Revision Log

proc_rdi_dspec_dw.py

Revision 448 (checked in by cbc, 13 years ago)	Add new Billy Mitchell configs.

Line
1	#!/usr/bin/env python
2	# Last modified: Time-stamp: <2010-12-09 16:13:21 haines>
3	"""
4	how to parse data, and assert what data and info goes into
5	creating and updating monthly netcdf files
6
7	RDI/Wavesmon processed adcp 2-D power spectrum (Dspec) as function of
8	frequency and direction
9
10	parser : sample date and time from file name, compute wave summary
11	based on George Voulgaris' matlab script (version 8, Feb 14, 2005,
12	polar_waves_cur_rdi.m) and additional parameters.
13	creator : lat, lon, z, time, freq, dir, Sxx(time, freq, dir), Sf(time, freq),
14	Stheta(time, dir), Stheta_swell(time, dir), Stheta_wind(time, dir),
15	Hs, Hs_swell, Hs_wind,
16	Tp, Tp_swell, Tp_wind, Tm, Tm_swell, Tm_wind,
17	Dp, Dp_swell, Dp_wind, Dm, Dm_swell, Dm_wind,
18
19	updater : time, Sxx(time, freq, dir), Sf(time, freq),
20	Stheta(time, dir), Stheta_swell(time, dir), Stheta_wind(time, dir),
21	Hs, Hs_swell, Hs_wind,
22	Tp, Tp_swell, Tp_wind, Tm, Tm_swell, Tm_wind,
23	Dp, Dp_swell, Dp_wind, Dm, Dm_swell, Dm_wind,
24
25	check that freq and dir have not changed from what is in current
26	NetCDF file
27
28	Examples
29	--------
30
31	>> (parse, create, update) = load_processors(module_name_without_dot_py)
32	For example,
33	>> (parse, create, update) = load_processors('proc_rdi_logdata_adcp')
34	or
35	>> si = get_config(cn+'.sensor_info')
36	>> (parse, create, update) = load_processors(si['adcp']['proc_module'])
37
38	Then use the generic name of processor to parse data, create or update
39	monthly output file
40
41	>> lines = load_data(filename)
42	>> data = parse(platform_info, sensor_info, lines)
43	>> create(platform_info, sensor_info, data)
44	or
45	>> update(platform_info, sensor_info, data)
46
47	"""
48
49	from raw2proc import *
50	from procutil import *
51	from ncutil import *
52
53	now_dt = datetime.utcnow()
54	now_dt.replace(microsecond=0)
55
56	def parser(platform_info, sensor_info, lines):
57	"""
58	parse and assign wave spectra data from RDI ADCP Dspec
59	and compute wave statistics and parameters
60
61	Notes
62	-----
63	1. adapted from polar_waves_cur_rdi.m (Version 8 - February 14, 2005)
64	by George Voulgaris
65	Coastal Processes & Sediment Dynamics Lab
66	Department of Geological Sciences
67	University of South Carolina, Columbia, SC 29205
68	Email: gvoulgaris@geol.sc.edu
69	1. should only be one line in each file of comma-delimited data
70
71	"""
72	import numpy
73	from datetime import datetime
74	from time import strptime
75
76	# get sample datetime from filename
77	fn = sensor_info['fn']
78	# print " ... %s" % (fn,)
79	if sensor_info['utc_offset']:
80	sample_dt = filt_datetime(fn) + \
81	timedelta(hours=sensor_info['utc_offset'])
82	else:
83	sample_dt = filt_datetime(fn)
84
85	# extract header (first 6 lines)
86	rdi = []
87	for line in [lines[2], lines[4], lines[5]]:
88	# split line and parse float and integers
89	sw = re.split(' ', line)
90	for s in sw:
91	m = re.search(REAL_RE_STR, s)
92	if m:
93	rdi.append(float(m.groups()[0]))
94
95	# assign specific fields
96	n = len(rdi)
97	ndir = float(rdi[0]) # Number of directions (no units)
98	nfreq = float(rdi[1]) # Number of frequencies (no units)
99	freq_bw = float(rdi[2]) # Frequency bandwidth (Hz)
100	Do = float(rdi[3]) # Starting direction (degrees from True North)
101
102	if ndir!=sensor_info['ndir']:
103	print 'Number of direction bins reported in data ('+ \
104	str(ndir)+') does not match config number ('+ \
105	str(sensor_info['ndir'])+'). \nCheck for change at sensor.'
