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Parameter polja v Modelbuilderju - prazen?


Zgradil sem model, saj imam kot vhodni parameter dva parametra: funkcijski sloj in polje. Toda ko zaženem model, parameter polja nima izbranih vrednosti. Kako naj povem graditelju modelov, da želim polja iz izbranega sloja lastnosti kot vrednosti v parametru Field?


Sliši se, kot da želite izpeljati polje iz sloja, to lahko storite samo v orodju za skripte, o čemer razpravljamo tukaj. Logiko modela boste morali pretvoriti v kodo python.


Nastavitev parametrov orodja za skripte

Skoraj vsa orodja imajo parametre, njihove vrednosti pa nastavite v pogovornem oknu ali v skriptu. Ko se orodje zažene, se vrednosti parametrov pošljejo v izvorno kodo vašega orodja. Orodje prebere te vrednosti in nadaljuje s svojim delom.

Parametre orodja za skripte lahko nastavite s čarovnikom za dodajanje skripta. V pogovornem oknu Lastnosti orodja lahko tudi dodate, izbrišete in spremenite parametre orodja za skripte. Če želite dostopati do lastnosti orodja za skripte, z desno miškino tipko kliknite orodje, kliknite Lastnosti in nato jeziček Parametri.

Ne glede na to, ali nastavite parametre v čarovniku za dodajanje skripta ali v pogovornem oknu Lastnosti, so postopki (kot je opisano tukaj) enaki.

Če želite dodati nov parameter, kliknite prvo prazno celico v stolpcu Prikazno ime in vnesite ime parametra. To ime bo prikazano v pogovornem oknu orodja in lahko vsebuje presledke. Pri sintaksi Python bo ime parametra prikazno ime s presledki, nadomeščenimi s podčrtaji (_).

Po vnosu prikazanega imena parametra izberite vrsto podatkov za parameter s klikom v celico Data Type, kot je prikazano spodaj.

Vsak parameter ima dodatne lastnosti, ki jih lahko nastavite, kot je prikazano spodaj.

Lahko je obvezno, neobvezno ali izpeljano. Izpeljano pomeni, da uporabnik vašega orodja ne vnese vrednosti za parameter. Izvedeni tipi so vedno izhodni parametri.

Lahko vhod ali izhod. Če je parameter tipa Izpeljano, je smer vedno enaka izhodu.

Multivalue je Da, če želite seznam vrednosti, in Ne, če želite eno vrednost.

Privzeta vrednost parametra. Če je podatkovni tip parametra bodisi nabor funkcij bodisi nabor zapisov, se privzeto nadomesti s shemo.

Če naj privzeta vrednost parametra prihaja iz nastavitve okolja, ta lastnost vsebuje ime nastavitve okolja.

Če želite, da se za parameter vnesejo samo določeni nabori podatkov ali vrednosti, lahko podate filter. Obstaja šest vrst filtrov, vrsta filtra, ki ga lahko izberete, je odvisna od podatkovne vrste parametra.

Ta lastnost velja za izpeljane izhodne parametre in vrste podatkov vhodnih parametrov. Za izpeljane izhodne parametre je možnost Pridobljeno iz mogoče nastaviti na parameter, ki vsebuje definicijo izhoda. Za vhodne parametre je Pridobljeno iz nastavljeno na parameter, ki vsebuje informacije, potrebne za vnos.

Ta lastnost velja samo za izhodne parametre. Vrednost je lokacija datoteke sloja (.lyr), ki vsebuje simboliko za prikaz izhoda.

Obstajajo tri možnosti izbire vrste:

  • A Obvezno parameter od uporabnika zahteva vhodno vrednost. Orodja ni mogoče izvesti, dokler uporabnik ne navede vrednosti.
  • An Neobvezno parameter ne zahteva vrednosti od uporabnika.
  • A Izpeljano parameter je samo za izhodne parametre (glejte smer spodaj). Izpeljani izhodni parameter se ne prikaže v pogovornem oknu orodja.

