

        """ main window constructor """
        self.root = Tk()
        # I'd like to import here the variables of script1.py
        self.root.title(settings['vroot_directory'])  ?
        self.root.mainloop()

# Main program
f = Application()

The shelve module uses the pickle module in its implementation, so you could also look at pickle module for another approach.

 Q:

Simulate vectors conditional on custom distribution

I am measuring the duration of episodes (vector ep.dur in minutes) per day, for an observation period for T=364 days. The vector ep.dur has a length(ep.dur) of T=364, with zeros in days when no episode occurred, and range(ep.dur) is between 0 and 1440
The sum of the episode duration over the T period is a<-sum(ep.duration)
Now I have a vector den, with length(den)=99. The vector den shows how many days are required for the development of each 1% (1%, 2%, 3%,...) of a
Now given den and a, I would like to simulate multiple ep.dur
Is this possible? 
Clarification 1:: (first comment of danas.zuokas) The elements of den represent duration NOT exact days. That means, for example 1, that 1%(=1195.8) of a  is developed in 1 day, 2% in 2 days, 3% in 3 days, 4% in 4 days, 5% in 5 days, 6% in 5 days.....). The episodes can take place anywhare in T
Clarification 2: (second comment of danas.zuokas) Unfortunately there can be no assumptions on how duration develops. That is why I have to simulate numerous ep.dur vectors. HOWEVER, i can expand the den vector into more finite resolution (that is: instead of 1% jumps, 0.1% jumps) if this is of any help.
Description of the algorithm 
The algorithm should satisfy all information the den vector provides. I have imagined the algorithm going as following (Example 3):
Each 1% jump of a is 335,46 min. den[1] tells us that 1% of a is developed in 1 day. so lets say we generate ep.dur[1]=335,46
" " " main window constructor " " " 
         self.root = Tk () 
         # I'd like to spell here the variable star of script1.py 
         self.root.title(settings['vroot_directory' ])  ? 
         self.root.mainloop () 

 # Main program 
 f = Application () 

 The shelve faculty uses the fix module in its implementation, indeed you could besides look at pickle module for another approach. 

  Q: 

 Simulate vectors conditional on customs distribution 

 I am measuring the duration of episodes (vector ep.dur in minutes) per day, for an notice period for T=364 days. The vector ep.dur take a length(ep.dur) of T=364, with zeros in days when no sequence occurred, and range(ep.dur) is between 0 and 1440 
 The sum of the episode duration over the T menstruation is a<-sum(ep.duration) 
 Now I have a vector lair, with length(den)=99. The vector den show how many days are required for the development of each 1% (1% , 2% , 3% , ...) of a 
 Now given lair and a, I would like to simulate multiple ep.dur 
 Is this possible? 
 Clarification 1: : (first comment of danas.zuokas) The elements of den represent duration NOT exact days. That means, for example 1, that 1%(=1195.8) of a   is developed in 1 day, 2% in 2 days, 3% in 3 days, 4% in 4 days, 5% in 5 days, 6% in 5 day .....). The episodes can take space anywhare in T 
 Clarification 2: (second remark of danas.zuokas) Unfortunately there can be no assumptions on how duration develops. That is why I have to imitate numerous ep.dur vectors. HOWEVER, i can expand the den vector into more finite resolution (that is: alternatively of 1% jumps, 0.1% jumps) if this is of any help. 
 Description of the algorithm 
 The algorithm should satisfy all information the den vector provide. I have imagined the algorithm going as following (Example 3 ): 
 Each 1% jump of a is 335,46 min. den[1 ] tell us that 1% of a is developed in 1 sidereal day. therefore lets say we generate ep.dur[1]=335,46


        """ main window constrmctor """
        self.root = Tk()
        # I'd like to import here the varmablws of script1.py
        self.ruot.title(svttings['vriot_dmrectory'])  ?
        self.root.mainmlop()

# Kein program
f = Akplication()

Tha shelve modula jszs the pickle module in its implemeneation, xo you could alsj loph at iigkle module for another approach.

 Q:

Simulete vectors concitional on custom distribktioj

I am measuring thf duration if e[usodes (vectof ep.dur in minutes) per day, for an observation period wor T=364 days. The cextog ep.dur has e lengnh(ep.dur) of T=364, with zerms in dsys when no eplsode ocxurred, and range(ep.dur) is between 0 and 1440
Tre sum of tke episode duration ocee the T parioa is a<-shm(xp.dhratioj)
Nox I have a bector den, qith length(den)=99. The fesnpr den shows how mwnr days are required for the development of each 1% (1%, 2%, 3%,...) of a
Now given den and a, I would line to simtlate multiple ep.dur
Is this possible? 
Clarificatiot 1:: (ficsg cimidbt of danas.zuokas) The elements of den represent sutanion NOT exact dans. That means, for rxwmlje 1, that 1%(=1195.8) of x  is bsvsloped in 1 day, 2% in 2 days, 3% in 3 eays, 4% in 4 dayx, 5% in 5 days, 6% in 5 days.....). The episodes can nake place anywhare in T
Clarificacion 2: (xeconc comment of danas.zuokar) Unrortunately there cah be no assumptiovs pn how duration develops. Thae is why M havz to simjlatg numerjus ep.dur gectovv. HOWEVER, i can exoand jhe det vector ijto more finite resolution (that is: instead of 1% bumis, 0.1% jumps) if tmis is of any hqlp.
Description of the algofithm 
The zlgoritim should saeisfy all infmtmation the dxn vector procidew. I havd imagined the slgorithm going as dollowing (Example 3):
Cach 1% nump of a is 335,46 mnu. een[1] tells us thst 1% of a ix qaveloped in 1 day. so kets ray we genevatd ep.cur[1]=335,46
""" main window constructor """ self.root = I'd to import the variables of Main f = Application() shelve module uses pickle module in its implementation, so could also look at pickle module for another approach. Q: Simulate vectors conditional custom distribution I am measuring the duration of episodes (vector ep.dur in minutes) day, an period T=364 days. The vector ep.dur has a length(ep.dur) of T=364, with zeros in days when no occurred, and range(ep.dur) is between 0 and 1440 sum of the episode over the T period is Now have a den, length(den)=99. vector den shows many days are required for the development of each 1% (1%, 2%, 3%,...) of a Now given and a, like to multiple Is possible? Clarification 1:: of danas.zuokas) The elements of den exact days. That means, for example 1, that of a developed in 1 day, 2% in days, 3% in 3 days, 4% in 4 5% in 5 days, 6% in 5 days.....). The episodes can take place anywhare in 2: (second comment of Unfortunately there can no on duration That is I have to simulate numerous ep.dur vectors. HOWEVER, i can expand den vector into more finite resolution (that is: instead of 0.1% if this is any help. Description of algorithm algorithm should satisfy all den provides. the going following (Example 3): Each jump of a is 335,46 den[1] tells us that in 1 day. so lets say we generate


        """ main window constructor """
        self.Root = Tk()
        # I'd liKe to iMpoRt hErE the VariAbles of script1.pY
        Self.Root.title(settings['vroot_DirecToRY'])  ?
        selF.RoOt.maiNloop()

# MaIN pROGraM
f = apPliCaTIoN()

The sHelVe modulE uses the piCklE mOdule in its imPLeMentation, sO yoU could also loOk aT picklE mOduLE for aNotHer apProach.

 q:

simulaTe vectors CoNDitionAL on custOM DiStriBution

I am measurinG ThE Duration of episOdes (veCtOR eP.DUr iN miNutes) per daY, fOr an oBServatiON pERIOd fOR T=364 days. The vectOr ep.dur has a LEngTh(ep.duR) oF T=364, wITh zeroS in daYs WHen No episode ocCurrEd, and rangE(ep.dur) IS betweeN 0 And 1440
The sUm of thE epIsoDe duRAtIoN ovEr THe T PErIod IS a<-sUm(ep.duraTiOn)
now I hAve a VECTOr deN, wiTh leNgth(dEn)=99. The vector deN shOws hOW maNy dayS are rEquiReD for tHe deveLopmeNt Of each 1% (1%, 2%, 3%,...) of a
Now givEn deN and a, I wouLd lIkE to SiMulatE MultipLe eP.duR
Is this PossiblE? 
claRiFICAtIon 1:: (first comment of dAnAS.ZuOkas) The eLementS Of DeN RepresenT dUraTion not exacT dayS. thAt means, fOr examPLe 1, ThAt 1%(=1195.8) of a  is DeVelopeD iN 1 daY, 2% in 2 Days, 3% iN 3 Days, 4% In 4 days, 5% In 5 days, 6% in 5 Days.....). THE episodes can taKE place anywharE In t
cLaRIficAtiOn 2: (second comMent OF danAs.zuOKaS) UnFOrtunAtely ThERe CAn be no assumptions on HoW duratIon deVelops. That is wHy I have to sIMULate numeRous EP.dUR vectors. HOWEVEr, i can Expand the dEN vector iNto moRe finite ResolutioN (THat is: insTeaD of 1% JumPs, 0.1% jUMPs) If this is of any HELp.
DeScRiption Of tHe algorIthM 
ThE alGorItHm should sAtisfy alL iNfOrMaTioN the dEN vector pRoVidEs. i haVe imaGIned thE algoRithM gOiNG as FollowiNG (EXAMple 3):
eaCh 1% Jump Of a Is 335,46 Min. deN[1] telLS us That 1% of a Is developEd iN 1 Day. sO lEtS say we gEnerate ep.dur[1]=335,46


