Class: URBANopt::RNM::GeojsonInput

Inherits:
Object
  • Object
show all
Defined in:
lib/urbanopt/rnm/geojson_input.rb

Overview

creating a class to process parse and process the geographic information for buildings, streets and substations

Constant Summary collapse

UG_RATIO_DEFAULT =

Set constants

0.9
ONLY_LV_CONSUMERS_DEFAULT = 
true
MAX_LV_NODES_DEFAULT = 
1

Instance Method Summary collapse

Instance Method Details

#consumer_coordinates(building, street) ⇒ Object

defining a method to find the coordinates of the closest node of each building building to the closest street, to be used for the customers_ext.txt file the method receives as arguments every nodes of 1 building and the array containing all the street nodes computed before and it returns the coordinates and id of the closest node of the building to the street The new algorithm developed calculates an approximate distance: (x+y)/2, of each building-node with each street-node and compares it with the “minimum_distance” this approximate distance has been defined in order to be able to disregard all the distances which are greater than the “minimum distance” computed until that moment, without being required to compute the Pithagorean Theorem, which requires a long computational time. Therefore (x+y)/2 has been computed knowing that: if the minimum length of the hypothenuse of a right triangle is when the triangle is isosceles so when the hyphothenuse (d) is equal to d = sqrt(2)*x (where x is the distance among the nodes on the x-axis), so we can assume that x = (x+y)/2, than if d = sqrt(2)*((x+y)/2) > (x+y)/2 > minimum_distance than it confirmes that x and y can be disregarded and there is no need to compute the real distance for that node since the approximate distance value (which represents the minimum possible distance for the sum of those catheti) is greater than the minimum_distance that it is been compared with. This process it is iterated for all the distances among the building-nodes with the street-nodes until an approximate distance (x+y)/2 is lower than the minimum distance computed until that moment and in that case the real distance with the Pythagorean Theorem is computed and compared with the minimum distance.



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# File 'lib/urbanopt/rnm/geojson_input.rb', line 73

def consumer_coordinates(building, street)
  dist_min = 5000 # assuming a first fitticious minimum distance that will be replaced later on by the real minimum distance
  # iterating the distance among each node of each street and each node of each building until the minimum distance is found
  for j in 0..building.length - 1 # assessing each building node of the considered building
    for i in 0..street.length - 1 # assessing each street node
      y = building[j][1] - street[i][1] # calculating the distance on the y-axis
      x = building[j][0] - street[i][0] # calculating the distance on the x-axis
      distance_approx = (x + y) / 2 # finding the "approximate" distance of each building node with each street node, in order to reduce computational time (considering that if the sum of the 2 cathets divided by 2 is lower than the minimum distance, than the real distance of this building node to the closest street-node will be further processed to see if it can be a "candidate" for the minimum distance)
      if distance_approx < dist_min # if the the new distance found is lower than the minimum one than the real distance considering this building-node and this street-node will be computed
        distance = (x**2 + y**2)**0.5 # the real distance between the building node and the street node is computed
        if distance < dist_min # if the new distance is lower than the minimum distance found until that moment, than this new "distance" value will be set as the minimum distance between the building node and the street node
          dist_min = distance
          chosen_coord = building[j] # assigning the node coordinates values and id of the building with the minimium distance to the street to chose_coord variable
        end
      end
      end
  end
  return chosen_coord
end

#coordinates(hash, lat, lon, coordinates, k, i) ⇒ Object

defining a method to set each street nodes to a uniform distance among eachothers, valid for both streets and buildings the method is passing as arguments the hash with each feature info from the geojson file, the latitude and longitude to be converted to UTM, the array containing the already processed nodes, the index defining the position of the lat and lon passed in this method and the index defining the reached position in the array with the processed nodes this method returns the array with the processed nodes and its index



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# File 'lib/urbanopt/rnm/geojson_input.rb', line 23