Izpeljani izhodni parameter ima pet načinov uporabe, in sicer:

  • Izhod je enak vhodu, na primer Izračunaj polje. Izračunaj polje spremeni vrednosti določenega polja v vhodni tabeli - ne ustvari nove tabele ali spremeni vhodne sheme. Druga orodja, katerih izhod je enak vhodu, najdete v orodni vrstici Urejanje.
  • Orodje spremeni shemo vnosa, na primer Dodaj polje. Dodaj polje doda polje v vhodno tabelo - ne ustvari nove izhodne tabele.
  • Orodje ustvari izhodne podatke z uporabo informacij v drugih parametrih, kot je orodje Create Feature Class. Z orodjem Create Feature Class določite delovni prostor in ime novega razreda funkcij in razred funkcij je ustvarjen za vas.
  • Orodje prikaže skalarno vrednost v nasprotju z naborom podatkov. Get Count, na primer, izpiše dolgo celo število (število zapisov). Kadar koli orodje prikaže skalarno vrednost, je izhod izpeljan.
  • Orodje bo ustvarilo podatke na znani lokaciji. Na primer, morda imate skript, ki posodablja obstoječo tabelo v znanem delovnem prostoru. Uporabniku ni treba predložiti te tabele v pogovornem oknu ali pri skriptiranju.

Če je vaše orodje za skripte izpeljalo izhodne podatke, morate v svojem skriptu nastaviti vrednost izpeljanega izhodnega parametra s funkcijo SetParameterAsText ().

Vsa orodja bi morala imeti rezultate

Vsa orodja za skripte morajo imeti izhodne parametre, da jih je mogoče uporabiti v ModelBuilder. Temeljna ideja ModelBuilderja je povezati izhod orodja z vhodi drugih orodij, in če vaše orodje za skripte nima izhodnega parametra, v ModelBuilderju ni zelo koristno. Izpišete lahko vsaj logično vrednost, ki vsebuje true, če je orodje uspešno zaključeno, sicer pa false.

Izpeljani izhod, ki spreminja vhodni parameter

Spodnja ilustracija prikazuje hipotetično orodje za skripte, Update Field Values, ki se uporablja v ModelBuilder. (Za namene razprave organizacija uporablja vrednosti posodobitvenih polj za preučitev vsebine nabora znanih besedilnih polj in odpravlja napake pri črkovanju in napake z velikimi črkami.) Vrednosti posodobitvenih polj ne ustvarijo novega razreda lastnosti, vendar polje za posodobitev vrednosti v vhodnem razredu lastnosti.

Pravilna opredelitev parametrov vrednosti polja za posodobitev je prikazana na spodnji sliki, kjer ima vrednost vrednosti polja za posodobitev parameter izhodnega razreda funkcije s tipom, nastavljenim na Izpeljano. Ker je izhod enak vhodu za to orodje, je Pridobljeno iz nastavljeno na vhodni parameter. (Pridobljeno iz uporablja ime parametra, ki je prikazno ime s presledki, nadomeščenimi s podčrtaji.)

Izpeljani izhod, ki ne spreminja vhodnega parametra

Spodnja ilustracija prikazuje drugačno orodje, pri katerem je izhod izpeljan, vendar ni pridobljen iz nobenega vhodnega parametra (Pridobljeno iz je prazno). V tem primeru hipotetično orodje Post Data to Repository kopira vhodni razred lastnosti v znani delovni prostor (repozitorij), nato doda in zapolni polje za datum / čas.

Nastavitev izhodne vrednosti

V zgornjem ilustriranem modelu upoštevajte, da je orodje za funkcije kopiranja prazno (belo namesto rumeno). Razlog je v tem, da spremenljivka izhodnih lastnosti, čeprav zelena, ne vsebuje vrednosti - ime in lokacija izhodnih lastnosti ni znana, dokler se orodje ne zažene. V tem primeru mora vaš skript določiti izhodno vrednost z uporabo funkcije ArcPy SetParameterAsText (). Funkcija SetParameterAsText () bo nastavila vrednost izhodnega parametra z uporabo besedilnega niza ali predmeta, kot je tabela vrednosti.

Pred izvedbo orodja je mogoče navesti vrednost izhoda z vnosom kode za preverjanje orodja.

Tu je primer kode, ki uporablja SetParameterAsText (), ki temelji na zgoraj opisanem delu, ki ga opravi skript Post Data to Repository.

Izhodne vrednosti namesto podatkov

Zgornji primeri kažejo izpisovanje izpeljanih naborov podatkov. Nekatera orodja pa namesto naborov podatkov izpišejo vrednosti, na primer orodje Get Count, ki izpiše podatkovni tip Long, ki vsebuje število vrstic v tabeli. Pogoste so izhodne vrednosti namesto naborov podatkov. Morda imate lastne skripte, ki izvajajo analizo na več povezanih naborih podatkov in ne izpisujejo nič več kot nekaj številk ali logično vrednost pass / fail.