106
107	if nfreq!=sensor_info['nfreq']:
108	print 'Number of frequencies reported in data ('+ \
109	str(nfreq)+') does not match config number ('+ \
110	str(sensor_info['nfreq'])+'). \nCheck for change at sensor. '
111
112	Dtheta = 360./ndir
113	D = Do + numpy.arange(ndir)*Dtheta
114	D = numpy.mod(D,360)
115	Df = 1./nfreq
116	f = numpy.arange(1,nfreq+1)*Df
117
118	# some data checks
119	if Df != freq_bw:
120	# frequency resolution should be the same as freq_bw
121	print "Df (%f) not equal to freq_bw (%f)" % (Df, freq_bw)
122
123	# set up dict of data
124	data = {
125	'dt' : numpy.array(numpy.ones((1,), dtype=object)*numpy.nan),
126	'time' : numpy.array(numpy.ones((1,), dtype=long)*numpy.nan),
127	'dirs' : numpy.array(numpy.ones((ndir,), dtype=float)*numpy.nan),
128	'freqs' : numpy.array(numpy.ones((nfreq,), dtype=float)*numpy.nan),
129	'Sxx' : numpy.array(numpy.ones((1,nfreq,ndir), dtype=float)*numpy.nan),
130	'Sf' : numpy.array(numpy.ones((1,nfreq), dtype=float)*numpy.nan),
131	'Stheta' : numpy.array(numpy.ones((1,ndir), dtype=float)*numpy.nan),
132	'Stheta_swell' : numpy.array(numpy.ones((1,ndir), dtype=float)*numpy.nan),
133	'Stheta_wind' : numpy.array(numpy.ones((1,ndir), dtype=float)*numpy.nan),
134	'Hs' : numpy.array(numpy.ones((1,), dtype=float)*numpy.nan),
135	'Hs_swell' : numpy.array(numpy.ones((1,), dtype=float)*numpy.nan),
136	'Hs_wind' : numpy.array(numpy.ones((1,), dtype=float)*numpy.nan),
137	'Tm' : numpy.array(numpy.ones((1,), dtype=float)*numpy.nan),
138	'Tm_swell' : numpy.array(numpy.ones((1,), dtype=float)*numpy.nan),
139	'Tm_wind' : numpy.array(numpy.ones((1,), dtype=float)*numpy.nan),
140	'Tp' : numpy.array(numpy.ones((1,), dtype=float)*numpy.nan),
141	'Tp_swell' : numpy.array(numpy.ones((1,), dtype=float)*numpy.nan),
142	'Tp_wind' : numpy.array(numpy.ones((1,), dtype=float)*numpy.nan),
143	'Dm' : numpy.array(numpy.ones((1,), dtype=float)*numpy.nan),
144	'Dm_swell' : numpy.array(numpy.ones((1,), dtype=float)*numpy.nan),
145	'Dm_wind' : numpy.array(numpy.ones((1,), dtype=float)*numpy.nan),
146	'Dp' : numpy.array(numpy.ones((1,), dtype=float)*numpy.nan),
147	'Dp_swell' : numpy.array(numpy.ones((1,), dtype=float)*numpy.nan),
148	'Dp_wind' : numpy.array(numpy.ones((1,), dtype=float)*numpy.nan),
149	}
150
151	# throw a dummy datetime into dt so we can return data with no data
152	# if we encounter an unrecoverable error while parsing the file
153	data['dt'][:] = datetime(1970,1,1,0,0,0)
154
155	j = 0
156	Sxx = numpy.array(numpy.ones((nfreq,ndir), dtype=float)*numpy.nan)
157	# each line is a freq, each column is a direction
158	for line in lines[6:]:
159	rdi = []
160	# split line and parse float and integers
161	sw = re.split(' ', line)
162	for s in sw:
163	m = re.search(REAL_RE_STR, s)
164	if m:
165	rdi.append(float(m.groups()[0]))
166
167	if len(rdi) == ndir:
168	Sxx[j][:] = numpy.array(rdi[:]) # cross spectrum as mm^2/Hz/deg
169	j = j+1
170
171	# Did we get the number of data rows that we expected? Should equal nfreq
172	if j != nfreq:
173	print "Number of data rows %d does not match expected number %d" % (j, nfreq)
174	print " .... skipping %s" % (fn,)
175	return data
176
177	Sxx = Sxx/360./1000./1000. # convert cross spectrum to units of m^2/Hz/deg
178	# NOTE make fupper location dependent?? (add to config_files??)
