Revision Log

proc_nortek_wpa_adcp.py

Revision 213 (checked in by haines, 16 years ago)	ncutil--added functions; proc2latest--moved to procutil; configs--added the computed mean_water_depth where measured and mean_water_depth_time_period

Line
1	#!/usr/bin/env python
2	# Last modified: Time-stamp: <2008-10-16 17:02:54 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 current profile data
8
9	parser : sample date and time, currents, water temperature, pressure and water_depth
10
11	creator : lat, lon, z, time, ens, u, v, w, water_depth, water_temp (at tranducer depth), pressure
12	updator : time, ens, u, v, w, water_depth, water_temp (at tranducer depth), pressure
13
14
15	Examples
16	--------
17
18	>> (parse, create, update) = load_processors('proc_rdi_logdata_adcp')
19	or
20	>> si = get_config(cn+'.sensor_info')
21	>> (parse, create, update) = load_processors(si['adcp']['proc_module'])
22
23	>> lines = load_data(filename)
24	>> data = parse(platform_info, sensor_info, lines)
25	>> create(platform_info, sensor_info, data) or
26	>> update(platform_info, sensor_info, data)
27
28	"""
29
30
31	from raw2proc import *
32	from procutil import *
33	from ncutil import *
34
35	import seawater
36
37	now_dt = datetime.utcnow()
38	now_dt.replace(microsecond=0)
39
40	def parser(platform_info, sensor_info, lines):
41	"""
42	parse and assign ocean profile current data from Nortek AWAC ADCP Data
43
44	Notes
45	-----
46	1. This parser requires date/time be parsed from .wap for each to
47	get sig_wave_ht for determining depth of each bin and surface mask
48	and check time same as in .wpa file.
49
50	2. multiple profiles in one file separated by header w/time,
51	pitch, roll, heading, ducer pressure, bottom temp, top bin#
52	bottom bin# (??). The profile data is several lines one for
53	each bin.
54
55	MM DD YYYY HH MM SS ERR STATUS BATT SNDSPD HDG PITCH ROLL PRESS WTEMP ?? ?? TBIN BBIN
56	07 31 2008 23 54 00 0 48 18.2 1525.8 270.1 -2.4 0.2 10.503 21.64 0 0 3 34
57	1 0.9 0.071 24.04 0.029 0.065 -0.058 123 126 124
58	2 1.4 0.089 342.38 -0.027 0.085 -0.057 110 111 113
59	3 1.9 0.065 310.03 -0.050 0.042 -0.063 102 104 104
60	4 2.4 0.063 46.93 0.046 0.043 -0.045 93 95 99
61	5 2.9 0.049 355.33 -0.004 0.049 -0.047 87 89 92
62	...
63	NBIN DEPTH SPEED DIR U V W E1? E2? E3?
64	32 16.4 0.184 331.76 -0.087 0.162 -0.162 26 25 27
65	33 16.9 0.137 288.70 -0.130 0.044 -0.181 26 24 26
66	34 17.4 0.070 32.78 0.038 0.059 -0.248 25 25 26
67
68	3. not sure if depth column is hab or down from surface?
