Exemplo n.º 1
0
def prepare_edge(edge_shapefile, building_shapefile):
    """Create edge graph with grouped building demands.
    """
    # load buildings and sum by type and nearest edge ID
    # 1. read shapefile to DataFrame (with special geometry column)
    # 2. group DataFrame by columns 'nearest' (ID of nearest edge) and 'type'
    #    (residential, commercial, industrial, other)
    # 3. sum by group and unstack, i.e. convert secondary index 'type' to columns
    buildings = pdshp.read_shp(building_shapefile)
    building_type_mapping = { 
        'church': 'other', 
        'farm': 'other',
        'hospital': 'residential',  
        'hotel': 'commercial',
        'house': 'residential',
        'office': 'commercial',
        'retail': 'commercial', 
        'school': 'commercial',  
        'yes': 'other'}
    buildings.replace(to_replace={'type': building_type_mapping}, inplace=True)
    buildings_grouped = buildings.groupby(['nearest', 'type'])
    total_area = buildings_grouped.sum()['AREA'].unstack()
    
    # load edges (streets) and join with summed areas 
    # 1. read shapefile to DataFrame (with geometry column)
    # 2. join DataFrame total_area on index (=ID)
    # 3. fill missing values with 0
    edge = pdshp.read_shp(edge_shapefile)
    edge = edge.set_index('Edge')
    edge = edge.join(total_area)
    edge = edge.fillna(0)
    return edge
Exemplo n.º 2
0
def prepare_edge(edge_shapefile, building_shapefile):
    """Create edge graph with grouped building demands.
    """
    # load buildings and sum by type and nearest edge ID
    # 1. read shapefile to DataFrame (with special geometry column)
    # 2. group DataFrame by columns 'nearest' (ID of nearest edge) and 'type'
    #    (residential, commercial, industrial, other)
    # 3. sum by group and unstack, i.e. convert secondary index 'type' to columns
    buildings = pdshp.read_shp(building_shapefile)
    building_type_mapping = {
        'church': 'other',
        'farm': 'other',
        'hospital': 'residential',
        'hotel': 'commercial',
        'house': 'residential',
        'office': 'commercial',
        'retail': 'commercial',
        'school': 'commercial',
        'yes': 'other'
    }
    buildings.replace(to_replace={'type': building_type_mapping}, inplace=True)
    buildings_grouped = buildings.groupby(['nearest', 'type'])
    total_area = buildings_grouped.sum()['AREA'].unstack()

    # load edges (streets) and join with summed areas
    # 1. read shapefile to DataFrame (with geometry column)
    # 2. join DataFrame total_area on index (=ID)
    # 3. fill missing values with 0
    edge = pdshp.read_shp(edge_shapefile)
    edge = edge.set_index('Edge')
    edge = edge.join(total_area)
    edge = edge.fillna(0)
    return edge
Exemplo n.º 3
0
def run_scenario(scenario):
    # scenario name
    sce = scenario.__name__
    sce_nice_name = sce.replace('_', ' ').title()

    # prepare input data
    data = rivus.read_excel(data_spreadsheet)
    vertex = pdshp.read_shp(vertex_shapefile)
    edge = prepare_edge(edge_shapefile, building_shapefile)

    # apply scenario function to input data
    data, vertex, edge = scenario(data, vertex, edge)

    # create & solve model
    model = rivus.create_model(data, vertex, edge)
    prob = model.create()
    optim = SolverFactory('gurobi')
    optim = setup_solver(optim)
    result = optim.solve(prob, tee=True)
    prob.load(result)

    # create result directory if not existent
    result_dir = os.path.join('result', os.path.basename(base_directory))
    if not os.path.exists(result_dir):
        os.makedirs(result_dir)

    # report
    rivus.report(prob, os.path.join(result_dir, 'report.xlsx'))

    # plots
    for com, plot_type in [('Elec', 'caps'), ('Heat', 'caps'), ('Gas', 'caps'),
                           ('Elec', 'peak'), ('Heat', 'peak')]:

