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source: lmdz_wrf/trunk/tools/drawing_tools.py @ 261

Last change on this file since 261 was 254, checked in by lfita, 10 years ago
Adding 'K s-1' in 'units_lunits'
File size: 167.2 KB

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1	# -- coding: iso-8859-15 --
2	#import pylab as plt
3	# From http://stackoverflow.com/questions/13336823/matplotlib-python-error
4	import numpy as np
5	import matplotlib as mpl
6	mpl.use('Agg')
7	import matplotlib.pyplot as plt
8	from mpl_toolkits.basemap import Basemap
9	import os
10	from netCDF4 import Dataset as NetCDFFile
11
12	errormsg = 'ERROR -- error -- ERROR -- error'
13	warnmsg = 'WARNING -- waring -- WARNING -- warning'
14
15	fillValue = 1.e20
16
17	####### Funtions
18	# searchInlist:
19	# datetimeStr_datetime:
20	# dateStr_date:
21	# numVector_String:
22	# timeref_datetime:
23	# slice_variable:
24	# interpolate_locs:
25	# datetimeStr_conversion:
26	# percendone:
27	# netCDFdatetime_realdatetime:
28	# file_nlines:
29	# variables_values:
30	# check_colorBar:
31	# units_lunits:
32	# ASCII_LaTeX:
33	# pretty_int:
34	# DegGradSec_deg:
35	# intT2dt:
36	# lonlat_values:
37	# date_CFtime:
38	# pot_values:
39	# CFtimes_plot:
40	# color_lines:
41	# output_kind:
42	# check_arguments:
43	# Str_Bool:
44	# plot_points:
45	# plot_2Dfield:
46	# plot_2Dfield_easy:
47	# plot_topo_geogrid:
48	# plot_topo_geogrid_boxes:
49	# plot_2D_shadow:
50	# plot_2D_shadow_time: Plotting a 2D field with one of the axes being time
51	# plot_Neighbourghood_evol:Plotting neighbourghood evolution# plot_Trajectories
52	# plot_2D_shadow_contour:
53	# plot_2D_shadow_contour_time:
54	# dxdy_lonlat: Function to provide lon/lat 2D lilke-matrices from any sort of dx,dy values
55	# plot_2D_shadow_line:
56	# plot_lines: Function to plot a collection of lines
57
58	# From nc_var_tools.py
59	def searchInlist(listname, nameFind):
60	""" Function to search a value within a list
61	listname = list
62	nameFind = value to find
63	>>> searInlist(['1', '2', '3', '5'], '5')
64	True
65	"""
66	for x in listname:
67	if x == nameFind:
68	return True
69	return False
70
71	def datetimeStr_datetime(StringDT):
72	""" Function to transform a string date ([YYYY]-[MM]-[DD]_[HH]:[MI]:[SS] format) to a date object
73	>>> datetimeStr_datetime('1976-02-17_00:00:00')
74	1976-02-17 00:00:00
75	"""
76	import datetime as dt
77
78	fname = 'datetimeStr_datetime'
79
80	dateD = np.zeros((3), dtype=int)
81	timeT = np.zeros((3), dtype=int)
82
83	dateD[0] = int(StringDT[0:4])
84	dateD[1] = int(StringDT[5:7])
85	dateD[2] = int(StringDT[8:10])
86
87	trefT = StringDT.find(':')
88	if not trefT == -1:
89	# print ' ' + fname + ': refdate with time!'
90	timeT[0] = int(StringDT[11:13])
91	timeT[1] = int(StringDT[14:16])
92	timeT[2] = int(StringDT[17:19])
93
94	if int(dateD[0]) == 0:
95	print warnmsg
96	print ' ' + fname + ': 0 reference year!! changing to 1'
97	dateD[0] = 1
98
99	newdatetime = dt.datetime(dateD[0], dateD[1], dateD[2], timeT[0], timeT[1], timeT[2])
100
101	return newdatetime
102
103	def dateStr_date(StringDate):
104	""" Function to transform a string date ([YYYY]-[MM]-[DD] format) to a date object
105	>>> dateStr_date('1976-02-17')
106	1976-02-17
107	"""
108	import datetime as dt
109
110	dateD = StringDate.split('-')
111	if int(dateD[0]) == 0:
112	print warnmsg
113	print ' dateStr_date: 0 reference year!! changing to 1'
114	dateD[0] = 1
115	newdate = dt.date(int(dateD[0]), int(dateD[1]), int(dateD[2]))
116	return newdate
117
118	def numVector_String(vec,char):
119	""" Function to transform a vector of numbers to a single string [char] separated
120	numVector_String(vec,char)
121	vec= vector with the numerical values
122	char= single character to split the values
123	>>> print numVector_String(np.arange(10),' ')
124	0 1 2 3 4 5 6 7 8 9
125	"""
126	fname = 'numVector_String'
127
128	if vec == 'h':
129	print fname + '_____________________________________________________________'
130	print numVector_String.__doc__
131	quit()
132
133	Nvals = len(vec)
134
135	string=''
136	for i in range(Nvals):
137	if i == 0:
138	string = str(vec[i])
139	else:
140	string = string + char + str(vec[i])
141
142	return string
143
144	def timeref_datetime(refd, timeval, tu):
145	""" Function to transform from a [timeval] in [tu] units from the time referece [tref] to datetime object
146	refd: time of reference (as datetime object)
147	timeval: time value (as [tu] from [tref])
148	tu: time units
149	>>> timeref = date(1949,12,1,0,0,0)
150	>>> timeref_datetime(timeref, 229784.36, hours)
151	1976-02-17 08:21:36
152	"""
153	import datetime as dt
154	import numpy as np
155
156	## Not in timedelta
157	# if tu == 'years':
158	# realdate = refdate + dt.timedelta(years=float(timeval))
159	# elif tu == 'months':
160	# realdate = refdate + dt.timedelta(months=float(timeval))
161	if tu == 'weeks':
162	realdate = refd + dt.timedelta(weeks=float(timeval))
163	elif tu == 'days':
164	realdate = refd + dt.timedelta(days=float(timeval))
165	elif tu == 'hours':
166	realdate = refd + dt.timedelta(hours=float(timeval))
167	elif tu == 'minutes':
168	realdate = refd + dt.timedelta(minutes=float(timeval))
169	elif tu == 'seconds':
170	realdate = refd + dt.timedelta(seconds=float(timeval))
171	elif tu == 'milliseconds':
172	realdate = refd + dt.timedelta(milliseconds=float(timeval))
173	else:
174	print errormsg
175	print ' timeref_datetime: time units "' + tu + '" not ready!!!!'
176	quit(-1)
177
178	return realdate
179
180	def slice_variable(varobj, dimslice):
181	""" Function to return a slice of a given variable according to values to its
182	dimensions
183	slice_variable(varobj, dims)
184	varobj= object wit the variable
185	dimslice= [[dimname1]:[value1]\|[[dimname2]:[value2], ...] pairs of dimension
186	[value]:
187	* [integer]: which value of the dimension
188	* -1: all along the dimension
189	* [beg]:[end] slice from [beg] to [end]
190	"""
191	fname = 'slice_variable'
192
193	if varobj == 'h':
194	print fname + '_____________________________________________________________'
195	print slice_variable.__doc__
196	quit()
197
198	vardims = varobj.dimensions
199	Ndimvar = len(vardims)
200
201	Ndimcut = len(dimslice.split('\|'))
202	dimsl = dimslice.split('\|')
203
204	varvalsdim = []
205	dimnslice = []
206
207	for idd in range(Ndimvar):
208	for idc in range(Ndimcut):
209	dimcutn = dimsl[idc].split(':')[0]
210	dimcutv = dimsl[idc].split(':')[1]
211	if vardims[idd] == dimcutn:
212	posfrac = dimcutv.find(':')
213	if posfrac != -1:
214	inifrac = int(dimcutv.split(':')[0])
215	endfrac = int(dimcutv.split(':')[1])
216	varvalsdim.append(slice(inifrac,endfrac))
217	dimnslice.append(vardims[idd])
218	else:
219	if int(dimcutv) == -1:
220	varvalsdim.append(slice(0,varobj.shape[idd]))
221	dimnslice.append(vardims[idd])
222	else:
223	varvalsdim.append(int(dimcutv))
224	break
225
226	varvalues = varobj[tuple(varvalsdim)]
227
228	return varvalues, dimnslice
229
230	def interpolate_locs(locs,coords,kinterp):
231	""" Function to provide interpolate locations on a given axis
232	interpolate_locs(locs,axis,kinterp)
233	locs= locations to interpolate
234	coords= axis values with the reference of coordinates
235	kinterp: kind of interpolation
236	'lin': linear
237	>>> coordinates = np.arange((10), dtype=np.float)
238	>>> values = np.array([-1.2, 2.4, 5.6, 7.8, 12.0])
239	>>> interpolate_locs(values,coordinates,'lin')
240	[ -1.2 2.4 5.6 7.8 13. ]
241	>>> coordinates[0] = 0.5
242	>>> coordinates[2] = 2.5
243	>>> interpolate_locs(values,coordinates,'lin')
244	[ -3.4 1.93333333 5.6 7.8 13. ]
245	"""
246
247	fname = 'interpolate_locs'
248
249	if locs == 'h':
250	print fname + '_____________________________________________________________'
251	print interpolate_locs.__doc__
252	quit()
253
254	Nlocs = locs.shape[0]
255	Ncoords = coords.shape[0]
256
257	dcoords = coords[Ncoords-1] - coords[0]
258
259	intlocs = np.zeros((Nlocs), dtype=np.float)
260	minc = np.min(coords)
261	maxc = np.max(coords)
262
263	for iloc in range(Nlocs):
264	for icor in range(Ncoords-1):
265	if locs[iloc] < minc and dcoords > 0.:
266	a = 0.
267	b = 1. / (coords[1] - coords[0])
268	c = coords[0]
269	elif locs[iloc] > maxc and dcoords > 0.:
270	a = (Ncoords-1)*1.
271	b = 1. / (coords[Ncoords-1] - coords[Ncoords-2])
272	c = coords[Ncoords-2]
273	elif locs[iloc] < minc and dcoords < 0.:
274	a = (Ncoords-1)*1.
275	b = 1. / (coords[Ncoords-1] - coords[Ncoords-2])
276	c = coords[Ncoords-2]
277	elif locs[iloc] > maxc and dcoords < 0.:
278	a = 0.
279	b = 1. / (coords[1] - coords[0])
280	c = coords[0]
281	elif locs[iloc] >= coords[icor] and locs[iloc] < coords[icor+1] and dcoords > 0.:
282	a = icor*1.
283	b = 1. / (coords[icor+1] - coords[icor])
284	c = coords[icor]
285	print coords[icor], locs[iloc], coords[icor+1], ':', icor, '->', a, b
286	elif locs[iloc] <= coords[icor] and locs[iloc] > coords[icor+1] and dcoords < 0.:
287	a = icor*1.
288	b = 1. / (coords[icor+1] - coords[icor])
289	c = coords[icor]
290
291	if kinterp == 'lin':
292	intlocs[iloc] = a + (locs[iloc] - c)*b
293	else:
294	print errormsg
295	print ' ' + fname + ": interpolation kind '" + kinterp + "' not ready !!!!!"
296	quit(-1)
297
298	return intlocs
299
300	def datetimeStr_conversion(StringDT,typeSi,typeSo):
301	""" Function to transform a string date to an another date object
302	StringDT= string with the date and time
303	typeSi= type of datetime string input
304	typeSo= type of datetime string output
305	[typeSi/o]
306	'cfTime': [time],[units]; ]time in CF-convention format [units] = [tunits] since [refdate]
307	'matYmdHMS': numerical vector with [[YYYY], [MM], [DD], [HH], [MI], [SS]]
308	'YmdHMS': [YYYY][MM][DD][HH][MI][SS] format
309	'Y-m-d_H:M:S': [YYYY]-[MM]-[DD]_[HH]:[MI]:[SS] format
310	'Y-m-d H:M:S': [YYYY]-[MM]-[DD] [HH]:[MI]:[SS] format
311	'Y/m/d H-M-S': [YYYY]/[MM]/[DD] [HH]-[MI]-[SS] format
312	'WRFdatetime': [Y], [Y], [Y], [Y], '-', [M], [M], '-', [D], [D], '_', [H],
313	[H], ':', [M], [M], ':', [S], [S]
314	>>> datetimeStr_conversion('1976-02-17_08:32:05','Y-m-d_H:M:S','matYmdHMS')
315	[1976 2 17 8 32 5]
316	>>> datetimeStr_conversion(str(137880)+',minutes since 1979-12-01_00:00:00','cfTime','Y/m/d H-M-S')
317	1980/03/05 18-00-00
318	"""
319	import datetime as dt
320
321	fname = 'datetimeStr_conversion'
322
323	if StringDT[0:1] == 'h':
324	print fname + '_____________________________________________________________'
325	print datetimeStr_conversion.__doc__
326	quit()
327
328	if typeSi == 'cfTime':
329	timeval = np.float(StringDT.split(',')[0])
330	tunits = StringDT.split(',')[1].split(' ')[0]
331	Srefdate = StringDT.split(',')[1].split(' ')[2]
332
333	# Does reference date contain a time value [YYYY]-[MM]-[DD] [HH]:[MI]:[SS]
334	##
335	yrref=Srefdate[0:4]
336	monref=Srefdate[5:7]
337	dayref=Srefdate[8:10]
338
339	trefT = Srefdate.find(':')
340	if not trefT == -1:
341	# print ' ' + fname + ': refdate with time!'
342	horref=Srefdate[11:13]
343	minref=Srefdate[14:16]
344	secref=Srefdate[17:19]
345	refdate = datetimeStr_datetime( yrref + '-' + monref + '-' + dayref + \
346	'_' + horref + ':' + minref + ':' + secref)
347	else:
348	refdate = datetimeStr_datetime( yrref + '-' + monref + '-' + dayref + \
349	+ '_00:00:00')
350
351	if tunits == 'weeks':
352	newdate = refdate + dt.timedelta(weeks=float(timeval))
353	elif tunits == 'days':
354	newdate = refdate + dt.timedelta(days=float(timeval))
355	elif tunits == 'hours':
356	newdate = refdate + dt.timedelta(hours=float(timeval))
357	elif tunits == 'minutes':
358	newdate = refdate + dt.timedelta(minutes=float(timeval))
359	elif tunits == 'seconds':
360	newdate = refdate + dt.timedelta(seconds=float(timeval))
361	elif tunits == 'milliseconds':
362	newdate = refdate + dt.timedelta(milliseconds=float(timeval))
363	else:
364	print errormsg
365	print ' timeref_datetime: time units "' + tunits + '" not ready!!!!'
366	quit(-1)
367
368	yr = newdate.year
369	mo = newdate.month
370	da = newdate.day
371	ho = newdate.hour
372	mi = newdate.minute
373	se = newdate.second
374	elif typeSi == 'matYmdHMS':
375	yr = StringDT[0]
376	mo = StringDT[1]
377	da = StringDT[2]
378	ho = StringDT[3]
379	mi = StringDT[4]
380	se = StringDT[5]
381	elif typeSi == 'YmdHMS':
382	yr = int(StringDT[0:4])
383	mo = int(StringDT[4:6])
384	da = int(StringDT[6:8])
385	ho = int(StringDT[8:10])
386	mi = int(StringDT[10:12])
387	se = int(StringDT[12:14])
388	elif typeSi == 'Y-m-d_H:M:S':
389	dateDT = StringDT.split('_')
390	dateD = dateDT[0].split('-')
391	timeT = dateDT[1].split(':')
392	yr = int(dateD[0])
393	mo = int(dateD[1])
394	da = int(dateD[2])
395	ho = int(timeT[0])
396	mi = int(timeT[1])
397	se = int(timeT[2])
398	elif typeSi == 'Y-m-d H:M:S':
399	dateDT = StringDT.split(' ')
400	dateD = dateDT[0].split('-')
401	timeT = dateDT[1].split(':')
402	yr = int(dateD[0])
403	mo = int(dateD[1])
404	da = int(dateD[2])
405	ho = int(timeT[0])
406	mi = int(timeT[1])
407	se = int(timeT[2])
408	elif typeSi == 'Y/m/d H-M-S':
409	dateDT = StringDT.split(' ')
410	dateD = dateDT[0].split('/')
411	timeT = dateDT[1].split('-')
412	yr = int(dateD[0])
413	mo = int(dateD[1])
414	da = int(dateD[2])
415	ho = int(timeT[0])
416	mi = int(timeT[1])
417	se = int(timeT[2])
418	elif typeSi == 'WRFdatetime':
419	yr = int(StringDT[0])1000 + int(StringDT[1])100 + int(StringDT[2])*10 + \
420	int(StringDT[3])
421	mo = int(StringDT[5])*10 + int(StringDT[6])
422	da = int(StringDT[8])*10 + int(StringDT[9])
423	ho = int(StringDT[11])*10 + int(StringDT[12])
424	mi = int(StringDT[14])*10 + int(StringDT[15])
425	se = int(StringDT[17])*10 + int(StringDT[18])
426	else:
427	print errormsg
428	print ' ' + fname + ': type of String input date "' + typeSi + \
429	'" not ready !!!!'
430	quit(-1)
431
432	if typeSo == 'matYmdHMS':
433	dateYmdHMS = np.zeros((6), dtype=int)
434	dateYmdHMS[0] = yr
435	dateYmdHMS[1] = mo
436	dateYmdHMS[2] = da
437	dateYmdHMS[3] = ho
438	dateYmdHMS[4] = mi
439	dateYmdHMS[5] = se
440	elif typeSo == 'YmdHMS':
441	dateYmdHMS = str(yr).zfill(4) + str(mo).zfill(2) + str(da).zfill(2) + \
442	str(ho).zfill(2) + str(mi).zfill(2) + str(se).zfill(2)
443	elif typeSo == 'Y-m-d_H:M:S':
444	dateYmdHMS = str(yr).zfill(4) + '-' + str(mo).zfill(2) + '-' + \
445	str(da).zfill(2) + '_' + str(ho).zfill(2) + ':' + str(mi).zfill(2) + ':' + \
446	str(se).zfill(2)
447	elif typeSo == 'Y-m-d H:M:S':
448	dateYmdHMS = str(yr).zfill(4) + '-' + str(mo).zfill(2) + '-' + \
449	str(da).zfill(2) + ' ' + str(ho).zfill(2) + ':' + str(mi).zfill(2) + ':' + \
450	str(se).zfill(2)
451	elif typeSo == 'Y/m/d H-M-S':
452	dateYmdHMS = str(yr).zfill(4) + '/' + str(mo).zfill(2) + '/' + \
453	str(da).zfill(2) + ' ' + str(ho).zfill(2) + '-' + str(mi).zfill(2) + '-' + \
454	str(se).zfill(2)
455	elif typeSo == 'WRFdatetime':
456	dateYmdHMS = []
457	yM = yr/1000
458	yC = (yr-yM*1000)/100
459	yD = (yr-yM1000-yC100)/10
460	yU = yr-yM1000-yC100-yD*10
461
462	mD = mo/10
463	mU = mo-mD*10
464
465	dD = da/10
466	dU = da-dD*10
467
468	hD = ho/10
469	hU = ho-hD*10
470
471	miD = mi/10
472	miU = mi-miD*10
473
474	sD = se/10
475	sU = se-sD*10
476
477	dateYmdHMS.append(str(yM))
478	dateYmdHMS.append(str(yC))
479	dateYmdHMS.append(str(yD))
480	dateYmdHMS.append(str(yU))
481	dateYmdHMS.append('-')
482	dateYmdHMS.append(str(mD))
483	dateYmdHMS.append(str(mU))
484	dateYmdHMS.append('-')
485	dateYmdHMS.append(str(dD))
486	dateYmdHMS.append(str(dU))
487	dateYmdHMS.append('_')
488	dateYmdHMS.append(str(hD))
489	dateYmdHMS.append(str(hU))
490	dateYmdHMS.append(':')
491	dateYmdHMS.append(str(miD))
492	dateYmdHMS.append(str(miU))
493	dateYmdHMS.append(':')
494	dateYmdHMS.append(str(sD))
495	dateYmdHMS.append(str(sU))
496	else:
497	print errormsg
498	print ' ' + fname + ': type of output date "' + typeSo + '" not ready !!!!'
499	quit(-1)
500
501	return dateYmdHMS
502
503	def percendone(nvals,tot,percen,msg):
504	""" Function to provide the percentage of an action across the matrix
505	nvals=number of values
506	tot=total number of values
507	percen=percentage frequency for which the message is wanted
508	msg= message
509	"""
510	from sys import stdout
511
512	num = int(tot * percen/100)
513	if (nvals%num == 0):
514	print '\r ' + msg + '{0:8.3g}'.format(nvals*100./tot) + ' %',
515	stdout.flush()
516
517	return ''
518
519	def netCDFdatetime_realdatetime(units, tcalendar, times):
520	""" Function to transfrom from netCDF CF-compilant times to real time
521	"""
522	import datetime as dt
523
524	txtunits = units.split(' ')
525	tunits = txtunits[0]
526	Srefdate = txtunits[len(txtunits) - 1]
527
528	# Calendar type
529	##
530	is360 = False
531	if tcalendar is not None:
532	print ' netCDFdatetime_realdatetime: There is a calendar attribute'
533	if tcalendar == '365_day' or tcalendar == 'noleap':
534	print ' netCDFdatetime_realdatetime: No leap years!'
535	isleapcal = False
536	elif tcalendar == 'proleptic_gregorian' or tcalendar == 'standard' or tcalendar == 'gregorian':
537	isleapcal = True
538	elif tcalendar == '360_day':
539	is360 = True
540	isleapcal = False
541	else:
542	print errormsg
543	print ' netCDFdatetime_realdatetime: Calendar "' + tcalendar + '" not prepared!'
544	quit(-1)
545
546	# Does reference date contain a time value [YYYY]-[MM]-[DD] [HH]:[MI]:[SS]
547	##
548	timeval = Srefdate.find(':')
549
550	if not timeval == -1:
551	print ' netCDFdatetime_realdatetime: refdate with time!'
552	refdate = datetimeStr_datetime(Srefdate)
553	else:
554	refdate = dateStr_date(Srefdate + '_00:00:00')
555
556	dimt = len(times)
557	# datetype = type(dt.datetime(1972,02,01))
558	# realdates = np.array(dimt, datetype)
559	# print realdates
560
561	## Not in timedelta
562	# if tunits == 'years':
563	# for it in range(dimt):
564	# realdate = refdate + dt.timedelta(years=float(times[it]))
565	# realdates[it] = int(realdate.year)
566	# elif tunits == 'months':
567	# for it in range(dimt):
568	# realdate = refdate + dt.timedelta(months=float(times[it]))
569	# realdates[it] = int(realdate.year)
570	# realdates = []
571	realdates = np.zeros((dimt, 6), dtype=int)
572	if tunits == 'weeks':
573	for it in range(dimt):
574	realdate = refdate + dt.timedelta(weeks=float(times[it]))
575	realdates[it,:]=[realdate.year, realdate.month, realdate.day, realdate.hour, realdate.minute, realdate.second]
576	elif tunits == 'days':
577	for it in range(dimt):
578	realdate = refdate + dt.timedelta(days=float(times[it]))
579	realdates[it,:]=[realdate.year, realdate.month, realdate.day, realdate.hour, realdate.minute, realdate.second]
580	elif tunits == 'hours':
581	for it in range(dimt):
582	realdate = refdate + dt.timedelta(hours=float(times[it]))
583	# if not isleapcal:
584	# Nleapdays = cal.leapdays(int(refdate.year), int(realdate.year))
585	# realdate = realdate - dt.timedelta(days=Nleapdays)
586	# if is360:
587	# Nyears360 = int(realdate.year) - int(refdate.year) + 1
588	# realdate = realdate -dt.timedelta(days=Nyears360*5)
589	# realdates[it] = realdate
590	# realdates = refdate + dt.timedelta(hours=float(times))
591	realdates[it,:]=[realdate.year, realdate.month, realdate.day, realdate.hour, realdate.minute, realdate.second]
592	elif tunits == 'minutes':
593	for it in range(dimt):
594	realdate = refdate + dt.timedelta(minutes=float(times[it]))
595	realdates[it,:]=[realdate.year, realdate.month, realdate.day, realdate.hour, realdate.minute, realdate.second]
596	elif tunits == 'seconds':
597	for it in range(dimt):
598	realdate = refdate + dt.timedelta(seconds=float(times[it]))
599	realdates[it,:]=[realdate.year, realdate.month, realdate.day, realdate.hour, realdate.minute, realdate.second]
600	elif tunits == 'milliseconds':
601	for it in range(dimt):
602	realdate = refdate + dt.timedelta(milliseconds=float(times[it]))
603	realdates[it,:]=[realdate.year, realdate.month, realdate.day, realdate.hour, realdate.minute, realdate.second]
604	elif tunits == 'microseconds':
605	for it in range(dimt):
606	realdate = refdate + dt.timedelta(microseconds=float(times[it]))
607	realdates[it,:]=[realdate.year, realdate.month, realdate.day, realdate.hour, realdate.minute, realdate.second]
608	else:
609	print errormsg
610	print ' netCDFdatetime_realdatetime: time units "' + tunits + '" is not ready!!!'
611	quit(-1)
612
613	return realdates
614
615	def file_nlines(filen):
616	""" Function to provide the number of lines of a file
617	filen= name of the file
618	>>> file_nlines('trajectory.dat')
619	49
620	"""
621	fname = 'file_nlines'
622
623	if not os.path.isfile(filen):
624	print errormsg
625	print ' ' + fname + ' file: "' + filen + '" does not exist !!'
626	quit(-1)
627
628	fo = open(filen,'r')
629
630	nlines=0
631	for line in fo: nlines = nlines + 1
632
633	fo.close()
634
635	return nlines
636
637	def realdatetime1_CFcompilant(time, Srefdate, tunits):
638	""" Function to transform a matrix with a real time value ([year, month, day,
639	hour, minute, second]) to a netCDF one
640	time= matrix with time
641	Srefdate= reference date ([YYYY][MM][DD][HH][MI][SS] format)
642	tunits= units of time respect to Srefdate
643	>>> realdatetime1_CFcompilant([1976, 2, 17, 8, 20, 0], '19491201000000', 'hours')
644	229784.33333333
645	"""
646
647	import datetime as dt
648	yrref=int(Srefdate[0:4])
649	monref=int(Srefdate[4:6])
650	dayref=int(Srefdate[6:8])
651	horref=int(Srefdate[8:10])
652	minref=int(Srefdate[10:12])
653	secref=int(Srefdate[12:14])
654
655	refdate=dt.datetime(yrref, monref, dayref, horref, minref, secref)
656
657	if tunits == 'weeks':
658	cfdate = dt.datetime(time[0],time[1],time[2],time[3],time[4],time[5])-refdate
659	cfdates = (cfdate.days + cfdate.seconds/(3600.*24.))/7.