69
70
71	"""
72
73	# get sample datetime from filename
74	fn = sensor_info['fn']
75	sample_dt_start = filt_datetime(fn)[0]
76
77	nbins = sensor_info['nbins'] # Number of bins in data
78	nbursts = len(lines)/(nbins+1)
79
80	data = {
81	'dt' : numpy.array(numpy.ones((nbursts,), dtype=object)*numpy.nan),
82	'time' : numpy.array(numpy.ones((nbursts,), dtype=long)*numpy.nan),
83	'z' : numpy.array(numpy.ones((nbins,), dtype=float)*numpy.nan),
84	'u' : numpy.array(numpy.ones((nbursts,nbins), dtype=float)*numpy.nan),
85	'v' : numpy.array(numpy.ones((nbursts,nbins), dtype=float)*numpy.nan),
86	'w' : numpy.array(numpy.ones((nbursts,nbins), dtype=float)*numpy.nan),
87	'e1' : numpy.array(numpy.ones((nbursts,nbins), dtype=int)*numpy.nan),
88	'e2' : numpy.array(numpy.ones((nbursts,nbins), dtype=int)*numpy.nan),
89	'e3' : numpy.array(numpy.ones((nbursts,nbins), dtype=int)*numpy.nan),
90	'wd' : numpy.array(numpy.ones((nbursts), dtype=float)*numpy.nan),
91	'wl' : numpy.array(numpy.ones((nbursts), dtype=float)*numpy.nan),
92	'water_temp' : numpy.array(numpy.ones((nbursts), dtype=float)*numpy.nan),
93	'pressure' : numpy.array(numpy.ones((nbursts), dtype=float)*numpy.nan),
94	}
95
96	# these items can also be teased out of raw adcp but for now get from config file
97	th = sensor_info['transducer_ht'] # Transducer height above bottom (meters)
98	bh = sensor_info['blanking_ht'] # Blanking height above Transducer (meters)
99	bin_size = sensor_info['bin_size'] # Bin Size (meters)
100
101	# compute height for each bin above the bottom
102	bins = numpy.arange(1,nbins+1)
103	# bin_habs = (bins*bin_size+bin_size/2)+th+bh
104	bin_habs = (bins*bin_size+bin_size/2)+th+bh
105
106	# added by SH -- 15 Oct 2008
107	# raw2proc:ticket:27 adjust bin_habs along beam to nadir
108	# Nortek awac beam angle is fixed at 25 deg
109	# adjustment is cos(25 deg) (which is approx .90*height)
110	bin_habs = (bin_habsnumpy.cos(25.numpy.pi/180))
111	iaboveblank = bin_habs > th+bh+(bin_size)
112
113	# current profile count
114	i = 0
115
116	wpa = []
117	for line in lines:
118	wpa = []
119	# split line and parse float and integers
120	sw = re.split(' ', line)
121	for s in sw:
122	m = re.search(REAL_RE_STR, s)
123	if m:
124	wpa.append(float(m.groups()[0]))
125
126	if len(wpa)==19:
127	# get sample datetime from data
128	sample_str = '%02d-%02d-%4d %02d:%02d:%02d' % tuple(wpa[0:6])
129	if sensor_info['utc_offset']:
130	sample_dt = scanf_datetime(sample_str, fmt='%m-%d-%Y %H:%M:%S') + \
131	timedelta(hours=sensor_info['utc_offset'])
132	else:
133	sample_dt = scanf_datetime(sample_str, fmt='%m-%d-%Y %H:%M:%S')
134
135	# these items can also be teased out of raw adcp but for now get from config file
136	# th = sensor_info['transducer_ht'] # Transducer height above bottom (meters)
137
138	error_code = int(wpa[6])
139	status_code = int(wpa[7])
140	battery_voltage = wpa[8] # volts
141	sound_speed = wpa[9] # m/s
142	heading = wpa[10] # deg
143	pitch = wpa[11] # deg
144	roll = wpa[12] # deg
145
146	pressure = wpa[13] # dbar
147	# pressure (dbar) converted to water depth
148	wd = th + seawater.depth(pressure, platform_info['lat']) # m
149	temperature = wpa[14] # deg C
150
151	start_bin = int(wpa[17]) # first good bin from transducer (?)
