schain-jc Commit - r1021:2aa9b10a9a70

Operation MAD2Writer done Task #343

Juan C. Espinoza -

r1021:2aa9b10a9a70

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r1021:2aa9b10a9a70

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schainpy/model/io/__init__.py +2 0

             from jroIO_param import *
             from jroIO_hf import *
+            from jroIO_madrigal import *
             from bltrIO_param import *
             from jroIO_bltr import *
             from jroIO_mira35c import *

schainpy/model/io/bltrIO_param.py +1 -3

                     self.dataOut.data_output = self.buffer
                     self.dataOut.utctimeInit = self.time
                     self.dataOut.utctime = self.dataOut.utctimeInit
-                    self.dataOut.counter_records = self.counter_records
-                    self.dataOut.nrecords = self.nrecords
                     self.dataOut.useLocalTime = False
                     self.dataOut.paramInterval = 157
                     self.dataOut.timezone = self.timezone
                     self.dataOut.site = self.siteFile
-                    self.dataOut.nrecords = self.nrecords
+                    self.dataOut.nrecords = self.nrecords/self.nmodes
                     self.dataOut.sizeOfFile = self.sizeOfFile
                     self.dataOut.lat = self.lat
                     self.dataOut.lon = self.lon

schainpy/model/io/jroIO_madrigal.py +145 -277

@@ -7,124 +7,146 Created on Aug 1, 2017
7	import os	7	import os
8	import sys	8	import sys
9	import time	9	import time
		10	import json
10	import datetime	11	import datetime
11		12
12	import numpy	13	import numpy
13		14
14	from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation
15	from schainpy.model.data.jrodata import Parameters
16	from schainpy.model.data.jroheaderIO import RadarControllerHeader, SystemHeader
17	from schainpy.model.graphics.jroplot_parameters import WindProfilerPlot
18	from schainpy.model.io.jroIO_base import *
19
20	try:	15	try:
21	import madrigal	16	import madrigal
22	import madrigal.cedar	17	import madrigal.cedar
23	from madrigal.cedar import MadrigalCatalogRecord
24	except:	18	except:
25	print 'You should install "madrigal library" module if you want to read/write Madrigal data'	19	print 'You should install "madrigal library" module if you want to read/write Madrigal data'
26		20
		21	from schainpy.model.proc.jroproc_base import Operation
		22	from schainpy.model.data.jrodata import Parameters
		23
		24	MISSING = -32767
		25	DEF_CATALOG = {
		26	'principleInvestigator': 'Marco Milla',
		27	'expPurpose': None,
		28	'expMode': None,
		29	'cycleTime': None,
		30	'correlativeExp': None,
		31	'sciRemarks': None,
		32	'instRemarks': None
		33	}
		34	DEF_HEADER = {
		35	'kindatDesc': None,
		36	'analyst': 'Jicamarca User',
		37	'comments': None,
		38	'history': None
		39	}
		40	MNEMONICS = {
		41	10: 'jro',
		42	11: 'jbr',
		43	840: 'jul',
		44	13: 'jas',
		45	1000: 'pbr',
		46	1001: 'hbr',
		47	1002: 'obr',
		48	}
		49
		50	def load_json(obj):
		51	'''
		52	Parse json as string instead of unicode
		53	'''
		54
		55	if isinstance(obj, str):
		56	obj = json.loads(obj)
		57
		58	return {str(k): load_json(v) if isinstance(v, dict) else str(v) if isinstance(v, unicode) else v
		59	for k, v in obj.items()}
27		60
28	class MADWriter(Operation):
29		61
30	def __init__(self):	62	class MAD2Writer(Operation):
31		63
32	Operation.__init__(self)	64	def __init__(self, **kwargs):
		65
		66	Operation.__init__(self, **kwargs)
33	self.dataOut = Parameters()	67	self.dataOut = Parameters()
34	self.path = None	68	self.path = None
35	self.dataOut = None	69	self.dataOut = None
36	self.flagIsNewFile=1	70	self.ext = '.dat'
37	self.ext = ".hdf5"
38		71
