-
Notifications
You must be signed in to change notification settings - Fork 0
/
model.py
361 lines (317 loc) · 14.3 KB
/
model.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
#Used to read Models
from pyspec.spectrum import spectrum
#from .fileio import dicafile,compfile,sbibfile
import param as paramMod
import fileio
import config
import fit
from glob import glob
import os,sys,re
import numpy as np
import sqlite3
import zlib
import dalekDB
import cPickle
import pdb
class model(object):
#class to store one model
@classmethod
def fromDB(cls, conn, modelID, GARunID=None):
curs = conn.cursor()
#retrieving origSpec:
if GARunID != None:
origSpec = curs.execute('select SN_SPECTRUM from GA_RUN where id=%s' % GARunID).fetchall()[0][0]
else: origSpec = None
#Retrieving the Model
(machineName, execTime, wFactor, errorString, ficaLog,
abundanceID, dicaID,
lumVphID, spectrumID) = curs.execute('select MACHINE, TIME, W, ERROR, FICA_LOG, '
'ABUNDANCE_ID, DICA_ID, LUMVPH_ID, SPECTRUM_ID '
'from FICA_MODEL where FICA_MODEL.ID=%s' % modelID).fetchall()[0]
if spectrumID == "None":
specFlag = -1
else:
specFlag = 0
#getting dica params
colNames = zip(*curs.execute('PRAGMA table_info(fica_dica)').fetchall())[1]
colNames = map(str, colNames)
colValues = curs.execute('select * from fica_dica where id=%s' % dicaID).fetchall()[0]
dicaDict = dict(zip([dalekDB.convertFields2Dica[item] for item in colNames[1:]], colValues[1:]))
lum, vph = curs.execute('select LUM, VPH from FICA_LUMVPH where FICA_LUMVPH.ID=%s' % lumVphID).fetchall()[0]
dicaDict['log_lbol'] = lum
dicaDict['v_ph'] = vph
dica = paramMod.dica(initDica=dicaDict, mode='fromDict')
#getting abundances
colNames = zip(*curs.execute('PRAGMA table_info(fica_abundance)').fetchall())[1]
colNames = map(str, colNames)
colValues = curs.execute('select * from fica_abundance where id=%s' % abundanceID).fetchall()[0]
compDict = dict(zip(colNames[1:], colValues[1:]))
comp = paramMod.comp(initComp=compDict, t=dica['t'])
comp._setNiDecay()
#getting aSpec
if specFlag == 0:
wl = dalekDB.createWLGrid(dicaDict['wl']*1e4, dicaDict['grid']*1e4, dicaDict['mu'])
intens = curs.execute('select spectrum from fica_spectrum where id=%s' % abundanceID).fetchall()[0][0]
aSpec = spectrum(wl,intens)
elif specFlag == -1:
aSpec = spectrum(zip(np.linspace(2000,20000,20),range(1,21)))
sbib={'llist':[]}
llist=sbib['llist']
wParam=[]
#getting llist
colValues = curs.execute('select eqw, shift, rest, atom, ion, param1, param2, param3 '
'from FICA_LLIST where model_id=%d' % modelID).fetchall()
#checking if llist exists for current model
if colValues == []: llist = None
else:
colNames = zip(*curs.execute('PRAGMA table_info(fica_llist)').fetchall())[1]
colNames = [(str(item.lower()), '|S2') if item=='ATOM' else (str(item.lower()), float)
for item in colNames[2:]]
llist = np.array(colValues, dtype=colNames)
""" Commented out until wParams becomes important, W is safed none the less
#getting wParams
colValues = curs.execute('select XS, VS, LOGRH, TE, TR, W '
'from FICA_WPARAM where FICA_WPARAM.model_id=%d' % model_id).fetchall()
#checking if WParams exists for current model
if colValues == []: llist = None
else:
colNames = zip(*curs.execute('PRAGMA table_info(fica_WPARAM)').fetchall())[1]
colNames = [(item.lower(), '|S2') if item=='ATOM' else (item.lower(), float)
for item in colNames[2:]]
llist = np.array(colValues, dtype=colNames)
"""
wParam=None
curParam = paramMod.param(initDica=dica, initComp=comp)
return cls(aSpec, curParam, wFactor, machineName=machineName, execTime=execTime,
wParam=wParam, error=errorString, ficaLog=ficaLog,
llist=None, origSpec=origSpec, specFlag=specFlag)
@classmethod
def fromPath(cls, basePath='.',machineName=None, param=None, origSpec=None, fitFunc=None, t=None, execTime=None):
if param == None:
dicaData=fileio.dicafile(os.path.join(basePath,'dica.dat')).read_data()
compData=fileio.compfile(os.path.join(basePath,'comp.ind')).read_data()
dica=paramMod.dica(dicaData, mode='fromPath', t=t)
comp=paramMod.comp(compData, t=dica['t'])
param = paramMod.param(initDica=dica,initComp=comp)
aSpecPath=os.path.join(basePath,'spct.dat')
try:
aSpec=spectrum(aSpecPath,usecols=(0,2))
sbib=fileio.sbibfile(os.path.join(basePath,'sbib.dat')).read_data()
llist=sbib['llist']
wParams=fileio.ststfile(os.path.join(basePath,'stst.dat')).getWParams()
specFlag=0
except:
print "Creating fake Spectrum @%s"%basePath
aSpec=spectrum(zip(np.linspace(2000,20000,20),range(1,21)))
sbib={'llist':[]}
llist=sbib['llist']
wParams=[]
specFlag=-1
log=list(file(os.path.join(basePath,'fica.log')))
#error=list(file(os.path.join(basePath,'error.log')))
if wParams!=[]:
w = wParams[-1][0][-1]
else:
w = -1
return cls(aSpec, param, w, machineName=None, execTime=execTime, wParam=wParams,
error=None, ficaLog=log, llist=llist, origSpec=origSpec,
specFlag=specFlag, fitFunc=fitFunc)
def __init__(self, aSpec, param, w, machineName=None, execTime=None,
wParam=None, error=None, ficaLog = None,
llist=None, origSpec=None, specFlag = -1, fitFunc=None):
self.param = param
self.w = w
self.machine = machineName
self.execTime = execTime
self.wParam = wParam
self.log = ficaLog
self.error = error
self.llist = llist
self.origSpec = origSpec
self.specFlag = specFlag
self.aSpec = aSpec
#Initializing subspec, divspec....
if origSpec!=None:
tmpAspec=self.aSpec.interpolate(xref=origSpec.x)
self.divSpec=tmpAspec/origSpec
self.subSpec=fit.getSubSpec(tmpAspec,origSpec)
else:
self.subSpec = None
self.divSpec = None
if fitFunc == None:
self.fitness = 0
else:
self.fitness = fitFunc(self)
def __getitem__(self,key):
if key.lower()=='llist':
return self.llist
elif key.lower()=='w':
return self.w
elif key.lower().startswith('aspec'):
return self.aSpec
elif key.lower().startswith('divpec'):
return self.divSpec
elif key.lower().startswith('addspec'):
return self.addSpec
elif key.lower().startswith('subspec'):
return self.subSpec
elif key.lower().startswith('err'):
return self.error
elif key.lower().startswith('divspec'):
return self.divSpec
else:
return self.param[key]
def toDB(self, conn, dicaID=None, storeLList=False, storeWParam=False):
#save model to db
curs = conn.cursor()
dica = self.param.dica.data.copy()
comp = self.param.comp.data.copy()
aSpec = self.aSpec.y
comp.pop('Ni')
comp.pop('Co')
comp.pop('Fe')
lum = dica.pop('log_lbol')
vph = dica.pop('v_ph')
dicaFields = [dalekDB.convertDica2Fields[item] for item in dica.keys()]
dicaValues = dica.values()
compFields = comp.keys()
compValues = comp.values()
#Inserting dica values
if dicaID == None:
curs.execute('insert into FICA_DICA (%s) values (%s)'
% (','.join(dicaFields), ','.join('?' * len(dicaValues))),
dicaValues)
dicaID = curs.lastrowid
#Inserting lum, vph values
curs.execute('insert into FICA_LUMVPH (LUM, VPH) VALUES (?, ?)', (lum,vph) )
lumVphID = curs.lastrowid
#Inserting comp values
curs.execute('insert into FICA_ABUNDANCE (%s) VALUES (%s)'
% (','.join(compFields), ','.join('?' * len(compValues))),
compValues)
compID = curs.lastrowid
#Inserting spectrum wl values
if self.specFlag == -1:
specID = "None"
if self.specFlag == 0:
zASpec = sqlite3.Binary(zlib.compress(cPickle.dumps(aSpec)))
curs.execute('insert into FICA_SPECTRUM (SPECTRUM) VALUES (?)', (zASpec,))
specID = curs.lastrowid
if not hasattr(model,'error'):
self.error = "None"
if self.log != None:
ficaLog = sqlite3.Binary(zlib.compress(cPickle.dumps(self.log)))
else:
ficaLog = 'None'
#merging the dataset
curs.execute('insert into FICA_MODEL'
'(MACHINE, TIME, W, ERROR, FICA_LOG, '
'ABUNDANCE_ID, DICA_ID, LUMVPH_ID, SPECTRUM_ID)'
'VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)',
