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
| import numpy as np
import netCDF4 as nc
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import os, math
import show_ssha_map as ssm
from mpl_toolkits.basemap import Basemap
def find_max(data_matrix):
new_data = []
for i in range(len(data_matrix)):
new_data.append(max(data_matrix[i]))
print("data_matrix最大值为", max(new_data))
def find_min(data_matrix):
new_data = []
for i in range(len(data_matrix)):
new_data.append(min(data_matrix[i]))
print(min(new_data))
def variables_extact(file):
lon_list = []
lat_list = []
ssha_list = []
ssha_w_list = []
for f in file:
'''第一重数据集--*.nc Dataset'''
dataset_nc = nc.Dataset(f)
'''第二重数据集--groups Dataset'''
data_01 = dataset_nc.groups['data_01']
lon = (data_01.variables['longitude'][:])
lat = (data_01.variables['latitude'][:])
'''第三重数据集--ku Dataset'''
ku = data_01.groups['ku']
ssha = (ku.variables['ssha'][:])
data_lon = np.array(lon)
data_lat = np.array(lat)
data_ssha_all = np.array(ssha)
data_ssha = np.where(data_ssha_all != 32767, data_ssha_all * 100, 0)
data_ssha_w = data_ssha * 100
lon_list.extend(data_lon)
lat_list.extend(data_lat)
ssha_list.extend(data_ssha)
ssha_w_list.extend(data_ssha_w)
lon_all = np.array(lon_list)
lat_all = np.array(lat_list)
ssha_all = np.array(ssha_list)
ssha_w_all = np.array(ssha_w_list)
print(lon_all, lon_all.shape)
print(lat_all, lat_all.shape)
print(ssha_all, ssha_all.shape)
return lon_all, lat_all, ssha_all, ssha_w_all
def show_ssha_3d(lon, lat, ssha, ssha_w):
lon_lat_ssha = np.vstack((lon, lat, ssha_w)).T
print(lon.shape, lat.shape, ssha_w.shape)
print(lon_lat_ssha[:, 2], lon_lat_ssha.shape)
ssha_mean_list = []
for a in range(360):
for b in range(180):
sum = 0
k_list = []
for i, j, k in lon_lat_ssha:
if all((abs(i) > a, abs(i) <= a + 1, abs(j) > b, abs(j) <= b + 1)):
sum += 1
k_list.extend([k])
mean_ssha = math.fsum(k_list) / sum
print(sum, k_list, mean_ssha)
ssha_mean_list.extend([mean_ssha])
print(ssha_mean_list)
map = Basemap(projection='cyl', llcrnrlat=-90., urcrnrlat=90., llcrnrlon=0., urcrnrlon=361., resolution='l',
lat_0=0, lon_0=180)
map.drawmapboundary()
map.fillcontinents(color='gray', lake_color='aqua')
map.drawstates()
map.drawcoastlines()
lons, lats = map.makegrid(1, 6598)
lats = lats[::-1]
x, y = map(lon, lat)
map.drawparallels(np.arange(-90., 91., 30.), labels=[1, 0, 0, 0], fontsize=12)
map.drawmeridians(np.arange(-180., 181., 60.), labels=[0, 0, 0, 1], fontsize=12)
contour_map = map.contour(x, y, ssha_mean_list, 15, linewidths=1.5)
plt.savefig(output_path + 'interpolote.png')
plt.show(contour_map)
'''
fig = plt.figure()
# ax = Axes3D(fig)
ax2 = plt.axes(projection='3d')
X = lon
Y = lat
X, Y = np.meshgrid(X, Y)
print(len(X), len(Y), len(ssha))
Z = np.expand_dims(ssha, axis=1)
ax2.plot_surface(X, Y, Z, alpha=0.3, cmap='rainbow')
# ax2.contour(X, Y, Z, zdir='z', offset=-3, cmap="rainbow")
# ax2.contourf(X, Y, Z, zdir='z', offset=-3, cmap="rainbow")
plt.savefig(output_path + '_3d.png')
plt.show()
'''
if __name__ == '__main__':
postfix = '.nc'
input_path = '/Users/leo/Desktop/MarineTechTest5/Data_Jason3/'
output_path = '/Users/leo/Desktop/MarineTechTest5/Results/'
if not os.path.exists(output_path):
os.mkdir(output_path)
file_list = os.listdir(input_path)
day_list = []
for i in file_list:
if i.endswith(postfix):
file_nc = input_path + i
day_list.append(file_nc)
print(day_list)
lon, lat, ssha, ssha_w = variables_extact(day_list)
show_ssha_3d(lon, lat, ssha, ssha_w)
|