660	elif tunits == 'days':
661	cfdate = dt.datetime(time[0],time[1],time[2],time[3],time[4],time[5]) - refdate
662	cfdates = cfdate.days + cfdate.seconds/(3600.*24.)
663	elif tunits == 'hours':
664	cfdate = dt.datetime(time[0],time[1],time[2],time[3],time[4],time[5]) - refdate
665	cfdates = cfdate.days*24. + cfdate.seconds/3600.
666	elif tunits == 'minutes':
667	cfdate = dt.datetime(time[0],time[1],time[2],time[3],time[4],time[5]) - refdate
668	cfdates = cfdate.days24.60. + cfdate.seconds/60.
669	elif tunits == 'seconds':
670	cfdate = dt.datetime(time[0],time[1],time[2],time[3],time[4],time[5]) - refdate
671	cfdates = cfdate.days24.3600. + cfdate.seconds
672	elif tunits == 'milliseconds':
673	cfdate = dt.datetime(time[0],time[1],time[2],time[3],time[4],time[5]) - refdate
674	cfdates = cfdate.days1000.24.3600. + cfdate.seconds1000.
675	elif tunits == 'microseconds':
676	cfdate = dt.datetime(time[0],time[1],time[2],time[3],time[4],times[5]) - refdate
677	cfdates = cfdate.days1000000.24.3600. + cfdate.seconds1000000.
678	else:
679	print errormsg
680	print ' ' + fname + ': time units "' + tunits + '" is not ready!!!'
681	quit(-1)
682
683	return cfdates
684
685	def basicvardef(varobj, vstname, vlname, vunits):
686	""" Function to give the basic attributes to a variable
687	varobj= netCDF variable object
688	vstname= standard name of the variable
689	vlname= long name of the variable
690	vunits= units of the variable
691	"""
692	attr = varobj.setncattr('standard_name', vstname)
693	attr = varobj.setncattr('long_name', vlname)
694	attr = varobj.setncattr('units', vunits)
695
696	return
697
698	def variables_values(varName):
699	""" Function to provide values to plot the different variables
700	variables_values(varName)
701	[varName]= name of the variable
702	return: [var name], [std name], [minimum], [maximum],
703	[long name]('\|' for spaces), [units], [color palette] (following:
704	http://matplotlib.org/1.3.1/examples/color/colormaps_reference.html)
705	[varn]: original name of the variable
706	NOTE: It might be better doing it with an external ASII file. But then we
707	got an extra dependency...
708	>>> variables_values('WRFght')
709	['z', 'geopotential_height', 0.0, 80000.0, 'geopotential\|height', 'm2s-2', 'rainbow']
710	"""
711	fname='variables_values'
712
713	if varName == 'h':
714	print fname + '_____________________________________________________________'
715	print variables_values.__doc__
716	quit()
717
718	# Variable name might come with a statistical surname...
719	stats=['min','max','mean','stdv']
720
721	ifst = False
722	for st in stats:
723	if varName.find(st) > -1:
724	print ' '+ fname + ": varibale '" + varName + "' with a statistical "+\
725	" surname: '",st,"' !!"
726	Lst = len(st)
727	LvarName = len(varName)
728	varn = varName[0:LvarName - Lst]
729	ifst = True
730	break
731	if not ifst:
732	varn = varName
733
734	if varn[0:6] == 'varDIM':
735	# Variable from a dimension (all with 'varDIM' prefix)
736	Lvarn = len(varn)
737	varvals = [varn[6:Lvarn+1], varn[6:Lvarn+1], 0., 1., \
738	"variable\|from\|size\|of\|dimension\|'" + varn[6:Lvarn+1] + "'", '1', 'rainbox']
739	elif varn == 'a_tht' or varn == 'LA_THT':
740	varvals = ['ath', 'total_thermal_plume_cover', 0., 1., \
741	'total\|column\|thermal\|plume\|cover', '1', 'YlGnBu']
742	elif varn == 'bils' or varn == 'LBILS':
743	varvals = ['bils', 'surface_total_heat_flux', -100., 100., \
744	'surface\|total\|heat\|flux', 'Wm-2', 'seismic']
745	elif varn == 'landcat' or varn == 'category':
746	varvals = ['landcat', 'land_categories', 0., 22., 'land\|categories', '1', \
747	'rainbow']
748	elif varn == 'c' or varn == 'QCLOUD' or varn == 'oliq' or varn == 'OLIQ':
749	varvals = ['c', 'condensed_water_mixing_ratio', 0., 3.e-4, \
750	'condensed\|water\|mixing\|ratio', 'kgkg-1', 'BuPu']
751	elif varn == 'ci' or varn == 'iwcon' or varn == 'LIWCON':
752	varvals = ['ci', 'cloud_iced_water_mixing_ratio', 0., 0.0003, \
753	'cloud\|iced\|water\|mixing\|ratio', 'kgkg-1', 'Purples']
754	elif varn == 'cl' or varn == 'lwcon' or varn == 'LLWCON':
755	varvals = ['cl', 'cloud_liquidwater_mixing_ratio', 0., 0.0003, \
756	'cloud\|liquid\|water\|mixing\|ratio', 'kgkg-1', 'Blues']
757	elif varn == 'cld' or varn == 'CLDFRA' or varn == 'rneb' or varn == 'lrneb' or \
758	varn == 'LRNEB':
759	varvals = ['cld', 'cloud_area_fraction', 0., 1., 'cloud\|fraction', '1', \
760	'gist_gray']
761	elif varn == 'cldc' or varn == 'rnebcon' or varn == 'lrnebcon' or \
762	varn == 'LRNEBCON':
763	varvals = ['cldc', 'convective_cloud_area_fraction', 0., 1., \
764	'convective\|cloud\|fraction', '1', 'gist_gray']
765	elif varn == 'cldl' or varn == 'rnebls' or varn == 'lrnebls' or varn == 'LRNEBLS':
766	varvals = ['cldl', 'large_scale_cloud_area_fraction', 0., 1., \
767	'large\|scale\|cloud\|fraction', '1', 'gist_gray']
768	elif varn == 'clt' or varn == 'CLT' or varn == 'cldt' or \
769	varn == 'Total cloudiness':
770	varvals = ['clt', 'cloud_area_fraction', 0., 1., 'total\|cloud\|cover', '1', \
771	'gist_gray']
772	elif varn == 'cll' or varn == 'cldl' or varn == 'LCLDL' or \
773	varn == 'Low-level cloudiness':
774	varvals = ['cll', 'low_level_cloud_area_fraction', 0., 1., \
775	'low\|level\|(p\|>\|680\|hPa)\|cloud\|fraction', '1', 'gist_gray']
776	elif varn == 'clm' or varn == 'cldm' or varn == 'LCLDM' or \
777	varn == 'Mid-level cloudiness':
778	varvals = ['clm', 'mid_level_cloud_area_fraction', 0., 1., \
779	'medium\|level\|(440\|<\|p\|<\|680\|hPa)\|cloud\|fraction', '1', 'gist_gray']
780	elif varn == 'clh' or varn == 'cldh' or varn == 'LCLDH' or \
781	varn == 'High-level cloudiness':
782	varvals = ['clh', 'high_level_cloud_area_fraction', 0., 1., \
783	'high\|level\|(p\|<\|440\|hPa)\|cloud\|fraction', '1', 'gist_gray']
784	elif varn == 'clmf' or varn == 'fbase' or varn == 'LFBASE':
785	varvals = ['clmf', 'cloud_base_max_flux', -0.3, 0.3, 'cloud\|base\|max\|flux', \
786	'kgm-2s-1', 'seismic']
787	elif varn == 'clp' or varn == 'pbase' or varn == 'LPBASE':
788	varvals = ['clp', 'cloud_base_pressure', -0.3, 0.3, 'cloud\|base\|pressure', \
789	'Pa', 'Reds']
790	elif varn == 'cpt' or varn == 'ptconv' or varn == 'LPTCONV':
791	varvals = ['cpt', 'convective_point', 0., 1., 'convective\|point', '1', \
792	'seismic']
793	elif varn == 'dqajs' or varn == 'LDQAJS':
794	varvals = ['dqajs', 'dry_adjustment_water_vapor_tendency', -0.0003, 0.0003, \
795	'dry\|adjustment\|water\|vapor\|tendency', 'kg/kg/s', 'seismic']
796	elif varn == 'dqcon' or varn == 'LDQCON':
797	varvals = ['dqcon', 'convective_water_vapor_tendency', -3e-8, 3.e-8, \
798	'convective\|water\|vapor\|tendency', 'kg/kg/s', 'seismic']
799	elif varn == 'dqdyn' or varn == 'LDQDYN':
800	varvals = ['dqdyn', 'dynamics_water_vapor_tendency', -3.e-7, 3.e-7, \
801	'dynamics\|water\|vapor\|tendency', 'kg/kg/s', 'seismic']
802	elif varn == 'dqeva' or varn == 'LDQEVA':
803	varvals = ['dqeva', 'evaporation_water_vapor_tendency', -3.e-6, 3.e-6, \
804	'evaporation\|water\|vapor\|tendency', 'kg/kg/s', 'seismic']
805	elif varn == 'dqlscst' or varn == 'LDQLSCST':
806	varvals = ['dqlscst', 'stratocumulus_water_vapor_tendency', -3.e-7, 3.e-7, \
807	'stratocumulus\|water\|vapor\|tendency', 'kg/kg/s', 'seismic']
808	elif varn == 'dqlscth' or varn == 'LDQLSCTH':
809	varvals = ['dqlscth', 'thermals_water_vapor_tendency', -3.e-7, 3.e-7, \
810	'thermal\|plumes\|water\|vapor\|tendency', 'kg/kg/s', 'seismic']
811	elif varn == 'dqlsc' or varn == 'LDQLSC':
812	varvals = ['dqlsc', 'condensation_water_vapor_tendency', -3.e-6, 3.e-6, \
813	'condensation\|water\|vapor\|tendency', 'kg/kg/s', 'seismic']
814	elif varn == 'dqphy' or varn == 'LDQPHY':
815	varvals = ['dqphy', 'physics_water_vapor_tendency', -3.e-7, 3.e-7, \
816	'physics\|water\|vapor\|tendency', 'kg/kg/s', 'seismic']
817	elif varn == 'dqthe' or varn == 'LDQTHE':
818	varvals = ['dqthe', 'thermals_water_vapor_tendency', -3.e-7, 3.e-7, \
819	'thermal\|plumes\|water\|vapor\|tendency', 'kg/kg/s', 'seismic']
820	elif varn == 'dqvdf' or varn == 'LDQVDF':
821	varvals = ['dqvdf', 'vertical_difussion_water_vapor_tendency', -3.e-8, 3.e-8,\
822	'vertical\|difussion\|water\|vapor\|tendency', 'kg/kg/s', 'seismic']
823	elif varn == 'dqwak' or varn == 'LDQWAK':
824	varvals = ['dqwak', 'wake_water_vapor_tendency', -3.e-7, 3.e-7, \
825	'wake\|water\|vapor\|tendency', 'kg/kg/s', 'seismic']
826	elif varn == 'dta' or varn == 'tnt' or varn == 'LTNT':
827	varvals = ['dta', 'tendency_air_temperature', -3.e-3, 3.e-3, \
828	'tendency\|of\|air\|temperature', 'K/s', 'seismic']
829	elif varn == 'dtac' or varn == 'tntc' or varn == 'LTNTC':
830	varvals = ['dtac', 'moist_convection_tendency_air_temperature', -3.e-3, \
831	3.e-3, 'moist\|convection\|tendency\|of\|air\|temperature', 'K/s', 'seismic']
832	elif varn == 'dtar' or varn == 'tntr' or varn == 'LTNTR':
833	varvals = ['dtar', 'radiative_heating_tendency_air_temperature', -3.e-3, \
834	3.e-3, 'radiative\|heating\|tendency\|of\|air\|temperature', 'K/s', 'seismic']
835	elif varn == 'dtascpbl' or varn == 'tntscpbl' or varn == 'LTNTSCPBL':
836	varvals = ['dtascpbl', \
837	'stratiform_cloud_precipitation_BL_mixing_tendency_air_temperature', \
838	-3.e-6, 3.e-6, \
839	'stratiform\|cloud\|precipitation\|Boundary\|Layer\|mixing\|tendency\|air\|' +
840	'temperature', 'K/s', 'seismic']
841	elif varn == 'dtajs' or varn == 'LDTAJS':
842	varvals = ['dtajs', 'dry_adjustment_thermal_tendency', -3.e-5, 3.e-5, \
843	'dry\|adjustment\|thermal\|tendency', 'K/s', 'seismic']
844	elif varn == 'dtcon' or varn == 'LDTCON':
845	varvals = ['dtcon', 'convective_thermal_tendency', -3.e-5, 3.e-5, \
846	'convective\|thermal\|tendency', 'K/s', 'seismic']
847	elif varn == 'dtdyn' or varn == 'LDTDYN':
848	varvals = ['dtdyn', 'dynamics_thermal_tendency', -3.e-4, 3.e-4, \
849	'dynamics\|thermal\|tendency', 'K/s', 'seismic']
850	elif varn == 'dteva' or varn == 'LDTEVA':
851	varvals = ['dteva', 'evaporation_thermal_tendency', -3.e-3, 3.e-3, \
852	'evaporation\|thermal\|tendency', 'K/s', 'seismic']
853	elif varn == 'dtlscst' or varn == 'LDTLSCST':
854	varvals = ['dtlscst', 'stratocumulus_thermal_tendency', -3.e-4, 3.e-4, \
855	'stratocumulus\|thermal\|tendency', 'K/s', 'seismic']
856	elif varn == 'dtlscth' or varn == 'LDTLSCTH':
857	varvals = ['dtlscth', 'thermals_thermal_tendency', -3.e-4, 3.e-4, \
858	'thermal\|plumes\|thermal\|tendency', 'K/s', 'seismic']
859	elif varn == 'dtlsc' or varn == 'LDTLSC':
860	varvals = ['dtlsc', 'condensation_thermal_tendency', -3.e-3, 3.e-3, \
861	'condensation\|thermal\|tendency', 'K/s', 'seismic']
862	elif varn == 'dtlwr' or varn == 'LDTLWR':
863	varvals = ['dtlwr', 'long_wave_thermal_tendency', -3.e-3, 3.e-3, \
864	'long\|wave\|radiation\|thermal\|tendency', 'K/s', 'seismic']
865	elif varn == 'dtphy' or varn == 'LDTPHY':
866	varvals = ['dtphy', 'physics_thermal_tendency', -3.e-4, 3.e-4, \
867	'physics\|thermal\|tendency', 'K/s', 'seismic']
868	elif varn == 'dtsw0' or varn == 'LDTSW0':
869	varvals = ['dtsw0', 'cloudy_sky_short_wave_thermal_tendency', -3.e-4, 3.e-4, \
870	'cloudy\|sky\|short\|wave\|radiation\|thermal\|tendency', 'K/s', 'seismic']
871	elif varn == 'dtthe' or varn == 'LDTTHE':
872	varvals = ['dtthe', 'thermals_thermal_tendency', -3.e-4, 3.e-4, \
873	'thermal\|plumes\|thermal\|tendency', 'K/s', 'seismic']
874	elif varn == 'dtvdf' or varn == 'LDTVDF':
875	varvals = ['dtvdf', 'vertical_difussion_thermal_tendency', -3.e-5, 3.e-5, \
876	'vertical\|difussion\|thermal\|tendency', 'K/s', 'seismic']
877	elif varn == 'dtwak' or varn == 'LDTWAK':
878	varvals = ['dtwak', 'wake_thermal_tendency', -3.e-4, 3.e-4, \
879	'wake\|thermal\|tendency', 'K/s', 'seismic']
880	elif varn == 'ducon' or varn == 'LDUCON':
881	varvals = ['ducon', 'convective_eastward_wind_tendency', -3.e-3, 3.e-3, \
882	'convective\|eastward\|wind\|tendency', 'ms-2', 'seismic']
883	elif varn == 'dudyn' or varn == 'LDUDYN':
884	varvals = ['dudyn', 'dynamics_eastward_wind_tendency', -3.e-3, 3.e-3, \
885	'dynamics\|eastward\|wind\|tendency', 'ms-2', 'seismic']
886	elif varn == 'duvdf' or varn == 'LDUVDF':
887	varvals = ['duvdf', 'vertical_difussion_eastward_wind_tendency', -3.e-3, \
888	3.e-3, 'vertical\|difussion\|eastward\|wind\|tendency', 'ms-2', 'seismic']
889	elif varn == 'dvcon' or varn == 'LDVCON':
890	varvals = ['dvcon', 'convective_difussion_northward_wind_tendency', -3.e-3, \
891	3.e-3, 'convective\|northward\|wind\|tendency', 'ms-2', 'seismic']
892	elif varn == 'dvdyn' or varn == 'LDVDYN':
893	varvals = ['dvdyn', 'dynamics_northward_wind_tendency', -3.e-3, \
894	3.e-3, 'dynamics\|difussion\|northward\|wind\|tendency', 'ms-2', 'seismic']
895	elif varn == 'dvvdf' or varn == 'LDVVDF':
896	varvals = ['dvvdf', 'vertical_difussion_northward_wind_tendency', -3.e-3, \
897	3.e-3, 'vertical\|difussion\|northward\|wind\|tendency', 'ms-2', 'seismic']
898	elif varn == 'evspsbl' or varn == 'LEVAP' or varn == 'evap':
899	varvals = ['evspsbl', 'water_evaporation_flux', 0., 1.5e-4, \
900	'water\|evaporation\|flux', 'kgm-2s-1', 'Blues']
901	elif varn == 'g' or varn == 'QGRAUPEL':
902	varvals = ['g', 'grauepl_mixing_ratio', 0., 0.0003, 'graupel\|mixing\|ratio', \
903	'kgkg-1', 'Purples']
904	elif varn == 'h2o' or varn == 'LH2O':
905	varvals = ['h2o', 'water_mass_fraction', 0., 3.e-2, \
906	'mass\|fraction\|of\|water', '1', 'Blues']
907	elif varn == 'h' or varn == 'QHAIL':
908	varvals = ['h', 'hail_mixing_ratio', 0., 0.0003, 'hail\|mixing\|ratio', \
909	'kgkg-1', 'Purples']
910	elif varn == 'hfls' or varn == 'LH' or varn == 'LFLAT' or varn == 'flat':
911	varvals = ['hfls', 'surface_upward_latent_heat_flux', -400., 400., \
912	'upward\|latnt\|heat\|flux\|at\|the\|surface', 'Wm-2', 'seismic']
913	elif varn == 'hfss' or varn == 'LSENS' or varn == 'sens':
914	varvals = ['hfss', 'surface_upward_sensible_heat_flux', -150., 150., \
915	'upward\|sensible\|heat\|flux\|at\|the\|surface', 'Wm-2', 'seismic']
916	elif varn == 'hus' or varn == 'WRFrh' or varn == 'LMDZrh' or varn == 'rhum' or \
917	varn == 'LRHUM':
918	varvals = ['hus', 'specific_humidity', 0., 1., 'specific\|humidty', '1', \
919	'BuPu']
920	elif varn == 'huss' or varn == 'WRFrhs' or varn == 'LMDZrhs' or varn == 'rh2m' or\
921	varn == 'LRH2M':
922	varvals = ['huss', 'specific_humidity', 0., 1., 'specific\|humidty\|at\|2m', \
923	'1', 'BuPu']
924	elif varn == 'i' or varn == 'QICE':
925	varvals = ['i', 'iced_water_mixing_ratio', 0., 0.0003, \
926	'iced\|water\|mixing\|ratio', 'kgkg-1', 'Purples']
927	elif varn == 'lat' or varn == 'XLAT' or varn == 'XLAT_M' or varn == 'latitude':
928	varvals = ['lat', 'latitude', -90., 90., 'latitude', 'degrees North', \
929	'seismic']
930	elif varn == 'lcl' or varn == 's_lcl' or varn == 'ls_lcl' or varn == 'LS_LCL':
931	varvals = ['lcl', 'condensation_level', 0., 2500., 'level\|of\|condensation', \
932	'm', 'Greens']
933	elif varn == 'lambdath' or varn == 'lambda_th' or varn == 'LLAMBDA_TH':
934	varvals = ['lambdath', 'thermal_plume_vertical_velocity', -30., 30., \
935	'thermal\|plume\|vertical\|velocity', 'm/s', 'seismic']
936	elif varn == 'lmaxth' or varn == 'LLMAXTH':
937	varvals = ['lmaxth', 'upper_level_thermals', 0., 100., 'upper\|level\|thermals'\
938	, '1', 'Greens']
939	elif varn == 'lon' or varn == 'XLONG' or varn == 'XLONG_M':
940	varvals = ['lon', 'longitude', -180., 180., 'longitude', 'degrees East', \
941	'seismic']
942	elif varn == 'longitude':
943	varvals = ['lon', 'longitude', 0., 360., 'longitude', 'degrees East', \
944	'seismic']
945	elif varn == 'orog' or varn == 'HGT' or varn == 'HGT_M':
946	varvals = ['orog', 'orography', 0., 3000., 'surface\|altitude', 'm','terrain']
947	elif varn == 'pfc' or varn == 'plfc' or varn == 'LPLFC':
948	varvals = ['pfc', 'pressure_free_convection', 100., 1100., \
949	'pressure\|free\|convection', 'hPa', 'BuPu']
950	elif varn == 'plcl' or varn == 'LPLCL':
951	varvals = ['plcl', 'pressure_lifting_condensation_level', 700., 1100., \
952	'pressure\|lifting\|condensation\|level', 'hPa', 'BuPu']
953	elif varn == 'pr' or varn == 'RAINTOT' or varn == 'precip' or \
954	varn == 'LPRECIP' or varn == 'Precip Totale liq+sol':
955	varvals = ['pr', 'precipitation_flux', 0., 1.e-4, 'precipitation\|flux', \
956	'kgm-2s-1', 'BuPu']
957	elif varn == 'prprof' or varn == 'vprecip' or varn == 'LVPRECIP':
958	varvals = ['prprof', 'precipitation_profile', 0., 1.e-3, \
959	'precipitation\|profile', 'kg/m2/s', 'BuPu']
960	elif varn == 'prprofci' or varn == 'pr_con_i' or varn == 'LPR_CON_I':
961	varvals = ['prprofci', 'precipitation_profile_convective_i', 0., 1.e-3, \
962	'precipitation\|profile\|convective\|i', 'kg/m2/s', 'BuPu']
963	elif varn == 'prprofcl' or varn == 'pr_con_l' or varn == 'LPR_CON_L':
964	varvals = ['prprofcl', 'precipitation_profile_convective_l', 0., 1.e-3, \
965	'precipitation\|profile\|convective\|l', 'kg/m2/s', 'BuPu']
966	elif varn == 'prprofli' or varn == 'pr_lsc_i' or varn == 'LPR_LSC_I':
967	varvals = ['prprofli', 'precipitation_profile_large_scale_i', 0., 1.e-3, \
968	'precipitation\|profile\|large\|scale\|i', 'kg/m2/s', 'BuPu']
969	elif varn == 'prprofll' or varn == 'pr_lsc_l' or varn == 'LPR_LSC_L':
970	varvals = ['prprofll', 'precipitation_profile_large_scale_l', 0., 1.e-3, \
971	'precipitation\|profile\|large\|scale\|l', 'kg/m2/s', 'BuPu']
972	elif varn == 'pracc' or varn == 'ACRAINTOT':
973	varvals = ['pracc', 'precipitation_amount', 0., 100., \
974	'accumulated\|precipitation', 'kgm-2', 'BuPu']
975	elif varn == 'prc' or varn == 'LPLUC' or varn == 'pluc' or varn == 'WRFprc':
976	varvals = ['prc', 'convective_precipitation_flux', 0., 2.e-4, \
977	'convective\|precipitation\|flux', 'kgm-2s-1', 'Blues']
978	elif varn == 'prci' or varn == 'pr_con_i' or varn == 'LPR_CON_I':
979	varvals = ['prci', 'convective_ice_precipitation_flux', 0., 0.003, \
980	'convective\|ice\|precipitation\|flux', 'kgm-2s-1', 'Purples']
981	elif varn == 'prcl' or varn == 'pr_con_l' or varn == 'LPR_CON_L':
982	varvals = ['prcl', 'convective_liquid_precipitation_flux', 0., 0.003, \
983	'convective\|liquid\|precipitation\|flux', 'kgm-2s-1', 'Blues']
984	elif varn == 'pres' or varn == 'presnivs' or varn == 'pressure' or \
985	varn == 'lpres' or varn == 'LPRES':
986	varvals = ['pres', 'air_pressure', 0., 103000., 'air\|pressure', 'Pa', \
987	'Blues']
988	elif varn == 'prls' or varn == 'WRFprls' or varn == 'LPLUL' or varn == 'plul':
989	varvals = ['prls', 'large_scale_precipitation_flux', 0., 2.e-4, \
990	'large\|scale\|precipitation\|flux', 'kgm-2s-1', 'Blues']
991	elif varn == 'prsn' or varn == 'SNOW' or varn == 'snow' or varn == 'LSNOW':
992	varvals = ['prsn', 'snowfall', 0., 1.e-4, 'snowfall\|flux', 'kgm-2s-1', 'BuPu']
993	elif varn == 'prw' or varn == 'WRFprh':
994	varvals = ['prw', 'atmosphere_water_vapor_content', 0., 10., \
995	'water\|vapor"path', 'kgm-2', 'Blues']
996	elif varn == 'ps' or varn == 'psfc' or varn =='PSFC' or varn == 'psol' or \
997	varn == 'Surface Pressure':
998	varvals=['ps', 'surface_air_pressure', 85000., 105400., 'surface\|pressure', \
999	'hPa', 'cool']
1000	elif varn == 'psl' or varn == 'mslp' or varn =='WRFmslp':
1001	varvals=['psl', 'air_pressure_at_sea_level', 85000., 104000., \
1002	'mean\|sea\|level\|pressure', 'Pa', 'Greens']
1003	elif varn == 'qth' or varn == 'q_th' or varn == 'LQ_TH':
1004	varvals = ['qth', 'thermal_plume_total_water_content', 0., 25., \
1005	'total\|water\|cotent\|in\|thermal\|plume', 'mm', 'YlOrRd']
1006	elif varn == 'r' or varn == 'QVAPOR' or varn == 'ovap' or varn == 'LOVAP':
1007	varvals = ['r', 'water_mixing_ratio', 0., 0.03, 'water\|mixing\|ratio', \
1008	'kgkg-1', 'BuPu']
1009	elif varn == 'rsds' or varn == 'SWdnSFC' or varn == 'SWdn at surface' or \
1010	varn == 'SWDOWN':
1011	varvals=['rsds', 'surface_downwelling_shortwave_flux_in_air', 0., 1200., \
1012	'downward\|SW\|surface\|radiation', 'Wm-2' ,'Reds']
1013	elif varn == 'rsdsacc':
1014	varvals=['rsdsacc', 'accumulated_surface_downwelling_shortwave_flux_in_air', \
1015	0., 1200., 'accumulated\|downward\|SW\|surface\|radiation', 'Wm-2' ,'Reds']
1016	elif varn == 'rvor' or varn == 'WRFrvor':
1017	varvals = ['rvor', 'air_relative_vorticity', -2.5E-3, 2.5E-3, \
1018	'air\|relative\|vorticity', 's-1', 'seismic']
1019	elif varn == 'rvors' or varn == 'WRFrvors':
1020	varvals = ['rvors', 'surface_air_relative_vorticity', -2.5E-3, 2.5E-3, \
1021	'surface\|air\|relative\|vorticity', 's-1', 'seismic']
1022	elif varn == 's' or varn == 'QSNOW':
1023	varvals = ['s', 'snow_mixing_ratio', 0., 0.0003, 'snow\|mixing\|ratio', \
1024	'kgkg-1', 'Purples']
1025	elif varn == 'stherm' or varn == 'LS_THERM':
1026	varvals = ['stherm', 'thermals_excess', 0., 0.8, 'thermals\|excess', 'K', \
1027	'Reds']
1028	elif varn == 'ta' or varn == 'WRFt' or varn == 'temp' or varn == 'LTEMP' or \
1029	varn == 'Air temperature':
1030	varvals = ['ta', 'air_temperature', 195., 320., 'air\|temperature', 'K', \
1031	'YlOrRd']
1032	elif varn == 'tah' or varn == 'theta' or varn == 'LTHETA':
1033	varvals = ['tah', 'potential_air_temperature', 195., 320., \
1034	'potential\|air\|temperature', 'K', 'YlOrRd']
1035	elif varn == 'tas' or varn == 'T2' or varn == 't2m' or varn == 'T2M' or \
1036	varn == 'Temperature 2m':
1037	varvals = ['tas', 'air_temperature', 240., 310., 'air\|temperature\|at\|2m', ' \
1038	K', 'YlOrRd']
1039	elif varn == 'tds' or varn == 'TH2':
1040	varvals = ['tds', 'air_dew_point_temperature', 240., 310., \
1041	'air\|dew\|point\|temperature\|at\|2m', 'K', 'YlGnBu']
1042	elif varn == 'tke' or varn == 'TKE' or varn == 'tke' or varn == 'LTKE':
1043	varvals = ['tke', 'turbulent_kinetic_energy', 0., 0.003, \
1044	'turbulent\|kinetic\|energy', 'm2/s2', 'Reds']
1045	elif varn == 'time'or varn == 'time_counter':
1046	varvals = ['time', 'time', 0., 1000., 'time', \
1047	'hours\|since\|1949/12/01\|00:00:00', 'Reds']
1048	elif varn == 'tmla' or varn == 's_pblt' or varn == 'LS_PBLT':
1049	varvals = ['tmla', 'atmosphere_top_boundary_layer_temperature', 250., 330., \
1050	'atmosphere\|top\|boundary\|layer\|temperature', 'K', 'Reds']
1051	elif varn == 'ua' or varn == 'vitu' or varn == 'U' or varn == 'Zonal wind' or \
1052	varn == 'LVITU':
1053	varvals = ['ua', 'eastward_wind', -30., 30., 'eastward\|wind', 'ms-1', \
1054	'seismic']
1055	elif varn == 'uas' or varn == 'u10m' or varn == 'U10' or varn =='Vent zonal 10m':
1056	varvals = ['uas', 'eastward_wind', -30., 30., 'eastward\|2m\|wind', \
1057	'ms-1', 'seismic']
1058	elif varn == 'va' or varn == 'vitv' or varn == 'V' or varn == 'Meridional wind' \
1059	or varn == 'LVITV':
1060	varvals = ['va', 'northward_wind', -30., 30., 'northward\|wind', 'ms-1', \
1061	'seismic']
1062	elif varn == 'vas' or varn == 'v10m' or varn == 'V10' or \
1063	varn =='Vent meridien 10m':
1064	varvals = ['vas', 'northward_wind', -30., 30., 'northward\|2m\|wind', 'ms-1', \
1065	'seismic']
1066	elif varn == 'wakedeltaq' or varn == 'wake_deltaq' or varn == 'lwake_deltaq' or \
1067	varn == 'LWAKE_DELTAQ':
1068	varvals = ['wakedeltaq', 'wake_delta_vapor', -0.003, 0.003, \
1069	'wake\|delta\|mixing\|ratio', '-', 'seismic']
1070	elif varn == 'wakedeltat' or varn == 'wake_deltat' or varn == 'lwake_deltat' or \
1071	varn == 'LWAKE_DELTAT':
1072	varvals = ['wakedeltat', 'wake_delta_temp', -0.003, 0.003, \
1073	'wake\|delta\|temperature', '-', 'seismic']
1074	elif varn == 'wakeh' or varn == 'wake_h' or varn == 'LWAKE_H':
1075	varvals = ['wakeh', 'wake_height', 0., 1000., 'height\|of\|the\|wakes', 'm', \
1076	'YlOrRd']
1077	elif varn == 'wakeomg' or varn == 'wake_omg' or varn == 'lwake_omg' or \
1078	varn == 'LWAKE_OMG':
1079	varvals = ['wakeomg', 'wake_omega', 0., 3., 'wake\|omega', \
1080	'-', 'BuGn']
1081	elif varn == 'wakes' or varn == 'wake_s' or varn == 'LWAKE_S':
1082	varvals = ['wakes', 'wake_area_fraction', 0., 0.5, 'wake\|spatial\|fraction', \
1083	'1', 'BuGn']
1084	elif varn == 'wa' or varn == 'W' or varn == 'Vertical wind':
1085	varvals = ['wa', 'upward_wind', -10., 10., 'upward\|wind', 'ms-1', \
1086	'seismic']
1087	elif varn == 'wap' or varn == 'vitw' or varn == 'LVITW':
1088	varvals = ['wap', 'upward_wind', -3.e-10, 3.e-10, 'upward\|wind', 'mPa-1', \
1089	'seismic']
1090	elif varn == 'wss' or varn == 'SPDUV':
1091	varvals = ['wss', 'air_velocity', 0., 30., 'surface\|horizontal\|wind\|speed', \
1092	'ms-1', 'Reds']
1093	elif varn == 'xtime' or varn == 'XTIME':
1094	varvals = ['xtime', 'time', 0., 1.e5, 'time', \
1095	'minutes\|since\|simulation\|start', 'Reds']
1096	elif varn == 'x' or varn == 'X':
1097	varvals = ['x', 'x', 0., 100., 'x', '-', 'Reds']
1098	elif varn == 'y' or varn == 'Y':
1099	varvals = ['y', 'y', 0., 100., 'y', '-', 'Blues']
1100	elif varn == 'z' or varn == 'Z':
1101	varvals = ['z', 'z', 0., 100., 'z', '-', 'Greens']
1102	elif varn == 'zg' or varn == 'WRFght' or varn == 'Geopotential height' or \
1103	varn == 'geop' or varn == 'LGEOP':
1104	varvals = ['zg', 'geopotential_height', 0., 80000., 'geopotential\|height', \
1105	'm2s-2', 'rainbow']
1106	elif varn == 'zmaxth' or varn == 'zmax_th' or varn == 'LZMAX_TH':
1107	varvals = ['zmaxth', 'thermal_plume_height', 0., 4000., \
1108	'maximum\|thermals\|plume\|height', 'm', 'YlOrRd']
1109	elif varn == 'zmla' or varn == 's_pblh' or varn == 'LS_PBLH':
1110	varvals = ['zmla', 'atmosphere_boundary_layer_thickness', 0., 2500., \
1111	'atmosphere\|boundary\|layer\|thickness', 'm', 'Blues']
1112	else:
1113	print errormsg
1114	print ' ' + fname + ': variable ' + varn + ' not defined !!!'