("None", float(self.execTime), float(self.w), "error not implemented atm", ficaLog, compID, dicaID, lumVphID, specID))
#returning FICA_MODEL_ID
modelID = curs.lastrowid
#Storing Line List
if storeLList:
#Inserting line list
for line in self.llist:
curLine = list(line)
curLine[4]=dalekDB.transformIon[curLine[4].lower()]
curLine = tuple([modelID] + [float(item) if i!=3 else str(item) for i, item in enumerate(curLine)])
curs.execute('insert into FICA_LLIST (MODEL_ID, EQW, SHIFT, REST, ATOM, ION, PARAM1, PARAM2, PARAM3)'
'values (?, ?, ?, ?, ?, ?, ?, ?, ?)',
curLine)
#Storing whole wParams
if storeWParam:
for i, wParam in enumerate(self.wParam):
for line in wParam:
curLine=[modelID, i] + map(float,list(line))
curs.execute('insert into FICA_WPARAM (MODEL_ID, WSET_ID, XS, VS, LOGRH, TE, TR, W)'
'values (?, ?, ?, ?, ?, ?, ?, ?)',
curLine)
#Returning the modelID
return modelID
class modelGrid(object):
#modelGrid
def __init__(self, runDirs=None, multiParam=None, paramList=None, origSpec=None):
tmpParam=[]
if paramList!=None:
self.grid=np.array(paramList)
del paramList
else:
for dir,param in zip(runDirs,multiParam.paramGrid.flatten()):
#print "Reading Directory %s"%dir
tmpParam.append(model(param,dir))
self.grid=np.array(tmpParam)
del tmpParam
#self.grid.reshape(multiParam.paramGrid.shape)
if origSpec!=None: self.origSpec=origSpec
else: self.origSpec=config.getOrigSpec(preProcess=True)
self._initSpecs()
self.specMask=self.specFlag==0
self._initSpecs()
def _filterSpectra(self):
self.grid=self.grid[self.specMask]
def _initSpecs(self):
self._getSubSpec()
self._getDivSpec()
#self._getAddSpec()
self._getSpecFlag()
self._getFitness()
def __getitem__(self,key):
if key=='tobedetermined':
print
elif isinstance(key,int):
return self.grid[key]
elif key.lower().startswith('fit'):
return self.fitness
elif key.lower().startswith('divspec'):
return self.divSpec
elif key.lower().startswith('specflag'):
return self.specFlag
elif key.lower().startswith('subspec'):
return self.subSpec
elif key.lower().startswith('addspec'):
return self.addSpec
elif key.lower().startswith('llis'):
return [item['llist'] for item in self.grid]
else:
getFunc=np.vectorize(lambda item:item[key])
return getFunc(self.grid)
def _getSubSpec(self):
#vecFunc=np.vectorize(lambda item:fit.getSubSpec(item['aspec'],self.origSpec))
#self.subSpec=vecFunc(self.grid)
self.subSpec=np.array([item.subSpec for item in self.grid])
def _getAddSpec(self):
raise Exception('add not implemented yet')
vecFunc=np.vectorize(lambda item:fit.getAddSpec(item['aspec'],self.origSpec))
self.addSpec=vecFunc(self.grid)
def _getSpecFlag(self):
vecFunc=np.vectorize(lambda item:item.specFlag)
self.specFlag=vecFunc(self.grid)
def _getDivSpec(self):
self.divSpec=np.array([item.divSpec for item in self.grid])
def _getFitness(self):
self.fitness=np.array([item.fitness for item in self.grid])
#def divSpecGetter(item):
# tmpAspec=item['aspec']
# tmpAspec=tmpAspec.interpolate(xref=self.origSpec.x)
# return tmpAspec/self.origSpec
#vecFunc=np.vectorize(divSpecGetter)
#self.divSpec=vecFunc(self.grid)
def toDB(self, conn, dicaID=None, storeLList=None, storeWParam=None):
modelGridIDs = []
#saving all models to db
for model in self.grid:
modelGridIDs.append(model.toDB(conn, dicaID=dicaID,
storeLList=storeLList,
storeWParam=storeWParam))
return np.array(modelGridIDs)
#simple Functions to extract merits
#getGridInt=np.vectorize(lambda item: fit.getDiffIntBin(item,1)[1][0])
getGridInt=np.vectorize(lambda item: fit.getInt(item))
getGridUV=np.vectorize(lambda item: fit.getUVInt(item,norm=False))
getGridOptical=np.vectorize(lambda item: fit.getInt(item,lower=3950.,norm=False))
getGridUVComp=np.vectorize(lambda item: fit.getUVIntComp(item))
getGridSlope=np.vectorize(lambda item: fit.getIntSlope(item))
getGridSlope=np.vectorize(lambda item: fit.getSlope(item))