152	wpa_nbins = int(wpa[18]) # Number of bins
153	# check this is same as in sensor_info
154
155	# initialize for new profile
156	hab = numpy.ones(nbins)*numpy.nan
157	spd = numpy.ones(nbins)*numpy.nan
158	dir = numpy.ones(nbins)*numpy.nan
159	u = numpy.ones(nbins)*numpy.nan
160	v = numpy.ones(nbins)*numpy.nan
161	w = numpy.ones(nbins)*numpy.nan
162	e1 = numpy.array(numpy.ones((nbins), dtype=int)*numpy.nan)
163	e2 = numpy.array(numpy.ones((nbins), dtype=int)*numpy.nan)
164	e3 = numpy.array(numpy.ones((nbins), dtype=int)*numpy.nan)
165
166	elif len(wpa)==10:
167	# current profile data at each bin
168	bin_number = wpa[0]
169	j = wpa[0]-1
170	hab[j] = wpa[1]
171
172	spd[j] = wpa[2] # m/s
173	dir[j] = wpa[3] # deg N
174
175	u[j] = wpa[4] # m/s
176	v[j] = wpa[5] # m/s
177	w[j] = wpa[6] # m/s
178
179	e1[j] = int(wpa[7]) # echo dB ??
180	e2[j] = int(wpa[8]) #
181	e3[j] = int(wpa[9]) #
182
183	# ibad = (current_spd==-32768) \| (current_dir==-32768)
184	# current_spd[ibad] = numpy.nan
185	# current_dir[ibad] = numpy.nan
186
187	# if done reading profile, just read data for last bin
188	if bin_number==nbins:
189	# compute water mask
190	# if positive is up, in water is less than zero depth
191	bin_depths = (bin_habs)-(wd)
192	iwater = bin_depths+bin_size/2 < 0
193	iwater = iwater*iaboveblank
194
195	# use nominal water depth (MSL) averaged from full pressure record
196	# this should be checked/recalulated every so often
197	z = bin_habs+platform_info['mean_water_depth']
198
199	data['dt'][i] = sample_dt # sample datetime
200	data['time'][i] = dt2es(sample_dt) # sample time in epoch seconds
201	data['z'] = z
202	data['wd'][i] = -1*wd
203	data['wl'][i] = platform_info['mean_water_depth'] - (-1*wd)
204	data['water_temp'][i] = temperature
205	data['pressure'][i] = pressure
206
207	data['u'][i][iwater] = u[iwater]
208	data['v'][i][iwater] = v[iwater]
209	data['w'][i][iwater] = w[iwater]
210
211	data['e1'][i] = e1
212	data['e2'][i] = e2
213	data['e3'][i] = e3
214
215	# ready for next burst
216	i = i+1
217	# if j+1==nbins
218	# if len(wpa)==19 elif ==10
219	# for line
220
221	return data
222
223
224	def creator(platform_info, sensor_info, data):
225	#
226	#
227	title_str = sensor_info['description']+' at '+ platform_info['location']
228
229	if 'mean_water_depth' in platform_info.keys():
230	msl_str = platform_info['mean_water_depth']
231	else:
232	msl_str = 'None'
233	if 'mean_water_depth_time_period' in platform_info.keys():
234	msl_tp_str = platform_info['mean_water_depth_time_period']
235	else:
236	msl_tp_str = 'None'
237
238	global_atts = {
239	'title' : title_str,
240	'institution' : 'University of North Carolina at Chapel Hill (UNC-CH)',
241	'institution_url' : 'http://nccoos.unc.edu',
242	'institution_dods_url' : 'http://nccoos.unc.edu',
243	'metadata_url' : 'http://nccoos.unc.edu',
244	'references' : 'http://nccoos.unc.edu',
245	'contact' : 'Sara Haines (haines@email.unc.edu)',
246	#
247	'source' : 'fixed-profiler (acoustic doppler) observation',
248	'history' : 'raw2proc using ' + sensor_info['process_module'],
249	'comment' : 'File created using pycdf'+pycdfVersion()+' and numpy '+pycdfArrayPkg(),
250	# conventions
251	'Conventions' : 'CF-1.0; SEACOOS-CDL-v2.0',
252	# SEACOOS CDL codes
253	'format_category_code' : 'fixed-profiler',
254	'institution_code' : platform_info['institution'],
255	'platform_code' : platform_info['id'],
256	'package_code' : sensor_info['id'],
257	# institution specific
258	'project' : 'North Carolina Coastal Ocean Observing System (NCCOOS)',
259	'project_url' : 'http://nccoos.unc.edu',
260	# timeframe of data contained in file yyyy-mm-dd HH:MM:SS
261	# first date in monthly file