1115	quit(-1)
1116
1117	return varvals
1118
1119
1120	####### ####### ####### ####### ####### ####### ####### ####### ####### #######
1121
1122	def check_colorBar(cbarn):
1123	""" Check if the given colorbar exists in matplotlib
1124	"""
1125	fname = 'check_colorBar'
1126
1127	# Possible color bars
1128	colorbars = ['binary', 'Blues', 'BuGn', 'BuPu', 'gist_yarg', 'GnBu', 'Greens', \
1129	'Greys', 'Oranges', 'OrRd', 'PuBu', 'PuBuGn', 'PuRd', 'Purples', 'RdPu', \
1130	'Reds', 'YlGn', 'YlGnBu', 'YlOrBr', 'YlOrRd', 'afmhot', 'autumn', 'bone', \
1131	'cool', 'copper', 'gist_gray', 'gist_heat', 'gray', 'hot', 'pink', 'spring', \
1132	'summer', 'winter', 'BrBG', 'bwr', 'coolwarm', 'PiYG', 'PRGn', 'PuOr', 'RdBu', \
1133	'RdGy', 'RdYlBu', 'RdYlGn', 'seismic', 'Accent', 'Dark2', 'hsv', 'Paired', \
1134	'Pastel1', 'Pastel2', 'Set1', 'Set2', 'Set3', 'spectral', 'gist_earth', \
1135	'gist_ncar', 'gist_rainbow', 'gist_stern', 'jet', 'brg', 'CMRmap', 'cubehelix',\
1136	'gnuplot', 'gnuplot2', 'ocean', 'rainbow', 'terrain', 'flag', 'prism']
1137
1138	if not searchInlist(colorbars,cbarn):
1139	print warnmsg
1140	print ' ' + fname + ' color bar: "' + cbarn + '" does not exist !!'
1141	print ' a standard one will be use instead !!'
1142
1143	return
1144
1145	def units_lunits(u):
1146	""" Fucntion to provide LaTeX equivalences from a given units
1147	u= units to transform
1148	>>> units_lunits('kgkg-1')
1149	'$kgkg^{-1}$'
1150	"""
1151	fname = 'units_lunits'
1152
1153	if u == 'h':
1154	print fname + '_____________________________________________________________'
1155	print units_lunits.__doc__
1156	quit()
1157
1158	# Units which does not change
1159	same = ['1', 'category', 'day', 'degrees East', 'degrees Nord', 'degrees North', \
1160	'g', 'hour', 'hPa', 'K', 'Km', 'kg', 'km', 'm', 'minute', 'mm', 'month', 'Pa', \
1161	's', 'second', 'um', 'year', '-']
1162
1163	if searchInlist(same,u):
1164	lu = '$' + u + '$'
1165	elif len(u.split(' ')) > 1 and u.split(' ')[1] == 'since':
1166	uparts = u.split(' ')
1167	ip=0
1168	for up in uparts:
1169	if ip == 0:
1170	lu = '$' + up
1171	else:
1172	lu = lu + '\ ' + up
1173	ip=ip+1
1174	lu = lu + '$'
1175	else:
1176	if u == '': lu='-'
1177	elif u == 'C': lu='$^{\circ}C$'
1178	elif u == 'days': lu='$day$'
1179	elif u == 'degrees_east': lu='$degrees\ East$'
1180	elif u == 'degree_east': lu='$degrees\ East$'
1181	elif u == 'degrees longitude': lu='$degrees\ East$'
1182	elif u == 'degrees latitude': lu='$degrees\ North$'
1183	elif u == 'degrees_north': lu='$degrees\ North$'
1184	elif u == 'degree_north': lu='$degrees\ North$'
1185	elif u == 'deg C': lu='$^{\circ}C$'
1186	elif u == 'degC': lu='$^{\circ}C$'
1187	elif u == 'deg K': lu='$K$'
1188	elif u == 'degK': lu='$K$'
1189	elif u == 'hours': lu='$hour$'
1190	elif u == 'J/kg': lu='$Jkg^{-1}$'
1191	elif u == 'Jkg-1': lu='$Jkg^{-1}$'
1192	elif u == 'K/m': lu='$Km^{-1}$'
1193	elif u == 'Km-1': lu='$Km^{-1}$'
1194	elif u == 'K/s': lu='$Ks^{-1}$'
1195	elif u == 'Ks-1': lu='$Ks^{-1}$'
1196	elif u == 'K s-1': lu='$Ks^{-1}$'
1197	elif u == 'kg/kg': lu='$kgkg^{-1}$'
1198	elif u == 'kgkg-1': lu='$kgkg^{-1}$'
1199	elif u == 'kg kg-1': lu='$kgkg^{-1}$'
1200	elif u == '(kg/kg)/s': lu='$kgkg^{-1}s^{-1}$'
1201	elif u == 'kgkg-1s-1': lu='$kgkg^{-1}s^{-1}$'
1202	elif u == 'kg/m2': lu='$kgm^{-2}$'
1203	elif u == 'kgm-2': lu='$kgm^{-2}$'
1204	elif u == 'kg m-2': lu='$kgm^{-2}$'
1205	elif u == 'Kg m-2': lu='$kgm^{-2}$'
1206	elif u == 'kg/m2/s': lu='$kgm^{-2}s^{-1}$'
1207	elif u == 'kg/(m2*s)': lu='$kgm^{-2}s^{-1}$'
1208	elif u == 'kg/(s*m2)': lu='$kgm^{-2}s^{-1}$'
1209	elif u == 'kgm-2s-1': lu='$kgm^{-2}s^{-1}$'
1210	elif u == '1/m': lu='$m^{-1}$'
1211	elif u == 'm-1': lu='$m^{-1}$'
1212	elif u == 'm2/s': lu='$m2s^{-1}$'
1213	elif u == 'm2s-1': lu='$m2s^{-1}$'
1214	elif u == 'm2/s2': lu='$m2s^{-2}$'
1215	elif u == 'm/s': lu='$ms^{-1}$'
1216	elif u == 'mmh-3': lu='$mmh^{-3}$'
1217	elif u == 'ms-1': lu='$ms^{-1}$'
1218	elif u == 'm s-1': lu='$ms^{-1}$'
1219	elif u == 'm/s2': lu='$ms^{-2}$'
1220	elif u == 'ms-2': lu='$ms^{-2}$'
1221	elif u == 'minutes': lu='$minute$'
1222	elif u == 'Pa/s': lu='$Pas^{-1}$'
1223	elif u == 'Pas-1': lu='$Pas^{-1}$'
1224	elif u == 'W m-2': lu='$Wm^{-2}$'
1225	elif u == 'Wm-2': lu='$Wm^{-2}$'
1226	elif u == 'W/m2': lu='$Wm^{-2}$'
1227	elif u == '1/s': lu='$s^{-1}$'
1228	elif u == 's-1': lu='$s^{-1}$'
1229	elif u == 'seconds': lu='$second$'
1230	elif u == '%': lu='\%'
1231	else:
1232	print errormsg
1233	print ' ' + fname + ': units "' + u + '" not ready!!!!'
1234	quit(-1)
1235
1236	return lu
1237
1238	def ASCII_LaTeX(ln):
1239	""" Function to transform from an ASCII line to LaTeX codification
1240	>>> ASCII_LaTeX('Laboratoire de MÃ©tÃ©orologie Dynamique perÃ² HovmÃ¶ller')
1241	Laboratoire de M\'et\'eorologie Dynamique per\`o Hovm\"oller
1242	"""
1243	fname='ASCII_LaTeX'
1244
1245	if ln == 'h':
1246	print fname + '_____________________________________________________________'
1247	print ASCII_LaTeX.__doc__
1248	quit()
1249
1250	newln = ln.replace('\\', '\\textbackslash')
1251
1252	newln = newln.replace('Ã¡', "\\'a")
1253	newln = newln.replace('Ã©', "\\'e")
1254	newln = newln.replace('Ã', "\\'i")
1255	newln = newln.replace('Ã³', "\\'o")
1256	newln = newln.replace('Ãº', "\\'u")
1257
1258	newln = newln.replace('Ã ', "\\`a")
1259	newln = newln.replace('Ãš', "\\`e")
1260	newln = newln.replace('Ã¬', "\\`i")
1261	newln = newln.replace('Ã²', "\\`o")
1262	newln = newln.replace('Ã¹', "\\`u")
1263
1264	newln = newln.replace('Ã¢', "\\^a")
1265	newln = newln.replace('Ãª', "\\^e")
1266	newln = newln.replace('Ã®', "\\^i")
1267	newln = newln.replace('ÃŽ', "\\^o")
1268	newln = newln.replace('Ã»', "\\^u")
1269
1270	newln = newln.replace('Ã€', '\\"a')
1271	newln = newln.replace('Ã«', '\\"e')
1272	newln = newln.replace('Ã¯', '\\"i')
1273	newln = newln.replace('Ã¶', '\\"o')
1274	newln = newln.replace('ÃŒ', '\\"u')
1275
1276	newln = newln.replace('Ã§', '\c{c}')
1277	newln = newln.replace('Ã±', '\~{n}')
1278
1279	newln = newln.replace('Ã', "\\'A")
1280	newln = newln.replace('Ã', "\\'E")
1281	newln = newln.replace('Ã', "\\'I")
1282	newln = newln.replace('Ã', "\\'O")
1283	newln = newln.replace('Ã', "\\'U")
1284
1285	newln = newln.replace('Ã', "\\`A")
1286	newln = newln.replace('Ã', "\\`E")
1287	newln = newln.replace('Ã', "\\`I")
1288	newln = newln.replace('Ã', "\\`O")
1289	newln = newln.replace('Ã', "\\`U")
1290
1291	newln = newln.replace('Ã', "\\^A")
1292	newln = newln.replace('Ã', "\\^E")
1293	newln = newln.replace('Ã', "\\^I")
1294	newln = newln.replace('Ã', "\\^O")
1295	newln = newln.replace('Ã', "\\^U")
1296
1297	newln = newln.replace('Ã', '\\"A')
1298	newln = newln.replace('Ã', '\\"E')
1299	newln = newln.replace('Ã', '\\"I')
1300	newln = newln.replace('Ã', '\\"O')
1301	newln = newln.replace('Ã', '\\"U')
1302
1303	newln = newln.replace('Ã', '\\c{C}')
1304	newln = newln.replace('Ã', '\\~{N}')
1305
1306	newln = newln.replace('Â¡', '!`')
1307	newln = newln.replace('Â¿', 'Â¿`')
1308	newln = newln.replace('%', '\\%')
1309	newln = newln.replace('#', '\\#')
1310	newln = newln.replace('&', '\\&')
1311	newln = newln.replace('$', '\\$')
1312	newln = newln.replace('_', '\\_')
1313	newln = newln.replace('Â·', '\\textperiodcentered')
1314	newln = newln.replace('<', '$<$')
1315	newln = newln.replace('>', '$>$')
1316	newln = newln.replace('ï', '*')
1317	# newln = newln.replace('Âº', '$^{\\circ}$')
1318	newln = newln.replace('Âª', '$^{a}$')
1319	newln = newln.replace('Âº', '$^{o}$')
1320	newln = newln.replace('Â°', '$^{\\circ}$')
1321	newln = newln.replace('\n', '\\\\\n')
1322	newln = newln.replace('\t', '\\medskip')
1323
1324	return newln
1325
1326	def pretty_int(minv,maxv,Nint):
1327	""" Function to plot nice intervals
1328	minv= minimum value
1329	maxv= maximum value
1330	Nint= number of intervals
1331	>>> pretty_int(23.50,67.21,5)
1332	[ 25. 30. 35. 40. 45. 50. 55. 60. 65.]
1333	>>> pretty_int(-23.50,67.21,15)
1334	[ 0. 20. 40. 60.]
1335	pretty_int(14.75,25.25,5)
1336	[ 16. 18. 20. 22. 24.]
1337	"""
1338	fname = 'pretty_int'
1339	nice_int = [1,2,5]
1340
1341	# print 'minv: ',minv,'maxv:',maxv,'Nint:',Nint
1342
1343	interval = np.abs(maxv - minv)
1344
1345	potinterval = np.log10(interval)
1346
1347	Ipotint = int(potinterval)
1348	intvalue = np.float(interval / np.float(Nint))
1349
1350	# new
1351	potinterval = np.log10(intvalue)
1352	Ipotint = int(potinterval)
1353
1354	# print 'interval:', interval, 'intavlue:', intvalue, 'potinterval:', potinterval, \
1355	# 'Ipotint:', Ipotint, 'intvalue:', intvalue
1356
1357	mindist = 10.e15
1358	for inice in nice_int:
1359	# print inice,':',inice10.Ipotint,np.abs(inice10.**Ipotint - intvalue),mindist
1360	if np.abs(inice10.*Ipotint - intvalue) < mindist:
1361	mindist = np.abs(inice10.*Ipotint - intvalue)
1362	closestint = inice
1363
1364	Ibeg = int(minv / (closestint10.*Ipotint))
1365
1366	values = []
1367	val = closestint(Ibeg)10.**(Ipotint)
1368
1369	# print 'closestint:',closestint,'Ibeg:',Ibeg,'val:',val
1370
1371	while val < maxv:
1372	values.append(val)
1373	val = val + closestint10.*Ipotint
1374
1375	return np.array(values, dtype=np.float)
1376
1377	def DegGradSec_deg(grad,deg,sec):
1378	""" Function to transform from a coordinate in grad deg sec to degrees (decimal)
1379	>>> DegGradSec_deg(39.,49.,26.)
1380	39.8238888889
1381	"""
1382	fname = 'DegGradSec_deg'
1383
1384	if grad == 'h':
1385	print fname + '_____________________________________________________________'
1386	print DegGradSec_deg.__doc__
1387	quit()
1388
1389	deg = grad + deg/60. + sec/3600.
1390
1391	return deg
1392
1393	def intT2dt(intT,tu):
1394	""" Function to provide an 'timedelta' object from a given interval value
1395	intT= interval value
1396	tu= interval units, [tu]= 'd': day, 'w': week, 'h': hour, 'i': minute, 's': second,
1397	'l': milisecond
1398
1399	>>> intT2dt(3.5,'s')
1400	0:00:03.500000
1401
1402	>>> intT2dt(3.5,'w')
1403	24 days, 12:00:00
1404	"""
1405	import datetime as dt
1406
1407	fname = 'intT2dt'
1408
1409	if tu == 'w':
1410	dtv = dt.timedelta(weeks=np.float(intT))
1411	elif tu == 'd':
1412	dtv = dt.timedelta(days=np.float(intT))
1413	elif tu == 'h':
1414	dtv = dt.timedelta(hours=np.float(intT))
1415	elif tu == 'i':
1416	dtv = dt.timedelta(minutes=np.float(intT))
1417	elif tu == 's':
1418	dtv = dt.timedelta(seconds=np.float(intT))
1419	elif tu == 'l':
1420	dtv = dt.timedelta(milliseconds=np.float(intT))
1421	else:
1422	print errormsg
1423	print ' ' + fname + ': time units "' + tu + '" not ready!!!!'
1424	quit(-1)
1425
1426	return dtv
1427
1428	def lonlat_values(mapfile,lonvn,latvn):
1429	""" Function to obtain the lon/lat matrices from a given netCDF file
1430	lonlat_values(mapfile,lonvn,latvn)
1431	[mapfile]= netCDF file name
1432	[lonvn]= variable name with the longitudes
1433	[latvn]= variable name with the latitudes
1434	"""
1435
1436	fname = 'lonlat_values'
1437
1438	if mapfile == 'h':
1439	print fname + '_____________________________________________________________'
1440	print lonlat_values.__doc__
1441	quit()
1442
1443	if not os.path.isfile(mapfile):
1444	print errormsg
1445	print ' ' + fname + ": map file '" + mapfile + "' does not exist !!"
1446	quit(-1)
1447
1448	ncobj = NetCDFFile(mapfile, 'r')
1449	lonobj = ncobj.variables[lonvn]
1450	latobj = ncobj.variables[latvn]
1451
1452	if len(lonobj.shape) == 3:
1453	lonv = lonobj[0,:,:]
1454	latv = latobj[0,:,:]
1455	elif len(lonobj.shape) == 2:
1456	lonv = lonobj[:,:]
1457	latv = latobj[:,:]
1458	elif len(lonobj.shape) == 1:
1459	lon0 = lonobj[:]
1460	lat0 = latobj[:]
1461	lonv = np.zeros( (len(lat0),len(lon0)), dtype=np.float )
1462	latv = np.zeros( (len(lat0),len(lon0)), dtype=np.float )
1463	for iy in range(len(lat0)):
1464	lonv[iy,:] = lon0
1465	for ix in range(len(lon0)):
1466	latv[:,ix] = lat0
1467	else:
1468	print errormsg
1469	print ' ' + fname + ': lon/lat variables shape:',lonobj.shape,'not ready!!'
1470	quit(-1)
1471
1472	return lonv, latv
1473
1474	def date_CFtime(ind,refd,tunits):
1475	""" Function to transform from a given date object a CF-convention time
1476	ind= date object to transform
1477	refd= reference date
1478	tunits= units for time
1479	>>> date_CFtime(dt.datetime(1976,02,17,08,30,00), dt.datetime(1949,12,01,00,00,00), 'seconds')
1480	827224200.0
1481	"""
1482	import datetime as dt
1483
1484	fname = 'date_CFtime'
1485
1486	dt = ind - refd
1487
1488	if tunits == 'weeks':
1489	value = dt.days/7. + dt.seconds/(3600.24.7.)
1490	elif tunits == 'days':
1491	value = dt.days + dt.seconds/(3600.*24.)
1492	elif tunits == 'hours':
1493	value = dt.days*24. + dt.seconds/(3600.)
1494	elif tunits == 'minutes':
1495	value = dt.days24.60. + dt.seconds/(60.)
1496	elif tunits == 'seconds':
1497	value = dt.days24.3600. + dt.seconds
1498	elif tunits == 'milliseconds':
1499	value = dt.days24.3600.1000. + dt.seconds1000.
1500	else:
1501	print errormsg
1502	print ' ' + fname + ': reference time units "' + trefu + '" not ready!!!!'