Izhodni parametri, ki vsebujejo podatkovne tipe vrednosti (na primer Long ali Boolean), so vedno izpeljani in ne obvezni.


Delo s spremenljivkami

Za popolno definicijo in razpravo o spremenljivkah preberite Elementi modela.

Spremenljivko lahko predstavljate kot vsebnik, ki vsebuje vrednost, ki jo je mogoče spremeniti. V kontekstu modela je mogoče ustvariti spremenljivko in njeno vrednost uporabiti kot vrednost parametra orodja.

Obstajata dve osnovni vrsti spremenljivk: podatkovne spremenljivke in spremenljivke vrednosti.

Podatkovne spremenljivke so obravnavane v razdelku Ustvarjanje procesov modelov, ki prikazuje, kako ustvariti podatkovne spremenljivke in jih povezati s parametri orodja.

Vrednostne spremenljivke so stvari, kot so nizi, števila, logične vrednosti (resnične / napačne vrednosti), prostorske reference, linearne enote, ekstenzije itd. Skratka, spremenljivke vrednosti vsebujejo vse, razen sklicev na podatke na disku.

Med podatkovnimi in vrednostnimi spremenljivkami so le manjše razlike.

  • Podatkovne spremenljivke lahko ustvarite s pomočjo spremenljivk vrednosti orodja Dodaj podatke ne morete.
  • Izvedene spremenljivke podatkov se ustvarijo samodejno za vas, ko dodate orodje v ModelBuilder.
  • Podatkovna spremenljivka se samodejno ustvari za vas, ko v pogovornem oknu orodja vnesete vrednost za parameter nabora podatkov. Spremenljivke vrednosti se na ta način ne ustvarijo samodejno, ker bi s tem hitro pretrgali vaš diagram do te mere, da bi bil neberljiv. Vendar lahko kadar koli ustvarite spremenljivko za parameter orodja.
    Preberite več o izdelavi spremenljivk iz parametrov.

Vrednostne spremenljivke so povezane s parametri orodja na enak način kot podatkovne spremenljivke.

Ta razdelek se osredotoča na splošno ustvarjanje in upravljanje spremenljivk, zato velja tako za spremenljivke podatkov kot vrednosti.

Zakaj ustvarjati spremenljivke?


Izdelava spremenljivk iz parametrov

Spremenljivko lahko naredite iz katerega koli parametra orodja. Z desno miškino tipko kliknite orodje in pokažite na Make Variable & gt From Parameter, nato kliknite parameter, kot je prikazano spodaj.

Tako narejene spremenljivke se samodejno povežejo z orodjem.


Ustvarjanje samostojnih spremenljivk

Drug način za ustvarjanje spremenljivke podatkov ali vrednosti je, da z desno miškino tipko kliknete diagram modela in kliknete Ustvari spremenljivko. Pojavilo se bo naslednje pogovorno okno, kjer boste izbrali vrsto podatkov spremenljivke, ki jo želite ustvariti.
Preberite več o spremenljivih vrstah podatkov.

Ko izberete podatkovni tip za spremenljivko in kliknete V redu, bo ustvarjena nova prazna spremenljivka. Nato lahko spremenljivko povežete z enim ali več orodji, nastavite vrednost za spremenljivko in / ali jo spremenite v modelni parameter.

Možnost Multivalue bo ustvarila spremenljivko, ki omogoča vnos več vrednosti, kot je prikazano spodaj.

Vsak parameter in spremenljivka ima povezan podatkovni tip. Na primer, prvi parameter orodja za raztapljanje je & ltin_features & gt, njegov podatkovni tip pa je plast lastnosti.

Na spremenljivko lahko z desno miškino tipko kliknete, kliknete Lastnosti in spremenite podatkovni tip spremenljivke na zavihku Data Type. Seznam podatkovnih vrst, ki jih lahko izberete, so tisti, ki podpirajo trenutni podatkovni tip. Primer, ki sledi, kaže, kako je lahko spreminjanje podatkovnega tipa koristno.

