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source: lmdz_wrf/trunk/tools/drawing_tools.py @ 410

Last change on this file since 410 was 399, checked in by lfita, 10 years ago
Adding `found' when the dimension is already found in slice_variable
File size: 181.2 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	import nc_var_tools as ncvar
12
13	errormsg = 'ERROR -- error -- ERROR -- error'
14	warnmsg = 'WARNING -- waring -- WARNING -- warning'
15
16	fillValue = 1.e20
17
18	####### Funtions
19	# searchInlist:
20	# datetimeStr_datetime:
21	# dateStr_date:
22	# numVector_String:
23	# timeref_datetime:
24	# slice_variable:
25	# interpolate_locs:
26	# datetimeStr_conversion:
27	# percendone:
28	# netCDFdatetime_realdatetime:
29	# file_nlines:
30	# variables_values:
31	# check_colorBar:
32	# units_lunits:
33	# ASCII_LaTeX:
34	# pretty_int:
35	# DegGradSec_deg:
36	# intT2dt:
37	# lonlat_values:
38	# date_CFtime:
39	# pot_values:
40	# CFtimes_plot:
41	# color_lines:
42	# output_kind:
43	# check_arguments:
44	# Str_Bool:
45	# plot_points:
46	# plot_2Dfield:
47	# plot_2Dfield_easy:
48	# plot_topo_geogrid:
49	# plot_topo_geogrid_boxes:
50	# plot_2D_shadow:
51	# plot_2D_shadow_time: Plotting a 2D field with one of the axes being time
52	# plot_Neighbourghood_evol:Plotting neighbourghood evolution# plot_Trajectories
53	# plot_2D_shadow_contour:
54	# plot_2D_shadow_contour_time:
55	# dxdy_lonlat: Function to provide lon/lat 2D lilke-matrices from any sort of dx,dy values
56	# plot_2D_shadow_line:
57	# plot_lines: Function to plot a collection of lines
58
59	# From nc_var_tools.py
60	def searchInlist(listname, nameFind):
61	""" Function to search a value within a list
62	listname = list
63	nameFind = value to find
64	>>> searInlist(['1', '2', '3', '5'], '5')
65	True
66	"""
67	for x in listname:
68	if x == nameFind:
69	return True
70	return False
71
72	def datetimeStr_datetime(StringDT):
73	""" Function to transform a string date ([YYYY]-[MM]-[DD]_[HH]:[MI]:[SS] format) to a date object
74	>>> datetimeStr_datetime('1976-02-17_00:00:00')
75	1976-02-17 00:00:00
76	"""
77	import datetime as dt
78
79	fname = 'datetimeStr_datetime'
80
81	dateD = np.zeros((3), dtype=int)
82	timeT = np.zeros((3), dtype=int)
83
84	dateD[0] = int(StringDT[0:4])
85	dateD[1] = int(StringDT[5:7])
86	dateD[2] = int(StringDT[8:10])
87
88	trefT = StringDT.find(':')
89	if not trefT == -1:
90	# print ' ' + fname + ': refdate with time!'
91	timeT[0] = int(StringDT[11:13])
92	timeT[1] = int(StringDT[14:16])
93	timeT[2] = int(StringDT[17:19])
94
95	if int(dateD[0]) == 0:
96	print warnmsg
97	print ' ' + fname + ': 0 reference year!! changing to 1'
98	dateD[0] = 1
99
100	newdatetime = dt.datetime(dateD[0], dateD[1], dateD[2], timeT[0], timeT[1], timeT[2])
101
102	return newdatetime
103
104	def dateStr_date(StringDate):
105	""" Function to transform a string date ([YYYY]-[MM]-[DD] format) to a date object
106	>>> dateStr_date('1976-02-17')
107	1976-02-17
108	"""
109	import datetime as dt
110
111	dateD = StringDate.split('-')
112	if int(dateD[0]) == 0:
113	print warnmsg
114	print ' dateStr_date: 0 reference year!! changing to 1'
115	dateD[0] = 1
116	newdate = dt.date(int(dateD[0]), int(dateD[1]), int(dateD[2]))
117	return newdate
118
119	def numVector_String(vec,char):
120	""" Function to transform a vector of numbers to a single string [char] separated
121	numVector_String(vec,char)
122	vec= vector with the numerical values
123	char= single character to split the values
124	>>> print numVector_String(np.arange(10),' ')
125	0 1 2 3 4 5 6 7 8 9
126	"""
127	fname = 'numVector_String'
128
129	if vec == 'h':
130	print fname + '_____________________________________________________________'
131	print numVector_String.__doc__
132	quit()
133
134	Nvals = len(vec)
135
136	string=''
137	for i in range(Nvals):
138	if i == 0:
139	string = str(vec[i])
140	else:
141	string = string + char + str(vec[i])
142
143	return string
144
145	def timeref_datetime(refd, timeval, tu):
146	""" Function to transform from a [timeval] in [tu] units from the time referece [tref] to datetime object
147	refd: time of reference (as datetime object)
148	timeval: time value (as [tu] from [tref])
149	tu: time units
150	>>> timeref = date(1949,12,1,0,0,0)
151	>>> timeref_datetime(timeref, 229784.36, hours)
152	1976-02-17 08:21:36
153	"""
154	import datetime as dt
155	import numpy as np
156
157	## Not in timedelta
158	# if tu == 'years':
159	# realdate = refdate + dt.timedelta(years=float(timeval))
160	# elif tu == 'months':
161	# realdate = refdate + dt.timedelta(months=float(timeval))
162	if tu == 'weeks':
163	realdate = refd + dt.timedelta(weeks=float(timeval))
164	elif tu == 'days':
165	realdate = refd + dt.timedelta(days=float(timeval))
166	elif tu == 'hours':
167	realdate = refd + dt.timedelta(hours=float(timeval))
168	elif tu == 'minutes':
169	realdate = refd + dt.timedelta(minutes=float(timeval))
170	elif tu == 'seconds':
171	realdate = refd + dt.timedelta(seconds=float(timeval))
172	elif tu == 'milliseconds':
173	realdate = refd + dt.timedelta(milliseconds=float(timeval))
174	else:
175	print errormsg
176	print ' timeref_datetime: time units "' + tu + '" not ready!!!!'
177	quit(-1)
178
179	return realdate
180
181	def slice_variable(varobj, dimslice):
182	""" Function to return a slice of a given variable according to values to its
183	dimensions
184	slice_variable(varobj, dims)
185	varobj= object wit the variable
186	dimslice= [[dimname1]:[value1]\|[[dimname2]:[value2], ...] pairs of dimension
187	[value]:
188	* [integer]: which value of the dimension
189	* -1: all along the dimension
190	* [beg]:[end] slice from [beg] to [end]
191	"""
192	fname = 'slice_variable'
193
194	if varobj == 'h':
195	print fname + '_____________________________________________________________'
196	print slice_variable.__doc__
197	quit()
198
199	vardims = varobj.dimensions
200	Ndimvar = len(vardims)
201
202	Ndimcut = len(dimslice.split('\|'))
203	if Ndimcut == 0:
204	Ndimcut = 1
205	dimcut = list(dimslice)
206
207	dimsl = dimslice.split('\|')
208
209	varvalsdim = []
210	dimnslice = []
211
212	for idd in range(Ndimvar):
213	found = False
214	for idc in range(Ndimcut):
215	dimcutn = dimsl[idc].split(':')[0]
216	dimcutv = dimsl[idc].split(':')[1]
217	if vardims[idd] == dimcutn:
218	posfrac = dimcutv.find('@')
219	if posfrac != -1:
220	inifrac = int(dimcutv.split('@')[0])
221	endfrac = int(dimcutv.split('@')[1])
222	varvalsdim.append(slice(inifrac,endfrac))
223	dimnslice.append(vardims[idd])
224	else:
225	if int(dimcutv) == -1:
226	varvalsdim.append(slice(0,varobj.shape[idd]))
227	dimnslice.append(vardims[idd])
228	elif int(dimcutv) == -9:
229	varvalsdim.append(int(varobj.shape[idd])-1)
230	else:
231	varvalsdim.append(int(dimcutv))
232	found = True
233	break
234	if not found and not searchInlist(dimnslice,vardims[idd]):
235	varvalsdim.append(slice(0,varobj.shape[idd]))
236	dimnslice.append(vardims[idd])
237
238	varvalues = varobj[tuple(varvalsdim)]
239
240	return varvalues, dimnslice
241
242	def interpolate_locs(locs,coords,kinterp):
243	""" Function to provide interpolate locations on a given axis
244	interpolate_locs(locs,axis,kinterp)
245	locs= locations to interpolate
246	coords= axis values with the reference of coordinates
247	kinterp: kind of interpolation
248	'lin': linear
249	>>> coordinates = np.arange((10), dtype=np.float)
250	>>> values = np.array([-1.2, 2.4, 5.6, 7.8, 12.0])
251	>>> interpolate_locs(values,coordinates,'lin')
252	[ -1.2 2.4 5.6 7.8 13. ]
253	>>> coordinates[0] = 0.5
254	>>> coordinates[2] = 2.5
255	>>> interpolate_locs(values,coordinates,'lin')
256	[ -3.4 1.93333333 5.6 7.8 13. ]
257	"""
258
259	fname = 'interpolate_locs'
260
261	if locs == 'h':
262	print fname + '_____________________________________________________________'
263	print interpolate_locs.__doc__
264	quit()
265
266	Nlocs = locs.shape[0]
267	Ncoords = coords.shape[0]
268
269	dcoords = coords[Ncoords-1] - coords[0]
270
271	intlocs = np.zeros((Nlocs), dtype=np.float)
272	minc = np.min(coords)
273	maxc = np.max(coords)
274
275	for iloc in range(Nlocs):
276	for icor in range(Ncoords-1):
277	if locs[iloc] < minc and dcoords > 0.:
278	a = 0.
279	b = 1. / (coords[1] - coords[0])
280	c = coords[0]
281	elif locs[iloc] > maxc and dcoords > 0.:
282	a = (Ncoords-1)*1.
283	b = 1. / (coords[Ncoords-1] - coords[Ncoords-2])
284	c = coords[Ncoords-2]
285	elif locs[iloc] < minc and dcoords < 0.:
286	a = (Ncoords-1)*1.
287	b = 1. / (coords[Ncoords-1] - coords[Ncoords-2])
288	c = coords[Ncoords-2]
289	elif locs[iloc] > maxc and dcoords < 0.:
290	a = 0.
291	b = 1. / (coords[1] - coords[0])
292	c = coords[0]
293	elif locs[iloc] >= coords[icor] and locs[iloc] < coords[icor+1] and dcoords > 0.:
294	a = icor*1.
295	b = 1. / (coords[icor+1] - coords[icor])
296	c = coords[icor]
297	print coords[icor], locs[iloc], coords[icor+1], ':', icor, '->', a, b
298	elif locs[iloc] <= coords[icor] and locs[iloc] > coords[icor+1] and dcoords < 0.:
299	a = icor*1.
300	b = 1. / (coords[icor+1] - coords[icor])
301	c = coords[icor]
302
303	if kinterp == 'lin':
304	intlocs[iloc] = a + (locs[iloc] - c)*b
305	else:
306	print errormsg
307	print ' ' + fname + ": interpolation kind '" + kinterp + "' not ready !!!!!"
308	quit(-1)
309
310	return intlocs
311
312	def datetimeStr_conversion(StringDT,typeSi,typeSo):
313	""" Function to transform a string date to an another date object
314	StringDT= string with the date and time
315	typeSi= type of datetime string input
316	typeSo= type of datetime string output
317	[typeSi/o]
318	'cfTime': [time],[units]; ]time in CF-convention format [units] = [tunits] since [refdate]
319	'matYmdHMS': numerical vector with [[YYYY], [MM], [DD], [HH], [MI], [SS]]
320	'YmdHMS': [YYYY][MM][DD][HH][MI][SS] format
321	'Y-m-d_H:M:S': [YYYY]-[MM]-[DD]_[HH]:[MI]:[SS] format
322	'Y-m-d H:M:S': [YYYY]-[MM]-[DD] [HH]:[MI]:[SS] format
323	'Y/m/d H-M-S': [YYYY]/[MM]/[DD] [HH]-[MI]-[SS] format
324	'WRFdatetime': [Y], [Y], [Y], [Y], '-', [M], [M], '-', [D], [D], '_', [H],
325	[H], ':', [M], [M], ':', [S], [S]
326	>>> datetimeStr_conversion('1976-02-17_08:32:05','Y-m-d_H:M:S','matYmdHMS')
327	[1976 2 17 8 32 5]
328	>>> datetimeStr_conversion(str(137880)+',minutes since 1979-12-01_00:00:00','cfTime','Y/m/d H-M-S')
329	1980/03/05 18-00-00
330	"""
331	import datetime as dt
332
333	fname = 'datetimeStr_conversion'
334
335	if StringDT[0:1] == 'h':
336	print fname + '_____________________________________________________________'
337	print datetimeStr_conversion.__doc__
338	quit()
339
340	if typeSi == 'cfTime':
341	timeval = np.float(StringDT.split(',')[0])
342	tunits = StringDT.split(',')[1].split(' ')[0]
343	Srefdate = StringDT.split(',')[1].split(' ')[2]
344
345	# Does reference date contain a time value [YYYY]-[MM]-[DD] [HH]:[MI]:[SS]
346	##
347	yrref=Srefdate[0:4]
348	monref=Srefdate[5:7]
349	dayref=Srefdate[8:10]
350
351	trefT = Srefdate.find(':')
352	if not trefT == -1:
353	# print ' ' + fname + ': refdate with time!'
354	horref=Srefdate[11:13]
355	minref=Srefdate[14:16]
356	secref=Srefdate[17:19]
357	refdate = datetimeStr_datetime( yrref + '-' + monref + '-' + dayref + \
358	'_' + horref + ':' + minref + ':' + secref)
359	else:
360	refdate = datetimeStr_datetime( yrref + '-' + monref + '-' + dayref + \
361	+ '_00:00:00')
362
363	if tunits == 'weeks':
364	newdate = refdate + dt.timedelta(weeks=float(timeval))
365	elif tunits == 'days':
366	newdate = refdate + dt.timedelta(days=float(timeval))
367	elif tunits == 'hours':
368	newdate = refdate + dt.timedelta(hours=float(timeval))
369	elif tunits == 'minutes':
370	newdate = refdate + dt.timedelta(minutes=float(timeval))
371	elif tunits == 'seconds':
372	newdate = refdate + dt.timedelta(seconds=float(timeval))
373	elif tunits == 'milliseconds':
374	newdate = refdate + dt.timedelta(milliseconds=float(timeval))
375	else:
376	print errormsg
377	print ' timeref_datetime: time units "' + tunits + '" not ready!!!!'
378	quit(-1)
379
380	yr = newdate.year
381	mo = newdate.month
382	da = newdate.day
383	ho = newdate.hour
384	mi = newdate.minute
385	se = newdate.second
386	elif typeSi == 'matYmdHMS':
387	yr = StringDT[0]
388	mo = StringDT[1]
389	da = StringDT[2]
390	ho = StringDT[3]
391	mi = StringDT[4]
392	se = StringDT[5]
393	elif typeSi == 'YmdHMS':
394	yr = int(StringDT[0:4])
395	mo = int(StringDT[4:6])
396	da = int(StringDT[6:8])
397	ho = int(StringDT[8:10])
398	mi = int(StringDT[10:12])
399	se = int(StringDT[12:14])
400	elif typeSi == 'Y-m-d_H:M:S':
401	dateDT = StringDT.split('_')
402	dateD = dateDT[0].split('-')
403	timeT = dateDT[1].split(':')
404	yr = int(dateD[0])
405	mo = int(dateD[1])
406	da = int(dateD[2])
407	ho = int(timeT[0])
408	mi = int(timeT[1])
409	se = int(timeT[2])
410	elif typeSi == 'Y-m-d H:M:S':
411	dateDT = StringDT.split(' ')
412	dateD = dateDT[0].split('-')
413	timeT = dateDT[1].split(':')
414	yr = int(dateD[0])
415	mo = int(dateD[1])
416	da = int(dateD[2])
417	ho = int(timeT[0])
418	mi = int(timeT[1])
419	se = int(timeT[2])
420	elif typeSi == 'Y/m/d H-M-S':
421	dateDT = StringDT.split(' ')
422	dateD = dateDT[0].split('/')
423	timeT = dateDT[1].split('-')
424	yr = int(dateD[0])
425	mo = int(dateD[1])
426	da = int(dateD[2])
427	ho = int(timeT[0])
428	mi = int(timeT[1])
429	se = int(timeT[2])
430	elif typeSi == 'WRFdatetime':
431	yr = int(StringDT[0])1000 + int(StringDT[1])100 + int(StringDT[2])*10 + \
432	int(StringDT[3])
433	mo = int(StringDT[5])*10 + int(StringDT[6])
434	da = int(StringDT[8])*10 + int(StringDT[9])
435	ho = int(StringDT[11])*10 + int(StringDT[12])
436	mi = int(StringDT[14])*10 + int(StringDT[15])
437	se = int(StringDT[17])*10 + int(StringDT[18])
438	else:
439	print errormsg
440	print ' ' + fname + ': type of String input date "' + typeSi + \
441	'" not ready !!!!'
442	quit(-1)
443
444	if typeSo == 'matYmdHMS':
445	dateYmdHMS = np.zeros((6), dtype=int)
446	dateYmdHMS[0] = yr
447	dateYmdHMS[1] = mo
448	dateYmdHMS[2] = da
449	dateYmdHMS[3] = ho
450	dateYmdHMS[4] = mi
451	dateYmdHMS[5] = se
452	elif typeSo == 'YmdHMS':
453	dateYmdHMS = str(yr).zfill(4) + str(mo).zfill(2) + str(da).zfill(2) + \
454	str(ho).zfill(2) + str(mi).zfill(2) + str(se).zfill(2)
455	elif typeSo == 'Y-m-d_H:M:S':
456	dateYmdHMS = str(yr).zfill(4) + '-' + str(mo).zfill(2) + '-' + \
457	str(da).zfill(2) + '_' + str(ho).zfill(2) + ':' + str(mi).zfill(2) + ':' + \
458	str(se).zfill(2)
459	elif typeSo == 'Y-m-d H:M:S':
460	dateYmdHMS = str(yr).zfill(4) + '-' + str(mo).zfill(2) + '-' + \
461	str(da).zfill(2) + ' ' + str(ho).zfill(2) + ':' + str(mi).zfill(2) + ':' + \
462	str(se).zfill(2)
463	elif typeSo == 'Y/m/d H-M-S':
464	dateYmdHMS = str(yr).zfill(4) + '/' + str(mo).zfill(2) + '/' + \
465	str(da).zfill(2) + ' ' + str(ho).zfill(2) + '-' + str(mi).zfill(2) + '-' + \
466	str(se).zfill(2)
467	elif typeSo == 'WRFdatetime':
468	dateYmdHMS = []
469	yM = yr/1000
470	yC = (yr-yM*1000)/100
471	yD = (yr-yM1000-yC100)/10
472	yU = yr-yM1000-yC100-yD*10
473
474	mD = mo/10
475	mU = mo-mD*10
476
477	dD = da/10
478	dU = da-dD*10
479
480	hD = ho/10
481	hU = ho-hD*10
482
483	miD = mi/10
484	miU = mi-miD*10
485
486	sD = se/10
487	sU = se-sD*10
488
489	dateYmdHMS.append(str(yM))
490	dateYmdHMS.append(str(yC))
491	dateYmdHMS.append(str(yD))
492	dateYmdHMS.append(str(yU))
493	dateYmdHMS.append('-')
494	dateYmdHMS.append(str(mD))
495	dateYmdHMS.append(str(mU))
496	dateYmdHMS.append('-')
497	dateYmdHMS.append(str(dD))
498	dateYmdHMS.append(str(dU))
499	dateYmdHMS.append('_')
500	dateYmdHMS.append(str(hD))
501	dateYmdHMS.append(str(hU))
502	dateYmdHMS.append(':')
503	dateYmdHMS.append(str(miD))
504	dateYmdHMS.append(str(miU))
505	dateYmdHMS.append(':')
506	dateYmdHMS.append(str(sD))
507	dateYmdHMS.append(str(sU))
508	else:
509	print errormsg
510	print ' ' + fname + ': type of output date "' + typeSo + '" not ready !!!!'
511	quit(-1)
512
513	return dateYmdHMS
514
515	def percendone(nvals,tot,percen,msg):
516	""" Function to provide the percentage of an action across the matrix
517	nvals=number of values
518	tot=total number of values
519	percen=percentage frequency for which the message is wanted
520	msg= message
521	"""
522	from sys import stdout
523
524	num = int(tot * percen/100)
525	if (nvals%num == 0):
526	print '\r ' + msg + '{0:8.3g}'.format(nvals*100./tot) + ' %',
527	stdout.flush()
528
529	return ''
530
531	def netCDFdatetime_realdatetime(units, tcalendar, times):
532	""" Function to transfrom from netCDF CF-compilant times to real time
533	"""
534	import datetime as dt
535
536	txtunits = units.split(' ')
537	tunits = txtunits[0]
538	Srefdate = txtunits[len(txtunits) - 1]
539
540	# Calendar type
541	##
542	is360 = False
543	if tcalendar is not None:
544	print ' netCDFdatetime_realdatetime: There is a calendar attribute'
545	if tcalendar == '365_day' or tcalendar == 'noleap':
546	print ' netCDFdatetime_realdatetime: No leap years!'
547	isleapcal = False
548	elif tcalendar == 'proleptic_gregorian' or tcalendar == 'standard' or tcalendar == 'gregorian':
549	isleapcal = True
550	elif tcalendar == '360_day':
551	is360 = True
552	isleapcal = False
553	else:
554	print errormsg
555	print ' netCDFdatetime_realdatetime: Calendar "' + tcalendar + '" not prepared!'
556	quit(-1)
557
558	# Does reference date contain a time value [YYYY]-[MM]-[DD] [HH]:[MI]:[SS]
559	##
560	timeval = Srefdate.find(':')
561
562	if not timeval == -1:
563	print ' netCDFdatetime_realdatetime: refdate with time!'
564	refdate = datetimeStr_datetime(Srefdate)
565	else:
566	refdate = dateStr_date(Srefdate + '_00:00:00')
567
568	dimt = len(times)
569	# datetype = type(dt.datetime(1972,02,01))
570	# realdates = np.array(dimt, datetype)
571	# print realdates
572
573	## Not in timedelta
574	# if tunits == 'years':
575	# for it in range(dimt):
576	# realdate = refdate + dt.timedelta(years=float(times[it]))
577	# realdates[it] = int(realdate.year)
578	# elif tunits == 'months':
579	# for it in range(dimt):
580	# realdate = refdate + dt.timedelta(months=float(times[it]))
581	# realdates[it] = int(realdate.year)
582	# realdates = []
583	realdates = np.zeros((dimt, 6), dtype=int)
584	if tunits == 'weeks':
585	for it in range(dimt):
586	realdate = refdate + dt.timedelta(weeks=float(times[it]))
587	realdates[it,:]=[realdate.year, realdate.month, realdate.day, realdate.hour, realdate.minute, realdate.second]
588	elif tunits == 'days':
589	for it in range(dimt):
590	realdate = refdate + dt.timedelta(days=float(times[it]))
591	realdates[it,:]=[realdate.year, realdate.month, realdate.day, realdate.hour, realdate.minute, realdate.second]
592	elif tunits == 'hours':
593	for it in range(dimt):
594	realdate = refdate + dt.timedelta(hours=float(times[it]))
595	# if not isleapcal:
596	# Nleapdays = cal.leapdays(int(refdate.year), int(realdate.year))
597	# realdate = realdate - dt.timedelta(days=Nleapdays)
598	# if is360:
599	# Nyears360 = int(realdate.year) - int(refdate.year) + 1
600	# realdate = realdate -dt.timedelta(days=Nyears360*5)
601	# realdates[it] = realdate
602	# realdates = refdate + dt.timedelta(hours=float(times))
603	realdates[it,:]=[realdate.year, realdate.month, realdate.day, realdate.hour, realdate.minute, realdate.second]
604	elif tunits == 'minutes':
605	for it in range(dimt):
606	realdate = refdate + dt.timedelta(minutes=float(times[it]))
607	realdates[it,:]=[realdate.year, realdate.month, realdate.day, realdate.hour, realdate.minute, realdate.second]
608	elif tunits == 'seconds':
609	for it in range(dimt):
610	realdate = refdate + dt.timedelta(seconds=float(times[it]))
611	realdates[it,:]=[realdate.year, realdate.month, realdate.day, realdate.hour, realdate.minute, realdate.second]
612	elif tunits == 'milliseconds':
613	for it in range(dimt):
614	realdate = refdate + dt.timedelta(milliseconds=float(times[it]))
615	realdates[it,:]=[realdate.year, realdate.month, realdate.day, realdate.hour, realdate.minute, realdate.second]
616	elif tunits == 'microseconds':
617	for it in range(dimt):
618	realdate = refdate + dt.timedelta(microseconds=float(times[it]))
619	realdates[it,:]=[realdate.year, realdate.month, realdate.day, realdate.hour, realdate.minute, realdate.second]
620	else:
621	print errormsg
622	print ' netCDFdatetime_realdatetime: time units "' + tunits + '" is not ready!!!'
623	quit(-1)
624
625	return realdates
626
627	def file_nlines(filen):
628	""" Function to provide the number of lines of a file
629	filen= name of the file
630	>>> file_nlines('trajectory.dat')
631	49
632	"""
633	fname = 'file_nlines'
634
635	if not os.path.isfile(filen):
636	print errormsg
637	print ' ' + fname + ' file: "' + filen + '" does not exist !!'
638	quit(-1)
639
640	fo = open(filen,'r')
641
642	nlines=0
643	for line in fo: nlines = nlines + 1
644
645	fo.close()
646
647	return nlines
648
649	def realdatetime1_CFcompilant(time, Srefdate, tunits):
650	""" Function to transform a matrix with a real time value ([year, month, day,
651	hour, minute, second]) to a netCDF one
652	time= matrix with time
653	Srefdate= reference date ([YYYY][MM][DD][HH][MI][SS] format)
654	tunits= units of time respect to Srefdate
655	>>> realdatetime1_CFcompilant([1976, 2, 17, 8, 20, 0], '19491201000000', 'hours')
656	229784.33333333
657	"""
658
659	import datetime as dt
660	yrref=int(Srefdate[0:4])
661	monref=int(Srefdate[4:6])
662	dayref=int(Srefdate[6:8])
663	horref=int(Srefdate[8:10])
664	minref=int(Srefdate[10:12])
665	secref=int(Srefdate[12:14])
666
667	refdate=dt.datetime(yrref, monref, dayref, horref, minref, secref)
668
669	if tunits == 'weeks':
670	cfdate = dt.datetime(time[0],time[1],time[2],time[3],time[4],time[5])-refdate
671	cfdates = (cfdate.days + cfdate.seconds/(3600.*24.))/7.
672	elif tunits == 'days':
673	cfdate = dt.datetime(time[0],time[1],time[2],time[3],time[4],time[5]) - refdate
674	cfdates = cfdate.days + cfdate.seconds/(3600.*24.)
675	elif tunits == 'hours':
676	cfdate = dt.datetime(time[0],time[1],time[2],time[3],time[4],time[5]) - refdate
677	cfdates = cfdate.days*24. + cfdate.seconds/3600.
678	elif tunits == 'minutes':
679	cfdate = dt.datetime(time[0],time[1],time[2],time[3],time[4],time[5]) - refdate
680	cfdates = cfdate.days24.60. + cfdate.seconds/60.
681	elif tunits == 'seconds':
682	cfdate = dt.datetime(time[0],time[1],time[2],time[3],time[4],time[5]) - refdate
683	cfdates = cfdate.days24.3600. + cfdate.seconds
684	elif tunits == 'milliseconds':
685	cfdate = dt.datetime(time[0],time[1],time[2],time[3],time[4],time[5]) - refdate
686	cfdates = cfdate.days1000.24.3600. + cfdate.seconds1000.
687	elif tunits == 'microseconds':
688	cfdate = dt.datetime(time[0],time[1],time[2],time[3],time[4],times[5]) - refdate
689	cfdates = cfdate.days1000000.24.3600. + cfdate.seconds1000000.
690	else:
691	print errormsg
692	print ' ' + fname + ': time units "' + tunits + '" is not ready!!!'
693	quit(-1)
694
695	return cfdates
696
697	def basicvardef(varobj, vstname, vlname, vunits):
698	""" Function to give the basic attributes to a variable
699	varobj= netCDF variable object
700	vstname= standard name of the variable
701	vlname= long name of the variable
702	vunits= units of the variable
703	"""
704	attr = varobj.setncattr('standard_name', vstname)
705	attr = varobj.setncattr('long_name', vlname)
706	attr = varobj.setncattr('units', vunits)
707
708	return
709
710	def variables_values(varName):
711	""" Function to provide values to plot the different variables values from ASCII file
712	'variables_values.dat'
713	variables_values(varName)
714	[varName]= name of the variable
715	return: [var name], [std name], [minimum], [maximum],
716	[long name]('\|' for spaces), [units], [color palette] (following:
717	http://matplotlib.org/1.3.1/examples/color/colormaps_reference.html)
718	[varn]: original name of the variable
719	NOTE: It might be better doing it with an external ASII file. But then we
720	got an extra dependency...