1503	quit(-1)
1504
1505	return value
1506
1507	def pot_values(values, uvals):
1508	""" Function to modify a seies of values by their potency of 10
1509	pot_values(values, uvals)
1510	values= values to modify
1511	uvals= units of the values
1512	>>> vals = np.sin(np.arange(20)np.pi/5.+0.01)10.e-5
1513	>>> pot_values(vals,'ms-1')
1514	(array([ 0.00000000e+00, 5.87785252e-01, 9.51056516e-01,
1515	9.51056516e-01, 5.87785252e-01, 1.22464680e-16,
1516	-5.87785252e-01, -9.51056516e-01, -9.51056516e-01,
1517	-5.87785252e-01, -2.44929360e-16, 5.87785252e-01,
1518	9.51056516e-01, 9.51056516e-01, 5.87785252e-01,
1519	3.67394040e-16, -5.87785252e-01, -9.51056516e-01,
1520	-9.51056516e-01, -5.87785252e-01]), -4, 'x10e-4 ms-1', 'x10e-4')
1521	"""
1522
1523	fname = 'pot_values'
1524
1525	if np.min(values) != 0.:
1526	potmin = int( np.log10( np.abs(np.min(values)) ) )
1527	else:
1528	potmin = 0
1529
1530	if np.max(values) != 0.:
1531	potmax = int( np.log10( np.abs(np.max(values)) ) )
1532	else:
1533	potmax = 0
1534
1535	if potmin * potmax > 9:
1536	potval = -np.min([np.abs(potmin), np.abs(potmax)]) * np.abs(potmin) / potmin
1537
1538	newvalues = values10.*potval
1539	potvalue = - potval
1540	potS = 'x10e' + str(potvalue)
1541	newunits = potS + ' ' + uvals
1542	else:
1543	newvalues = values
1544	potvalue = None
1545	potS = ''
1546	newunits = uvals
1547
1548	return newvalues, potvalue, newunits, potS
1549
1550	def CFtimes_plot(timev,units,kind,tfmt):
1551	""" Function to provide a list of string values from a CF time values in order
1552	to use them in a plot, according to the series of characteristics.
1553	String outputs will be suited to the 'human-like' output
1554	timev= time values (real values)
1555	units= units string according to CF conventions ([tunits] since
1556	[YYYY]-[MM]-[DD] [[HH]:[MI]:[SS]])
1557	kind= kind of output
1558	'Nval': according to a given number of values as 'Nval',[Nval]
1559	'exct': according to an exact time unit as 'exct',[tunit];
1560	tunit= [Nunits],[tu]; [tu]= 'c': centuries, 'y': year, 'm': month,
1561	'w': week, 'd': day, 'h': hour, 'i': minute, 's': second,
1562	'l': milisecond
1563	tfmt= desired format
1564	>>> CFtimes_plot(np.arange(100)*1.,'hours since 1979-12-01 00:00:00', 'Nval,5',"%Y/%m/%d %H:%M:%S")
1565	0.0 1979/12/01 00:00:00
1566	24.75 1979/12/02 00:45:00
1567	49.5 1979/12/03 01:30:00
1568	74.25 1979/12/04 02:15:00
1569	99.0 1979/12/05 03:00:00
1570	>>> CFtimes_plot(np.arange(100)*1.,'hours since 1979-12-01 00:00:00', 'exct,2,d',"%Y/%m/%d %H:%M:%S")
1571	0.0 1979/12/01 00:00:00
1572	48.0 1979/12/03 00:00:00
1573	96.0 1979/12/05 00:00:00
1574	144.0 1979/12/07 00:00:00
1575	"""
1576	import datetime as dt
1577
1578	# Seconds between 0001 and 1901 Jan - 01
1579	secs0001_1901=59958144000.
1580
1581	fname = 'CFtimes_plot'
1582
1583	if timev == 'h':
1584	print fname + '_____________________________________________________________'
1585	print CFtimes_plot.__doc__
1586	quit()
1587
1588	# Does reference date contain a time value [YYYY]-[MM]-[DD] [HH]:[MI]:[SS]
1589	##
1590	trefT = units.find(':')
1591	txtunits = units.split(' ')
1592	Ntxtunits = len(txtunits)
1593
1594	if Ntxtunits == 3:
1595	Srefdate = txtunits[Ntxtunits - 1]
1596	else:
1597	Srefdate = txtunits[Ntxtunits - 2]
1598
1599	if not trefT == -1:
1600	# print ' ' + fname + ': refdate with time!'
1601	if Ntxtunits == 3:
1602	refdate = datetimeStr_datetime(Srefdate)
1603	else:
1604	refdate = datetimeStr_datetime(Srefdate + '_' + txtunits[Ntxtunits - 1])
1605	else:
1606	refdate = datetimeStr_datetime(Srefdate + '_00:00:00')
1607
1608	trefunits=units.split(' ')[0]
1609	if trefunits == 'weeks':
1610	trefu = 'w'
1611	elif trefunits == 'days':
1612	trefu = 'd'
1613	elif trefunits == 'hours':
1614	trefu = 'h'
1615	elif trefunits == 'minutes':
1616	trefu = 'm'
1617	elif trefunits == 'seconds':
1618	trefu = 's'
1619	elif trefunits == 'milliseconds':
1620	trefu = 'l'
1621	else:
1622	print errormsg
1623	print ' ' + fname + ': reference time units "' + trefu + '" not ready!!!!'
1624	quit(-1)
1625
1626	okind=kind.split(',')[0]
1627	dtv = len(timev)
1628
1629	if refdate.year == 1:
1630	print warnmsg
1631	print ' ' + fname + ': changing reference date: ',refdate, \
1632	'to 1901-01-01_00:00:00 !!!'
1633	refdate = datetimeStr_datetime('1901-01-01_00:00:00')
1634	if trefu == 'w': timev = timev - secs0001_1901/(7.24.3600.)
1635	if trefu == 'd': timev = timev - secs0001_1901/(24.*3600.)
1636	if trefu == 'h': timev = timev - secs0001_1901/(3600.)
1637	if trefu == 'm': timev = timev - secs0001_1901/(60.)
1638	if trefu == 's': timev = timev - secs0001_1901
1639	if trefu == 'l': timev = timev - secs0001_1901*1000.
1640
1641	firstT = timev[0]
1642	lastT = timev[dtv-1]
1643
1644	# First and last times as datetime objects
1645	firstTdt = timeref_datetime(refdate, firstT, trefunits)
1646	lastTdt = timeref_datetime(refdate, lastT, trefunits)
1647
1648	# First and last times as [year, mon, day, hour, minut, second] vectors
1649	firstTvec = np.zeros((6), dtype= np.float)
1650	lastTvec = np.zeros((6), dtype= np.float)
1651	chTvec = np.zeros((6), dtype= bool)
1652
1653	firstTvec = np.array([firstTdt.year, firstTdt.month, firstTdt.day, firstTdt.hour,\
1654	firstTdt.minute, firstTdt.second])
1655	lastTvec = np.array([lastTdt.year, lastTdt.month, lastTdt.day, lastTdt.hour, \
1656	lastTdt.minute, lastTdt.second])
1657
1658	chdate= lastTvec - firstTvec
1659	chTvec = np.where (chdate != 0., True, False)
1660
1661	timeout = []
1662	if okind == 'Nval':
1663	nvalues = int(kind.split(',')[1])
1664	intervT = (lastT - firstT)/(nvalues-1)
1665	dtintervT = intT2dt(intervT, trefu)
1666
1667	for it in range(nvalues):
1668	timeout.append(firstTdt + dtintervT*it)
1669	elif okind == 'exct':
1670	Nunits = int(kind.split(',')[1])
1671	tu = kind.split(',')[2]
1672
1673	# Generic incremental dt [seconds] according to all the possibilities ['c', 'y', 'm',
1674	# 'w', 'd', 'h', 'i', 's', 'l'], some of them approximated (because they are not
1675	# already necessary!)
1676	basedt = np.zeros((9), dtype=np.float)
1677	basedt[0] = (365.100. + 25.)24.*3600.
1678	basedt[1] = 365.24.3600.
1679	basedt[2] = 31.24.3600.
1680	basedt[3] = 7.24.3600.
1681	basedt[4] = 24.*3600.
1682	basedt[5] = 3600.
1683	basedt[6] = 60.
1684	basedt[7] = 1.
1685	basedt[8] = 1000.
1686
1687	# Increment according to the units of the CF dates
1688	if trefunits == 'weeks':
1689	basedt = basedt/(7.24.3600.)
1690	elif trefunits == 'days':
1691	basedt = basedt/(24.*3600.)
1692	elif trefunits == 'hours':
1693	basedt = basedt/(3600.)
1694	elif trefunits == 'minutes':
1695	basedt = basedt/(60.)
1696	elif trefunits == 'seconds':
1697	basedt = basedt
1698	elif trefunits == 'milliseconds':
1699	basedt = basedt*1000.
1700
1701	if tu == 'c':
1702	ti = firstTvec[0]
1703	tf = lastTvec[0]
1704	centi = firstTvec[0] / 100
1705
1706	for it in range((tf - ti)/(Nunits*100) + 1):
1707	timeout.append(dt.datetime(centi+itNunits100, 1, 1, 0, 0, 0))
1708	elif tu == 'y':
1709	ti = firstTvec[0]
1710	tf = lastTvec[0]
1711	yeari = firstTvec[0]
1712
1713	for it in range((tf - ti)/(Nunits) + 1):
1714	timeout.append(dt.datetime(yeari+it*Nunits, 1, 1, 0, 0, 0))
1715	elif tu == 'm':
1716	ti = firstTvec[1]
1717	tf = lastTvec[1]
1718	yr = firstTvec[0]
1719	mon = firstTvec[1]
1720
1721	for it in range((tf - ti)/(Nunits) + 1):
1722	mon = mon+it*Nunits
1723	if mon > 12:
1724	yr = yr + 1
1725	mon = 1
1726
1727	timeout.append(dt.datetime(yr, mon, 1, 0, 0, 0))
1728	elif tu == 'w':
1729	datev=firstTdt
1730	it=0
1731	while datev <= lastTdt:
1732	datev = firstTdt + dt.timedelta(days=7Nunitsit)
1733	timeout.append(datev)
1734	it = it + 1
1735	elif tu == 'd':
1736	# datev=firstTdt
1737	yr = firstTvec[0]
1738	mon = firstTvec[1]
1739	day = firstTvec[2]
1740
1741	if np.sum(firstTvec[2:5]) > 0:
1742	firstTdt = dt.datetime(yr, mon, day+1, 0, 0, 0)
1743	datev = dt.datetime(yr, mon, day+1, 0, 0, 0)
1744	else:
1745	firstTdt = dt.datetime(yr, mon, day, 0, 0, 0)
1746	datev = dt.datetime(yr, mon, day, 0, 0, 0)
1747
1748	it=0
1749	while datev <= lastTdt:
1750	datev = firstTdt + dt.timedelta(days=Nunits*it)
1751	timeout.append(datev)
1752	it = it + 1
1753	elif tu == 'h':
1754	datev=firstTdt
1755	yr = firstTvec[0]
1756	mon = firstTvec[1]
1757	day = firstTvec[2]
1758	hour = firstTvec[3]
1759
1760	if np.sum(firstTvec[4:5]) > 0 or np.mod(hour,Nunits) != 0:
1761	tadvance = 2*Nunits
1762	if tadvance >= 24:
1763	firstTdt = dt.datetime(yr, mon, day+1, 0, 0, 0)
1764	datev = dt.datetime(yr, mon, day+1, 0, 0, 0)
1765	else:
1766	firstTdt = dt.datetime(yr, mon, day, Nunits, 0, 0)
1767	datev = dt.datetime(yr, mon, day, Nunits, 0, 0)
1768	else:
1769	firstTdt = dt.datetime(yr, mon, day, hour, 0, 0)
1770	datev = dt.datetime(yr, mon, day, hour, 0, 0)
1771
1772	it=0
1773	while datev <= lastTdt:
1774	datev = firstTdt + dt.timedelta(seconds=Nunits3600it)
1775	timeout.append(datev)
1776	it = it + 1
1777	elif tu == 'i':
1778	datev=firstTdt
1779	yr = firstTvec[0]
1780	mon = firstTvec[1]
1781	day = firstTvec[2]
1782	hour = firstTvec[3]
1783	minu = firstTvec[4]
1784
1785	if firstTvec[5] > 0 or np.mod(minu,Nunits) != 0:
1786	tadvance = 2*Nunits
1787	if tadvance >= 60:
1788	firstTdt = dt.datetime(yr, mon, day, hour, 0, 0)
1789	datev = dt.datetime(yr, mon, day, hour, 0, 0)
1790	else:
1791	firstTdt = dt.datetime(yr, mon, day, hour, Nunits, 0)
1792	datev = dt.datetime(yr, mon, day, hour, Nunits, 0)
1793	else:
1794	firstTdt = dt.datetime(yr, mon, day, hour, minu, 0)
1795	datev = dt.datetime(yr, mon, day, hour, minu, 0)
1796	it=0
1797	while datev <= lastTdt:
1798	datev = firstTdt + dt.timedelta(seconds=Nunits60it)
1799	timeout.append(datev)
1800	it = it + 1
1801	elif tu == 's':
1802	datev=firstTdt
1803	it=0
1804	while datev <= lastTdt:
1805	datev = firstTdt + dt.timedelta(seconds=Nunits)
1806	timeout.append(datev)
1807	it = it + 1
1808	elif tu == 'l':
1809	datev=firstTdt
1810	it=0
1811	while datev <= lastTdt:
1812	datev = firstTdt + dt.timedelta(seconds=Nunits*it/1000.)
1813	timeout.append(datev)
1814	it = it + 1
1815	else:
1816	print errormsg
1817	print ' ' + fname + ': exact units "' + tu + '" not ready!!!!!'
1818	quit(-1)
1819
1820	else:
1821	print errormsg
1822	print ' ' + fname + ': output kind "' + okind + '" not ready!!!!'
1823	quit(-1)
1824
1825	dtout = len(timeout)
1826
1827	timeoutS = []
1828	timeoutv = np.zeros((dtout), dtype=np.float)
1829
1830	for it in range(dtout):
1831	timeoutS.append(timeout[it].strftime(tfmt))
1832	timeoutv[it] = date_CFtime(timeout[it], refdate, trefunits)
1833
1834	# print it,':',timeoutv[it], timeoutS[it]
1835
1836	return timeoutv, timeoutS
1837
1838	def color_lines(Nlines):
1839	""" Function to provide a color list to plot lines
1840	color_lines(Nlines)
1841	Nlines= number of lines
1842	"""
1843
1844	fname = 'color_lines'
1845
1846	colors = ['r', 'b', 'g', 'p', 'g']
1847
1848	colorv = []
1849
1850	colorv.append('k')
1851	for icol in range(Nlines):
1852	colorv.append(colors[icol])
1853
1854
1855	return colorv
1856
1857	def output_kind(kindf, namef, close):
1858	""" Function to generate the output of the figure
1859	kindf= kind of the output
1860	null: show in screen
1861	[jpg/pdf/png/ps]: standard output types
1862	namef= name of the figure (without extension)
1863	close= if the graph has to be close or not [True/False]
1864	"""
1865	fname = 'output_kind'
1866
1867	if kindf == 'h':
1868	print fname + '_____________________________________________________________'
1869	print output_kind.__doc__
1870	quit()
1871
1872	if kindf == 'null':
1873	print 'showing figure...'
1874	plt.show()
1875	elif kindf == 'gif':
1876	plt.savefig(namef + ".gif")
1877	if close: print "Successfully generation of figure '" + namef + ".jpg' !!!"
1878	elif kindf == 'jpg':
1879	plt.savefig(namef + ".jpg")
1880	if close: print "Successfully generation of figure '" + namef + ".jpg' !!!"
1881	elif kindf == 'pdf':
1882	plt.savefig(namef + ".pdf")
1883	if close: print "Successfully generation of figure '" + namef + ".pdf' !!!"
1884	elif kindf == 'png':
1885	plt.savefig(namef + ".png")
1886	if close: print "Successfully generation of figure '" + namef + ".png' !!!"
1887	elif kindf == 'ps':
1888	plt.savefig(namef + ".ps")
1889	if close: print "Successfully generation of figure '" + namef + ".ps' !!!"
1890	else:
1891	print errormsg
1892	print ' ' + fname + ' output format: "' + kindf + '" not ready !!'
1893	print errormsg
1894	quit(-1)
1895
1896	if close:
1897	plt.close()
1898
1899	return
1900
1901	def check_arguments(funcname,Nargs,args,char,expectargs):
1902	""" Function to check the number of arguments if they are coincident
1903	check_arguments(funcname,Nargs,args,char)
1904	funcname= name of the function/program to check
1905	Nargs= theoretical number of arguments
1906	args= passed arguments
1907	char= character used to split the arguments
1908	"""
1909
1910	fname = 'check_arguments'
1911
1912	Nvals = len(args.split(char))
1913	if Nvals != Nargs:
1914	print errormsg
1915	print ' ' + fname + ': wrong number of arguments:',Nvals," passed to '", \
1916	funcname, "' which requires:",Nargs,'!!'
1917	print ' given arguments:',args.split(char)
1918	print ' expected arguments:',expectargs
1919	quit(-1)
1920
1921	return
1922
1923	def Str_Bool(val):
1924	""" Function to transform from a String value to a boolean one
1925	>>> Str_Bool('True')
1926	True
1927	>>> Str_Bool('0')
1928	False
1929	>>> Str_Bool('no')
1930	False
1931	"""
1932
1933	fname = 'Str_Bool'
1934
1935	if val == 'True' or val == '1' or val == 'yes':
1936	boolv = True
1937	elif val == 'False' or val == '0' or val== 'no':
1938	boolv = False
1939	else:
1940	print errormsg
1941	print ' ' + fname + ": value '" + val + "' not ready!!"
1942	quit(-1)
1943
1944	return boolv
1945
1946	def plot_TimeSeries(valtimes, vunits, tunits, hfileout, vtit, ttit, tkind, tformat, \
1947	tit, linesn, lloc, kfig):
1948	""" Function to draw time-series
1949	valtimes= list of arrays to plot [vals1[1values, 1times], [...,valsM[Mvals,Mtimes]])
1950	vunits= units of the values
1951	tunits= units of the times
1952	hfileout= header of the output figure. Final name: [hfileout]_[vtit].[kfig]
1953	vtit= variable title to be used in the graph
1954	ttit= time title to be used in the graph
1955	tkind= kind of time values to appear in the x-axis
1956	'Nval': according to a given number of values as 'Nval',[Nval]
1957	'exct': according to an exact time unit as 'exct',[tunit];
1958	tunit= [Nunits],[tu]; [tu]= 'c': centuries, 'y': year, 'm': month,
1959	'w': week, 'd': day, 'h': hour, 'i': minute, 's': second,
1960	'l': milisecond
1961	tformat= desired format of times
1962	tit= title of the graph
1963	linesn= list of values fot the legend
1964	lloc= location of the legend (-1, autmoatic)
1965	1: 'upper right', 2: 'upper left', 3: 'lower left', 4: 'lower right',
1966	5: 'right', 6: 'center left', 7: 'center right', 8: 'lower center',
1967	9: 'upper center', 10: 'center'
1968	kfig= type of figure: jpg, png, pds, ps
1969	"""
1970	fname = 'plot_TimeSeries'
1971
1972	if valtimes == 'h':
1973	print fname + '_____________________________________________________________'
1974	print plot_TimeSeries.__doc__
1975	quit()
1976
1977
1978	# Canging line kinds every 7 lines (end of standard colors)
1979	linekinds=['.-','x-','o-']
1980
1981	Nlines = len(valtimes)
1982
1983	Nvalues = []
1984	Ntimes = []
1985
1986	for il in range(Nlines):
1987	array = valtimes[il]
1988
1989	if Nlines == 1:
1990	print warnmsg
1991	print ' ' + fname + ': drawing only one line!'
1992
1993	Nvalues.append(array.shape[1])
1994	Ntimes.append(array.shape[0])
1995	tmin = np.min(array[1])
1996	tmax = np.max(array[1])
1997	vmin = np.min(array[0])
1998	vmax = np.max(array[0])
1999	else:
2000	Nvalues.append(array.shape[1])
2001	Ntimes.append(array.shape[0])
2002	tmin = np.min(array[1,:])
2003	tmax = np.max(array[1,:])
2004	vmin = np.min(array[0,:])
2005	vmax = np.max(array[0,:])
2006
2007	if il == 0:
2008	xmin = tmin
2009	xmax = tmax
2010	ymin = vmin
2011	ymax = vmax
2012	else:
2013	if tmin < xmin: xmin = tmin
2014	if tmax > xmax: xmax = tmax
2015	if vmin < ymin: ymin = vmin
2016	if vmax > ymax: ymax = vmax
2017
2018	dx = np.max(Ntimes)
2019	dy = np.min(Nvalues)
2020
2021	plt.rc('text', usetex=True)
2022
2023	print vtit
2024	if vtit == 'ps':
2025	plt.ylim(98000.,ymax)
2026	else:
2027	plt.ylim(ymin,ymax)
2028
2029	plt.xlim(xmin,xmax)
2030	# print 'x lim:',xmin,xmax
2031	# print 'y lim:',ymin,ymax
2032
2033	N7lines=0
2034	for il in range(Nlines):
2035	array = valtimes[il]
2036	if vtit == 'ps':
2037	array[0,:] = np.where(array[0,:] < 98000., None, array[0,:])
2038	plt.plot(array[1,:],array[0,:], linekinds[N7lines], label= linesn[il])
2039	if il == 6: N7lines = N7lines + 1
2040
2041	timevals = np.arange(xmin,xmax)*1.
2042
2043	tpos, tlabels = CFtimes_plot(timevals, tunits, tkind, tformat)
2044
2045	if len(tpos) > 10:
2046	print warnmsg
2047	print ' ' + fname + ': with "' + tkind + '" there are', len(tpos), 'xticks !'
2048
2049	plt.xticks(tpos, tlabels)
2050	# plt.Axes.set_xticklabels(tlabels)
2051
2052	plt.legend(loc=lloc)
2053	plt.xlabel(ttit)
2054	plt.ylabel(vtit + " (" + vunits + ")")
2055	plt.title(tit)
2056
2057	figname = hfileout + '_' + vtit
2058
2059	output_kind(kfig, figname, True)
2060
2061	return
2062
2063	#Nt = 50
2064	#Nlines = 3
2065
2066	#vtvalsv = []
2067
2068	#valsv = np.zeros((2,Nt), dtype=np.float)
2069	## First
2070	#valsv[0,:] = np.arange(Nt)
2071	#valsv[1,:] = np.arange(Nt)*180.
2072	#vtvalsv.append(valsv)
2073	#del(valsv)
2074
2075	#valsv = np.zeros((2,Nt/2), dtype=np.float)
2076	## Second
2077	#valsv[0,:] = np.arange(Nt/2)
2078	#valsv[1,:] = np.arange(Nt/2)180.2.
2079	#vtvalsv.append(valsv)
2080	#del(valsv)
2081
2082	#valsv = np.zeros((2,Nt/4), dtype=np.float)
2083	## Third
2084	#valsv[0,:] = np.arange(Nt/4)
2085	#valsv[1,:] = np.arange(Nt/4)180.4.
2086	#vtvalsv.append(valsv)
2087	#del(valsv)
2088
2089	#varu='mm'
2090	#timeu='seconds'
2091
2092	#title='test'
2093	#linesname = ['line 1', 'line 2', 'line 3']
2094
2095	#plot_TimeSeries(vtvalsv, units_lunits(varu), timeu, 'test', 'vartest', 'time', title, linesname, 'png')
2096	#quit()
2097
2098	def plot_points(xval, yval, ifile, vtit, kfig, mapv):
2099	""" plotting points
2100	[x/yval]: x,y values to plot
2101	vn,vm= minmum and maximum values to plot
2102	unit= units of the variable
2103	ifile= name of the input file
2104	vtit= title of the variable
2105	kfig= kind of figure (jpg, pdf, png)
2106	mapv= map characteristics: [proj],[res]
2107	see full documentation: http://matplotlib.org/basemap/
2108	[proj]: projection
2109	* 'cyl', cilindric
2110	[res]: resolution:
2111	* 'c', crude
2112	* 'l', low
2113	* 'i', intermediate
2114	* 'h', high
2115	* 'f', full
2116	"""
2117	fname = 'plot_points'
2118
2119	dx=xval.shape[0]
2120	dy=yval.shape[0]
2121
2122	plt.rc('text', usetex=True)
2123
2124	if not mapv is None:
2125	lon00 = np.where(xval[:] < 0., 360. + olon[:], olon[:])
2126	lat00 = yval[:]
2127	lon0 = np.zeros( (len(lat00),len(lon00)), dtype=np.float )
2128	lat0 = np.zeros( (len(lat00),len(lon00)), dtype=np.float )
2129
2130	for iy in range(len(lat00)):
2131	lon0[iy,:] = lon00
2132	for ix in range(len(lon00)):
2133	lat0[:,ix] = lat00
2134
2135	map_proj=mapv.split(',')[0]
2136	map_res=mapv.split(',')[1]
2137
2138	nlon = lon0[0,0]
2139	xlon = lon0[dy-1,dx-1]
2140	nlat = lat0[0,0]
2141	xlat = lat0[dy-1,dx-1]
2142
2143	lon2 = lon0[dy/2,dx/2]
2144	lat2 = lat0[dy/2,dx/2]
2145
2146	print 'lon2:', lon2, 'lat2:', lat2, 'SW pt:', nlon, ',', nlat, 'NE pt:', \
2147	xlon, ',', xlat
2148
2149	if map_proj == 'cyl':
2150	m = Basemap(projection=map_proj, llcrnrlon=nlon, llcrnrlat=nlat, \
2151	urcrnrlon=xlon, urcrnrlat= xlat, resolution=map_res)
2152	elif map_proj == 'lcc':
2153	m = Basemap(projection=map_proj, lat_0=lat2, lon_0=lon2, llcrnrlon=nlon, \
2154	llcrnrlat=nlat, urcrnrlon=xlon, urcrnrlat= xlat, resolution=map_res)
2155
2156	lons, lats = np.meshgrid(olon[:], olat[:])
2157	lons = np.where(lons < 0., lons + 360., lons)
2158
2159	x,y = m(lons,lats)
2160	plt.plot(x,y)
2161
2162	m.drawcoastlines()
2163
2164	meridians = pretty_int(nlon,xlon,5)
2165	m.drawmeridians(meridians,labels=[True,False,False,True])
2166
2167	parallels = pretty_int(nlat,xlat,5)
2168	m.drawparallels(parallels,labels=[False,True,True,False])
2169	else:
2170	# plt.xlim(0,dx-1)
2171	# plt.ylim(0,dy-1)
2172
2173	plt.plot(xval, yval, '.')
2174
2175	figname = ifile.replace('.','_') + '_' + vtit
2176	graphtit = vtit.replace('_','\_')
2177
2178	plt.title(graphtit)
2179
2180	output_kind(kfig, figname, True)
2181
2182	return
2183
2184	def plot_2Dfield(varv,dimn,colorbar,vn,vx,unit,olon,olat,ifile,vtit,zvalue,time,tk, \
2185	tkt,tobj,tvals,tind,kfig,mapv,reva):
2186	""" Adding labels and other staff to the graph
2187	varv= 2D values to plot
2188	dimn= dimension names to plot
2189	colorbar= name of the color bar to use
2190	vn,vm= minmum and maximum values to plot
2191	unit= units of the variable
2192	olon,olat= longitude, latitude objects
2193	ifile= name of the input file
2194	vtit= title of the variable
2195	zvalue= value on the z axis
2196	time= value on the time axis
2197	tk= kind of time (WRF)
2198	tkt= kind of time taken
2199	tobj= tim object
2200	tvals= values of the time variable
2201	tind= time index
2202	kfig= kind of figure (jpg, pdf, png)
2203	mapv= map characteristics: [proj],[res]
2204	see full documentation: http://matplotlib.org/basemap/
2205	[proj]: projection
2206	* 'cyl', cilindric
2207	[res]: resolution:
2208	* 'c', crude
2209	* 'l', low
2210	* 'i', intermediate
2211	* 'h', high
2212	* 'f', full
2213	reva= reverse the axes (x-->y, y-->x)
2214	"""
2215	## import matplotlib as mpl
2216	## mpl.use('Agg')
2217	## import matplotlib.pyplot as plt
2218
2219	if reva:
2220	print ' reversing the axes of the figure (x-->y, y-->x)!!'
