MIVIT/test_mivit.py at master · EarthCubeInGeo/MIVIT · GitHub

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
# test_mivit.py


import numpy as np
import datetime as dt
try:
    import ConfigParser as configparser
except:
    import configparser
import mivit
import copy


def test():

    targtime = dt.datetime(2017,5,28,6,35)

    # # get SuperDARN data
    # pt = mivit.PlotMethod(cmap='seismic',plot_type='pcolormesh',label='SuperDARN Velocity',vmin=-40,vmax=40)
    # sd_data = []
    # davitpy_kwargs = {'src':'local','fileType':'fitex','local_dirfmt':'./TestDataSets/SuperDARN/'}
    # for rad in ['bks','fhe','fhw','kap','pgr','sas','wal']:
    #     sd = mivit.helper.superdarn.velocity(targtime,rad,davitpy_kwargs=davitpy_kwargs)
    #     sd_data.append(mivit.DataVisualization(sd, pt))
    sd_data = []


    # get mango data
    pt = mivit.PlotMethod(cmap='gist_gray',plot_type='pcolormesh', label='MANGO', vmin=0, vmax=255)
    mangopy_kwargs = {'datadir':'./TestDataSets/MANGO'}
    mango_data = mivit.helper.mango.mosaic(targtime,mangopy_kwargs=mangopy_kwargs)
    mango = mivit.DataVisualization(mango_data, pt)


    # set up madrigalWeb credentials
    config = configparser.ConfigParser()
    config.read('config.ini')
    user_fullname = config.get('DEFAULT', 'MADRIGAL_FULLNAME')
    user_email = config.get('DEFAULT', 'MADRIGAL_EMAIL')
    user_affiliation = config.get('DEFAULT', 'MADRIGAL_AFFILIATION')
    user_info = {'fullname':user_fullname,'email':user_email,'affiliation':user_affiliation}
    madrigal_dir = './TestDataSets'


    # get GPS TEC
    pt = mivit.PlotMethod(cmap='magma',plot_type='contourf', label='GPS TEC', alpha=0.2, levels=25, vmin=0, vmax=20)
    pt2 = mivit.PlotMethod(cmap='magma',plot_type='contour', label='GPS TEC', levels=25, vmin=0, vmax=20)
    tec_dat = mivit.helper.madrigal.gps.tec(targtime,user_info, madrigal_dir=madrigal_dir)
    tec = mivit.DataVisualization(tec_dat,[pt,pt2])


    # get Millston Hill FPI data
    fpi_g_dat = mivit.helper.madrigal.fpi.Tn(targtime, 'green', user_info, madrigal_dir=madrigal_dir)
    pt = mivit.PlotMethod(cmap='Greens', plot_type='scatter', label='FPI Tn', vmin=min(fpi_g_dat.values), vmax=max(fpi_g_dat.values), s=100)
    fpi_g = mivit.DataVisualization(fpi_g_dat, pt)

    pt = mivit.PlotMethod(color='green', plot_type='quiver', label='FPI Vn', width=0.002)
    fpi_vec_g_dat = mivit.helper.madrigal.fpi.Vn(targtime, 'green', user_info, madrigal_dir=madrigal_dir)
    fpi_vec_g = mivit.DataVisualization(fpi_vec_g_dat, pt)

    fpi_r_dat = mivit.helper.madrigal.fpi.Tn(targtime, 'red', user_info, madrigal_dir=madrigal_dir)
    pt = mivit.PlotMethod(cmap='Reds', plot_type='scatter', label='FPI Tn', vmin=min(fpi_r_dat.values), vmax=max(fpi_r_dat.values), s=30)
    fpi_r = mivit.DataVisualization(fpi_r_dat, pt)

    pt = mivit.PlotMethod(color='red', plot_type='quiver', label='FPI Vn', width=0.002)
    fpi_vec_r_dat = mivit.helper.madrigal.fpi.Vn(targtime, 'red', user_info, madrigal_dir=madrigal_dir)
    fpi_vec_r = mivit.DataVisualization(fpi_vec_r_dat, pt)