721	>>> variables_values('WRFght')
722	['z', 'geopotential_height', 0.0, 80000.0, 'geopotential\|height', 'm2s-2', 'rainbow']
723	"""
724	import subprocess as sub
725
726	fname='variables_values'
727
728	if varName == 'h':
729	print fname + '_____________________________________________________________'
730	print variables_values.__doc__
731	quit()
732
733	# This does not work....
734	# folderins = sub.Popen(["pwd"], stdout=sub.PIPE)
735	# folder = list(folderins.communicate())[0].replace('\n','')
736	# From http://stackoverflow.com/questions/4934806/how-can-i-find-scripts-directory-with-python
737	folder = os.path.dirname(os.path.realpath(__file__))
738
739	infile = folder + '/variables_values.dat'
740
741	if not os.path.isfile(infile):
742	print errormsg
743	print ' ' + fname + ": File '" + infile + "' does not exist !!"
744	quit(-1)
745
746	# Variable name might come with a statistical surname...
747	stats=['min','max','mean','stdv', 'sum']
748
749	# Variables with a statistical section on their name...
750	NOstatsvars = ['zmaxth', 'zmax_th', 'lmax_th', 'lmaxth']
751
752	ifst = False
753	if not searchInlist(NOstatsvars, varName.lower()):
754	for st in stats:
755	if varName.find(st) > -1:
756	print ' '+ fname + ": varibale '" + varName + "' with a " + \
757	"statistical surname: '",st,"' !!"
758	Lst = len(st)
759	LvarName = len(varName)
760	varn = varName[0:LvarName - Lst]
761	ifst = True
762	break
763	if not ifst:
764	varn = varName
765
766	ncf = open(infile, 'r')
767
768	for line in ncf:
769	if line[0:1] != '#':
770	values = line.replace('\n','').split(',')
771	if len(values) != 8:
772	print errormsg
773	print "problem in varibale:'", values[0], \
774	'it should have 8 values and it has',len(values)
775	quit(-1)
776
777	if varn[0:6] == 'varDIM':
778	# Variable from a dimension (all with 'varDIM' prefix)
779	Lvarn = len(varn)
780	varvals = [varn[6:Lvarn+1], varn[6:Lvarn+1], 0., 1., \
781	"variable\|from\|size\|of\|dimension\|'" + varn[6:Lvarn+1] + "'", '1', \
782	'rainbow']
783	else:
784	varvals = [values[1].replace(' ',''), values[2].replace(' ',''), \
785	np.float(values[3]), np.float(values[4]),values[5].replace(' ',''),\
786	values[6].replace(' ',''), values[7].replace(' ','')]
787	if values[0] == varn:
788	ncf.close()
789	return varvals
790	break
791
792	print errormsg
793	print ' ' + fname + ": variable '" + varn + "' not defined !!!"
794	ncf.close()
795	quit(-1)
796
797	return
798
799	def variables_values_old(varName):
800	""" Function to provide values to plot the different variables
801	variables_values(varName)
802	[varName]= name of the variable
803	return: [var name], [std name], [minimum], [maximum],
804	[long name]('\|' for spaces), [units], [color palette] (following:
805	http://matplotlib.org/1.3.1/examples/color/colormaps_reference.html)
806	[varn]: original name of the variable
807	NOTE: It might be better doing it with an external ASII file. But then we
808	got an extra dependency...
809	>>> variables_values('WRFght')
810	['z', 'geopotential_height', 0.0, 80000.0, 'geopotential\|height', 'm2s-2', 'rainbow']
811	"""
812	fname='variables_values'
813
814	if varName == 'h':
815	print fname + '_____________________________________________________________'
816	print variables_values.__doc__
817	quit()
818
819	# Variable name might come with a statistical surname...
820	stats=['min','max','mean','stdv', 'sum']
821
822	ifst = False
823	for st in stats:
824	if varName.find(st) > -1:
825	print ' '+ fname + ": varibale '" + varName + "' with a statistical "+\
826	" surname: '",st,"' !!"
827	Lst = len(st)
828	LvarName = len(varName)
829	varn = varName[0:LvarName - Lst]
830	ifst = True
831	break
832	if not ifst:
833	varn = varName
834
835	if varn[0:6] == 'varDIM':
836	# Variable from a dimension (all with 'varDIM' prefix)
837	Lvarn = len(varn)
838	varvals = [varn[6:Lvarn+1], varn[6:Lvarn+1], 0., 1., \
839	"variable\|from\|size\|of\|dimension\|'" + varn[6:Lvarn+1] + "'", '1', 'rainbox']
840	elif varn == 'a_tht' or varn == 'LA_THT':
841	varvals = ['ath', 'total_thermal_plume_cover', 0., 1., \
842	'total\|column\|thermal\|plume\|cover', '1', 'YlGnBu']
843	elif varn == 'acprc' or varn == 'RAINC':
844	varvals = ['acprc', 'accumulated_cmulus_precipitation', 0., 3.e4, \
845	'accumulated\|cmulus\|precipitation', 'mm', 'Blues']
846	elif varn == 'acprnc' or varn == 'RAINNC':
847	varvals = ['acprnc', 'accumulated_non-cmulus_precipitation', 0., 3.e4, \
848	'accumulated\|non-cmulus\|precipitation', 'mm', 'Blues']
849	elif varn == 'bils' or varn == 'LBILS':
850	varvals = ['bils', 'surface_total_heat_flux', -100., 100., \
851	'surface\|total\|heat\|flux', 'Wm-2', 'seismic']
852	elif varn == 'landcat' or varn == 'category':
853	varvals = ['landcat', 'land_categories', 0., 22., 'land\|categories', '1', \
854	'rainbow']
855	elif varn == 'c' or varn == 'QCLOUD' or varn == 'oliq' or varn == 'OLIQ':
856	varvals = ['c', 'condensed_water_mixing_ratio', 0., 3.e-4, \
857	'condensed\|water\|mixing\|ratio', 'kgkg-1', 'BuPu']
858	elif varn == 'ci' or varn == 'iwcon' or varn == 'LIWCON':
859	varvals = ['ci', 'cloud_iced_water_mixing_ratio', 0., 0.0003, \
860	'cloud\|iced\|water\|mixing\|ratio', 'kgkg-1', 'Purples']
861	elif varn == 'cl' or varn == 'lwcon' or varn == 'LLWCON':
862	varvals = ['cl', 'cloud_liquidwater_mixing_ratio', 0., 0.0003, \
863	'cloud\|liquid\|water\|mixing\|ratio', 'kgkg-1', 'Blues']
864	elif varn == 'cld' or varn == 'CLDFRA' or varn == 'rneb' or varn == 'lrneb' or \
865	varn == 'LRNEB':
866	varvals = ['cld', 'cloud_area_fraction', 0., 1., 'cloud\|fraction', '1', \
867	'gist_gray']
868	elif varn == 'cldc' or varn == 'rnebcon' or varn == 'lrnebcon' or \
869	varn == 'LRNEBCON':
870	varvals = ['cldc', 'convective_cloud_area_fraction', 0., 1., \
871	'convective\|cloud\|fraction', '1', 'gist_gray']
872	elif varn == 'cldl' or varn == 'rnebls' or varn == 'lrnebls' or varn == 'LRNEBLS':
873	varvals = ['cldl', 'large_scale_cloud_area_fraction', 0., 1., \
874	'large\|scale\|cloud\|fraction', '1', 'gist_gray']
875	elif varn == 'clt' or varn == 'CLT' or varn == 'cldt' or \
876	varn == 'Total cloudiness':
877	varvals = ['clt', 'cloud_area_fraction', 0., 1., 'total\|cloud\|cover', '1', \
878	'gist_gray']
879	elif varn == 'cll' or varn == 'cldl' or varn == 'LCLDL' or \
880	varn == 'Low-level cloudiness':
881	varvals = ['cll', 'low_level_cloud_area_fraction', 0., 1., \
882	'low\|level\|(p\|>\|680\|hPa)\|cloud\|fraction', '1', 'gist_gray']
883	elif varn == 'clm' or varn == 'cldm' or varn == 'LCLDM' or \
884	varn == 'Mid-level cloudiness':
885	varvals = ['clm', 'mid_level_cloud_area_fraction', 0., 1., \
886	'medium\|level\|(440\|<\|p\|<\|680\|hPa)\|cloud\|fraction', '1', 'gist_gray']
887	elif varn == 'clh' or varn == 'cldh' or varn == 'LCLDH' or \
888	varn == 'High-level cloudiness':
889	varvals = ['clh', 'high_level_cloud_area_fraction', 0., 1., \
890	'high\|level\|(p\|<\|440\|hPa)\|cloud\|fraction', '1', 'gist_gray']
891	elif varn == 'clmf' or varn == 'fbase' or varn == 'LFBASE':
892	varvals = ['clmf', 'cloud_base_max_flux', -0.3, 0.3, 'cloud\|base\|max\|flux', \
893	'kgm-2s-1', 'seismic']
894	elif varn == 'clp' or varn == 'pbase' or varn == 'LPBASE':
895	varvals = ['clp', 'cloud_base_pressure', -0.3, 0.3, 'cloud\|base\|pressure', \
896	'Pa', 'Reds']
897	elif varn == 'cpt' or varn == 'ptconv' or varn == 'LPTCONV':
898	varvals = ['cpt', 'convective_point', 0., 1., 'convective\|point', '1', \
899	'seismic']
900	elif varn == 'dqajs' or varn == 'LDQAJS':
901	varvals = ['dqajs', 'dry_adjustment_water_vapor_tendency', -0.0003, 0.0003, \
902	'dry\|adjustment\|water\|vapor\|tendency', 'kg/kg/s', 'seismic']
903	elif varn == 'dqcon' or varn == 'LDQCON':
904	varvals = ['dqcon', 'convective_water_vapor_tendency', -3e-8, 3.e-8, \
905	'convective\|water\|vapor\|tendency', 'kg/kg/s', 'seismic']
906	elif varn == 'dqdyn' or varn == 'LDQDYN':
907	varvals = ['dqdyn', 'dynamics_water_vapor_tendency', -3.e-7, 3.e-7, \
908	'dynamics\|water\|vapor\|tendency', 'kg/kg/s', 'seismic']
909	elif varn == 'dqeva' or varn == 'LDQEVA':
910	varvals = ['dqeva', 'evaporation_water_vapor_tendency', -3.e-6, 3.e-6, \
911	'evaporation\|water\|vapor\|tendency', 'kg/kg/s', 'seismic']
912	elif varn == 'dqlscst' or varn == 'LDQLSCST':
913	varvals = ['dqlscst', 'stratocumulus_water_vapor_tendency', -3.e-7, 3.e-7, \
914	'stratocumulus\|water\|vapor\|tendency', 'kg/kg/s', 'seismic']
915	elif varn == 'dqlscth' or varn == 'LDQLSCTH':
916	varvals = ['dqlscth', 'thermals_water_vapor_tendency', -3.e-7, 3.e-7, \
917	'thermal\|plumes\|water\|vapor\|tendency', 'kg/kg/s', 'seismic']
918	elif varn == 'dqlsc' or varn == 'LDQLSC':
919	varvals = ['dqlsc', 'condensation_water_vapor_tendency', -3.e-6, 3.e-6, \
920	'condensation\|water\|vapor\|tendency', 'kg/kg/s', 'seismic']
921	elif varn == 'dqphy' or varn == 'LDQPHY':
922	varvals = ['dqphy', 'physics_water_vapor_tendency', -3.e-7, 3.e-7, \
923	'physics\|water\|vapor\|tendency', 'kg/kg/s', 'seismic']
924	elif varn == 'dqthe' or varn == 'LDQTHE':
925	varvals = ['dqthe', 'thermals_water_vapor_tendency', -3.e-7, 3.e-7, \
926	'thermal\|plumes\|water\|vapor\|tendency', 'kg/kg/s', 'seismic']
927	elif varn == 'dqvdf' or varn == 'LDQVDF':
928	varvals = ['dqvdf', 'vertical_difussion_water_vapor_tendency', -3.e-8, 3.e-8,\
929	'vertical\|difussion\|water\|vapor\|tendency', 'kg/kg/s', 'seismic']
930	elif varn == 'dqwak' or varn == 'LDQWAK':
931	varvals = ['dqwak', 'wake_water_vapor_tendency', -3.e-7, 3.e-7, \
932	'wake\|water\|vapor\|tendency', 'kg/kg/s', 'seismic']
933	elif varn == 'dta' or varn == 'tnt' or varn == 'LTNT':
934	varvals = ['dta', 'tendency_air_temperature', -3.e-3, 3.e-3, \
935	'tendency\|of\|air\|temperature', 'K/s', 'seismic']
936	elif varn == 'dtac' or varn == 'tntc' or varn == 'LTNTC':
937	varvals = ['dtac', 'moist_convection_tendency_air_temperature', -3.e-3, \
938	3.e-3, 'moist\|convection\|tendency\|of\|air\|temperature', 'K/s', 'seismic']
939	elif varn == 'dtar' or varn == 'tntr' or varn == 'LTNTR':
940	varvals = ['dtar', 'radiative_heating_tendency_air_temperature', -3.e-3, \
941	3.e-3, 'radiative\|heating\|tendency\|of\|air\|temperature', 'K/s', 'seismic']
942	elif varn == 'dtascpbl' or varn == 'tntscpbl' or varn == 'LTNTSCPBL':
943	varvals = ['dtascpbl', \
944	'stratiform_cloud_precipitation_BL_mixing_tendency_air_temperature', \
945	-3.e-6, 3.e-6, \
946	'stratiform\|cloud\|precipitation\|Boundary\|Layer\|mixing\|tendency\|air\|' +
947	'temperature', 'K/s', 'seismic']
948	elif varn == 'dtajs' or varn == 'LDTAJS':
949	varvals = ['dtajs', 'dry_adjustment_thermal_tendency', -3.e-5, 3.e-5, \
950	'dry\|adjustment\|thermal\|tendency', 'K/s', 'seismic']
951	elif varn == 'dtcon' or varn == 'LDTCON':
952	varvals = ['dtcon', 'convective_thermal_tendency', -3.e-5, 3.e-5, \
953	'convective\|thermal\|tendency', 'K/s', 'seismic']
954	elif varn == 'dtdyn' or varn == 'LDTDYN':
955	varvals = ['dtdyn', 'dynamics_thermal_tendency', -3.e-4, 3.e-4, \
956	'dynamics\|thermal\|tendency', 'K/s', 'seismic']
957	elif varn == 'dteva' or varn == 'LDTEVA':
958	varvals = ['dteva', 'evaporation_thermal_tendency', -3.e-3, 3.e-3, \
959	'evaporation\|thermal\|tendency', 'K/s', 'seismic']
960	elif varn == 'dtlscst' or varn == 'LDTLSCST':
961	varvals = ['dtlscst', 'stratocumulus_thermal_tendency', -3.e-4, 3.e-4, \
962	'stratocumulus\|thermal\|tendency', 'K/s', 'seismic']
963	elif varn == 'dtlscth' or varn == 'LDTLSCTH':
964	varvals = ['dtlscth', 'thermals_thermal_tendency', -3.e-4, 3.e-4, \
965	'thermal\|plumes\|thermal\|tendency', 'K/s', 'seismic']
966	elif varn == 'dtlsc' or varn == 'LDTLSC':
967	varvals = ['dtlsc', 'condensation_thermal_tendency', -3.e-3, 3.e-3, \
968	'condensation\|thermal\|tendency', 'K/s', 'seismic']
969	elif varn == 'dtlwr' or varn == 'LDTLWR':
970	varvals = ['dtlwr', 'long_wave_thermal_tendency', -3.e-3, 3.e-3, \
971	'long\|wave\|radiation\|thermal\|tendency', 'K/s', 'seismic']
972	elif varn == 'dtphy' or varn == 'LDTPHY':
973	varvals = ['dtphy', 'physics_thermal_tendency', -3.e-4, 3.e-4, \
974	'physics\|thermal\|tendency', 'K/s', 'seismic']
975	elif varn == 'dtsw0' or varn == 'LDTSW0':
976	varvals = ['dtsw0', 'cloudy_sky_short_wave_thermal_tendency', -3.e-4, 3.e-4, \
977	'cloudy\|sky\|short\|wave\|radiation\|thermal\|tendency', 'K/s', 'seismic']
978	elif varn == 'dtthe' or varn == 'LDTTHE':
979	varvals = ['dtthe', 'thermals_thermal_tendency', -3.e-4, 3.e-4, \
980	'thermal\|plumes\|thermal\|tendency', 'K/s', 'seismic']
981	elif varn == 'dtvdf' or varn == 'LDTVDF':
982	varvals = ['dtvdf', 'vertical_difussion_thermal_tendency', -3.e-5, 3.e-5, \
983	'vertical\|difussion\|thermal\|tendency', 'K/s', 'seismic']
984	elif varn == 'dtwak' or varn == 'LDTWAK':
985	varvals = ['dtwak', 'wake_thermal_tendency', -3.e-4, 3.e-4, \
986	'wake\|thermal\|tendency', 'K/s', 'seismic']
987	elif varn == 'ducon' or varn == 'LDUCON':
988	varvals = ['ducon', 'convective_eastward_wind_tendency', -3.e-3, 3.e-3, \
989	'convective\|eastward\|wind\|tendency', 'ms-2', 'seismic']
990	elif varn == 'dudyn' or varn == 'LDUDYN':
991	varvals = ['dudyn', 'dynamics_eastward_wind_tendency', -3.e-3, 3.e-3, \
992	'dynamics\|eastward\|wind\|tendency', 'ms-2', 'seismic']
993	elif varn == 'duvdf' or varn == 'LDUVDF':
994	varvals = ['duvdf', 'vertical_difussion_eastward_wind_tendency', -3.e-3, \
995	3.e-3, 'vertical\|difussion\|eastward\|wind\|tendency', 'ms-2', 'seismic']
996	elif varn == 'dvcon' or varn == 'LDVCON':
997	varvals = ['dvcon', 'convective_difussion_northward_wind_tendency', -3.e-3, \
998	3.e-3, 'convective\|northward\|wind\|tendency', 'ms-2', 'seismic']
999	elif varn == 'dvdyn' or varn == 'LDVDYN':
1000	varvals = ['dvdyn', 'dynamics_northward_wind_tendency', -3.e-3, \
1001	3.e-3, 'dynamics\|difussion\|northward\|wind\|tendency', 'ms-2', 'seismic']
1002	elif varn == 'dvvdf' or varn == 'LDVVDF':
1003	varvals = ['dvvdf', 'vertical_difussion_northward_wind_tendency', -3.e-3, \
1004	3.e-3, 'vertical\|difussion\|northward\|wind\|tendency', 'ms-2', 'seismic']
1005	elif varn == 'etau' or varn == 'ZNU':
1006	varvals = ['etau', 'etau', 0., 1, 'eta values on half (mass) levels', '-', \
1007	'reds']
1008	elif varn == 'evspsbl' or varn == 'LEVAP' or varn == 'evap' or varn == 'SFCEVPde':
1009	varvals = ['evspsbl', 'water_evaporation_flux', 0., 1.5e-4, \
1010	'water\|evaporation\|flux', 'kgm-2s-1', 'Blues']
1011	elif varn == 'evspsbl' or varn == 'SFCEVPde':
1012	varvals = ['evspsblac', 'water_evaporation_flux_ac', 0., 1.5e-4, \
1013	'accumulated\|water\|evaporation\|flux', 'kgm-2', 'Blues']
1014	elif varn == 'g' or varn == 'QGRAUPEL':
1015	varvals = ['g', 'grauepl_mixing_ratio', 0., 0.0003, 'graupel\|mixing\|ratio', \
1016	'kgkg-1', 'Purples']
1017	elif varn == 'h2o' or varn == 'LH2O':
1018	varvals = ['h2o', 'water_mass_fraction', 0., 3.e-2, \
1019	'mass\|fraction\|of\|water', '1', 'Blues']
1020	elif varn == 'h' or varn == 'QHAIL':
1021	varvals = ['h', 'hail_mixing_ratio', 0., 0.0003, 'hail\|mixing\|ratio', \
1022	'kgkg-1', 'Purples']
1023	elif varn == 'hfls' or varn == 'LH' or varn == 'LFLAT' or varn == 'flat':
1024	varvals = ['hfls', 'surface_upward_latent_heat_flux', -400., 400., \
1025	'upward\|latnt\|heat\|flux\|at\|the\|surface', 'Wm-2', 'seismic']
1026	elif varn == 'hfss' or varn == 'LSENS' or varn == 'sens' or varn == 'HFX':
1027	varvals = ['hfss', 'surface_upward_sensible_heat_flux', -150., 150., \
1028	'upward\|sensible\|heat\|flux\|at\|the\|surface', 'Wm-2', 'seismic']
1029	elif varn == 'hfso' or varn == 'GRDFLX':
1030	varvals = ['hfso', 'downward_heat_flux_in_soil', -150., 150., \
1031	'Downward\|soil\|heat\|flux', 'Wm-2', 'seismic']
1032	elif varn == 'hus' or varn == 'WRFrh' or varn == 'LMDZrh' or varn == 'rhum' or \
1033	varn == 'LRHUM':
1034	varvals = ['hus', 'specific_humidity', 0., 1., 'specific\|humidty', '1', \
1035	'BuPu']
1036	elif varn == 'huss' or varn == 'WRFrhs' or varn == 'LMDZrhs' or varn == 'rh2m' or\
1037	varn == 'LRH2M':
1038	varvals = ['huss', 'specific_humidity', 0., 1., 'specific\|humidty\|at\|2m', \
1039	'1', 'BuPu']
1040	elif varn == 'i' or varn == 'QICE':
1041	varvals = ['i', 'iced_water_mixing_ratio', 0., 0.0003, \
1042	'iced\|water\|mixing\|ratio', 'kgkg-1', 'Purples']
1043	elif varn == 'lat' or varn == 'XLAT' or varn == 'XLAT_M' or varn == 'latitude':
1044	varvals = ['lat', 'latitude', -90., 90., 'latitude', 'degrees North', \
1045	'seismic']
1046	elif varn == 'lcl' or varn == 's_lcl' or varn == 'ls_lcl' or varn == 'LS_LCL':
1047	varvals = ['lcl', 'condensation_level', 0., 2500., 'level\|of\|condensation', \
1048	'm', 'Greens']
1049	elif varn == 'lambdath' or varn == 'lambda_th' or varn == 'LLAMBDA_TH':
1050	varvals = ['lambdath', 'thermal_plume_vertical_velocity', -30., 30., \
1051	'thermal\|plume\|vertical\|velocity', 'm/s', 'seismic']
1052	elif varn == 'lmaxth' or varn == 'LLMAXTH':
1053	varvals = ['lmaxth', 'upper_level_thermals', 0., 100., 'upper\|level\|thermals'\
1054	, '1', 'Greens']
1055	elif varn == 'lon' or varn == 'XLONG' or varn == 'XLONG_M':
1056	varvals = ['lon', 'longitude', -180., 180., 'longitude', 'degrees East', \
1057	'seismic']
1058	elif varn == 'longitude':
1059	varvals = ['lon', 'longitude', 0., 360., 'longitude', 'degrees East', \
1060	'seismic']
1061	elif varn == 'orog' or varn == 'HGT' or varn == 'HGT_M':
1062	varvals = ['orog', 'orography', 0., 3000., 'surface\|altitude', 'm','terrain']
1063	elif varn == 'pfc' or varn == 'plfc' or varn == 'LPLFC':
1064	varvals = ['pfc', 'pressure_free_convection', 100., 1100., \
1065	'pressure\|free\|convection', 'hPa', 'BuPu']
1066	elif varn == 'plcl' or varn == 'LPLCL':
1067	varvals = ['plcl', 'pressure_lifting_condensation_level', 700., 1100., \
1068	'pressure\|lifting\|condensation\|level', 'hPa', 'BuPu']
1069	elif varn == 'pr' or varn == 'RAINTOT' or varn == 'precip' or \
1070	varn == 'LPRECIP' or varn == 'Precip Totale liq+sol':
1071	varvals = ['pr', 'precipitation_flux', 0., 1.e-4, 'precipitation\|flux', \
1072	'kgm-2s-1', 'BuPu']
1073	elif varn == 'prprof' or varn == 'vprecip' or varn == 'LVPRECIP':
1074	varvals = ['prprof', 'precipitation_profile', 0., 1.e-3, \
1075	'precipitation\|profile', 'kg/m2/s', 'BuPu']
1076	elif varn == 'prprofci' or varn == 'pr_con_i' or varn == 'LPR_CON_I':
1077	varvals = ['prprofci', 'precipitation_profile_convective_i', 0., 1.e-3, \
1078	'precipitation\|profile\|convective\|i', 'kg/m2/s', 'BuPu']
1079	elif varn == 'prprofcl' or varn == 'pr_con_l' or varn == 'LPR_CON_L':
1080	varvals = ['prprofcl', 'precipitation_profile_convective_l', 0., 1.e-3, \
1081	'precipitation\|profile\|convective\|l', 'kg/m2/s', 'BuPu']
1082	elif varn == 'prprofli' or varn == 'pr_lsc_i' or varn == 'LPR_LSC_I':
1083	varvals = ['prprofli', 'precipitation_profile_large_scale_i', 0., 1.e-3, \
1084	'precipitation\|profile\|large\|scale\|i', 'kg/m2/s', 'BuPu']
1085	elif varn == 'prprofll' or varn == 'pr_lsc_l' or varn == 'LPR_LSC_L':
1086	varvals = ['prprofll', 'precipitation_profile_large_scale_l', 0., 1.e-3, \
1087	'precipitation\|profile\|large\|scale\|l', 'kg/m2/s', 'BuPu']
1088	elif varn == 'pracc' or varn == 'ACRAINTOT':
1089	varvals = ['pracc', 'precipitation_amount', 0., 100., \
1090	'accumulated\|precipitation', 'kgm-2', 'BuPu']
1091	elif varn == 'prc' or varn == 'LPLUC' or varn == 'pluc' or varn == 'WRFprc' or \
1092	varn == 'RAINCde':
1093	varvals = ['prc', 'convective_precipitation_flux', 0., 2.e-4, \
1094	'convective\|precipitation\|flux', 'kgm-2s-1', 'Blues']
1095	elif varn == 'prci' or varn == 'pr_con_i' or varn == 'LPR_CON_I':
1096	varvals = ['prci', 'convective_ice_precipitation_flux', 0., 0.003, \
1097	'convective\|ice\|precipitation\|flux', 'kgm-2s-1', 'Purples']
1098	elif varn == 'prcl' or varn == 'pr_con_l' or varn == 'LPR_CON_L':
1099	varvals = ['prcl', 'convective_liquid_precipitation_flux', 0., 0.003, \