2221	varv = np.transpose(varv)
2222	dimn0 = []
2223	dimn0.append(dimn[1] + '')
2224	dimn0.append(dimn[0] + '')
2225	dimn = dimn0
2226
2227	fname = 'plot_2Dfield'
2228	dx=varv.shape[1]
2229	dy=varv.shape[0]
2230
2231	plt.rc('text', usetex=True)
2232	# plt.rc('font', family='serif')
2233
2234	if not mapv is None:
2235	if len(olon[:].shape) == 3:
2236	lon0 = np.where(olon[0,] < 0., 360. + olon[0,], olon[0,])
2237	lat0 = olat[0,]
2238	elif len(olon[:].shape) == 2:
2239	lon0 = np.where(olon[:] < 0., 360. + olon[:], olon[:])
2240	lat0 = olat[:]
2241	elif len(olon[:].shape) == 1:
2242	lon00 = np.where(olon[:] < 0., 360. + olon[:], olon[:])
2243	lat00 = olat[:]
2244	lon0 = np.zeros( (len(lat00),len(lon00)), dtype=np.float )
2245	lat0 = np.zeros( (len(lat00),len(lon00)), dtype=np.float )
2246
2247	for iy in range(len(lat00)):
2248	lon0[iy,:] = lon00
2249	for ix in range(len(lon00)):
2250	lat0[:,ix] = lat00
2251
2252	map_proj=mapv.split(',')[0]
2253	map_res=mapv.split(',')[1]
2254
2255	nlon = lon0[0,0]
2256	xlon = lon0[dy-1,dx-1]
2257	nlat = lat0[0,0]
2258	xlat = lat0[dy-1,dx-1]
2259
2260	lon2 = lon0[dy/2,dx/2]
2261	lat2 = lat0[dy/2,dx/2]
2262
2263	print ' lon2:', lon2, 'lat2:', lat2, 'SW pt:', nlon, ',', nlat, 'NE pt:', \
2264	xlon, ',', xlat
2265
2266	if map_proj == 'cyl':
2267	m = Basemap(projection=map_proj, llcrnrlon=nlon, llcrnrlat=nlat, \
2268	urcrnrlon=xlon, urcrnrlat= xlat, resolution=map_res)
2269	elif map_proj == 'lcc':
2270	m = Basemap(projection=map_proj, lat_0=lat2, lon_0=lon2, llcrnrlon=nlon, \
2271	llcrnrlat=nlat, urcrnrlon=xlon, urcrnrlat= xlat, resolution=map_res)
2272
2273	if len(olon[:].shape) == 1:
2274	lons, lats = np.meshgrid(olon[:], olat[:])
2275	else:
2276	lons = olon[0,:]
2277	lats = olat[:,0]
2278
2279	lons = np.where(lons < 0., lons + 360., lons)
2280
2281	x,y = m(lons,lats)
2282	plt.pcolormesh(x,y,varv, cmap=plt.get_cmap(colorbar), vmin=vn, vmax=vx)
2283	cbar = plt.colorbar()
2284
2285	m.drawcoastlines()
2286	# if (nlon > 180. or xlon > 180.):
2287	# nlon0 = nlon
2288	# xlon0 = xlon
2289	# if (nlon > 180.): nlon0 = nlon - 360.
2290	# if (xlon > 180.): xlon0 = xlon - 360.
2291	# meridians = pretty_int(nlon0,xlon0,5)
2292	# meridians = np.where(meridians < 0., meridians + 360., meridians)
2293	# else:
2294	# meridians = pretty_int(nlon,xlon,5)
2295
2296	meridians = pretty_int(nlon,xlon,5)
2297
2298	m.drawmeridians(meridians,labels=[True,False,False,True])
2299	parallels = pretty_int(nlat,xlat,5)
2300	m.drawparallels(parallels,labels=[False,True,True,False])
2301
2302	else:
2303	plt.xlim(0,dx-1)
2304	plt.ylim(0,dy-1)
2305
2306	plt.pcolormesh(varv, cmap=plt.get_cmap(colorbar), vmin=vn, vmax=vx)
2307	cbar = plt.colorbar()
2308
2309	plt.xlabel(dimn[1].replace('_','\_'))
2310	plt.ylabel(dimn[0].replace('_','\_'))
2311
2312	# set the limits of the plot to the limits of the data
2313	# plt.axis([x.min(), x.max(), y.min(), y.max()])
2314
2315	# plt.plot(varv)
2316	cbar.set_label(unit)
2317
2318	figname = ifile.replace('.','_') + '_' + vtit
2319	graphtit = vtit.replace('_','\_')
2320
2321	if zvalue != 'null':
2322	graphtit = graphtit + ' at z= ' + zvalue
2323	figname = figname + '_z' + zvalue
2324	if tkt == 'tstep':
2325	graphtit = graphtit + ' at time-step= ' + time.split(',')[1]
2326	figname = figname + '_t' + time.split(',')[1].zfill(4)
2327	elif tkt == 'CFdate':
2328	graphtit = graphtit + ' at ' + tobj.strfmt("%Y%m%d%H%M%S")
2329	figname = figname + '_t' + tobj.strfmt("%Y%m%d%H%M%S")
2330
2331	if tk == 'WRF':
2332	# datev = str(timevals[timeind][0:9])
2333	datev = tvals[tind][0] + tvals[tind][1] + tvals[tind][2] + \
2334	timevals[timeind][3] + timevals[timeind][4] + timevals[timeind][5] + \
2335	timevals[timeind][6] + timevals[timeind][7] + timevals[timeind][8] + \
2336	timevals[timeind][9]
2337	# timev = str(timevals[timeind][11:18])
2338	timev = timevals[timeind][11] + timevals[timeind][12] + \
2339	timevals[timeind][13] + timevals[timeind][14] + timevals[timeind][15] + \
2340	timevals[timeind][16] + timevals[timeind][17] + timevals[timeind][18]
2341	graphtit = vtit.replace('_','\_') + ' (' + datev + ' ' + timev + ')'
2342
2343	plt.title(graphtit)
2344
2345	output_kind(kfig, figname, True)
2346
2347	return
2348
2349	def plot_2Dfield_easy(varv,dimxv,dimyv,dimn,colorbar,vn,vx,unit,ifile,vtit,kfig,reva):
2350	""" Adding labels and other staff to the graph
2351	varv= 2D values to plot
2352	dim[x/y]v = values at the axes of x and y
2353	dimn= dimension names to plot
2354	colorbar= name of the color bar to use
2355	vn,vm= minmum and maximum values to plot
2356	unit= units of the variable
2357	ifile= name of the input file
2358	vtit= title of the variable
2359	kfig= kind of figure (jpg, pdf, png)
2360	reva= reverse the axes (x-->y, y-->x)
2361	"""
2362	## import matplotlib as mpl
2363	## mpl.use('Agg')
2364	## import matplotlib.pyplot as plt
2365	fname = 'plot_2Dfield'
2366
2367	if reva:
2368	print ' reversing the axes of the figure (x-->y, y-->x)!!'
2369	varv = np.transpose(varv)
2370	dimn0 = []
2371	dimn0.append(dimn[1] + '')
2372	dimn0.append(dimn[0] + '')
2373	dimn = dimn0
2374	if len(dimyv.shape) == 2:
2375	x = np.transpose(dimyv)
2376	else:
2377	if len(dimxv.shape) == 2:
2378	ddx = len(dimyv)
2379	ddy = dimxv.shape[1]
2380	else:
2381	ddx = len(dimyv)
2382	ddy = len(dimxv)
2383
2384	x = np.zeros((ddy,ddx), dtype=np.float)
2385	for j in range(ddy):
2386	x[j,:] = dimyv
2387
2388	if len(dimxv.shape) == 2:
2389	y = np.transpose(dimxv)
2390	else:
2391	if len(dimyv.shape) == 2:
2392	ddx = dimyv.shape[0]
2393	ddy = len(dimxv)
2394	else:
2395	ddx = len(dimyv)
2396	ddy = len(dimxv)
2397
2398	y = np.zeros((ddy,ddx), dtype=np.float)
2399	for i in range(ddx):
2400	y[:,i] = dimxv
2401	else:
2402	if len(dimxv.shape) == 2:
2403	x = dimxv
2404	else:
2405	x = np.zeros((len(dimyv),len(dimxv)), dtype=np.float)
2406	for j in range(len(dimyv)):
2407	x[j,:] = dimxv
2408
2409	if len(dimyv.shape) == 2:
2410	y = dimyv
2411	else:
2412	y = np.zeros((len(dimyv),len(dimxv)), dtype=np.float)
2413	for i in range(len(dimxv)):
2414	x[:,i] = dimyv
2415
2416	dx=varv.shape[1]
2417	dy=varv.shape[0]
2418
2419	plt.rc('text', usetex=True)
2420	plt.xlim(0,dx-1)
2421	plt.ylim(0,dy-1)
2422
2423	plt.pcolormesh(x, y, varv, cmap=plt.get_cmap(colorbar), vmin=vn, vmax=vx)
2424	# plt.pcolormesh(varv, cmap=plt.get_cmap(colorbar), vmin=vn, vmax=vx)
2425	cbar = plt.colorbar()
2426
2427	plt.xlabel(dimn[1].replace('_','\_'))
2428	plt.ylabel(dimn[0].replace('_','\_'))
2429
2430	# set the limits of the plot to the limits of the data
2431	plt.axis([x.min(), x.max(), y.min(), y.max()])
2432	# if varv.shape[1] / varv.shape[0] > 10:
2433	# plt.axes().set_aspect(0.001)
2434	# else:
2435	# plt.axes().set_aspect(np.float(varv.shape[0])/np.float(varv.shape[1]))
2436
2437	cbar.set_label(unit)
2438
2439	figname = ifile.replace('.','_') + '_' + vtit
2440	graphtit = vtit.replace('_','\_')
2441
2442	plt.title(graphtit)
2443
2444	output_kind(kfig, figname, True)
2445
2446	return
2447
2448	def plot_Trajectories(lonval, latval, linesn, olon, olat, lonlatLims, gtit, kfig, \
2449	mapv, obsname):
2450	""" plotting points
2451	[lon/latval]= lon,lat values to plot (as list of vectors)
2452	linesn: name of the lines
2453	o[lon/lat]= object with the longitudes and the latitudes of the map to plot
2454	lonlatLims: limits of longitudes and latitudes [lonmin, latmin, lonmax, latmax]
2455	gtit= title of the graph
2456	kfig= kind of figure (jpg, pdf, png)
2457	mapv= map characteristics: [proj],[res]
2458	see full documentation: http://matplotlib.org/basemap/
2459	[proj]: projection
2460	* 'cyl', cilindric
2461	* 'lcc', lambert conformal
2462	[res]: resolution:
2463	* 'c', crude
2464	* 'l', low
2465	* 'i', intermediate
2466	* 'h', high
2467	* 'f', full
2468	obsname= name of the observations in graph (can be None for without).
2469	Observational trajectory would be the last one
2470	"""
2471	fname = 'plot_Trajectories'
2472
2473	if lonval == 'h':
2474	print fname + '_____________________________________________________________'
2475	print plot_Trajectories.__doc__
2476	quit()
2477
2478	# Canging line kinds every 7 lines (end of standard colors)
2479	linekinds=['.-','x-','o-']
2480
2481	Ntraj = len(lonval)
2482
2483	if obsname is not None:
2484	Ntraj = Ntraj - 1
2485
2486	N7lines = 0
2487
2488	plt.rc('text', usetex=True)
2489
2490	if not mapv is None:
2491	if len(olon[:].shape) == 3:
2492	# lon0 = np.where(olon[0,] < 0., 360. + olon[0,], olon[0,])
2493	lon0 = olon[0,]
2494	lat0 = olat[0,]
2495	elif len(olon[:].shape) == 2:
2496	# lon0 = np.where(olon[:] < 0., 360. + olon[:], olon[:])
2497	lon0 = olon[:]
2498	lat0 = olat[:]
2499	elif len(olon[:].shape) == 1:
2500	# lon00 = np.where(olon[:] < 0., 360. + olon[:], olon[:])
2501	lon00 = olon[:]
2502	lat00 = olat[:]
2503	lon0 = np.zeros( (len(lat00),len(lon00)), dtype=np.float )
2504	lat0 = np.zeros( (len(lat00),len(lon00)), dtype=np.float )
2505
2506	for iy in range(len(lat00)):
2507	lon0[iy,:] = lon00
2508	for ix in range(len(lon00)):
2509	lat0[:,ix] = lat00
2510
2511	map_proj=mapv.split(',')[0]
2512	map_res=mapv.split(',')[1]
2513
2514	dx = lon0.shape[1]
2515	dy = lon0.shape[0]
2516
2517	nlon = lon0[0,0]
2518	xlon = lon0[dy-1,dx-1]
2519	nlat = lat0[0,0]
2520	xlat = lat0[dy-1,dx-1]
2521
2522	lon2 = lon0[dy/2,dx/2]
2523	lat2 = lat0[dy/2,dx/2]
2524
2525	if lonlatLims is not None:
2526	plt.xlim(lonlatLims[0], lonlatLims[2])
2527	plt.ylim(lonlatLims[1], lonlatLims[3])
2528	if map_proj == 'cyl':
2529	nlon = lonlatLims[0]
2530	nlat = lonlatLims[1]
2531	xlon = lonlatLims[2]
2532	xlat = lonlatLims[3]
2533
2534	print 'lon2:', lon2, 'lat2:', lat2, 'SW pt:', nlon, ',', nlat, 'NE pt:', \
2535	xlon, ',', xlat
2536
2537	if map_proj == 'cyl':
2538	m = Basemap(projection=map_proj, llcrnrlon=nlon, llcrnrlat=nlat, \
2539	urcrnrlon=xlon, urcrnrlat= xlat, resolution=map_res)
2540	elif map_proj == 'lcc':
2541	m = Basemap(projection=map_proj, lat_0=lat2, lon_0=lon2, llcrnrlon=nlon, \
2542	llcrnrlat=nlat, urcrnrlon=xlon, urcrnrlat= xlat, resolution=map_res)
2543
2544	if len(olon.shape) == 3:
2545	# lons, lats = np.meshgrid(olon[0,:,:], olat[0,:,:])
2546	lons = olon[0,:,:]
2547	lats = olat[0,:,:]
2548
2549	elif len(olon.shape) == 2:
2550	# lons, lats = np.meshgrid(olon[:,:], olat[:,:])
2551	lons = olon[:,:]
2552	lats = olat[:,:]
2553	else:
2554	print errormsg
2555	print ' ' + fname + ': shapes of lon/lat objects', olon.shape, \
2556	'not ready!!!'
2557
2558	for il in range(Ntraj):
2559	plt.plot(lonval[il], latval[il], linekinds[N7lines], label= linesn[il])
2560	if il == 6: N7lines = N7lines + 1
2561
2562	m.drawcoastlines()
2563
2564	meridians = pretty_int(nlon,xlon,5)
2565	m.drawmeridians(meridians,labels=[True,False,False,True])
2566
2567	parallels = pretty_int(nlat,xlat,5)
2568	m.drawparallels(parallels,labels=[False,True,True,False])
2569
2570	plt.xlabel('W-E')
2571	plt.ylabel('S-N')
2572
2573	else:
2574	if len(olon.shape) == 3:
2575	dx = olon.shape[2]
2576	dy = olon.shape[1]
2577	elif len(olon.shape) == 2:
2578	dx = olon.shape[1]
2579	dy = olon.shape[0]
2580	else:
2581	print errormsg
2582	print ' ' + fname + ': shapes of lon/lat objects', olon.shape, \
2583	'not ready!!!'
2584
2585	if lonlatLims is not None:
2586	plt.xlim(lonlatLims[0], lonlatLims[2])
2587	plt.ylim(lonlatLims[1], lonlatLims[3])
2588	else:
2589	plt.xlim(np.min(olon[:]),np.max(olon[:]))
2590	plt.ylim(np.min(olat[:]),np.max(olat[:]))
2591
2592	for il in range(Ntraj):
2593	plt.plot(lonval[il], latval[il], linekinds[N7lines], label= linesn[il])
2594	if il == 6: N7lines = N7lines + 1
2595
2596	plt.xlabel('x-axis')
2597	plt.ylabel('y-axis')
2598
2599	figname = 'trajectories'
2600	graphtit = gtit
2601
2602	if obsname is not None:
2603	plt.plot(lonval[Ntraj], latval[Ntraj], linestyle='-', color='k', \
2604	linewidth=3, label= obsname)
2605
2606	plt.title(graphtit)
2607	plt.legend()
2608
2609	output_kind(kfig, figname, True)
2610
2611	return
2612
2613	def plot_topo_geogrid(varv, olon, olat, mint, maxt, lonlatLims, gtit, kfig, mapv, \
2614	closeif):
2615	""" plotting geo_em.d[nn].nc topography from WPS files
2616	plot_topo_geogrid(domf, mint, maxt, gtit, kfig, mapv)
2617	varv= topography values
2618	o[lon/lat]= longitude and latitude objects
2619	[min/max]t: minimum and maximum values of topography to draw
2620	lonlatLims: limits of longitudes and latitudes [lonmin, latmin, lonmax, latmax]
2621	gtit= title of the graph
2622	kfig= kind of figure (jpg, pdf, png)
2623	mapv= map characteristics: [proj],[res]
2624	see full documentation: http://matplotlib.org/basemap/
2625	[proj]: projection
2626	* 'cyl', cilindric
2627	* 'lcc', lamvbert conformal
2628	[res]: resolution:
2629	* 'c', crude
2630	* 'l', low
2631	* 'i', intermediate
2632	* 'h', high
2633	* 'f', full
2634	closeif= Boolean value if the figure has to be closed
2635	"""
2636	fname = 'plot_topo_geogrid'
2637
2638	if varv == 'h':
2639	print fname + '_____________________________________________________________'
2640	print plot_topo_geogrid.__doc__
2641	quit()
2642
2643	dx=varv.shape[1]
2644	dy=varv.shape[0]
2645
2646	plt.rc('text', usetex=True)
2647	# plt.rc('font', family='serif')
2648
2649	if not mapv is None:
2650	if len(olon[:].shape) == 3:
2651	lon0 = olon[0,]
2652	lat0 = olat[0,]
2653	elif len(olon[:].shape) == 2:
2654	lon0 = olon[:]
2655	lat0 = olat[:]
2656	elif len(olon[:].shape) == 1:
2657	lon00 = olon[:]
2658	lat00 = olat[:]
2659	lon0 = np.zeros( (len(lat00),len(lon00)), dtype=np.float )
2660	lat0 = np.zeros( (len(lat00),len(lon00)), dtype=np.float )
2661
2662	for iy in range(len(lat00)):
2663	lon0[iy,:] = lon00
2664	for ix in range(len(lon00)):
2665	lat0[:,ix] = lat00
2666
2667	map_proj=mapv.split(',')[0]
2668	map_res=mapv.split(',')[1]
2669	dx = lon0.shape[1]
2670	dy = lon0.shape[0]
2671
2672	if lonlatLims is not None:
2673	print ' ' + fname + ': cutting the domain to plot !!!!'
2674	print ' limits: W-E', lonlatLims[0], lonlatLims[2]
2675	print ' limits: N-S', lonlatLims[1], lonlatLims[3]
2676	nlon = lonlatLims[0]
2677	xlon = lonlatLims[2]
2678	nlat = lonlatLims[1]
2679	xlat = lonlatLims[3]
2680
2681	if map_proj == 'lcc':
2682	lon2 = (lonlatLims[0] + lonlatLims[2])/2.
2683	lat2 = (lonlatLims[1] + lonlatLims[3])/2.
2684	else:
2685	nlon = lon0[0,0]
2686	xlon = lon0[dy-1,dx-1]
2687	nlat = lat0[0,0]
2688	xlat = lat0[dy-1,dx-1]
2689	lon2 = lon0[dy/2,dx/2]
2690	lat2 = lat0[dy/2,dx/2]
2691
2692	plt.xlim(nlon, xlon)
2693	plt.ylim(nlat, xlat)
2694	print 'lon2:', lon2, 'lat2:', lat2, 'SW pt:', nlon, ',', nlat, 'NE pt:', \
2695	xlon, ',', xlat
2696
2697	if map_proj == 'cyl':
2698	m = Basemap(projection=map_proj, llcrnrlon=nlon, llcrnrlat=nlat, \
2699	urcrnrlon=xlon, urcrnrlat= xlat, resolution=map_res)
2700	elif map_proj == 'lcc':
2701	m = Basemap(projection=map_proj, lat_0=lat2, lon_0=lon2, llcrnrlon=nlon, \
2702	llcrnrlat=nlat, urcrnrlon=xlon, urcrnrlat= xlat, resolution=map_res)
2703	else:
2704	print errormsg
2705	print ' ' + fname + ": map projection '" + map_proj + "' not ready !!"
2706	quit(-1)
2707
2708	if len(olon[:].shape) == 1:
2709	lons, lats = np.meshgrid(olon[:], olat[:])
2710	else:
2711	if len(olon[:].shape) == 3:
2712	lons = olon[0,:,:]
2713	lats = olat[0,:,:]
2714	else:
2715	lons = olon[:]
2716	lats = olat[:]
2717
2718	x,y = m(lons,lats)
2719
2720	plt.pcolormesh(x,y,varv, cmap=plt.get_cmap('terrain'), vmin=mint, vmax=maxt)
2721	cbar = plt.colorbar()
2722
2723	m.drawcoastlines()
2724
2725	meridians = pretty_int(nlon,xlon,5)
2726	m.drawmeridians(meridians,labels=[True,False,False,True])
2727
2728	parallels = pretty_int(nlat,xlat,5)
2729	m.drawparallels(parallels,labels=[False,True,True,False])
2730
2731	plt.xlabel('W-E')
2732	plt.ylabel('S-N')
2733	else:
2734	print emsg
2735	print ' ' + fname + ': A projection parameter is needed None given !!'
2736	quit(-1)
2737
2738	figname = 'domain'
2739	graphtit = gtit.replace('_','\_')
2740	cbar.set_label('height ($m$)')
2741
2742	plt.title(graphtit)
2743
2744	output_kind(kfig, figname, closeif)
2745
2746	return
2747
2748	def plot_topo_geogrid_boxes(varv, boxesX, boxesY, boxlabels, olon, olat, mint, maxt, \
2749	lonlatLims, gtit, kfig, mapv, closeif):
2750	""" plotting geo_em.d[nn].nc topography from WPS files
2751	plot_topo_geogrid(domf, mint, maxt, gtit, kfig, mapv)
2752	varv= topography values
2753	boxesX/Y= 4-line sets to draw the boxes
2754	boxlabels= labels for the legend of the boxes
2755	o[lon/lat]= longitude and latitude objects
2756	[min/max]t: minimum and maximum values of topography to draw
2757	lonlatLims: limits of longitudes and latitudes [lonmin, latmin, lonmax, latmax]
2758	gtit= title of the graph
2759	kfig= kind of figure (jpg, pdf, png)
2760	mapv= map characteristics: [proj],[res]
2761	see full documentation: http://matplotlib.org/basemap/
2762	[proj]: projection
2763	* 'cyl', cilindric
2764	* 'lcc', lamvbert conformal
2765	[res]: resolution:
2766	* 'c', crude
2767	* 'l', low
2768	* 'i', intermediate
2769	* 'h', high
2770	* 'f', full
2771	closeif= Boolean value if the figure has to be closed
2772	"""
2773	fname = 'plot_topo_geogrid'
2774
2775	if varv == 'h':
2776	print fname + '_____________________________________________________________'
2777	print plot_topo_geogrid.__doc__
2778	quit()
2779
2780	cols = color_lines(len(boxlabels))
2781
2782	dx=varv.shape[1]
2783	dy=varv.shape[0]
2784
2785	plt.rc('text', usetex=True)
2786	# plt.rc('font', family='serif')
2787
2788	if not mapv is None:
2789	if len(olon[:].shape) == 3:
2790	lon0 = olon[0,]
2791	lat0 = olat[0,]
2792	elif len(olon[:].shape) == 2:
2793	lon0 = olon[:]
2794	lat0 = olat[:]
2795	elif len(olon[:].shape) == 1:
2796	lon00 = olon[:]
2797	lat00 = olat[:]
2798	lon0 = np.zeros( (len(lat00),len(lon00)), dtype=np.float )
2799	lat0 = np.zeros( (len(lat00),len(lon00)), dtype=np.float )
2800
2801	for iy in range(len(lat00)):
2802	lon0[iy,:] = lon00
2803	for ix in range(len(lon00)):
2804	lat0[:,ix] = lat00
2805
2806	map_proj=mapv.split(',')[0]
2807	map_res=mapv.split(',')[1]
2808	dx = lon0.shape[1]
2809	dy = lon0.shape[0]
2810
2811	if lonlatLims is not None:
2812	print ' ' + fname + ': cutting the domain to plot !!!!'
2813	print ' limits: W-E', lonlatLims[0], lonlatLims[2]
2814	print ' limits: N-S', lonlatLims[1], lonlatLims[3]
2815	nlon = lonlatLims[0]
2816	xlon = lonlatLims[2]
2817	nlat = lonlatLims[1]
2818	xlat = lonlatLims[3]
2819
2820	if map_proj == 'lcc':
2821	lon2 = (lonlatLims[0] + lonlatLims[2])/2.
2822	lat2 = (lonlatLims[1] + lonlatLims[3])/2.