179	fupper = 0.65 # upper freq limit 0.65 Hz or wave periods less than T~1.538s
180	iswell = f<=1/10. # swell band for T>10s
181	iwind = (f>1/10.) * (f<=fupper) # wind band 1/fupper<T<10s
182	# NOTE about python boolean overloaded operator '*' == and == bitwise_and()
183	iall = f<=fupper # all wave freq upper limit
184
185	# compute non-directional spectrum by integrating over all angles
186	# Sxx(freq, dir) sum axis=1 is along direction
187	Sf = Sxx.sum(axis=1)*Dtheta
188	# Sxx(freq, dir) axis=0 is along freq
189	Stheta = Sxx[iall].sum(axis=0)*Df
190	Stheta_s = Sxx[iswell].sum(axis=0)*Df
191	Stheta_w = Sxx[iwind].sum(axis=0)*Df
192
193	# compute zeroth-, first- and second-moment from the non-directional spectrum
194	# all frequency ranges
195	m0 = Sf[iall].sum()*Df
196	m1 = (f[iall]Sf[iall]).sum()Df
197	m2 = ((f[iall]*2)Sf[iall]).sum()*Df
198	# swell band
199	m0s = Sf[iswell].sum()*Df
200	m1s = (f[iswell]Sf[iswell]).sum()Df
201	m2s = ((f[iswell]*2)Sf[iswell]).sum()*Df
202	# wind band
203	m0w = Sf[iwind].sum()*Df
204	m1w = (f[iwind]Sf[iwind]).sum()Df
205	m2w = ((f[iwind]*2)Sf[iwind]).sum()*Df
206
207	# Significant Wave Height (Hs)
208	Hs = 4*numpy.sqrt(m0)
209	Hss = 4*numpy.sqrt(m0s)
210	Hsw = 4*numpy.sqrt(m0w)
211
212	# Mean Wave Period (Tm)
213	Tm = m0/m1
214	Tms = m0s/m1s
215	Tmw = m0w/m1w
216
217	# Peak Wave Period (Tp)
218	# imax = Sf[iall]==Sf[iall].max()
219	# Fp = f(imax)
220	# Tp = 1/Fp[0]
221	# This wave parameters added by SH (not in GV's matlab script)
222	# one-liner version of above
223	Tp = 1/(f[Sf[iall]==Sf[iall].max()][0])
224	Tps = 1/(f[Sf[iswell]==Sf[iswell].max()][0])
225
226	# account for offset of iwind by iswell in finding peak wind freq
227	nswell = len(f[iswell])
228	false_swell = numpy.array([False for i in range(nswell)])
229	imax = Sf[iwind]==Sf[iwind].max()
230	imax = numpy.concatenate((false_swell,imax))
231	Tpw = 1/(f[imax][0])
232
233	# mean direction of wave approach used by Kuik et al (1989)
234	# Mean wave direction as a function of frequency
235	# for all freq, wind and swell bands as adapted from GV's code
236	# (polar_waves_cur_rdi.m, version 8)
237	pi = numpy.pi
238	ac = numpy.cos(D*pi/180)
239	as = numpy.sin(D*pi/180)
240
241	ch0 = (acSthetaDtheta).sum()
242	sh0 = (asSthetaDtheta).sum()
243	Dm = numpy.arctan2(sh0,ch0)*180/pi
244	if Dm<0: Dm = Dm+360.