def coordinates(hash, lat, lon, coordinates, k, i)
  lat_lon = GeoUtm::LatLon.new(lat, lon)
  z = 0 # default value for surface elevation
  uniform_distance = 15 # values set as uniform distance among nodes
  utm = lat_lon.to_utm # converting latitude and longitude to UTM
  x_utm = utm.e.round(2) # UTM x-distance from the origin
  y_utm = utm.n.round(2) # UTM y-distance from the origin
  identifier = hash['properties']['id']
  # creating streetmap nodes every 10 m for each road, considering the angle of each road
  if k != 0
    distance_y = y_utm - coordinates[i - 1][1]
    distance_x = x_utm - coordinates[i - 1][0]
    distance = (distance_x**2 + distance_y**2)**0.5
    intervals = (distance / uniform_distance).to_i
    # creating variables for x, y for each node, with the right street inclination
    x_uniform = uniform_distance * (distance_x / distance)
    y_uniform = uniform_distance * (distance_y / distance)
    n = 1 # counter to keep track when the number of intervals for each "distnce" is reached
    # creating nodes in the coordinates array with the right street inclination and uniform distance among each others
    while n <= intervals
      id = identifier.to_s + "_#{i}"
      coordinates[i] = (coordinates[i - 1][0] + x_uniform).round(2), (coordinates[i - 1][1] + y_uniform).round(2), z, id
      i += 1
      n += 1
    end
    # when the last interval of each road is reached, the last node values are given as the streets coordinates
    # imported from the street.json file
    id = identifier.to_s + "_#{i}"
    coordinates[i] = x_utm.round(2), y_utm.round(2), z, id
    i += 1
  else
    # in the 1st node of each road, the coordinates are tacken directly from the streetmap.json file
    id = identifier.to_s + "_#{i}"
    coordinates[i] = x_utm, y_utm, z, id
    i += 1
  end
  return coordinates, i
end

#coordinates_feature_hash(geojson_hash, scenario_features = []) ⇒ Object

creating a method passing the GeoJSON file from URBANopt as the argument to define options that can be modified by the user streets and building and primary substation coordinates and returning the street coordinates array, the building coordinates array and the tot number of buildings in the project



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# File 'lib/urbanopt/rnm/geojson_input.rb', line 111

def coordinates_feature_hash(geojson_hash, scenario_features = [])
  i = 0 # index representing the number of street_nodes
  building_number = 0 # variable created to keep track the number of buildings in the project
  street_number = 0 # variable created to keep track the number of streets in the project
  substation_number = 0 # variable created to keep track the number of substations in the project
  customers_coordinates = [] # array containing the coordinates and id of the closest node of each building to the street
  street_coordinates = [] # array containing every street node coordinates and id
  coordinates_buildings = [] # array containing every building node coordinates and id
  building_ids = [] # array containing building_ids to retrieve urbanopt results later
  building_floors = [] # array containing numb of floors for each building
  substation_location = []
  utm_zone = 0
  streets = geojson_hash
  puts "SCENARIO FEATURES: #{scenario_features}"
  # parsing the options defined by the user to run the RNM-US with a certain % of UG cables and designing the network with only LV nodes
  # to be consistent in case several case-studies are run to have an homogenous parameter on how to compare the same buildings with different energy consumption
  # Use defaults and warn user if these fields are unset
  if streets.key?('project') && streets['project'].key?('underground_cables_ratio')
    ug_ratio = streets['project']['underground_cables_ratio'].to_f
    puts "RNM-US gem INFO: using underground_cables_ratio of #{ug_ratio}"
  else
    ug_ratio = UG_RATIO_DEFAULT
    puts "RNM-US gem WARNING: field ['project']['underground_cables_ratio'] not specified in Feature File...using default value of #{UG_RATIO_DEFAULT}"
  end
  if streets.key?('project') && streets['project'].key?('only_lv_consumers')
    only_lv_consumers = streets['project']['only_lv_consumers']
    puts "RNM-US gem INFO: using only_lv_consumers ratio of #{only_lv_consumers}"
  else
    only_lv_consumers = ONLY_LV_CONSUMERS_DEFAULT
    puts "RNM-US gem WARNING: field ['project']['only_lv_consumers'] not specified in Feature File...using default value of #{ONLY_LV_CONSUMERS_DEFAULT}"
  end
  if streets.key?('project') && streets['project'].key?('max_number_of_lv_nodes_per_building')
    max_num_lv_nodes = streets['project']['max_number_of_lv_nodes_per_building']
    puts "RNM-US gem INFO: using at max #{max_num_lv_nodes} lv nodes per building"
  else
    max_num_lv_nodes = MAX_LV_NODES_DEFAULT
    puts "RNM-US gem WARNING: field ['project']['max_number_of_lv_nodes_per_building'] not specified in Feature File...using default value of #{MAX_LV_NODES_DEFAULT}"
  end
  # each features (linestring, multilinestring and polygon) are processed in an external method, to create intermediate nodes
  # for a better graphical representation of the district
  # "Point" geometry is ignored (site origin feature)
  # put error if there is not this info, and use default values
  streets['features'].each do |street|
    # the geojson file is read and according to the "type" of feature (linestring, multilinestring, polygon)
    # a different loop is executed to fill every node coordinates in a specific array
    if street['properties']['type'] == 'District System' && street['properties']['district_system_type'] == 'Electrical Substation'
      substation_location[substation_number] = sub_coordinates(street['geometry']['coordinates'][0], street['properties']['id'])
      substation_number += 1
    elsif street['geometry']['type'] == 'LineString' && street['properties']['type'] == 'Road'
      each_street = [] # defining an array for each street, that includes all the nodes coordinates for each street
      i = 0 # index representing the number of street_nodes
      for k in 0..street['geometry']['coordinates'].length - 1
        each_street, i = coordinates(street, street['geometry']['coordinates'][k][1], street['geometry']['coordinates'][k][0], each_street, k, i)
          end
      street_coordinates[street_number] = each_street
      street_number += 1
    elsif street['geometry']['type'] == 'MultiLineString' && street['properties']['type'] == 'Road'
      each_street = []
      i = 0 # index representing the number of street_nodes
      for k in 0..street['geometry']['coordinates'].length - 1
        for j in 0..street['geometry']['coordinates'][k].length - 1
          each_street, i = coordinates(street, street['geometry']['coordinates'][k][j][1], street['geometry']['coordinates'][k][j][0], each_street, j, i)
          end
      end
      street_coordinates[street_number] = each_street
      street_number += 1
    elsif street['geometry']['type'] == 'Polygon' && street['properties']['type'] == 'Building' && scenario_features.include?(street['properties']['id'])
      puts "------ processing Building #{ street['properties']['name']} --------"
      # this loop goes through each polygon and assign it a building and is not correct for buildings with
      # inner courtyards (it would create 2 buildings)
      # This assumes the outward footprint is the first polygon -- we only process the first one here
      for k in 0..0
        h = 0 # index representing number of nodes for each single building
        building = [] # array containing every building node coordinates and id of 1 building
        for j in 0..street['geometry']['coordinates'][k].length - 1
          building, h = URBANopt::RNM::GeojsonInput.new.coordinates(street, street['geometry']['coordinates'][k][j][1], street['geometry']['coordinates'][k][j][0], building, j, h)
        end
        coordinates_buildings[building_number] = building # inserting in each index the nodes coordinates and id of each building
        building_ids[building_number] = street['properties']['id']
        building_floors[building_number] = street['properties']['number_of_stories']
        if building_number == 0
          utm = GeoUtm::LatLon.new(street['geometry']['coordinates'][k][j][1], street['geometry']['coordinates'][k][j][0]).to_utm
          utm_zone = utm.zone
        end
        building_number += 1
      end
    end
  end