2823	else:
2824	nlon = np.min(lon0)
2825	xlon = np.max(lon0)
2826	nlat = np.min(lat0)
2827	xlat = np.max(lat0)
2828	lon2 = lon0[dy/2,dx/2]
2829	lat2 = lat0[dy/2,dx/2]
2830
2831	plt.xlim(nlon, xlon)
2832	plt.ylim(nlat, xlat)
2833	print 'lon2:', lon2, 'lat2:', lat2, 'SW pt:', nlon, ',', nlat, 'NE pt:', \
2834	xlon, ',', xlat
2835
2836	if map_proj == 'cyl':
2837	m = Basemap(projection=map_proj, llcrnrlon=nlon, llcrnrlat=nlat, \
2838	urcrnrlon=xlon, urcrnrlat= xlat, resolution=map_res)
2839	elif map_proj == 'lcc':
2840	m = Basemap(projection=map_proj, lat_0=lat2, lon_0=lon2, llcrnrlon=nlon, \
2841	llcrnrlat=nlat, urcrnrlon=xlon, urcrnrlat= xlat, resolution=map_res)
2842
2843	if len(olon[:].shape) == 1:
2844	lons, lats = np.meshgrid(olon[:], olat[:])
2845	else:
2846	if len(olon[:].shape) == 3:
2847	lons = olon[0,:,:]
2848	lats = olat[0,:,:]
2849	else:
2850	lons = olon[:]
2851	lats = olat[:]
2852
2853	x,y = m(lons,lats)
2854
2855	plt.pcolormesh(x,y,varv, cmap=plt.get_cmap('terrain'), vmin=mint, vmax=maxt)
2856	cbar = plt.colorbar()
2857
2858	Nboxes = len(boxesX)/4
2859	for ibox in range(Nboxes):
2860	plt.plot(boxesX[ibox4], boxesY[ibox4], linestyle='-', linewidth=3, \
2861	label=boxlabels[ibox], color=cols[ibox])
2862	plt.plot(boxesX[ibox4+1], boxesY[ibox4+1], linestyle='-', linewidth=3, \
2863	color=cols[ibox])
2864	plt.plot(boxesX[ibox4+2], boxesY[ibox4+2], linestyle='-', linewidth=3, \
2865	color=cols[ibox])
2866	plt.plot(boxesX[ibox4+3], boxesY[ibox4+3], linestyle='-', linewidth=3, \
2867	color=cols[ibox])
2868
2869	m.drawcoastlines()
2870
2871	meridians = pretty_int(nlon,xlon,5)
2872	m.drawmeridians(meridians,labels=[True,False,False,True])
2873
2874	parallels = pretty_int(nlat,xlat,5)
2875	m.drawparallels(parallels,labels=[False,True,True,False])
2876
2877	plt.xlabel('W-E')
2878	plt.ylabel('S-N')
2879	else:
2880	print emsg
2881	print ' ' + fname + ': A projection parameter is needed None given !!'
2882	quit(-1)
2883
2884	figname = 'domain_boxes'
2885	graphtit = gtit.replace('_','\_')
2886	cbar.set_label('height ($m$)')
2887
2888	plt.title(graphtit)
2889	plt.legend()
2890
2891	output_kind(kfig, figname, closeif)
2892
2893	return
2894
2895	def plot_2D_shadow(varsv,vnames,dimxv,dimyv,dimxu,dimyu,dimn, \
2896	colorbar,vs,uts,vtit,kfig,reva,mapv,ifclose):
2897	""" Adding labels and other staff to the graph
2898	varsv= 2D values to plot with shading
2899	vnames= variable names for the figure
2900	dim[x/y]v = values at the axes of x and y
2901	dim[x/y]u = units at the axes of x and y
2902	dimn= dimension names to plot
2903	colorbar= name of the color bar to use
2904	vs= minmum and maximum values to plot in shadow or:
2905	'Srange': for full range
2906	'Saroundmean@val': for mean-xtrm,mean+xtrm where xtrm = np.min(mean-min@val,max@val-mean)
2907	'Saroundminmax@val': for minval,maxval
2908	'Saroundpercentile@val': for median-xtrm,median+xtrm where xtrm = np.min(median-percentile_(val),
2909	percentile_(100-val)-median)
2910	'Smean@val': for -xtrm,xtrm where xtrm = np.min(mean-min@val,max@val-mean)
2911	'Smedian@val': for -xtrm,xtrm where xtrm = np.min(median-min@val,max@val-median)
2912	'Spercentile@val': for -xtrm,xtrm where xtrm = np.min(median-percentile_(val),
2913	percentile_(100-val)-median)
2914	uts= units of the variable to shadow
2915	vtit= title of the variable
2916	kfig= kind of figure (jpg, pdf, png)
2917	reva= ('\|' for combination)
2918	* 'transpose': reverse the axes (x-->y, y-->x)
2919	* 'flip'@[x/y]: flip the axis x or y
2920	mapv= map characteristics: [proj],[res]
2921	see full documentation: http://matplotlib.org/basemap/
2922	[proj]: projection
2923	* 'cyl', cilindric
2924	* 'lcc', lambert conformal
2925	[res]: resolution:
2926	* 'c', crude
2927	* 'l', low
2928	* 'i', intermediate
2929	* 'h', high
2930	* 'f', full
2931	ifclose= boolean value whether figure should be close (finish) or not
2932	"""
2933	## import matplotlib as mpl
2934	## mpl.use('Agg')
2935	## import matplotlib.pyplot as plt
2936	fname = 'plot_2D_shadow'
2937
2938	# print dimyv[73,21]
2939	# dimyv[73,21] = -dimyv[73,21]
2940	# print 'Lluis dimsv: ',np.min(dimxv), np.max(dimxv), ':', np.min(dimyv), np.max(dimyv)
2941
2942	if varsv == 'h':
2943	print fname + '_____________________________________________________________'
2944	print plot_2D_shadow.__doc__
2945	quit()
2946
2947	if len(varsv.shape) != 2:
2948	print errormsg
2949	print ' ' + fname + ': wrong variable shape:',varv.shape,'is has to be 2D!!'
2950	quit(-1)
2951
2952	reva0 = ''
2953	if reva.find('\|') != 0:
2954	revas = reva.split('\|')
2955	else:
2956	revas = [reva]
2957	reva0 = reva
2958
2959	for rev in revas:
2960	if reva[0:4] == 'flip':
2961	reva0 = 'flip'
2962	if len(reva.split('@')) != 2:
2963	print errormsg
2964	print ' ' + fname + ': flip is given', reva, 'but not axis!'
2965	quit(-1)
2966
2967	if rev == 'transpose':
2968	print ' reversing the axes of the figure (x-->y, y-->x)!!'
2969	varsv = np.transpose(varsv)
2970	dxv = dimyv
2971	dyv = dimxv
2972	dimxv = dxv
2973	dimyv = dyv
2974
2975	if len(dimxv[:].shape) == 3:
2976	xdims = '1,2'
2977	elif len(dimxv[:].shape) == 2:
2978	xdims = '0,1'
2979	elif len(dimxv[:].shape) == 1:
2980	xdims = '0'
2981
2982	if len(dimyv[:].shape) == 3:
2983	ydims = '1,2'
2984	elif len(dimyv[:].shape) == 2:
2985	ydims = '0,1'
2986	elif len(dimyv[:].shape) == 1:
2987	ydims = '0'
2988
2989	lon0, lat0 = dxdy_lonlat(dimxv,dimyv, xdims, ydims)
2990
2991	if not mapv is None:
2992	map_proj=mapv.split(',')[0]
2993	map_res=mapv.split(',')[1]
2994
2995	dx = lon0.shape[1]
2996	dy = lat0.shape[0]
2997
2998	nlon = lon0[0,0]
2999	xlon = lon0[dy-1,dx-1]
3000	nlat = lat0[0,0]
3001	xlat = lat0[dy-1,dx-1]
3002
3003	# Thats too much! :)
3004	# if lonlatLims is not None:
3005	# print ' ' + fname + ': cutting the domain to plot !!!!'
3006	# plt.xlim(lonlatLims[0], lonlatLims[2])
3007	# plt.ylim(lonlatLims[1], lonlatLims[3])
3008	# print ' limits: W-E', lonlatLims[0], lonlatLims[2]
3009	# print ' limits: N-S', lonlatLims[1], lonlatLims[3]
3010
3011	# if map_proj == 'cyl':
3012	# nlon = lonlatLims[0]
3013	# nlat = lonlatLims[1]
3014	# xlon = lonlatLims[2]
3015	# xlat = lonlatLims[3]
3016	# elif map_proj == 'lcc':
3017	# lon2 = (lonlatLims[0] + lonlatLims[2])/2.
3018	# lat2 = (lonlatLims[1] + lonlatLims[3])/2.
3019	# nlon = lonlatLims[0]
3020	# xlon = lonlatLims[2]
3021	# nlat = lonlatLims[1]
3022	# xlat = lonlatLims[3]
3023
3024	lon2 = lon0[dy/2,dx/2]
3025	lat2 = lat0[dy/2,dx/2]
3026
3027	print 'lon2:', lon2, 'lat2:', lat2, 'SW pt:', nlon, ',', nlat, 'NE pt:', \
3028	xlon, ',', xlat
3029
3030	if map_proj == 'cyl':
3031	m = Basemap(projection=map_proj, llcrnrlon=nlon, llcrnrlat=nlat, \
3032	urcrnrlon=xlon, urcrnrlat= xlat, resolution=map_res)
3033	elif map_proj == 'lcc':
3034	m = Basemap(projection=map_proj, lat_0=lat2, lon_0=lon2, llcrnrlon=nlon, \
3035	llcrnrlat=nlat, urcrnrlon=xlon, urcrnrlat= xlat, resolution=map_res)
3036	else:
3037	print errormsg
3038	print ' ' + fname + ": map projection '" + map_proj + "' not defined!!!"
3039	print ' available: cyl, lcc'
3040	quit(-1)
3041
3042	x,y = m(lon0,lat0)
3043
3044	else:
3045	x = lon0
3046	y = lat0
3047
3048	vsend = np.zeros((2), dtype=np.float)
3049	# Changing limits of the colors
3050	if type(vs[0]) != type(np.float(1.)):
3051	if vs[0] == 'Srange':
3052	vsend[0] = np.min(varsv)
3053	elif vs[0][0:11] == 'Saroundmean':
3054	meanv = np.mean(varsv)
3055	permean = np.float(vs[0].split('@')[1])
3056	minv = np.min(varsv)*permean
3057	maxv = np.max(varsv)*permean
3058	minextrm = np.min([np.abs(meanv-minv), np.abs(maxv-meanv)])
3059	vsend[0] = meanv-minextrm
3060	vsend[1] = meanv+minextrm
3061	elif vs[0][0:13] == 'Saroundminmax':
3062	permean = np.float(vs[0].split('@')[1])
3063	minv = np.min(varsv)*permean
3064	maxv = np.max(varsv)*permean
3065	vsend[0] = minv
3066	vsend[1] = maxv
3067	elif vs[0][0:17] == 'Saroundpercentile':
3068	medianv = np.median(varsv)
3069	valper = np.float(vs[0].split('@')[1])
3070	minv = np.percentile(varsv, valper)
3071	maxv = np.percentile(varsv, 100.-valper)
3072	minextrm = np.min([np.abs(medianv-minv), np.abs(maxv-medianv)])
3073	vsend[0] = medianv-minextrm
3074	vsend[1] = medianv+minextrm
3075	elif vs[0][0:5] == 'Smean':
3076	meanv = np.mean(varsv)
3077	permean = np.float(vs[0].split('@')[1])
3078	minv = np.min(varsv)*permean
3079	maxv = np.max(varsv)*permean
3080	minextrm = np.min([np.abs(meanv-minv), np.abs(maxv-meanv)])
3081	vsend[0] = -minextrm
3082	vsend[1] = minextrm
3083	elif vs[0][0:7] == 'Smedian':
3084	medianv = np.median(varsv)
3085	permedian = np.float(vs[0].split('@')[1])
3086	minv = np.min(varsv)*permedian
3087	maxv = np.max(varsv)*permedian
3088	minextrm = np.min([np.abs(medianv-minv), np.abs(maxv-medianv)])
3089	vsend[0] = -minextrm
3090	vsend[1] = minextrm
3091	elif vs[0][0:11] == 'Spercentile':
3092	medianv = np.median(varsv)
3093	valper = np.float(vs[0].split('@')[1])
3094	minv = np.percentile(varsv, valper)
3095	maxv = np.percentile(varsv, 100.-valper)
3096	minextrm = np.min([np.abs(medianv-minv), np.abs(maxv-medianv)])
3097	vsend[0] = -minextrm
3098	vsend[1] = minextrm
3099	else:
3100	print errormsg
3101	print ' ' + fname + ": range '" + vs[0] + "' not ready!!!"
3102	quit(-1)
3103	print ' ' + fname + ': modified shadow min,max:',vsend
3104	else:
3105	vsend[0] = vs[0]
3106
3107	if type(vs[0]) != type(np.float(1.)):
3108	if vs[1] == 'range':
3109	vsend[1] = np.max(varsv)
3110	else:
3111	vsend[1] = vs[1]
3112
3113	plt.rc('text', usetex=True)
3114
3115	plt.pcolormesh(x, y, varsv, cmap=plt.get_cmap(colorbar), vmin=vsend[0], vmax=vsend[1])
3116	cbar = plt.colorbar()
3117
3118	if not mapv is None:
3119	m.drawcoastlines()
3120
3121	meridians = pretty_int(nlon,xlon,5)
3122	m.drawmeridians(meridians,labels=[True,False,False,True])
3123	parallels = pretty_int(nlat,xlat,5)
3124	m.drawparallels(parallels,labels=[False,True,True,False])
3125
3126	plt.xlabel('W-E')
3127	plt.ylabel('S-N')
3128	else:
3129	plt.xlabel(variables_values(dimn[1])[0].replace('_','\_') + ' (' + \
3130	units_lunits(dimxu) + ')')
3131	plt.ylabel(variables_values(dimn[0])[0].replace('_','\_') + ' (' + \
3132	units_lunits(dimyu) + ')')
3133
3134	txpos = pretty_int(x.min(),x.max(),5)
3135	typos = pretty_int(y.min(),y.max(),5)
3136	txlabels = list(txpos)
3137	for i in range(len(txlabels)): txlabels[i] = str(txlabels[i])
3138	tylabels = list(typos)
3139	for i in range(len(tylabels)): tylabels[i] = str(tylabels[i])
3140
3141	# set the limits of the plot to the limits of the data
3142
3143	if searchInlist(revas,'transpose'):
3144	x0 = y
3145	y0 = x
3146	x = x0
3147	y = y0
3148	# print 'Lluis reva0:',reva0,'x min,max:',x.min(),x.max(),' y min,max:',y.min(),y.max()
3149
3150	if reva0 == 'flip':
3151	if reva.split('@')[1] == 'x':
3152	plt.axis([x.max(), x.min(), y.min(), y.max()])
3153	elif reva.split('@')[1] == 'y':
3154	plt.axis([x.min(), x.max(), y.max(), y.min()])
3155	else:
3156	plt.axis([x.max(), x.min(), y.max(), y.min()])
3157	else:
3158	plt.axis([x.min(), x.max(), y.min(), y.max()])
3159
3160	if mapv is None:
3161	plt.xticks(txpos, txlabels)
3162	plt.yticks(typos, tylabels)
3163
3164	# units labels
3165	cbar.set_label(vnames.replace('_','\_') + ' (' + units_lunits(uts) + ')')
3166
3167	figname = '2Dfields_shadow'
3168	graphtit = vtit.replace('_','\_').replace('&','\&')
3169
3170	plt.title(graphtit)
3171
3172	output_kind(kfig, figname, ifclose)
3173
3174	return
3175
3176	#Nvals=50
3177	#vals1 = np.zeros((Nvals,Nvals), dtype= np.float)
3178	#for j in range(Nvals):
3179	# for i in range(Nvals):
3180	# vals1[j,i]=np.sqrt((j-Nvals/2)2. + (i-Nvals/2)2.)
3181
3182	#plot_2D_shadow(vals1, 'var1', np.arange(50)1., np.arange(50)1., 'ms-1', \
3183	# 'm', ['lat','lon'], 'rainbow', [0, Nvals], 'ms-1', 'test var1', 'pdf', 'None', \
3184	# None, True)
3185	#quit()
3186
3187	def plot_2D_shadow_time(varsv,vnames,dimxv,dimyv,dimxu,dimyu,dimn,colorbar,vs,uts, \
3188	vtit,kfig,reva,taxis,tpos,tlabs,ifclose):
3189	""" Plotting a 2D field with one of the axes being time
3190	varsv= 2D values to plot with shading
3191	vnames= shading variable name for the figure
3192	dim[x/y]v= values at the axes of x and y
3193	dim[x/y]u= units at the axes of x and y
3194	dimn= dimension names to plot
3195	colorbar= name of the color bar to use
3196	vs= minmum and maximum values to plot in shadow or:
3197	'Srange': for full range
3198	'Saroundmean@val': for mean-xtrm,mean+xtrm where xtrm = np.min(mean-min@val,max@val-mean)
3199	'Saroundminmax@val': for minval,maxval
3200	'Saroundpercentile@val': for median-xtrm,median+xtrm where xtrm = np.min(median-percentile_(val),
3201	percentile_(100-val)-median)
3202	'Smean@val': for -xtrm,xtrm where xtrm = np.min(mean-min@val,max@val-mean)
3203	'Smedian@val': for -xtrm,xtrm where xtrm = np.min(median-min@val,max@val-median)
3204	'Spercentile@val': for -xtrm,xtrm where xtrm = np.min(median-percentile_(val),
3205	percentile_(100-val)-median)
3206	uts= units of the variable to shadow
3207	vtit= title of the variable
3208	kfig= kind of figure (jpg, pdf, png)
3209	reva=
3210	* 'transpose': reverse the axes (x-->y, y-->x)
3211	* 'flip'@[x/y]: flip the axis x or y
3212	taxis= Which is the time-axis
3213	tpos= positions of the time ticks
3214	tlabs= labels of the time ticks
3215	ifclose= boolean value whether figure should be close (finish) or not
3216	"""
3217	fname = 'plot_2D_shadow_time'
3218
3219	if varsv == 'h':
3220	print fname + '_____________________________________________________________'
3221	print plot_2D_shadow_time.__doc__
3222	quit()
3223
3224	# Definning ticks labels
3225	if taxis == 'x':
3226	txpos = tpos
3227	txlabels = tlabs
3228	plxlabel = dimxu
3229	typos = pretty_int(np.min(dimyv),np.max(dimyv),10)
3230	tylabels = list(typos)
3231	for i in range(len(tylabels)): tylabels[i] = str(tylabels[i])
3232	plylabel = variables_values(dimn[0])[0].replace('_','\_') + ' (' + \
3233	units_lunits(dimyu) + ')'
3234	else:
3235	txpos = pretty_int(np.min(dimxv),np.max(dimxv),10)
3236	txlabels = list(txpos)
3237	plxlabel = variables_values(dimn[1])[0].replace('_','\_') + ' (' + \
3238	units_lunits(dimxu) + ')'
3239	typos = tpos
3240	tylabels = tlabs
3241	plylabel = dimyu
3242
3243	# Transposing/flipping axis
3244	if reva.find('\|') != 0:
3245	revas = reva.split('\|')
3246	reva0 = ''
3247	else:
3248	revas = [reva]
3249	reva0 = reva
3250
3251	for rev in revas:
3252	if rev[0:4] == 'flip':
3253	reva0 = 'flip'
3254	if len(reva.split('@')) != 2:
3255	print errormsg
3256	print ' ' + fname + ': flip is given', reva, 'but not axis!'
3257	quit(-1)
3258	else:
3259	print " flipping '" + rev.split('@')[1] + "' axis !"
3260
3261	if rev == 'transpose':
3262	print ' reversing the axes of the figure (x-->y, y-->x)!!'
3263	# Flipping values of variable
3264	varsv = np.transpose(varsv)
3265	dxv = dimyv
3266	dyv = dimxv
3267	dimxv = dxv
3268	dimyv = dyv
3269
3270	if len(dimxv.shape) == 3:
3271	dxget='1,2'
3272	elif len(dimxv.shape) == 2:
3273	dxget='0,1'
3274	elif len(dimxv.shape) == 1:
3275	dxget='0'
3276	else:
3277	print errormsg
3278	print ' ' + fname + ': shape of x-values:',dimxv.shape,'not ready!!'
3279	quit(-1)
3280
3281	if len(dimyv.shape) == 3:
3282	dyget='1,2'
3283	elif len(dimyv.shape) == 2:
3284	dyget='0,1'
3285	elif len(dimyv.shape) == 1:
3286	dyget='0'
3287	else:
3288	print errormsg
3289	print ' ' + fname + ': shape of y-values:',dimyv.shape,'not ready!!'
3290	quit(-1)
3291
3292	x,y = dxdy_lonlat(dimxv,dimyv,dxget,dyget)
3293
3294	plt.rc('text', usetex=True)
3295
3296	vsend = np.zeros((2), dtype=np.float)
3297	# Changing limits of the colors
3298	if type(vs[0]) != type(np.float(1.)):
3299	if vs[0] == 'Srange':
3300	vsend[0] = np.min(varsv)
3301	elif vs[0][0:11] == 'Saroundmean':
3302	meanv = np.mean(varsv)
3303	permean = np.float(vs[0].split('@')[1])
3304	minv = np.min(varsv)*permean
3305	maxv = np.max(varsv)*permean
3306	minextrm = np.min([np.abs(meanv-minv), np.abs(maxv-meanv)])
3307	vsend[0] = meanv-minextrm
3308	vsend[1] = meanv+minextrm
3309	elif vs[0][0:13] == 'Saroundminmax':
3310	permean = np.float(vs[0].split('@')[1])
3311	minv = np.min(varsv)*permean
3312	maxv = np.max(varsv)*permean
3313	vsend[0] = minv
3314	vsend[1] = maxv
3315	elif vs[0][0:17] == 'Saroundpercentile':
3316	medianv = np.median(varsv)
3317	valper = np.float(vs[0].split('@')[1])
3318	minv = np.percentile(varsv, valper)
3319	maxv = np.percentile(varsv, 100.-valper)
3320	minextrm = np.min([np.abs(medianv-minv), np.abs(maxv-medianv)])
3321	vsend[0] = medianv-minextrm
3322	vsend[1] = medianv+minextrm
3323	elif vs[0][0:5] == 'Smean':
3324	meanv = np.mean(varsv)
3325	permean = np.float(vs[0].split('@')[1])
3326	minv = np.min(varsv)*permean
3327	maxv = np.max(varsv)*permean
3328	minextrm = np.min([np.abs(meanv-minv), np.abs(maxv-meanv)])
3329	vsend[0] = -minextrm
3330	vsend[1] = minextrm
3331	elif vs[0][0:7] == 'Smedian':
3332	medianv = np.median(varsv)
3333	permedian = np.float(vs[0].split('@')[1])
3334	minv = np.min(varsv)*permedian
3335	maxv = np.max(varsv)*permedian
3336	minextrm = np.min([np.abs(medianv-minv), np.abs(maxv-medianv)])
3337	vsend[0] = -minextrm
3338	vsend[1] = minextrm
3339	elif vs[0][0:11] == 'Spercentile':
3340	medianv = np.median(varsv)
3341	valper = np.float(vs[0].split('@')[1])
3342	minv = np.percentile(varsv, valper)
3343	maxv = np.percentile(varsv, 100.-valper)
3344	minextrm = np.min([np.abs(medianv-minv), np.abs(maxv-medianv)])
3345	vsend[0] = -minextrm
3346	vsend[1] = minextrm
3347	else:
3348	print errormsg
3349	print ' ' + fname + ": range '" + vs[0] + "' not ready!!!"
3350	quit(-1)
3351	print ' ' + fname + ': modified shadow min,max:',vsend
3352	else:
3353	vsend[0] = vs[0]
3354
3355	if type(vs[0]) != type(np.float(1.)):
3356	if vs[1] == 'range':
3357	vsend[1] = np.max(varsv)
3358	else:
3359	vsend[1] = vs[1]
3360
3361	plt.pcolormesh(x, y, varsv, cmap=plt.get_cmap(colorbar), vmin=vsend[0], vmax=vsend[1])
3362	cbar = plt.colorbar()
3363
3364	# print 'Lluis reva0:',reva0,'x min,max:',x.min(),x.max(),' y min,max:',y.min(),y.max()
3365
3366	# set the limits of the plot to the limits of the data
3367	if reva0 == 'flip':
3368	if reva.split('@')[1] == 'x':
3369	plt.axis([x.max(), x.min(), y.min(), y.max()])
3370	elif reva.split('@')[1] == 'y':
3371	plt.axis([x.min(), x.max(), y.max(), y.min()])
3372	else:
3373	plt.axis([x.max(), x.min(), y.max(), y.min()])
3374	else:
3375	plt.axis([x.min(), x.max(), y.min(), y.max()])
3376
3377	if searchInlist(revas, 'transpose'):
3378	plt.xticks(typos, tylabels)
3379	plt.yticks(txpos, txlabels)
3380	plt.xlabel(plylabel)
3381	plt.ylabel(plxlabel)
3382	else:
3383	plt.xticks(txpos, txlabels)
3384	plt.yticks(typos, tylabels)
3385	plt.xlabel(plxlabel)
3386	plt.ylabel(plylabel)
3387
3388	# units labels
3389	cbar.set_label(vnames.replace('_','\_') + ' (' + units_lunits(uts) + ')')
3390
3391	figname = '2Dfields_shadow_time'
3392	graphtit = vtit.replace('_','\_').replace('&','\&')
3393
3394	plt.title(graphtit)
3395
3396	output_kind(kfig, figname, ifclose)
3397
3398	return
3399
3400	def plot_2D_shadow_contour(varsv,varcv,vnames,dimxv,dimyv,dimxu,dimyu,dimn, \
3401	colorbar,ckind,clabfmt,vs,vc,uts,vtit,kfig,reva,mapv):
3402	""" Adding labels and other staff to the graph
3403	varsv= 2D values to plot with shading
3404	varcv= 2D values to plot with contours
3405	vnames= variable names for the figure
3406	dim[x/y]v = values at the axes of x and y
3407	dim[x/y]u = units at the axes of x and y
3408	dimn= dimension names to plot
3409	colorbar= name of the color bar to use
3410	ckind= contour kind
3411	'cmap': as it gets from colorbar
3412	'fixc,[colname]': fixed color [colname], all stright lines
3413	'fixsigc,[colname]': fixed color [colname], >0 stright, <0 dashed line
3414	clabfmt= format of the labels in the contour plot (None, no labels)
3415	vs= minmum and maximum values to plot in shadow
3416	vc= vector with the levels for the contour
3417	uts= units of the variable [u-shadow, u-contour]
3418	vtit= title of the variable
3419	kfig= kind of figure (jpg, pdf, png)
3420	reva=
3421	* 'transpose': reverse the axes (x-->y, y-->x)
3422	* 'flip'@[x/y]: flip the axis x or y
3423	mapv= map characteristics: [proj],[res]
3424	see full documentation: http://matplotlib.org/basemap/
3425	[proj]: projection
3426	* 'cyl', cilindric
3427	* 'lcc', lamvbert conformal
3428	[res]: resolution:
3429	* 'c', crude
3430	* 'l', low
3431	* 'i', intermediate
3432	* 'h', high
3433	* 'f', full
3434	"""
3435	## import matplotlib as mpl
3436	## mpl.use('Agg')
3437	## import matplotlib.pyplot as plt
3438	fname = 'plot_2D_shadow_contour'
3439
3440	if varsv == 'h':
3441	print fname + '_____________________________________________________________'
3442	print plot_2D_shadow_contour.__doc__
3443	quit()
3444
3445	if reva[0:4] == 'flip':
3446	reva0 = 'flip'
3447	if len(reva.split('@')) != 2:
3448	print errormsg
3449	print ' ' + fname + ': flip is given', reva, 'but not axis!'