1100	'convective\|liquid\|precipitation\|flux', 'kgm-2s-1', 'Blues']
1101	elif varn == 'pres' or varn == 'presnivs' or varn == 'pressure' or \
1102	varn == 'lpres' or varn == 'LPRES':
1103	varvals = ['pres', 'air_pressure', 0., 103000., 'air\|pressure', 'Pa', \
1104	'Blues']
1105	elif varn == 'prls' or varn == 'WRFprls' or varn == 'LPLUL' or varn == 'plul' or \
1106	varn == 'RAINNCde':
1107	varvals = ['prls', 'large_scale_precipitation_flux', 0., 2.e-4, \
1108	'large\|scale\|precipitation\|flux', 'kgm-2s-1', 'Blues']
1109	elif varn == 'prsn' or varn == 'SNOW' or varn == 'snow' or varn == 'LSNOW':
1110	varvals = ['prsn', 'snowfall', 0., 1.e-4, 'snowfall\|flux', 'kgm-2s-1', 'BuPu']
1111	elif varn == 'prw' or varn == 'WRFprh':
1112	varvals = ['prw', 'atmosphere_water_vapor_content', 0., 10., \
1113	'water\|vapor"path', 'kgm-2', 'Blues']
1114	elif varn == 'ps' or varn == 'psfc' or varn =='PSFC' or varn == 'psol' or \
1115	varn == 'Surface Pressure':
1116	varvals=['ps', 'surface_air_pressure', 85000., 105400., 'surface\|pressure', \
1117	'hPa', 'cool']
1118	elif varn == 'psl' or varn == 'mslp' or varn =='WRFmslp':
1119	varvals=['psl', 'air_pressure_at_sea_level', 85000., 104000., \
1120	'mean\|sea\|level\|pressure', 'Pa', 'Greens']
1121	elif varn == 'qth' or varn == 'q_th' or varn == 'LQ_TH':
1122	varvals = ['qth', 'thermal_plume_total_water_content', 0., 25., \
1123	'total\|water\|cotent\|in\|thermal\|plume', 'mm', 'YlOrRd']
1124	elif varn == 'r' or varn == 'QVAPOR' or varn == 'ovap' or varn == 'LOVAP':
1125	varvals = ['r', 'water_mixing_ratio', 0., 0.03, 'water\|mixing\|ratio', \
1126	'kgkg-1', 'BuPu']
1127	elif varn == 'r2' or varn == 'Q2':
1128	varvals = ['r2', 'water_mixing_ratio_at_2m', 0., 0.03, 'water\|mixing\|' + \
1129	'ratio\|at\|2\|m','kgkg-1', 'BuPu']
1130	elif varn == 'rsds' or varn == 'SWdnSFC' or varn == 'SWdn at surface' or \
1131	varn == 'SWDOWN':
1132	varvals=['rsds', 'surface_downwelling_shortwave_flux_in_air', 0., 1200., \
1133	'downward\|SW\|surface\|radiation', 'Wm-2' ,'Reds']
1134	elif varn == 'rsdsacc':
1135	varvals=['rsdsacc', 'accumulated_surface_downwelling_shortwave_flux_in_air', \
1136	0., 1200., 'accumulated\|downward\|SW\|surface\|radiation', 'Wm-2' ,'Reds']
1137	elif varn == 'rvor' or varn == 'WRFrvor':
1138	varvals = ['rvor', 'air_relative_vorticity', -2.5E-3, 2.5E-3, \
1139	'air\|relative\|vorticity', 's-1', 'seismic']
1140	elif varn == 'rvors' or varn == 'WRFrvors':
1141	varvals = ['rvors', 'surface_air_relative_vorticity', -2.5E-3, 2.5E-3, \
1142	'surface\|air\|relative\|vorticity', 's-1', 'seismic']
1143	elif varn == 's' or varn == 'QSNOW':
1144	varvals = ['s', 'snow_mixing_ratio', 0., 0.0003, 'snow\|mixing\|ratio', \
1145	'kgkg-1', 'Purples']
1146	elif varn == 'stherm' or varn == 'LS_THERM':
1147	varvals = ['stherm', 'thermals_excess', 0., 0.8, 'thermals\|excess', 'K', \
1148	'Reds']
1149	elif varn == 'ta' or varn == 'WRFt' or varn == 'temp' or varn == 'LTEMP' or \
1150	varn == 'Air temperature':
1151	varvals = ['ta', 'air_temperature', 195., 320., 'air\|temperature', 'K', \
1152	'YlOrRd']
1153	elif varn == 'tah' or varn == 'theta' or varn == 'LTHETA':
1154	varvals = ['tah', 'potential_air_temperature', 195., 320., \
1155	'potential\|air\|temperature', 'K', 'YlOrRd']
1156	elif varn == 'tas' or varn == 'T2' or varn == 't2m' or varn == 'T2M' or \
1157	varn == 'Temperature 2m':
1158	varvals = ['tas', 'air_temperature', 240., 310., 'air\|temperature\|at\|2m', ' \
1159	K', 'YlOrRd']
1160	elif varn == 'tds' or varn == 'TH2':
1161	varvals = ['tds', 'air_dew_point_temperature', 240., 310., \
1162	'air\|dew\|point\|temperature\|at\|2m', 'K', 'YlGnBu']
1163	elif varn == 'tke' or varn == 'TKE' or varn == 'tke' or varn == 'LTKE':
1164	varvals = ['tke', 'turbulent_kinetic_energy', 0., 0.003, \
1165	'turbulent\|kinetic\|energy', 'm2/s2', 'Reds']
1166	elif varn == 'time'or varn == 'time_counter':
1167	varvals = ['time', 'time', 0., 1000., 'time', \
1168	'hours\|since\|1949/12/01\|00:00:00', 'Reds']
1169	elif varn == 'tmla' or varn == 's_pblt' or varn == 'LS_PBLT':
1170	varvals = ['tmla', 'atmosphere_top_boundary_layer_temperature', 250., 330., \
1171	'atmosphere\|top\|boundary\|layer\|temperature', 'K', 'Reds']
1172	elif varn == 'ua' or varn == 'vitu' or varn == 'U' or varn == 'Zonal wind' or \
1173	varn == 'LVITU':
1174	varvals = ['ua', 'eastward_wind', -30., 30., 'eastward\|wind', 'ms-1', \
1175	'seismic']
1176	elif varn == 'uas' or varn == 'u10m' or varn == 'U10' or varn =='Vent zonal 10m':
1177	varvals = ['uas', 'eastward_wind', -30., 30., 'eastward\|2m\|wind', \
1178	'ms-1', 'seismic']
1179	elif varn == 'va' or varn == 'vitv' or varn == 'V' or varn == 'Meridional wind' \
1180	or varn == 'LVITV':
1181	varvals = ['va', 'northward_wind', -30., 30., 'northward\|wind', 'ms-1', \
1182	'seismic']
1183	elif varn == 'vas' or varn == 'v10m' or varn == 'V10' or \
1184	varn =='Vent meridien 10m':
1185	varvals = ['vas', 'northward_wind', -30., 30., 'northward\|2m\|wind', 'ms-1', \
1186	'seismic']
1187	elif varn == 'wakedeltaq' or varn == 'wake_deltaq' or varn == 'lwake_deltaq' or \
1188	varn == 'LWAKE_DELTAQ':
1189	varvals = ['wakedeltaq', 'wake_delta_vapor', -0.003, 0.003, \
1190	'wake\|delta\|mixing\|ratio', '-', 'seismic']
1191	elif varn == 'wakedeltat' or varn == 'wake_deltat' or varn == 'lwake_deltat' or \
1192	varn == 'LWAKE_DELTAT':
1193	varvals = ['wakedeltat', 'wake_delta_temp', -0.003, 0.003, \
1194	'wake\|delta\|temperature', '-', 'seismic']
1195	elif varn == 'wakeh' or varn == 'wake_h' or varn == 'LWAKE_H':
1196	varvals = ['wakeh', 'wake_height', 0., 1000., 'height\|of\|the\|wakes', 'm', \
1197	'YlOrRd']
1198	elif varn == 'wakeomg' or varn == 'wake_omg' or varn == 'lwake_omg' or \
1199	varn == 'LWAKE_OMG':
1200	varvals = ['wakeomg', 'wake_omega', 0., 3., 'wake\|omega', \
1201	'-', 'BuGn']
1202	elif varn == 'wakes' or varn == 'wake_s' or varn == 'LWAKE_S':
1203	varvals = ['wakes', 'wake_area_fraction', 0., 0.5, 'wake\|spatial\|fraction', \
1204	'1', 'BuGn']
1205	elif varn == 'wa' or varn == 'W' or varn == 'Vertical wind':
1206	varvals = ['wa', 'upward_wind', -10., 10., 'upward\|wind', 'ms-1', \
1207	'seismic']
1208	elif varn == 'wap' or varn == 'vitw' or varn == 'LVITW':
1209	varvals = ['wap', 'upward_wind', -3.e-10, 3.e-10, 'upward\|wind', 'mPa-1', \
1210	'seismic']
1211	elif varn == 'wss' or varn == 'SPDUV':
1212	varvals = ['wss', 'air_velocity', 0., 30., 'surface\|horizontal\|wind\|speed', \
1213	'ms-1', 'Reds']
1214	# Water budget
1215	# Water budget de-accumulated
1216	elif varn == 'ccond' or varn == 'CCOND' or varn == 'ACCCONDde':
1217	varvals = ['ccond', 'cw_cond', 0., 30., \
1218	'cloud\|water\|condensation', 'mm', 'Reds']
1219	elif varn == 'wbr' or varn == 'ACQVAPORde':
1220	varvals = ['wbr', 'wbr', 0., 30., 'Water\|Budget\|water\|wapor', 'mm', 'Blues']
1221	elif varn == 'diabh' or varn == 'DIABH' or varn == 'ACDIABHde':
1222	varvals = ['diabh', 'diabh', 0., 30., 'diabatic\|heating', 'K', 'Reds']
1223	elif varn == 'wbpw' or varn == 'WBPW' or varn == 'WBACPWde':
1224	varvals = ['wbpw', 'water_budget_pw', 0., 30., 'Water\|Budget\|water\|content',\
1225	'mms-1', 'Reds']
1226	elif varn == 'wbf' or varn == 'WBACF' or varn == 'WBACFde':
1227	varvals = ['wbf', 'water_budget_hfcqv', 0., 30., \
1228	'Water\|Budget\|horizontal\|convergence\|of\|water\|vapour\|(+,\|' + \
1229	'conv.;\|-,\|div.)', 'mms-1', 'Reds']
1230	elif varn == 'wbfc' or varn == 'WBFC' or varn == 'WBACFCde':
1231	varvals = ['wbfc', 'water_budget_fc', 0., 30., \
1232	'Water\|Budget\|horizontal\|convergence\|of\|cloud\|(+,\|conv.;\|-,\|' +\
1233	'div.)', 'mms-1', 'Reds']
1234	elif varn == 'wbfp' or varn == 'WBFP' or varn == 'WBACFPde':
1235	varvals = ['wbfp', 'water_budget_cfp', 0., 30., \
1236	'Water\|Budget\|horizontal\|convergence\|of\|precipitation\|(+,\|' + \
1237	'conv.;\|-,\|div.)', 'mms-1', 'Reds']
1238	elif varn == 'wbz' or varn == 'WBZ' or varn == 'WBACZde':
1239	varvals = ['wbz', 'water_budget_z', 0., 30., \
1240	'Water\|Budget\|vertical\|convergence\|of\|water\|vapour\|(+,\|conv.' +\
1241	';\|-,\|div.)', 'mms-1', 'Reds']
1242	elif varn == 'wbc' or varn == 'WBC' or varn == 'WBACCde':
1243	varvals = ['wbc', 'water_budget_c', 0., 30., \
1244	'Water\|Budget\|Cloud\|water\|species','mms-1', 'Reds']
1245	elif varn == 'wbqvd' or varn == 'WBQVD' or varn == 'WBACQVDde':
1246	varvals = ['wbqvd', 'water_budget_qvd', 0., 30., \
1247	'Water\|Budget\|water\|vapour\|divergence', 'mms-1', 'Reds']
1248	elif varn == 'wbqvblten' or varn == 'WBQVBLTEN' or varn == 'WBACQVBLTENde':
1249	varvals = ['wbqvblten', 'water_budget_qv_blten', 0., 30., \
1250	'Water\|Budget\|QV\|tendency\|due\|to\|pbl\|parameterization', \
1251	'kg kg-1 s-1', 'Reds']
1252	elif varn == 'wbqvcuten' or varn == 'WBQVCUTEN' or varn == 'WBACQVCUTENde':
1253	varvals = ['wbqvcuten', 'water_budget_qv_cuten', 0., 30., \
1254	'Water\|Budget\|QV\|tendency\|due\|to\|cu\|parameterization', \
1255	'kg kg-1 s-1', 'Reds']
1256	elif varn == 'wbqvshten' or varn == 'WBQVSHTEN' or varn == 'WBACQVSHTENde':
1257	varvals = ['wbqvshten', 'water_budget_qv_shten', 0., 30., \
1258	'Water\|Budget\|QV\|tendency\|due\|to\|shallow\|cu\|parameterization', \
1259	'kg kg-1 s-1', 'Reds']
1260	elif varn == 'wbpr' or varn == 'WBP' or varn == 'WBACPde':
1261	varvals = ['wbpr', 'water_budget_pr', 0., 30., \
1262	'Water\|Budget\|recipitation', 'mms-1', 'Reds']
1263	elif varn == 'wbpw' or varn == 'WBPW' or varn == 'WBACPWde':
1264	varvals = ['wbpw', 'water_budget_pw', 0., 30., \
1265	'Water\|Budget\|water\|content', 'mms-1', 'Reds']
1266	elif varn == 'wbcondt' or varn == 'WBCONDT' or varn == 'WBACCONDTde':
1267	varvals = ['wbcondt', 'water_budget_condt', 0., 30., \
1268	'Water\|Budget\|condensation\|and\|deposition', 'mms-1', 'Reds']
1269	elif varn == 'wbqcm' or varn == 'WBQCM' or varn == 'WBACQCMde':
1270	varvals = ['wbqcm', 'water_budget_qcm', 0., 30., \
1271	'Water\|Budget\|hydrometeor\|change\|and\|convergence', 'mms-1', 'Reds']
1272	elif varn == 'wbsi' or varn == 'WBSI' or varn == 'WBACSIde':
1273	varvals = ['wbsi', 'water_budget_si', 0., 30., \
1274	'Water\|Budget\|hydrometeor\|sink', 'mms-1', 'Reds']
1275	elif varn == 'wbso' or varn == 'WBSO' or varn == 'WBACSOde':
1276	varvals = ['wbso', 'water_budget_so', 0., 30., \
1277	'Water\|Budget\|hydrometeor\|source', 'mms-1', 'Reds']
1278	# Water Budget accumulated
1279	elif varn == 'ccondac' or varn == 'ACCCOND':
1280	varvals = ['ccondac', 'cw_cond_ac', 0., 30., \
1281	'accumulated\|cloud\|water\|condensation', 'mm', 'Reds']
1282	elif varn == 'rac' or varn == 'ACQVAPOR':
1283	varvals = ['rac', 'ac_r', 0., 30., 'accumualted\|water\|wapor', 'mm', 'Blues']
1284	elif varn == 'diabhac' or varn == 'ACDIABH':
1285	varvals = ['diabhac', 'diabh_ac', 0., 30., 'accumualted\|diabatic\|heating', \
1286	'K', 'Reds']
1287	elif varn == 'wbpwac' or varn == 'WBACPW':
1288	varvals = ['wbpwac', 'water_budget_pw_ac', 0., 30., \
1289	'Water\|Budget\|accumulated\|water\|content', 'mm', 'Reds']
1290	elif varn == 'wbfac' or varn == 'WBACF':
1291	varvals = ['wbfac', 'water_budget_hfcqv_ac', 0., 30., \
1292	'Water\|Budget\|accumulated\|horizontal\|convergence\|of\|water\|vapour\|(+,\|' + \
1293	'conv.;\|-,\|div.)', 'mm', 'Reds']
1294	elif varn == 'wbfcac' or varn == 'WBACFC':
1295	varvals = ['wbfcac', 'water_budget_fc_ac', 0., 30., \
1296	'Water\|Budget\|accumulated\|horizontal\|convergence\|of\|cloud\|(+,\|conv.;\|-,\|' +\
1297	'div.)', 'mm', 'Reds']
1298	elif varn == 'wbfpac' or varn == 'WBACFP':
1299	varvals = ['wbfpac', 'water_budget_cfp_ac', 0., 30., \
1300	'Water\|Budget\|accumulated\|horizontal\|convergence\|of\|precipitation\|(+,\|' + \
1301	'conv.;\|-,\|div.)', 'mm', 'Reds']
1302	elif varn == 'wbzac' or varn == 'WBACZ':
1303	varvals = ['wbzac', 'water_budget_z_ac', 0., 30., \
1304	'Water\|Budget\|accumulated\|vertical\|convergence\|of\|water\|vapour\|(+,\|conv.' +\
1305	';\|-,\|div.)', 'mm', 'Reds']
1306	elif varn == 'wbcac' or varn == 'WBACC':
1307	varvals = ['wbcac', 'water_budget_c_ac', 0., 30., \
1308	'Water\|Budget\|accumulated\|Cloud\|water\|species','mm', 'Reds']
1309	elif varn == 'wbqvdac' or varn == 'WBACQVD':
1310	varvals = ['wbqvdac', 'water_budget_qvd_ac', 0., 30., \
1311	'Water\|Budget\|accumulated\|water\|vapour\|divergence', 'mm', 'Reds']
1312	elif varn == 'wbqvbltenac' or varn == 'WBACQVBLTEN':
1313	varvals = ['wbqvbltenac', 'water_budget_qv_blten_ac', 0., 30., \
1314	'Water\|Budget\|accumulated\|QV\|tendency\|due\|to\|pbl\|parameterization', \
1315	'kg kg-1 s-1', 'Reds']
1316	elif varn == 'wbqvcutenac' or varn == 'WBACQVCUTEN':
1317	varvals = ['wbqvcutenac', 'water_budget_qv_cuten_ac', 0., 30., \
1318	'Water\|Budget\|accumulated\|QV\|tendency\|due\|to\|cu\|parameterization', \
1319	'kg kg-1 s-1', 'Reds']
1320	elif varn == 'wbqvshtenac' or varn == 'WBACQVSHTEN':
1321	varvals = ['wbqvshtenac', 'water_budget_qv_shten_ac', 0., 30., \
1322	'Water\|Budget\|accumulated\|QV\|tendency\|due\|to\|shallow\|cu\|parameterization', \
1323	'kg kg-1 s-1', 'Reds']
1324	elif varn == 'wbprac' or varn == 'WBACP':
1325	varvals = ['wbprac', 'water_budget_pr_ac', 0., 30., \
1326	'Water\|Budget\|accumulated\|precipitation', 'mm', 'Reds']
1327	elif varn == 'wbpwac' or varn == 'WBACPW':
1328	varvals = ['wbpwac', 'water_budget_pw_ac', 0., 30., \
1329	'Water\|Budget\|accumulated\|water\|content', 'mm', 'Reds']
1330	elif varn == 'wbcondtac' or varn == 'WBACCONDT':
1331	varvals = ['wbcondtac', 'water_budget_condt_ac', 0., 30., \
1332	'Water\|Budget\|accumulated\|condensation\|and\|deposition', 'mm', 'Reds']
1333	elif varn == 'wbqcmac' or varn == 'WBACQCM':
1334	varvals = ['wbqcmac', 'water_budget_qcm_ac', 0., 30., \
1335	'Water\|Budget\|accumulated\|hydrometeor\|change\|and\|convergence', 'mm', 'Reds']
1336	elif varn == 'wbsiac' or varn == 'WBACSI':
1337	varvals = ['wbsiac', 'water_budget_si_ac', 0., 30., \
1338	'Water\|Budget\|accumulated\|hydrometeor\|sink', 'mm', 'Reds']
1339	elif varn == 'wbsoac' or varn == 'WBACSO':
1340	varvals = ['wbsoac', 'water_budget_so_ac', 0., 30., \
1341	'Water\|Budget\|accumulated\|hydrometeor\|source', 'mm', 'Reds']
1342
1343	elif varn == 'xtime' or varn == 'XTIME':
1344	varvals = ['xtime', 'time', 0., 1.e5, 'time', \
1345	'minutes\|since\|simulation\|start', 'Reds']
1346	elif varn == 'x' or varn == 'X':
1347	varvals = ['x', 'x', 0., 100., 'x', '-', 'Reds']
1348	elif varn == 'y' or varn == 'Y':
1349	varvals = ['y', 'y', 0., 100., 'y', '-', 'Blues']
1350	elif varn == 'z' or varn == 'Z':
1351	varvals = ['z', 'z', 0., 100., 'z', '-', 'Greens']
1352	elif varn == 'zg' or varn == 'WRFght' or varn == 'Geopotential height' or \
1353	varn == 'geop' or varn == 'LGEOP':
1354	varvals = ['zg', 'geopotential_height', 0., 80000., 'geopotential\|height', \
1355	'm2s-2', 'rainbow']
1356	elif varn == 'zmaxth' or varn == 'zmax_th' or varn == 'LZMAX_TH':
1357	varvals = ['zmaxth', 'thermal_plume_height', 0., 4000., \
1358	'maximum\|thermals\|plume\|height', 'm', 'YlOrRd']
1359	elif varn == 'zmla' or varn == 's_pblh' or varn == 'LS_PBLH':
1360	varvals = ['zmla', 'atmosphere_boundary_layer_thickness', 0., 2500., \
1361	'atmosphere\|boundary\|layer\|thickness', 'm', 'Blues']
1362	else:
1363	print errormsg
1364	print ' ' + fname + ": variable '" + varn + "' not defined !!!"
1365	quit(-1)
1366
1367	return varvals
1368
1369	####### ####### ####### ####### ####### ####### ####### ####### ####### #######
1370
1371	def check_colorBar(cbarn):
1372	""" Check if the given colorbar exists in matplotlib
1373	"""
1374	fname = 'check_colorBar'
1375
1376	# Possible color bars
1377	colorbars = ['binary', 'Blues', 'BuGn', 'BuPu', 'gist_yarg', 'GnBu', 'Greens', \
1378	'Greys', 'Oranges', 'OrRd', 'PuBu', 'PuBuGn', 'PuRd', 'Purples', 'RdPu', \
1379	'Reds', 'YlGn', 'YlGnBu', 'YlOrBr', 'YlOrRd', 'afmhot', 'autumn', 'bone', \
1380	'cool', 'copper', 'gist_gray', 'gist_heat', 'gray', 'hot', 'pink', 'spring', \
1381	'summer', 'winter', 'BrBG', 'bwr', 'coolwarm', 'PiYG', 'PRGn', 'PuOr', 'RdBu', \
1382	'RdGy', 'RdYlBu', 'RdYlGn', 'seismic', 'Accent', 'Dark2', 'hsv', 'Paired', \
1383	'Pastel1', 'Pastel2', 'Set1', 'Set2', 'Set3', 'spectral', 'gist_earth', \
1384	'gist_ncar', 'gist_rainbow', 'gist_stern', 'jet', 'brg', 'CMRmap', 'cubehelix',\
1385	'gnuplot', 'gnuplot2', 'ocean', 'rainbow', 'terrain', 'flag', 'prism']
1386
1387	if not searchInlist(colorbars,cbarn):
1388	print warnmsg
1389	print ' ' + fname + ' color bar: "' + cbarn + '" does not exist !!'
1390	print ' a standard one will be use instead !!'
1391
1392	return
1393
1394	def units_lunits(u):
1395	""" Fucntion to provide LaTeX equivalences from a given units
1396	u= units to transform
1397	>>> units_lunits('kgkg-1')
1398	'$kgkg^{-1}$'
1399	"""
1400	fname = 'units_lunits'
1401
1402	if u == 'h':
1403	print fname + '_____________________________________________________________'
1404	print units_lunits.__doc__
1405	quit()
1406
1407	# Units which does not change
1408	same = ['1', 'category', 'day', 'degrees East', 'degrees Nord', 'degrees North', \
1409	'g', 'hour', 'hPa', 'K', 'Km', 'kg', 'km', 'm', 'minute', 'mm', 'month', 'Pa', \
1410	's', 'second', 'um', 'year', '-']
1411
1412	if searchInlist(same,u):
1413	lu = '$' + u + '$'
1414	elif len(u.split(' ')) > 1 and u.split(' ')[1] == 'since':
1415	uparts = u.split(' ')
1416	ip=0
1417	for up in uparts:
1418	if ip == 0:
1419	lu = '$' + up
1420	else:
1421	lu = lu + '\ ' + up
1422	ip=ip+1
1423	lu = lu + '$'
1424	else:
1425	if u == '': lu='-'
1426	elif u == 'C': lu='$^{\circ}C$'
1427	elif u == 'days': lu='$day$'
1428	elif u == 'degrees_east': lu='$degrees\ East$'
1429	elif u == 'degree_east': lu='$degrees\ East$'
1430	elif u == 'degrees longitude': lu='$degrees\ East$'
1431	elif u == 'degrees latitude': lu='$degrees\ North$'
1432	elif u == 'degrees_north': lu='$degrees\ North$'
1433	elif u == 'degree_north': lu='$degrees\ North$'
1434	elif u == 'deg C': lu='$^{\circ}C$'
1435	elif u == 'degC': lu='$^{\circ}C$'
1436	elif u == 'deg K': lu='$K$'
1437	elif u == 'degK': lu='$K$'
1438	elif u == 'hours': lu='$hour$'
1439	elif u == 'J/kg': lu='$Jkg^{-1}$'
1440	elif u == 'Jkg-1': lu='$Jkg^{-1}$'
1441	elif u == 'K/m': lu='$Km^{-1}$'
1442	elif u == 'Km-1': lu='$Km^{-1}$'
1443	elif u == 'K/s': lu='$Ks^{-1}$'
1444	elif u == 'Ks-1': lu='$Ks^{-1}$'
1445	elif u == 'K s-1': lu='$Ks^{-1}$'
1446	elif u == 'kg/kg': lu='$kgkg^{-1}$'
1447	elif u == 'kgkg-1': lu='$kgkg^{-1}$'
1448	elif u == 'kg kg-1': lu='$kgkg^{-1}$'
1449	elif u == '(kg/kg)/s': lu='$kgkg^{-1}s^{-1}$'
1450	elif u == 'kgkg-1s-1': lu='$kgkg^{-1}s^{-1}$'
1451	elif u == 'kg kg-1 s-1': lu='$kgkg^{-1}s^{-1}$'
1452	elif u == 'kg/m2': lu='$kgm^{-2}$'
1453	elif u == 'kgm-2': lu='$kgm^{-2}$'
1454	elif u == 'kg m-2': lu='$kgm^{-2}$'
1455	elif u == 'Kg m-2': lu='$kgm^{-2}$'
1456	elif u == 'kg/m2/s': lu='$kgm^{-2}s^{-1}$'
1457	elif u == 'kg/(m2*s)': lu='$kgm^{-2}s^{-1}$'
1458	elif u == 'kg/(s*m2)': lu='$kgm^{-2}s^{-1}$'
1459	elif u == 'kgm-2s-1': lu='$kgm^{-2}s^{-1}$'
1460	elif u == 'kg m-2 s-1': lu='$kgm^{-2}s^{-1}$'
1461	elif u == '1/m': lu='$m^{-1}$'
1462	elif u == 'm-1': lu='$m^{-1}$'
1463	elif u == 'm2/s': lu='$m2s^{-1}$'
1464	elif u == 'm2s-1': lu='$m2s^{-1}$'
1465	elif u == 'm2/s2': lu='$m2s^{-2}$'
1466	elif u == 'm/s': lu='$ms^{-1}$'
1467	elif u == 'mmh-3': lu='$mmh^{-3}$'
1468	elif u == 'ms-1': lu='$ms^{-1}$'
1469	elif u == 'm s-1': lu='$ms^{-1}$'
1470	elif u == 'm/s2': lu='$ms^{-2}$'
1471	elif u == 'ms-2': lu='$ms^{-2}$'
1472	elif u == 'minutes': lu='$minute$'
1473	elif u == 'Pa/s': lu='$Pas^{-1}$'
1474	elif u == 'Pas-1': lu='$Pas^{-1}$'
1475	elif u == 'W m-2': lu='$Wm^{-2}$'
1476	elif u == 'Wm-2': lu='$Wm^{-2}$'
1477	elif u == 'W/m2': lu='$Wm^{-2}$'
1478	elif u == '1/s': lu='$s^{-1}$'
1479	elif u == 's-1': lu='$s^{-1}$'
1480	elif u == 'seconds': lu='$second$'
1481	elif u == '%': lu='\%'
1482	else:
1483	print errormsg
1484	print ' ' + fname + ': units "' + u + '" not ready!!!!'