245
246	ch0s = (acStheta_sDtheta).sum()
247	sh0s = (asStheta_sDtheta).sum()
248	Dms = numpy.arctan2(sh0s,ch0s)*180/pi
249	if Dms<0: Dms = Dms+360.
250
251	ch0w = (acStheta_wDtheta).sum()
252	sh0w = (asStheta_wDtheta).sum()
253	Dmw = numpy.arctan2(sh0w,ch0w)*180/pi
254	if Dmw<0: Dmw = Dmw+360.
255
256	# Peak Wave Direction (Dp) defined as the direction which
257	# corresponds to the "Peak frequency", or Fp. Peak frequency is the
258	# frequency at which the "Spectral density function" is at a
259	# maximum. The spectral density function gives the dependence
260	# with frequency of the energy of the waves considered. also
261	# known as the one-dimensional spectrum or energy spectrum.
262	# Definitions from Metocean Glossary
263	# http://www.ifremer.fr/web-com/glossary
264	#
265	# This wave parameter added by SH (not in GV's matlab script)
266	imax = Sf[iall]==Sf[iall].max()
267	idir = numpy.squeeze(Sxx[imax,:]==Sxx[imax,:].max())
268	Dp = D[idir][0]
269
270	imax = Sf[iswell]==Sf[iswell].max()
271	idir = numpy.squeeze(Sxx[imax,:]==Sxx[imax,:].max())
272	Dps = D[idir][0]
273
274	imax = Sf[iwind]==Sf[iwind].max()
275	# account for swell offset of swell freq in finding max wind freq
276	imax = numpy.concatenate((false_swell, imax))
277	idir = numpy.squeeze(Sxx[imax,:]==Sxx[imax,:].max())
278	Dpw = D[idir][0]
279
280	#---------------------------------------------------------------
281	data['dt'][0] = sample_dt
282	data['time'][0] = dt2es(sample_dt) # sample time in epoch seconds
283	data['dirs'] = D
284	data['freqs'] = f
285
286	data['Sxx'][0] = Sxx # full directional spectrum (m^2/Hz/deg)
287	data['Sf'][0] = Sf # non-directional spectrum (m^2/Hz)
288	data['Stheta'][0] = Stheta # Energy from all freq from each direction
289	data['Stheta_swell'][0] = Stheta_s
290	data['Stheta_wind'][0] = Stheta_w
291
292	data['Hs'][0] = Hs
293	data['Hs_swell'][0] = Hss
294	data['Hs_wind'][0] = Hsw
295
296	data['Tm'][0] = Tm
297	data['Tm_swell'][0] = Tms
298	data['Tm_wind'][0] = Tmw
299
300	data['Tp'][0] = Tp
301	data['Tp_swell'][0] = Tps
302	data['Tp_wind'][0] = Tpw
303
304	data['Dm'][0] = Dm
305	data['Dm_swell'][0] = Dms
306	data['Dm_wind'][0] = Dmw
307
308	data['Dp'][0] = Dp
309	data['Dp_swell'][0] = Dps
310	data['Dp_wind'][0] = Dpw
311
312	# print " Waves: All / Swell / Wind"
313	# print " Hs (m): %g /%g /%g" % (Hs, Hss, Hsw)
314	# print " Tm (s): %g /%g /%g" % (Tm, Tms, Tmw)
315	# print " Dm (N): %g /%g /%g" % (Dm, Dms, Dmw)
316	# print " Dp (N): %g /%g /%g" % (Dp, Dps, Dpw)
317
318	return data
319
320	def creator(platform_info, sensor_info, data):
321	#
322	#
323	title_str = sensor_info['description']+' at '+ platform_info['location']
324	global_atts = {
325	'title' : title_str,
326	'institution' : 'University of North Carolina at Chapel Hill (UNC-CH)',
327	'institution_url' : 'http://nccoos.unc.edu',
328	'institution_dods_url' : 'http://nccoos.unc.edu',
329	'metadata_url' : 'http://nccoos.unc.edu',
330	'references' : 'http://nccoos.unc.edu',
331	'contact' : 'Sara Haines (haines@email.unc.edu)',