3450	quit(-1)
3451	else:
3452	reva0 = reva
3453
3454	if reva0 == 'transpose':
3455	print ' reversing the axes of the figure (x-->y, y-->x)!!'
3456	varsv = np.transpose(varsv)
3457	varcv = np.transpose(varcv)
3458	dxv = dimyv
3459	dyv = dimxv
3460	dimxv = dxv
3461	dimyv = dyv
3462
3463	if not mapv is None:
3464	if len(dimxv[:].shape) == 3:
3465	lon0 = dimxv[0,]
3466	lat0 = dimyv[0,]
3467	elif len(dimxv[:].shape) == 2:
3468	lon0 = dimxv[:]
3469	lat0 = dimyv[:]
3470	elif len(dimxv[:].shape) == 1:
3471	lon00 = dimxv[:]
3472	lat00 = dimyv[:]
3473	lon0 = np.zeros( (len(lat00),len(lon00)), dtype=np.float )
3474	lat0 = np.zeros( (len(lat00),len(lon00)), dtype=np.float )
3475
3476	for iy in range(len(lat00)):
3477	lon0[iy,:] = lon00
3478	for ix in range(len(lon00)):
3479	lat0[:,ix] = lat00
3480
3481	map_proj=mapv.split(',')[0]
3482	map_res=mapv.split(',')[1]
3483
3484	dx = lon0.shape[1]
3485	dy = lon0.shape[0]
3486
3487	nlon = lon0[0,0]
3488	xlon = lon0[dy-1,dx-1]
3489	nlat = lat0[0,0]
3490	xlat = lat0[dy-1,dx-1]
3491
3492	# Thats too much! :)
3493	# if lonlatLims is not None:
3494	# print ' ' + fname + ': cutting the domain to plot !!!!'
3495	# plt.xlim(lonlatLims[0], lonlatLims[2])
3496	# plt.ylim(lonlatLims[1], lonlatLims[3])
3497	# print ' limits: W-E', lonlatLims[0], lonlatLims[2]
3498	# print ' limits: N-S', lonlatLims[1], lonlatLims[3]
3499
3500	# if map_proj == 'cyl':
3501	# nlon = lonlatLims[0]
3502	# nlat = lonlatLims[1]
3503	# xlon = lonlatLims[2]
3504	# xlat = lonlatLims[3]
3505	# elif map_proj == 'lcc':
3506	# lon2 = (lonlatLims[0] + lonlatLims[2])/2.
3507	# lat2 = (lonlatLims[1] + lonlatLims[3])/2.
3508	# nlon = lonlatLims[0]
3509	# xlon = lonlatLims[2]
3510	# nlat = lonlatLims[1]
3511	# xlat = lonlatLims[3]
3512
3513	lon2 = lon0[dy/2,dx/2]
3514	lat2 = lat0[dy/2,dx/2]
3515
3516	print 'lon2:', lon2, 'lat2:', lat2, 'SW pt:', nlon, ',', nlat, 'NE pt:', \
3517	xlon, ',', xlat
3518
3519	if map_proj == 'cyl':
3520	m = Basemap(projection=map_proj, llcrnrlon=nlon, llcrnrlat=nlat, \
3521	urcrnrlon=xlon, urcrnrlat= xlat, resolution=map_res)
3522	elif map_proj == 'lcc':
3523	m = Basemap(projection=map_proj, lat_0=lat2, lon_0=lon2, llcrnrlon=nlon, \
3524	llcrnrlat=nlat, urcrnrlon=xlon, urcrnrlat= xlat, resolution=map_res)
3525
3526	if len(dimxv.shape) == 1:
3527	lons, lats = np.meshgrid(dimxv, dimyv)
3528	else:
3529	if len(dimxv.shape) == 3:
3530	lons = dimxv[0,:,:]
3531	lats = dimyv[0,:,:]
3532	else:
3533	lons = dimxv[:]
3534	lats = dimyv[:]
3535
3536	x,y = m(lons,lats)
3537
3538	else:
3539	if len(dimxv.shape) == 2:
3540	x = dimxv
3541	else:
3542	if len(dimyv.shape) == 1:
3543	x = np.zeros((len(dimyv),len(dimxv)), dtype=np.float)
3544	for j in range(len(dimyv)):
3545	x[j,:] = dimxv
3546	else:
3547	x = np.zeros((dimyv.shape), dtype=np.float)
3548	if x.shape[0] == dimxv.shape[0]:
3549	for j in range(x.shape[1]):
3550	x[:,j] = dimxv
3551	else:
3552	for j in range(x.shape[0]):
3553	x[j,:] = dimxv
3554
3555	if len(dimyv.shape) == 2:
3556	y = dimyv
3557	else:
3558	if len(dimxv.shape) == 1:
3559	y = np.zeros((len(dimyv),len(dimxv)), dtype=np.float)
3560	for i in range(len(dimxv)):
3561	y[:,i] = dimyv
3562	else:
3563	y = np.zeros((dimxv.shape), dtype=np.float)
3564	if y.shape[0] == dimyv.shape[0]:
3565	for i in range(y.shape[1]):
3566	y[:,i] = dimyv
3567	else:
3568	for i in range(y.shape[0]):
3569	y[i,:] = dimyv
3570
3571	# plt.rc('text', usetex=True)
3572
3573	plt.pcolormesh(x, y, varsv, cmap=plt.get_cmap(colorbar), vmin=vs[0], vmax=vs[1])
3574	cbar = plt.colorbar()
3575
3576	# contour
3577	##
3578	contkind = ckind.split(',')[0]
3579	if contkind == 'cmap':
3580	cplot = plt.contour(x, y, varcv, levels=vc)
3581	elif contkind == 'fixc':
3582	plt.rcParams['contour.negative_linestyle'] = 'solid'
3583	coln = ckind.split(',')[1]
3584	cplot = plt.contour(x, y, varcv, levels=vc, colors=coln)
3585	elif contkind == 'fixsigc':
3586	coln = ckind.split(',')[1]
3587	cplot = plt.contour(x, y, varcv, levels=vc, colors=coln)
3588	else:
3589	print errormsg
3590	print ' ' + fname + ': contour kind "' + contkind + '" not defined !!!!!'
3591	quit(-1)
3592
3593	if clabfmt is not None:
3594	plt.clabel(cplot, fmt=clabfmt)
3595	mincntS = format(vc[0], clabfmt[1:len(clabfmt)])
3596	maxcntS = format(vc[len(vc)-1], clabfmt[1:len(clabfmt)])
3597	else:
3598	mincntS = '{:g}'.format(vc[0])
3599	maxcntS = '{:g}'.format(vc[len(vc)-1])
3600
3601	if not mapv is None:
3602	m.drawcoastlines()
3603
3604	meridians = pretty_int(nlon,xlon,5)
3605	m.drawmeridians(meridians,labels=[True,False,False,True])
3606	parallels = pretty_int(nlat,xlat,5)
3607	m.drawparallels(parallels,labels=[False,True,True,False])
3608
3609	plt.xlabel('W-E')
3610	plt.ylabel('S-N')
3611	else:
3612	plt.xlabel(variables_values(dimn[1])[0] + ' (' + units_lunits(dimxu) + ')')
3613	plt.ylabel(variables_values(dimn[0])[0] + ' (' + units_lunits(dimyu) + ')')
3614
3615	txpos = pretty_int(x.min(),x.max(),10)
3616	typos = pretty_int(y.min(),y.max(),10)
3617	txlabels = list(txpos)
3618	for i in range(len(txlabels)): txlabels[i] = str(txlabels[i])
3619	tylabels = list(typos)
3620	for i in range(len(tylabels)): tylabels[i] = str(tylabels[i])
3621
3622	# set the limits of the plot to the limits of the data
3623	if reva0 == 'flip':
3624	if reva.split('@')[1] == 'x':
3625	plt.axis([x.max(), x.min(), y.min(), y.max()])
3626	else:
3627	plt.axis([x.min(), x.max(), y.max(), y.min()])
3628	else:
3629	plt.axis([x.min(), x.max(), y.min(), y.max()])
3630
3631	plt.xticks(txpos, txlabels)
3632	plt.yticks(typos, tylabels)
3633
3634	# units labels
3635	cbar.set_label(vnames[0].replace('_','\_') + ' (' + units_lunits(uts[0]) + ')')
3636	plt.annotate(vnames[1].replace('_','\_') +' (' + units_lunits(uts[1]) + ') [' + \
3637	mincntS + ', ' + maxcntS + ']', xy=(0.55,0.04), xycoords='figure fraction', \
3638	color=coln)
3639
3640	figname = '2Dfields_shadow-contour'
3641	graphtit = vtit.replace('_','\_').replace('&','\&')
3642
3643	plt.title(graphtit)
3644
3645	output_kind(kfig, figname, True)
3646
3647	return
3648
3649	#Nvals=50
3650	#vals1 = np.zeros((Nvals,Nvals), dtype= np.float)
3651	#vals2 = np.zeros((Nvals,Nvals), dtype= np.float)
3652	#for j in range(Nvals):
3653	# for i in range(Nvals):
3654	# vals1[j,i]=np.sqrt((j-Nvals/2)2. + (i-Nvals/2)2.)
3655	# vals2[j,i]=np.sqrt((j-Nvals/2)2. + (i-Nvals/2)2.) - Nvals/2
3656
3657	#prettylev=pretty_int(-Nvals/2,Nvals/2,10)
3658
3659	#plot_2D_shadow_contour(vals1, vals2, ['var1', 'var2'], np.arange(50)*1., \
3660	# np.arange(50)*1., ['x-axis','y-axis'], 'rainbow', 'fixc,b', "%.2f", [0, Nvals], \
3661	# prettylev, ['$ms^{-1}$','$kJm^{-1}s^{-1}$'], 'test var1 & var2', 'pdf', False)
3662
3663	def plot_2D_shadow_contour_time(varsv,varcv,vnames,valv,timv,timpos,timlab,valu, \
3664	timeu,axist,dimn,colorbar,ckind,clabfmt,vs,vc,uts,vtit,kfig,reva,mapv):
3665	""" Adding labels and other staff to the graph
3666	varsv= 2D values to plot with shading
3667	varcv= 2D values to plot with contours
3668	vnames= variable names for the figure
3669	valv = values at the axes which is not time
3670	timv = values for the axis time
3671	timpos = positions at the axis time
3672	timlab = labes at the axis time
3673	valu = units at the axes which is not time
3674	timeu = units at the axes which is not time
3675	axist = which is the axis time
3676	dimn= dimension names to plot
3677	colorbar= name of the color bar to use
3678	ckind= contour kind
3679	'cmap': as it gets from colorbar
3680	'fixc,[colname]': fixed color [colname], all stright lines
3681	'fixsigc,[colname]': fixed color [colname], >0 stright, <0 dashed line
3682	clabfmt= format of the labels in the contour plot (None, no labels)
3683	vs= minmum and maximum values to plot in shadow
3684	vc= vector with the levels for the contour
3685	uts= units of the variable [u-shadow, u-contour]
3686	vtit= title of the variable
3687	kfig= kind of figure (jpg, pdf, png)
3688	reva=
3689	* 'transpose': reverse the axes (x-->y, y-->x)
3690	* 'flip'@[x/y]: flip the axis x or y
3691	mapv= map characteristics: [proj],[res]
3692	see full documentation: http://matplotlib.org/basemap/
3693	[proj]: projection
3694	* 'cyl', cilindric
3695	* 'lcc', lamvbert conformal
3696	[res]: resolution:
3697	* 'c', crude
3698	* 'l', low
3699	* 'i', intermediate
3700	* 'h', high
3701	* 'f', full
3702	"""
3703	## import matplotlib as mpl
3704	## mpl.use('Agg')
3705	## import matplotlib.pyplot as plt
3706	fname = 'plot_2D_shadow_contour'
3707
3708	if varsv == 'h':
3709	print fname + '_____________________________________________________________'
3710	print plot_2D_shadow_contour.__doc__
3711	quit()
3712
3713	if axist == 'x':
3714	dimxv = timv.copy()
3715	dimyv = valv.copy()
3716	else:
3717	dimxv = valv.copy()
3718	dimyv = timv.copy()
3719
3720	if reva[0:4] == 'flip':
3721	reva0 = 'flip'
3722	if len(reva.split('@')) != 2:
3723	print errormsg
3724	print ' ' + fname + ': flip is given', reva, 'but not axis!'
3725	quit(-1)
3726	else:
3727	reva0 = reva
3728
3729	if reva0 == 'transpose':
3730	if axist == 'x':
3731	axist = 'y'
3732	else:
3733	axist = 'x'
3734
3735	if not mapv is None:
3736	if len(dimxv[:].shape) == 3:
3737	lon0 = dimxv[0,]
3738	lat0 = dimyv[0,]
3739	elif len(dimxv[:].shape) == 2:
3740	lon0 = dimxv[:]
3741	lat0 = dimyv[:]
3742	elif len(dimxv[:].shape) == 1:
3743	lon00 = dimxv[:]
3744	lat00 = dimyv[:]
3745	lon0 = np.zeros( (len(lat00),len(lon00)), dtype=np.float )
3746	lat0 = np.zeros( (len(lat00),len(lon00)), dtype=np.float )
3747
3748	for iy in range(len(lat00)):
3749	lon0[iy,:] = lon00
3750	for ix in range(len(lon00)):
3751	lat0[:,ix] = lat00
3752	if reva0 == 'transpose':
3753	print ' reversing the axes of the figure (x-->y, y-->x)!!'
3754	varsv = np.transpose(varsv)
3755	varcv = np.transpose(varcv)
3756	lon0 = np.transpose(lon0)
3757	lat0 = np.transpose(lat0)
3758
3759	map_proj=mapv.split(',')[0]
3760	map_res=mapv.split(',')[1]
3761
3762	dx = lon0.shape[1]
3763	dy = lon0.shape[0]
3764
3765	nlon = lon0[0,0]
3766	xlon = lon0[dy-1,dx-1]
3767	nlat = lat0[0,0]
3768	xlat = lat0[dy-1,dx-1]
3769
3770	# Thats too much! :)
3771	# if lonlatLims is not None:
3772	# print ' ' + fname + ': cutting the domain to plot !!!!'
3773	# plt.xlim(lonlatLims[0], lonlatLims[2])
3774	# plt.ylim(lonlatLims[1], lonlatLims[3])
3775	# print ' limits: W-E', lonlatLims[0], lonlatLims[2]
3776	# print ' limits: N-S', lonlatLims[1], lonlatLims[3]
3777
3778	# if map_proj == 'cyl':
3779	# nlon = lonlatLims[0]
3780	# nlat = lonlatLims[1]
3781	# xlon = lonlatLims[2]
3782	# xlat = lonlatLims[3]
3783	# elif map_proj == 'lcc':
3784	# lon2 = (lonlatLims[0] + lonlatLims[2])/2.
3785	# lat2 = (lonlatLims[1] + lonlatLims[3])/2.
3786	# nlon = lonlatLims[0]
3787	# xlon = lonlatLims[2]
3788	# nlat = lonlatLims[1]
3789	# xlat = lonlatLims[3]
3790
3791	lon2 = lon0[dy/2,dx/2]
3792	lat2 = lat0[dy/2,dx/2]
3793
3794	print 'lon2:', lon2, 'lat2:', lat2, 'SW pt:', nlon, ',', nlat, 'NE pt:', \
3795	xlon, ',', xlat
3796
3797	if map_proj == 'cyl':
3798	m = Basemap(projection=map_proj, llcrnrlon=nlon, llcrnrlat=nlat, \
3799	urcrnrlon=xlon, urcrnrlat= xlat, resolution=map_res)
3800	elif map_proj == 'lcc':
3801	m = Basemap(projection=map_proj, lat_0=lat2, lon_0=lon2, llcrnrlon=nlon, \
3802	llcrnrlat=nlat, urcrnrlon=xlon, urcrnrlat= xlat, resolution=map_res)
3803
3804	if len(dimxv.shape) == 1:
3805	lons, lats = np.meshgrid(dimxv, dimyv)
3806	else:
3807	if len(dimxv.shape) == 3:
3808	lons = dimxv[0,:,:]
3809	lats = dimyv[0,:,:]
3810	else:
3811	lons = dimxv[:]
3812	lats = dimyv[:]
3813
3814	x,y = m(lons,lats)
3815
3816	else:
3817	if reva0 == 'transpose':
3818	print ' reversing the axes of the figure (x-->y, y-->x)!!'
3819	varsv = np.transpose(varsv)
3820	varcv = np.transpose(varcv)
3821	dimn0 = []
3822	dimn0.append(dimn[1] + '')
3823	dimn0.append(dimn[0] + '')
3824	dimn = dimn0
3825	if len(dimyv.shape) == 2:
3826	x = np.transpose(dimyv)
3827	else:
3828	if len(dimxv.shape) == 2:
3829	ddx = len(dimyv)
3830	ddy = dimxv.shape[1]
3831	else:
3832	ddx = len(dimyv)
3833	ddy = len(dimxv)
3834
3835	x = np.zeros((ddy,ddx), dtype=np.float)
3836	for j in range(ddy):
3837	x[j,:] = dimyv
3838
3839	if len(dimxv.shape) == 2:
3840	y = np.transpose(dimxv)
3841	else:
3842	if len(dimyv.shape) == 2:
3843	ddx = dimyv.shape[0]
3844	ddy = len(dimxv)
3845	else:
3846	ddx = len(dimyv)
3847	ddy = len(dimxv)
3848
3849	y = np.zeros((ddy,ddx), dtype=np.float)
3850	for i in range(ddx):
3851	y[:,i] = dimxv
3852	else:
3853	if len(dimxv.shape) == 2:
3854	x = dimxv
3855	else:
3856	if len(dimyv.shape) == 1:
3857	x = np.zeros((len(dimyv),len(dimxv)), dtype=np.float)
3858	for j in range(len(dimyv)):
3859	x[j,:] = dimxv
3860	else:
3861	x = np.zeros((dimyv.shape), dtype=np.float)
3862	if x.shape[0] == dimxv.shape[0]:
3863	for j in range(x.shape[1]):
3864	x[:,j] = dimxv
3865	else:
3866	for j in range(x.shape[0]):
3867	x[j,:] = dimxv
3868
3869	if len(dimyv.shape) == 2:
3870	y = dimyv
3871	else:
3872	if len(dimxv.shape) == 1:
3873	y = np.zeros((len(dimyv),len(dimxv)), dtype=np.float)
3874	for i in range(len(dimxv)):
3875	y[:,i] = dimyv
3876	else:
3877	y = np.zeros((dimxv.shape), dtype=np.float)
3878	if y.shape[0] == dimyv.shape[0]:
3879	for i in range(y.shape[1]):
3880	y[:,i] = dimyv
3881	else:
3882	for i in range(y.shape[0]):
3883	y[i,:] = dimyv
3884
3885	dx=varsv.shape[1]
3886	dy=varsv.shape[0]
3887
3888	plt.rc('text', usetex=True)
3889
3890	if axist == 'x':
3891	valpos = pretty_int(y.min(),y.max(),10)
3892	vallabels = list(valpos)
3893	for i in range(len(vallabels)): vallabels[i] = str(vallabels[i])
3894	else:
3895	valpos = pretty_int(x.min(),x.max(),10)
3896	vallabels = list(valpos)
3897	for i in range(len(vallabels)): vallabels[i] = str(vallabels[i])
3898
3899	if reva0 == 'flip':
3900	if reva.split('@')[1] == 'x':
3901	varsv[:,0:dx-1] = varsv[:,dx-1:0:-1]
3902	varcv[:,0:dx-1] = varcv[:,dx-1:0:-1]
3903	plt.xticks(valpos, vallabels[::-1])
3904	else:
3905	varsv[0:dy-1,:] = varsv[dy-1:0:-1,:]
3906	varcv[0:dy-1,:] = varcv[dy-1:0:-1,:]
3907	plt.yticks(valpos, vallabels[::-1])
3908	else:
3909	plt.xlim(0,dx-1)
3910	plt.ylim(0,dy-1)
3911
3912	plt.pcolormesh(x, y, varsv, cmap=plt.get_cmap(colorbar), vmin=vs[0], vmax=vs[1])
3913	cbar = plt.colorbar()
3914
3915	# contour
3916	##
3917	contkind = ckind.split(',')[0]
3918	if contkind == 'cmap':
3919	cplot = plt.contour(x, y, varcv, levels=vc)
3920	elif contkind == 'fixc':
3921	plt.rcParams['contour.negative_linestyle'] = 'solid'
3922	coln = ckind.split(',')[1]
3923	cplot = plt.contour(x, y, varcv, levels=vc, colors=coln)
3924	elif contkind == 'fixsigc':
3925	coln = ckind.split(',')[1]
3926	cplot = plt.contour(x, y, varcv, levels=vc, colors=coln)
3927	else:
3928	print errormsg
3929	print ' ' + fname + ': contour kind "' + contkind + '" not defined !!!!!'
3930	quit(-1)
3931
3932	if clabfmt is not None:
3933	plt.clabel(cplot, fmt=clabfmt)
3934	mincntS = format(vc[0], clabfmt[1:len(clabfmt)])
3935	maxcntS = format(vc[len(vc)-1], clabfmt[1:len(clabfmt)])
3936	else:
3937	mincntS = '{:g}'.format(vc[0])
3938	maxcntS = '{:g}'.format(vc[len(vc)-1])
3939
3940	if not mapv is None:
3941	m.drawcoastlines()
3942
3943	meridians = pretty_int(nlon,xlon,5)
3944	m.drawmeridians(meridians,labels=[True,False,False,True])
3945	parallels = pretty_int(nlat,xlat,5)
3946	m.drawparallels(parallels,labels=[False,True,True,False])
3947
3948	plt.xlabel('W-E')
3949	plt.ylabel('S-N')
3950	else:
3951	if axist == 'x':
3952	plt.xlabel(timeu)
3953	plt.xticks(timpos, timlab)
3954	plt.ylabel(variables_values(dimn[0])[0] + ' (' + units_lunits(valu) + ')')
3955	plt.yticks(valpos, vallabels)
3956	else:
3957	plt.xlabel(variables_values(dimn[1])[0] + ' (' + units_lunits(valu) + ')')
3958	plt.xticks(valpos, vallabels)
3959	plt.ylabel(timeu)
3960	plt.yticks(timpos, timlab)
3961
3962	# set the limits of the plot to the limits of the data
3963	plt.axis([x.min(), x.max(), y.min(), y.max()])
3964
3965	# units labels
3966	cbar.set_label(vnames[0].replace('_','\_') + ' (' + units_lunits(uts[0]) + ')')
3967	plt.annotate(vnames[1].replace('_','\_') +' (' + units_lunits(uts[1]) + ') [' + \
3968	mincntS + ', ' + maxcntS + ']', xy=(0.55,0.04), xycoords='figure fraction', \
3969	color=coln)
3970
3971	figname = '2Dfields_shadow-contour'
3972	graphtit = vtit.replace('_','\_').replace('&','\&')
3973
3974	plt.title(graphtit)
3975
3976	output_kind(kfig, figname, True)
3977
3978	return
3979
3980	def dxdy_lonlat(dxv,dyv,ddx,ddy):
3981	""" Function to provide lon/lat 2D lilke-matrices from any sort of dx,dy values
3982	dxdy_lonlat(dxv,dyv,Lv,lv)
3983	dx: values for the x
3984	dy: values for the y
3985	ddx: ',' list of which dimensions to use from values along x
3986	ddy: ',' list of which dimensions to use from values along y
3987	"""
3988
3989	fname = 'dxdy_lonlat'
3990
3991	if ddx.find(',') > -1:
3992	dxk = 2
3993	ddxv = ddx.split(',')
3994	ddxy = int(ddxv[0])
3995	ddxx = int(ddxv[1])
3996	else:
3997	dxk = 1
3998	ddxy = int(ddx)
3999	ddxx = int(ddx)
4000
4001	if ddy.find(',') > -1:
4002	dyk = 2
4003	ddyv = ddy.split(',')
4004	ddyy = int(ddyv[0])
4005	ddyx = int(ddyv[1])
4006	else:
4007	dyk = 1
4008	ddyy = int(ddy)
4009	ddyx = int(ddy)
4010
4011	ddxxv = dxv.shape[ddxx]
4012	ddxyv = dxv.shape[ddxy]
4013	ddyxv = dyv.shape[ddyx]
4014	ddyyv = dyv.shape[ddyy]
4015
4016	slicex = []
4017	if len(dxv.shape) > 1:
4018	for idim in range(len(dxv.shape)):
4019	if idim == ddxx or idim == ddxy:
4020	slicex.append(slice(0,dxv.shape[idim]))
4021	else:
4022	slicex.append(0)
4023	else:
4024	slicex.append(slice(0,len(dxv)))
4025
4026	slicey = []
4027	if len(dyv.shape) > 1:
4028	for idim in range(len(dyv.shape)):
4029	if idim == ddyx or idim == ddyy:
4030	slicey.append(slice(0,dyv.shape[idim]))
4031	else:
4032	slicey.append(0)
4033	else:
4034	slicey.append(slice(0,len(dyv)))
4035
4036	# print ' ' + fname + ' Lluis shapes dxv:',dxv.shape,'dyv:',dyv.shape
4037	# print ' ' + fname + ' Lluis slicex:',slicex,'slicey:',slicey
4038
4039	if dxk == 2 and dyk == 2:
4040	if ddxxv != ddyxv:
4041	print errormsg
4042	print ' ' + fname + ': wrong dx dimensions! ddxx=',ddxxv,'ddyx=',ddyxv
4043	print ' choose another for x:',dxv.shape,'or y:',dyv.shape
4044	quit(-1)
4045	if ddxyv != ddyyv:
4046	print errormsg
4047	print ' ' + fname + ': wrong dy dimensions! ddxy=',ddxyv,'ddyy=',ddyv
4048	print ' choose another for x:',dxv.shape,'or y:',dyv.shape
4049	quit(-1)
4050	dx = ddxxv
4051	dy = ddxyv
4052
4053	print ' ' + fname + ': final dimension 2D lon/lat-like matrices:',dy,',',dx
4054	lonv = np.zeros((dy,dx), dtype=np.float)
4055	latv = np.zeros((dy,dx), dtype=np.float)
4056
4057
4058	lonv = dxv[tuple(slicex)]
4059	latv = dyv[tuple(slicey)]
4060
4061	elif dxk == 2 and dyk == 1:
4062	if not ddxxv == ddyxv and not ddxyv == ddyyv:
4063	print errormsg
4064	print ' ' + fname + ': wrong dimensions! ddxx=',ddxxv,'ddyx=',ddyxv, \
4065	'ddyx=',ddyxv,'ddyy=',ddyyv
4066	print ' choose another for x:',dxv.shape,'or y:',dyv.shape
4067	quit(-1)
4068	dx = ddxvv
4069	dy = ddxyv
4070
4071	print ' ' + fname + ': final dimension 2D lon/lat-like matrices:',dy,',',dx
4072	lonv = np.zeros((dy,dx), dtype=np.float)
4073	latv = np.zeros((dy,dx), dtype=np.float)
4074	lonv = dxv[tuple(slicex)]
4075
4076	if ddxxv == ddyxv:
4077	for iy in range(dy):
4078	latv[iy,:] = dyv[tuple(slicey)]
4079	else:
4080	for ix in range(dx):
4081	latv[:,ix] = dyv[tuple(slicey)]
4082
4083	elif dxk == 1 and dyk == 2:
4084	if not ddxxv == ddyxv and not ddxyv == ddyyv:
4085	print errormsg
4086	print ' ' + fname + ': wrong dimensions! ddxx=',ddxxv,'ddyx=',ddyxv, \
4087	'ddyx=',ddyxv,'ddyy=',ddyyv
4088	print ' choose another for x:',dxv.shape,'or y:',dyv.shape
4089	quit(-1)
4090	dx = ddyxv
4091	dy = ddyyv
4092
4093	print ' ' + fname + ': final dimension 2D lon/lat-like matrices:',dy,',',dx
4094	lonv = np.zeros((dy,dx), dtype=np.float)
4095	latv = np.zeros((dy,dx), dtype=np.float)
4096
4097	latv = dyv[tuple(slicey)]
4098
4099	if ddyxv == ddxxv:
4100	for iy in range(dy):
4101	lonv[iy,:] = dxv[tuple(slicex)]
4102	else:
4103	for ix in range(dx):
4104	lonv[:,ix] = dxv[tuple(slicex)]
4105
4106
4107	elif dxk == 1 and dyk == 1:
4108	dx = ddxxv
4109	dy = ddyyv
4110
4111	# print 'dx:',dx,'dy:',dy
4112
4113	lonv = np.zeros((dy,dx), dtype=np.float)
4114	latv = np.zeros((dy,dx), dtype=np.float)
4115
4116	for iy in range(dy):
4117	lonv[iy,:] = dxv[tuple(slicex)]
4118	for ix in range(dx):
4119	latv[:,ix] = dyv[tuple(slicey)]
4120
4121	return lonv,latv
4122
4123	def plot_2D_shadow_line(varsv,varlv,vnames,vnamel,dimxv,dimyv,dimxu,dimyu,dimn, \
4124	colorbar,colln,vs,uts,utl,vtit,kfig,reva,mapv,ifclose):
4125	""" Plotting a 2D field with shadows and another one with a line
4126	varsv= 2D values to plot with shading
4127	varlv= 1D values to plot with line
4128	vnames= variable names for the shadow variable in the figure
4129	vnamel= variable names for the line varibale in the figure
4130	dim[x/y]v = values at the axes of x and y
4131	dim[x/y]u = units at the axes of x and y
4132	dimn= dimension names to plot
4133	colorbar= name of the color bar to use
4134	colln= color for the line
4135	vs= minmum and maximum values to plot in shadow
4136	uts= units of the variable to shadow
4137	utl= units of the variable to line
4138	vtit= title of the variable
4139	kfig= kind of figure (jpg, pdf, png)
4140	reva=
4141	* 'transpose': reverse the axes (x-->y, y-->x)
4142	* 'flip'@[x/y]: flip the axis x or y
4143	mapv= map characteristics: [proj],[res]
4144	see full documentation: http://matplotlib.org/basemap/
4145	[proj]: projection
4146	* 'cyl', cilindric
4147	* 'lcc', lambert conformal
4148	[res]: resolution:
4149	* 'c', crude
4150	* 'l', low
4151	* 'i', intermediate
4152	* 'h', high
4153	* 'f', full
4154	ifclose= boolean value whether figure should be close (finish) or not
4155	"""
4156	## import matplotlib as mpl
4157	## mpl.use('Agg')
4158	## import matplotlib.pyplot as plt
4159	fname = 'plot_2D_shadow_line'
4160
4161	if varsv == 'h':
4162	print fname + '_____________________________________________________________'
4163	print plot_2D_shadow_line.__doc__
4164	quit()
4165
4166	if reva[0:4] == 'flip':
4167	reva0 = 'flip'
4168	if len(reva.split('@')) != 2:
4169	print errormsg
4170	print ' ' + fname + ': flip is given', reva, 'but not axis!'