        # two plot variants
        for plot_annotations in [False, True]:
            # create plot
            fig = rivus.plot(prob,
                             com,
                             mapscale=False,
                             tick_labels=False,
                             plot_demand=(plot_type == 'peak'),
                             annotations=plot_annotations)
            plt.title('')

            # save to file
            for ext, transp in [('png', True), ('png', False), ('pdf', True)]:
                transp_str = ('-transp' if transp and ext != 'pdf' else '')
                annote_str = ('-annote' if plot_annotations else '')

                # determine figure filename from scenario name, plot type,
                # commodity, transparency, annotations and extension
                fig_filename = '{}-{}-{}{}{}.{}'.format(
                    sce, plot_type, com, transp_str, annote_str, ext)
                fig_filename = os.path.join(result_dir, fig_filename)
                fig.savefig(fig_filename,
                            dpi=300,
                            bbox_inches='tight',
                            transparent=transp)

    return prob
Exemplo n.º 4
0
def run_scenario(scenario):
    # scenario name
    sce = scenario.__name__
    sce_nice_name = sce.replace('_', ' ').title()
    
    # prepare input data 
    data = rivus.read_excel(data_spreadsheet)
    vertex = pdshp.read_shp(vertex_shapefile)    
    edge = prepare_edge(edge_shapefile, building_shapefile)
    
    # apply scenario function to input data
    data, vertex, edge = scenario(data, vertex, edge)
    
    # create & solve model
    model = rivus.create_model(data, vertex, edge)
    prob = model.create()
    optim = SolverFactory('gurobi')
    optim = setup_solver(optim)
    result = optim.solve(prob, tee=True)
    prob.load(result)
        
    # create result directory if not existent
    result_dir = os.path.join('result', os.path.basename(base_directory))
    if not os.path.exists(result_dir):
        os.makedirs(result_dir)
    
    # report    
    rivus.report(prob, os.path.join(result_dir, 'report.xlsx'))
    
    # plots
    for com, plot_type in [('Elec', 'caps'), ('Heat', 'caps'), ('Gas', 'caps'),
                           ('Elec', 'peak'), ('Heat', 'peak')]:
        
        # two plot variants
        for plot_annotations in [False, True]:
            # create plot
            fig = rivus.plot(prob, com, mapscale=False, tick_labels=False, 
                             plot_demand=(plot_type == 'peak'),
                             annotations=plot_annotations)
            plt.title('')
            
            # save to file
            for ext, transp in [('png', True), ('png', False), ('pdf', True)]:
                transp_str = ('-transp' if transp and ext != 'pdf' else '')
                annote_str = ('-annote' if plot_annotations else '')
                
                # determine figure filename from scenario name, plot type, 
                # commodity, transparency, annotations and extension
                fig_filename = '{}-{}-{}{}{}.{}'.format(
                    sce, plot_type, com, transp_str, annote_str, ext) 
                fig_filename = os.path.join(result_dir, fig_filename)
                fig.savefig(fig_filename, dpi=300, bbox_inches='tight', 
                            transparent=transp)
                
    return prob
Exemplo n.º 5
0
def run_scenario(scenario, result_dir):
    # scenario name
    sce = scenario.__name__
    sce_nice_name = sce.replace('_', ' ').title()

    # prepare input data
    data = rivus.read_excel(data_spreadsheet)
    vertex = pdshp.read_shp(vertex_shapefile)
    edge = prepare_edge(edge_shapefile, building_shapefile)

    # apply scenario function to input data
    data, vertex, edge = scenario(data, vertex, edge)

    log_filename = os.path.join(result_dir, sce+'.log')

    # create & solve model
    model = rivus.create_model(
        data, vertex, edge,
        peak_multiplier=lambda x:scale_peak_demand(x, peak_demand_prefactor))
    
    # scale peak demand according to pickled urbs findings
    #reduced_peak = scale_peak_demand(model, peak_demand_prefactor)
    #model.peak = reduced_peak
    
    prob = model.create()
    optim = SolverFactory('gurobi')
    optim = setup_solver(optim, logfile=log_filename)
    result = optim.solve(prob, tee=True)
    prob.load(result)