1485	quit(-1)
1486
1487	return lu
1488
1489	def ASCII_LaTeX(ln):
1490	""" Function to transform from an ASCII line to LaTeX codification
1491	>>> ASCII_LaTeX('Laboratoire de MÃ©tÃ©orologie Dynamique perÃ² HovmÃ¶ller')
1492	Laboratoire de M\'et\'eorologie Dynamique per\`o Hovm\"oller
1493	"""
1494	fname='ASCII_LaTeX'
1495
1496	if ln == 'h':
1497	print fname + '_____________________________________________________________'
1498	print ASCII_LaTeX.__doc__
1499	quit()
1500
1501	newln = ln.replace('\\', '\\textbackslash')
1502
1503	newln = newln.replace('Ã¡', "\\'a")
1504	newln = newln.replace('Ã©', "\\'e")
1505	newln = newln.replace('Ã', "\\'i")
1506	newln = newln.replace('Ã³', "\\'o")
1507	newln = newln.replace('Ãº', "\\'u")
1508
1509	newln = newln.replace('Ã ', "\\`a")
1510	newln = newln.replace('Ãš', "\\`e")
1511	newln = newln.replace('Ã¬', "\\`i")
1512	newln = newln.replace('Ã²', "\\`o")
1513	newln = newln.replace('Ã¹', "\\`u")
1514
1515	newln = newln.replace('Ã¢', "\\^a")
1516	newln = newln.replace('Ãª', "\\^e")
1517	newln = newln.replace('Ã®', "\\^i")
1518	newln = newln.replace('ÃŽ', "\\^o")
1519	newln = newln.replace('Ã»', "\\^u")
1520
1521	newln = newln.replace('Ã€', '\\"a')
1522	newln = newln.replace('Ã«', '\\"e')
1523	newln = newln.replace('Ã¯', '\\"i')
1524	newln = newln.replace('Ã¶', '\\"o')
1525	newln = newln.replace('ÃŒ', '\\"u')
1526
1527	newln = newln.replace('Ã§', '\c{c}')
1528	newln = newln.replace('Ã±', '\~{n}')
1529
1530	newln = newln.replace('Ã', "\\'A")
1531	newln = newln.replace('Ã', "\\'E")
1532	newln = newln.replace('Ã', "\\'I")
1533	newln = newln.replace('Ã', "\\'O")
1534	newln = newln.replace('Ã', "\\'U")
1535
1536	newln = newln.replace('Ã', "\\`A")
1537	newln = newln.replace('Ã', "\\`E")
1538	newln = newln.replace('Ã', "\\`I")
1539	newln = newln.replace('Ã', "\\`O")
1540	newln = newln.replace('Ã', "\\`U")
1541
1542	newln = newln.replace('Ã', "\\^A")
1543	newln = newln.replace('Ã', "\\^E")
1544	newln = newln.replace('Ã', "\\^I")
1545	newln = newln.replace('Ã', "\\^O")
1546	newln = newln.replace('Ã', "\\^U")
1547
1548	newln = newln.replace('Ã', '\\"A')
1549	newln = newln.replace('Ã', '\\"E')
1550	newln = newln.replace('Ã', '\\"I')
1551	newln = newln.replace('Ã', '\\"O')
1552	newln = newln.replace('Ã', '\\"U')
1553
1554	newln = newln.replace('Ã', '\\c{C}')
1555	newln = newln.replace('Ã', '\\~{N}')
1556
1557	newln = newln.replace('Â¡', '!`')
1558	newln = newln.replace('Â¿', 'Â¿`')
1559	newln = newln.replace('%', '\\%')
1560	newln = newln.replace('#', '\\#')
1561	newln = newln.replace('&', '\\&')
1562	newln = newln.replace('$', '\\$')
1563	newln = newln.replace('_', '\\_')
1564	newln = newln.replace('Â·', '\\textperiodcentered')
1565	newln = newln.replace('<', '$<$')
1566	newln = newln.replace('>', '$>$')
1567	newln = newln.replace('ï', '*')
1568	# newln = newln.replace('Âº', '$^{\\circ}$')
1569	newln = newln.replace('Âª', '$^{a}$')
1570	newln = newln.replace('Âº', '$^{o}$')
1571	newln = newln.replace('Â°', '$^{\\circ}$')
1572	newln = newln.replace('\n', '\\\\\n')
1573	newln = newln.replace('\t', '\\medskip')
1574
1575	return newln
1576
1577	def pretty_int(minv,maxv,Nint):
1578	""" Function to plot nice intervals
1579	minv= minimum value
1580	maxv= maximum value
1581	Nint= number of intervals
1582	>>> pretty_int(23.50,67.21,5)
1583	[ 25. 30. 35. 40. 45. 50. 55. 60. 65.]
1584	>>> pretty_int(-23.50,67.21,15)
1585	[ 0. 20. 40. 60.]
1586	pretty_int(14.75,25.25,5)
1587	[ 16. 18. 20. 22. 24.]
1588	"""
1589	fname = 'pretty_int'
1590	nice_int = [1,2,5]
1591
1592	# print 'minv: ',minv,'maxv:',maxv,'Nint:',Nint
1593
1594	interval = np.abs(maxv - minv)
1595
1596	potinterval = np.log10(interval)
1597	Ipotint = int(potinterval)
1598	intvalue = np.float(interval / np.float(Nint))
1599
1600	# new
1601	potinterval = np.log10(intvalue)
1602	Ipotint = int(potinterval)
1603
1604	# print 'interval:', interval, 'intavlue:', intvalue, 'potinterval:', potinterval, \
1605	# 'Ipotint:', Ipotint, 'intvalue:', intvalue
1606
1607	mindist = 10.e15
1608	for inice in nice_int:
1609	# print inice,':',inice10.Ipotint,np.abs(inice10.**Ipotint - intvalue),mindist
1610	if np.abs(inice10.*Ipotint - intvalue) < mindist:
1611	mindist = np.abs(inice10.*Ipotint - intvalue)
1612	closestint = inice
1613
1614	Ibeg = int(minv / (closestint10.*Ipotint))
1615
1616	values = []
1617	val = closestint(Ibeg)10.**(Ipotint)
1618
1619	# print 'closestint:',closestint,'Ibeg:',Ibeg,'val:',val
1620
1621	while val < maxv:
1622	values.append(val)
1623	val = val + closestint10.*Ipotint
1624
1625	return np.array(values, dtype=np.float)
1626
1627	def DegGradSec_deg(grad,deg,sec):
1628	""" Function to transform from a coordinate in grad deg sec to degrees (decimal)
1629	>>> DegGradSec_deg(39.,49.,26.)
1630	39.8238888889
1631	"""
1632	fname = 'DegGradSec_deg'
1633
1634	if grad == 'h':
1635	print fname + '_____________________________________________________________'
1636	print DegGradSec_deg.__doc__
1637	quit()
1638
1639	deg = grad + deg/60. + sec/3600.
1640
1641	return deg
1642
1643	def intT2dt(intT,tu):
1644	""" Function to provide an 'timedelta' object from a given interval value
1645	intT= interval value
1646	tu= interval units, [tu]= 'd': day, 'w': week, 'h': hour, 'i': minute, 's': second,
1647	'l': milisecond
1648
1649	>>> intT2dt(3.5,'s')
1650	0:00:03.500000
1651
1652	>>> intT2dt(3.5,'w')
1653	24 days, 12:00:00
1654	"""
1655	import datetime as dt
1656
1657	fname = 'intT2dt'
1658
1659	if tu == 'w':
1660	dtv = dt.timedelta(weeks=np.float(intT))
1661	elif tu == 'd':
1662	dtv = dt.timedelta(days=np.float(intT))
1663	elif tu == 'h':
1664	dtv = dt.timedelta(hours=np.float(intT))
1665	elif tu == 'i':
1666	dtv = dt.timedelta(minutes=np.float(intT))
1667	elif tu == 's':
1668	dtv = dt.timedelta(seconds=np.float(intT))
1669	elif tu == 'l':
1670	dtv = dt.timedelta(milliseconds=np.float(intT))
1671	else:
1672	print errormsg
1673	print ' ' + fname + ': time units "' + tu + '" not ready!!!!'
1674	quit(-1)
1675
1676	return dtv
1677
1678	def lonlat_values(mapfile,lonvn,latvn):
1679	""" Function to obtain the lon/lat matrices from a given netCDF file
1680	lonlat_values(mapfile,lonvn,latvn)
1681	[mapfile]= netCDF file name
1682	[lonvn]= variable name with the longitudes
1683	[latvn]= variable name with the latitudes
1684	"""
1685
1686	fname = 'lonlat_values'
1687
1688	if mapfile == 'h':
1689	print fname + '_____________________________________________________________'
1690	print lonlat_values.__doc__
1691	quit()
1692
1693	if not os.path.isfile(mapfile):
1694	print errormsg
1695	print ' ' + fname + ": map file '" + mapfile + "' does not exist !!"
1696	quit(-1)
1697
1698	ncobj = NetCDFFile(mapfile, 'r')
1699	lonobj = ncobj.variables[lonvn]
1700	latobj = ncobj.variables[latvn]
1701
1702	if len(lonobj.shape) == 3:
1703	lonv = lonobj[0,:,:]
1704	latv = latobj[0,:,:]
1705	elif len(lonobj.shape) == 2:
1706	lonv = lonobj[:,:]
1707	latv = latobj[:,:]
1708	elif len(lonobj.shape) == 1:
1709	lon0 = lonobj[:]
1710	lat0 = latobj[:]
1711	lonv = np.zeros( (len(lat0),len(lon0)), dtype=np.float )
1712	latv = np.zeros( (len(lat0),len(lon0)), dtype=np.float )
1713	for iy in range(len(lat0)):
1714	lonv[iy,:] = lon0
1715	for ix in range(len(lon0)):
1716	latv[:,ix] = lat0
1717	else:
1718	print errormsg
1719	print ' ' + fname + ': lon/lat variables shape:',lonobj.shape,'not ready!!'
1720	quit(-1)
1721
1722	return lonv, latv
1723
1724	def date_CFtime(ind,refd,tunits):
1725	""" Function to transform from a given date object a CF-convention time
1726	ind= date object to transform
1727	refd= reference date
1728	tunits= units for time
1729	>>> date_CFtime(dt.datetime(1976,02,17,08,30,00), dt.datetime(1949,12,01,00,00,00), 'seconds')
1730	827224200.0
1731	"""
1732	import datetime as dt
1733
1734	fname = 'date_CFtime'
1735
1736	dt = ind - refd
1737
1738	if tunits == 'weeks':
1739	value = dt.days/7. + dt.seconds/(3600.24.7.)
1740	elif tunits == 'days':
1741	value = dt.days + dt.seconds/(3600.*24.)
1742	elif tunits == 'hours':
1743	value = dt.days*24. + dt.seconds/(3600.)
1744	elif tunits == 'minutes':
1745	value = dt.days24.60. + dt.seconds/(60.)
1746	elif tunits == 'seconds':
1747	value = dt.days24.3600. + dt.seconds
1748	elif tunits == 'milliseconds':
1749	value = dt.days24.3600.1000. + dt.seconds1000.
1750	else:
1751	print errormsg
1752	print ' ' + fname + ': reference time units "' + trefu + '" not ready!!!!'
1753	quit(-1)
1754
1755	return value
1756
1757	def pot_values(values, uvals):
1758	""" Function to modify a seies of values by their potency of 10
1759	pot_values(values, uvals)
1760	values= values to modify
1761	uvals= units of the values
1762	>>> vals = np.sin(np.arange(20)np.pi/5.+0.01)10.e-5
1763	>>> pot_values(vals,'ms-1')
1764	(array([ 0.00000000e+00, 5.87785252e-01, 9.51056516e-01,
1765	9.51056516e-01, 5.87785252e-01, 1.22464680e-16,
1766	-5.87785252e-01, -9.51056516e-01, -9.51056516e-01,
1767	-5.87785252e-01, -2.44929360e-16, 5.87785252e-01,
1768	9.51056516e-01, 9.51056516e-01, 5.87785252e-01,
1769	3.67394040e-16, -5.87785252e-01, -9.51056516e-01,
1770	-9.51056516e-01, -5.87785252e-01]), -4, 'x10e-4 ms-1', 'x10e-4')
1771	"""
1772
1773	fname = 'pot_values'
1774
1775	if np.min(values) != 0.:
1776	potmin = int( np.log10( np.abs(np.min(values)) ) )
1777	else:
1778	potmin = 0
1779
1780	if np.max(values) != 0.:
1781	potmax = int( np.log10( np.abs(np.max(values)) ) )
1782	else:
1783	potmax = 0
1784
1785	if potmin * potmax > 9:
1786	potval = -np.min([np.abs(potmin), np.abs(potmax)]) * np.abs(potmin) / potmin
1787
1788	newvalues = values10.*potval
1789	potvalue = - potval
1790	potS = 'x10e' + str(potvalue)
1791	newunits = potS + ' ' + uvals
1792	else:
1793	newvalues = values
1794	potvalue = None
1795	potS = ''
1796	newunits = uvals
1797
1798	return newvalues, potvalue, newunits, potS
1799
1800	def CFtimes_plot(timev,units,kind,tfmt):
1801	""" Function to provide a list of string values from a CF time values in order
1802	to use them in a plot, according to the series of characteristics.
1803	String outputs will be suited to the 'human-like' output
1804	timev= time values (real values)
1805	units= units string according to CF conventions ([tunits] since
1806	[YYYY]-[MM]-[DD] [[HH]:[MI]:[SS]])
1807	kind= kind of output
1808	'Nval': according to a given number of values as 'Nval',[Nval]
1809	'exct': according to an exact time unit as 'exct',[tunit];
1810	tunit= [Nunits],[tu]; [tu]= 'c': centuries, 'y': year, 'm': month,
1811	'w': week, 'd': day, 'h': hour, 'i': minute, 's': second,
1812	'l': milisecond
1813	tfmt= desired format
1814	>>> CFtimes_plot(np.arange(100)*1.,'hours since 1979-12-01 00:00:00', 'Nval,5',"%Y/%m/%d %H:%M:%S")
1815	0.0 1979/12/01 00:00:00
1816	24.75 1979/12/02 00:45:00
1817	49.5 1979/12/03 01:30:00
1818	74.25 1979/12/04 02:15:00
1819	99.0 1979/12/05 03:00:00
1820	>>> CFtimes_plot(np.arange(100)*1.,'hours since 1979-12-01 00:00:00', 'exct,2,d',"%Y/%m/%d %H:%M:%S")
1821	0.0 1979/12/01 00:00:00
1822	48.0 1979/12/03 00:00:00
1823	96.0 1979/12/05 00:00:00
1824	144.0 1979/12/07 00:00:00
1825	"""
1826	import datetime as dt
1827
1828	# Seconds between 0001 and 1901 Jan - 01
1829	secs0001_1901=59958144000.
1830
1831	fname = 'CFtimes_plot'
1832
1833	if timev == 'h':
1834	print fname + '_____________________________________________________________'
1835	print CFtimes_plot.__doc__
1836	quit()
1837
1838	# Does reference date contain a time value [YYYY]-[MM]-[DD] [HH]:[MI]:[SS]
1839	##
1840	trefT = units.find(':')
1841	txtunits = units.split(' ')
1842	Ntxtunits = len(txtunits)
1843
1844	if Ntxtunits == 3:
1845	Srefdate = txtunits[Ntxtunits - 1]
1846	else:
1847	Srefdate = txtunits[Ntxtunits - 2]
1848
1849	if not trefT == -1:
1850	# print ' ' + fname + ': refdate with time!'
1851	if Ntxtunits == 3:
1852	refdate = datetimeStr_datetime(Srefdate)
1853	else:
1854	refdate = datetimeStr_datetime(Srefdate + '_' + txtunits[Ntxtunits - 1])
1855	else:
1856	refdate = datetimeStr_datetime(Srefdate + '_00:00:00')
1857
1858	trefunits=units.split(' ')[0]
1859	if trefunits == 'weeks':
1860	trefu = 'w'
1861	elif trefunits == 'days':
1862	trefu = 'd'
1863	elif trefunits == 'hours':
1864	trefu = 'h'
1865	elif trefunits == 'minutes':
1866	trefu = 'm'
1867	elif trefunits == 'seconds':
1868	trefu = 's'
1869	elif trefunits == 'milliseconds':
1870	trefu = 'l'
1871	else:
1872	print errormsg
1873	print ' ' + fname + ': reference time units "' + trefu + '" not ready!!!!'
1874	quit(-1)
1875
1876	okind=kind.split(',')[0]
1877	dtv = len(timev)
1878
1879	if refdate.year == 1:
1880	print warnmsg
1881	print ' ' + fname + ': changing reference date: ',refdate, \
1882	'to 1901-01-01_00:00:00 !!!'
1883	refdate = datetimeStr_datetime('1901-01-01_00:00:00')
1884	if trefu == 'w': timev = timev - secs0001_1901/(7.24.3600.)
1885	if trefu == 'd': timev = timev - secs0001_1901/(24.*3600.)
1886	if trefu == 'h': timev = timev - secs0001_1901/(3600.)
1887	if trefu == 'm': timev = timev - secs0001_1901/(60.)
1888	if trefu == 's': timev = timev - secs0001_1901
1889	if trefu == 'l': timev = timev - secs0001_1901*1000.
1890
1891	firstT = timev[0]
1892	lastT = timev[dtv-1]
1893
1894	# First and last times as datetime objects
1895	firstTdt = timeref_datetime(refdate, firstT, trefunits)
1896	lastTdt = timeref_datetime(refdate, lastT, trefunits)
1897
1898	# First and last times as [year, mon, day, hour, minut, second] vectors
1899	firstTvec = np.zeros((6), dtype= np.float)
1900	lastTvec = np.zeros((6), dtype= np.float)
1901	chTvec = np.zeros((6), dtype= bool)
1902
1903	firstTvec = np.array([firstTdt.year, firstTdt.month, firstTdt.day, firstTdt.hour,\
1904	firstTdt.minute, firstTdt.second])
1905	lastTvec = np.array([lastTdt.year, lastTdt.month, lastTdt.day, lastTdt.hour, \
1906	lastTdt.minute, lastTdt.second])
1907
1908	chdate= lastTvec - firstTvec
1909	chTvec = np.where (chdate != 0., True, False)
1910
1911	timeout = []
1912	if okind == 'Nval':
1913	nvalues = int(kind.split(',')[1])
1914	intervT = (lastT - firstT)/(nvalues-1)
1915	dtintervT = intT2dt(intervT, trefu)
1916
1917	for it in range(nvalues):
1918	timeout.append(firstTdt + dtintervT*it)
1919	elif okind == 'exct':
1920	Nunits = int(kind.split(',')[1])
1921	tu = kind.split(',')[2]
1922
1923	# Generic incremental dt [seconds] according to all the possibilities ['c', 'y', 'm',
1924	# 'w', 'd', 'h', 'i', 's', 'l'], some of them approximated (because they are not
1925	# already necessary!)
1926	basedt = np.zeros((9), dtype=np.float)
1927	basedt[0] = (365.100. + 25.)24.*3600.
1928	basedt[1] = 365.24.3600.
1929	basedt[2] = 31.24.3600.
1930	basedt[3] = 7.24.3600.
1931	basedt[4] = 24.*3600.
1932	basedt[5] = 3600.
1933	basedt[6] = 60.
1934	basedt[7] = 1.
1935	basedt[8] = 1000.
1936
1937	# Increment according to the units of the CF dates
1938	if trefunits == 'weeks':
1939	basedt = basedt/(7.24.3600.)
1940	elif trefunits == 'days':
1941	basedt = basedt/(24.*3600.)
1942	elif trefunits == 'hours':
1943	basedt = basedt/(3600.)
1944	elif trefunits == 'minutes':
1945	basedt = basedt/(60.)
1946	elif trefunits == 'seconds':
1947	basedt = basedt
1948	elif trefunits == 'milliseconds':
1949	basedt = basedt*1000.
1950
1951	if tu == 'c':
1952	ti = firstTvec[0]
1953	tf = lastTvec[0]
1954	centi = firstTvec[0] / 100
1955
1956	for it in range((tf - ti)/(Nunits*100) + 1):
1957	timeout.append(dt.datetime(centi+itNunits100, 1, 1, 0, 0, 0))
1958	elif tu == 'y':
1959	ti = firstTvec[0]
1960	tf = lastTvec[0]
1961	yeari = firstTvec[0]
1962
1963	for it in range((tf - ti)/(Nunits) + 1):
1964	timeout.append(dt.datetime(yeari+it*Nunits, 1, 1, 0, 0, 0))
1965	elif tu == 'm':
1966	ti = firstTvec[1]
1967	tf = lastTvec[1]
1968	yr = firstTvec[0]
1969	mon = firstTvec[1]
1970
1971	for it in range((tf - ti)/(Nunits) + 1):
1972	mon = mon+it*Nunits
1973	if mon > 12:
1974	yr = yr + 1
1975	mon = 1
1976
1977	timeout.append(dt.datetime(yr, mon, 1, 0, 0, 0))
1978	elif tu == 'w':
1979	datev=firstTdt
1980	it=0
1981	while datev <= lastTdt:
1982	datev = firstTdt + dt.timedelta(days=7Nunitsit)
1983	timeout.append(datev)
1984	it = it + 1
1985	elif tu == 'd':
1986	# datev=firstTdt
1987	yr = firstTvec[0]
1988	mon = firstTvec[1]
1989	day = firstTvec[2]
1990
1991	if np.sum(firstTvec[2:5]) > 0:
1992	firstTdt = dt.datetime(yr, mon, day+1, 0, 0, 0)
1993	datev = dt.datetime(yr, mon, day+1, 0, 0, 0)
1994	else:
1995	firstTdt = dt.datetime(yr, mon, day, 0, 0, 0)
1996	datev = dt.datetime(yr, mon, day, 0, 0, 0)
1997
1998	it=0
1999	while datev <= lastTdt:
2000	datev = firstTdt + dt.timedelta(days=Nunits*it)
2001	timeout.append(datev)
2002	it = it + 1
2003	elif tu == 'h':
2004	datev=firstTdt
2005	yr = firstTvec[0]
2006	mon = firstTvec[1]
2007	day = firstTvec[2]
2008	hour = firstTvec[3]
2009
2010	if np.sum(firstTvec[4:5]) > 0 or np.mod(hour,Nunits) != 0:
2011	tadvance = 2*Nunits
2012	if tadvance >= 24:
2013	firstTdt = dt.datetime(yr, mon, day+1, 0, 0, 0)
2014	datev = dt.datetime(yr, mon, day+1, 0, 0, 0)
2015	else:
2016	firstTdt = dt.datetime(yr, mon, day, Nunits, 0, 0)
2017	datev = dt.datetime(yr, mon, day, Nunits, 0, 0)
2018	else:
2019	firstTdt = dt.datetime(yr, mon, day, hour, 0, 0)
2020	datev = dt.datetime(yr, mon, day, hour, 0, 0)
2021
2022	it=0
2023	while datev <= lastTdt:
2024	datev = firstTdt + dt.timedelta(seconds=Nunits3600it)
2025	timeout.append(datev)
2026	it = it + 1
2027	elif tu == 'i':
2028	datev=firstTdt
2029	yr = firstTvec[0]
2030	mon = firstTvec[1]
2031	day = firstTvec[2]
2032	hour = firstTvec[3]
2033	minu = firstTvec[4]
2034
2035	if firstTvec[5] > 0 or np.mod(minu,Nunits) != 0:
2036	tadvance = 2*Nunits
2037	if tadvance >= 60:
2038	firstTdt = dt.datetime(yr, mon, day, hour, 0, 0)
2039	datev = dt.datetime(yr, mon, day, hour, 0, 0)
2040	else:
2041	firstTdt = dt.datetime(yr, mon, day, hour, Nunits, 0)
2042	datev = dt.datetime(yr, mon, day, hour, Nunits, 0)
2043	else:
2044	firstTdt = dt.datetime(yr, mon, day, hour, minu, 0)
2045	datev = dt.datetime(yr, mon, day, hour, minu, 0)
2046	it=0
2047	while datev <= lastTdt:
2048	datev = firstTdt + dt.timedelta(seconds=Nunits60it)
2049	timeout.append(datev)
2050	it = it + 1
2051	elif tu == 's':
2052	datev=firstTdt
2053	it=0
2054	while datev <= lastTdt:
2055	datev = firstTdt + dt.timedelta(seconds=Nunits)
2056	timeout.append(datev)
2057	it = it + 1
2058	elif tu == 'l':
2059	datev=firstTdt
2060	it=0
2061	while datev <= lastTdt:
2062	datev = firstTdt + dt.timedelta(seconds=Nunits*it/1000.)
2063	timeout.append(datev)
2064	it = it + 1
2065	else:
2066	print errormsg
2067	print ' ' + fname + ': exact units "' + tu + '" not ready!!!!!'
2068	quit(-1)
2069
2070	else:
2071	print errormsg
2072	print ' ' + fname + ': output kind "' + okind + '" not ready!!!!'
2073	quit(-1)
2074
2075	dtout = len(timeout)
2076
2077	timeoutS = []
2078	timeoutv = np.zeros((dtout), dtype=np.float)
2079
2080	for it in range(dtout):
2081	timeoutS.append(timeout[it].strftime(tfmt))
2082	timeoutv[it] = date_CFtime(timeout[it], refdate, trefunits)
2083
2084	# print it,':',timeoutv[it], timeoutS[it]
2085
2086	if len(timeoutv) < 1 or len(timeoutS) < 1:
2087	print errormsg
2088	print ' ' + fname + ': no time values are generated!'
2089	print ' values passed:',timev
2090	print ' units:',units
2091	print ' desired kind:',kind
2092	print ' format:',tfmt
2093	print ' function values ___ __ _'
2094	print ' reference date:',refdate
2095	print ' first date:',firstTdt
2096	print ' last date:',lastTdt
2097	print ' icrement:',basedt,trefunits
2098
2099	quit(-1)
2100
2101	return timeoutv, timeoutS
2102
2103	def color_lines(Nlines):
2104	""" Function to provide a color list to plot lines
2105	color_lines(Nlines)
2106	Nlines= number of lines
2107	"""
2108
2109	fname = 'color_lines'
2110
2111	colors = ['r', 'b', 'g', 'p', 'g']
2112
2113	colorv = []
2114
2115	colorv.append('k')
2116	for icol in range(Nlines):
2117	colorv.append(colors[icol])
2118
2119
2120	return colorv
2121
2122	def output_kind(kindf, namef, close):
2123	""" Function to generate the output of the figure
2124	kindf= kind of the output
2125	null: show in screen
2126	[jpg/pdf/png/ps]: standard output types
2127	namef= name of the figure (without extension)
2128	close= if the graph has to be close or not [True/False]
2129	"""
2130	fname = 'output_kind'
2131
2132	if kindf == 'h':
2133	print fname + '_____________________________________________________________'
2134	print output_kind.__doc__
2135	quit()
2136
2137	if kindf == 'null':
2138	print 'showing figure...'
2139	plt.show()
2140	elif kindf == 'gif':
2141	plt.savefig(namef + ".gif")
2142	if close: print "Successfully generation of figure '" + namef + ".jpg' !!!"
2143	elif kindf == 'jpg':
2144	plt.savefig(namef + ".jpg")
2145	if close: print "Successfully generation of figure '" + namef + ".jpg' !!!"
2146	elif kindf == 'pdf':
2147	plt.savefig(namef + ".pdf")
2148	if close: print "Successfully generation of figure '" + namef + ".pdf' !!!"
2149	elif kindf == 'png':
2150	plt.savefig(namef + ".png")
2151	if close: print "Successfully generation of figure '" + namef + ".png' !!!"
2152	elif kindf == 'ps':
2153	plt.savefig(namef + ".ps")
2154	if close: print "Successfully generation of figure '" + namef + ".ps' !!!"
2155	else:
2156	print errormsg
2157	print ' ' + fname + ' output format: "' + kindf + '" not ready !!'
2158	print errormsg
2159	quit(-1)
2160
2161	if close:
2162	plt.close()
2163
2164	return
2165
2166	def check_arguments(funcname,Nargs,args,char,expectargs):
2167	""" Function to check the number of arguments if they are coincident
2168	check_arguments(funcname,Nargs,args,char)
2169	funcname= name of the function/program to check
2170	Nargs= theoretical number of arguments
2171	args= passed arguments
2172	char= character used to split the arguments
2173	"""
2174
2175	fname = 'check_arguments'
2176
2177	Nvals = len(args.split(char))
2178	if Nvals != Nargs:
2179	print errormsg
2180	print ' ' + fname + ': wrong number of arguments:',Nvals," passed to '", \
2181	funcname, "' which requires:",Nargs,'!!'
2182	print ' given arguments:',args.split(char)
2183	print ' expected arguments:',expectargs
2184	quit(-1)
2185
2186	return
2187
2188	def Str_Bool(val):
2189	""" Function to transform from a String value to a boolean one
2190	>>> Str_Bool('True')
2191	True
2192	>>> Str_Bool('0')
2193	False
2194	>>> Str_Bool('no')
2195	False
2196	"""
2197
2198	fname = 'Str_Bool'
2199
2200	if val == 'True' or val == '1' or val == 'yes':
2201	boolv = True
2202	elif val == 'False' or val == '0' or val== 'no':
2203	boolv = False
2204	else:
2205	print errormsg
2206	print ' ' + fname + ": value '" + val + "' not ready!!"
2207	quit(-1)
2208
2209	return boolv
2210
2211	def plot_TimeSeries(valtimes, vunits, tunits, hfileout, vtit, ttit, tkind, tformat, \
2212	tit, linesn, lloc, kfig):
2213	""" Function to draw time-series
2214	valtimes= list of arrays to plot [vals1[1values, 1times], [...,valsM[Mvals,Mtimes]])
2215	vunits= units of the values
2216	tunits= units of the times
2217	hfileout= header of the output figure. Final name: [hfileout]_[vtit].[kfig]
2218	vtit= variable title to be used in the graph
2219	ttit= time title to be used in the graph
2220	tkind= kind of time values to appear in the x-axis
2221	'Nval': according to a given number of values as 'Nval',[Nval]
2222	'exct': according to an exact time unit as 'exct',[tunit];
2223	tunit= [Nunits],[tu]; [tu]= 'c': centuries, 'y': year, 'm': month,
2224	'w': week, 'd': day, 'h': hour, 'i': minute, 's': second,
2225	'l': milisecond
2226	tformat= desired format of times
2227	tit= title of the graph
2228	linesn= list of values fot the legend
2229	lloc= location of the legend (-1, autmoatic)
2230	1: 'upper right', 2: 'upper left', 3: 'lower left', 4: 'lower right',
2231	5: 'right', 6: 'center left', 7: 'center right', 8: 'lower center',
2232	9: 'upper center', 10: 'center'
2233	kfig= type of figure: jpg, png, pds, ps
2234	"""
2235	fname = 'plot_TimeSeries'
2236
2237	if valtimes == 'h':
2238	print fname + '_____________________________________________________________'
2239	print plot_TimeSeries.__doc__
2240	quit()
2241
2242
2243	# Canging line kinds every 7 lines (end of standard colors)
2244	linekinds=['.-','x-','o-']
2245
2246	Nlines = len(valtimes)
2247
2248	Nvalues = []
2249	Ntimes = []
2250
2251	for il in range(Nlines):
2252	array = valtimes[il]
2253
2254	if Nlines == 1:
2255	print warnmsg
2256	print ' ' + fname + ': drawing only one line!'
