1 |
|
---|
2 |
|
---|
3 |
""" |
---|
4 |
how to parse data, and assert what data and info goes into |
---|
5 |
creating and updating monthly netcdf files |
---|
6 |
|
---|
7 |
CODAR SeaSonde Total Sea Surface Currents (LLUV TOT4) |
---|
8 |
|
---|
9 |
parser : sample date and time from header (%TimeStamp:) |
---|
10 |
table time version (%TableType:) |
---|
11 |
creator : lat, lon, z, time, u(time, lat, lon), v(time, lat, lon), |
---|
12 |
updater : time, u(time, lat, lon), v(time, lat, lon), |
---|
13 |
|
---|
14 |
Check that grid that totals are calculated over has not changed. |
---|
15 |
(%Origin, %GridAxis, %GridAxisType, %GridSpacing all the same) |
---|
16 |
|
---|
17 |
Examples |
---|
18 |
-------- |
---|
19 |
|
---|
20 |
>> (parse, create, update) = load_processors(module_name_without_dot_py) |
---|
21 |
For example, |
---|
22 |
>> (parse, create, update) = load_processors('proc_rdi_logdata_adcp') |
---|
23 |
or |
---|
24 |
>> si = get_config(cn+'.sensor_info') |
---|
25 |
>> (parse, create, update) = load_processors(si['adcp']['proc_module']) |
---|
26 |
|
---|
27 |
Then use the generic name of processor to parse data, create or update |
---|
28 |
monthly output file |
---|
29 |
|
---|
30 |
>> lines = load_data(filename) |
---|
31 |
>> data = parse(platform_info, sensor_info, lines) |
---|
32 |
>> create(platform_info, sensor_info, data) |
---|
33 |
or |
---|
34 |
>> update(platform_info, sensor_info, data) |
---|
35 |
|
---|
36 |
""" |
---|
37 |
|
---|
38 |
from raw2proc import * |
---|
39 |
from procutil import * |
---|
40 |
from ncutil import * |
---|
41 |
|
---|
42 |
now_dt = datetime.utcnow() |
---|
43 |
now_dt.replace(microsecond=0) |
---|
44 |
|
---|
45 |
def parser(platform_info, sensor_info, lines): |
---|
46 |
""" |
---|
47 |
parse and assign data to variables from CODAR Totals LLUV format |
---|
48 |
|
---|
49 |
Notes |
---|
50 |
----- |
---|
51 |
1. Requires grid definition obtained from sensor_info |
---|
52 |
For best coverage of totals, this includes overlapping foot print of HATY, DUCK, LISL and CEDR |
---|
53 |
|
---|
54 |
""" |
---|
55 |
|
---|
56 |
import numpy |
---|
57 |
from datetime import datetime |
---|
58 |
from time import strptime |
---|
59 |
from StringIO import StringIO |
---|
60 |
from matplotlib.mlab import griddata |
---|
61 |
|
---|
62 |
|
---|
63 |
minlat, maxlat = platform_info['lat'] |
---|
64 |
minlon, maxlon = platform_info['lon'] |
---|
65 |
nlat = platform_info['nlat'] |
---|
66 |
nlon = platform_info['nlon'] |
---|
67 |
yi = numpy.linspace(minlat, maxlat, nlat) |
---|
68 |
xi = numpy.linspace(minlon, maxlon, nlon) |
---|
69 |
|
---|
70 |
data = { |
---|
71 |
'dt' : numpy.array(numpy.ones((1,), dtype=object)*numpy.nan), |
---|
72 |
'time' : numpy.array(numpy.ones((1,), dtype=long)*numpy.nan), |
---|
73 |
'lon' : numpy.array(numpy.ones((nlon,), dtype=float)*numpy.nan), |
---|
74 |
'lat' : numpy.array(numpy.ones((nlat,), dtype=float)*numpy.nan), |
---|
75 |
'u' : numpy.array(numpy.ones((1,nlon,nlat), dtype=float)*numpy.nan), |
---|
76 |
'v' : numpy.array(numpy.ones((1,nlon,nlat), dtype=float)*numpy.nan), |
---|
77 |
} |
---|
78 |
|
---|
79 |
sample_dt, ftype, ncol, nrow = (None, None, None, None) |
---|
80 |
|
---|
81 |
m = re.findall(r'^(%.*):\s*(.*)$', ''.join(lines), re.MULTILINE) |
---|
82 |
for k,v in m: |
---|
83 |
if k == '%TimeStamp': |
---|
84 |
sample_dt = scanf_datetime(v, fmt='%Y %m %d %H %M %S') |
---|
85 |
elif k == '%TableType': |
---|
86 |
ftype = v |
---|
87 |