4171	quit(-1)
4172	else:
4173	reva0 = reva
4174
4175	if reva0 == 'transpose':
4176	print ' reversing the axes of the figure (x-->y, y-->x)!!'
4177	varsv = np.transpose(varsv)
4178	dxv = dimyv
4179	dyv = dimxv
4180	dimxv = dxv
4181	dimyv = dyv
4182
4183	if len(dimxv[:].shape) == 3:
4184	lon0 = dimxv[0,]
4185	elif len(dimxv[:].shape) == 2:
4186	lon0 = dimxv[:]
4187
4188	if len(dimyv[:].shape) == 3:
4189	lat0 = dimyv[0,]
4190	elif len(dimyv[:].shape) == 2:
4191	lat0 = dimyv[:]
4192
4193	if len(dimxv[:].shape) == 1 and len(dimyv[:].shape) == 1:
4194	lon00 = dimxv[:]
4195	lon0 = np.zeros( (len(lat00),len(lon00)), dtype=np.float )
4196
4197	for iy in range(len(lat00)):
4198	lon0[iy,:] = lon00
4199	for ix in range(len(lon00)):
4200	lat0[:,ix] = lat00
4201
4202	if not mapv is None:
4203	map_proj=mapv.split(',')[0]
4204	map_res=mapv.split(',')[1]
4205
4206	dx = lon0.shape[1]
4207	dy = lat0.shape[0]
4208
4209	nlon = lon0[0,0]
4210	xlon = lon0[dy-1,dx-1]
4211	nlat = lat0[0,0]
4212	xlat = lat0[dy-1,dx-1]
4213
4214	# Thats too much! :)
4215	# if lonlatLims is not None:
4216	# print ' ' + fname + ': cutting the domain to plot !!!!'
4217	# plt.xlim(lonlatLims[0], lonlatLims[2])
4218	# plt.ylim(lonlatLims[1], lonlatLims[3])
4219	# print ' limits: W-E', lonlatLims[0], lonlatLims[2]
4220	# print ' limits: N-S', lonlatLims[1], lonlatLims[3]
4221
4222	# if map_proj == 'cyl':
4223	# nlon = lonlatLims[0]
4224	# nlat = lonlatLims[1]
4225	# xlon = lonlatLims[2]
4226	# xlat = lonlatLims[3]
4227	# elif map_proj == 'lcc':
4228	# lon2 = (lonlatLims[0] + lonlatLims[2])/2.
4229	# lat2 = (lonlatLims[1] + lonlatLims[3])/2.
4230	# nlon = lonlatLims[0]
4231	# xlon = lonlatLims[2]
4232	# nlat = lonlatLims[1]
4233	# xlat = lonlatLims[3]
4234
4235	lon2 = lon0[dy/2,dx/2]
4236	lat2 = lat0[dy/2,dx/2]
4237
4238	print 'lon2:', lon2, 'lat2:', lat2, 'SW pt:', nlon, ',', nlat, 'NE pt:', \
4239	xlon, ',', xlat
4240
4241	if map_proj == 'cyl':
4242	m = Basemap(projection=map_proj, llcrnrlon=nlon, llcrnrlat=nlat, \
4243	urcrnrlon=xlon, urcrnrlat= xlat, resolution=map_res)
4244	elif map_proj == 'lcc':
4245	m = Basemap(projection=map_proj, lat_0=lat2, lon_0=lon2, llcrnrlon=nlon, \
4246	llcrnrlat=nlat, urcrnrlon=xlon, urcrnrlat= xlat, resolution=map_res)
4247	else:
4248	print errormsg
4249	print ' ' + fname + ": map projection '" + map_proj + "' not defined!!!"
4250	print ' available: cyl, lcc'
4251	quit(-1)
4252
4253	if len(dimxv.shape) == 1:
4254	lons, lats = np.meshgrid(dimxv, dimyv)
4255	else:
4256	if len(dimxv.shape) == 3:
4257	lons = dimxv[0,:,:]
4258	else:
4259	lons = dimxv[:]
4260
4261	if len(dimyv.shape) == 3:
4262	lats = dimyv[0,:,:]
4263	else:
4264	lats = dimyv[:]
4265
4266	x,y = m(lons,lats)
4267
4268	else:
4269	if len(dimxv.shape) == 3:
4270	x = dimxv[0,:,:]
4271	elif len(dimxv.shape) == 2:
4272	x = dimxv
4273	else:
4274	# Attempt of simplier way...
4275	# x = np.zeros((lon0.shape), dtype=np.float)
4276	# for j in range(lon0.shape[0]):
4277	# x[j,:] = dimxv
4278
4279	## This way is too complicated and maybe not necessary ? (assuming dimxv.shape == dimyv.shape)
4280	if len(dimyv.shape) == 1:
4281	x = np.zeros((len(dimyv),len(dimxv)), dtype=np.float)
4282	for j in range(len(dimxv)):
4283	x[j,:] = dimxv
4284	else:
4285	x = np.zeros((dimyv.shape), dtype=np.float)
4286	if x.shape[0] == dimxv.shape[0]:
4287	for j in range(x.shape[1]):
4288	x[:,j] = dimxv
4289	else:
4290	for j in range(x.shape[0]):
4291	x[j,:] = dimxv
4292
4293	if len(dimyv.shape) == 3:
4294	y = dimyv[0,:,:]
4295	elif len(dimyv.shape) == 2:
4296	y = dimyv
4297	else:
4298	# y = np.zeros((lat0.shape), dtype=np.float)
4299	# for i in range(lat0.shape[1]):
4300	# x[:,i] = dimyv
4301
4302	# Idem
4303	if len(dimxv.shape) == 1:
4304	y = np.zeros((len(dimyv),len(dimxv)), dtype=np.float)
4305	for i in range(len(dimxv)):
4306	y[:,i] = dimyv
4307	else:
4308	y = np.zeros((dimxv.shape), dtype=np.float)
4309	if y.shape[0] == dimyv.shape[0]:
4310	for i in range(y.shape[1]):
4311	y[:,i] = dimyv
4312	else:
4313	for j in range(y.shape[0]):
4314	y[j,:] = dimyv
4315
4316	plt.rc('text', usetex=True)
4317
4318	plt.pcolormesh(x, y, varsv, cmap=plt.get_cmap(colorbar), vmin=vs[0], vmax=vs[1])
4319	cbar = plt.colorbar()
4320
4321	if not mapv is None:
4322	m.drawcoastlines()
4323
4324	meridians = pretty_int(nlon,xlon,5)
4325	m.drawmeridians(meridians,labels=[True,False,False,True])
4326	parallels = pretty_int(nlat,xlat,5)
4327	m.drawparallels(parallels,labels=[False,True,True,False])
4328
4329	plt.xlabel('W-E')
4330	plt.ylabel('S-N')
4331	else:
4332	plt.xlabel(variables_values(dimn[1])[0] + ' (' + units_lunits(dimxu) + ')')
4333	plt.ylabel(variables_values(dimn[0])[0] + ' (' + units_lunits(dimyu) + ')')
4334
4335	# Line
4336	##
4337
4338	if reva0 == 'flip' and reva.split('@')[1] == 'y':
4339	b=-np.max(y[0,:])/np.max(varlv)
4340	a=np.max(y[0,:])
4341	else:
4342	b=np.max(y[0,:])/np.max(varlv)
4343	a=0.
4344
4345	newlinv = varlv*b+a
4346	if reva0 == 'transpose':
4347	plt.plot(newlinv, x[0,:], '-', color=colln, linewidth=2)
4348	else:
4349	plt.plot(x[0,:], newlinv, '-', color=colln, linewidth=2)
4350
4351	txpos = pretty_int(x.min(),x.max(),10)
4352	typos = pretty_int(y.min(),y.max(),10)
4353	txlabels = list(txpos)
4354	for i in range(len(txlabels)): txlabels[i] = str(txlabels[i])
4355	tylabels = list(typos)
4356	for i in range(len(tylabels)): tylabels[i] = str(tylabels[i])
4357
4358	tllabels = pretty_int(np.min(varlv),np.max(varlv),len(txlabels))
4359	for it in range(len(tllabels)):
4360	yval = (tllabels[it]*b+a)
4361	plt.plot([x.max()*0.97, x.max()], [yval, yval], '-', color='k')
4362	plt.annotate(tllabels[it], xy=(1.01,tllabels[it]/np.max(varlv)), \
4363	xycoords='axes fraction')
4364
4365	# set the limits of the plot to the limits of the data
4366	if reva0 == 'flip':
4367	if reva.split('@')[1] == 'x':
4368	plt.axis([x.max(), x.min(), y.min(), y.max()])
4369	else:
4370	plt.axis([x.min(), x.max(), y.max(), y.min()])
4371	else:
4372	plt.axis([x.min(), x.max(), y.min(), y.max()])
4373
4374	plt.tick_params(axis='y',right='off')
4375	if mapv is None:
4376	plt.xticks(txpos, txlabels)
4377	plt.yticks(typos, tylabels)
4378
4379	tllabels = pretty_int(np.min(varlv),np.max(varlv),len(txlabels))
4380	for it in range(len(tllabels)):
4381	plt.annotate(tllabels[it], xy=(1.01,tllabels[it]/np.max(varlv)), xycoords='axes fraction')
4382
4383	# units labels
4384	cbar.set_label(vnames.replace('_','\_') + ' (' + units_lunits(uts) + ')')
4385
4386	plt.annotate(vnamel +' (' + units_lunits(utl) + ')', xy=(0.75,0.04),
4387	xycoords='figure fraction', color=colln)
4388	figname = '2Dfields_shadow_line'
4389	graphtit = vtit.replace('_','\_').replace('&','\&')
4390
4391	plt.title(graphtit)
4392
4393	output_kind(kfig, figname, ifclose)
4394
4395	return
4396
4397	def plot_Neighbourghood_evol(varsv, dxv, dyv, vnames, ttits, tpos, tlabels, colorbar, \
4398	Nng, vs, uts, gtit, kfig, ifclose):
4399	""" Plotting neighbourghood evolution
4400	varsv= 2D values to plot with shading
4401	vnames= shading variable name for the figure
4402	d[x/y]v= values at the axes of x and y
4403	ttits= titles of both time axis
4404	tpos= positions of the time ticks
4405	tlabels= labels of the time ticks
4406	colorbar= name of the color bar to use
4407	Nng= Number of grid points of the full side of the box (odd value)
4408	vs= minmum and maximum values to plot in shadow or:
4409	'Srange': for full range
4410	'Saroundmean@val': for mean-xtrm,mean+xtrm where xtrm = np.min(mean-min@val,max@val-mean)
4411	'Saroundminmax@val': for minval,maxval
4412	'Saroundpercentile@val': for median-xtrm,median+xtrm where xtrm = np.min(median-percentile_(val),
4413	percentile_(100-val)-median)
4414	'Smean@val': for -xtrm,xtrm where xtrm = np.min(mean-min@val,max@val-mean)
4415	'Smedian@val': for -xtrm,xtrm where xtrm = np.min(median-min@val,max@val-median)
4416	'Spercentile@val': for -xtrm,xtrm where xtrm = np.min(median-percentile_(val),
4417	percentile_(100-val)-median)
4418	uts= units of the variable to shadow
4419	gtit= title of the graph
4420	kfig= kind of figure (jpg, pdf, png)
4421	ifclose= boolean value whether figure should be close (finish) or not
4422	"""
4423	import numpy.ma as ma
4424
4425	fname = 'plot_Neighbourghood_evol'
4426
4427	if varsv == 'h':
4428	print fname + '_____________________________________________________________'
4429	print plot_Neighbourghood_evol.__doc__
4430	quit()
4431
4432	if len(varsv.shape) != 2:
4433	print errormsg
4434	print ' ' + fname + ': wrong number of dimensions of the values: ', \
4435	varsv.shape
4436	quit(-1)
4437
4438	varsvmask = ma.masked_equal(varsv,fillValue)
4439
4440	vsend = np.zeros((2), dtype=np.float)
4441	# Changing limits of the colors
4442	if type(vs[0]) != type(np.float(1.)):
4443	if vs[0] == 'Srange':
4444	vsend[0] = np.min(varsvmask)
4445	elif vs[0][0:11] == 'Saroundmean':
4446	meanv = np.mean(varsvmask)
4447	permean = np.float(vs[0].split('@')[1])
4448	minv = np.min(varsvmask)*permean
4449	maxv = np.max(varsvmask)*permean
4450	minextrm = np.min([np.abs(meanv-minv), np.abs(maxv-meanv)])
4451	vsend[0] = meanv-minextrm
4452	vsend[1] = meanv+minextrm
4453	elif vs[0][0:13] == 'Saroundminmax':
4454	permean = np.float(vs[0].split('@')[1])
4455	minv = np.min(varsvmask)*permean
4456	maxv = np.max(varsvmask)*permean
4457	vsend[0] = minv
4458	vsend[1] = maxv
4459	elif vs[0][0:17] == 'Saroundpercentile':
4460	medianv = np.median(varsvmask)
4461	valper = np.float(vs[0].split('@')[1])
4462	minv = np.percentile(varsvmask, valper)
4463	maxv = np.percentile(varsvmask, 100.-valper)
4464	minextrm = np.min([np.abs(medianv-minv), np.abs(maxv-medianv)])
4465	vsend[0] = medianv-minextrm
4466	vsend[1] = medianv+minextrm
4467	elif vs[0][0:5] == 'Smean':
4468	meanv = np.mean(varsvmask)
4469	permean = np.float(vs[0].split('@')[1])
4470	minv = np.min(varsvmask)*permean
4471	maxv = np.max(varsvmask)*permean
4472	minextrm = np.min([np.abs(meanv-minv), np.abs(maxv-meanv)])
4473	vsend[0] = -minextrm
4474	vsend[1] = minextrm
4475	elif vs[0][0:7] == 'Smedian':
4476	medianv = np.median(varsvmask)
4477	permedian = np.float(vs[0].split('@')[1])
4478	minv = np.min(varsvmask)*permedian
4479	maxv = np.max(varsvmask)*permedian
4480	minextrm = np.min([np.abs(medianv-minv), np.abs(maxv-medianv)])
4481	vsend[0] = -minextrm
4482	vsend[1] = minextrm
4483	elif vs[0][0:11] == 'Spercentile':
4484	medianv = np.median(varsvmask)
4485	valper = np.float(vs[0].split('@')[1])
4486	minv = np.percentile(varsvmask, valper)
4487	maxv = np.percentile(varsvmask, 100.-valper)
4488	minextrm = np.min([np.abs(medianv-minv), np.abs(maxv-medianv)])
4489	vsend[0] = -minextrm
4490	vsend[1] = minextrm
4491	else:
4492	print errormsg
4493	print ' ' + fname + ": range '" + vs[0] + "' not ready!!!"
4494	quit(-1)
4495	print ' ' + fname + ': modified shadow min,max:',vsend
4496	else:
4497	vsend[0] = vs[0]
4498
4499	if type(vs[0]) != type(np.float(1.)):
4500	if vs[1] == 'range':
4501	vsend[1] = np.max(varsv)
4502	else:
4503	vsend[1] = vs[1]
4504
4505	plt.rc('text', usetex=True)
4506
4507	# plt.pcolormesh(dxv, dyv, varsv, cmap=plt.get_cmap(colorbar), vmin=vsend[0], vmax=vsend[1])
4508	plt.pcolormesh(varsvmask, cmap=plt.get_cmap(colorbar), vmin=vsend[0], vmax=vsend[1])
4509	cbar = plt.colorbar()
4510
4511	newtposx = (tpos[0][:] - np.min(dxv)) * len(dxv) * Nng / (np.max(dxv) - np.min(dxv))
4512	newtposy = (tpos[1][:] - np.min(dyv)) * len(dyv) * Nng / (np.max(dyv) - np.min(dyv))
4513
4514	plt.xticks(newtposx, tlabels[0])
4515	plt.yticks(newtposy, tlabels[1])
4516	plt.xlabel(ttits[0])
4517	plt.ylabel(ttits[1])
4518
4519	plt.axes().set_aspect('equal')
4520	# From: http://stackoverflow.com/questions/14406214/moving-x-axis-to-the-top-of-a-plot-in-matplotlib
4521	plt.axes().xaxis.tick_top
4522	plt.axes().xaxis.set_ticks_position('top')
4523
4524	# units labels
4525	cbar.set_label(vnames.replace('_','\_') + ' (' + units_lunits(uts) + ')')
4526
4527	figname = 'Neighbourghood_evol'
4528	graphtit = gtit.replace('_','\_').replace('&','\&')
4529
4530	plt.title(graphtit, position=(0.5,1.05))
4531
4532	output_kind(kfig, figname, ifclose)
4533
4534	return
4535
4536	def plot_lines(vardv, varvv, vaxis, dtit, linesn, vtit, vunit, gtit, gloc, kfig):
4537	""" Function to plot a collection of lines
4538	vardv= list of set of dimension values
4539	varvv= list of set of values
4540	vaxis= which axis will be used for the values ('x', or 'y')
4541	dtit= title for the common dimension
4542	linesn= names for the legend
4543	vtit= title for the vaxis
4544	vunit= units of the vaxis
4545	gtit= main title
4546	gloc= location of the legend (-1, autmoatic)
4547	1: 'upper right', 2: 'upper left', 3: 'lower left', 4: 'lower right',
4548	5: 'right', 6: 'center left', 7: 'center right', 8: 'lower center',
4549	9: 'upper center', 10: 'center'
4550	kfig= kind of figure
4551	plot_lines([np.arange(10)], [np.sin(np.arange(10)*np.pi/2.5)], 'y', 'time (s)', \
4552	['2.5'], 'sin', '-', 'sinus frequency dependency', 'pdf')
4553	"""
4554	fname = 'plot_lines'
4555
4556	if vardv == 'h':
4557	print fname + '_____________________________________________________________'
4558	print plot_lines.__doc__
4559	quit()
4560
4561	# Canging line kinds every 7 lines (end of standard colors)
4562	linekinds=['.-','x-','o-']
4563
4564	Ntraj = len(vardv)
4565
4566	N7lines = 0
4567
4568	plt.rc('text', usetex=True)
4569
4570	if vaxis == 'x':
4571	for il in range(Ntraj):
4572	plt.plot(varvv[il], vardv[il], linekinds[N7lines], label= linesn[il])
4573	if il == 6: N7lines = N7lines + 1
4574
4575	plt.xlabel(vtit + ' (' + vunit + ')')
4576	plt.ylabel(dtit)
4577	plt.xlim(np.min(varvv[:]),np.max(varvv[:]))
4578	plt.ylim(np.min(vardv[:]),np.max(vardv[:]))
4579
4580	else:
4581	for il in range(Ntraj):
4582	plt.plot(vardv[il], varvv[il], linekinds[N7lines], label= linesn[il])
4583	if il == 6: N7lines = N7lines + 1
4584
4585	plt.xlabel(dtit)
4586	plt.ylabel(vtit + ' (' + vunit + ')')
4587	plt.xlim(np.min(vardv[:]),np.max(vardv[:]))
4588	plt.ylim(np.min(varvv[:]),np.max(varvv[:]))
4589
4590	figname = 'lines'
4591	graphtit = gtit
4592
4593	plt.title(graphtit)
4594	plt.legend(loc=gloc)
4595
4596	output_kind(kfig, figname, True)
4597
4598	return
4599
4600	def plot_lines_time(vardv, varvv, vaxis, dtit, linesn, vtit, vunit, tpos, tlabs, \
4601	gtit, gloc, kfig):
4602	""" Function to plot a collection of lines with a time axis
4603	vardv= list of set of dimension values
4604	varvv= list of set of values
4605	vaxis= which axis will be used for the time values ('x', or 'y')
4606	dtit= title for the common dimension
4607	linesn= names for the legend
4608	vtit= title for the vaxis
4609	vunit= units of the vaxis
4610	tpos= positions of the time ticks
4611	tlabs= labels of the time ticks
4612	gtit= main title
4613	gloc= location of the legend (-1, autmoatic)
4614	1: 'upper right', 2: 'upper left', 3: 'lower left', 4: 'lower right',
4615	5: 'right', 6: 'center left', 7: 'center right', 8: 'lower center',
4616	9: 'upper center', 10: 'center'
4617	kfig= kind of figure
4618	plot_lines([np.arange(10)], [np.sin(np.arange(10)*np.pi/2.5)], 'y', 'time (s)', \
4619	['2.5'], 'sin', '-', 'sinus frequency dependency', 'pdf')
4620	"""
4621	fname = 'plot_lines'
4622
4623	if vardv == 'h':
4624	print fname + '_____________________________________________________________'
4625	print plot_lines.__doc__
4626	quit()
4627
4628	# Canging line kinds every 7 lines (end of standard colors)
4629	linekinds=['.-','x-','o-']
4630
4631	Ntraj = len(vardv)
4632
4633	N7lines = 0
4634
4635	plt.rc('text', usetex=True)
4636	varTvv = []
4637	varTdv = []
4638
4639	if vaxis == 'x':
4640	for il in range(Ntraj):
4641	plt.plot(varvv[il], vardv[il], linekinds[N7lines], label= linesn[il])
4642	varTvv = varTvv + list(varvv[il])
4643	varTdv = varTdv + list(vardv[il])
4644	if il == 6: N7lines = N7lines + 1
4645
4646	plt.xlabel(vtit + ' (' + vunit + ')')
4647	plt.ylabel(dtit)
4648	plt.xlim(np.min(varTvv),np.max(varTvv))
4649	plt.ylim(np.min(varTdv),np.max(varTdv))
4650	plt.yticks(tpos, tlabs)
4651	else:
4652	for il in range(Ntraj):
4653	plt.plot(vardv[il], varvv[il], linekinds[N7lines], label= linesn[il])
4654	varTvv = varTvv + list(varvv[il])
4655	varTdv = varTdv + list(vardv[il])
4656	if il == 6: N7lines = N7lines + 1
4657
4658	plt.xlabel(dtit)
4659	plt.ylabel(vtit + ' (' + vunit + ')')
4660
4661	plt.xlim(np.min(varTdv),np.max(varTdv))
4662	plt.ylim(np.min(varTvv),np.max(varTvv))
4663	plt.xticks(tpos, tlabs)
4664
4665	figname = 'lines_time'
4666	graphtit = gtit
4667
4668	plt.title(graphtit)
4669	plt.legend(loc=gloc)
4670
4671	output_kind(kfig, figname, True)
4672
4673	return

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