2257
2258	Nvalues.append(array.shape[1])
2259	Ntimes.append(array.shape[0])
2260	tmin = np.min(array[1])
2261	tmax = np.max(array[1])
2262	vmin = np.min(array[0])
2263	vmax = np.max(array[0])
2264	else:
2265	Nvalues.append(array.shape[1])
2266	Ntimes.append(array.shape[0])
2267	tmin = np.min(array[1,:])
2268	tmax = np.max(array[1,:])
2269	vmin = np.min(array[0,:])
2270	vmax = np.max(array[0,:])
2271
2272	if il == 0:
2273	xmin = tmin
2274	xmax = tmax
2275	ymin = vmin
2276	ymax = vmax
2277	else:
2278	if tmin < xmin: xmin = tmin
2279	if tmax > xmax: xmax = tmax
2280	if vmin < ymin: ymin = vmin
2281	if vmax > ymax: ymax = vmax
2282
2283	dx = np.max(Ntimes)
2284	dy = np.min(Nvalues)
2285
2286	plt.rc('text', usetex=True)
2287
2288	print vtit
2289	if vtit == 'ps':
2290	plt.ylim(98000.,ymax)
2291	else:
2292	plt.ylim(ymin,ymax)
2293
2294	plt.xlim(xmin,xmax)
2295	# print 'x lim:',xmin,xmax
2296	# print 'y lim:',ymin,ymax
2297
2298	N7lines=0
2299	for il in range(Nlines):
2300	array = valtimes[il]
2301	if vtit == 'ps':
2302	array[0,:] = np.where(array[0,:] < 98000., None, array[0,:])
2303	plt.plot(array[1,:],array[0,:], linekinds[N7lines], label= linesn[il])
2304	if il == 6: N7lines = N7lines + 1
2305
2306	timevals = np.arange(xmin,xmax)*1.
2307
2308	tpos, tlabels = CFtimes_plot(timevals, tunits, tkind, tformat)
2309
2310	if len(tpos) > 10:
2311	print warnmsg
2312	print ' ' + fname + ': with "' + tkind + '" there are', len(tpos), 'xticks !'
2313
2314	plt.xticks(tpos, tlabels)
2315	# plt.Axes.set_xticklabels(tlabels)
2316
2317	plt.legend(loc=lloc)
2318	plt.xlabel(ttit)
2319	plt.ylabel(vtit + " (" + vunits + ")")
2320	plt.title(tit).replace('_','\_').replace('&','\&')
2321
2322	figname = hfileout + '_' + vtit
2323
2324	output_kind(kfig, figname, True)
2325
2326	return
2327
2328	#Nt = 50
2329	#Nlines = 3
2330
2331	#vtvalsv = []
2332
2333	#valsv = np.zeros((2,Nt), dtype=np.float)
2334	## First
2335	#valsv[0,:] = np.arange(Nt)
2336	#valsv[1,:] = np.arange(Nt)*180.
2337	#vtvalsv.append(valsv)
2338	#del(valsv)
2339
2340	#valsv = np.zeros((2,Nt/2), dtype=np.float)
2341	## Second
2342	#valsv[0,:] = np.arange(Nt/2)
2343	#valsv[1,:] = np.arange(Nt/2)180.2.
2344	#vtvalsv.append(valsv)
2345	#del(valsv)
2346
2347	#valsv = np.zeros((2,Nt/4), dtype=np.float)
2348	## Third
2349	#valsv[0,:] = np.arange(Nt/4)
2350	#valsv[1,:] = np.arange(Nt/4)180.4.
2351	#vtvalsv.append(valsv)
2352	#del(valsv)
2353
2354	#varu='mm'
2355	#timeu='seconds'
2356
2357	#title='test'
2358	#linesname = ['line 1', 'line 2', 'line 3']
2359
2360	#plot_TimeSeries(vtvalsv, units_lunits(varu), timeu, 'test', 'vartest', 'time', title, linesname, 'png')
2361	#quit()
2362
2363	def plot_points(xval, yval, ifile, vtit, kfig, mapv):
2364	""" plotting points
2365	[x/yval]: x,y values to plot
2366	vn,vm= minmum and maximum values to plot
2367	unit= units of the variable
2368	ifile= name of the input file
2369	vtit= title of the variable
2370	kfig= kind of figure (jpg, pdf, png)
2371	mapv= map characteristics: [proj],[res]
2372	see full documentation: http://matplotlib.org/basemap/
2373	[proj]: projection
2374	* 'cyl', cilindric
2375	[res]: resolution:
2376	* 'c', crude
2377	* 'l', low
2378	* 'i', intermediate
2379	* 'h', high
2380	* 'f', full
2381	"""
2382	fname = 'plot_points'
2383
2384	dx=xval.shape[0]
2385	dy=yval.shape[0]
2386
2387	plt.rc('text', usetex=True)
2388
2389	if not mapv is None:
2390	lon00 = np.where(xval[:] < 0., 360. + olon[:], olon[:])
2391	lat00 = yval[:]
2392	lon0 = np.zeros( (len(lat00),len(lon00)), dtype=np.float )
2393	lat0 = np.zeros( (len(lat00),len(lon00)), dtype=np.float )
2394
2395	for iy in range(len(lat00)):
2396	lon0[iy,:] = lon00
2397	for ix in range(len(lon00)):
2398	lat0[:,ix] = lat00
2399
2400	map_proj=mapv.split(',')[0]
2401	map_res=mapv.split(',')[1]
2402
2403	nlon = lon0[0,0]
2404	xlon = lon0[dy-1,dx-1]
2405	nlat = lat0[0,0]
2406	xlat = lat0[dy-1,dx-1]
2407
2408	lon2 = lon0[dy/2,dx/2]
2409	lat2 = lat0[dy/2,dx/2]
2410
2411	print 'lon2:', lon2, 'lat2:', lat2, 'SW pt:', nlon, ',', nlat, 'NE pt:', \
2412	xlon, ',', xlat
2413
2414	if map_proj == 'cyl':
2415	m = Basemap(projection=map_proj, llcrnrlon=nlon, llcrnrlat=nlat, \
2416	urcrnrlon=xlon, urcrnrlat= xlat, resolution=map_res)
2417	elif map_proj == 'lcc':
2418	m = Basemap(projection=map_proj, lat_0=lat2, lon_0=lon2, llcrnrlon=nlon, \
2419	llcrnrlat=nlat, urcrnrlon=xlon, urcrnrlat= xlat, resolution=map_res)
2420
2421	lons, lats = np.meshgrid(olon[:], olat[:])
2422	lons = np.where(lons < 0., lons + 360., lons)
2423
2424	x,y = m(lons,lats)
2425	plt.plot(x,y)
2426
2427	m.drawcoastlines()
2428
2429	meridians = pretty_int(nlon,xlon,5)
2430	m.drawmeridians(meridians,labels=[True,False,False,True])
2431
2432	parallels = pretty_int(nlat,xlat,5)
2433	m.drawparallels(parallels,labels=[False,True,True,False])
2434	else:
2435	# plt.xlim(0,dx-1)
2436	# plt.ylim(0,dy-1)
2437
2438	plt.plot(xval, yval, '.')
2439
2440	figname = ifile.replace('.','_') + '_' + vtit
2441	graphtit = vtit.replace('_','\_').replace('&','\&')
2442
2443	plt.title(graphtit)
2444
2445	output_kind(kfig, figname, True)
2446
2447	return
2448
2449	def plot_2Dfield(varv,dimn,colorbar,vn,vx,unit,olon,olat,ifile,vtit,zvalue,time,tk, \
2450	tkt,tobj,tvals,tind,kfig,mapv,reva):
2451	""" Adding labels and other staff to the graph
2452	varv= 2D values to plot
2453	dimn= dimension names to plot
2454	colorbar= name of the color bar to use
2455	vn,vm= minmum and maximum values to plot
2456	unit= units of the variable
2457	olon,olat= longitude, latitude objects
2458	ifile= name of the input file
2459	vtit= title of the variable
2460	zvalue= value on the z axis
2461	time= value on the time axis
2462	tk= kind of time (WRF)
2463	tkt= kind of time taken
2464	tobj= tim object
2465	tvals= values of the time variable
2466	tind= time index
2467	kfig= kind of figure (jpg, pdf, png)
2468	mapv= map characteristics: [proj],[res]
2469	see full documentation: http://matplotlib.org/basemap/
2470	[proj]: projection
2471	* 'cyl', cilindric
2472	[res]: resolution:
2473	* 'c', crude
2474	* 'l', low
2475	* 'i', intermediate
2476	* 'h', high
2477	* 'f', full
2478	reva= reverse the axes (x-->y, y-->x)
2479	"""
2480	## import matplotlib as mpl
2481	## mpl.use('Agg')
2482	## import matplotlib.pyplot as plt
2483
2484	if reva:
2485	print ' reversing the axes of the figure (x-->y, y-->x)!!'
2486	varv = np.transpose(varv)
2487	dimn0 = []
2488	dimn0.append(dimn[1] + '')
2489	dimn0.append(dimn[0] + '')
2490	dimn = dimn0
2491
2492	fname = 'plot_2Dfield'
2493	dx=varv.shape[1]
2494	dy=varv.shape[0]
2495
2496	plt.rc('text', usetex=True)
2497	# plt.rc('font', family='serif')
2498
2499	if not mapv is None:
2500	if len(olon[:].shape) == 3:
2501	lon0 = np.where(olon[0,] < 0., 360. + olon[0,], olon[0,])
2502	lat0 = olat[0,]
2503	elif len(olon[:].shape) == 2:
2504	lon0 = np.where(olon[:] < 0., 360. + olon[:], olon[:])
2505	lat0 = olat[:]
2506	elif len(olon[:].shape) == 1:
2507	lon00 = np.where(olon[:] < 0., 360. + olon[:], olon[:])
2508	lat00 = olat[:]
2509	lon0 = np.zeros( (len(lat00),len(lon00)), dtype=np.float )
2510	lat0 = np.zeros( (len(lat00),len(lon00)), dtype=np.float )
2511
2512	for iy in range(len(lat00)):
2513	lon0[iy,:] = lon00
2514	for ix in range(len(lon00)):
2515	lat0[:,ix] = lat00
2516
2517	map_proj=mapv.split(',')[0]
2518	map_res=mapv.split(',')[1]
2519
2520	nlon = lon0[0,0]
2521	xlon = lon0[dy-1,dx-1]
2522	nlat = lat0[0,0]
2523	xlat = lat0[dy-1,dx-1]
2524
2525	lon2 = lon0[dy/2,dx/2]
2526	lat2 = lat0[dy/2,dx/2]
2527
2528	print ' lon2:', lon2, 'lat2:', lat2, 'SW pt:', nlon, ',', nlat, 'NE pt:', \
2529	xlon, ',', xlat
2530
2531	if map_proj == 'cyl':
2532	m = Basemap(projection=map_proj, llcrnrlon=nlon, llcrnrlat=nlat, \
2533	urcrnrlon=xlon, urcrnrlat= xlat, resolution=map_res)
2534	elif map_proj == 'lcc':
2535	m = Basemap(projection=map_proj, lat_0=lat2, lon_0=lon2, llcrnrlon=nlon, \
2536	llcrnrlat=nlat, urcrnrlon=xlon, urcrnrlat= xlat, resolution=map_res)
2537
2538	if len(olon[:].shape) == 1:
2539	lons, lats = np.meshgrid(olon[:], olat[:])
2540	else:
2541	lons = olon[0,:]
2542	lats = olat[:,0]
2543
2544	lons = np.where(lons < 0., lons + 360., lons)
2545
2546	x,y = m(lons,lats)
2547	plt.pcolormesh(x,y,varv, cmap=plt.get_cmap(colorbar), vmin=vn, vmax=vx)
2548	cbar = plt.colorbar()
2549
2550	m.drawcoastlines()
2551	# if (nlon > 180. or xlon > 180.):
2552	# nlon0 = nlon
2553	# xlon0 = xlon
2554	# if (nlon > 180.): nlon0 = nlon - 360.
2555	# if (xlon > 180.): xlon0 = xlon - 360.
2556	# meridians = pretty_int(nlon0,xlon0,5)
2557	# meridians = np.where(meridians < 0., meridians + 360., meridians)
2558	# else:
2559	# meridians = pretty_int(nlon,xlon,5)
2560
2561	meridians = pretty_int(nlon,xlon,5)
2562
2563	m.drawmeridians(meridians,labels=[True,False,False,True])
2564	parallels = pretty_int(nlat,xlat,5)
2565	m.drawparallels(parallels,labels=[False,True,True,False])
2566
2567	else:
2568	plt.xlim(0,dx-1)
2569	plt.ylim(0,dy-1)
2570
2571	plt.pcolormesh(varv, cmap=plt.get_cmap(colorbar), vmin=vn, vmax=vx)
2572	cbar = plt.colorbar()
2573
2574	plt.xlabel(dimn[1].replace('_','\_'))
2575	plt.ylabel(dimn[0].replace('_','\_'))
2576
2577	# set the limits of the plot to the limits of the data
2578	# plt.axis([x.min(), x.max(), y.min(), y.max()])
2579
2580	# plt.plot(varv)
2581	cbar.set_label(unit)
2582
2583	figname = ifile.replace('.','_') + '_' + vtit
2584	graphtit = vtit.replace('_','\_').replace('&','\&')
2585
2586	if zvalue != 'null':
2587	graphtit = graphtit + ' at z= ' + zvalue
2588	figname = figname + '_z' + zvalue
2589	if tkt == 'tstep':
2590	graphtit = graphtit + ' at time-step= ' + time.split(',')[1]
2591	figname = figname + '_t' + time.split(',')[1].zfill(4)
2592	elif tkt == 'CFdate':
2593	graphtit = graphtit + ' at ' + tobj.strfmt("%Y%m%d%H%M%S")
2594	figname = figname + '_t' + tobj.strfmt("%Y%m%d%H%M%S")
2595
2596	if tk == 'WRF':
2597	# datev = str(timevals[timeind][0:9])
2598	datev = tvals[tind][0] + tvals[tind][1] + tvals[tind][2] + \
2599	timevals[timeind][3] + timevals[timeind][4] + timevals[timeind][5] + \
2600	timevals[timeind][6] + timevals[timeind][7] + timevals[timeind][8] + \
2601	timevals[timeind][9]
2602	# timev = str(timevals[timeind][11:18])
2603	timev = timevals[timeind][11] + timevals[timeind][12] + \
2604	timevals[timeind][13] + timevals[timeind][14] + timevals[timeind][15] + \
2605	timevals[timeind][16] + timevals[timeind][17] + timevals[timeind][18]
2606	graphtit = vtit.replace('_','\_') + ' (' + datev + ' ' + timev + ')'
2607
2608	plt.title(graphtit)
2609
2610	output_kind(kfig, figname, True)
2611
2612	return
2613
2614	def plot_2Dfield_easy(varv,dimxv,dimyv,dimn,colorbar,vn,vx,unit,ifile,vtit,kfig,reva):
2615	""" Adding labels and other staff to the graph
2616	varv= 2D values to plot
2617	dim[x/y]v = values at the axes of x and y
2618	dimn= dimension names to plot
2619	colorbar= name of the color bar to use
2620	vn,vm= minmum and maximum values to plot
2621	unit= units of the variable
2622	ifile= name of the input file
2623	vtit= title of the variable
2624	kfig= kind of figure (jpg, pdf, png)
2625	reva= reverse the axes (x-->y, y-->x)
2626	"""
2627	## import matplotlib as mpl
2628	## mpl.use('Agg')
2629	## import matplotlib.pyplot as plt
2630	fname = 'plot_2Dfield'
2631
2632	if reva:
2633	print ' reversing the axes of the figure (x-->y, y-->x)!!'
2634	varv = np.transpose(varv)
2635	dimn0 = []
2636	dimn0.append(dimn[1] + '')
2637	dimn0.append(dimn[0] + '')
2638	dimn = dimn0
2639	if len(dimyv.shape) == 2:
2640	x = np.transpose(dimyv)
2641	else:
2642	if len(dimxv.shape) == 2:
2643	ddx = len(dimyv)
2644	ddy = dimxv.shape[1]
2645	else:
2646	ddx = len(dimyv)
2647	ddy = len(dimxv)
2648
2649	x = np.zeros((ddy,ddx), dtype=np.float)
2650	for j in range(ddy):
2651	x[j,:] = dimyv
2652
2653	if len(dimxv.shape) == 2:
2654	y = np.transpose(dimxv)
2655	else:
2656	if len(dimyv.shape) == 2:
2657	ddx = dimyv.shape[0]
2658	ddy = len(dimxv)
2659	else:
2660	ddx = len(dimyv)
2661	ddy = len(dimxv)
2662
2663	y = np.zeros((ddy,ddx), dtype=np.float)
2664	for i in range(ddx):
2665	y[:,i] = dimxv
2666	else:
2667	if len(dimxv.shape) == 2:
2668	x = dimxv
2669	else:
2670	x = np.zeros((len(dimyv),len(dimxv)), dtype=np.float)
2671	for j in range(len(dimyv)):
2672	x[j,:] = dimxv
2673
2674	if len(dimyv.shape) == 2:
2675	y = dimyv
2676	else:
2677	y = np.zeros((len(dimyv),len(dimxv)), dtype=np.float)
2678	for i in range(len(dimxv)):
2679	x[:,i] = dimyv
2680
2681	dx=varv.shape[1]
2682	dy=varv.shape[0]
2683
2684	plt.rc('text', usetex=True)
2685	plt.xlim(0,dx-1)
2686	plt.ylim(0,dy-1)
2687
2688	plt.pcolormesh(x, y, varv, cmap=plt.get_cmap(colorbar), vmin=vn, vmax=vx)
2689	# plt.pcolormesh(varv, cmap=plt.get_cmap(colorbar), vmin=vn, vmax=vx)
2690	cbar = plt.colorbar()
2691
2692	plt.xlabel(dimn[1].replace('_','\_'))
2693	plt.ylabel(dimn[0].replace('_','\_'))
2694
2695	# set the limits of the plot to the limits of the data
2696	plt.axis([x.min(), x.max(), y.min(), y.max()])
2697	# if varv.shape[1] / varv.shape[0] > 10:
2698	# plt.axes().set_aspect(0.001)
2699	# else:
2700	# plt.axes().set_aspect(np.float(varv.shape[0])/np.float(varv.shape[1]))
2701
2702	cbar.set_label(unit)
2703
2704	figname = ifile.replace('.','_') + '_' + vtit
2705	graphtit = vtit.replace('_','\_').replace('&','\&')
2706
2707	plt.title(graphtit)
2708
2709	output_kind(kfig, figname, True)
2710
2711	return
2712
2713	def plot_Trajectories(lonval, latval, linesn, olon, olat, lonlatLims, gtit, kfig, \
2714	mapv, obsname):
2715	""" plotting points
2716	[lon/latval]= lon,lat values to plot (as list of vectors)
2717	linesn: name of the lines
2718	o[lon/lat]= object with the longitudes and the latitudes of the map to plot
2719	lonlatLims: limits of longitudes and latitudes [lonmin, latmin, lonmax, latmax]
2720	gtit= title of the graph
2721	kfig= kind of figure (jpg, pdf, png)
2722	mapv= map characteristics: [proj],[res]
2723	see full documentation: http://matplotlib.org/basemap/
2724	[proj]: projection
2725	* 'cyl', cilindric
2726	* 'lcc', lambert conformal
2727	[res]: resolution:
2728	* 'c', crude
2729	* 'l', low
2730	* 'i', intermediate
2731	* 'h', high
2732	* 'f', full
2733	obsname= name of the observations in graph (can be None for without).
2734	Observational trajectory would be the last one
2735	"""
2736	fname = 'plot_Trajectories'
2737
2738	if lonval == 'h':
2739	print fname + '_____________________________________________________________'
2740	print plot_Trajectories.__doc__
2741	quit()
2742
2743	# Canging line kinds every 7 lines (end of standard colors)
2744	linekinds=['.-','x-','o-']
2745
2746	Ntraj = len(lonval)
2747
2748	if obsname is not None:
2749	Ntraj = Ntraj - 1
2750
2751	N7lines = 0
2752
2753	plt.rc('text', usetex=True)
2754
2755	if not mapv is None:
2756	if len(olon[:].shape) == 3:
2757	# lon0 = np.where(olon[0,] < 0., 360. + olon[0,], olon[0,])
2758	lon0 = olon[0,]
2759	lat0 = olat[0,]
2760	elif len(olon[:].shape) == 2:
2761	# lon0 = np.where(olon[:] < 0., 360. + olon[:], olon[:])
2762	lon0 = olon[:]
2763	lat0 = olat[:]
2764	elif len(olon[:].shape) == 1:
2765	# lon00 = np.where(olon[:] < 0., 360. + olon[:], olon[:])
2766	lon00 = olon[:]
2767	lat00 = olat[:]
2768	lon0 = np.zeros( (len(lat00),len(lon00)), dtype=np.float )
2769	lat0 = np.zeros( (len(lat00),len(lon00)), dtype=np.float )
2770
2771	for iy in range(len(lat00)):
2772	lon0[iy,:] = lon00
2773	for ix in range(len(lon00)):
2774	lat0[:,ix] = lat00
2775
2776	map_proj=mapv.split(',')[0]
2777	map_res=mapv.split(',')[1]
2778
2779	dx = lon0.shape[1]
2780	dy = lon0.shape[0]
2781
2782	nlon = lon0[0,0]
2783	xlon = lon0[dy-1,dx-1]
2784	nlat = lat0[0,0]
2785	xlat = lat0[dy-1,dx-1]
2786
2787	lon2 = lon0[dy/2,dx/2]
2788	lat2 = lat0[dy/2,dx/2]
2789
2790	if lonlatLims is not None:
2791	plt.xlim(lonlatLims[0], lonlatLims[2])
2792	plt.ylim(lonlatLims[1], lonlatLims[3])
2793	if map_proj == 'cyl':
2794	nlon = lonlatLims[0]
2795	nlat = lonlatLims[1]
2796	xlon = lonlatLims[2]
2797	xlat = lonlatLims[3]
2798
2799	print 'lon2:', lon2, 'lat2:', lat2, 'SW pt:', nlon, ',', nlat, 'NE pt:', \
2800	xlon, ',', xlat
2801
2802	if map_proj == 'cyl':
2803	m = Basemap(projection=map_proj, llcrnrlon=nlon, llcrnrlat=nlat, \
2804	urcrnrlon=xlon, urcrnrlat= xlat, resolution=map_res)
2805	elif map_proj == 'lcc':
2806	m = Basemap(projection=map_proj, lat_0=lat2, lon_0=lon2, llcrnrlon=nlon, \
2807	llcrnrlat=nlat, urcrnrlon=xlon, urcrnrlat= xlat, resolution=map_res)
2808
2809	if len(olon.shape) == 3:
2810	# lons, lats = np.meshgrid(olon[0,:,:], olat[0,:,:])
2811	lons = olon[0,:,:]
2812	lats = olat[0,:,:]
2813
2814	elif len(olon.shape) == 2:
2815	# lons, lats = np.meshgrid(olon[:,:], olat[:,:])
2816	lons = olon[:,:]
2817	lats = olat[:,:]
2818	else:
2819	print errormsg
2820	print ' ' + fname + ': shapes of lon/lat objects', olon.shape, \
2821	'not ready!!!'
2822
2823	for il in range(Ntraj):
2824	plt.plot(lonval[il], latval[il], linekinds[N7lines], label= linesn[il])
2825	if il == 6: N7lines = N7lines + 1
2826
2827	m.drawcoastlines()
2828
2829	meridians = pretty_int(nlon,xlon,5)
2830	m.drawmeridians(meridians,labels=[True,False,False,True])
2831
2832	parallels = pretty_int(nlat,xlat,5)
2833	m.drawparallels(parallels,labels=[False,True,True,False])
2834
2835	plt.xlabel('W-E')
2836	plt.ylabel('S-N')
2837
2838	else:
2839	if len(olon.shape) == 3:
2840	dx = olon.shape[2]
2841	dy = olon.shape[1]
2842	elif len(olon.shape) == 2:
2843	dx = olon.shape[1]
2844	dy = olon.shape[0]
2845	else:
2846	print errormsg
2847	print ' ' + fname + ': shapes of lon/lat objects', olon.shape, \
2848	'not ready!!!'
2849
2850	if lonlatLims is not None:
2851	plt.xlim(lonlatLims[0], lonlatLims[2])
2852	plt.ylim(lonlatLims[1], lonlatLims[3])
2853	else:
2854	plt.xlim(np.min(olon[:]),np.max(olon[:]))
2855	plt.ylim(np.min(olat[:]),np.max(olat[:]))
2856
2857	for il in range(Ntraj):
2858	plt.plot(lonval[il], latval[il], linekinds[N7lines], label= linesn[il])
2859	if il == 6: N7lines = N7lines + 1
2860
2861	plt.xlabel('x-axis')
2862	plt.ylabel('y-axis')
2863
2864	figname = 'trajectories'
2865	graphtit = gtit
2866
2867	if obsname is not None:
2868	plt.plot(lonval[Ntraj], latval[Ntraj], linestyle='-', color='k', \
2869	linewidth=3, label= obsname)
2870
2871	plt.title(graphtit.replace('_','\_').replace('&','\&'))
2872	plt.legend()
2873
2874	output_kind(kfig, figname, True)
2875
2876	return
2877
2878	def plot_topo_geogrid(varv, olon, olat, mint, maxt, lonlatLims, gtit, kfig, mapv, \
2879	closeif):
2880	""" plotting geo_em.d[nn].nc topography from WPS files
2881	plot_topo_geogrid(domf, mint, maxt, gtit, kfig, mapv)
2882	varv= topography values
2883	o[lon/lat]= longitude and latitude objects
2884	[min/max]t: minimum and maximum values of topography to draw
2885	lonlatLims: limits of longitudes and latitudes [lonmin, latmin, lonmax, latmax]
2886	gtit= title of the graph
2887	kfig= kind of figure (jpg, pdf, png)
2888	mapv= map characteristics: [proj],[res]
2889	see full documentation: http://matplotlib.org/basemap/
2890	[proj]: projection
2891	* 'cyl', cilindric
2892	* 'lcc', lamvbert conformal
2893	[res]: resolution:
2894	* 'c', crude
2895	* 'l', low
2896	* 'i', intermediate
2897	* 'h', high
2898	* 'f', full
2899	closeif= Boolean value if the figure has to be closed
2900	"""
2901	fname = 'plot_topo_geogrid'
2902
2903	if varv == 'h':
2904	print fname + '_____________________________________________________________'
2905	print plot_topo_geogrid.__doc__
2906	quit()
2907
2908	dx=varv.shape[1]
2909	dy=varv.shape[0]
2910
2911	plt.rc('text', usetex=True)
2912	# plt.rc('font', family='serif')
2913
2914	if not mapv is None:
2915	if len(olon[:].shape) == 3:
2916	lon0 = olon[0,]
2917	lat0 = olat[0,]
2918	elif len(olon[:].shape) == 2:
2919	lon0 = olon[:]
2920	lat0 = olat[:]
2921	elif len(olon[:].shape) == 1:
2922	lon00 = olon[:]
2923	lat00 = olat[:]
2924	lon0 = np.zeros( (len(lat00),len(lon00)), dtype=np.float )
2925	lat0 = np.zeros( (len(lat00),len(lon00)), dtype=np.float )
2926
2927	for iy in range(len(lat00)):
2928	lon0[iy,:] = lon00
2929	for ix in range(len(lon00)):
2930	lat0[:,ix] = lat00
2931
2932	map_proj=mapv.split(',')[0]
2933	map_res=mapv.split(',')[1]
2934	dx = lon0.shape[1]
2935	dy = lon0.shape[0]
2936
2937	if lonlatLims is not None:
2938	print ' ' + fname + ': cutting the domain to plot !!!!'
2939	print ' limits: W-E', lonlatLims[0], lonlatLims[2]
2940	print ' limits: N-S', lonlatLims[1], lonlatLims[3]
2941	nlon = lonlatLims[0]
2942	xlon = lonlatLims[2]
2943	nlat = lonlatLims[1]
2944	xlat = lonlatLims[3]
2945
2946	if map_proj == 'lcc':
2947	lon2 = (lonlatLims[0] + lonlatLims[2])/2.
2948	lat2 = (lonlatLims[1] + lonlatLims[3])/2.