elif k == '%TableColumns': |
---|
88 |
ncol = int(v) |
---|
89 |
elif k == '%TableRows': |
---|
90 |
nrow = int(v) |
---|
91 |
|
---|
92 |
if nrow>2: |
---|
93 |
|
---|
94 |
s = StringIO(''.join(lines)) |
---|
95 |
s.seek(0) |
---|
96 |
d = numpy.loadtxt(s, comments='%') |
---|
97 |
|
---|
98 |
|
---|
99 |
if 'TOT4' in ftype: |
---|
100 |
lon = d[:,0] |
---|
101 |
lat = d[:,1] |
---|
102 |
wu = d[:,2] |
---|
103 |
wv = d[:,3] |
---|
104 |
gridflag = d[:,4] |
---|
105 |
wu_std_qual = d[:,5] |
---|
106 |
wv_std_qual = d[:,6] |
---|
107 |
cov_qual = d[:,7] |
---|
108 |
x_dist = d[:,8] |
---|
109 |
y_dist = d[:,9] |
---|
110 |
rang = d[:,10] |
---|
111 |
bearing = d[:,11] |
---|
112 |
vel_mag = d[:,12] |
---|
113 |
vel_dir = d[:,13] |
---|
114 |
s1 = d[:,14] |
---|
115 |
s2 = d[:,15] |
---|
116 |
s3 = d[:,16] |
---|
117 |
s4 = d[:,17] |
---|
118 |
s5 = d[:,18] |
---|
119 |
s6 = d[:,19] |
---|
120 |
|
---|
121 |
try: |
---|
122 |
uim = griddata(lon, lat, wu, xi, yi) |
---|
123 |
vim = griddata(lon, lat, wv, xi, yi) |
---|
124 |
|
---|
125 |
ui = uim.filled(fill_value=numpy.nan) |
---|
126 |
vi = vim.filled(fill_value=numpy.nan) |
---|
127 |
|
---|
128 |
except IndexError: |
---|
129 |
print "raw2proc: IndexError in griddata() -- skipping data" |
---|
130 |
|
---|
131 |
|
---|
132 |
i = 0 |
---|
133 |
data['dt'][i] = sample_dt |
---|
134 |
data['time'][i] = dt2es(sample_dt) |
---|
135 |
data['lon'] = xi |
---|
136 |
data['lat'] = yi |
---|
137 |
|
---|
138 |
if nrow and nrow>2: |
---|
139 |
|
---|
140 |
data['u'][i] = ui.T |
---|
141 |
data['v'][i] = vi.T |
---|
142 |
|
---|
143 |
return data |
---|
144 |
|
---|
145 |
def creator(platform_info, sensor_info, data): |
---|
146 |
|
---|
147 |
|
---|
148 |
title_str = sensor_info['description']+' at '+ platform_info['location'] |
---|
149 |
global_atts = { |
---|
150 |
'title' : title_str, |
---|
151 |
'institution' : 'University of North Carolina at Chapel Hill (UNC-CH)', |
---|
152 |
'institution_url' : 'http://nccoos.unc.edu', |
---|
153 |
'institution_dods_url' : 'http://nccoos.unc.edu', |
---|
154 |
'metadata_url' : 'http://nccoos.unc.edu', |
---|
155 |
'references' : 'http://nccoos.unc.edu', |
---|
156 |
'contact' : 'Sara Haines (haines@email.unc.edu)', |
---|
157 |
|
---|
158 |
'source' : 'surface current observation', |
---|
159 |
'history' : 'raw2proc using ' + sensor_info['process_module'], |
---|
160 |
'comment' : 'File created using pycdf'+pycdfVersion()+' and numpy '+pycdfArrayPkg(), |
---|
161 |
|
---|
162 |
'Conventions' : 'CF-1.0; SEACOOS-CDL-v2.0', |
---|
163 |
|
---|
164 |
'format_category_code' : 'fixed-map', |
---|
165 |
'institution_code' : platform_info['institution'], |
---|
166 |
'platform_code' : platform_info['id'], |
---|
167 |
'package_code' : sensor_info['id'], |
---|
168 |
|
---|
169 |
'project' : 'North Carolina Coastal Ocean Observing System (NCCOOS)', |
---|
170 |
'project_url' : 'http://nccoos.org', |
---|
171 |
|
---|
172 |
'start_date' : data['dt'][0].strftime("%Y-%m-%d %H:%M:%S"), |
---|
173 |
'end_date' : data['dt'][-1].strftime("%Y-%m-%d %H:%M:%S"), |
---|
174 |
'release_date' : now_dt.strftime("%Y-%m-%d %H:%M:%S"), |
---|
175 |
|
---|
176 |
'creation_date' : now_dt.strftime("%Y-%m-%d %H:%M:%S"), |
---|
177 |
'modification_date' : now_dt.strftime("%Y-%m-%d %H:%M:%S"), |
---|
178 |
'process_level' : 'level1', |
---|
179 |
|
---|
180 |
|
---|
181 |
'_FillValue' : numpy.nan, |
---|
182 |
} |
---|
183 |
|
---|
184 |
var_atts = { |
---|
185 |
|
---|
186 |
'time' : {'short_name': 'time', |
