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proc_remtech_rawdata_pa0.py

Revision 188 (checked in by cbc, 16 years ago)	Saving work.

Line
1	#!/usr/bin/env python
2	"""
3	Parse sodar data and assert what data and info goes into
4	creating and updating monthly netcdf files.
5
6	>> (parse, create, update) = load_processors('proc_rdi_rawdata_sodar')
7	>> data = parse(lines)
8	>> create(platform_info, sensor_info, data)
9	>> update(platform_info, sensor_info, data)
10	"""
11
12	from sodar import rawData
13	import numpy as n
14	from datetime import timedelta
15	from procutil import scanf_datetime, dt2es
16
17	def parser(platform_info, sensor_info, lines):
18	"""
19	Parse and assign wind profile data from raw Sodar file.
20	"""
21
22	rawDataObject = rawdata.RawData('\n'.join(lines))
23
24	numIntervals = len(rawDataObject)
25	numAltitudes = sensor_info[num_altitudes]
26
27	data = {
28	'block' : n.array(n.ones((numIntervals,), dtype=int) * n.nan),
29	'dt' : n.array(n.ones((numIntervals,), dtype=object) * n.nan),
30	'es' : n.array(n.ones((numIntervals,), dtype=long) * n.nan),
31	'val1' : n.array(n.ones((numIntervals,), dtype=int) * n.nan),
32	'val2' : n.array(n.ones((numIntervals,), dtype=int) * n.nan),
33	'val3' : n.array(n.ones((numIntervals,), dtype=int) * n.nan),
34	'val4' : n.array(n.ones((numIntervals,), dtype=int) * n.nan),
35	'spu1' : n.array(n.ones((numIntervals,), dtype=int) * n.nan),
36	'spu2' : n.array(n.ones((numIntervals,), dtype=int) * n.nan),
37	'spu3' : n.array(n.ones((numIntervals,), dtype=int) * n.nan),
38	'spu4' : n.array(n.ones((numIntervals,), dtype=int) * n.nan),
39	'nois1' : n.array(n.ones((numIntervals,), dtype=int) * n.nan),
40	'nois2' : n.array(n.ones((numIntervals,), dtype=int) * n.nan),
41	'nois3' : n.array(n.ones((numIntervals,), dtype=int) * n.nan),
42	'nois4' : n.array(n.ones((numIntervals,), dtype=int) * n.nan),
43	'femax' : n.array(n.ones((numIntervals,), dtype=int) * n.nan),
44	'softw' : n.array(n.ones((numIntervals,), dtype=int) * n.nan),
45	'fe11' : n.array(n.ones((numIntervals,), dtype=int) * n.nan),
46	'fe12' : n.array(n.ones((numIntervals,), dtype=int) * n.nan),
47	'fe21' : n.array(n.ones((numIntervals,), dtype=int) * n.nan),
48	'fe22' : n.array(n.ones((numIntervals,), dtype=int) * n.nan),
49	'snr1' : n.array(n.ones((numIntervals,), dtype=int) * n.nan),
50	'snr2' : n.array(n.ones((numIntervals,), dtype=int) * n.nan),
51	'snr3' : n.array(n.ones((numIntervals,), dtype=int) * n.nan),
52	'snr4' : n.array(n.ones((numIntervals,), dtype=int) * n.nan),
53	'check' : n.array(n.ones((numIntervals,), dtype=int) * n.nan),
54	'jam' : n.array(n.ones((numIntervals,), dtype=int) * n.nan),
55	'z' : n.array(n.ones((numAltitudes,), dtype=float) * n.nan),
56	'u' : n.array(n.ones((numIntervals,
57	numAltitudes), dtype=float) * n.nan),
58	'v' : n.array(n.ones((numIntervals,
59	numAltitudes), dtype=float) * n.nan),
60	'w' : n.array(n.ones((numIntervals,
61	numAltitudes), dtype=float) * n.nan),
62	'echo' : n.array(n.ones((numIntervals,
63	numAltitudes), dtype = int) * n.nan),
64	}
65
66	for sample in rawDataObject:
67	i = rawDataObject.index(sample)
68
69	data['block'][i] = int(sample['BL#'])
70
71	dt = {'month' : int(sample['MONTH']),
72	'day' : int(sample['DAY']),