2949	else:
2950	nlon = lon0[0,0]
2951	xlon = lon0[dy-1,dx-1]
2952	nlat = lat0[0,0]
2953	xlat = lat0[dy-1,dx-1]
2954	lon2 = lon0[dy/2,dx/2]
2955	lat2 = lat0[dy/2,dx/2]
2956
2957	plt.xlim(nlon, xlon)
2958	plt.ylim(nlat, xlat)
2959	print 'lon2:', lon2, 'lat2:', lat2, 'SW pt:', nlon, ',', nlat, 'NE pt:', \
2960	xlon, ',', xlat
2961
2962	if map_proj == 'cyl':
2963	m = Basemap(projection=map_proj, llcrnrlon=nlon, llcrnrlat=nlat, \
2964	urcrnrlon=xlon, urcrnrlat= xlat, resolution=map_res)
2965	elif map_proj == 'lcc':
2966	m = Basemap(projection=map_proj, lat_0=lat2, lon_0=lon2, llcrnrlon=nlon, \
2967	llcrnrlat=nlat, urcrnrlon=xlon, urcrnrlat= xlat, resolution=map_res)
2968	else:
2969	print errormsg
2970	print ' ' + fname + ": map projection '" + map_proj + "' not ready !!"
2971	quit(-1)
2972
2973	if len(olon[:].shape) == 1:
2974	lons, lats = np.meshgrid(olon[:], olat[:])
2975	else:
2976	if len(olon[:].shape) == 3:
2977	lons = olon[0,:,:]
2978	lats = olat[0,:,:]
2979	else:
2980	lons = olon[:]
2981	lats = olat[:]
2982
2983	x,y = m(lons,lats)
2984
2985	plt.pcolormesh(x,y,varv, cmap=plt.get_cmap('terrain'), vmin=mint, vmax=maxt)
2986	cbar = plt.colorbar()
2987
2988	m.drawcoastlines()
2989
2990	meridians = pretty_int(nlon,xlon,5)
2991	m.drawmeridians(meridians,labels=[True,False,False,True])
2992
2993	parallels = pretty_int(nlat,xlat,5)
2994	m.drawparallels(parallels,labels=[False,True,True,False])
2995
2996	plt.xlabel('W-E')
2997	plt.ylabel('S-N')
2998	else:
2999	print emsg
3000	print ' ' + fname + ': A projection parameter is needed None given !!'
3001	quit(-1)
3002
3003	figname = 'domain'
3004	graphtit = gtit.replace('_','\_')
3005	cbar.set_label('height ($m$)')
3006
3007	plt.title(graphtit.replace('_','\_').replace('&','\&'))
3008
3009	output_kind(kfig, figname, closeif)
3010
3011	return
3012
3013	def plot_topo_geogrid_boxes(varv, boxesX, boxesY, boxlabels, olon, olat, mint, maxt, \
3014	lonlatLims, gtit, kfig, mapv, closeif):
3015	""" plotting geo_em.d[nn].nc topography from WPS files
3016	plot_topo_geogrid(domf, mint, maxt, gtit, kfig, mapv)
3017	varv= topography values
3018	boxesX/Y= 4-line sets to draw the boxes
3019	boxlabels= labels for the legend of the boxes
3020	o[lon/lat]= longitude and latitude objects
3021	[min/max]t: minimum and maximum values of topography to draw
3022	lonlatLims: limits of longitudes and latitudes [lonmin, latmin, lonmax, latmax]
3023	gtit= title of the graph
3024	kfig= kind of figure (jpg, pdf, png)
3025	mapv= map characteristics: [proj],[res]
3026	see full documentation: http://matplotlib.org/basemap/
3027	[proj]: projection
3028	* 'cyl', cilindric
3029	* 'lcc', lamvbert conformal
3030	[res]: resolution:
3031	* 'c', crude
3032	* 'l', low
3033	* 'i', intermediate
3034	* 'h', high
3035	* 'f', full
3036	closeif= Boolean value if the figure has to be closed
3037	"""
3038	fname = 'plot_topo_geogrid'
3039
3040	if varv == 'h':
3041	print fname + '_____________________________________________________________'
3042	print plot_topo_geogrid.__doc__
3043	quit()
3044
3045	cols = color_lines(len(boxlabels))
3046
3047	dx=varv.shape[1]
3048	dy=varv.shape[0]
3049
3050	plt.rc('text', usetex=True)
3051	# plt.rc('font', family='serif')
3052
3053	if not mapv is None:
3054	if len(olon[:].shape) == 3:
3055	lon0 = olon[0,]
3056	lat0 = olat[0,]
3057	elif len(olon[:].shape) == 2:
3058	lon0 = olon[:]
3059	lat0 = olat[:]
3060	elif len(olon[:].shape) == 1:
3061	lon00 = olon[:]
3062	lat00 = olat[:]
3063	lon0 = np.zeros( (len(lat00),len(lon00)), dtype=np.float )
3064	lat0 = np.zeros( (len(lat00),len(lon00)), dtype=np.float )
3065
3066	for iy in range(len(lat00)):
3067	lon0[iy,:] = lon00
3068	for ix in range(len(lon00)):
3069	lat0[:,ix] = lat00
3070
3071	map_proj=mapv.split(',')[0]
3072	map_res=mapv.split(',')[1]
3073	dx = lon0.shape[1]
3074	dy = lon0.shape[0]
3075
3076	if lonlatLims is not None:
3077	print ' ' + fname + ': cutting the domain to plot !!!!'
3078	print ' limits: W-E', lonlatLims[0], lonlatLims[2]
3079	print ' limits: N-S', lonlatLims[1], lonlatLims[3]
3080	nlon = lonlatLims[0]
3081	xlon = lonlatLims[2]
3082	nlat = lonlatLims[1]
3083	xlat = lonlatLims[3]
3084
3085	if map_proj == 'lcc':
3086	lon2 = (lonlatLims[0] + lonlatLims[2])/2.
3087	lat2 = (lonlatLims[1] + lonlatLims[3])/2.
3088	else:
3089	nlon = np.min(lon0)
3090	xlon = np.max(lon0)
3091	nlat = np.min(lat0)
3092	xlat = np.max(lat0)
3093	lon2 = lon0[dy/2,dx/2]
3094	lat2 = lat0[dy/2,dx/2]
3095
3096	plt.xlim(nlon, xlon)
3097	plt.ylim(nlat, xlat)
3098	print 'lon2:', lon2, 'lat2:', lat2, 'SW pt:', nlon, ',', nlat, 'NE pt:', \
3099	xlon, ',', xlat
3100
3101	if map_proj == 'cyl':
3102	m = Basemap(projection=map_proj, llcrnrlon=nlon, llcrnrlat=nlat, \
3103	urcrnrlon=xlon, urcrnrlat= xlat, resolution=map_res)
3104	elif map_proj == 'lcc':
3105	m = Basemap(projection=map_proj, lat_0=lat2, lon_0=lon2, llcrnrlon=nlon, \
3106	llcrnrlat=nlat, urcrnrlon=xlon, urcrnrlat= xlat, resolution=map_res)
3107
3108	if len(olon[:].shape) == 1:
3109	lons, lats = np.meshgrid(olon[:], olat[:])
3110	else:
3111	if len(olon[:].shape) == 3:
3112	lons = olon[0,:,:]
3113	lats = olat[0,:,:]
3114	else:
3115	lons = olon[:]
3116	lats = olat[:]
3117
3118	x,y = m(lons,lats)
3119
3120	plt.pcolormesh(x,y,varv, cmap=plt.get_cmap('terrain'), vmin=mint, vmax=maxt)
3121	cbar = plt.colorbar()
3122
3123	Nboxes = len(boxesX)/4
3124	for ibox in range(Nboxes):
3125	plt.plot(boxesX[ibox4], boxesY[ibox4], linestyle='-', linewidth=3, \
3126	label=boxlabels[ibox], color=cols[ibox])
3127	plt.plot(boxesX[ibox4+1], boxesY[ibox4+1], linestyle='-', linewidth=3, \
3128	color=cols[ibox])
3129	plt.plot(boxesX[ibox4+2], boxesY[ibox4+2], linestyle='-', linewidth=3, \
3130	color=cols[ibox])
3131	plt.plot(boxesX[ibox4+3], boxesY[ibox4+3], linestyle='-', linewidth=3, \
3132	color=cols[ibox])
3133
3134	m.drawcoastlines()
3135
3136	meridians = pretty_int(nlon,xlon,5)
3137	m.drawmeridians(meridians,labels=[True,False,False,True])
3138
3139	parallels = pretty_int(nlat,xlat,5)
3140	m.drawparallels(parallels,labels=[False,True,True,False])
3141
3142	plt.xlabel('W-E')
3143	plt.ylabel('S-N')
3144	else:
3145	print emsg
3146	print ' ' + fname + ': A projection parameter is needed None given !!'
3147	quit(-1)
3148
3149	figname = 'domain_boxes'
3150	graphtit = gtit.replace('_','\_').replace('&','\&')
3151	cbar.set_label('height ($m$)')
3152
3153	plt.title(graphtit)
3154	plt.legend()
3155
3156	output_kind(kfig, figname, closeif)
3157
3158	return
3159
3160	def plot_2D_shadow(varsv,vnames,dimxv,dimyv,dimxu,dimyu,dimn, \
3161	colorbar,vs,uts,vtit,kfig,reva,mapv,ifclose):
3162	""" Adding labels and other staff to the graph
3163	varsv= 2D values to plot with shading
3164	vnames= variable names for the figure
3165	dim[x/y]v = values at the axes of x and y
3166	dim[x/y]u = units at the axes of x and y
3167	dimn= dimension names to plot
3168	colorbar= name of the color bar to use
3169	vs= minmum and maximum values to plot in shadow or:
3170	'Srange': for full range
3171	'Saroundmean@val': for mean-xtrm,mean+xtrm where xtrm = np.min(mean-min@val,max@val-mean)
3172	'Saroundminmax@val': for minval,maxval
3173	'Saroundpercentile@val': for median-xtrm,median+xtrm where xtrm = np.min(median-percentile_(val),
3174	percentile_(100-val)-median)
3175	'Smean@val': for -xtrm,xtrm where xtrm = np.min(mean-min@val,max@val-mean)
3176	'Smedian@val': for -xtrm,xtrm where xtrm = np.min(median-min@val,max@val-median)
3177	'Spercentile@val': for -xtrm,xtrm where xtrm = np.min(median-percentile_(val),
3178	percentile_(100-val)-median)
3179	uts= units of the variable to shadow
3180	vtit= title of the variable
3181	kfig= kind of figure (jpg, pdf, png)
3182	reva= ('\|' for combination)
3183	* 'transpose': reverse the axes (x-->y, y-->x)
3184	* 'flip'@[x/y]: flip the axis x or y
3185	mapv= map characteristics: [proj],[res]
3186	see full documentation: http://matplotlib.org/basemap/
3187	[proj]: projection
3188	* 'cyl', cilindric
3189	* 'lcc', lambert conformal
3190	[res]: resolution:
3191	* 'c', crude
3192	* 'l', low
3193	* 'i', intermediate
3194	* 'h', high
3195	* 'f', full
3196	ifclose= boolean value whether figure should be close (finish) or not
3197	"""
3198	## import matplotlib as mpl
3199	## mpl.use('Agg')
3200	## import matplotlib.pyplot as plt
3201	fname = 'plot_2D_shadow'
3202
3203	# print dimyv[73,21]
3204	# dimyv[73,21] = -dimyv[73,21]
3205	# print 'Lluis dimsv: ',np.min(dimxv), np.max(dimxv), ':', np.min(dimyv), np.max(dimyv)
3206
3207	if varsv == 'h':
3208	print fname + '_____________________________________________________________'
3209	print plot_2D_shadow.__doc__
3210	quit()
3211
3212	if len(varsv.shape) != 2:
3213	print errormsg
3214	print ' ' + fname + ': wrong variable shape:',varv.shape,'is has to be 2D!!'
3215	quit(-1)
3216
3217	reva0 = ''
3218	if reva.find('\|') != 0:
3219	revas = reva.split('\|')
3220	else:
3221	revas = [reva]
3222	reva0 = reva
3223
3224	for rev in revas:
3225	if reva[0:4] == 'flip':
3226	reva0 = 'flip'
3227	if len(reva.split('@')) != 2:
3228	print errormsg
3229	print ' ' + fname + ': flip is given', reva, 'but not axis!'
3230	quit(-1)
3231
3232	if rev == 'transpose':
3233	print ' reversing the axes of the figure (x-->y, y-->x)!!'
3234	varsv = np.transpose(varsv)
3235	dxv = dimyv
3236	dyv = dimxv
3237	dimxv = dxv
3238	dimyv = dyv
3239
3240	if len(dimxv[:].shape) == 3:
3241	xdims = '1,2'
3242	elif len(dimxv[:].shape) == 2:
3243	xdims = '0,1'
3244	elif len(dimxv[:].shape) == 1:
3245	xdims = '0'
3246
3247	if len(dimyv[:].shape) == 3:
3248	ydims = '1,2'
3249	elif len(dimyv[:].shape) == 2:
3250	ydims = '0,1'
3251	elif len(dimyv[:].shape) == 1:
3252	ydims = '0'
3253
3254	lon0, lat0 = dxdy_lonlat(dimxv,dimyv, xdims, ydims)
3255
3256	if not mapv is None:
3257	map_proj=mapv.split(',')[0]
3258	map_res=mapv.split(',')[1]
3259
3260	dx = lon0.shape[1]
3261	dy = lat0.shape[0]
3262
3263	nlon = lon0[0,0]
3264	xlon = lon0[dy-1,dx-1]
3265	nlat = lat0[0,0]
3266	xlat = lat0[dy-1,dx-1]
3267
3268	# Thats too much! :)
3269	# if lonlatLims is not None:
3270	# print ' ' + fname + ': cutting the domain to plot !!!!'
3271	# plt.xlim(lonlatLims[0], lonlatLims[2])
3272	# plt.ylim(lonlatLims[1], lonlatLims[3])
3273	# print ' limits: W-E', lonlatLims[0], lonlatLims[2]
3274	# print ' limits: N-S', lonlatLims[1], lonlatLims[3]
3275
3276	# if map_proj == 'cyl':
3277	# nlon = lonlatLims[0]
3278	# nlat = lonlatLims[1]
3279	# xlon = lonlatLims[2]
3280	# xlat = lonlatLims[3]
3281	# elif map_proj == 'lcc':
3282	# lon2 = (lonlatLims[0] + lonlatLims[2])/2.
3283	# lat2 = (lonlatLims[1] + lonlatLims[3])/2.
3284	# nlon = lonlatLims[0]
3285	# xlon = lonlatLims[2]
3286	# nlat = lonlatLims[1]
3287	# xlat = lonlatLims[3]
3288
3289	lon2 = lon0[dy/2,dx/2]
3290	lat2 = lat0[dy/2,dx/2]
3291
3292	print 'lon2:', lon2, 'lat2:', lat2, 'SW pt:', nlon, ',', nlat, 'NE pt:', \
3293	xlon, ',', xlat
3294
3295	if map_proj == 'cyl':
3296	m = Basemap(projection=map_proj, llcrnrlon=nlon, llcrnrlat=nlat, \
3297	urcrnrlon=xlon, urcrnrlat= xlat, resolution=map_res)
3298	elif map_proj == 'lcc':
3299	m = Basemap(projection=map_proj, lat_0=lat2, lon_0=lon2, llcrnrlon=nlon, \
3300	llcrnrlat=nlat, urcrnrlon=xlon, urcrnrlat= xlat, resolution=map_res)
3301	else:
3302	print errormsg
3303	print ' ' + fname + ": map projection '" + map_proj + "' not defined!!!"
3304	print ' available: cyl, lcc'
3305	quit(-1)
3306
3307	x,y = m(lon0,lat0)
3308
3309	else:
3310	x = lon0
3311	y = lat0
3312
3313	vsend = np.zeros((2), dtype=np.float)
3314	# Changing limits of the colors
3315	if type(vs[0]) != type(np.float(1.)):
3316	if vs[0] == 'Srange':
3317	vsend[0] = np.min(varsv)
3318	elif vs[0][0:11] == 'Saroundmean':
3319	meanv = np.mean(varsv)
3320	permean = np.float(vs[0].split('@')[1])
3321	minv = np.min(varsv)*permean
3322	maxv = np.max(varsv)*permean
3323	minextrm = np.min([np.abs(meanv-minv), np.abs(maxv-meanv)])
3324	vsend[0] = meanv-minextrm
3325	vsend[1] = meanv+minextrm
3326	elif vs[0][0:13] == 'Saroundminmax':
3327	permean = np.float(vs[0].split('@')[1])
3328	minv = np.min(varsv)*permean
3329	maxv = np.max(varsv)*permean
3330	vsend[0] = minv
3331	vsend[1] = maxv
3332	elif vs[0][0:17] == 'Saroundpercentile':
3333	medianv = np.median(varsv)
3334	valper = np.float(vs[0].split('@')[1])
3335	minv = np.percentile(varsv, valper)
3336	maxv = np.percentile(varsv, 100.-valper)
3337	minextrm = np.min([np.abs(medianv-minv), np.abs(maxv-medianv)])
3338	vsend[0] = medianv-minextrm
3339	vsend[1] = medianv+minextrm
3340	elif vs[0][0:5] == 'Smean':
3341	meanv = np.mean(varsv)
3342	permean = np.float(vs[0].split('@')[1])
3343	minv = np.min(varsv)*permean
3344	maxv = np.max(varsv)*permean
3345	minextrm = np.min([np.abs(meanv-minv), np.abs(maxv-meanv)])
3346	vsend[0] = -minextrm
3347	vsend[1] = minextrm
3348	elif vs[0][0:7] == 'Smedian':
3349	medianv = np.median(varsv)
3350	permedian = np.float(vs[0].split('@')[1])
3351	minv = np.min(varsv)*permedian
3352	maxv = np.max(varsv)*permedian
3353	minextrm = np.min([np.abs(medianv-minv), np.abs(maxv-medianv)])
3354	vsend[0] = -minextrm
3355	vsend[1] = minextrm
3356	elif vs[0][0:11] == 'Spercentile':
3357	medianv = np.median(varsv)
3358	valper = np.float(vs[0].split('@')[1])
3359	minv = np.percentile(varsv, valper)
3360	maxv = np.percentile(varsv, 100.-valper)
3361	minextrm = np.min([np.abs(medianv-minv), np.abs(maxv-medianv)])
3362	vsend[0] = -minextrm
3363	vsend[1] = minextrm
3364	else:
3365	print errormsg
3366	print ' ' + fname + ": range '" + vs[0] + "' not ready!!!"
3367	quit(-1)
3368	print ' ' + fname + ': modified shadow min,max:',vsend
3369	else:
3370	vsend[0] = vs[0]
3371
3372	if type(vs[0]) != type(np.float(1.)):
3373	if vs[1] == 'range':
3374	vsend[1] = np.max(varsv)
3375	else:
3376	vsend[1] = vs[1]
3377
3378	plt.rc('text', usetex=True)
3379
3380	plt.pcolormesh(x, y, varsv, cmap=plt.get_cmap(colorbar), vmin=vsend[0], vmax=vsend[1])
3381	cbar = plt.colorbar()
3382
3383	if not mapv is None:
3384	m.drawcoastlines()
3385
3386	meridians = pretty_int(nlon,xlon,5)
3387	m.drawmeridians(meridians,labels=[True,False,False,True])
3388	parallels = pretty_int(nlat,xlat,5)
3389	m.drawparallels(parallels,labels=[False,True,True,False])
3390
3391	plt.xlabel('W-E')
3392	plt.ylabel('S-N')
3393	else:
3394	plt.xlabel(variables_values(dimn[1])[0].replace('_','\_') + ' (' + \
3395	units_lunits(dimxu) + ')')
3396	plt.ylabel(variables_values(dimn[0])[0].replace('_','\_') + ' (' + \
3397	units_lunits(dimyu) + ')')
3398
3399	txpos = pretty_int(x.min(),x.max(),5)
3400	typos = pretty_int(y.min(),y.max(),5)
3401	txlabels = list(txpos)
3402	for i in range(len(txlabels)): txlabels[i] = str(txlabels[i])
3403	tylabels = list(typos)
3404	for i in range(len(tylabels)): tylabels[i] = str(tylabels[i])
3405
3406	# set the limits of the plot to the limits of the data
3407
3408	if searchInlist(revas,'transpose'):
3409	x0 = y
3410	y0 = x
3411	x = x0
3412	y = y0
3413	# print 'Lluis reva0:',reva0,'x min,max:',x.min(),x.max(),' y min,max:',y.min(),y.max()
3414
3415	if reva0 == 'flip':
3416	if reva.split('@')[1] == 'x':
3417	plt.axis([x.max(), x.min(), y.min(), y.max()])
3418	elif reva.split('@')[1] == 'y':
3419	plt.axis([x.min(), x.max(), y.max(), y.min()])
3420	else:
3421	plt.axis([x.max(), x.min(), y.max(), y.min()])
3422	else:
3423	plt.axis([x.min(), x.max(), y.min(), y.max()])
3424
3425	if mapv is None:
3426	plt.xticks(txpos, txlabels)
3427	plt.yticks(typos, tylabels)
3428
3429	# units labels
3430	cbar.set_label(vnames.replace('_','\_') + ' (' + units_lunits(uts) + ')')
3431
3432	figname = '2Dfields_shadow'
3433	graphtit = vtit.replace('_','\_').replace('&','\&')
3434
3435	plt.title(graphtit)
3436
3437	output_kind(kfig, figname, ifclose)
3438
3439	return
3440
3441	#Nvals=50
3442	#vals1 = np.zeros((Nvals,Nvals), dtype= np.float)
3443	#for j in range(Nvals):
3444	# for i in range(Nvals):
3445	# vals1[j,i]=np.sqrt((j-Nvals/2)2. + (i-Nvals/2)2.)
3446
3447	#plot_2D_shadow(vals1, 'var1', np.arange(50)1., np.arange(50)1., 'ms-1', \
3448	# 'm', ['lat','lon'], 'rainbow', [0, Nvals], 'ms-1', 'test var1', 'pdf', 'None', \
3449	# None, True)
3450	#quit()
3451
3452	def plot_2D_shadow_time(varsv,vnames,dimxv,dimyv,dimxu,dimyu,dimn,colorbar,vs,uts, \
3453	vtit,kfig,reva,taxis,tpos,tlabs,ifclose):
3454	""" Plotting a 2D field with one of the axes being time
3455	varsv= 2D values to plot with shading
3456	vnames= shading variable name for the figure
3457	dim[x/y]v= values at the axes of x and y
3458	dim[x/y]u= units at the axes of x and y
3459	dimn= dimension names to plot
3460	colorbar= name of the color bar to use
3461	vs= minmum and maximum values to plot in shadow or:
3462	'Srange': for full range
3463	'Saroundmean@val': for mean-xtrm,mean+xtrm where xtrm = np.min(mean-min@val,max@val-mean)
3464	'Saroundminmax@val': for minval,maxval
3465	'Saroundpercentile@val': for median-xtrm,median+xtrm where xtrm = np.min(median-percentile_(val),
3466	percentile_(100-val)-median)
3467	'Smean@val': for -xtrm,xtrm where xtrm = np.min(mean-min@val,max@val-mean)
3468	'Smedian@val': for -xtrm,xtrm where xtrm = np.min(median-min@val,max@val-median)
3469	'Spercentile@val': for -xtrm,xtrm where xtrm = np.min(median-percentile_(val),
3470	percentile_(100-val)-median)
3471	uts= units of the variable to shadow
3472	vtit= title of the variable
3473	kfig= kind of figure (jpg, pdf, png)
3474	reva=
3475	* 'transpose': reverse the axes (x-->y, y-->x)
3476	* 'flip'@[x/y]: flip the axis x or y
3477	taxis= Which is the time-axis
3478	tpos= positions of the time ticks
3479	tlabs= labels of the time ticks
3480	ifclose= boolean value whether figure should be close (finish) or not
3481	"""
3482	fname = 'plot_2D_shadow_time'
3483
3484	if varsv == 'h':
3485	print fname + '_____________________________________________________________'
3486	print plot_2D_shadow_time.__doc__
3487	quit()
3488
3489	# Definning ticks labels
3490	if taxis == 'x':
3491	txpos = tpos
3492	txlabels = tlabs
3493	plxlabel = dimxu
3494	typos = pretty_int(np.min(dimyv),np.max(dimyv),10)
3495	tylabels = list(typos)
3496	for i in range(len(tylabels)): tylabels[i] = str(tylabels[i])
3497	plylabel = variables_values(dimn[0])[0].replace('_','\_') + ' (' + \
3498	units_lunits(dimyu) + ')'
3499	else:
3500	txpos = pretty_int(np.min(dimxv),np.max(dimxv),10)
3501	txlabels = list(txpos)
3502	plxlabel = variables_values(dimn[1])[0].replace('_','\_') + ' (' + \
3503	units_lunits(dimxu) + ')'
3504	typos = tpos
3505	tylabels = tlabs
3506	plylabel = dimyu
3507
3508	# Transposing/flipping axis
3509	if reva.find('\|') != 0:
3510	revas = reva.split('\|')
3511	reva0 = ''
3512	else:
3513	revas = [reva]
3514	reva0 = reva
3515
3516	for rev in revas:
3517	if rev[0:4] == 'flip':
3518	reva0 = 'flip'
3519	if len(reva.split('@')) != 2:
3520	print errormsg
3521	print ' ' + fname + ': flip is given', reva, 'but not axis!'
3522	quit(-1)
3523	else:
3524	print " flipping '" + rev.split('@')[1] + "' axis !"
3525
3526	if rev == 'transpose':
3527	print ' reversing the axes of the figure (x-->y, y-->x)!!'
3528	# Flipping values of variable
3529	varsv = np.transpose(varsv)
3530	dxv = dimyv
3531	dyv = dimxv
3532	dimxv = dxv
3533	dimyv = dyv
3534
3535	if len(dimxv.shape) == 3:
3536	dxget='1,2'
3537	elif len(dimxv.shape) == 2:
3538	dxget='0,1'
3539	elif len(dimxv.shape) == 1:
3540	dxget='0'
3541	else:
3542	print errormsg
3543	print ' ' + fname + ': shape of x-values:',dimxv.shape,'not ready!!'
3544	quit(-1)
3545
3546	if len(dimyv.shape) == 3:
3547	dyget='1,2'
3548	elif len(dimyv.shape) == 2:
3549	dyget='0,1'
3550	elif len(dimyv.shape) == 1:
3551	dyget='0'
3552	else:
3553	print errormsg
3554	print ' ' + fname + ': shape of y-values:',dimyv.shape,'not ready!!'
3555	quit(-1)
3556
3557	x,y = dxdy_lonlat(dimxv,dimyv,dxget,dyget)
3558
3559	plt.rc('text', usetex=True)
3560
3561	vsend = np.zeros((2), dtype=np.float)
3562	# Changing limits of the colors
3563	if type(vs[0]) != type(np.float(1.)):
3564	if vs[0] == 'Srange':
3565	vsend[0] = np.min(varsv)
3566	elif vs[0][0:11] == 'Saroundmean':
3567	meanv = np.mean(varsv)
3568	permean = np.float(vs[0].split('@')[1])
3569	minv = np.min(varsv)*permean
3570	maxv = np.max(varsv)*permean
3571	minextrm = np.min([np.abs(meanv-minv), np.abs(maxv-meanv)])
3572	vsend[0] = meanv-minextrm
3573	vsend[1] = meanv+minextrm
3574	elif vs[0][0:13] == 'Saroundminmax':
3575	permean = np.float(vs[0].split('@')[1])
3576	minv = np.min(varsv)*permean
3577	maxv = np.max(varsv)*permean
3578	vsend[0] = minv
3579	vsend[1] = maxv
3580	elif vs[0][0:17] == 'Saroundpercentile':
3581	medianv = np.median(varsv)
3582	valper = np.float(vs[0].split('@')[1])
3583	minv = np.percentile(varsv, valper)
3584	maxv = np.percentile(varsv, 100.-valper)
3585	minextrm = np.min([np.abs(medianv-minv), np.abs(maxv-medianv)])
3586	vsend[0] = medianv-minextrm
3587	vsend[1] = medianv+minextrm
3588	elif vs[0][0:5] == 'Smean':
3589	meanv = np.mean(varsv)
3590	permean = np.float(vs[0].split('@')[1])
3591	minv = np.min(varsv)*permean
3592	maxv = np.max(varsv)*permean
3593	minextrm = np.min([np.abs(meanv-minv), np.abs(maxv-meanv)])
3594	vsend[0] = -minextrm
3595	vsend[1] = minextrm
3596	elif vs[0][0:7] == 'Smedian':
3597	medianv = np.median(varsv)
3598	permedian = np.float(vs[0].split('@')[1])
3599	minv = np.min(varsv)*permedian
3600	maxv = np.max(varsv)*permedian
3601	minextrm = np.min([np.abs(medianv-minv), np.abs(maxv-medianv)])
3602	vsend[0] = -minextrm
3603	vsend[1] = minextrm
3604	elif vs[0][0:11] == 'Spercentile':
3605	medianv = np.median(varsv)
3606	valper = np.float(vs[0].split('@')[1])
3607	minv = np.percentile(varsv, valper)
3608	maxv = np.percentile(varsv, 100.-valper)
3609	minextrm = np.min([np.abs(medianv-minv), np.abs(maxv-medianv)])
3610	vsend[0] = -minextrm
3611	vsend[1] = minextrm
3612	else:
3613	print errormsg
3614	print ' ' + fname + ": range '" + vs[0] + "' not ready!!!"