---|
187 |
'long_name': 'Time', |
---|
188 |
'standard_name': 'time', |
---|
189 |
'units': 'seconds since 1970-1-1 00:00:00 -0', |
---|
190 |
'axis': 'T', |
---|
191 |
}, |
---|
192 |
'lat' : {'short_name': 'lat', |
---|
193 |
'long_name': 'Latitude', |
---|
194 |
'standard_name': 'latitude', |
---|
195 |
'reference':'geographic coordinates', |
---|
196 |
'units': 'degrees_north', |
---|
197 |
'valid_range':(-90.,90.), |
---|
198 |
'axis': 'Y', |
---|
199 |
}, |
---|
200 |
'lon' : {'short_name': 'lon', |
---|
201 |
'long_name': 'Longitude', |
---|
202 |
'standard_name': 'longitude', |
---|
203 |
'reference':'geographic coordinates', |
---|
204 |
'units': 'degrees_east', |
---|
205 |
'valid_range':(-180.,180.), |
---|
206 |
'axis': 'Y', |
---|
207 |
}, |
---|
208 |
'z' : {'short_name': 'z', |
---|
209 |
'long_name': 'Height', |
---|
210 |
'standard_name': 'height', |
---|
211 |
'reference':'zero at sea-surface', |
---|
212 |
'units': 'm', |
---|
213 |
'axis': 'Z', |
---|
214 |
}, |
---|
215 |
|
---|
216 |
'u' : {'short_name': 'u', |
---|
217 |
'long_name': 'E/W component of current', |
---|
218 |
'standard_name': 'eastward_current', |
---|
219 |
'units': 'cm sec-1', |
---|
220 |
'reference' : 'clockwise from True East', |
---|
221 |
}, |
---|
222 |
'v' : {'short_name': 'v', |
---|
223 |
'long_name': 'N/S component of current', |
---|
224 |
'standard_name': 'northward_current', |
---|
225 |
'units': 'cm sec-1', |
---|
226 |
'reference' : 'clockwise from True North', |
---|
227 |
}, |
---|
228 |
} |
---|
229 |
|
---|
230 |
|
---|
231 |
|
---|
232 |
dim_inits = ( |
---|
233 |
('ntime', NC.UNLIMITED), |
---|
234 |
('nlat', platform_info['nlat']), |
---|
235 |
('nlon', platform_info['nlon']), |
---|
236 |
('nz', 1), |
---|
237 |
) |
---|
238 |
|
---|
239 |
|
---|
240 |
|
---|
241 |
|
---|
242 |
|
---|
243 |
var_inits = ( |
---|
244 |
|
---|
245 |
('time', NC.INT, ('ntime',)), |
---|
246 |
('lat', NC.FLOAT, ('nlat',)), |
---|
247 |
('lon', NC.FLOAT, ('nlon',)), |
---|
248 |
('z', NC.FLOAT, ('nz',)), |
---|
249 |
|
---|
250 |
('u', NC.FLOAT, ('ntime','nlon','nlat')), |
---|
251 |
('v', NC.FLOAT, ('ntime','nlon','nlat')), |
---|
252 |
) |
---|
253 |
|
---|
254 |
|
---|
255 |
i = data['in'] |
---|
256 |
|
---|
257 |
|
---|
258 |
var_data = ( |
---|
259 |
('lat', data['lat']), |
---|
260 |
('lon', data['lon']), |
---|
261 |
('z', 0.), |
---|
262 |
|
---|
263 |
('time', data['time'][i]), |
---|
264 |
('u', data['u'][i]), |
---|
265 |
('v', data['v'][i]), |
---|
266 |
) |
---|
267 |
|
---|
268 |
return (global_atts, var_atts, dim_inits, var_inits, var_data) |
---|
269 |
|
---|
270 |
def updater(platform_info, sensor_info, data): |
---|
271 |
|
---|
272 |
global_atts = { |
---|
273 |
|
---|
274 |
|
---|
275 |
'end_date' : data['dt'][-1].strftime("%Y-%m-%d %H:%M:%S"), |
---|
276 |
'release_date' : now_dt.strftime("%Y-%m-%d %H:%M:%S"), |
---|
277 |
|
---|
278 |
'modification_date' : now_dt.strftime("%Y-%m-%d %H:%M:%S"), |
---|
279 |
} |
---|
280 |
|
---|
281 |
|
---|
282 |
|
---|
283 |
var_atts = {} |
---|
284 |
|
---|
285 |
|
---|
286 |
|
---|
287 |
|
---|
288 |
|
---|
289 |
|
---|
290 |
|
---|
291 |
|
---|
292 |
|
---|
293 |
|
---|
294 |
i = data['in'] |
---|
295 |
|
---|
296 |
|
---|
297 |
var_data = ( |
---|
298 |
('time', data['time'][i]), |
---|
299 |
('u', data['u'][i]), |
---|
300 |
('v', data['v'][i]), |
---|
301 |
) |
---|
302 |
|
---|
303 |
return (global_atts, var_atts, var_data) |
---|
304 |
|
---|
305 |
|
---|
306 |
|
---|