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Copy pathFig5_bubble_map.py
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Fig5_bubble_map.py
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import plotly.graph_objects as go
import pandas as pd
import numpy as np
import plotly.io as pio
import json
import urllib.request
with urllib.request.urlopen('https://services3.arcgis.com/bWPjFyq029ChCGur/arcgis/rest/services/'
'California_Electric_Utility_Service_Areas/FeatureServer/0/'
'query?where=OBJECTID%20%3E%3D%2056%20AND%20OBJECTID%20%3C%3D%2058&out'
'Fields=Acronym&outSR=4326&f=json') as url:
utilities = json.loads(url.read().decode())
# Read TMY 3 location data with geo-information
map_tmy = pd.read_excel('/Users/jiajiazheng/Box/Suh\'s lab/GSRs/Jiajia/3-Residential Solar-plus-storage/'
'Annual_Load_PV_BA_by_Location.xlsx', index_col=0) # a table listing TMY 3 location codes
map_loc = map_tmy.loc[:, 'ToU Assigned': 'Longitude']
map_loc = map_loc.rename(columns={"ToU Assigned": "Utility"})
# Define function to draw bubble map
def draw_bubble_map(obj, mode, year, c_cost):
"""
Draw bubble map showing the cost and emissions differences
"""
# Draw utility borders
fig = go.Figure()
for i in range(39):
df_SCE = pd.json_normalize(utilities['features'][0])['geometry.rings'].apply(
lambda r: [(j[0], j[1]) for j in r[i]])
fig.add_trace(go.Scattergeo(
showlegend=False,
lat=np.array(df_SCE[0])[:, 1],
lon=np.array(df_SCE[0])[:, 0],
mode="lines",
line=dict(width=0.5, color="darkgrey")
)
)
for i in range(99):
df_PGE = pd.json_normalize(utilities['features'][1])['geometry.rings'].apply(
lambda r: [(j[0], j[1]) for j in r[i]])
fig.add_trace(go.Scattergeo(
showlegend=False,
lat=np.array(df_PGE[0])[:, 1],
lon=np.array(df_PGE[0])[:, 0],
mode="lines",
line=dict(width=0.5, color="darkgrey")
)
)
for i in range(6):
df_SDGE = pd.json_normalize(utilities['features'][2])['geometry.rings'].apply(
lambda r: [(j[0], j[1]) for j in r[i]])
fig.add_trace(go.Scattergeo(
showlegend=False,
lat=np.array(df_SDGE[0])[:, 1],
lon=np.array(df_SDGE[0])[:, 0],
mode="lines",
line=dict(width=0.5, color="darkgrey")
)
)
# import results from csv files and sum up the GHG emissions and costs
df = pd.DataFrame()
for i in range(len(map_tmy)):
tmy_i = map_tmy.index[i]
utility = map_tmy.loc[tmy_i, 'ToU Assigned']
if obj == 'minCost':
df_results = pd.read_csv('/Users/jiajiazheng/Box/Suh\'s lab/GSRs/Jiajia/3-Residential Solar-plus-storage/'
'Results/results_%(obj)s_%(mode)s_%(year)s_cc_%(c_cost)s_RTP/'
'results_%(obj)s_%(tmy_code)s_%(utility)s_%(year)s_cc_%(c_cost)s.csv'
% {'obj': obj, 'mode': mode, 'year': year, 'tmy_code': tmy_i,
'utility': utility, 'c_cost': c_cost}, index_col=0)
else:
df_results = pd.read_csv('/Users/jiajiazheng/Box/Suh\'s lab/GSRs/Jiajia/3-Residential Solar-plus-storage/'
'Results/results_%(obj)s_%(mode)s_%(year)s/'
'results_%(obj)s_%(tmy_code)s_%(utility)s_%(year)s.csv'
% {'obj': obj, 'mode': mode, 'year': year, 'tmy_code': tmy_i,
'utility': utility}, index_col=0)
sum_i = pd.DataFrame(pd.to_numeric(df_results.sum()[-8:])).T
sum_i.insert(0, 'Location', tmy_i) # insert the file name to the far left of the df
df = df.append(sum_i) # calculate the sums of each column and keep the totals
df = df.merge(map_loc, how='left', on='Location')