39	return	72	return
40		73
41	def run(self, dataOut, path , ~~modetowrite~~,**kwargs):	74	def run(self, dataOut, path, oneDList, twoDParam='', twoDList='{}', metadata='{}', **kwargs):
42		75	'''
43	if self.flagIsNewFile:	76	Inputs:
44	flagdata = self.setup(dataOut, path, modetowrite)	77	path - path where files will be created
		78	oneDList - json of one-dimensional parameters in record where keys
		79	are Madrigal codes (integers or mnemonics) and values the corresponding
		80	dataOut attribute e.g: {
		81	'gdlatr': 'lat',
		82	'gdlonr': 'lon',
		83	'gdlat2':'lat',
		84	'glon2':'lon'}
		85	twoDParam - independent parameter to get the number of rows e.g:
		86	heighList
		87	twoDList - json of two-dimensional parameters in record where keys
		88	are Madrigal codes (integers or mnemonics) and values the corresponding
		89	dataOut attribute if multidimensional array specify as tupple
		90	('attr', pos) e.g: {
		91	'gdalt': 'heightList',
		92	'vn1p2': ('data_output', 0),
		93	'vn2p2': ('data_output', 1),
		94	'vn3': ('data_output', 2),
		95	'snl': ('data_SNR', 'db')
		96	}
		97	metadata - json of madrigal metadata (kinst, kindat, catalog and header)
		98	'''
		99	if not self.isConfig:
		100	self.setup(dataOut, path, oneDList, twoDParam, twoDList, metadata, **kwargs)
		101	self.isConfig = True
45		102
46	self.putData()	103	self.putData()
47	return	104	return
48		105
49	def setup(self, dataOut, path, ~~modetowrite~~):	106	def setup(self, dataOut, path, oneDList, twoDParam, twoDList, metadata, **kwargs):
50	'''	107	'''
51	Recovering data to write in new *.hdf5 file	108	Configure Operation
52	Inputs:
53	modew -- mode to write (1 or 2)
54	path -- destination path
55
56	'''	109	'''
57		110
58	self.im = modetowrite-1
59	if self.im!=0 and self.im!=1:
60	raise ValueError, 'Check "modetowrite" value. Must be egual to 1 or 2, "{}" is not valid. '.format(modetowrite)
61
62	self.dataOut = dataOut	111	self.dataOut = dataOut
63	self.nmodes = self.dataOut.nmodes	112	self.nmodes = self.dataOut.nmodes
64	self.nchannels = self.dataOut.nchannels
65	self.lat = self.dataOut.lat
66	self.lon = self.dataOut.lon
67	self.hcm = 3
68	self.thisDate = self.dataOut.utctimeInit
69	self.year = self.dataOut.year
70	self.month = self.dataOut.month
71	self.day = self.dataOut.day
72	self.path = path	113	self.path = path
		114	self.blocks = kwargs.get('blocks', None)
		115	self.counter = 0
		116	self.oneDList = load_json(oneDList)
		117	self.twoDList = load_json(twoDList)
		118	self.twoDParam = twoDParam
		119	meta = load_json(metadata)
		120	self.kinst = meta.get('kinst')
		121	self.kindat = meta.get('kindat')
		122	self.catalog = meta.get('catalog', DEF_CATALOG)
		123	self.header = meta.get('header', DEF_HEADER)
73		124
74	self.flagIsNewFile = 0	125	return
75
76	return 1
77		126
78	def setFile(self):	127	def setFile(self):
79	'''	128	'''
80	- Determining the file name for each mode of operation	129	Create new cedar file object
81	kinst - Kind of Instrument (mnemotic)
82	kindat - Kind of Data (mnemotic)
83
84	- Creating a cedarObject
85
86	'''	130	'''
87	lat_piura = -5.17
88	lat_huancayo = -12.04
89	lat_porcuya = -5.8
90
91	if '%2.2f' % self.lat == '%2.2f' % lat_piura:
92	self.instMnemonic = 'pbr'
93
94	elif '%2.2f' % self.lat == '%2.2f' % lat_huancayo:
95	self.instMnemonic = 'hbr'
96		131
97	elif '%2.2f' % self.lat == '%2.2f' % lat_porcuya:	132	self.mnemonic = MNEMONICS[self.kinst] #TODO get mnemonic from madrigal
98	self.instMnemonic = 'obr'	133	date = datetime.datetime.utcfromtimestamp(self.dataOut.utctime)
99	else: raise Warning, "The site of file read doesn't match any site known. Only file from Huancayo, Piura and Porcuya can be processed.\n Check the file "
100		134
101	mode = ['_mode1','_mode2']	135	filename = '%s%s_%s%s' % (self.mnemonic,
