/
share_of_electric_vehicles.py
95 lines (80 loc) · 2.63 KB
/
share_of_electric_vehicles.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
# -*- coding: utf-8 -*-
# ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: percent
# format_version: '1.2'
# jupytext_version: 1.2.4
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# %% [markdown]
# # Greenhouse gas emissions of passenger car traffic in Helsinki
#
# [](https://mybinder.org/v2/gh/juyrjola/ghg-helsinki/master?urlpath=lab/tree/share_of_electric_vehicles.ipynb)
#
# Click Run -> Run All Cells to run the calculations.
# %%
INPUT_DATASETS = ['jyrjola/ymparistotilastot']
import math
import re
import pandas as pd
import numpy as np
import importlib
for dataset in INPUT_DATASETS:
mod_path = dataset.replace('/', '.')
try:
mod = importlib.import_module('quilt.data.%s' % mod_path)
except ImportError:
import quilt
quilt.install(dataset)
from quilt.data.jyrjola import ymparistotilastot
from utils import dict_merge
import aplans_graphs
import plotly
import plotly.graph_objs as go
import cufflinks as cf
plotly.offline.init_notebook_mode(connected=True)
cf.set_config_file(offline=True)
# %%
df = ymparistotilastot.l34_polttoaine_tavoitteet().copy()
display(df.set_index(['Vuosi']))
df['Muut'] = df['Maakaasu'] + df['Bensiini/maakaasu'] + df['Bensiini/etanoli'] + df['Bensiini/sähkö'] + df['Sähkö']+ df['Diesel/sähkö'] + df['Muu/tuntemato']
for name in ['Bensiini', 'Diesel', 'Muut']:
df[name] = df[name] / df['Yhteensä']
start_year = df['Vuosi'].max()
start_val = df.loc[df['Vuosi'] == start_year]['Muut'].sum()
target_series = pd.Series([start_val, 0.20], index=[start_year, 2020])
target_series = target_series.reindex(range(target_series.index.min(), target_series.index.max() + 1))
target_series = target_series.interpolate(method='pchip')
data = [
go.Scatter(
x=df['Vuosi'], y=df['Bensiini'], name='Bensiini',
line=dict(color='#ffc61e'),
),
go.Scatter(
x=df['Vuosi'], y=df['Diesel'], name='Diesel',
line=dict(color='#c2a251'),
),
go.Scatter(
x=df['Vuosi'], y=df['Muut'], name='Muut',
line=dict(color='#00d7a7'),
),
go.Scatter(
x=target_series.index, y=target_series, name='Tavoite',
line=dict(color='#009246', dash='dash')
)
]
d = {
"xaxis": dict(title='Vuosi', fixedrange=True),
"yaxis": dict(fixedrange=True, tickformat=',.0%', rangemode="tozero"),
"title": "Helsingissä ensirekisteröidyjen henkilöautojen osuudet käyttövoiman mukaan",
}
fig = go.Figure(data=data, layout=d)
plotly.offline.iplot(fig)
aplans_graphs.post_graph(fig, 14)
# %%