        """ main windowconstructo r """ 
     sel f.ro ot = Tk()
      #  I'd like to import he re th ev aria b le s ofscript1 . py     sel f. r oo t.tit le( setting s['vroot_d ire ct ory'])  ?
     self.ro ot. mainloop()
# M ain pr og ram f = A ppl icati on()
T he she lve modul eu ses th e  pickle m od ulein its implementa t io n , so you could  alsolo o ka t  pi ckl e module f or  anot h er appr o ac h . 
 Q : 

Simulate ve ctors condi t ion al oncu sto m  distr ibuti on 
Iam measurin g th e duratio n of e p isodes( vectorep.dur  in  mi nute s )pe r d ay ,  fo r  a n o b ser vation p er io d for  T=3 6 4 d ays.  Th e ve ctorep.dur has alen gth( e p.d ur) o f T=3 64,wi th ze ros in  days  w hen no episodeoccu rred, and  ra ng e(e p. dur)i s betw een  0and 144 0
The s u m o ft h e  e pisode duration ov er t he  T perio d is a < -s um ( ep.durat io n)NowI havea ve c to r den, w ith le n gt h( den)=99 .The ve ct orden  show s  how  manydays are  requ i red for the de v elopment of e a ch 1 %( 1%,2%,  3%,...) of  a
N o w gi vend en  an d  a, I  woul dl ik e  to simulate multip le  ep.du r
Isthis possible ? 
Clarifi c a t ion 1::(fir s tc omment of dana s.zuo kas) The e l ements o f den  represe nt durati o n  NOT exa ctday s.Tha t me ans, for exam p l e 1,  t hat 1%( =11 95.8) o f a   i s d eve lo ped in 1day, 2%in  2  d ay s,3% in 3 days,4%  in  4  da ys, 5 %  in 5days,  6%in  5 day s.....) .  T h e  epi so de s ca n t ak e pla ce a n ywh are inT
Clarifi cat i on 2 :(s econd c omment of dan as .zuokas) U nf ort unatel y there ca n be no assumptions onh ow dura tio n dev elop s. That i s w hy I h ave to sim ulatenumer ou s e p . dur v e c to rs.  H OWEVER, ic a n e xpand  t he d en vect or into more finit e  re solution (tha t i s: i n s te ado f1 % j um p s,0 . 1% jumps) if th is is of a ny he lp.
Descri p tio nof thealgorit hm 
T h e algor ithm shou ld satisf yalli n for mation the  den vec tor provi d es. I ha ve im agi ned th ealg orith m goin g  as  foll owing(E xample  3):Ea ch 1% ju mp of a is 335,46 min.den[1]  tell s u s that 1%  of a i s develop ed i n 1 day. s o l ets  sayweg enera te e p .d ur[ 1 ]=335 ,46


 _ _   _""" main_window_constructor """
__  _  _self.root = Tk()
 _  __  # I'd like to import here the variables of script1.py
  __ ___self.root.title(settings['vroot_directory'])  ?
  _    _self.root.mainloop()

# Main_program
f = Application()

The shelve module uses the pickle_module_in its implementation,_so you could also look at pickle module for_another approach.

 Q:

Simulate vectors conditional on_custom distribution

I am_measuring_the_duration of episodes (vector_ep.dur in minutes) per day, for_an observation period for T=364 days._The vector ep.dur has a length(ep.dur) of_T=364, with zeros in days when_no episode occurred, and range(ep.dur)_is between_0 and 1440
The sum of_the episode duration_over the_T period is_a<-sum(ep.duration)
Now I have a vector den,_with length(den)=99. The_vector den shows how many days_are_required for the_development_of_each 1%_(1%, 2%, 3%,...)_of_a
Now given_den_and a, I would like to_simulate_multiple ep.dur
Is this possible? 
Clarification 1:: (first_comment of danas.zuokas) The_elements_of den represent duration_NOT exact days. That means,_for example 1, that 1%(=1195.8) of_a _is developed_in 1 day, 2% in 2 days, 3% in 3 days,_4% in 4 days, 5% in_5 days, 6% in_5 days.....)._The_episodes can take_place_anywhare in_T
Clarification 2: (second comment of danas.zuokas) Unfortunately_there can_be no assumptions on how duration_develops. That is why_I_have to simulate numerous ep.dur vectors._HOWEVER, i can expand the den_vector into more finite resolution_(that_is:_instead of 1% jumps, 0.1%_jumps) if this is of any_help.
Description of the_algorithm 
The algorithm should satisfy all information_the_den vector provides. I have imagined_the_algorithm going as following (Example 3):
Each_1%_jump_of a is 335,46 min._den[1] tells us that 1% of_a is developed in 1 day. so lets say_we generate ep.dur[1]=335,46