-
Notifications
You must be signed in to change notification settings - Fork 1
/
plot_EnglandWales_tempdeaths_temp_episodes.py
133 lines (109 loc) · 6.3 KB
/
plot_EnglandWales_tempdeaths_temp_episodes.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
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
# requires source activate pyn_env
from __future__ import division
import numpy as np
import pandas as pd
import matplotlib as mpl
mpl.use('Agg')
from matplotlib.lines import Line2D
import matplotlib.pyplot as plt
''' This script plots scatter plots '''
''' of temp deaths vs mean temp in heatwave/coldsnap episodes '''
''' across England '''
''' indicating COVID years '''
''' Eunice Lo '''
''' Created 18/04/2023 '''
if __name__ == "__main__":
# paths
in_dir = "/home/bridge/yl17544/papers_repos/EnglandWales_climate_vs_covid_paper/outputs/"
out_dir = "/home/bridge/yl17544/plots/EnglandWales_climate_vs_covid/"
# UKHSA-defined heatwaves in period 2016 to 2022, according to PhE reports. Dates are inclusive
hws = [["2016-07-18", "2016-07-22"], ["2016-08-22", "2016-08-26"], ["2016-09-12", "2016-09-17"], \
["2017-06-16", "2017-06-23"], ["2017-07-05", "2017-07-07"], \
["2018-06-25", "2018-06-27"], ["2018-06-30", "2018-07-10"], ["2018-07-21", "2018-07-29"], ["2018-08-01", "2018-08-09"], \
["2019-06-28", "2019-06-30"], ["2019-07-21", "2019-07-28"], ["2019-08-23", "2019-08-29"], \
["2020-06-23", "2020-06-27"], ["2020-07-30", "2020-08-01"], ["2020-08-05", "2020-08-15"], \
["2021-07-16", "2021-07-23"], ["2021-09-06", "2021-09-09"], \
["2022-06-16", "2022-06-19"], ["2022-07-10", "2022-07-25"], ["2022-07-30", "2022-08-05"], ["2022-08-08", "2022-08-17"], ["2022-08-23", "2022-08-25"]]
# cold waves in period
# defined here as dates on which a L3 Cold Weather Alert was issued for any region in England (see CWP England)
# excludes single-day alerts
css = [["2016-02-13", "2016-02-16"], ["2016-02-24", "2016-02-28"], ["2016-12-27", "2016-12-30"], \
["2017-01-04", "2017-01-06"], ["2017-01-11", "2017-01-14"], ["2017-01-19", "2017-01-28"], ["2017-02-08", "2017-02-12"], ["2017-11-29", "2017-12-02"], ["2017-12-07", "2017-12-17"], \
["2018-01-06", "2018-01-12"], ["2018-01-15", "2018-01-21"], ["2018-02-04", "2018-02-08"], ["2018-02-23", "2018-03-04"], ["2018-03-16", "2018-03-20"], \
["2019-01-21", "2019-01-25"], ["2019-01-28", "2019-02-03"], \
["2020-12-29", "2021-01-18"], \
["2021-01-22", "2021-02-02"], ["2021-02-08", "2021-02-12"], ["2021-11-26", "2021-11-29"], ["2021-12-20", "2021-12-23"], \
["2022-01-04", "2022-01-10"], ["2022-01-13", "2022-01-17"], ["2022-12-07", "2022-12-18"]]
# England and Wales mortality
regnames = ["NorthEastEngland", "NorthWestEngland", "YorkshireandHumber", "EastMidlands", \
"WestMidlands", "EastofEngland", "London", "SouthEastEngland", "SouthWestEngland", \
"Wales"]
temp_dflist = []
for reg in regnames:
temp_df= pd.read_csv(in_dir+"daily_attributable_deaths_ER1981-2022_yearround_21dayslag_MMT2to98_nsim100_1981-2022_"+\
reg+".csv", usecols=["date","tmean","mmt","est"])