    # get DMSP data
    pt = mivit.PlotMethod(cmap='jet',plot_type='scatter',label='DMSP Ni', vmin=0, vmax=3e10, s=20)
    dmsp_dat = mivit.helper.madrigal.dmsp.density(targtime-dt.timedelta(hours=1), targtime+dt.timedelta(hours=1), user_info, madrigal_dir=madrigal_dir)
    dmsp = mivit.DataVisualization(dmsp_dat, pt)

    pt = mivit.PlotMethod(cmap='jet',plot_type='quiver',label='DMSP Vi', width=0.002)
    dmsp_dat = mivit.helper.madrigal.dmsp.velocity(targtime-dt.timedelta(hours=1), targtime+dt.timedelta(hours=1), user_info, madrigal_dir=madrigal_dir)
    dmsp_vec = mivit.DataVisualization(dmsp_dat, pt)


    plot = mivit.Visualize([mango,tec,fpi_g,fpi_r,fpi_vec_g,fpi_vec_r,dmsp,dmsp_vec]+sd_data, map_features=['gridlines','coastlines','mag_gridlines'], map_extent=[-130,-65,20,50], map_proj='LambertConformal', map_proj_kwargs={'central_longitude':-100,'central_latitude':35})
    # plot = Visualize([dmsp,dmsp_vec], map_features=['gridlines','coastlines','mag_gridlines'], map_extent=[-130,-65,20,50], map_proj='LambertConformal', map_proj_kwargs={'central_longitude':-100,'central_latitude':35})
    plot.one_map()
    # plot.multi_map()


def test_mango():

    targtime = dt.datetime(2017,5,28,6,35)

    # get mango data
    mangopy_kwargs = {'datadir':'./TestDataSets/MANGO'}

    dataset = mivit.helper.mango.camera(targtime,'Hat Creek Observatory', mangopy_kwargs=mangopy_kwargs)
    pt = mivit.PlotMethod(cmap='jet',plot_type='scatter', label='MANGO', vmin=0, vmax=255, alpha=0.5, zorder=6)
    mango = mivit.DataVisualization(dataset, pt)

    dataset = mivit.helper.mango.mosaic(targtime, mangopy_kwargs=mangopy_kwargs)
    pt = mivit.PlotMethod(cmap='gist_gray',plot_type='pcolormesh', label='MANGO', vmin=0, vmax=255)
    mosaic = mivit.DataVisualization(dataset, pt)

    plot = mivit.Visualize([mango, mosaic], map_features=['gridlines','coastlines','mag_gridlines'], map_extent=[-130,-65,20,50], map_proj='LambertConformal', map_proj_kwargs={'central_longitude':-100,'central_latitude':35})
    plot.one_map()


def test_superdarn():

    targtime = dt.datetime(2017,5,28,6,35)

    ptv = mivit.PlotMethod(cmap='seismic',plot_type='pcolormesh',label='SuperDARN Velocity',vmin=-200,vmax=200)
    ptp = mivit.PlotMethod(cmap='Greens',plot_type='pcolormesh',label='SuperDARN Velocity',vmin=0,vmax=20)
    pts = mivit.PlotMethod(cmap='Oranges',plot_type='pcolormesh',label='SuperDARN Velocity',vmin=0,vmax=100)

    sd_data = []

    davitpy_kwargs = {'src':'local','fileType':'fitex','local_dirfmt':'./TestDataSets/SuperDARN/'}
    for rad in ['bks','fhe','fhw','kap','pgr','sas','wal']:

        # v = mivit.helper.superdarn.velocity(targtime,rad,davitpy_kwargs=davitpy_kwargs)
        # sd_data.append(mivit.DataVisualization(v, ptv))

        # p = mivit.helper.superdarn.power(targtime,rad,davitpy_kwargs=davitpy_kwargs)
        # sd_data.append(mivit.DataVisualization(p, ptp))

        s = mivit.helper.superdarn.spectralwidth(targtime,rad,davitpy_kwargs=davitpy_kwargs)
        sd_data.append(mivit.DataVisualization(s, pts))

    plot = mivit.Visualize(sd_data, map_features=['gridlines','coastlines','mag_gridlines'], map_extent=[-130,-65,20,50], map_proj='LambertConformal', map_proj_kwargs={'central_longitude':-100,'central_latitude':35})
    plot.one_map()

def test_amisr():

    targtime = dt.datetime(2017,5,28,6,35)

    filename = 'TestDataSets/20151107.003_lp_3min-fitcal.h5'

    dataset = mivit.helper.amisr.density(targtime, filename)
    pt = mivit.PlotMethod(cmap='jet', plot_type='scatter', label='PFISR Density', vmin=0., vmax=2.e11, s=10)
    data = mivit.DataVisualization(dataset, pt)

    plot = mivit.Visualize([data], map_features=['gridlines','coastlines','mag_gridlines'], map_extent=[-170,-135,50,75], map_proj='LambertConformal', map_proj_kwargs={'central_longitude':-150,'central_latitude':65})
    plot.one_map()


def main():
    test()
    # test_mango()
    # test_superdarn()
    # test_amisr()

if __name__ == '__main__':
    main()