-
Notifications
You must be signed in to change notification settings - Fork 5
/
tracks.py
523 lines (417 loc) · 17.3 KB
/
tracks.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
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
#The track program creates correspondences between basins at neighboring times.
#We want tracks that extend over the lifetime of each/all basins
import numpy as np
import netCDF4
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from mpl_toolkits.basemap import Basemap
from matplotlib.collections import LineCollection
from matplotlib.colors import ListedColormap, BoundaryNorm
import datetime as dt
import basinMetrics
import correspond
r2d = 180./np.pi
def form_track_site(dataCorr, iTimeStart, iTimeEnd, site0, trackOnlyMajor):
"""
Follow a given site throughout the correspondences "tree" and split tree into individual tracks
Arguments:
dataCorr - correspondence netCDF4 object
iTimeStart - time index to start track
iTimeEnd - last possible time in track
site0 - starting site
trackOnlyMajor - if True, track only major correspondences. if false, track major and minor correspondences.
"""
tracks_checkContinue = [[site0]]
trackList = []
while (len(tracks_checkContinue)>0):
#can continue a track if: have more times AND site corresponds to future site
basinTrack = tracks_checkContinue.pop()
nTimes = len(basinTrack); site0 = basinTrack[-1]
iTime = iTimeStart+nTimes-1 #0-indexing for time (time0 will have 1 site, time1 will have 2 sites)
if (iTime>=iTimeEnd): #no more times left
trackList.append(basinTrack)
continue
corrSites, corrTypes = correspond.get_correspondingSites(dataCorr, iTime, site0)
if (len(corrSites)<1): #don't connect to future site
trackList.append(basinTrack)
continue
if (trackOnlyMajor):
if (2 not in corrTypes):
#don't connect to future site
trackList.append(basinTrack)
continue
#if here, site0 connects to >=1 future sites so can continue track
nSites1 = len(corrSites)
for iSite1 in xrange(nSites1):
if (trackOnlyMajor):
#only consider branches that are major correspondences
if (corrTypes[iSite1]<2):
continue
#continue by duplicating the entire previous track
site1 = corrSites[iSite1]
tracks_checkContinue.append(basinTrack+[site1])
return trackList
def form_tracks_iTime(dataCorr, iTimeStart, iTimeEnd, sites0, trackOnlyMajor):
"""
Form tracks that started at a given time
Arguments:
dataCorr - correspondence netCDF4 object
iTimeStart - time index to start track
iTimeEnd - last possible time in track
sites0 - starting sites
trackOnlyMajor - if True, track only major correspondences. if false, track major and minor correspondences.
"""
trackList = []
for site in sites0:
#print "Forming tracks from correspondences for initial site {0} at time {1}".format(site, iTimeStart)
siteTracks = form_track_site(dataCorr, iTimeStart, iTimeEnd, site, trackOnlyMajor)
for t in siteTracks:
if (len(t)>1): #drop the noise/artifacts
trackList.append(t)
return trackList
def run_tracks_timeInterval(fNameTracks, fCorr, iTimeStart, iTimeEnd, timeStartGlobal, deltaTGlobal, fMetrics='', trackOnlyMajor=False):
"""
Find tracks for sites at [iTimeStart,iTimeEnd) out to iTimeEnd (at most).
Besides stitching the correspondences together, the tricky part is that we don't want to start a track for a basin at each timestep...only start a track at "genesis".