# isolate non-optimal temperature deaths
temp_df.loc[temp_df.tmean == temp_df.mmt, "est"] = 0 # best estimate
# put in list
temp_dflist.append(temp_df.loc[:,("date", "tmean", "est")])
# mean Tmean across England and Wales
engwales_df = temp_dflist[0].copy()
engwales_df["tmean"] = sum([df["tmean"] for df in temp_dflist])/len(temp_dflist)
# summed est deaths across England and Wales
engwales_df["est"] = sum([df["est"] for df in temp_dflist])
# lists for storing heatwave results
hws_avetemp = []
hws_culdeaths = []
for hw in hws:
# isolate heatwave
hw_df = engwales_df.loc[(engwales_df["date"]>=hw[0]) & (engwales_df["date"]<=hw[1])]
# heatwave mean temp
hws_avetemp.append(hw_df["tmean"].mean())
# heatwave deaths
hws_culdeaths.append(hw_df["est"].mean())
# lists for storing coldsnaps results
css_avetemp = []
css_culdeaths = []
for cs in css:
# isolate heatwave
cs_df = engwales_df.loc[(engwales_df["date"]>=cs[0]) & (engwales_df["date"]<=cs[1])]
# coldsnap mean temp
css_avetemp.append(cs_df["tmean"].mean())
# coldsnap deaths
css_culdeaths.append(cs_df["est"].mean())
# plot graph
font = {'size' : 10}
plt.rc('font', **font)
f, axs = plt.subplots(1, 2, figsize=(10,5))
# heatwaves
# colour code by year, grey for 2016-2019, other colours for 2020, 2021, 2022 (done manually)
cmaph = ["grey"]*12 + ["pink"]*3 + ["red"]*2 + ["darkred"]*5
axs[0].scatter(hws_avetemp, hws_culdeaths, c=cmaph, marker="d", s=50, alpha=0.9)
axs[0].set_title("Heatwaves")
axs[0].set_ylabel("Average temperature-related deaths (/day)")
axs[0].set_xlabel("Average temperature ($^{\circ}$C)")
# legend
hleg = [Line2D([0],[0], marker='d', label='2016-19', color="w", markerfacecolor='grey', markersize=10), \
Line2D([0],[0], marker='d', label='2020', color="w", markerfacecolor='pink', markersize=10), \
Line2D([0],[0], marker='d', label='2021', color="w", markerfacecolor='red', markersize=10), \
Line2D([0],[0], marker='d', label='2022', color="w", markerfacecolor='darkred', markersize=10)]
axs[0].legend(handles=hleg, loc='upper left')
# coldsnaps
# colour code by year
cmapc = ["grey"]*16 + ["lightskyblue"]*1 + ["blue"]*4 + ["darkblue"]*3
axs[1].scatter(css_avetemp, css_culdeaths, c=cmapc, marker="d", s=50, alpha=0.9)
axs[1].set_title("Cold snaps")
axs[1].set_ylabel("Average temperature-related deaths (/day)")
axs[1].set_xlabel("Average temperature ($^{\circ}$C)")
# legend
cleg = [Line2D([0],[0], marker='d', label='2016-19', color="w", markerfacecolor='grey', markersize=10), \
Line2D([0],[0], marker='d', label='2020', color="w", markerfacecolor='lightskyblue', markersize=10), \
Line2D([0],[0], marker='d', label='2021', color="w", markerfacecolor='blue', markersize=10), \
Line2D([0],[0], marker='d', label='2022', color="w", markerfacecolor='darkblue', markersize=10)]
axs[1].legend(handles=cleg, loc=0)
f.suptitle("England and Wales-wide results")
# save graph
plt.tight_layout(rect=[0, 0, 1, 0.95])
plt.savefig(out_dir+"EngWales_avetempmortality_vs_avetemp_all_heatwaves_coldwaves_officaldef_2016_2022.png", format="png", dpi=300)
plt.close("all")
print("Saved graph!")