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_plot_infrastructure_increased_capacity_on_map.py
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_plot_infrastructure_increased_capacity_on_map.py
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"""
In this file we plot infrastructure investments on a map of Norway
(much code is copied from plot_flow_on_map.py)
"""
import warnings
warnings.filterwarnings("ignore")
# IMPORTS
import numpy as np
import pandas as pd
import pickle
from mpl_toolkits.basemap import Basemap #for creating the background map
import matplotlib.pyplot as plt #for plotting on top of the background map
import matplotlib.patches as patches #import library for fancy arrows/edges
from Data.settings import *
from _plot_base_map import plot_base_map_start
def process_epsilon_edges(epsilon_edge, sel_time_period, sel_scenario, cumulative=False):
epsilon_edge = epsilon_edge.copy()
epsilon_edge = epsilon_edge[epsilon_edge['scenario']==sel_scenario]
if cumulative:
epsilon_edge = epsilon_edge[epsilon_edge['time_period']<=sel_time_period]
epsilon_edge = epsilon_edge.groupby(['from', 'to', 'scenario']).sum().reset_index()
else:
epsilon_edge = epsilon_edge[epsilon_edge['time_period'] == sel_time_period]
if sel_time_period==2023:
print(epsilon_edge)
return epsilon_edge
def plot_edge_expansion(df_edges, base_data, show_fig, save_fig, filename):
"""
Create a plot on the map of Norway with infrastructure investments
INPUT
def_edges: dataframe with binary expansion variables for edges
base_data: base model data (only used to extract N_NODES)
show_fig: indicate whether to show the figure
save_fig: indicate whether to save the figure (filename is determined automatically)
OUTPUT
figure that is shown and/or saved to disk if requested
"""
fig = plt.figure(figsize=(6,3))
ax = plt.axes([0,0,1,1])
####################
# b. Build a map
fig, ax, mapp, node_xy_offset, coordinate_mapping, node_x, node_y = plot_base_map_start(base_data)
for index, row in df_edges.iterrows():
From_node = row['from']
To_node = row['to']
weight = row['weight']
if weight > 0:
#get coordinates
x1, y1 = coordinate_mapping[From_node]
x2, y2 = coordinate_mapping[To_node]
#plot arrow
ax.arrow(x1, y1, x2-x1, y2-y1, width=0.1, color='blue', length_includes_head=True, head_width=0.3, head_length=0.3, zorder=100)
###############################
# d. Show and save the figure
from matplotlib.lines import Line2D
custom_lines = [Line2D([0], [0], color='blue', lw=3),]
plt.legend(custom_lines, ['Expanded infrastructure'])
#set size
scale = 1.3
plot_width = 5 #in inches
plot_height = scale * plot_width
plt.gcf().set_size_inches(plot_width, plot_height, forward=True) #TODO: FIND THE RIGH TSIZE
#save figure
if save_fig:
plt.savefig(filename, bbox_inches="tight")
#show figure
if show_fig:
plt.show()
def process_and_plot_expansion_edge(output, base_data, sel_time_period, sel_scenario="BB", cumulative=False, show_fig=True, save_fig=False):
# process data
print("Processing terminal infrasructure investments...")
df_edges = process_epsilon_edges(output.epsilon_edge, sel_time_period, sel_scenario, cumulative)
# make plot
print("Making plot...")
filename = f"Data/Output/Plots/EdgeExpansion/edge_expansion_plot_{sel_time_period}_{sel_scenario}_{cumulative}.png"
plot_edge_expansion(df_edges, base_data, show_fig, save_fig, filename)
def process_and_plot_expansion_node(output, base_data, sel_time_period, sel_scenario="BB", cumulative=False, show_fig=True, save_fig=False):
# process data
print("Processing terminal infrasructure investments...")
df_nodes = process_nu_nodes(output.nu_node, sel_time_period, sel_scenario, cumulative)
for mode in ["Sea", "Rail"]:
# make plot
print("Making plot...")
filename = f"Data/Output/Plots/NodeExpansion/{mode}/node_expansion_plot_{mode}_{sel_time_period}_{sel_scenario}_{cumulative}.png"
plot_node_expansion(df_nodes, base_data, mode, show_fig, save_fig, filename)
def process_nu_nodes(nu_node, sel_time_period, sel_scenario, cumulative):
nu_node = nu_node.copy()
nu_node = nu_node[nu_node['scenario']==sel_scenario]
if cumulative:
nu_node = nu_node[nu_node['time_period']<=sel_time_period]
nu_node = nu_node.groupby(['from', 'scenario', 'mode']).sum().reset_index()
else:
nu_node = nu_node[nu_node['time_period'] == sel_time_period]
return nu_node
def plot_node_expansion(df_nodes, base_data, mode, show_fig, save_fig, filename):
df_nodes = df_nodes.copy()
df_nodes = df_nodes[df_nodes['mode']==mode]
fig, ax, mapp, node_xy_offset, coordinate_mapping, node_x, node_y = plot_base_map_start(base_data)
#if the weight is larger than 0, plot the node
for index, row in df_nodes.iterrows():
node = row['from']
weight = row['weight']
if weight > 0:
x, y = coordinate_mapping[node]
ax.scatter(x, y, s=100, c='red', zorder=100)
#show and save the figure
from matplotlib.lines import Line2D
custom_lines = [Line2D([0], [0], color='red', lw=3),]
plt.legend(custom_lines, [f'Expanded Nodes for {mode}'])
#set size
scale = 1.3
plot_width = 5 #in inches
plot_height = scale * plot_width
plt.gcf().set_size_inches(plot_width, plot_height, forward=True) #TODO: FIND THE RIGH TSIZE
#save figure
if save_fig:
plt.savefig(filename, bbox_inches="tight")
#show figure
if show_fig:
plt.show()
#####################################################################################
# RUN ANALYSIS
# Read model output
analyses_type = 'SP' # EV , EEV, 'SP
scenario_type = "FuelScen" # 4Scen
emission_cap = False
carbon_fee = 'base' #high, intermediate, base
run_identifier = scenario_type+"_carbontax"+carbon_fee
if emission_cap:
run_identifier = run_identifier + "_emissioncap"
run_identifier2 = run_identifier+"_"+analyses_type
with open(r'Data//Output//'+run_identifier+'_basedata.pickle', 'rb') as output_file:
base_data = pickle.load(output_file)
with open(r'Data//Output//'+run_identifier2+'_results.pickle', 'rb') as data_file:
output = pickle.load(data_file)
# plot charging infra for multiple years
sel_scenario = "BB" #Choose "BB" or "PB", "OO"
if True:
#for t in [2023, 2028, 2034, 2040, 2050]:
for t in [2023, 2028, 2034, 2040, 2050]:
process_and_plot_expansion_edge(output, base_data, t, sel_scenario, cumulative=False, show_fig=False, save_fig=True)
process_and_plot_expansion_node(output, base_data, t, sel_scenario, cumulative=False, show_fig=False, save_fig=True)