332	#
333	'source' : 'directional wave (acoustic doppler) observation',
334	'history' : 'raw2proc using ' + sensor_info['process_module'],
335	'comment' : 'File created using pycdf'+pycdfVersion()+' and numpy '+pycdfArrayPkg(),
336	# conventions
337	'Conventions' : 'CF-1.0; SEACOOS-CDL-v2.0',
338	# SEACOOS CDL codes
339	'format_category_code' : 'directional waves',
340	'institution_code' : platform_info['institution'],
341	'platform_code' : platform_info['id'],
342	'package_code' : sensor_info['id'],
343	# institution specific
344	'project' : 'North Carolina Coastal Ocean Observing System (NCCOOS)',
345	'project_url' : 'http://nccoos.unc.edu',
346	# timeframe of data contained in file yyyy-mm-dd HH:MM:SS
347	'start_date' : data['dt'][0].strftime("%Y-%m-%d %H:%M:%S"),
348	'end_date' : data['dt'][-1].strftime("%Y-%m-%d %H:%M:%S"),
349	'release_date' : now_dt.strftime("%Y-%m-%d %H:%M:%S"),
350	#
351	'creation_date' : now_dt.strftime("%Y-%m-%d %H:%M:%S"),
352	'modification_date' : now_dt.strftime("%Y-%m-%d %H:%M:%S"),
353	'process_level' : 'level1',
354	#
355	# must type match to data (e.g. fillvalue is real if data is real)
356	'_FillValue' : -99999.,
357	}
358
359	var_atts = {
360	# coordinate variables
361	'time' : {'short_name': 'time',
362	'long_name': 'Time',
363	'standard_name': 'time',
364	'units': 'seconds since 1970-1-1 00:00:00 -0', # UTC
365	'axis': 'T',
366	},
367	'lat' : {'short_name': 'lat',
368	'long_name': 'Latitude',
369	'standard_name': 'latitude',
370	'reference':'geographic coordinates',
371	'units': 'degrees_north',
372	'valid_range':(-90.,90.),
373	'axis': 'Y',
374	},
375	'lon' : {'short_name': 'lon',
376	'long_name': 'Longitude',
377	'standard_name': 'longitude',
378	'reference':'geographic coordinates',
379	'units': 'degrees_east',
380	'valid_range':(-180.,180.),
381	'axis': 'Y',
382	},
383	'z' : {'short_name': 'z',
384	'long_name': 'Height',
385	'standard_name': 'height',
386	'reference':'zero at sea-surface',
387	'units': 'm',
388	'axis': 'Z',
389	},
390	'f' : {'short_name': 'f',
391	'long_name': 'Frequency',
392	'standard_name': 'frequency',
393	'units': 'Hz',
394	},
395	'd' : {'short_name': 'd',
396	'long_name': 'Direction',
397	'standard_name': 'direction',
398	'reference':'clock-wise from True North',
399	'units': 'deg',
400	},
401	# data variables
402	'Sxx' : {'short_name': 'Sxx',
403	'long_name': 'Directional Spectral Density Function',
404	'definition': 'Distribution of the wave energy with both frequency and direction',
405	'standard_name': 'wave_directional_spectral_density',
406	'units': 'm2 Hz-1 deg-1',
407	},
408	'Sf' : {'short_name': 'Sf',
409	'long_name': 'Spectral Density Function',
410	'definition': 'Distribution of the wave energy with frequency from all directions',
411	'standard_name': 'wave_spectral_density',
412	'units': 'm2 Hz-1',
413	},
414	'Stheta' : {'short_name': 'St',
415	'long_name': 'Spectral Density Function',
416	'definition': 'Distribution of the wave energy with direction from all frequencies',