102		136	date.strftime('%Y%m%d_%H%M%S'),
103	self.hdf5filename = '%s%4.4d%2.2d%2.2d%s%s' % (self.instMnemonic,	137	self.dataOut.mode,
104	self.year,
105	self.month,
106	self.day,
107	mode[self.im],
108	self.ext)	138	self.ext)
109		139
110	self.fullname=os.path.join(self.path,~~self~~.~~hdf5~~filename)	140	self.fullname = os.path.join(self.path, filename)
111		141
112	if os.path.isfile(self.fullname) :	142	if os.path.isfile(self.fullname) :
113	print "Destination path '%s' already exists. Previous file deleted. " %self.fullname	143	print "Destination path '%s' already exists. Previous file deleted. " %self.fullname
114	os.remove(self.fullname)	144	os.remove(self.fullname)
115		145
116	# Identify kinst and kindat
117	InstName = self.hdf5filename[0:3]
118	KinstList = [1000, 1001, 1002]
119	KinstId = {'pbr':0, 'hbr':1, 'obr':2} # pbr:piura, hbr:huancayo, obr:porcuya
120	KindatList = [1600, 1601] # mode 1, mode 2
121	self.type = KinstId[InstName]
122	self.kinst = KinstList[self.type]
123	self.kindat = KindatList[self.im]
124
125	try:	146	try:
		147	print '[Writing] creating file : %s' % (self.fullname)
126	self.cedarObj = madrigal.cedar.MadrigalCedarFile(self.fullname, True)	148	self.cedarObj = madrigal.cedar.MadrigalCedarFile(self.fullname, True)
127	except ValueError, ~~messag~~e:	149	except ValueError, e:
128	print '[Error]: Impossible to create a cedar object with "madrigal.cedar.MadrigalCedarFile" '	150	print '[Error]: Impossible to create a cedar object with "madrigal.cedar.MadrigalCedarFile" '
129	return	151	return
130		152
@@ -132,92 +154,17 class MADWriter(Operation):
132		154
133	def writeBlock(self):	155	def writeBlock(self):
134	'''	156	'''
135	- Selecting mode of operation:	157	Add data records to cedar file taking data from oneDList and twoDList
136		158	attributes.
137	bltr high resolution mode 1 - Low Atmosphere (0 - 3km) // bltr high resolution mode 2 - High Atmosphere (0 - 10km)	159	Allowed parameters in: parcodes.tab
138	msnr - Average Signal Noise Ratio in dB
139	hcm - 3 km
140
141	- Filling the cedarObject by a block: each array data entry is assigned a code that defines the parameter to write to the file
142
143	GDLATR - Reference geod latitude (deg)
144	GDLONR - Reference geographic longitude (deg)
145	GDLAT2 - Geodetic latitude of second inst (deg)
146	GLON2 - Geographic longitude of second inst (deg)
147
148	GDALT - Geodetic altitude (height) (km)
149	SNL - Log10 (signal to noise ratio)
150	VN1P2 - Neutral wind in direction 1 (eastward) (m/s), ie zonal wind
151	VN2P2 - Neutral wind in direction 2 (northward) (m/s), ie meridional wind
152	EL2 - Ending elevation angle (deg), ie vertical wind
153
154	Other parameters: /madrigal3/metadata/parcodes.tab
155
156	'''	160	'''
157		161
158	self.z_zon = self.dataOut.data_output[0,:,:]	162	startTime = datetime.datetime.utcfromtimestamp(self.dataOut.utctime)
159	self.z_mer =self.dataOut.data_output[1,:,:]	163	endTime = startTime + datetime.timedelta(seconds=self.dataOut.paramInterval)
160	self.z_ver = self.dataOut.data_output[2,:,:]	164	nrows = len(getattr(self.dataOut, self.twoDParam))
161
162	if self.im == 0:
163	h_select = numpy.where(numpy.bitwise_and(self.dataOut.height[0, :] >= 0., self.dataOut.height[0, :] <= self.hcm, numpy.isfinite(self.dataOut.height[0, :])))
164	else:
165	h_select = numpy.where(numpy.bitwise_and(self.dataOut.height[0, :] >= 0., self.dataOut.height[0, :] < 20, numpy.isfinite(self.dataOut.height[0, :])))
166
167	ht = h_select[0]
168
169	self.o_height = self.dataOut.height[self.im, ht]
170	self.o_zon = self.z_zon[ht, self.im]
171	self.o_mer = self.z_mer[ht, self.im]
172	self.o_ver = self.z_ver[ht, self.im]
173	o_snr = self.dataOut.data_SNR[ :, :, self.im]