262	'start_date' : data['dt'][0].strftime("%Y-%m-%d %H:%M:%S"),
263	# last date in monthly file
264	'end_date' : data['dt'][-1].strftime("%Y-%m-%d %H:%M:%S"),
265	'release_date' : now_dt.strftime("%Y-%m-%d %H:%M:%S"),
266	#
267	'mean_water_depth' : msl_str,
268	'mean_water_depth_time_period' : msl_tp_str,
269	#
270	'creation_date' : now_dt.strftime("%Y-%m-%d %H:%M:%S"),
271	'modification_date' : now_dt.strftime("%Y-%m-%d %H:%M:%S"),
272	'process_level' : 'level1',
273	#
274	# must type match to data (e.g. fillvalue is real if data is real)
275	'_FillValue' : numpy.nan,
276	}
277
278	var_atts = {
279	# coordinate variables
280	'time' : {'short_name': 'time',
281	'long_name': 'Time',
282	'standard_name': 'time',
283	'units': 'seconds since 1970-1-1 00:00:00 -0', # UTC
284	'axis': 'T',
285	},
286	'lat' : {'short_name': 'lat',
287	'long_name': 'Latitude',
288	'standard_name': 'latitude',
289	'reference':'geographic coordinates',
290	'units': 'degrees_north',
291	'valid_range':(-90.,90.),
292	'axis': 'Y',
293	},
294	'lon' : {'short_name': 'lon',
295	'long_name': 'Longitude',
296	'standard_name': 'longitude',
297	'reference':'geographic coordinates',
298	'units': 'degrees_east',
299	'valid_range':(-180.,180.),
300	'axis': 'Y',
301	},
302	'z' : {'short_name': 'z',
303	'long_name': 'Height',
304	'standard_name': 'height',
305	'reference':'zero at mean-sea-level',
306	'positive' : 'up',
307	'units': 'm',
308	'axis': 'Z',
309	},
310	# data variables
311	'u': {'short_name' : 'u',
312	'long_name': 'East/West Component of Current',
313	'standard_name': 'eastward_current',
314	'units': 'm s-1',
315	'reference': 'clockwise from True East',
316	},
317	'v': {'short_name' : 'v',
318	'long_name': 'North/South Component of Current',
319	'standard_name': 'northward_current',
320	'units': 'm s-1',
321	'reference': 'clockwise from True North',
322	},
323	'w': {'short_name' : 'w',
324	'long_name': 'Vertical Component of Current',
325	'standard_name': 'upward_current',
326	'units': 'm s-1',
327	'reference': 'clockwise from True North',
328	},
329	'e1': {'short_name' : 'e1',
330	'long_name': 'Echo Beam 1 (??)',
331	'standard_name': 'beam_echo',
332	'units': 'dB',
333	},
334	'e2': {'short_name' : 'e2',
335	'long_name': 'Echo Beam 2 (??)',
336	'standard_name': 'beam_echo',
337	'units': 'dB',
338	},
339	'e3': {'short_name' : 'e3',
340	'long_name': 'Echo Beam 3 (??)',
341	'standard_name': 'beam_echo',
342	'units': 'dB',
343	},
344	'wd': {'short_name': 'wd',
345	'long_name': 'Water Depth',
346	'standard_name': 'water_depth',
347	'reference':'zero at surface',
348	'positive' : 'up',
349	'units': 'm',
350	},
351	'wl': {'short_name': 'wl',
352	'long_name': 'Water Level',
353	'standard_name': 'water_level',
354	'reference':'MSL',
355	'reference_to_MSL' : 0.,
356	'reference_MSL_datum' : platform_info['mean_water_depth'],
357	'reference_MSL_datum_time_period' : platform_info['mean_water_depth_time_period'],
358	'positive' : 'up',
359	'z' : 0.,
360	'units': 'm',
361	},
362	'pressure': {'short_name': 'p',
363	'long_name': 'Pressure',
364	'standard_name': 'pressure',
365	'units': 'dbar',
366	},
367	'water_temp': {'short_name': 'wtemp',
368	'long_name': 'Water Temperature at Transducer',
369	'standard_name': 'water_temperature',
370	'units': 'deg C',
371	},
372	}
373
374
375	# dimension names use tuple so order of initialization is maintained
376	dim_inits = (
377	('ntime', NC.UNLIMITED),
378	('nlat', 1),
379	('nlon', 1),
380	('nz', sensor_info['nbins'])
381	)
382
383	# using tuple of tuples so order of initialization is maintained
384	# using dict for attributes order of init not important
385	# use dimension names not values
386	# (varName, varType, (dimName1, [dimName2], ...))