417	'standard_name': 'wave_directional_density',
418	'units': 'm2 deg-1',
419	},
420	'Stheta_swell' : {'short_name': 'Sts',
421	'long_name': 'Swell Spectral Density Function',
422	'definition': 'Distribution of the wave energy with direction from all swell frequencies',
423	'standard_name': 'swell_wave_directional_density',
424	'units': 'm2 deg-1',
425	},
426	'Stheta_wind' : {'short_name': 'Stw',
427	'long_name': 'Wind Spectral Density Function',
428	'definition': 'Distribution of the wave energy with direction from all Wind frequencies',
429	'standard_name': 'wind_wave_directional_density',
430	'units': 'm2 deg-1',
431	},
432	'Hs' : {'short_name': 'Hs',
433	'long_name': 'Significant Wave Height',
434	'definition': 'Four times the square root of the first moment of the wave spectrum (4*sqrt(m0))',
435	'standard_name': 'significant_wave_height',
436	'units': 'm',
437	},
438	'Hs_swell' : {'short_name': 'Hss',
439	'long_name': 'Significant Swell Wave Height',
440	'definition': 'Four times the square root of the first moment of the swell wave spectrum (4*sqrt(m0s))',
441	'standard_name': 'significant_swell_wave_height',
442	'units': 'm',
443	},
444	'Hs_wind' : {'short_name': 'Hsw',
445	'long_name': 'Significant Wind Wave Height',
446	'definition': 'Four times the square root of the first moment of the wind wave spectrum (4*sqrt(m0w))',
447	'standard_name': 'significant_wind_wave_height',
448	'units': 'm',
449	},
450	'Tp' : {'short_name': 'Tp',
451	'long_name': 'Peak Wave Period',
452	'definition': 'Period of strongest wave (Sf maximum)',
453	'standard_name': 'peak_wave_period',
454	'units': 'sec',
455	},
456	'Tp_swell' : {'short_name': 'Tps',
457	'long_name': 'Peak Swell Wave Period',
458	'definition': 'Period of strongest swell (Sfs energy maximum)',
459	'standard_name': 'peak_swell_wave_period',
460	'units': 'sec',
461	},
462	'Tp_wind' : {'short_name': 'Tpw',
463	'long_name': 'Peak Wind Wave Period',
464	'definition': 'Period of strongest wind wave (Sfw energy maximum)',
465	'standard_name': 'peak_wind_wave_period',
466	'units': 'sec',
467	},
468	'Tm' : {'short_name': 'Tm',
469	'long_name': 'Mean Wave Period',
470	'definition': 'Zero-moment of the non-directional spectrum divided by the first-moment (m0/m1)',
471	'standard_name': 'mean_wave_period',
472	'units': 'sec',
473	},
474	'Tm_swell' : {'short_name': 'Tms',
475	'long_name': 'Mean Swell Wave Period',
476	'definition': 'Zero-moment of the non-directional spectrum divided by the first-moment (m0s/m1s)',
477	'standard_name': 'mean_swell_wave_period',
478	'units': 'sec',
479	},
480	'Tm_wind' : {'short_name': 'Tmw',
481	'long_name': 'Mean Wind Wave Period',
482	'definition': 'Zero-moment of the non-directional spectrum divided by the first-moment (m0w/m1w)',
483	'standard_name': 'mean_wind_wave_period',
484	'units': 'sec',
485	},
486	'Dp' : {'short_name': 'Dp',
487	'long_name': 'Peak Wave Direction',
488	'definition': 'Direction from which strongest waves (wave energy) are coming (dir of max(S(Tp,dir)',