#Define genesis as:
#-basin not part of an existing track at that time
Arguments:
fNameTracks - output tracks file
fCorr - correspondence file output by tpvTrack
iTimeStart - global start time of tracks
iTimeEnd - global end time of tracks
timeStartGlobal - datetime.datetime of initial time
deltaTGlobal - datetime.timedelta of time spacing between input data
fMetrics - path to metrics netcdf file. If '', write different format track file
trackOnlyMajor - to track only major (True) or all correspondences (False)
"""
#store the basins that are part of tracks at each time
nTimes = iTimeEnd-iTimeStart
sitesInTrack = [[] for i in xrange(nTimes+1)] #need +1 since storing iTimeStart basins as well
dataTracks = write_tracks_metrics_netcdf_header(fNameTracks, 'test', nTimes, nTimes); iTrackGlobal=0
dataCorr = netCDF4.Dataset(fCorr, 'r')
for iTime in xrange(nTimes):
timeInd = iTime+iTimeStart
sites0, corrSites, typeCorr = correspond.read_corr_iTime(dataCorr, timeInd)
#ignore sites already trajectoried in an existing track
nSites0 = len(sites0)
notInPrev = np.ones(nSites0,dtype=int)
for iSite in xrange(nSites0):
site0 = sites0[iSite]
if (site0 in sitesInTrack[iTime]):
notInPrev[iSite] = 0
#print "{0}/{1} sites started at time {2}".format(np.sum(notInPrev>0), nSites0, timeInd) #this doesn't account for trackOnlyMajor
sites0 = sites0[notInPrev>0]
trackList = form_tracks_iTime(dataCorr, timeInd, iTimeEnd, sites0,trackOnlyMajor); print "Formed tracks for iTimeGlobal: ", timeInd
#update sitesInTrack
for trackSeq in trackList:
for i in xrange(len(trackSeq)):
#basin in trackSeq[i] is at index i [iTimeStart,iTimeEnd]
sitesInTrack[iTime+i].append(trackSeq[i])
#print sitesInTrack
#write to file
if (fMetrics==''):
write_tracks_cells(fNameTracks, trackList)
else:
dataMetrics = netCDF4.Dataset(fMetrics,'r')
#write_tracks_metrics_iTime(fNameTracks, timeInd, trackList, dataMetrics, timeStartGlobal, deltaTGlobal)
iTrackGlobal = write_tracks_metrics_iTime_netcdf(dataTracks, timeInd, iTrackGlobal, trackList, dataMetrics, timeStartGlobal, deltaTGlobal)
dataMetrics.close()
dataCorr.close()
dataTracks.close()
def write_tracks_cells(fNameTracks, trackList):
"""Writing text track output file of just site in track"""
print "Appending to file: "+fNameTracks
f = open(fNameTracks,'a')
for track in trackList:
s = ' '.join(str(i) for i in track)
f.write(s+'\n')
f.close()
def read_tracks_cells(fNameTracks):
"""Reading text track output file of just site in track"""
#return trackList
f = open(fNameTracks,'r')
trackList = []
for line in f:
cellStr = line.strip().split()
trackSeq = [int(i) for i in cellStr]
trackList.append(trackSeq)
return trackList
timeStringFormat = "%Y-%m-%d-%H"
def write_tracks_metrics_netcdf_header(fName, info, nTimesInTrackMax, nTimes):
'''
Make and write header of tracks tpvtrack netcdf output file.
For inputs,
nTimesInTrackMax: maximum length of track
nTimes: number of times in tracking interval
nTimesInTrackMax <= nTimes
Maybe dumping out the tracks to text file as:
iTimeStartTrack1 nTimesTrack1 timeStart timeEnd
(time1) metric1 metric2 ... for track1
(time2) metric1 metric2 ... for track1
-1
ends up taking alot of time since we read many small chunks from basinMetrics.
Writing tracks out as metric1[iTime,iTrack] will let us load basinMetrics[iTime], but we'll
see how costly it is to write to scattered locations within the file. There are probably intermediate buffers and such.
"ragged" or "vlen" arrays still don't make sense to me, so we'll use extra padding instead.
'''
data = netCDF4.Dataset(fName, 'w', format='NETCDF4')
data.description = info
# dimensions
data.createDimension('nTracks', None)
#data.createDimension('nTimesTrack', nTimesInTrackMax+1)
data.createDimension('nTimesTrack', None) #unlimited dimension
#data.createDimension('nTimes', nTimes)
data.createDimension('nTimes', nTimes+1) #[0:nTimesSeg-1 +1)
tNow = dt.datetime.now().strftime(timeStringFormat)
lenTime = len(tNow)
data.createDimension('lenTimeString', lenTime)
# variables
data.createVariable('timeStamp', str, ('nTimes',))
data.createVariable('iTimeStart', 'i4', ('nTracks',))
data.createVariable('lenTrack', 'i4', ('nTracks',))
data.createVariable('siteExtr', 'i4', ('nTracks','nTimesTrack',))
#for key in basinMetrics.metricKeys:
for iKey in xrange(len(basinMetrics.metricKeys)):
key = basinMetrics.metricKeys[iKey]; units=basinMetrics.metricUnits[iKey];info=basinMetrics.metricNames[iKey]
var_data = data.createVariable(key, 'f8', ('nTracks','nTimesTrack',))
var_data.units = units; var_data.long_name=info
return data
def write_tracks_metrics_iTime_netcdf(data, iTime0, iTrackGlobal0, trackList, dataMetrics, timeStartGlobal, deltaTGlobal):
"""Write all tracks that started at given time to tracks netcdf file"""
tStart = timeStartGlobal+deltaTGlobal*iTime0; tStart = tStart.strftime(timeStringFormat)
data.variables['timeStamp'][iTime0] = tStart
#quick fix to fill in timestamp for tracks ending at last possible time
tNext = timeStartGlobal+deltaTGlobal*(iTime0+1); tNext = tNext.strftime(timeStringFormat)
data.variables['timeStamp'][iTime0+1] = tNext
nTracks = len(trackList)
if (nTracks==0):
return iTrackGlobal0
trackLengths = np.array([len(track) for track in trackList], dtype=int)
maxLength = np.max(trackLengths)
print "Maximum track length={0} at time {1}".format(maxLength, tStart)
data.variables['lenTrack'][iTrackGlobal0:iTrackGlobal0+nTracks] = trackLengths[:]
data.variables['iTimeStart'][iTrackGlobal0:iTrackGlobal0+nTracks] = iTime0
for iTime in xrange(maxLength):
iTimeGlobal = iTime0+iTime
sites = dataMetrics.variables['sites'][iTimeGlobal,:]
for key in basinMetrics.metricKeys:
vals = dataMetrics.variables[key][iTimeGlobal,:]
for iTrack in xrange(nTracks):
if (trackLengths[iTrack]-1<iTime): #can only index an array of length 4 with [3]
continue
iTrackGlobal = iTrackGlobal0+iTrack
#print iTrackGlobal, iTime, trackList[iTrack]
site = trackList[iTrack][iTime]
iSite = np.where(sites==site)[0][0]
data.variables['siteExtr'][iTrackGlobal,iTime] = site
data.variables[key][iTrackGlobal, iTime] = vals[iSite]
iTrackGlobal = iTrackGlobal0+nTracks
return iTrackGlobal
def write_tracks_metrics_iTime(fSave, iTime0, trackList, dataMetrics, timeStartGlobal, deltaTGlobal):
'''
Write text file version of tracks file
format of text file is:
(header) metric1 metric2 ...