73	'year' : int(sample['YEAR']),
74	'hour' : int(sample['HOUR']),
75	'min' : int(sample['MIN']),
76	}
77	dt = '%(month)02d-%(day)02d-%(year)04d %(hour)02d:%02d(min)' % dt
78	dt = scanf_datetime(dt, fmt='%m-%d-%Y %H:%M')
79	if sensor_info['utc_offset']:
80	dt = dt + timedelta(hours=sensor_info['utc_offset'])
81	data['dt'][i] = dt
82
83	data['es'][i] = dt2es(dt)
84
85	data['val1'][i] = int(sample['VAL1'])
86	data['val2'][i] = int(sample['VAL2'])
87	data['val3'][i] = int(sample['VAL3'])
88	data['val4'][i] = int(sample['VAL4'])
89
90	data['spu1'][i] = int(sample['SPU1'])
91	data['spu2'][i] = int(sample['SPU2'])
92	data['spu3'][i] = int(sample['SPU3'])
93	data['spu4'][i] = int(sample['SPU4'])
94
95	data['nois1'][i] = int(sample['NOIS1'])
96	data['nois2'][i] = int(sample['NOIS2'])
97	data['nois3'][i] = int(sample['NOIS3'])
98	data['nois4'][i] = int(sample['NOIS4'])
99
100	data['femax'][i] = int(sample['FEMAX'])
101	data['softw'][i] = int(sample['SOFTW'])
102
103	data['fe11'][i] = int(sample['FE11'])
104	data['fe12'][i] = int(sample['FE12'])
105	data['fe21'][i] = int(sample['FE21'])
106	data['fe22'][i] = int(sample['FE22'])
107
108	data['snr1'][i] = int(sample['SNR1'])
109	data['snr2'][i] = int(sample['SNR2'])
110	data['snr3'][i] = int(sample['SNR3'])
111	data['snr4'][i] = int(sample['SNR4'])
112
113	data['check'][i] = int(sample['CHECK'])
114	data['jam'][i] = int(sample['JAM'])
115	return data
116
117	def creator(platform_info, sensor_info, data):
118	#
119	#
120	title_str = sensor_info['description']+' at '+ platform_info['location']
121	global_atts = {
122	'title' : title_str,
123	'institution' : 'Unversity of North Carolina at Chapel Hill (UNC-CH)',
124	'institution_url' : 'http://nccoos.unc.edu',
125	'institution_dods_url' : 'http://nccoos.unc.edu',
126	'metadata_url' : 'http://nccoos.unc.edu',
127	'references' : 'http://nccoos.unc.edu',
128	'contact' : 'Sara Haines (haines@email.unc.edu)',
129	#
130	'source' : 'fixed-profiler (acoustic doppler) observation',
131	'history' : 'Data processed by NCCOOS',
132	'comment' : 'File created using pycdf'+pycdfVersion()+' and numpy '+pycdfArrayPkg(),
133	# conventions
134	'Conventions' : 'CF-1.0; SEACOOS-CDL-v2.0',
135	# SEACOOS CDL codes
136	'format_category_code' : 'fixed-profiler',
137	'institution_code' : platform_info['instituion'],
138	'platform_code' : platform_info['id'],
139	'package_code' : sensor_info['id'],
140	# institution specific
141	'project' : 'North Carolina Coastal Ocean Observing System (NCCOOS)',
142	'project_url' : 'http://nccoos.unc.edu',
143	# timeframe of data contained in file yyyy-mm-dd HH:MM:SS
144	'start_date' : data['sample_dt'].strftime("%Y-%m-%d %H:%M:%S"),
145	'end_date' : data['sample_dt'].strftime("%Y-%m-%d %H:%M:%S"),
146	'release_date' : now.strftime("%Y-%m-%d %H:%M:%S"),
147	#
148	'creation_date' : now.strftime("%Y-%m-%d %H:%M:%S"),
149	'modification_date' : now.strftime("%Y-%m-%d %H:%M:%S"),
150	'process_level' : 'level1',
151	#
152	# must type match to data (e.g. fillvalue is real if data is real)
153	'_FillValue' : -99999.,
154	}
155
156	var_atts = {
157	# coordinate variables
158	'time' : {'short_name': 'time',
159	'long_name': 'Time',
160	'standard_name': 'time',
161	'units': 'seconds since 1970-1-1 00:00:00 -0', # UTC