3615	quit(-1)
3616	print ' ' + fname + ': modified shadow min,max:',vsend
3617	else:
3618	vsend[0] = vs[0]
3619
3620	if type(vs[0]) != type(np.float(1.)):
3621	if vs[1] == 'range':
3622	vsend[1] = np.max(varsv)
3623	else:
3624	vsend[1] = vs[1]
3625
3626	plt.pcolormesh(x, y, varsv, cmap=plt.get_cmap(colorbar), vmin=vsend[0], vmax=vsend[1])
3627	cbar = plt.colorbar()
3628
3629	# print 'Lluis reva0:',reva0,'x min,max:',x.min(),x.max(),' y min,max:',y.min(),y.max()
3630
3631	# set the limits of the plot to the limits of the data
3632	if reva0 == 'flip':
3633	if reva.split('@')[1] == 'x':
3634	plt.axis([x.max(), x.min(), y.min(), y.max()])
3635	elif reva.split('@')[1] == 'y':
3636	plt.axis([x.min(), x.max(), y.max(), y.min()])
3637	else:
3638	plt.axis([x.max(), x.min(), y.max(), y.min()])
3639	else:
3640	plt.axis([x.min(), x.max(), y.min(), y.max()])
3641
3642	if searchInlist(revas, 'transpose'):
3643	plt.xticks(typos, tylabels)
3644	plt.yticks(txpos, txlabels)
3645	plt.xlabel(plylabel)
3646	plt.ylabel(plxlabel)
3647	else:
3648	plt.xticks(txpos, txlabels)
3649	plt.yticks(typos, tylabels)
3650	plt.xlabel(plxlabel)
3651	plt.ylabel(plylabel)
3652
3653	# units labels
3654	cbar.set_label(vnames.replace('_','\_') + ' (' + units_lunits(uts) + ')')
3655
3656	figname = '2Dfields_shadow_time'
3657	graphtit = vtit.replace('_','\_').replace('&','\&')
3658
3659	plt.title(graphtit)
3660
3661	output_kind(kfig, figname, ifclose)
3662
3663	return
3664
3665	def plot_2D_shadow_contour(varsv,varcv,vnames,dimxv,dimyv,dimxu,dimyu,dimn, \
3666	colorbar,ckind,clabfmt,vs,vc,uts,vtit,kfig,reva,mapv):
3667	""" Adding labels and other staff to the graph
3668	varsv= 2D values to plot with shading
3669	varcv= 2D values to plot with contours
3670	vnames= variable names for the figure
3671	dim[x/y]v = values at the axes of x and y
3672	dim[x/y]u = units at the axes of x and y
3673	dimn= dimension names to plot
3674	colorbar= name of the color bar to use
3675	ckind= contour kind
3676	'cmap': as it gets from colorbar
3677	'fixc,[colname]': fixed color [colname], all stright lines
3678	'fixsigc,[colname]': fixed color [colname], >0 stright, <0 dashed line
3679	clabfmt= format of the labels in the contour plot (None, no labels)
3680	vs= minmum and maximum values to plot in shadow
3681	vc= vector with the levels for the contour
3682	uts= units of the variable [u-shadow, u-contour]
3683	vtit= title of the variable
3684	kfig= kind of figure (jpg, pdf, png)
3685	reva=
3686	* 'transpose': reverse the axes (x-->y, y-->x)
3687	* 'flip'@[x/y]: flip the axis x or y
3688	mapv= map characteristics: [proj],[res]
3689	see full documentation: http://matplotlib.org/basemap/
3690	[proj]: projection
3691	* 'cyl', cilindric
3692	* 'lcc', lamvbert conformal
3693	[res]: resolution:
3694	* 'c', crude
3695	* 'l', low
3696	* 'i', intermediate
3697	* 'h', high
3698	* 'f', full
3699	"""
3700	## import matplotlib as mpl
3701	## mpl.use('Agg')
3702	## import matplotlib.pyplot as plt
3703	fname = 'plot_2D_shadow_contour'
3704
3705
3706	if varsv == 'h':
3707	print fname + '_____________________________________________________________'
3708	print plot_2D_shadow_contour.__doc__
3709	quit()
3710
3711	if reva[0:4] == 'flip':
3712	reva0 = 'flip'
3713	if len(reva.split('@')) != 2:
3714	print errormsg
3715	print ' ' + fname + ': flip is given', reva, 'but not axis!'
3716	quit(-1)
3717	else:
3718	reva0 = reva
3719
3720	if reva0 == 'transpose':
3721	print ' reversing the axes of the figure (x-->y, y-->x)!!'
3722	varsv = np.transpose(varsv)
3723	varcv = np.transpose(varcv)
3724	dxv = dimyv
3725	dyv = dimxv
3726	dimxv = dxv
3727	dimyv = dyv
3728
3729	if not mapv is None:
3730	if len(dimxv[:].shape) == 3:
3731	lon0 = dimxv[0,]
3732	lat0 = dimyv[0,]
3733	elif len(dimxv[:].shape) == 2:
3734	lon0 = dimxv[:]
3735	lat0 = dimyv[:]
3736	elif len(dimxv[:].shape) == 1:
3737	lon00 = dimxv[:]
3738	lat00 = dimyv[:]
3739	lon0 = np.zeros( (len(lat00),len(lon00)), dtype=np.float )
3740	lat0 = np.zeros( (len(lat00),len(lon00)), dtype=np.float )
3741
3742	for iy in range(len(lat00)):
3743	lon0[iy,:] = lon00
3744	for ix in range(len(lon00)):
3745	lat0[:,ix] = lat00
3746
3747	map_proj=mapv.split(',')[0]
3748	map_res=mapv.split(',')[1]
3749
3750	dx = lon0.shape[1]
3751	dy = lon0.shape[0]
3752
3753	nlon = lon0[0,0]
3754	xlon = lon0[dy-1,dx-1]
3755	nlat = lat0[0,0]
3756	xlat = lat0[dy-1,dx-1]
3757
3758	# Thats too much! :)
3759	# if lonlatLims is not None:
3760	# print ' ' + fname + ': cutting the domain to plot !!!!'
3761	# plt.xlim(lonlatLims[0], lonlatLims[2])
3762	# plt.ylim(lonlatLims[1], lonlatLims[3])
3763	# print ' limits: W-E', lonlatLims[0], lonlatLims[2]
3764	# print ' limits: N-S', lonlatLims[1], lonlatLims[3]
3765
3766	# if map_proj == 'cyl':
3767	# nlon = lonlatLims[0]
3768	# nlat = lonlatLims[1]
3769	# xlon = lonlatLims[2]
3770	# xlat = lonlatLims[3]
3771	# elif map_proj == 'lcc':
3772	# lon2 = (lonlatLims[0] + lonlatLims[2])/2.
3773	# lat2 = (lonlatLims[1] + lonlatLims[3])/2.
3774	# nlon = lonlatLims[0]
3775	# xlon = lonlatLims[2]
3776	# nlat = lonlatLims[1]
3777	# xlat = lonlatLims[3]
3778
3779	lon2 = lon0[dy/2,dx/2]
3780	lat2 = lat0[dy/2,dx/2]
3781
3782	print 'lon2:', lon2, 'lat2:', lat2, 'SW pt:', nlon, ',', nlat, 'NE pt:', \
3783	xlon, ',', xlat
3784
3785	if map_proj == 'cyl':
3786	m = Basemap(projection=map_proj, llcrnrlon=nlon, llcrnrlat=nlat, \
3787	urcrnrlon=xlon, urcrnrlat= xlat, resolution=map_res)
3788	elif map_proj == 'lcc':
3789	m = Basemap(projection=map_proj, lat_0=lat2, lon_0=lon2, llcrnrlon=nlon, \
3790	llcrnrlat=nlat, urcrnrlon=xlon, urcrnrlat= xlat, resolution=map_res)
3791
3792	if len(dimxv.shape) == 1:
3793	lons, lats = np.meshgrid(dimxv, dimyv)
3794	else:
3795	if len(dimxv.shape) == 3:
3796	lons = dimxv[0,:,:]
3797	lats = dimyv[0,:,:]
3798	else:
3799	lons = dimxv[:]
3800	lats = dimyv[:]
3801
3802	x,y = m(lons,lats)
3803
3804	else:
3805	if len(dimxv.shape) == 2:
3806	x = dimxv
3807	else:
3808	if len(dimyv.shape) == 1:
3809	x = np.zeros((len(dimyv),len(dimxv)), dtype=np.float)
3810	for j in range(len(dimyv)):
3811	x[j,:] = dimxv
3812	else:
3813	x = np.zeros((dimyv.shape), dtype=np.float)
3814	if x.shape[0] == dimxv.shape[0]:
3815	for j in range(x.shape[1]):
3816	x[:,j] = dimxv
3817	else:
3818	for j in range(x.shape[0]):
3819	x[j,:] = dimxv
3820
3821	if len(dimyv.shape) == 2:
3822	y = dimyv
3823	else:
3824	if len(dimxv.shape) == 1:
3825	y = np.zeros((len(dimyv),len(dimxv)), dtype=np.float)
3826	for i in range(len(dimxv)):
3827	y[:,i] = dimyv
3828	else:
3829	y = np.zeros((dimxv.shape), dtype=np.float)
3830
3831	if y.shape[0] == dimyv.shape[0]:
3832	for i in range(y.shape[1]):
3833	y[i,:] = dimyv
3834	else:
3835	for i in range(y.shape[0]):
3836	y[i,:] = dimyv
3837
3838	plt.rc('text', usetex=True)
3839
3840	plt.pcolormesh(x, y, varsv, cmap=plt.get_cmap(colorbar), vmin=vs[0], vmax=vs[1])
3841	cbar = plt.colorbar()
3842
3843	# contour
3844	##
3845	contkind = ckind.split(',')[0]
3846	if contkind == 'cmap':
3847	cplot = plt.contour(x, y, varcv, levels=vc)
3848	elif contkind == 'fixc':
3849	plt.rcParams['contour.negative_linestyle'] = 'solid'
3850	coln = ckind.split(',')[1]
3851	cplot = plt.contour(x, y, varcv, levels=vc, colors=coln)
3852	elif contkind == 'fixsigc':
3853	coln = ckind.split(',')[1]
3854	cplot = plt.contour(x, y, varcv, levels=vc, colors=coln)
3855	else:
3856	print errormsg
3857	print ' ' + fname + ': contour kind "' + contkind + '" not defined !!!!!'
3858	quit(-1)
3859
3860	if clabfmt is not None:
3861	plt.clabel(cplot, fmt=clabfmt)
3862	mincntS = format(vc[0], clabfmt[1:len(clabfmt)])
3863	maxcntS = format(vc[len(vc)-1], clabfmt[1:len(clabfmt)])
3864	else:
3865	mincntS = '{:g}'.format(vc[0])
3866	maxcntS = '{:g}'.format(vc[len(vc)-1])
3867
3868	if not mapv is None:
3869	m.drawcoastlines()
3870
3871	meridians = pretty_int(nlon,xlon,5)
3872	m.drawmeridians(meridians,labels=[True,False,False,True])
3873	parallels = pretty_int(nlat,xlat,5)
3874	m.drawparallels(parallels,labels=[False,True,True,False])
3875
3876	plt.xlabel('W-E')
3877	plt.ylabel('S-N')
3878	else:
3879	plt.xlabel(variables_values(dimn[1])[0] + ' (' + units_lunits(dimxu) + ')')
3880	plt.ylabel(variables_values(dimn[0])[0] + ' (' + units_lunits(dimyu) + ')')
3881
3882	txpos = pretty_int(x.min(),x.max(),10)
3883	typos = pretty_int(y.min(),y.max(),10)
3884	txlabels = list(txpos)
3885	for i in range(len(txlabels)): txlabels[i] = str(txlabels[i])
3886	tylabels = list(typos)
3887	for i in range(len(tylabels)): tylabels[i] = str(tylabels[i])
3888
3889	# set the limits of the plot to the limits of the data
3890	if reva0 == 'flip':
3891	if reva.split('@')[1] == 'x':
3892	plt.axis([x.max(), x.min(), y.min(), y.max()])
3893	else:
3894	plt.axis([x.min(), x.max(), y.max(), y.min()])
3895	else:
3896	plt.axis([x.min(), x.max(), y.min(), y.max()])
3897
3898	plt.xticks(txpos, txlabels)
3899	plt.yticks(typos, tylabels)
3900
3901	# units labels
3902	cbar.set_label(vnames[0].replace('_','\_') + ' (' + units_lunits(uts[0]) + ')')
3903	plt.annotate(vnames[1].replace('_','\_') +' (' + units_lunits(uts[1]) + ') [' + \
3904	mincntS + ', ' + maxcntS + ']', xy=(0.55,0.04), xycoords='figure fraction', \
3905	color=coln)
3906
3907	figname = '2Dfields_shadow-contour'
3908	graphtit = vtit.replace('_','\_').replace('&','\&')
3909
3910	plt.title(graphtit)
3911
3912	output_kind(kfig, figname, True)
3913
3914	return
3915
3916	#Nvals=50
3917	#vals1 = np.zeros((Nvals,Nvals), dtype= np.float)
3918	#vals2 = np.zeros((Nvals,Nvals), dtype= np.float)
3919	#for j in range(Nvals):
3920	# for i in range(Nvals):
3921	# vals1[j,i]=np.sqrt((j-Nvals/2)2. + (i-Nvals/2)2.)
3922	# vals2[j,i]=np.sqrt((j-Nvals/2)2. + (i-Nvals/2)2.) - Nvals/2
3923
3924	#prettylev=pretty_int(-Nvals/2,Nvals/2,10)
3925
3926	#plot_2D_shadow_contour(vals1, vals2, ['var1', 'var2'], np.arange(50)*1., \
3927	# np.arange(50)*1., ['x-axis','y-axis'], 'rainbow', 'fixc,b', "%.2f", [0, Nvals], \
3928	# prettylev, ['$ms^{-1}$','$kJm^{-1}s^{-1}$'], 'test var1 & var2', 'pdf', False)
3929
3930	def plot_2D_shadow_contour_time(varsv,varcv,vnames,valv,timv,timpos,timlab,valu, \
3931	timeu,axist,dimn,colorbar,ckind,clabfmt,vs,vc,uts,vtit,kfig,reva,mapv):
3932	""" Adding labels and other staff to the graph
3933	varsv= 2D values to plot with shading
3934	varcv= 2D values to plot with contours
3935	vnames= variable names for the figure
3936	valv = values at the axes which is not time
3937	timv = values for the axis time
3938	timpos = positions at the axis time
3939	timlab = labes at the axis time
3940	valu = units at the axes which is not time
3941	timeu = units at the axes which is not time
3942	axist = which is the axis time
3943	dimn= dimension names to plot
3944	colorbar= name of the color bar to use
3945	ckind= contour kind
3946	'cmap': as it gets from colorbar
3947	'fixc,[colname]': fixed color [colname], all stright lines
3948	'fixsigc,[colname]': fixed color [colname], >0 stright, <0 dashed line
3949	clabfmt= format of the labels in the contour plot (None, no labels)
3950	vs= minmum and maximum values to plot in shadow
3951	vc= vector with the levels for the contour
3952	uts= units of the variable [u-shadow, u-contour]
3953	vtit= title of the variable
3954	kfig= kind of figure (jpg, pdf, png)
3955	reva=
3956	* 'transpose': reverse the axes (x-->y, y-->x)
3957	* 'flip'@[x/y]: flip the axis x or y
3958	mapv= map characteristics: [proj],[res]
3959	see full documentation: http://matplotlib.org/basemap/
3960	[proj]: projection
3961	* 'cyl', cilindric
3962	* 'lcc', lamvbert conformal
3963	[res]: resolution:
3964	* 'c', crude
3965	* 'l', low
3966	* 'i', intermediate
3967	* 'h', high
3968	* 'f', full
3969	"""
3970	## import matplotlib as mpl
3971	## mpl.use('Agg')
3972	## import matplotlib.pyplot as plt
3973	fname = 'plot_2D_shadow_contour'
3974
3975	if varsv == 'h':
3976	print fname + '_____________________________________________________________'
3977	print plot_2D_shadow_contour.__doc__
3978	quit()
3979
3980	if axist == 'x':
3981	dimxv = timv.copy()
3982	dimyv = valv.copy()
3983	else:
3984	dimxv = valv.copy()
3985	dimyv = timv.copy()
3986
3987	if reva[0:4] == 'flip':
3988	reva0 = 'flip'
3989	if len(reva.split('@')) != 2:
3990	print errormsg
3991	print ' ' + fname + ': flip is given', reva, 'but not axis!'
3992	quit(-1)
3993	else:
3994	reva0 = reva
3995
3996	if reva0 == 'transpose':
3997	if axist == 'x':
3998	axist = 'y'
3999	else:
4000	axist = 'x'
4001
4002	if not mapv is None:
4003	if len(dimxv[:].shape) == 3:
4004	lon0 = dimxv[0,]
4005	lat0 = dimyv[0,]
4006	elif len(dimxv[:].shape) == 2:
4007	lon0 = dimxv[:]
4008	lat0 = dimyv[:]
4009	elif len(dimxv[:].shape) == 1:
4010	lon00 = dimxv[:]
4011	lat00 = dimyv[:]
4012	lon0 = np.zeros( (len(lat00),len(lon00)), dtype=np.float )
4013	lat0 = np.zeros( (len(lat00),len(lon00)), dtype=np.float )
4014
4015	for iy in range(len(lat00)):
4016	lon0[iy,:] = lon00
4017	for ix in range(len(lon00)):
4018	lat0[:,ix] = lat00
4019	if reva0 == 'transpose':
4020	print ' reversing the axes of the figure (x-->y, y-->x)!!'
4021	varsv = np.transpose(varsv)
4022	varcv = np.transpose(varcv)
4023	lon0 = np.transpose(lon0)
4024	lat0 = np.transpose(lat0)
4025
4026	map_proj=mapv.split(',')[0]
4027	map_res=mapv.split(',')[1]
4028
4029	dx = lon0.shape[1]
4030	dy = lon0.shape[0]
4031
4032	nlon = lon0[0,0]
4033	xlon = lon0[dy-1,dx-1]
4034	nlat = lat0[0,0]
4035	xlat = lat0[dy-1,dx-1]
4036
4037	# Thats too much! :)
4038	# if lonlatLims is not None:
4039	# print ' ' + fname + ': cutting the domain to plot !!!!'
4040	# plt.xlim(lonlatLims[0], lonlatLims[2])
4041	# plt.ylim(lonlatLims[1], lonlatLims[3])
4042	# print ' limits: W-E', lonlatLims[0], lonlatLims[2]
4043	# print ' limits: N-S', lonlatLims[1], lonlatLims[3]
4044
4045	# if map_proj == 'cyl':
4046	# nlon = lonlatLims[0]
4047	# nlat = lonlatLims[1]
4048	# xlon = lonlatLims[2]
4049	# xlat = lonlatLims[3]
4050	# elif map_proj == 'lcc':
4051	# lon2 = (lonlatLims[0] + lonlatLims[2])/2.
4052	# lat2 = (lonlatLims[1] + lonlatLims[3])/2.
4053	# nlon = lonlatLims[0]
4054	# xlon = lonlatLims[2]
4055	# nlat = lonlatLims[1]
4056	# xlat = lonlatLims[3]
4057
4058	lon2 = lon0[dy/2,dx/2]
4059	lat2 = lat0[dy/2,dx/2]
4060
4061	print 'lon2:', lon2, 'lat2:', lat2, 'SW pt:', nlon, ',', nlat, 'NE pt:', \
4062	xlon, ',', xlat
4063
4064	if map_proj == 'cyl':
4065	m = Basemap(projection=map_proj, llcrnrlon=nlon, llcrnrlat=nlat, \
4066	urcrnrlon=xlon, urcrnrlat= xlat, resolution=map_res)
4067	elif map_proj == 'lcc':
4068	m = Basemap(projection=map_proj, lat_0=lat2, lon_0=lon2, llcrnrlon=nlon, \
4069	llcrnrlat=nlat, urcrnrlon=xlon, urcrnrlat= xlat, resolution=map_res)
4070
4071	if len(dimxv.shape) == 1:
4072	lons, lats = np.meshgrid(dimxv, dimyv)
4073	else:
4074	if len(dimxv.shape) == 3:
4075	lons = dimxv[0,:,:]
4076	lats = dimyv[0,:,:]
4077	else:
4078	lons = dimxv[:]
4079	lats = dimyv[:]
4080
4081	x,y = m(lons,lats)
4082
4083	else:
4084	if reva0 == 'transpose':
4085	print ' reversing the axes of the figure (x-->y, y-->x)!!'
4086	varsv = np.transpose(varsv)
4087	varcv = np.transpose(varcv)
4088	dimn0 = []
4089	dimn0.append(dimn[1] + '')
4090	dimn0.append(dimn[0] + '')
4091	dimn = dimn0
4092	if len(dimyv.shape) == 2:
4093	x = np.transpose(dimyv)
4094	else:
4095	if len(dimxv.shape) == 2:
4096	ddx = len(dimyv)
4097	ddy = dimxv.shape[1]
4098	else:
4099	ddx = len(dimyv)
4100	ddy = len(dimxv)
4101
4102	x = np.zeros((ddy,ddx), dtype=np.float)
4103	for j in range(ddy):
4104	x[j,:] = dimyv
4105
4106	if len(dimxv.shape) == 2:
4107	y = np.transpose(dimxv)
4108	else:
4109	if len(dimyv.shape) == 2:
4110	ddx = dimyv.shape[0]
4111	ddy = len(dimxv)
4112	else:
4113	ddx = len(dimyv)
4114	ddy = len(dimxv)
4115
4116	y = np.zeros((ddy,ddx), dtype=np.float)
4117	for i in range(ddx):
4118	y[:,i] = dimxv
4119	else:
4120	if len(dimxv.shape) == 2:
4121	x = dimxv
4122	else:
4123	if len(dimyv.shape) == 1:
4124	x = np.zeros((len(dimyv),len(dimxv)), dtype=np.float)
4125	for j in range(len(dimyv)):
4126	x[j,:] = dimxv
4127	else:
4128	x = np.zeros((dimyv.shape), dtype=np.float)
4129	if x.shape[0] == dimxv.shape[0]:
4130	for j in range(x.shape[1]):
4131	x[:,j] = dimxv
4132	else:
4133	for j in range(x.shape[0]):
4134	x[j,:] = dimxv
4135
4136	if len(dimyv.shape) == 2:
4137	y = dimyv
4138	else:
4139	if len(dimxv.shape) == 1:
4140	y = np.zeros((len(dimyv),len(dimxv)), dtype=np.float)
4141	for i in range(len(dimxv)):
4142	y[:,i] = dimyv
4143	else:
4144	y = np.zeros((dimxv.shape), dtype=np.float)
4145	if y.shape[0] == dimyv.shape[0]:
4146	for i in range(y.shape[1]):
4147	y[:,i] = dimyv
4148	else:
4149	for i in range(y.shape[0]):
4150	y[i,:] = dimyv
4151
4152	dx=varsv.shape[1]
4153	dy=varsv.shape[0]
4154
4155	plt.rc('text', usetex=True)
4156
4157	if axist == 'x':
4158	valpos = pretty_int(y.min(),y.max(),10)
4159	vallabels = list(valpos)
4160	for i in range(len(vallabels)): vallabels[i] = str(vallabels[i])
4161	else:
4162	valpos = pretty_int(x.min(),x.max(),10)
4163	vallabels = list(valpos)
4164	for i in range(len(vallabels)): vallabels[i] = str(vallabels[i])
4165
4166	if reva0 == 'flip':
4167	if reva.split('@')[1] == 'x':
4168	varsv[:,0:dx-1] = varsv[:,dx-1:0:-1]
4169	varcv[:,0:dx-1] = varcv[:,dx-1:0:-1]
4170	plt.xticks(valpos, vallabels[::-1])
4171	else:
4172	varsv[0:dy-1,:] = varsv[dy-1:0:-1,:]
4173	varcv[0:dy-1,:] = varcv[dy-1:0:-1,:]
4174	plt.yticks(valpos, vallabels[::-1])
4175	else:
4176	plt.xlim(0,dx-1)
4177	plt.ylim(0,dy-1)
4178
4179	plt.pcolormesh(x, y, varsv, cmap=plt.get_cmap(colorbar), vmin=vs[0], vmax=vs[1])
4180	cbar = plt.colorbar()
4181
4182	# contour
4183	##
4184	contkind = ckind.split(',')[0]
4185	if contkind == 'cmap':
4186	cplot = plt.contour(x, y, varcv, levels=vc)
4187	elif contkind == 'fixc':
4188	plt.rcParams['contour.negative_linestyle'] = 'solid'
4189	coln = ckind.split(',')[1]
4190	cplot = plt.contour(x, y, varcv, levels=vc, colors=coln)
4191	elif contkind == 'fixsigc':
4192	coln = ckind.split(',')[1]
4193	cplot = plt.contour(x, y, varcv, levels=vc, colors=coln)
4194	else:
4195	print errormsg
4196	print ' ' + fname + ': contour kind "' + contkind + '" not defined !!!!!'