489	'standard_name': 'peak_wave_from_direction',
490	'units': 'deg from N',
491	'reference': 'clockwise from True North',
492	},
493	'Dp_swell' : {'short_name': 'Dps',
494	'long_name': 'Peak Swell Wave Direction',
495	'definition': 'Direction from which strongest waves (swell energy) are coming (dir of max(S(Tps,dir)',
496	'standard_name': 'peak_swell_wave_from_direction',
497	'units': 'deg from N',
498	'reference': 'clockwise from True North',
499	},
500	'Dp_wind' : {'short_name': 'Dpw',
501	'long_name': 'Peak Wind Wave Direction',
502	'definition': 'Direction from which strongest waves (wind wave energy) are coming (dir of max(S(Tpw,dir)',
503	'standard_name': 'peak_wind_wave_from_direction',
504	'units': 'deg from N',
505	'reference': 'clockwise from True North',
506	},
507	'Dm' : {'short_name': 'Dm',
508	'long_name': 'Mean Wave Direction',
509	'definition': 'Mean direction from which strongest waves (wave energy max) are coming for all frequencies',
510	'standard_name': 'mean_wave_from_direction',
511	'units': 'deg from N',
512	'reference': 'clockwise from True North',
513	},
514	'Dm_swell' : {'short_name': 'Dms',
515	'long_name': 'Mean Swell Wave Direction',
516	'definition': 'Mean direction from which strongest waves (wave energy max) are coming for swell frequencies',
517	'standard_name': 'mean_swell_wave_from_direction',
518	'units': 'deg from N',
519	'reference': 'clockwise from True North',
520	},
521	'Dm_wind' : {'short_name': 'Dmw',
522	'long_name': 'Mean Wind Wave Direction',
523	'definition': 'Mean direction from which strongest waves (wave energy max) are coming for wind wave frequencies',
524	'standard_name': 'mean_wind_wave_from_direction',
525	'units': 'deg from N',
526	'reference': 'clockwise from True North',
527	},
528	}
529
530
531	# dimension names use tuple so order of initialization is maintained
532	dim_inits = (
533	('ntime', NC.UNLIMITED),
534	('nlat', 1),
535	('nlon', 1),
536	('nz', 1),
537	('nfreq', sensor_info['nfreq']),
538	('ndir', sensor_info['ndir']),
539	)
540
541	# using tuple of tuples so order of initialization is maintained
542	# using dict for attributes order of init not important
543	# use dimension names not values
544	# (varName, varType, (dimName1, [dimName2], ...))
545	var_inits = (
546	# coordinate variables
547	('time', NC.INT, ('ntime',)),
548	('lat', NC.FLOAT, ('nlat',)),
549	('lon', NC.FLOAT, ('nlon',)),
550	('z', NC.FLOAT, ('nz',)),
551	('f', NC.FLOAT, ('nfreq',)),
552	('d', NC.FLOAT, ('ndir',)),
553	# data variables
554	('Sxx', NC.FLOAT, ('ntime','nfreq','ndir')),
555	('Sf', NC.FLOAT, ('ntime','nfreq')),
556	('Stheta', NC.FLOAT, ('ntime','ndir')),
557	('Stheta_swell', NC.FLOAT, ('ntime','ndir')),
558	('Stheta_wind', NC.FLOAT, ('ntime','ndir')),
559	('Hs', NC.FLOAT, ('ntime',)),
560	('Hs_swell', NC.FLOAT, ('ntime',)),
561	('Hs_wind', NC.FLOAT, ('ntime',)),
562	('Tp', NC.FLOAT, ('ntime',)),
563	('Tp_swell', NC.FLOAT, ('ntime',)),
564	('Tp_wind', NC.FLOAT, ('ntime',)),
565	('Tm', NC.FLOAT, ('ntime',)),
