-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathlst_binning.py
200 lines (163 loc) · 6.78 KB
/
lst_binning.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
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
import os, sys
import matplotlib as mpl
if os.environ.get('DISPLAY','') == '':
print('no display found. Using non-interactive Agg backend')
mpl.use('Agg')
from matplotlib import pyplot as plt
import numpy as np
import datetime, time, re
from scio import scio
import SNAPfiletools as sft
import argparse
from datetime import datetime
import matplotlib.dates as mdates
from multiprocessing import Pool
from functools import partial
import pytz
import skyfield.api as sf
def get_ts_from_name(f):
return int(f.split('/')[-1])
def get_localtime_from_UTC(tstamp, mytz):
return datetime.fromtimestamp(int(tstamp),tz=pytz.utc).astimezone(tz=mytz)
def get_binned(fname,data_dir,coords,ctime_start,ctime_stop,nbins):
# data_dir='/project/s/sievers/mohanagr/uapishka_aug_oct_2022/data_auto_cross/'
# data_dir='/project/s/sievers/mohanagr/uapishka_franken_oct_nov_2022/data_auto_cross/SNAP3/'
# data_dir='/project/s/sievers/albatros/marion/albatros-hydroshack/data_auto_cross/'
data_subdirs = sft.time2fnames(ctime_start, ctime_stop, data_dir)
data_subdirs.sort()
new_dirs = [d+f'/{fname}.scio.bz2' for d in data_subdirs]
datpol00 = scio.read_files(new_dirs)
print("Files of ",fname, "read")
# print(data_subdirs)
# data_subdirs = ['/home/mohan/Projects/direct/16272/1627202093']
ts=sf.load.timescale()
# jd = tstart/86400+2440587.5
# t=ts.ut1_jd(jd)
earth_loc = sf.wgs84.latlon(*coords)
dH = 24/nbins
# print(earth_loc.lst_hours_at(t))
binned = [np.asarray([]).reshape(-1,2048)]*nbins
for i,pol00 in enumerate(datpol00):
tstamp = get_ts_from_name(data_subdirs[i])
if(i%10==0):
print(i+1,"files read.File timestamp is ", tstamp)
if(pol00 is None):
print("YO WTF, READ",fname, data_subdirs[i])
continue
mytstamps = tstamp+np.arange(0,pol00.shape[0])*6.44
myjds = mytstamps/86400 + 2440587.5
# print(ts.ut1_jd(myjds))
lsthrs=earth_loc.lst_hours_at(ts.ut1_jd(myjds))
# print("LST hours are:",lsthrs )
lstbins = np.floor(lsthrs/dH).astype(int)
# print(lstbins)
branch_points=list(np.where(np.diff(lstbins)!=0)[0])
branch_points.append(pol00.shape[0]-1)
# print(branch_points)
st=0
for i in range(len(branch_points)):
b=branch_points[i]
en=b+1
# print("branch point",b,"lst bin", lstbins[b])
# print(st,en,lstbins[b])
binned[lstbins[b]]=np.vstack([binned[lstbins[b]],pol00[st:en,:]]) #prealocated arrays would prolly speeden this up
st=en
return binned
# def myredux(xx):
# u=np.percentile(xx,99)
# b=np.percentile(xx,1)
# xx_clean=xx[(xx<=u)&(xx>=b)]
# return np.mean(xx_clean)
def myredux(bigarr):
# def redux(xx):
# u=np.percentile(xx,50)
# b=np.percentile(xx,1)
# xx_clean=xx[(xx<=u)&(xx>=b)]
# return np.mean(xx_clean)
# return np.apply_along_axis(redux,0,bigarr)
return np.median(bigarr,axis=0)
def reduce_binned(binned,nbins,nchan):
counts=np.zeros(nbins)
bmedian = np.zeros((nbins,nchan)) #bmedian = bin median
bmean = np.zeros((nbins,nchan))
for i in range(nbins):
counts[i]=binned[i].shape[0]