iTimeStartTrack1 nTimesTrack1 timeStart timeEnd
(time1) metric1 metric2 ... for track1
(time2) metric1 metric2 ... for track1
-1
iTimeStartTrack2 nTimesTrack2 timeStart timeEnd
(time1) metric1 metric2 ... for track2
(time2) metric1 metric2 ... for track2
-1
.
.
.
'''
f = open(fSave,'a')
#add the header if "appending" actually created the file
if (f.tell() == 0): #file pointer at 0th byte
varNames = basinMetrics.metricKeys
s = ' '.join(varNames);
f.write(s+'\n'); s = ''
for track in trackList:
s = ''
nTimes = len(track);
tStart = timeStartGlobal+deltaTGlobal*iTime0; tEnd = timeStartGlobal+deltaTGlobal*(iTime0+nTimes-1);
tStart = tStart.strftime(timeStringFormat)
tEnd = tEnd.strftime(timeStringFormat)
s += '{0} {1} {2} {3}\n'.format(iTime0, nTimes, tStart, tEnd)
for iTime in xrange(nTimes):
site = track[iTime]
vals = basinMetrics.get_metrics_basin(dataMetrics, iTime+iTime0, site)
valsStr = '';
for val in vals:
valsStr += '{0:g} '.format(val)
#valsStr = str(vals)[1:-1]
s += valsStr+'\n'
#end iTime
s += '-1\n'
f.write(s); s = ''
#end iTrack
f.close()
def read_tracks_metrics(fNameTracks, metricNames):
"""
#Input the name of the track file and list of metricNames strings.
#return list of numpy arrays list[iTrack][iTime,iMetric] with the metric properties of the tracked TPVs.
"""
nMetrics = len(metricNames)
data = netCDF4.Dataset(fNameTracks,'r')
nTracks = len(data.dimensions['nTracks'])
trackList = []
timeStartList = []
for iTrack in xrange(nTracks):
nTimes = data.variables['lenTrack'][iTrack]
iTimeStart = data.variables['iTimeStart'][iTrack]
timeStartList.append(data.variables['timeStamp'][iTimeStart])
trackVals = np.empty((nTimes,nMetrics),dtype=float)
for iMetric in xrange(nMetrics):
key = metricNames[iMetric]
trackVals[:,iMetric] = data.variables[key][iTrack,0:nTimes]
trackList.append(trackVals)
data.close()
return trackList, timeStartList
def plot_tracks_cells(fTracks, mesh, fDirSave):
"""Example plot of tracks in text tracks without metrics file on map"""
f = open(fTracks,'r')
m = Basemap(projection='ortho',lon_0=0,lat_0=89.5, resolution='l')
plt.figure()
m.drawcoastlines()
for line in f:
cellStr = line.strip().split()
trackList = [int(i) for i in cellStr]
if (len(trackList)<3):
continue
lat, lon = mesh.get_latLon_inds(np.array(trackList,dtype=int))
lat *= r2d; lon *= r2d
x,y = m(lon,lat)
#print lat; print lon
#mark beginning and ending of track
m.scatter(x[0],y[0], marker='+', color='g', s=45)
m.scatter(x[-1],y[-1], marker='o', color='r', s=10)
#plot track
m.plot(x,y, 'b-')
if (False):
plt.show()
else:
fName = 'tracks_debug.png'
fSave = fDirSave+fName
print "Picture of tracks from {0}: {1}".format(fTracks,fSave)
plt.savefig(fSave); plt.close()
f.close()
def plot_tracks_metrics(fTracks, fSave):
"""Example plot of tracks in netcdf tracks file on map colored by metric"""
metricNames = ['thetaExtr', 'latExtr', 'lonExtr']
latInd = metricNames.index('latExtr')
lonInd = metricNames.index('lonExtr')
varKey = 'thetaExtr'
varInd = metricNames.index(varKey); varMin = 270.; varMax = 310.; #varMin= 320.; varMax = 380.;
trackList, timeList = read_tracks_metrics(fTracks, metricNames)
m = Basemap(projection='ortho',lon_0=0,lat_0=89.5, resolution='l')
#ax = plt.figure()
ax = plt.gca()
m.drawcoastlines()
for iTrack,track in enumerate(trackList):