Spodnji primer prikazuje orodje Ustvari naključne točke z vhodno spremenljivko Omejevalni obseg kot parameter modela. Ta spremenljivka je podatkovni tip Extent, ki je v bistvu štiri številke, ki določajo geografski pravokotnik. Razmislite, da je te štiri številke mogoče pridobiti iz katerega koli razreda lastnosti, nabora podatkov, sloja, TIN-a, omrežja itd. Lahko rečemo, da ti zadnji podatkovni tipi (razred lastnosti, nabor funkcij itd.) Podpirajo podatkovni tip Extent, ker je iz njih mogoče pridobiti geografski obseg.

Če želite spremeniti vrsto podatkov Constraining Extent, z desno miškino tipko kliknite spremenljivko in kliknite lastnosti. Na zavihku Data Type izberite novo vrsto podatkov, kot je prikazano spodaj. Prikazane bodo samo tiste vrste podatkov, ki podpirajo trenutni tip podatkov (v tem primeru obseg). Podatkovni tip tabele na primer ne podpira ekstenzije, zato ne bo prikazan na seznamu.

Potem ko na primer spremenite podatkovni tip v razred lastnosti in odprete pogovorno okno orodja modela iz ArcToolbox, vnesete razred lastnosti za omejevalni obseg in ne štiri številke.

Če želite nastaviti vrednost spremenljivke, dvokliknite spremenljivko ali z desno miškino tipko kliknite Odpri. To bo odprlo nadzor uporabniškega vmesnika in vam omogočilo nastavitev vrednosti. S to metodo ne morete nastaviti vrednosti izpeljane spremenljivke vhod / izhod. Za spremenljivke izpeljanih podatkov vhod / izhod morate spremeniti vrednost spremenljivke vhodnih podatkov.
Preberite več o izvedenih spremenljivkah vhod / izhod.

OPOMBA: Ko je element spremenljivke povezan s parametrom orodja, ne morete nastaviti vrednosti spremenljivke v pogovornem oknu orodja, saj kontrolnik parametrov prikazuje ime povezane spremenljivke in ne njene vsebine. Če z uporabo kontrolnika parametrov orodja vnesete novo vrednost, bo ustvarjena in povezana nova spremenljivka, stara spremenljivka pa bo prekinjena.


Povezovanje spremenljivk z orodji

Obstajata dva načina, kako spremenljivke lahko povežete s parametri orodja, bodisi z orodjem Poveži bodisi v pogovornem oknu orodja.

  1. Kliknite orodje Connect.
  2. Kliknite spremenljivko, ki jo želite povezati z orodjem.
  3. Kliknite orodje, s katerim želite spremenljivko povezati.

Če želite uporabiti pogovorno okno orodja, z desno miškino tipko kliknite orodje in kliknite Odpri. Običajno nadzor parametrov v orodju omogoča izbiro modelnih spremenljivk. Vendar ni vedno tako. Razmislite o naslednjem preprostem modelu, ki vsebuje orodje Dodaj polje. Obstajajo tri samostojne spremenljivke, ki jih je treba povezati z orodjem za dodajanje polja. Kupci, Novo ime polja in Polje lahko vsebujejo ničle. Stranke je podatkovna spremenljivka, ustvarjena z orodjem Dodaj podatke.

V pogovornem oknu orodij lahko stranke povežete s parametrom vhodne tabele tako, da razkrijete spustni seznam, kot je prikazano spodaj. Podatkovne spremenljivke so prikazane z ikono. Na sliki so prikazane tudi razpoložljive plasti iz kazala vsebine ArcMap na spustnem seznamu z ikono. Ko izberete Stranke in kliknete V redu, bo vzpostavljena povezava med Strankami in orodjem Dodaj polje.

Za parameter Ime polja lahko na spustnem seznamu izberete spremenljivko Novo ime polja. Ker je spremenljivka Novo ime polja spremenljivka vrednosti in ne podatkovna spremenljivka, ne bo imela ikone.

Nazadnje orodje Dodaj polje vsebuje dva logična parametra, Field IsNullable in Field IsRequired. Vendar pa s kontrolnikom parametrov ne morete povezati spremenljivke z imenom Novo polje & # 8212, uporabljati morate orodje Povezava. Obstajajo tudi drugi kontrolniki parametrov, na primer Linear Unit, ki se obnašajo enako & # 8212morate uporabljati orodje Connect namesto pogovornega okna orodja.