    # report
    rivus.save(prob, os.path.join(result_dir, sce+'.pgz'))
    rivus.report(prob, os.path.join(result_dir, sce+'.xlsx'))
    
    # plot without buildings
    rivus.result_figures(prob, os.path.join(result_dir, sce))
    
    # plot with buildings and to_edge lines
    more_shapefiles = [{'name': 'to_edge',
                        'color': rivus.to_rgb(192, 192, 192),
                        'shapefile': to_edge_shapefile,
                        'zorder': 1,
                        'linewidth': 0.1}]
    rivus.result_figures(prob, os.path.join(result_dir, sce+'_bld'), 
                         buildings=(building_shapefile, False),
                         shapefiles=more_shapefiles)
    return prob
Exemplo n.º 6
0
    elif optim.name == 'glpk':
        # reference with list of options
        # execute 'glpsol --help'
        optim.set_options("tmlim=600")
        optim.set_options("mipgap=2e-2")
    else:
        print("Warning from setup_solver: no options set for solver "
            "'{}'!".format(optim.name))
    return optim

# load buildings and sum by type and nearest edge ID
# 1. read shapefile to DataFrame (with special geometry column)
# 2. group DataFrame by columns 'nearest' (ID of nearest edge) and 'type'
#    (residential, commercial, industrial, other)
# 3. sum by group and unstack, i.e. convert secondary index 'type' to columns
buildings = pdshp.read_shp(building_shapefile)
building_type_mapping = { 
'church': 'other', 
'farm': 'other',
'hospital': 'residential',  
'hotel': 'commercial',
'house': 'residential',
'office': 'commercial',
'retail': 'commercial', 
'school': 'commercial',  
'yes': 'other',
}
buildings.replace(to_replace={'type': building_type_mapping}, inplace=True)
buildings_grouped = buildings.groupby(['nearest', 'type'])
total_area = buildings_grouped.sum()['AREA'].unstack()
Exemplo n.º 7
0
        # reference with list of options
        # execute 'glpsol --help'
        optim.set_options("tmlim=600")
        optim.set_options("mipgap=2e-2")
    else:
        print("Warning from setup_solver: no options set for solver "
              "'{}'!".format(optim.name))
    return optim


# load buildings and sum by type and nearest edge ID
# 1. read shapefile to DataFrame (with special geometry column)
# 2. group DataFrame by columns 'nearest' (ID of nearest edge) and 'type'
#    (residential, commercial, industrial, other)
# 3. sum by group and unstack, i.e. convert secondary index 'type' to columns
buildings = pdshp.read_shp(building_shapefile)
building_type_mapping = {
    'church': 'other',
    'farm': 'other',
    'hospital': 'residential',
    'hotel': 'commercial',
    'house': 'residential',
    'office': 'commercial',
    'retail': 'commercial',
    'school': 'commercial',
    'yes': 'other',
}
buildings.replace(to_replace={'type': building_type_mapping}, inplace=True)
buildings_grouped = buildings.groupby(['nearest', 'type'])
total_area = buildings_grouped.sum()['AREA'].unstack()
Exemplo n.º 8
0
import pandashp
from shapely.geometry import LineString

edge_shp = 'spatial/output/tuscaloosa_roads.shp'

edges = pandashp.read_shp(edge_shp)

fields=['oneway','fclass','LENGTH_GEO','START_X','START_Y','END_X','END_Y']

edges = edges[[f for f in fields]]

edges.columns = ['oneway','fclass','miles','startlon','startlat','endlon','endlat']

# **********************
# Convert latlon points to LINESTRING format for mapping
# **********************
startpoints = []
endpoints = []
for i in range(len(edges)):
	sp = (edges['startlon'].values[i],edges['startlat'].values[i])
	startpoints.append(sp) # shapely pointfile of startpoint
	ep = (edges['endlon'].values[i],edges['endlat'].values[i])
	endpoints.append(ep) # shapely pointfile of endpoint

lines = []
for a,b in zip(startpoints,endpoints):
	l = LineString([a,b]) # line
	lines.append(l.wkt) # add as well-known-text format

print len(lines)