162	'axis': 'T',
163	},
164	'lat' : {'short_name': 'lat',
165	'long_name': 'Latitude',
166	'standard_name': 'latitude',
167	'reference':'geographic coordinates',
168	'units': 'degrees_north',
169	'valid_range':(-90.,90.),
170	'axis': 'Y',
171	},
172	'lon' : {'short_name': 'lon',
173	'long_name': 'Longtitude',
174	'standard_name': 'longtitude',
175	'reference':'geographic coordinates',
176	'units': 'degrees_east',
177	'valid_range':(-180.,180.),
178	'axis': 'Y',
179	},
180	'z' : {'short_name': 'z',
181	'long_name': 'Height',
182	'standard_name': 'height',
183	'reference':'zero at sea-surface',
184	'units': 'm',
185	'axis': 'Z',
186	},
187	# data variables
188	'u': {'long_name': 'East/West Component of Current',
189	'standard_name': 'eastward_current',
190	'units': 'm s-1',
191	'reference': 'clockwise from True East',
192	},
193	'v': {'long_name': 'North/South Component of Current',
194	'standard_name': 'northward_current',
195	'units': 'm s-1',
196	'reference': 'clockwise from True North',
197	},
198	'w': {'long_name': 'Upward/Downward Component of Current',
199	'standard_name': 'upward_current',
200	'units': 'm s-1',
201	'positive': 'up',
202	},
203	'back_scatter':{'long_name': 'Backscatter',
204	'standard_name': 'back_scatter',
205	'units': 'decibels',
206	},
207	'wtemp': {'long_name': 'Water Temperature',
208	'standard_name': 'water_temperature',
209	'units': 'degrees Celsius',
210	},
211	}
212
213
214	# integer values
215	ntime=NC.UNLIMITED
216	nlat=1
217	nlon=1
218	nz=sensor_info['nbins']
219
220	# dimension names use tuple so order of initialization is maintained
221	dimensions = ('ntime', 'nlat', 'nlon', 'nz')
222
223	# using tuple of tuples so order of initialization is maintained
224	# using dict for attributes order of init not important
225	# use dimension names not values
226	# (varName, varType, (dimName1, [dimName2], ...))
227	var_inits = (
228	# coordinate variables
229	('time', NC.INT, ('ntime',)),
230	('lat', NC.FLOAT, ('nlat',)),
231	('lon', NC.FLOAT, ('nlon',)),
232	('z', NC.FLOAT, ('nz',)),
233	# data variables
234	('u', NC.FLOAT, ('ntime', 'nz')),
235	('v', NC.FLOAT, ('ntime', 'nz')),
236	('w', NC.FLOAT, ('ntime', 'nz')),
237	('back_scatter', NC.FLOAT, ('ntime', 'nz')),
238	('wtemp', NC.FLOAT, ('ntime',)),
239	)
240
241	# var data
242	var_data = (
243	('lat', platform_info['lat']),
244	('lon', platform_info['lon']),
245	('z', []),
246	('u', []),
247	('v', []),
248	('w', []),
249	('back_scatter', []),
250	('wtemp', []),
251	)
252
253	return (global_atts, dimensions, var_inits, var_data)
254
255	def updater(platform_info, sensor_info, data):
256	#
257	global_atts = {
258	# timeframe of data contained in file yyyy-mm-dd HH:MM:SS
259	'end_date' : data['sample_dt'].strftime("%Y-%m-%d %H:%M:%S"),
260	'release_date' : now.strftime("%Y-%m-%d %H:%M:%S"),
261	#
262	'creation_date' : now.strftime("%Y-%m-%d %H:%M:%S"),
263	'modification_date' : now.strftime("%Y-%m-%d %H:%M:%S"),
264	}
265	# var data
266	var_data = (
267	('u', data['u']),
268	('v', data['v']),
269	('w', data['w']),
270	('back_scatter', data['back_scatter']),
271	('wtemp', data['wtemp']),
272	)
273	return (global_atts, var_data)
274	#
275

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