Prikaz veljavnih parametrov pri povezovanju spremenljivk

Ko z orodjem Connect povežete spremenljivko z orodjem, se predpostavlja, da je spremenljivka prvega parametra, katerega podatkovni tip se ujema s spremenljivko. Na primer, orodje Dodaj polje zajema dve logični spremenljivki, Field IsNullable in Field IsRequired. Če z orodjem Dodaj polje povežete logično spremenljivko, s katerim od teh dveh parametrov se bo povezala?
Če želite določiti, s katerim parametrom bo spremenljivka povezana, morate ModelBuilderju naročiti, da pri povezovanju podatkov z orodji prikaže seznam veljavnih parametrov, kot sledi:

  1. V meniju Orodja katere koli aplikacije ArcGIS, na primer ArcMap ali ArcCatalog, kliknite Možnosti.
  2. Kliknite zavihek Geoprocessing.
  3. Preverite možnost prikaza veljavnih parametrov, kot je prikazano spodaj.


Ustvarjanje spremenljivk za nastavitve okolja

Spremenljivke lahko ustvarite za nastavitve okolja. Z desno miškino tipko kliknite orodje in pokažite na Make Variable & gt From Environment. Lahko ustvarite tudi samostojno spremenljivko in jo povežete z nastavitvijo okolja modela.
Preberite več o modelnih okoljih.


Ustvarjanje preprostega modela

Ta razdelek vas vodi po korakih skozi ustvarjanje in izvajanje novega delujočega modela. Model zgolj ustvari novo tabelo baze podatkov in doda polje. Za dokončanje tega primera ne potrebujete nobenih podatkov, razen mape na disku. Namen enostavnosti tega modela je prikazati bistvene ključne koncepte oblikovanja modelov, namesto da bi prikazal geografske koncepte.

Prva naloga je ustvariti orodjarno po meri, ki bo vsebovala primer modela. Zaženite ArcCatalog, odprite okno ArcToolbox in ustvarite novo orodjarno. Poimenujte to orodjarno Primer preprostega modela (v praksi ime ni pomembno).
Preberite več o ustvarjanju orodjarne po meri

Naslednji korak je ustvariti nov model. Z desno miškino tipko kliknite orodno polje Simple Model Example in kliknite New & gt Model. Odpre se okno ModelBuilder in območje diagrama bo prazno.
Nato poiščite orodje Ustvari tabelo v oknu ArcToolbox. Najdete ga v orodjih za upravljanje podatkov & nabor orodij gt Table.
Preberite več o iskanju orodij v ArcToolbox

Povlecite in spustite orodje Ustvari tabelo na diagram ModelBuilder. Okno ModelBuilder bi zdaj moralo izgledati tako:

Dvokliknite orodje Ustvari tabelo, da odprete njegovo pogovorno okno (ali z desno miškino tipko kliknite Odpri). Navesti morate le dva zahtevana parametra, kot je prikazano spodaj: izhodno mesto (v tem primeru mapo z imenom E: OutputFolder & # 8212 lahko uporabite katero koli mapo v vašem sistemu) in ime izhodne tabele. Za ime izhodne tabele uporabite SimpleTable.dbf. Po izpolnitvi teh dveh parametrov kliknite V redu.

Model se mora zdaj prikazati, kot je prikazano spodaj. Modra ovala predstavlja vhodne podatke, zelena oval pa izhodne podatke. Upoštevajte, da je nalepka v izhodni tabeli zavita in je težko berljiva. Oval lahko spremenite tako, da ga kliknete, da se prikažejo modri ročaji za spreminjanje velikosti. Kliknite in pridržite ročico za spreminjanje velikosti in povlecite miško, da jo spremenite. Če kliknete na sredino ovala in povlečete miško, lahko oval prestavite kjer koli na območju diagrama ModelBuilder.

Po spremembi velikosti je model zdaj bolj berljiv.

V tem trenutku se še ni zgodilo nič, ker še niste izvedli modela & # 8212SimpleTable.dbf, ki še ne obstaja na disku. Razlog je v tem, da se v ModelBuilder, ko odprete pogovorno okno orodja in kliknete V redu, vrnete v okno ModelBuilder, ki ga orodje ne zažene. To se razlikuje od obnašanja orodja v oknu ArcToolbox in klika V redu, zaradi česar se orodje zažene.