  # raise error if no streets were found
  if street_number == 0
    raise 'ERROR: No roads were found in the Feature File. Road locations must be in the Feature File for RNM analysis.'
  end

  street_type = []
  for i in 0..street_number - 1
    # creating a class to define when the lines in each street have to be considered OH or UG
    street_type[i] = URBANopt::RNM::OhUgRate.new
    # obtaining the average height for each street and numb of buildings in each street
    street_type[i].height_building(coordinates_buildings, street_coordinates[i], building_floors)
  end
  street = []
  jj = 0
  # defining each street as "OH" or "UG" according to the threshold value obtained from the user's input
  for i in 0..street_number - 1
    street_type[i].classify_street_type(street_type, ug_ratio)
    for j in 0..street_coordinates[i].length - 1
      street[jj] = street_coordinates[i][j][0], street_coordinates[i][j][1], street_coordinates[i][j][2], street_coordinates[i][j][3], street_type[i].type
      jj += 1
    end
  end
  # an external method is called to find the coordinates of the closest node of each building to the street
  for i in 0..building_number - 1
    customers_coordinates[i] = consumer_coordinates(coordinates_buildings[i], street)
  end
  return street, customers_coordinates, coordinates_buildings, building_number, building_ids, substation_location, only_lv_consumers, max_num_lv_nodes, utm_zone # considering creating an hash as attribute
end

#sub_coordinates(points_coord, id) ⇒ Object

defining a method for parsing the coordinates of the substations to be passed in the RNM-US model choose the closes coord to the street or the one in the midle of the polygon since the sub is far away from district and streets



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# File 'lib/urbanopt/rnm/geojson_input.rb', line 95

def sub_coordinates(points_coord, id)
  x_utm = []
  y_utm = []
  for i in 0..points_coord.length - 1
    lat_lon = GeoUtm::LatLon.new(points_coord[i][1], points_coord[i][0])
    utm = lat_lon.to_utm # converting latitude and longitude to UTM
    x_utm[i] = utm.e.round(2) # UTM x-distance from the origin
    y_utm[i] = utm.n.round(2) # UTM y-distance from the origin
  end
  coord_sub = [(x_utm[0] + x_utm[2]) / 2, (y_utm[0] + y_utm[2]) / 2, 0, "sub_#{id}"]
  return coord_sub
end