df['GHG_diff'] = pd.to_numeric((df['GHG_total_PV+Batt'] - df['GHG_total_PVonly']) / df['GHG_total_PVonly'])
df['cost_diff'] = pd.to_numeric((df['cost_total_PV+Batt_withCC'] -
df['cost_total_PVonly_withCC']) / df['cost_total_PVonly_withCC'])
df['GHG_diff_pos'] = np.where(df['GHG_diff'] > 0, df['GHG_diff'], 0)
df['GHG_diff_neg'] = np.where(df['GHG_diff'] < 0, df['GHG_diff'], 0)
df['cost_diff_pos'] = np.where(df['cost_diff'] > 0, df['cost_diff'], 0)
df['cost_diff_neg'] = np.where(df['cost_diff'] < 0, df['cost_diff'], 0)
df.drop(df.columns[1:9], inplace=True, axis=1)
GHG_diff_mean = '{:.1%}'.format(df['GHG_diff'].mean())
cost_diff_mean = '{:.1%}'.format(df['cost_diff'].mean())
GHG_diff_mean_PGE = '{:.0%}'.format(df[df['Utility'] == "PGE"]['GHG_diff'].mean())
cost_diff_mean_PGE = '{:.0%}'.format(df[df['Utility'] == "PGE"]['cost_diff'].mean())
GHG_diff_mean_SCE = '{:.0%}'.format(df[df['Utility'] == "SCE"]['GHG_diff'].mean())
cost_diff_mean_SCE = '{:.0%}'.format(df[df['Utility'] == "SCE"]['cost_diff'].mean())
GHG_diff_mean_SDGE = '{:.0%}'.format(df[df['Utility'] == "SDGE"]['GHG_diff'].mean())
cost_diff_mean_SDGE = '{:.0%}'.format(df[df['Utility'] == "SDGE"]['cost_diff'].mean())
# add a dummy row for negative legend to show
df.loc[len(df.index)] = ['700000', "Dummy", 40, -120, 0, 0, 0.0000001, -0.0000001, 0.0000001, -0.0000001]
# create a list of parameters for four traces
trace_list = [[df['GHG_diff_pos'], 'GHG - higher', 'rgba(219, 67, 37, 0.8)', 'rgba(219, 67, 37, 0)'], # (236,91,18)
[df['GHG_diff_neg'], 'GHG - lower', 'rgba(70, 180, 155, 0.8)', 'rgba(70, 180, 155, 0)'],
[df['cost_diff_pos'], 'Cost - higher', 'rgba(0,0,0,0)', 'rgba(219, 67, 37, 1)'],
[df['cost_diff_neg'], 'Cost - lower', 'rgba(0,0,0,0)', 'rgba(57, 166, 143, 1)']]
# iterate and create each trace
for index in range(len(trace_list)):
fig.add_trace(go.Scattergeo(
locationmode='USA-states',
lon=df['Longitude'],
lat=df['Latitude'],
hoverinfo='text',
text=trace_list[index][0] * 100,
mode='markers',
name=trace_list[index][1],
# opacity=0.6,
marker=dict(
size=trace_list[index][0] * (-1) ** index, # use -1 to show bubbles of negative values
sizemode='area', # 'area' or 'diameter'
sizeref=2. * 0.5 / (40. ** 2),
symbol='circle',
color=trace_list[index][2], # circle fill color
line=dict(
width=1,
color=trace_list[index][3] # circle border color
)
)))
bb_lon = -118.2 # bubble legend location - longitude
bb_lat = 39.32
bb_size_data = [['5%', 0.05, bb_lon, bb_lat],
['15%', 0.15, bb_lon + 0.122, bb_lat - 0.36],
['25%', 0.25, bb_lon + 0.271, bb_lat - 0.79]]
bb_size = pd.DataFrame(bb_size_data, columns=['percentage', 'size', 'longitude', 'latitude'])
# add bubble size legend
fig.add_trace(go.Scattergeo(
showlegend=False,
locationmode='USA-states',
lon=bb_size['longitude'],
lat=bb_size['latitude'],
hoverinfo='text',
# text=round(bb_size['size'] * 100, 1),
mode='markers',
marker=dict(
size=bb_size['size'].tolist(),
sizemode='area',
sizeref=2. * 0.5 / (40. ** 2),
symbol='circle',
color='rgba(0, 0, 0, 0)',
line=dict(
width=1,
color='black'
)
)))
bb_x_txt = 0.699 # bubble legend text position - x
bb_y_txt = 0.648
# legend - bubble sizes
fig.add_annotation(x=bb_x_txt, y=bb_y_txt + 0.002, text="5%", showarrow=False,
font=dict(family="Helvetica", size=20))