if(counts[i]>0):
# bmean[i,:]=np.mean(binned[i],axis=0)
bmean[i,:] = np.mean(binned[i],axis=0)
bmedian[i,:]=np.median(binned[i],axis=0)
return {'counts':counts,'mean':bmean,'median':bmedian}
def reduce_binned_parallel(binned,nbins,nchan):
# counts=np.zeros(nbins)
# bmedian = np.zeros((nbins,nchan)) #bmedian = bin median
bmean = np.zeros((nbins,nchan))
# custom_mean = lambda bigarr: np.apply_along_axis(myredux,0,bigarr)
# custom_median = lambda bigarr: np.median(bigarr,axis=0)
with Pool(os.cpu_count()) as p:
means = p.map(myredux, binned)
# medians = p.map(custom_median,binned)
bmean[:] = np.asarray(means)
# bmedian[:] = np.asarray(medians)
return {'mean':bmean}
if __name__ == '__main__':
loc='uapishka'
ctime_start= 1661011607
ctime_stop = 1666620593
mytz=pytz.timezone('US/Eastern')
nbins = 720 # 2 minute bins
coords = [51.4641932, -68.2348603,300]
data_dir = '/project/s/sievers/mohanagr/uapishka_aug_oct_2022/data_auto_cross/'
plot_type = 'median'
sttime=get_localtime_from_UTC(ctime_start,mytz).strftime("%b-%d %H:%M")
entime=get_localtime_from_UTC(ctime_stop,mytz).strftime("%b-%d %H:%M")
t1=time.time()
binned=get_binned('pol00',data_dir,coords,ctime_start,ctime_stop,nbins)
t2=time.time()
print("time taken for binning",t2-t1)
t1=time.time()
statsp00=reduce_binned(binned,nbins,2048)
t2=time.time()
print("time taken for reduction",t2-t1)
binned=get_binned('pol11',data_dir,coords,ctime_start,ctime_stop,nbins)
statsp11=reduce_binned(binned,nbins,2048)
print("Done pol11")
binned=get_binned('pol01r',data_dir,coords,ctime_start,ctime_stop,nbins)
statsp01r=reduce_binned(binned,nbins,2048)
print("Done pol01r")
binned=get_binned('pol01i',data_dir,coords,ctime_start,ctime_stop,nbins)
statsp01i=reduce_binned(binned,nbins,2048)
print("Done pol01i")
pol01 =statsp01r[plot_type] + 1J*statsp01i[plot_type]
f=plt.gcf()
f.set_size_inches(15,15)
plt.suptitle(f'Plotting from: {sttime} to {entime}, plot type: {plot_type}')
myext=[0, 125, 24, 0]
plt.subplot(321)
plt.title("Pol00")
plt.imshow(np.log10(statsp00[plot_type]),vmin=7,vmax=8.2,extent=myext,aspect='auto')
plt.colorbar()
plt.subplot(323)
plt.title("Pol11")
plt.imshow(np.log10(statsp11[plot_type]),vmin=7,vmax=8.2,extent=myext,aspect='auto')
plt.colorbar()
plt.subplot(322)
plt.title("Pol01 mag")
plt.imshow(np.log10(np.abs(pol01)),vmin=3,vmax=8,extent=myext,aspect='auto')
plt.colorbar()
plt.subplot(324)
plt.title("Pol01 phase")
plt.imshow(np.angle(pol01),extent=myext,aspect='auto',cmap='RdBu')
plt.colorbar()
plt.subplot(313)
plt.title('bin count')
plt.plot(np.arange(0,nbins),statsp00['counts'])
output_path = f'/project/s/sievers/mohanagr/lst_{nbins}_{plot_type}_{ctime_start}_{ctime_stop}_{loc}.png'
plt.savefig(output_path)
print(output_path)
output_path = f'/project/s/sievers/mohanagr/lst_{nbins}_{ctime_start}_{ctime_stop}_{loc}.npz'
np.savez_compressed(output_path,\
p00mean=statsp00['mean'],p00median=statsp00['median'],\
p11mean=statsp11['mean'],p11median=statsp11['median'],\
p01rmean=statsp01r['mean'],p01rmedian=statsp01r['median'],\
p01imean=statsp01i['mean'],p01imedian=statsp01i['median'],
counts=statsp00['counts'])