Naslednji korak je dodajanje novega polja v SimpleTable.dbf. Poiščite orodje Add Field v oknu ArcToolbox, ki ga najdete v orodjih za upravljanje podatkov & gt Fields. Povlecite in spustite orodje Dodaj polje na območje diagrama, kot je prikazano spodaj.

Dvokliknite orodje Dodaj polje v ModelBuilder, da odprete njegovo pogovorno okno. Za parameter Vhodna tabela razkrijte spustni seznam in izberite vnos SimpleTable.dbf. Ikona poleg SimpleTable.dbf (prikazana spodaj) pomeni, da so vhodni podatki spremenljivka v modelu. Več o spremenljivkah boste izvedeli kasneje.

Za ime polja vnesite "Table_ID". Za ostale parametre uporabite privzete vrednosti. Kliknite V redu, da zaprete pogovorno okno. Zdaj bi moral biti vaš model videti tako:

Upoštevajte, kako je izhod Ustvari tabelo povezan kot vhod v polje Dodaj. Rezultat polja Dodaj je SimpleTable.dbf (2), ime, ki ga je ModelBuilder samodejno ustvaril.
Tu se dogaja nekaj zanimivega. Če odprete pogovorno okno orodja Dodaj polje, boste opazili, da noben od parametrov ne zahteva imena izhodne tabele. To je zato, ker bo polje dodano v vhodno tabelo, zato orodju ni treba imeti parametra izhodne tabele. Toda v ModelBuilder je izhod & # 8212SimpleTable.dbf (2). Ta izhod vsebuje novo polje, ki ga bo orodje Dodaj polje dodalo ob izvedbi modela. Razlog je v tem, da morajo v orodju ModelBuilder vsa orodja ustvariti izhod, tako da lahko izhod povežete z drugim orodjem. Če bi na primer morali dodati drugo polje, lahko dodate drugo orodje Dodaj polje in za vhod izberete SimpleTable.dbf (2), ki bo kot izhod ustvarilo SimpleTable.dbf (3). Na ta način lahko nanizate toliko poljub za dodajanje, kolikor potrebujete.

Model je zdaj pripravljen za zagon. Model lahko zaženete v oknu ModelBuilder tako, da kliknete gumb Zaženi ali v meniju Model kliknete Zaženi. Ko zaženete model, morajo imeti orodja in podatki senčna polja, kot je prikazano spodaj, kar pomeni, da so bila orodja izvedena in podatki ustvarjeni.

Model lahko izvedete tudi tako, da ga odprete v ArcToolbox. Najprej shranite model, tako da kliknete ukaz menija Model & gt Save, nato ukaz menija Model & gt Zapri, kot je prikazano spodaj.

V oknu ArcToolbox poiščite orodno polje Simple Model Example, ki ste ga ustvarili. Model, ki ste ga pravkar ustvarili, bo poimenovan Model, kot je prikazano spodaj.

Dvokliknite Model, da odprete pogovorno okno. Videti bi moralo kot spodaj.

Sporočilo To orodje nima parametrov in ni napaka. To preprosto pomeni, da ima model vse informacije, ki jih potrebuje za izvedbo, v tem primeru izhodno mapo, ime tabele in ime polja. Za izvedbo modela kliknite V redu. Pojavilo se bo pogovorno okno napredka in videli boste sporočila, ki sta jih napisala orodje Ustvari tabelo in Dodaj polje. Ko se model izvrši, lahko v ArcCatalogu odprete SimpleTable.dbf in si ogledate njegove lastnosti. Polja naj bodo tri: OID, Field1 in Table_ID. Prvi dve polji vedno ustvari orodje Ustvari tabelo. Za več informacij o teh dveh poljih glejte spodnji razdelek Ustvari polja tabele.

Ustvarjanje in izpostavljanje parametrov

Model, ki ste ga pravkar ustvarili, lahko uredite tako, da lahko uporabnik ob odprtju izbere ime tabele.
Prvi korak je odpreti okno ModelBuilder. V ArcToolbox z desno miškino tipko kliknite orodje za shranjeni model in kliknite Uredi. To bo odprlo okno ModelBuilder in diagram vsebuje vaš model.
V naslednjem koraku boste izdelali spremenljivko modela za izhodno tabelo, ki jo želite ustvariti, nato pa jo izpostavili kot parameter modela.
Z desno miškino tipko kliknite orodje Create Table, pokažite na Make Variable & gt From Parameter, nato kliknite Output Table, kot je prikazano spodaj.