174
175	o_snr = o_snr[ht, :]
176		165
177	ndiv = numpy.nansum((numpy.isfinite(o_snr)), 1)	166	rec = madrigal.cedar.MadrigalDataRecord(
178	ndiv = ndiv.astype(float)	167	self.kinst,
179
180	sel_div = numpy.where(ndiv == 0.)
181	ndiv[sel_div] = numpy.nan
182
183	if self.nchannels > 1:
184	msnr = numpy.nansum(o_snr, axis=1)
185	else:
186	msnr = o_snr
187
188	try:
189	self.msnr = 10 * numpy.log10(msnr / ndiv)
190	except ZeroDivisionError:
191	self.msnr = 10 * numpy.log10(msnr /1)
192	print 'Number of division (ndiv) egal to 1 by default. Check SNR'
193
194	time_t = time.gmtime(self.dataOut.time1)
195	year = time_t.tm_year
196	month = time_t.tm_mon
197	day = time_t.tm_mday
198	hour = time_t.tm_hour
199	minute = time_t.tm_min
200	second = time_t.tm_sec
201	timedate_0 = datetime.datetime(year, month, day, hour, minute, second)
202
203	# 1d parameters
204	GDLATR = self.lat
205	GDLONR = self.lon
206	GDLAT2 = self.lat
207	GLON2 = self.lon
208
209	# 2d parameters
210	GDALT = self.o_height
211
212	SNL = self.msnr
213	VN1P2 = self.o_zon
214	VN2P2 = self.o_mer
215	EL2 = self.o_ver
216	NROW = len(self.o_height)
217
218	startTime = timedate_0
219	endTime = startTime
220	self.dataRec = madrigal.cedar.MadrigalDataRecord(self.kinst,
221	self.kindat,	168	self.kindat,
222	startTime.year,	169	startTime.year,
223	startTime.month,	170	startTime.month,
@@ -225,151 +172,72 class MADWriter(Operation):
225	startTime.hour,	172	startTime.hour,
226	startTime.minute,	173	startTime.minute,
227	startTime.second,	174	startTime.second,
228	0,	175	startTime.microsecond/10000,
229	endTime.year,	176	endTime.year,
230	endTime.month,	177	endTime.month,
231	endTime.day,	178	endTime.day,
232	endTime.hour,	179	endTime.hour,
233	endTime.minute,	180	endTime.minute,
234	endTime.second,	181	endTime.second,
235	0,	182	endTime.microsecond/10000,
236	('gdlatr', 'gdlonr', 'gdlat2', 'glon2'),	183	self.oneDList.keys(),
237	('gdalt', 'snl', 'vn1p2', 'vn2p2', 'el2'),	184	self.twoDList.keys(),
238	NROW, ind2DList=['gdalt'])	185	nrows
		186	)
239		187
240	# Setting 1d values	188	# Setting 1d values
241	self.dataRec.set1D('gdlatr', GDLATR)	189	for key in self.oneDList:
242	self.dataRec.set1D('gdlonr', GDLONR)	190	rec.set1D(key, getattr(self.dataOut, self.oneDList[key]))
243	self.dataRec.set1D('gdlat2', GDLAT2)
244	self.dataRec.set1D('glon2', GLON2)
245		191
246	# Setting 2d values	192	# Setting 2d values
247	for n in range(self.o_height.shape[0]):	193	invalid = numpy.isnan(self.dataOut.data_output)
248	self.dataRec.set2D('gdalt', n, GDALT[n])	194	self.dataOut.data_output[invalid] = MISSING
249	self.dataRec.set2D('snl', n, SNL[n])	195	out = {}
250	self.dataRec.set2D('vn1p2', n, VN1P2[n])	196	for key, value in self.twoDList.items():
251	self.dataRec.set2D('vn2p2', n, VN2P2[n])	197	if isinstance(value, str):
252	self.dataRec.set2D('el2', n, EL2[n])	198	out[key] = getattr(self.dataOut, value)
253		199	elif isinstance(value, tuple):
254	# Appending new data record	200	attr, x = value
255	'''	201	if isinstance(x, (int, float)):
256	[MADRIGAL3]There are two ways to write to a MadrigalCedarFile. Either this method (write) is called after all the	202	out[key] = getattr(self.dataOut, attr)[int(x)]
257	records have been appended to the MadrigalCedarFile, or dump is called after a certain number of records are appended,	203	elif x.lower()=='db':
258	and then at the end dump is called a final time if there were any records not yet dumped, followed by addArray.	204	tmp = getattr(self.dataOut, attr)
259	'''	205	SNRavg = numpy.average(tmp, axis=0)
260		206	out[key] = 10*numpy.log10(SNRavg)
261	self.cedarObj.append(self.dataRec)	207
262	print ' [Writing] records {} (mode {}).'.format(self.dataOut.counter_records,self.im+1)	208	for n in range(nrows):