4197	quit(-1)
4198
4199	if clabfmt is not None:
4200	plt.clabel(cplot, fmt=clabfmt)
4201	mincntS = format(vc[0], clabfmt[1:len(clabfmt)])
4202	maxcntS = format(vc[len(vc)-1], clabfmt[1:len(clabfmt)])
4203	else:
4204	mincntS = '{:g}'.format(vc[0])
4205	maxcntS = '{:g}'.format(vc[len(vc)-1])
4206
4207	if not mapv is None:
4208	m.drawcoastlines()
4209
4210	meridians = pretty_int(nlon,xlon,5)
4211	m.drawmeridians(meridians,labels=[True,False,False,True])
4212	parallels = pretty_int(nlat,xlat,5)
4213	m.drawparallels(parallels,labels=[False,True,True,False])
4214
4215	plt.xlabel('W-E')
4216	plt.ylabel('S-N')
4217	else:
4218	if axist == 'x':
4219	plt.xlabel(timeu)
4220	plt.xticks(timpos, timlab)
4221	plt.ylabel(variables_values(dimn[0])[0] + ' (' + units_lunits(valu) + ')')
4222	plt.yticks(valpos, vallabels)
4223	else:
4224	plt.xlabel(variables_values(dimn[1])[0] + ' (' + units_lunits(valu) + ')')
4225	plt.xticks(valpos, vallabels)
4226	plt.ylabel(timeu)
4227	plt.yticks(timpos, timlab)
4228
4229	# set the limits of the plot to the limits of the data
4230	plt.axis([x.min(), x.max(), y.min(), y.max()])
4231
4232	# units labels
4233	cbar.set_label(vnames[0].replace('_','\_') + ' (' + units_lunits(uts[0]) + ')')
4234	plt.annotate(vnames[1].replace('_','\_') +' (' + units_lunits(uts[1]) + ') [' + \
4235	mincntS + ', ' + maxcntS + ']', xy=(0.55,0.04), xycoords='figure fraction', \
4236	color=coln)
4237
4238	figname = '2Dfields_shadow-contour'
4239	graphtit = vtit.replace('_','\_').replace('&','\&')
4240
4241	plt.title(graphtit)
4242
4243	output_kind(kfig, figname, True)
4244
4245	return
4246
4247	def dxdy_lonlat(dxv,dyv,ddx,ddy):
4248	""" Function to provide lon/lat 2D lilke-matrices from any sort of dx,dy values
4249	dxdy_lonlat(dxv,dyv,Lv,lv)
4250	dx: values for the x
4251	dy: values for the y
4252	ddx: ',' list of which dimensions to use from values along x
4253	ddy: ',' list of which dimensions to use from values along y
4254	"""
4255
4256	fname = 'dxdy_lonlat'
4257
4258	if ddx.find(',') > -1:
4259	dxk = 2
4260	ddxv = ddx.split(',')
4261	ddxy = int(ddxv[0])
4262	ddxx = int(ddxv[1])
4263	else:
4264	dxk = 1
4265	ddxy = int(ddx)
4266	ddxx = int(ddx)
4267
4268	if ddy.find(',') > -1:
4269	dyk = 2
4270	ddyv = ddy.split(',')
4271	ddyy = int(ddyv[0])
4272	ddyx = int(ddyv[1])
4273	else:
4274	dyk = 1
4275	ddyy = int(ddy)
4276	ddyx = int(ddy)
4277
4278	ddxxv = dxv.shape[ddxx]
4279	ddxyv = dxv.shape[ddxy]
4280	ddyxv = dyv.shape[ddyx]
4281	ddyyv = dyv.shape[ddyy]
4282
4283	slicex = []
4284	if len(dxv.shape) > 1:
4285	for idim in range(len(dxv.shape)):
4286	if idim == ddxx or idim == ddxy:
4287	slicex.append(slice(0,dxv.shape[idim]))
4288	else:
4289	slicex.append(0)
4290	else:
4291	slicex.append(slice(0,len(dxv)))
4292
4293	slicey = []
4294	if len(dyv.shape) > 1:
4295	for idim in range(len(dyv.shape)):
4296	if idim == ddyx or idim == ddyy:
4297	slicey.append(slice(0,dyv.shape[idim]))
4298	else:
4299	slicey.append(0)
4300	else:
4301	slicey.append(slice(0,len(dyv)))
4302
4303	# print ' ' + fname + ' Lluis shapes dxv:',dxv.shape,'dyv:',dyv.shape
4304	# print ' ' + fname + ' Lluis slicex:',slicex,'slicey:',slicey
4305
4306	if dxk == 2 and dyk == 2:
4307	if ddxxv != ddyxv:
4308	print errormsg
4309	print ' ' + fname + ': wrong dx dimensions! ddxx=',ddxxv,'ddyx=',ddyxv
4310	print ' choose another for x:',dxv.shape,'or y:',dyv.shape
4311	quit(-1)
4312	if ddxyv != ddyyv:
4313	print errormsg
4314	print ' ' + fname + ': wrong dy dimensions! ddxy=',ddxyv,'ddyy=',ddyv
4315	print ' choose another for x:',dxv.shape,'or y:',dyv.shape
4316	quit(-1)
4317	dx = ddxxv
4318	dy = ddxyv
4319
4320	print ' ' + fname + ': final dimension 2D lon/lat-like matrices:',dy,',',dx
4321	lonv = np.zeros((dy,dx), dtype=np.float)
4322	latv = np.zeros((dy,dx), dtype=np.float)
4323
4324
4325	lonv = dxv[tuple(slicex)]
4326	latv = dyv[tuple(slicey)]
4327
4328	elif dxk == 2 and dyk == 1:
4329	if not ddxxv == ddyxv and not ddxyv == ddyyv:
4330	print errormsg
4331	print ' ' + fname + ': wrong dimensions! ddxx=',ddxxv,'ddyx=',ddyxv, \
4332	'ddyx=',ddyxv,'ddyy=',ddyyv
4333	print ' choose another for x:',dxv.shape,'or y:',dyv.shape
4334	quit(-1)
4335	dx = ddxvv
4336	dy = ddxyv
4337
4338	print ' ' + fname + ': final dimension 2D lon/lat-like matrices:',dy,',',dx
4339	lonv = np.zeros((dy,dx), dtype=np.float)
4340	latv = np.zeros((dy,dx), dtype=np.float)
4341	lonv = dxv[tuple(slicex)]
4342
4343	if ddxxv == ddyxv:
4344	for iy in range(dy):
4345	latv[iy,:] = dyv[tuple(slicey)]
4346	else:
4347	for ix in range(dx):
4348	latv[:,ix] = dyv[tuple(slicey)]
4349
4350	elif dxk == 1 and dyk == 2:
4351	if not ddxxv == ddyxv and not ddxyv == ddyyv:
4352	print errormsg
4353	print ' ' + fname + ': wrong dimensions! ddxx=',ddxxv,'ddyx=',ddyxv, \
4354	'ddyx=',ddyxv,'ddyy=',ddyyv
4355	print ' choose another for x:',dxv.shape,'or y:',dyv.shape
4356	quit(-1)
4357	dx = ddyxv
4358	dy = ddyyv
4359
4360	print ' ' + fname + ': final dimension 2D lon/lat-like matrices:',dy,',',dx
4361	lonv = np.zeros((dy,dx), dtype=np.float)
4362	latv = np.zeros((dy,dx), dtype=np.float)
4363
4364	latv = dyv[tuple(slicey)]
4365
4366	if ddyxv == ddxxv:
4367	for iy in range(dy):
4368	lonv[iy,:] = dxv[tuple(slicex)]
4369	else:
4370	for ix in range(dx):
4371	lonv[:,ix] = dxv[tuple(slicex)]
4372
4373
4374	elif dxk == 1 and dyk == 1:
4375	dx = ddxxv
4376	dy = ddyyv
4377
4378	# print 'dx:',dx,'dy:',dy
4379
4380	lonv = np.zeros((dy,dx), dtype=np.float)
4381	latv = np.zeros((dy,dx), dtype=np.float)
4382
4383	for iy in range(dy):
4384	lonv[iy,:] = dxv[tuple(slicex)]
4385	for ix in range(dx):
4386	latv[:,ix] = dyv[tuple(slicey)]
4387
4388	return lonv,latv
4389
4390	def plot_2D_shadow_line(varsv,varlv,vnames,vnamel,dimxv,dimyv,dimxu,dimyu,dimn, \
4391	colorbar,colln,vs,uts,utl,vtit,kfig,reva,mapv,ifclose):
4392	""" Plotting a 2D field with shadows and another one with a line
4393	varsv= 2D values to plot with shading
4394	varlv= 1D values to plot with line
4395	vnames= variable names for the shadow variable in the figure
4396	vnamel= variable names for the line varibale in the figure
4397	dim[x/y]v = values at the axes of x and y
4398	dim[x/y]u = units at the axes of x and y
4399	dimn= dimension names to plot
4400	colorbar= name of the color bar to use
4401	colln= color for the line
4402	vs= minmum and maximum values to plot in shadow
4403	uts= units of the variable to shadow
4404	utl= units of the variable to line
4405	vtit= title of the variable
4406	kfig= kind of figure (jpg, pdf, png)
4407	reva=
4408	* 'transpose': reverse the axes (x-->y, y-->x)
4409	* 'flip'@[x/y]: flip the axis x or y
4410	mapv= map characteristics: [proj],[res]
4411	see full documentation: http://matplotlib.org/basemap/
4412	[proj]: projection
4413	* 'cyl', cilindric
4414	* 'lcc', lambert conformal
4415	[res]: resolution:
4416	* 'c', crude
4417	* 'l', low
4418	* 'i', intermediate
4419	* 'h', high
4420	* 'f', full
4421	ifclose= boolean value whether figure should be close (finish) or not
4422	"""
4423	## import matplotlib as mpl
4424	## mpl.use('Agg')
4425	## import matplotlib.pyplot as plt
4426	fname = 'plot_2D_shadow_line'
4427
4428	if varsv == 'h':
4429	print fname + '_____________________________________________________________'
4430	print plot_2D_shadow_line.__doc__
4431	quit()
4432
4433	if reva[0:4] == 'flip':
4434	reva0 = 'flip'
4435	if len(reva.split('@')) != 2:
4436	print errormsg
4437	print ' ' + fname + ': flip is given', reva, 'but not axis!'
4438	quit(-1)
4439	else:
4440	reva0 = reva
4441
4442	if reva0 == 'transpose':
4443	print ' reversing the axes of the figure (x-->y, y-->x)!!'
4444	varsv = np.transpose(varsv)
4445	dxv = dimyv
4446	dyv = dimxv
4447	dimxv = dxv
4448	dimyv = dyv
4449
4450	if len(dimxv[:].shape) == 3:
4451	lon0 = dimxv[0,]
4452	elif len(dimxv[:].shape) == 2:
4453	lon0 = dimxv[:]
4454
4455	if len(dimyv[:].shape) == 3:
4456	lat0 = dimyv[0,]
4457	elif len(dimyv[:].shape) == 2:
4458	lat0 = dimyv[:]
4459
4460	if len(dimxv[:].shape) == 1 and len(dimyv[:].shape) == 1:
4461	lon00 = dimxv[:]
4462	lon0 = np.zeros( (len(lat00),len(lon00)), dtype=np.float )
4463
4464	for iy in range(len(lat00)):
4465	lon0[iy,:] = lon00
4466	for ix in range(len(lon00)):
4467	lat0[:,ix] = lat00
4468
4469	if not mapv is None:
4470	map_proj=mapv.split(',')[0]
4471	map_res=mapv.split(',')[1]
4472
4473	dx = lon0.shape[1]
4474	dy = lat0.shape[0]
4475
4476	nlon = lon0[0,0]
4477	xlon = lon0[dy-1,dx-1]
4478	nlat = lat0[0,0]
4479	xlat = lat0[dy-1,dx-1]
4480
4481	# Thats too much! :)
4482	# if lonlatLims is not None:
4483	# print ' ' + fname + ': cutting the domain to plot !!!!'
4484	# plt.xlim(lonlatLims[0], lonlatLims[2])
4485	# plt.ylim(lonlatLims[1], lonlatLims[3])
4486	# print ' limits: W-E', lonlatLims[0], lonlatLims[2]
4487	# print ' limits: N-S', lonlatLims[1], lonlatLims[3]
4488
4489	# if map_proj == 'cyl':
4490	# nlon = lonlatLims[0]
4491	# nlat = lonlatLims[1]
4492	# xlon = lonlatLims[2]
4493	# xlat = lonlatLims[3]
4494	# elif map_proj == 'lcc':
4495	# lon2 = (lonlatLims[0] + lonlatLims[2])/2.
4496	# lat2 = (lonlatLims[1] + lonlatLims[3])/2.
4497	# nlon = lonlatLims[0]
4498	# xlon = lonlatLims[2]
4499	# nlat = lonlatLims[1]
4500	# xlat = lonlatLims[3]
4501
4502	lon2 = lon0[dy/2,dx/2]
4503	lat2 = lat0[dy/2,dx/2]
4504
4505	print 'lon2:', lon2, 'lat2:', lat2, 'SW pt:', nlon, ',', nlat, 'NE pt:', \
4506	xlon, ',', xlat
4507
4508	if map_proj == 'cyl':
4509	m = Basemap(projection=map_proj, llcrnrlon=nlon, llcrnrlat=nlat, \
4510	urcrnrlon=xlon, urcrnrlat= xlat, resolution=map_res)
4511	elif map_proj == 'lcc':
4512	m = Basemap(projection=map_proj, lat_0=lat2, lon_0=lon2, llcrnrlon=nlon, \
4513	llcrnrlat=nlat, urcrnrlon=xlon, urcrnrlat= xlat, resolution=map_res)
4514	else:
4515	print errormsg
4516	print ' ' + fname + ": map projection '" + map_proj + "' not defined!!!"
4517	print ' available: cyl, lcc'
4518	quit(-1)
4519
4520	if len(dimxv.shape) == 1:
4521	lons, lats = np.meshgrid(dimxv, dimyv)
4522	else:
4523	if len(dimxv.shape) == 3:
4524	lons = dimxv[0,:,:]
4525	else:
4526	lons = dimxv[:]
4527
4528	if len(dimyv.shape) == 3:
4529	lats = dimyv[0,:,:]
4530	else:
4531	lats = dimyv[:]
4532
4533	x,y = m(lons,lats)
4534
4535	else:
4536	if len(dimxv.shape) == 3:
4537	x = dimxv[0,:,:]
4538	elif len(dimxv.shape) == 2:
4539	x = dimxv
4540	else:
4541	# Attempt of simplier way...
4542	# x = np.zeros((lon0.shape), dtype=np.float)
4543	# for j in range(lon0.shape[0]):
4544	# x[j,:] = dimxv
4545
4546	## This way is too complicated and maybe not necessary ? (assuming dimxv.shape == dimyv.shape)
4547	if len(dimyv.shape) == 1:
4548	x = np.zeros((len(dimyv),len(dimxv)), dtype=np.float)
4549	for j in range(len(dimxv)):
4550	x[j,:] = dimxv
4551	else:
4552	x = np.zeros((dimyv.shape), dtype=np.float)
4553	if x.shape[0] == dimxv.shape[0]:
4554	for j in range(x.shape[1]):
4555	x[:,j] = dimxv
4556	else:
4557	for j in range(x.shape[0]):
4558	x[j,:] = dimxv
4559
4560	if len(dimyv.shape) == 3:
4561	y = dimyv[0,:,:]
4562	elif len(dimyv.shape) == 2:
4563	y = dimyv
4564	else:
4565	# y = np.zeros((lat0.shape), dtype=np.float)
4566	# for i in range(lat0.shape[1]):
4567	# x[:,i] = dimyv
4568
4569	# Idem
4570	if len(dimxv.shape) == 1:
4571	y = np.zeros((len(dimyv),len(dimxv)), dtype=np.float)
4572	for i in range(len(dimxv)):
4573	y[:,i] = dimyv
4574	else:
4575	y = np.zeros((dimxv.shape), dtype=np.float)
4576	if y.shape[0] == dimyv.shape[0]:
4577	for i in range(y.shape[1]):
4578	y[:,i] = dimyv
4579	else:
4580	for j in range(y.shape[0]):
4581	y[j,:] = dimyv
4582
4583	plt.rc('text', usetex=True)
4584
4585	plt.pcolormesh(x, y, varsv, cmap=plt.get_cmap(colorbar), vmin=vs[0], vmax=vs[1])
4586	cbar = plt.colorbar()
4587
4588	if not mapv is None:
4589	m.drawcoastlines()
4590
4591	meridians = pretty_int(nlon,xlon,5)
4592	m.drawmeridians(meridians,labels=[True,False,False,True])
4593	parallels = pretty_int(nlat,xlat,5)
4594	m.drawparallels(parallels,labels=[False,True,True,False])
4595
4596	plt.xlabel('W-E')
4597	plt.ylabel('S-N')
4598	else:
4599	plt.xlabel(variables_values(dimn[1])[0] + ' (' + units_lunits(dimxu) + ')')
4600	plt.ylabel(variables_values(dimn[0])[0] + ' (' + units_lunits(dimyu) + ')')
4601
4602	# Line
4603	##
4604
4605	if reva0 == 'flip' and reva.split('@')[1] == 'y':
4606	b=-np.max(y[0,:])/np.max(varlv)
4607	a=np.max(y[0,:])
4608	else:
4609	b=np.max(y[0,:])/np.max(varlv)
4610	a=0.
4611
4612	newlinv = varlv*b+a
4613	if reva0 == 'transpose':
4614	plt.plot(newlinv, x[0,:], '-', color=colln, linewidth=2)
4615	else:
4616	plt.plot(x[0,:], newlinv, '-', color=colln, linewidth=2)
4617
4618	txpos = pretty_int(x.min(),x.max(),10)
4619	typos = pretty_int(y.min(),y.max(),10)
4620	txlabels = list(txpos)
4621	for i in range(len(txlabels)): txlabels[i] = str(txlabels[i])
4622	tylabels = list(typos)
4623	for i in range(len(tylabels)): tylabels[i] = str(tylabels[i])
4624
4625	tllabels = pretty_int(np.min(varlv),np.max(varlv),len(txlabels))
4626	for it in range(len(tllabels)):
4627	yval = (tllabels[it]*b+a)
4628	plt.plot([x.max()*0.97, x.max()], [yval, yval], '-', color='k')
4629	plt.annotate(tllabels[it], xy=(1.01,tllabels[it]/np.max(varlv)), \
4630	xycoords='axes fraction')
4631
4632	# set the limits of the plot to the limits of the data
4633	if reva0 == 'flip':
4634	if reva.split('@')[1] == 'x':
4635	plt.axis([x.max(), x.min(), y.min(), y.max()])
4636	else:
4637	plt.axis([x.min(), x.max(), y.max(), y.min()])
4638	else:
4639	plt.axis([x.min(), x.max(), y.min(), y.max()])
4640
4641	plt.tick_params(axis='y',right='off')
4642	if mapv is None:
4643	plt.xticks(txpos, txlabels)
4644	plt.yticks(typos, tylabels)
4645
4646	tllabels = pretty_int(np.min(varlv),np.max(varlv),len(txlabels))
4647	for it in range(len(tllabels)):
4648	plt.annotate(tllabels[it], xy=(1.01,tllabels[it]/np.max(varlv)), xycoords='axes fraction')
4649
4650	# units labels
4651	cbar.set_label(vnames.replace('_','\_') + ' (' + units_lunits(uts) + ')')
4652
4653	plt.annotate(vnamel +' (' + units_lunits(utl) + ')', xy=(0.75,0.04),
4654	xycoords='figure fraction', color=colln)
4655	figname = '2Dfields_shadow_line'
4656	graphtit = vtit.replace('_','\_').replace('&','\&')
4657
4658	plt.title(graphtit)
4659
4660	output_kind(kfig, figname, ifclose)
4661
4662	return
4663
4664	def plot_Neighbourghood_evol(varsv, dxv, dyv, vnames, ttits, tpos, tlabels, colorbar, \
4665	Nng, vs, uts, gtit, kfig, ifclose):
4666	""" Plotting neighbourghood evolution
4667	varsv= 2D values to plot with shading
4668	vnames= shading variable name for the figure
4669	d[x/y]v= values at the axes of x and y
4670	ttits= titles of both time axis
4671	tpos= positions of the time ticks
4672	tlabels= labels of the time ticks
4673	colorbar= name of the color bar to use
4674	Nng= Number of grid points of the full side of the box (odd value)
4675	vs= minmum and maximum values to plot in shadow or:
4676	'Srange': for full range
4677	'Saroundmean@val': for mean-xtrm,mean+xtrm where xtrm = np.min(mean-min@val,max@val-mean)
4678	'Saroundminmax@val': for minval,maxval
4679	'Saroundpercentile@val': for median-xtrm,median+xtrm where xtrm = np.min(median-percentile_(val),
4680	percentile_(100-val)-median)
4681	'Smean@val': for -xtrm,xtrm where xtrm = np.min(mean-min@val,max@val-mean)
4682	'Smedian@val': for -xtrm,xtrm where xtrm = np.min(median-min@val,max@val-median)
4683	'Spercentile@val': for -xtrm,xtrm where xtrm = np.min(median-percentile_(val),
4684	percentile_(100-val)-median)
4685	uts= units of the variable to shadow
4686	gtit= title of the graph
4687	kfig= kind of figure (jpg, pdf, png)
4688	ifclose= boolean value whether figure should be close (finish) or not
4689	"""
4690	import numpy.ma as ma
4691
4692	fname = 'plot_Neighbourghood_evol'
4693
4694	if varsv == 'h':
4695	print fname + '_____________________________________________________________'
4696	print plot_Neighbourghood_evol.__doc__
4697	quit()
4698
4699	if len(varsv.shape) != 2:
4700	print errormsg
4701	print ' ' + fname + ': wrong number of dimensions of the values: ', \
4702	varsv.shape
4703	quit(-1)
4704
4705	varsvmask = ma.masked_equal(varsv,fillValue)
4706
4707	vsend = np.zeros((2), dtype=np.float)
4708	# Changing limits of the colors
4709	if type(vs[0]) != type(np.float(1.)):
4710	if vs[0] == 'Srange':
4711	vsend[0] = np.min(varsvmask)
4712	elif vs[0][0:11] == 'Saroundmean':
4713	meanv = np.mean(varsvmask)
4714	permean = np.float(vs[0].split('@')[1])
4715	minv = np.min(varsvmask)*permean
4716	maxv = np.max(varsvmask)*permean
4717	minextrm = np.min([np.abs(meanv-minv), np.abs(maxv-meanv)])
4718	vsend[0] = meanv-minextrm
4719	vsend[1] = meanv+minextrm
4720	elif vs[0][0:13] == 'Saroundminmax':
4721	permean = np.float(vs[0].split('@')[1])
4722	minv = np.min(varsvmask)*permean
4723	maxv = np.max(varsvmask)*permean
4724	vsend[0] = minv
4725	vsend[1] = maxv
4726	elif vs[0][0:17] == 'Saroundpercentile':
4727	medianv = np.median(varsvmask)
4728	valper = np.float(vs[0].split('@')[1])
4729	minv = np.percentile(varsvmask, valper)
4730	maxv = np.percentile(varsvmask, 100.-valper)
4731	minextrm = np.min([np.abs(medianv-minv), np.abs(maxv-medianv)])
4732	vsend[0] = medianv-minextrm
4733	vsend[1] = medianv+minextrm
4734	elif vs[0][0:5] == 'Smean':
4735	meanv = np.mean(varsvmask)
4736	permean = np.float(vs[0].split('@')[1])
4737	minv = np.min(varsvmask)*permean
4738	maxv = np.max(varsvmask)*permean
4739	minextrm = np.min([np.abs(meanv-minv), np.abs(maxv-meanv)])
4740	vsend[0] = -minextrm
4741	vsend[1] = minextrm
4742	elif vs[0][0:7] == 'Smedian':
4743	medianv = np.median(varsvmask)
4744	permedian = np.float(vs[0].split('@')[1])
4745	minv = np.min(varsvmask)*permedian
4746	maxv = np.max(varsvmask)*permedian
4747	minextrm = np.min([np.abs(medianv-minv), np.abs(maxv-medianv)])
4748	vsend[0] = -minextrm
4749	vsend[1] = minextrm
4750	elif vs[0][0:11] == 'Spercentile':
4751	medianv = np.median(varsvmask)
4752	valper = np.float(vs[0].split('@')[1])
4753	minv = np.percentile(varsvmask, valper)
4754	maxv = np.percentile(varsvmask, 100.-valper)
4755	minextrm = np.min([np.abs(medianv-minv), np.abs(maxv-medianv)])
4756	vsend[0] = -minextrm
4757	vsend[1] = minextrm
4758	else:
4759	print errormsg
4760	print ' ' + fname + ": range '" + vs[0] + "' not ready!!!"
4761	quit(-1)
4762	print ' ' + fname + ': modified shadow min,max:',vsend
4763	else:
4764	vsend[0] = vs[0]
4765
4766	if type(vs[0]) != type(np.float(1.)):
4767	if vs[1] == 'range':
4768	vsend[1] = np.max(varsv)
4769	else:
4770	vsend[1] = vs[1]
4771
4772	plt.rc('text', usetex=True)
4773
4774	# plt.pcolormesh(dxv, dyv, varsv, cmap=plt.get_cmap(colorbar), vmin=vsend[0], vmax=vsend[1])
4775	plt.pcolormesh(varsvmask, cmap=plt.get_cmap(colorbar), vmin=vsend[0], vmax=vsend[1])
4776	cbar = plt.colorbar()
4777
4778	newtposx = (tpos[0][:] - np.min(dxv)) * len(dxv) * Nng / (np.max(dxv) - np.min(dxv))
4779	newtposy = (tpos[1][:] - np.min(dyv)) * len(dyv) * Nng / (np.max(dyv) - np.min(dyv))
4780
4781	plt.xticks(newtposx, tlabels[0])
4782	plt.yticks(newtposy, tlabels[1])
4783	plt.xlabel(ttits[0])
4784	plt.ylabel(ttits[1])
4785
4786	plt.axes().set_aspect('equal')
4787	# From: http://stackoverflow.com/questions/14406214/moving-x-axis-to-the-top-of-a-plot-in-matplotlib
4788	plt.axes().xaxis.tick_top
4789	plt.axes().xaxis.set_ticks_position('top')
4790
4791	# units labels
4792	cbar.set_label(vnames.replace('_','\_') + ' (' + units_lunits(uts) + ')')
4793
4794	figname = 'Neighbourghood_evol'
4795	graphtit = gtit.replace('_','\_').replace('&','\&')
4796
4797	plt.title(graphtit, position=(0.5,1.05))
4798
4799	output_kind(kfig, figname, ifclose)
4800
4801	return
4802
4803	def plot_lines(vardv, varvv, vaxis, dtit, linesn, vtit, vunit, gtit, gloc, kfig):
4804	""" Function to plot a collection of lines
4805	vardv= list of set of dimension values
4806	varvv= list of set of values
4807	vaxis= which axis will be used for the values ('x', or 'y')
4808	dtit= title for the common dimension
4809	linesn= names for the legend
4810	vtit= title for the vaxis
4811	vunit= units of the vaxis
4812	gtit= main title
4813	gloc= location of the legend (-1, autmoatic)
4814	1: 'upper right', 2: 'upper left', 3: 'lower left', 4: 'lower right',
4815	5: 'right', 6: 'center left', 7: 'center right', 8: 'lower center',
4816	9: 'upper center', 10: 'center'
4817	kfig= kind of figure
4818	plot_lines([np.arange(10)], [np.sin(np.arange(10)*np.pi/2.5)], 'y', 'time (s)', \
4819	['2.5'], 'sin', '-', 'sinus frequency dependency', 'pdf')
4820	"""
4821	fname = 'plot_lines'
4822
4823	if vardv == 'h':
4824	print fname + '_____________________________________________________________'
4825	print plot_lines.__doc__
4826	quit()
4827
4828	# Canging line kinds every 7 lines (end of standard colors)
4829	linekinds=['.-','x-','o-']
4830
4831	Ntraj = len(vardv)
4832
4833	N7lines = 0
4834
4835	plt.rc('text', usetex=True)
4836
4837	if vaxis == 'x':
4838	for il in range(Ntraj):
4839	plt.plot(varvv[il], vardv[il], linekinds[N7lines], label= linesn[il])
4840	if il == 6: N7lines = N7lines + 1
4841
4842	plt.xlabel(vtit + ' (' + vunit + ')')
4843	plt.ylabel(dtit)
4844	plt.xlim(np.min(varvv[:]),np.max(varvv[:]))
4845	plt.ylim(np.min(vardv[:]),np.max(vardv[:]))
4846
4847	else:
4848	for il in range(Ntraj):
4849	plt.plot(vardv[il], varvv[il], linekinds[N7lines], label= linesn[il])
4850	if il == 6: N7lines = N7lines + 1
4851
4852	plt.xlabel(dtit)
4853	plt.ylabel(vtit + ' (' + vunit + ')')
4854	plt.xlim(np.min(vardv[:]),np.max(vardv[:]))
4855	plt.ylim(np.min(varvv[:]),np.max(varvv[:]))
4856
4857	figname = 'lines'
4858	graphtit = gtit.replace('_','\_').replace('&','\&')
4859
4860	plt.title(graphtit)
4861	plt.legend(loc=gloc)
4862
4863	output_kind(kfig, figname, True)
4864
4865	return
4866
4867	def plot_lines_time(vardv, varvv, vaxis, dtit, linesn, vtit, vunit, tpos, tlabs, \
4868	gtit, gloc, kfig):
4869	""" Function to plot a collection of lines with a time axis
4870	vardv= list of set of dimension values
4871	varvv= list of set of values
4872	vaxis= which axis will be used for the time values ('x', or 'y')
4873	dtit= title for the common dimension
4874	linesn= names for the legend
4875	vtit= title for the vaxis
4876	vunit= units of the vaxis
4877	tpos= positions of the time ticks
4878	tlabs= labels of the time ticks
4879	gtit= main title
4880	gloc= location of the legend (-1, autmoatic)
4881	1: 'upper right', 2: 'upper left', 3: 'lower left', 4: 'lower right',
4882	5: 'right', 6: 'center left', 7: 'center right', 8: 'lower center',
4883	9: 'upper center', 10: 'center'
4884	kfig= kind of figure
4885	plot_lines([np.arange(10)], [np.sin(np.arange(10)*np.pi/2.5)], 'y', 'time (s)', \
4886	['2.5'], 'sin', '-', 'sinus frequency dependency', 'pdf')
4887	"""
4888	fname = 'plot_lines'
4889
4890	if vardv == 'h':
4891	print fname + '_____________________________________________________________'
4892	print plot_lines.__doc__
4893	quit()
4894
4895	# Canging line kinds every 7 lines (end of standard colors)
4896	linekinds=['.-','x-','o-']
4897
4898	Ntraj = len(vardv)
4899
4900	N7lines = 0
4901
4902	plt.rc('text', usetex=True)
4903	varTvv = []
4904	varTdv = []
4905
4906	if vaxis == 'x':
4907	for il in range(Ntraj):
4908	plt.plot(varvv[il], vardv[il], linekinds[N7lines], label= linesn[il])
4909	varTvv = varTvv + list(varvv[il])
4910	varTdv = varTdv + list(vardv[il])
4911	if il == 6: N7lines = N7lines + 1
4912
4913	plt.xlabel(vtit + ' (' + vunit + ')')
4914	plt.ylabel(dtit)
4915	plt.xlim(np.min(varTvv),np.max(varTvv))
4916	plt.ylim(np.min(varTdv),np.max(varTdv))
4917	plt.yticks(tpos, tlabs)
4918	else:
4919	for il in range(Ntraj):
4920	plt.plot(vardv[il], varvv[il], linekinds[N7lines], label= linesn[il])
4921	varTvv = varTvv + list(varvv[il])
4922	varTdv = varTdv + list(vardv[il])
4923	if il == 6: N7lines = N7lines + 1
4924
4925	plt.xlabel(dtit)
4926	plt.ylabel(vtit + ' (' + vunit + ')')
4927
4928	plt.xlim(np.min(varTdv),np.max(varTdv))
4929	plt.ylim(np.min(varTvv),np.max(varTvv))
4930	plt.xticks(tpos, tlabs)
4931
4932	figname = 'lines_time'
4933	graphtit = gtit.replace('_','\_').replace('&','\&')
4934
4935	plt.title(graphtit)
4936	plt.legend(loc=gloc)
4937
4938	output_kind(kfig, figname, True)
4939
4940	return

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