Na oznaki Izhodna tabela se mora prikazati svetlo modra ovalna oblika. Ta oval se bo verjetno pojavil na vrhu ovalnega dela OutputFolder, zato ga boste morali premakniti tako, da kliknete in povlečete ovalni izhodni tabeli.
Nato z desno miškino tipko kliknite Output Table in kliknite Model Parameter, kot je prikazano spodaj.

Oval izhodne tabele bi moral imeti poleg sebe črko P, kar pomeni, da gre za modelni parameter, kot je prikazano spodaj.

Shranite in zaprite okno ModelBuilder kot prej in dvokliknite orodje za model, da odprete njegovo pogovorno okno. Zdaj bi moral izgledati tako:

Parameter izhodne tabele je izpolnjen z SimpleTable.dbf, ker model še vedno vsebuje SimpleTable.dbf kot vrednost za spremenljivko izhodne tabele.
Ikona opozorila se prikaže, ker ste predhodno zagnali model in izhod SimpleTable.dbf obstaja.
Če je parameter izhodne tabele prednastavljen na SimpleTable.dbf, je enako kot privzeta vrednost za parameter, kar je v nekaterih situacijah, ko želite, da uporabniki tega orodja zahtevajo vnos vrednosti izhodne tabele, namesto da jim dovolite, nerodno sprejme privzeto.
Če želite odstraniti SimpleTable.dbf kot privzeto vrednost, uredite orodje, da odprete okno ModelBuilder. Dvokliknite oval izhodne tabele, da odprete kontrolnik parametrov in izbrišete besedilo SimpleTable.dbf. Kliknite V redu in vaš model se bo prikazal na naslednji način:

Vsi podatkovni elementi in spremenljivke (ovali), razen OutputFolder, so beli, kar pomeni, da so prazni. Vsa orodja so prav tako bela, kar pomeni, da nimajo dovolj informacij za izvajanje. Če zdaj model zaženete iz okna ModelBuilder, se prikaže sporočilo "Noben proces ni pripravljen za zagon". Če pa zaženete modelno orodje iz ArcToolbox in navedete ime izhodne tabele, se bo orodje izvedlo.

Upoštevajte, da imena izhodnih podatkov, SimpleTable.dbf in SimpleTable.dbf (2), niso bila spremenjena. To ni težava & # 8212ko je orodje zagnano, bo dejansko ustvarjeno ime tabele ime, ki ga vnese uporabnik. V modelu sta SimpleTable.dbf in SimpleTable.dbf (2) zgolj oznaki in ne dejanski imeni izhoda.
Preberite več o oznakah elementov v modelih

Shranite in zaprite model kot prej, nato dvokliknite orodje za model, da odprete pogovorno okno. Parameter izhodne tabele bo zdaj prazen.

Zdaj lahko za tabelo navedete drugo ime. Ustvarjen bo na izhodni lokaciji in bo vseboval polje Table_ID.

Ustvari polja tabele

Ko zaženete ta primer modela, bo tabela, ki jo ustvarite, dejansko vsebovala tri polja: OID, Field1 in Table_ID. (Prepričajte se, da ima ime tabele, ki jo ustvarite, pripono .dbf. Če te pripone ne dodate, bo ustvarjena tabela INFO s polji: Rowid, OBJECTID, FIELD1 in TABLE_ID).

Polji OID in Field1 se samodejno ustvarijo z orodjem Ustvari tabelo. Orodje Ustvari tabelo mora ustvariti veljavno tabelo ArcGIS, kar pomeni, da mora tabela imeti polje identifikatorja objekta (OID), ki ga uporablja izključno ArcGIS, in vsaj eno uporabniško polje (polje 1). Orodje Ustvari tabelo ne ve, ali mu boste dodali polja, zato mora ta polja vedno dodati, da ustvari veljavno tabelo. Polja, ki jih dodate Ustvari tabelo, so odvisna od vrste ustvarjene tabele (INFO, dbf ali zbirka geodata).

Če poznate to vedenje orodja Ustvari tabelo, ga lahko v svojem modelu upoštevate tako, da dodate orodje Delete Field, da izbrišete Field1. Z enakimi tehnikami kot prej povlecite in spustite orodje Delete Field v model, dvokliknite orodje Delete Field in nato za vnos izberite SimpleTable.dbf (2). Model bi moral izgledati na naslednji način.