387	var_inits = (
388	# coordinate variables
389	('time', NC.INT, ('ntime',)),
390	('lat', NC.FLOAT, ('nlat',)),
391	('lon', NC.FLOAT, ('nlon',)),
392	('z', NC.FLOAT, ('nz',)),
393	# data variables
394	('u', NC.FLOAT, ('ntime', 'nz')),
395	('v', NC.FLOAT, ('ntime', 'nz')),
396	('w', NC.FLOAT, ('ntime', 'nz')),
397	('e1', NC.INT, ('ntime', 'nz')),
398	('e2', NC.INT, ('ntime', 'nz')),
399	('e3', NC.INT, ('ntime', 'nz')),
400	('wd', NC.FLOAT, ('ntime',)),
401	('wl', NC.FLOAT, ('ntime',)),
402	('pressure', NC.FLOAT, ('ntime',)),
403	('water_temp', NC.FLOAT, ('ntime',)),
404	)
405
406	# subset data only to month being processed (see raw2proc.process())
407	i = data['in']
408
409	# var data
410	var_data = (
411	('lat', platform_info['lat']),
412	('lon', platform_info['lon']),
413	('z', data['z']),
414	#
415	('time', data['time'][i]),
416	('u', data['u'][i]),
417	('v', data['v'][i]),
418	('w', data['w'][i]),
419	('e1', data['e1'][i]),
420	('e2', data['e2'][i]),
421	('e3', data['e3'][i]),
422	('wd', data['wd'][i]),
423	('wl', data['wl'][i]),
424	('pressure', data['pressure'][i]),
425	('water_temp', data['water_temp'][i]),
426	)
427
428	return (global_atts, var_atts, dim_inits, var_inits, var_data)
429
430	def updater(platform_info, sensor_info, data):
431	#
432	global_atts = {
433	# update times of data contained in file (yyyy-mm-dd HH:MM:SS)
434	# last date in monthly file
435	'end_date' : data['dt'][-1].strftime("%Y-%m-%d %H:%M:%S"),
436	'release_date' : now_dt.strftime("%Y-%m-%d %H:%M:%S"),
437	#
438	'modification_date' : now_dt.strftime("%Y-%m-%d %H:%M:%S"),
439	}
440
441	# data variables
442	# update any variable attributes like range, min, max
443	var_atts = {}
444	# var_atts = {
445	# 'u': {'max': max(data.u),
446	# 'min': min(data.v),
447	# },
448	# 'v': {'max': max(data.u),
449	# 'min': min(data.v),
450	# },
451	# }
452
453	# subset data only to month being processed (see raw2proc.process())
454	i = data['in']
455
456	# data
457	var_data = (
458	('time', data['time'][i]),
459	('u', data['u'][i]),
460	('v', data['v'][i]),
461	('w', data['w'][i]),
462	('e1', data['e1'][i]),
463	('e2', data['e2'][i]),
464	('e3', data['e3'][i]),
465	('wd', data['wd'][i]),
466	('wl', data['wl'][i]),
467	('pressure', data['pressure'][i]),
468	('water_temp', data['water_temp'][i]),
469	)
470
471	return (global_atts, var_atts, var_data)
472	#

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