-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathweather.py
181 lines (164 loc) · 7.23 KB
/
weather.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
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
import random
import numpy as np
from pyecharts import options as opts
from pyecharts.charts import Bar, Grid, Line
import json
import pandas as pd
def get_weather_now():
"""生成当前时间下的天气预报信息"""
# 定义时间和天气数据
time_slots = ["08:00", "11:00", "14:00", "17:00", "20:00", "23:00", "02:00", "05:00"]
weather_conditions = ["晴", "多云", "阵雨", "小雨", "阴"]
# 定义气温、降水、风速、风向、气压、湿度和云量的范围
temperature_range = (20, 30)
precipitation_range = (0, 0.3)
wind_speed_range = (2.6, 3.3)
wind_directions = ["西北风", "东北风"]
pressure_range = (944.4, 946.8)
humidity_range = (73, 96.8)
cloudiness_range = (80, 100)
weather = random.choice(weather_conditions)
temperature = round(random.uniform(temperature_range[0], temperature_range[1]), 1) # ℃
precipitation = round(random.uniform(precipitation_range[0], precipitation_range[1]), 2) # mm
wind_speed = round(random.uniform(wind_speed_range[0], wind_speed_range[1]), 1) # m/s
wind_direction = random.choice(wind_directions)
pressure = round(random.uniform(pressure_range[0], pressure_range[1]), 1) # hPa
humidity = round(random.uniform(humidity_range[0], humidity_range[1]), 1) # %
cloudiness = round(random.uniform(cloudiness_range[0], cloudiness_range[1]), 1) # %
return json.dumps({
'weather':weather,'temperature':str(temperature)+'℃',
'precipitation':str(precipitation)+'mm', 'wind_speed':str(wind_speed)+'m/s',
'wind_direction':wind_direction, 'pressure':str(pressure)+'hPa',
'humidity':str(humidity)+'%', 'cloudiness':str(cloudiness)+'%',
})
def get_weather_future():
"""生成未来某一天的8个时间点的天气预报信息，如果需要预测几天，则写个循环，把这个代码执行多次"""
# 定义时间和天气数据
time_slots = ["08:00", "11:00", "14:00", "17:00", "20:00", "23:00", "02:00", "05:00"]
weather_conditions = ["晴", "多云", "阵雨", "小雨", "阴"]
# 定义气温、降水、风速、风向、气压、湿度和云量的范围
temperature_range = (20, 30)
precipitation_range = (0, 0.3)
wind_speed_range = (2.6, 3.3)
wind_directions = ["西北风", "东北风","北风","南风","西风","东南风","西南风"]
pressure_range = (944.4, 946.8)
humidity_range = (73, 96.8)
cloudiness_range = (80, 100)
weather = random.choice(weather_conditions)
temperature = round(random.uniform(temperature_range[0], temperature_range[1]), 1) # ℃
precipitation = round(random.uniform(precipitation_range[0], precipitation_range[1]), 2) # mm
wind_speed = round(random.uniform(wind_speed_range[0], wind_speed_range[1]), 1) # m/s
wind_direction = random.choice(wind_directions)
pressure = round(random.uniform(pressure_range[0], pressure_range[1]), 1) # hPa
humidity = round(random.uniform(humidity_range[0], humidity_range[1]), 1) # %
cloudiness = round(random.uniform(cloudiness_range[0], cloudiness_range[1]), 1) # %
res=pd.DataFrame([{
'time':time_slots[i], 'weather':weather,'temperature':str(temperature)+'℃',
'precipitation':str(precipitation)+'mm', 'wind_speed':str(wind_speed)+'m/s',
'wind_direction':wind_direction, 'pressure':str(pressure)+'hPa',
'humidity':str(humidity)+'%', 'cloudiness':str(cloudiness)+'%',
} for i in range(len(time_slots))])
return res.to_json()
import json
import pandas as pd
from datetime import datetime
def get_weather_data(file_path):
df = pd.read_csv(file_path)
times = df.iloc[-119*6::6]['DATATIME'].tolist()
windspeed = df.iloc[-119*6::6]['ROUND(A.WS,1)'].tolist()
winddirection = df.iloc[-119*6::6]['WINDDIRECTION'].tolist()
humidity = df.iloc[-119*6::6]['HUMIDITY'].tolist()
dt_objects = [datetime.strptime(time, "%Y-%m-%d %H:%M:%S") for time in times]
iso_datetimes = [dt_object.strftime("%Y-%m-%dT%H:%M:%S.000Z") for dt_object in dt_objects]
dates = [time.split(' ')[0] for time in times]
dates = sorted(list(set(dates)))
# 读取json文件内容,返回字典格式
with open('./weather_data.json','r')as fp:
dic = json.load(fp)
data_list = dic['data']
data_ret_list = []
for i, data in enumerate(data_list):
data['time'] = iso_datetimes[i]
data['windSpeed'] = windspeed[i]
data['R'] = winddirection[i]
data['waveHeight'] = humidity[i]
data_ret_list.append(data)
dic['data'] = data_ret_list
forecast_list = dic['forecast']
fore_ret_list = []
dic['forecast'] = []
for i, fore in enumerate(forecast_list):
if i >= len(dates):
break;
fore['localDate'] = dates[i]
fore_ret_list.append(fore)
dic['forecast'] = fore_ret_list
json_data = json.dumps(dic)
return json_data
def generate_data_list(new_min, new_max):
data = np.histogram(np.random.normal(0, 1, 1000), bins=12, density=False)[0].tolist()
min_value = min(data)
max_value = max(data)
normalized_data = [(x - min_value) / (max_value - min_value) * (new_max - new_min) + new_min for x in data]
return [round(x, 2) for x in normalized_data]
def grid_mutil_yaxis() -> Grid:
"""生成某一个风场的历史天气信息数据"""
x_data = ["{}月".format(i) for i in range(1, 13)]
bar = (
Bar()
.add_xaxis(x_data)
.add_yaxis(
"平均风速",
generate_data_list(0, 12.5),
yaxis_index=1,
color="#5793f3",
)
.extend_axis(
yaxis=opts.AxisOpts(
name="风速",
type_="value",
min_=0,
max_=15,
position="right",
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color="#d14a61")
),
axislabel_opts=opts.LabelOpts(formatter="{value} m/s"),
)
)
.extend_axis(
yaxis=opts.AxisOpts(
type_="value",
name="温度",
min_=-5,
max_=35,
position="left",
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color="#675bba")
),
axislabel_opts=opts.LabelOpts(formatter="{value} °C"),
splitline_opts=opts.SplitLineOpts(
is_show=True, linestyle_opts=opts.LineStyleOpts(opacity=1)
),
)
)
.set_global_opts(
title_opts=opts.TitleOpts(title="气候背景", subtitle="2022年平均风速和平均气温"),
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"),
)
)
line = (
Line()
.add_xaxis(x_data)
.add_yaxis(
"平均气温",
generate_data_list(0, 30),
yaxis_index=2,
color="#675bba",
label_opts=opts.LabelOpts(is_show=False),
)
)
bar.overlap(line)
return Grid().add(
bar, opts.GridOpts(pos_left="5%", pos_right="20%", pos_top="17%"), is_control_axis_index=True
)