566	('Tm_swell', NC.FLOAT, ('ntime',)),
567	('Tm_wind', NC.FLOAT, ('ntime',)),
568	('Dp', NC.FLOAT, ('ntime',)),
569	('Dp_swell', NC.FLOAT, ('ntime',)),
570	('Dp_wind', NC.FLOAT, ('ntime',)),
571	('Dm', NC.FLOAT, ('ntime',)),
572	('Dm_swell', NC.FLOAT, ('ntime',)),
573	('Dm_wind', NC.FLOAT, ('ntime',)),
574	)
575
576	# subset data only to month being processed (see raw2proc.process())
577	i = data['in']
578
579	# var data
580	var_data = (
581	('lat', platform_info['lat']),
582	('lon', platform_info['lon']),
583	('z', 0),
584	('f', data['freqs']),
585	('d', data['dirs']),
586	#
587	('time', data['time'][i]),
588	('Sxx', data['Sxx'][i]),
589	('Sf', data['Sf'][i]),
590	('Stheta', data['Stheta'][i]),
591	('Stheta_swell', data['Stheta_swell'][i]),
592	('Stheta_wind', data['Stheta_wind'][i]),
593	('Hs', data['Hs'][i]),
594	('Hs_swell', data['Hs_swell'][i]),
595	('Hs_wind', data['Hs_wind'][i]),
596	('Tp', data['Tp'][i]),
597	('Tp_swell', data['Tp_swell'][i]),
598	('Tp_wind', data['Tp_wind'][i]),
599	('Tm', data['Tm'][i]),
600	('Tm_swell', data['Tm_swell'][i]),
601	('Tm_wind', data['Tm_wind'][i]),
602	('Dp', data['Dp'][i]),
603	('Dp_swell', data['Dp_swell'][i]),
604	('Dp_wind', data['Dp_wind'][i]),
605	('Dm', data['Dm'][i]),
606	('Dm_swell', data['Tm_swell'][i]),
607	('Dm_wind', data['Tm_wind'][i]),
608	)
609
610	return (global_atts, var_atts, dim_inits, var_inits, var_data)
611
612	def updater(platform_info, sensor_info, data):
613	#
614	global_atts = {
615	# update times of data contained in file (yyyy-mm-dd HH:MM:SS)
616	# last date in monthly file
617	'end_date' : data['dt'][-1].strftime("%Y-%m-%d %H:%M:%S"),
618	'release_date' : now_dt.strftime("%Y-%m-%d %H:%M:%S"),
619	#
620	'modification_date' : now_dt.strftime("%Y-%m-%d %H:%M:%S"),
621	}
622
623	# data variables
624	# update any variable attributes like range, min, max
625	var_atts = {}
626	# var_atts = {
627	# 'u': {'max': max(data.u),
628	# 'min': min(data.v),
629	# },
630	# 'v': {'max': max(data.u),
631	# 'min': min(data.v),
632	# },
633	# }
634
635	# subset data only to month being processed (see raw2proc.process())
636	i = data['in']
637
638	# data
639	var_data = (
640	('time', data['time'][i]),
641	('Sxx', data['Sxx'][i]),
642	('Sf', data['Sf'][i]),
643	('Stheta', data['Stheta'][i]),
644	('Stheta_swell', data['Stheta_swell'][i]),
645	('Stheta_wind', data['Stheta_wind'][i]),
646	('Hs', data['Hs'][i]),
647	('Hs_swell', data['Hs_swell'][i]),
648	('Hs_wind', data['Hs_wind'][i]),
649	('Tp', data['Tp'][i]),
650	('Tp_swell', data['Tp_swell'][i]),
651	('Tp_wind', data['Tp_wind'][i]),
652	('Tm', data['Tm'][i]),
653	('Tm_swell', data['Tm_swell'][i]),
654	('Tm_wind', data['Tm_wind'][i]),
655	('Dp', data['Dp'][i]),
656	('Dp_swell', data['Dp_swell'][i]),
657	('Dp_wind', data['Dp_wind'][i]),
658	('Dm', data['Dm'][i]),
659	('Dm_swell', data['Dm_swell'][i]),
660	('Dm_wind', data['Dm_wind'][i]),
661	)
662
663	return (global_atts, var_atts, var_data)
664
665	#

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