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        "description": "# Urban Weather Generator\n\n[![Build Status](https://travis-ci.org/ladybug-tools/uwg.svg?branch=master)](https://travis-ci.org/ladybug-tools/uwg)\n\nThe 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:\n\n* Building geometry (including building height, ground coverage, window:wall area, and facade:site area)\n* Building use (including program type, HVAC systems, and occupancy/equipment scheduling)\n* Cooling system heat rejection to the outdoors (for Summer)\n* Indoor heat leakage to the outdoors (for Winter)\n* Urban materials (including the thermal mass, albedo and emissivity of roads, walls, and roofs)\n* Anthropogenic heat from traffic (including traffic schedules)\n* Vegetation coverage (both trees and shrubs)\n* Atmospheric heat transfer from urban boundary and canopy layers\n\nThe [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).\n\nThis repository is a Python translation of the original [MATLAB Urban Weather Generator](https://github.com/hansukyang/UWG_Matlab).\n\n# Example\nHere 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```\n\n```python\nfrom uwg import uwg\n\n# Define the .epw, .uwg filenames to create an uwg object.\n# uwg will look for the .epw file in the uwg/resources/epw folder,\n# and the .uwg file in the uwg/resources/parameters folder.\nepw_filename = \"SGP_Singapore.486980_IWEC.epw\"      # .epw file name\nparam_filename = \"initialize_singapore.uwg\"         # .uwg file name\n\n# Initialize the UWG object and run the simulation\nuwg_ = uwg(epw_filename, param_filename)\nuwg_.run()\n```\n\nHere 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).\n\n```\n# =================================================\n# REQUIRED PARAMETERS\n# =================================================\n\n# Urban characteristics\nbldHeight,10,     # average building height (m)\nbldDensity,0.5,   # urban area building plan density (0-1)\nverToHor,0.8,     # urban area vertical to horizontal ratio\nh_mix,1,           # fraction of building HVAC waste heat set to the street canyon [as opposed to the roof]\ncharLength,1000,  # dimension of a square that encompasses the whole neighborhood [aka. characteristic length] (m)\nalbRoad,0.1,      # road albedo (0 - 1)\ndRoad,0.5,        # road pavement thickness (m)\nkRoad,1,          # road pavement conductivity (W/m K)\ncRoad,1600000,    # road volumetric heat capacity (J/m^3 K)\nsensAnth,20,      # non-building sensible heat at street level [aka. heat from cars, pedestrians, street cooking, etc. ] (W/m^2)\nlatAnth,2,        # non-building latent heat (W/m^2) (currently not used)\n\n# Climate Zone (Eg. City)   Zone number\n# 1A(Miami)                     1\n# 2A(Houston)                   2\n# 2B(Phoenix)                   3\n# 3A(Atlanta)                   4\n# 3B-CA(Los Angeles)            5\n# 3B(Las Vegas)                 6\n# 3C(San Francisco)             7\n# 4A(Baltimore)                 8\n# 4B(Albuquerque)               9\n# 4C(Seattle)                   10\n# 5A(Chicago)                   11\n# 5B(Boulder)                   12\n# 6A(Minneapolis)               13\n# 6B(Helena)                    14\n# 7(Duluth)                     15\n# 8(Fairbanks)                  16\n\nzone,1,\n\n# Vegetation parameters\nvegCover,0.2,     # Fraction of the urban ground covered in grass/shrubs only (0-1)\ntreeCoverage,0.1, # Fraction of