nTimes = track.shape[0]
if (True):
if (nTimes<8):
continue
lat = track[:,latInd]
lon = track[:,lonInd]
x,y = m(lon,lat)
print timeList[iTrack], nTimes; print lat; print lon
#mark beginning and ending of track
m.scatter(x[0],y[0], marker='+', color='g', s=45)
m.scatter(x[-1],y[-1], marker='o', color='r', s=10)
#plot track, with color representing value
#m.plot(x,y, 'b-')
vals = track[:,varInd]
colorline(x, y, z=vals, cmap=plt.get_cmap('RdBu_r'), norm=plt.Normalize(varMin, varMax), linewidth=3, alpha=1.0, ax=ax)
#plt.colorbar()
s = 'Tracks {0}, [{1},{2}]'.format(varKey, varMin, varMax)
plt.title(s)
if (False):
plt.show()
else:
print "Saving image of tracks from {0}: {1}".format(fTracks,fSave)
plt.savefig(fSave); plt.close()
def demo_plotMetrics(fTracks):
"""Example plot of track metrics"""
metricNames = ['thetaExtr', 'latExtr']
#latInd = metricNames.index('latExtr')
trackList = read_tracks_metrics(fTracks, metricNames)
for iMetric,metricName in enumerate(metricNames):
plt.figure()
for track in trackList:
nTimes = track.shape[0]
if (True):
if (nTimes<4):
continue
#lat = track[:,latInd]
plt.plot(track[:,iMetric])
plt.title(metricName)
plt.show()
def demo_compareMetrics(fTracks):
"""Example joint-plot of 2 metrics"""
metricNames = ['rEquiv', 'vortMean']
#latInd = metricNames.index('latExtr')
trackList = read_tracks_metrics(fTracks, metricNames)
plt.figure()
for track in trackList:
nTimes = track.shape[0]; #print track, track.shape
if (True):
if (nTimes<4):
continue
#lat = track[:,latInd]
plt.scatter(track[:,0], track[:,1])
s = '{0} vs {1}'.format(metricNames[0], metricNames[1])
plt.title(s)
plt.tight_layout()
plt.ylim([1.e-6, 2.e-4]); plt.semilogy()
plt.show()
def demo_plotLifetimes(fTracks):
"""Example histogram of track lifetimes"""
metricNames = ['latExtr']
trackList = read_tracks_metrics(fTracks, metricNames)
plt.figure()
vals = []
for track in trackList:
nTimes = track.shape[0]
vals.append(nTimes)
if (np.sum(track[:,0]>70.)>7*4):
print nTimes, track
if (True):
vals = [i for i in vals if i>6]
plt.hist(vals, cumulative=True, bins=20)
plt.title('Lifetime (timesteps)')
plt.show()
#The following 2 fcts are taken from:
# http://nbviewer.ipython.org/github/dpsanders/matplotlib-examples/blob/master/colorline.ipynb
def make_segments(x, y):
'''
Create list of line segments from x and y coordinates, in the correct format for LineCollection:
an array of the form numlines x (points per line) x 2 (x and y) array
'''
points = np.array([x, y]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)
return segments
# Interface to LineCollection:
def colorline(x, y, z=None, cmap=plt.get_cmap('Blues_r'), norm=plt.Normalize(0.0, 1.0), linewidth=3, alpha=1.0, ax=plt.gca()):
'''
Plot a colored line with coordinates x and y
Optionally specify colors in the array z
Optionally specify a colormap, a norm function and a line width
'''
# Default colors equally spaced on [0,1]:
if z is None:
z = np.linspace(0.0, 1.0, len(x))
# Special case if a single number:
if not hasattr(z, "__iter__"): # to check for numerical input -- this is a hack
z = np.array([z])
z = np.asarray(z)
segments = make_segments(x, y)
lc = LineCollection(segments, array=z, cmap=cmap, norm=norm, linewidth=linewidth, alpha=alpha)
#ax = plt.gca()
ax.add_collection(lc)
return lc