		209	for key in out:
		210	rec.set2D(key, n, out[key][n])
		211
		212	self.cedarObj.append(rec)
263	self.cedarObj.dump()	213	self.cedarObj.dump()
264		214	print '[Writing] Record No. {} (mode {}).'.format(
265		215	self.counter,
266		216	self.dataOut.mode
		217	)
267		218
268	def setHeader(self):	219	def setHeader(self):
269	'''	220	'''
270	- Creating self.catHeadObj	221	Create an add catalog and header to cedar file
271	- Adding information catalog
272	- Writing file header
273
274	'''	222	'''
275	self.catHeadObj = madrigal.cedar.CatalogHeaderCreator(self.fullname)
276	kindatDesc, comments, analyst, history, principleInvestigator = self._info_BLTR()
277
278	self.catHeadObj.createCatalog(principleInvestigator="Jarjar",
279	expPurpose='characterize the atmospheric dynamics in this region where frequently it happens the El Nino',
280	sciRemarks="http://madrigal3.haystack.mit.edu/static/CEDARMadrigalHdf5Format.pdf")
281
282	self.catHeadObj.createHeader(kindatDesc, analyst, comments, history)
283		223
284	self.catHeadObj.write()	224	header = madrigal.cedar.CatalogHeaderCreator(self.fullname)
285		225	header.createCatalog(**self.catalog)
286	print '[File created] path: %s' % (self.fullname)	226	header.createHeader(**self.header)
		227	header.write()
287		228
288	def putData(self):	229	def putData(self):
289		230
290	if self.dataOut.flagNoData:	231	if self.dataOut.flagNoData:
291	return 0	232	return 0
292		233
293	if self.~~dataOut~~.counter~~_records~~ == 1:	234	if self.counter == 0:
294	self.setFile()	235	self.setFile()
295	print '[Writing] Setting new hdf5 file for the mode {}'.format(self.im+1)
296		236
297	if self.~~dataOut~~.counter~~_records~~ <= self.dataOut.nrecords:	237	if self.counter <= self.dataOut.nrecords:
298	self.writeBlock()	238	self.writeBlock()
		239	self.counter += 1
299		240
300		241	if self.counter == self.dataOut.nrecords or self.counter == self.blocks:
301	if self.dataOut.counter_records == self.dataOut.nrecords:
302	self.cedarObj.addArray()
303
304	self.setHeader()	242	self.setHeader()
305	self.~~flagIsNewFile~~ = 1	243	self.counter = 0
306
307	def _info_BLTR(self):
308
309	kindatDesc = '''--This header is for KINDAT = %d''' % self.kindat
310	history = None
311	analyst = '''Jarjar'''
312	principleInvestigator = '''
313	Jarjar
314	Radio Observatorio de Jicamarca
315	Instituto Geofisico del Peru
316
317	'''
318	if self.type == 1:
319	comments = '''
320
321	--These data are provided by two Boundary Layer and Tropospheric Radar (BLTR) deployed at two different locations at Peru(GMT-5), one of them at Piura(5.17 S, 80.64W) and another located at Huancayo (12.04 S, 75.32 W).
322
323	--The purpose of conducting these observations is to measure wind in the differents levels of height, this radar makes measurements the Zonal(U), Meridional(V) and Vertical(W) wind velocities component in northcoast from Peru. And the main purpose of these mensurations is to characterize the atmospheric dynamics in this region where frequently it happens the 'El Nino Phenomenon'
324
325	--In Kindat = 1600, contains information of wind velocities component since 0 Km to 3 Km.
326
327	--In Kindat = 1601, contains information of wind velocities component since 0 Km to 10 Km.
328
329	--The Huancayo-BLTR is a VHF Profiler Radar System is a 3 channel coherent receiver pulsed radar utilising state-of-the-art software and computing techniques to acquire, decode, and translate signals obtained from partial reflection echoes in the troposphere, lower stratosphere and mesosphere. It uses an array of three horizontal spaced and vertically directed receiving antennas. The data is recorded thirty seconds, averaged to one minute mean values of Height, Zonal, Meridional and Vertical wind.