the urban ground covered in trees (0-1)\nvegStart,4,       # The month in which vegetation starts to evapotranspire (leaves are out)\nvegEnd,10,        # The month in which vegetation stops evapotranspiring (leaves fall)\nalbVeg,0.25,      # Vegetation albedo\nlatGrss,0.4,      # Fraction of the heat absorbed by grass that is latent (goes to evaporating water)\nlatTree,0.6,      # Fraction of the heat absorbed by trees that is latent (goes to evaporating water)\nrurVegCover,0.9,  # Fraction of the rural ground covered by vegetation\n\n# Traffic schedule [1 to 24 hour],\nSchTraffic,\n0.2,0.2,0.2,0.2,0.2,0.4,0.7,0.9,0.9,0.6,0.6,0.6,0.6,0.6,0.7,0.8,0.9,0.9,0.8,0.8,0.7,0.3,0.2,0.2, # Weekday\n0.2,0.2,0.2,0.2,0.2,0.3,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.6,0.7,0.7,0.7,0.7,0.5,0.4,0.3,0.2,0.2, # Saturday\n0.2,0.2,0.2,0.2,0.2,0.3,0.4,0.4,0.4,0.4,0.4,0.4,0.4,0.4,0.4,0.4,0.4,0.4,0.4,0.4,0.3,0.3,0.2,0.2, # Sunday\n\n# Fraction of building stock for each DOE Building type (pre-80's build, 80's-present build, new)\n# Note that sum(bld) must be equal to 1\nbld,\n0,0,0,    # FullServiceRestaurant\n0,0,0,    # Hospital\n0,0,0,    # LargeHotel\n0,0.4,0,  # LargeOffice\n0,0,0,    # MediumOffice\n0,0.6,0,  # MidRiseApartment\n0,0,0,    # OutPatient\n0,0,0,    # PrimarySchool\n0,0,0,    # QuickServiceRestaurant\n0,0,0,    # SecondarySchool\n0,0,0,    # SmallHotel\n0,0,0,    # SmallOffice\n0,0,0,    # Stand-aloneRetail\n0,0,0,    # StripMall\n0,0,0,    # SuperMarket\n0,0,0,    # Warehouse\n\n# =================================================\n# OPTIONAL URBAN PARAMETERS\n# =================================================\n\nalbRoof,0.5,  # roof albedo (0 - 1)\nvegRoof,0.1,  # Fraction of the roofs covered in grass/shrubs (0-1)\nglzR,0.5,     # Glazing Ratio. If not provided, all buildings are assumed to have 40% glazing ratio\nhvac,0,       # HVAC TYPE; 0 = Fully Conditioned (21C-24C); 1 = Mixed Mode Natural Ventilation (19C-29C + windows open >22C); 2 = Unconditioned (windows open >22C)\n\n# =================================================\n# OPTIONAL PARAMETERS FOR SIMULATION CONTROL,\n# =================================================\n\n# Simulation parameters,\nMonth,1,        # starting month (1-12)\nDay,1,          # starting day (1-31)\nnDay,31,        # number of days to run simultion\ndtSim,300,      # simulation time step (s)\ndtWeather,3600, # weather time step (s)\n\nautosize,0,     # autosize HVAC (1 for yes; 0 for no)\nsensOcc,100,    # Sensible heat per occupant (W)\nLatFOcc,0.3,    # Latent heat fraction from occupant (normally 0.3)\nRadFOcc,0.2,    # Radiant heat fraction from occupant (normally 0.2)\nRadFEquip,0.5,  # Radiant heat fraction from equipment (normally 0.5)\nRadFLight,0.7,  # Radiant heat fraction from light (normally 0.7)\n\n#Urban climate parameters\nh_ubl1,1000,    # ubl height - day (m)\nh_ubl2,80,      # ubl height - night (m)\nh_ref,150,      # inversion height (m)\nh_temp,2,       # temperature height (m)\nh_wind,10,      # wind height (m)\nc_circ,1.2,     # circulation coefficient (default = 1.2 per Bruno (2012))\nc_exch,1,       # exchange coefficient (default = 1; ref Bruno (2014))\nmaxDay,150,     # max day threshold (W/m^2)\nmaxNight,20,    # max night threshold (W/m^2)\nwindMin,1,      # min wind speed (m/s)\nh_obs,0.1,      # rural average obstacle height (m)\n```\n\n\n",
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