def reanalyse(directory):
"""Return constants for all pickled rivus results in directory
Args:
directory: a directory with 1 or multiple pickled rivus instances
Returns:
tuple (demand, cost, Pmax, Kappa_hub, Kappa_process) of concatenated
DataFrames
"""
glob_pattern = os.path.join(directory, '*.pgz')
pickle_filenames = glob.glob(glob_pattern)
demand = {}
cost = {}
Pmax = {}
Kappa_hub = {}
Kappa_process = {}
for pf in pickle_filenames:
# load original problem object including solution
prob = rivus.load(pf)
# truncate directory name and extension from pickle filename
# remove 'scenario_' prefix, if present
scenario_name = os.path.splitext(os.path.basename(pf))[0]
scenario_name = scenario_name.replace('scenario_', '')
# retrieve costs and capacities from result
constants = rivus.get_constants(prob)
# assign dict values per scenario
cost[scenario_name] = constants[0]
Pmax[scenario_name] = constants[1]
Kappa_hub[scenario_name] = constants[2]
Kappa_process[scenario_name] = constants[3]
demand[scenario_name] = prob.peak
# merge into single dataframe
demand = pd.concat(demand, axis=1)
cost = pd.concat(cost, axis=1)
Pmax = pd.concat(Pmax, axis=1)
Kappa_hub = pd.concat(Kappa_hub, axis=1)
Kappa_process = pd.concat(Kappa_process, axis=1)
return demand, cost, Pmax, Kappa_hub, Kappa_process
def replot(directory):
"""Recreate result figures for all pickled rivus results in directory
Args:
directory: a directory with 1 or multiple pickled rivus instances
Returns:
Nothing
"""
glob_pattern = os.path.join(directory, '*.pgz')
pickle_filenames = glob.glob(glob_pattern)
data_dir = os.path.join('data', os.path.basename(directory).split('-')[0])
# if directory = 'result/moosh' try to find a suitable building shapefile
# in 'data/moosh'
buildings = None
building_filename = os.path.join(data_dir, 'building')
if os.path.exists(building_filename + '.shp'):
buildings = (building_filename, False) # if True, color buildings
# if data/.../to_edge exists, paint it
shapefiles = None
to_edge_filename = os.path.join(data_dir, 'to_edge')
if os.path.exists(to_edge_filename + '.shp'):
shapefiles = [{
'name': 'to_edge',
'color': rivus.to_rgb(192, 192, 192),
'shapefile': to_edge_filename,
'zorder': 1,
'linewidth': 0.1
}]
for pf in pickle_filenames:
prob = rivus.load(pf)
figure_basename = os.path.splitext(pf)[0]
if buildings:
figure_basename += '_bld'
rivus.result_figures(prob,
figure_basename,
buildings=buildings,
shapefiles=shapefiles)
def replot(directory):
"""Recreate result figures for all pickled rivus results in directory
Args:
directory: a directory with 1 or multiple pickled rivus instances
Returns:
Nothing
"""
glob_pattern = os.path.join(directory, '*.pgz')
pickle_filenames = glob.glob(glob_pattern)
data_dir = os.path.join('data', os.path.basename(directory).split('-')[0])
# if directory = 'result/moosh' try to find a suitable building shapefile
# in 'data/moosh'
buildings = None
building_filename = os.path.join(data_dir, 'building')
if os.path.exists(building_filename+'.shp'):
buildings = (building_filename, False) # if True, color buildings
# if data/.../to_edge exists, paint it
shapefiles = None
to_edge_filename = os.path.join(data_dir, 'to_edge')
if os.path.exists(to_edge_filename+'.shp'):
shapefiles = [{'name': 'to_edge',
'color': rivus.to_rgb(192, 192, 192),
'shapefile': to_edge_filename,
'zorder': 1,
'linewidth': 0.1}]
for pf in pickle_filenames:
prob = rivus.load(pf)
figure_basename = os.path.splitext(pf)[0]
if buildings:
figure_basename += '_bld'
rivus.result_figures(prob, figure_basename,
buildings=buildings,
shapefiles=shapefiles)
import geopandas
import os
import pandas as pd
import pandashp as pdshp
import rivus
from coopr.opt.base import SolverFactory
from datetime import datetime
base_directory = os.path.join('data', 'haag15')
building_shapefile = os.path.join(base_directory, 'building')
edge_shapefile = os.path.join(base_directory, 'edge')
to_edge_shapefile = os.path.join(base_directory, 'to_edge')
vertex_shapefile = os.path.join(base_directory, 'vertex')
data_spreadsheet = os.path.join(base_directory, 'data.xlsx')
peak_demand_prefactor = rivus.load('urbs-peak-demand-reduction.pgz')
def scale_peak_demand(model, multiplier):
""" scale rivus peak demand DataFrame by multiplier
Args:
model: a rivus model instance
multiplier: a DataFrame indexed by Cluster
(rows) and commodity (columns)
Returns:
"""
reduced_peak = []
for name, group in (model.peak.join(model.params['edge']['Cluster'])
.groupby('Cluster')):
reduced_peak.append(group.drop('Cluster', axis=1) *