330
331	--The Huancayo-BLTR was installed in January 2010. This instrument was designed and constructed by Genesis Soft Pty. Ltd. Is constituted by three groups of spaced antennas (distributed) forming an isosceles triangle.
332
333
334	Station _______ Geographic Coord ______ Geomagnetic Coord
335
336	_______________ Latitude _ Longitude __ Latitude _ Longitude
337
338	Huancayo (HUA) __12.04 S ___ 75.32 W _____ -12.05 ____ 352.85
339	Piura (PIU) _____ 5.17 S ___ 80.64 W ______ 5.18 ____ 350.93
340
341	WIND OBSERVATIONS
342
343	--To obtain wind the BLTR uses Spaced Antenna technique (e.g., Briggs 1984). The scatter and reflection it still provided by variations in the refractive index as in the Doppler method(Gage and Basley,1978; Balsley and Gage 1982; Larsen and Rottger 1982), but instead of using the Doppler shift to derive the velocity components, the cross-correlation between signals in an array of three horizontally spaced and vertically directed receiving antennas is used.
344
345	......................................................................
346	For more information, consult the following references:
347	- Balsley, B. B., and K. S. Gage., On the use of radars for operational wind profiling, Bull. Amer. Meteor.Soc.,63, 1009-1018, 1982.
348
349	- Briggs, B. H., The analysis of spaced sensor data by correations techniques, Handbook for MAP, Vol. 13, SCOTEP Secretariat, University of Illinois, Urbana, 166-186, 1984.
350
351	- Gage, K. S., and B.B. Balsley., Doppler radar probing of the clear atmosphere, Bull. Amer. Meteor.Soc., 59, 1074-1093, 1978.
352
353	- Larsen, M. F., The Spaced Antenna Technique for Radar Wind Profiling, Journal of Atm. and Ocean. Technology. , Vol.6, 920-937, 1989.
354
355	- Larsen, M. F., A method for single radar voracity measurements?, Handbook for MAP,SCOSTEP Secretariat, University of the Illinois, Urban, in press, 1989.
356	......................................................................
357
358	ACKNOWLEDGEMENTS:
359
360	--The Piura and Huancayo BLTR are part of the network of instruments operated by the Jicamarca Radio Observatory.
361
362	--The Jicamarca Radio Observatory is a facility of the Instituto Geofisico del Peru operated with support from the NSF Cooperative Agreement ATM-0432565 through Cornell University
363
364	......................................................................
365
366	Further questions and comments should be addressed to:
367	Radio Observatorio de Jicamarca
368	Instituto Geofisico del Peru
369	Lima, Peru
370	Web URL: http://jro.igp.gob.pe
371	......................................................................
372	'''
373
374	return kindatDesc, comments, analyst, history, principleInvestigator
375

schainpy/model/proc/bltrproc_parameters.py +11 -1

                     '''
                     ProcessingUnit.__init__(self, **kwargs)
                     self.dataOut = Parameters()
+                    self.isConfig = False
-                def run(self, mode, snr_threshold=None):
+                def setup(self, mode):
+                    '''
                     '''
+                    self.dataOut.mode = mode
+                def run(self, mode, snr_threshold=None):
+                    '''
                     Inputs:
                         mode = High resolution (0) or Low resolution (1) data
                         snr_threshold = snr filter value
                     '''
+                    if not self.isConfig:
+                        self.setup(mode)
+                        self.isConfig = True
                     if self.dataIn.type == 'Parameters':
                         self.dataOut.copy(self.dataIn)

schainpy/__init__.pyc removed binary 0 0