fig.add_annotation(x=bb_x_txt, y=bb_y_txt - 0.032, text="15%", showarrow=False,
font=dict(family="Helvetica", size=20))
fig.add_annotation(x=bb_x_txt, y=bb_y_txt - 0.068, text="25%", showarrow=False,
font=dict(family="Helvetica", size=20))
# mark of utility territory
fig.add_annotation(x=0.38, y=0.75, text='PG&E', showarrow=False, font=dict(family="Helvetica", size=20))
fig.add_annotation(x=0.66, y=0.31, text='SCE', showarrow=False, font=dict(family="Helvetica", size=20))
fig.add_annotation(x=0.61, y=0.074, text='SDG&E', showarrow=False, font=dict(family="Helvetica", size=20))
# legend - mean values of all samples
# fig.add_annotation(x=bb_x_txt + 0.1, y=bb_y_txt - 0.15,
# text=f"Mean value <br> GHG difference: {GHG_diff_mean} <br> Cost difference: {cost_diff_mean}",
# showarrow=False, font=dict(family="Helvetica", size=14))
# summary table of mean values by utility
sum_x_txt = 0.24
sum_y_txt = 0.02
fig.add_annotation(x=sum_x_txt, y=sum_y_txt + 0.066, text='PG&E', showarrow=False,
font=dict(family="Helvetica", size=20))
fig.add_annotation(x=sum_x_txt, y=sum_y_txt + 0.033, text='SCE', showarrow=False,
font=dict(family="Helvetica", size=20))
fig.add_annotation(x=sum_x_txt, y=sum_y_txt, text='SDG&E', showarrow=False,
font=dict(family="Helvetica", size=20))
fig.add_annotation(x=sum_x_txt + 0.16, y=sum_y_txt + 0.099, text='GHG diff Cost diff',
showarrow=False, font=dict(family="Helvetica", size=20))
fig.add_annotation(x=sum_x_txt + 0.16, y=sum_y_txt + 0.066, text=f"{GHG_diff_mean_PGE} {cost_diff_mean_PGE}",
showarrow=False, font=dict(family="Helvetica", size=20))
fig.add_annotation(x=sum_x_txt + 0.16, y=sum_y_txt + 0.033, text=f"{GHG_diff_mean_SCE} {cost_diff_mean_SCE}",
showarrow=False, font=dict(family="Helvetica", size=20))
fig.add_annotation(x=sum_x_txt + 0.16, y=sum_y_txt, text=f"{GHG_diff_mean_SDGE} {cost_diff_mean_SDGE}",
showarrow=False, font=dict(family="Helvetica", size=20))
fig.update_annotations(align="left")
fig.update_layout(
# title=go.layout.Title(
# text=f'{mode} {year}, carbon cost = $0/tonneCO2, upper',
# # text=f'{mode}, {year}, minGHG',
# x=0.5, y=0.95,
# font=dict(family='Helvetica', size=18)),
geo=go.layout.Geo(
resolution=50,
scope='usa',
showframe=False,
showland=True,
showsubunits=True,
showcoastlines=True,
coastlinecolor="grey",
landcolor="rgb(235, 235, 235)",
subunitcolor="grey",
countrycolor="grey",
countrywidth=0.5,
subunitwidth=0.5,
center=dict(lon=-119.5, lat=37.3),
projection=dict(scale=0.4),
lonaxis=dict(range=[-127.0, -124.9]),
lataxis=dict(range=[35.2, 39.5]),
),
# showlegend=True,
# legend_traceorder='reversed',
legend=dict(
itemsizing='constant', x=0.6, y=0.83,
title='',
title_font_family='Helvetica',
title_font_size=18,
font=dict(family='Helvetica', size=20), bgcolor='rgba(0,0,0,0)')
)
# fig.show()
pio.write_image(fig,
'/Users/jiajiazheng/Box/Suh\'s lab/GSRs/Jiajia/3-Residential Solar-plus-storage/Visualization/'
'Bubble_map_%(obj)s_%(mode)s_%(year)s_cc_%(c_cost)s_RTP.jpg'
% {'obj': obj, 'mode': mode, 'year': year, 'c_cost': c_cost},
format='jpg', scale=3, width=1000, height=1000, validate=True, engine='auto')
draw_bubble_map('minCost', 'ExportOnly', 2040, 0)
draw_bubble_map('minCost', 'ImportOnly', 2040, 0)
draw_bubble_map('minCost', 'Unconstrained', 2040, 0)