaporating water)
        rurVegCover,0.9,  # Fraction of the rural ground covered by vegetation
        
        # Traffic schedule [1 to 24 hour],
        SchTraffic,
        0.2,0.2,0.2,0.2,0.2,0.4,0.7,0.9,0.9,0.6,0.6,0.6,0.6,0.6,0.7,0.8,0.9,0.9,0.8,0.8,0.7,0.3,0.2,0.2, # Weekday
        0.2,0.2,0.2,0.2,0.2,0.3,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.6,0.7,0.7,0.7,0.7,0.5,0.4,0.3,0.2,0.2, # Saturday
        0.2,0.2,0.2,0.2,0.2,0.3,0.4,0.4,0.4,0.4,0.4,0.4,0.4,0.4,0.4,0.4,0.4,0.4,0.4,0.4,0.3,0.3,0.2,0.2, # Sunday
        
        # Fraction of building stock for each DOE Building type (pre-80's build, 80's-present build, new)
        # Note that sum(bld) must be equal to 1
        bld,
        0,0,0,    # FullServiceRestaurant
        0,0,0,    # Hospital
        0,0,0,    # LargeHotel
        0,0.4,0,  # LargeOffice
        0,0,0,    # MediumOffice
        0,0.6,0,  # MidRiseApartment
        0,0,0,    # OutPatient
        0,0,0,    # PrimarySchool
        0,0,0,    # QuickServiceRestaurant
        0,0,0,    # SecondarySchool
        0,0,0,    # SmallHotel
        0,0,0,    # SmallOffice
        0,0,0,    # Stand-aloneRetail
        0,0,0,    # StripMall
        0,0,0,    # SuperMarket
        0,0,0,    # Warehouse
        
        # =================================================
        # OPTIONAL URBAN PARAMETERS
        # =================================================
        
        albRoof,0.5,  # roof albedo (0 - 1)
        vegRoof,0.1,  # Fraction of the roofs covered in grass/shrubs (0-1)
        glzR,0.5,     # Glazing Ratio. If not provided, all buildings are assumed to have 40% glazing ratio
        hvac,0,       # HVAC TYPE; 0 = Fully Conditioned (21C-24C); 1 = Mixed Mode Natural Ventilation (19C-29C + windows open >22C); 2 = Unconditioned (windows open >22C)
        
        # =================================================
        # OPTIONAL PARAMETERS FOR SIMULATION CONTROL,
        # =================================================
        
        # Simulation parameters,
        Month,1,        # starting month (1-12)
        Day,1,          # starting day (1-31)
        nDay,31,        # number of days to run simultion
        dtSim,300,      # simulation time step (s)
        dtWeather,3600, # weather time step (s)
        
        autosize,0,     # autosize HVAC (1 for yes; 0 for no)
        sensOcc,100,    # Sensible heat per occupant (W)
        LatFOcc,0.3,    # Latent heat fraction from occupant (normally 0.3)
        RadFOcc,0.2,    # Radiant heat fraction from occupant (normally 0.2)
        RadFEquip,0.5,  # Radiant heat fraction from equipment (normally 0.5)
        RadFLight,0.7,  # Radiant heat fraction from light (normally 0.7)
        
        #Urban climate parameters
        h_ubl1,1000,    # ubl height - day (m)
        h_ubl2,80,      # ubl height - night (m)
        h_ref,150,      # inversion height (m)
        h_temp,2,       # temperature height (m)
        h_wind,10,      # wind height (m)
        c_circ,1.2,     # circulation coefficient (default = 1.2 per Bruno (2012))
        c_exch,1,       # exchange coefficient (default = 1; ref Bruno (2014))
        maxDay,150,     # max day threshold (W/m^2)
        maxNight,20,    # max night threshold (W/m^2)
        windMin,1,      # min wind speed (m/s)
        h_obs,0.1,      # rural average obstacle height (m)
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.6
Classifier: Operating System :: OS Independent