Odprite orodje Delete Field. V kontrolniku parametra Drop Field kliknite Add Field, da dodate novo polje in ga preimenujete v Field1, kot je prikazano spodaj.

Ko zaženete ta model, mora imeti izhodna tabela samo dve polji, OID in Table_ID.

Zaključki in ključne točke

Ta preprost primer modela prikazuje nekaj ključnih konceptov o izdelavi modelov:


Izpeljava parametrov modela, prilagajanje najmanjših kvadratov vs reševanje sistemov enačb

PS: Naj vam predgovorim s tem, da komaj imam idejo, kako postaviti to vprašanje tako razgaljeno s svojimi neumnostmi.

Poskušam model 5 parametrov prilagoditi gravitacijskim lečam. Za to imam 2 razreda leč z 2 in 4 slikami.

Za primer 4 slik uporabljam metodo najmanjših kvadratov, ki ustreza parametrom.

Za primer 2 slike imam enačbe v sistemu enačb, ki jih rešim, da najdem parametre.

Zdaj oba delujeta, toda ko zaženem model nazaj, da predvidim, kje naj bodo moje slike, se primer 2 primerja natančno, medtem ko ima primer 4 slike nekaj odstopanj. Na splošno pričakujem odstopanja med vgrajenim modelom in resničnostjo, tako da je to dobro. Moja težava je v primeru 2 slike, ki se dosledno ujema z opaženimi slikami, vendar ustvarja čudne vrednosti parametrov.

Zaradi česar se sprašujem, ali bo rešitev SoE obstajala, ali se bo vedno natančno ujemala z vhodnimi podatki, ne glede na to, ali nima fizičnega smisla?

Medtem ko na drugi strani uporablja nekaj podobnega pristopu najmanjših kvadratov, čeprav resničnosti ne reproducira natančno, ima vgrajeno vsaj nekaj doslednosti?


Razvoj in uporaba avtomatizirane ocene na osnovi GIS za prednostno obravnavanje možnosti obnove mokrišč

Nedavno kartiranje geografskih informacijskih sistemov (GIS) oregonskih plimskih mokrišč je odkrilo več kot 2000 potencialnih območij obnove. Glede na veliko število možnosti obnove smo razvili avtomatizirana GIS orodja, ki pomagajo upravljavcem virov pri določanju prednostnih območij z manj hidrološkimi spremembami in ugodnejšimi meritvami krajinskega merila. Izliv in porečje reke Coos je bil uporabljen kot primer regionalne uporabe. Z uporabo skriptnih tehnik smo razvili devet GIS orodij, s katerimi smo razvrstili 530 potencialnih mest za obnovo z uporabo široko dostopnih podatkovnih nizov. Ocenjeni parametri so bili omejeni na dejavnike, ki vplivajo na hidroperiod območja v več merilih in odražajo upoštevanje ekoloških načel. Izhod iz tabelarnega modela je bil uporabljen za določanje prednosti potencialnih mest obnove. Uvrstitve prednostnih nalog so bile izračunane z uporabo tristopenjskega tehtanega seštevka, ki so ga določili izvajalci restavracije Coos v estuariju. Standardizirane stopnje so se gibale od 0,479–1,000 na lestvici od nič do ena. Višje stopnje kažejo na ugodnejše meritve krajinskega obsega in manj kumulativne hidrološke spremembe. Izhodni podatki modela, standardizirani rezultati parametrov in prednostne razvrstitve potencialnih lokacij za obnovo so bili shranjeni v zbirki podatkov Microsoft Access skupaj z bazo geodatov, ki vsebuje prostorsko geometrijo. Ta avtomatizirana orodja predstavljajo ponovljive in prilagodljive metode za ocenjevanje in določanje prednostnih nalog velikega števila potencialnih lokacij za obnovo v Oregonu.

To je predogled naročniške vsebine, dostop prek vaše institucije.


Mdhntd

Zakaj ekipa Event Horizon Telescope ni omenila Strelca A *?

Iskanje območja med dvema krivuljama z Integrate

Kaj pomeni & # 12418 & # 12398 v tem stavku?

Ali je v redu, če pred pogajanji o zaposlitvi za polni delovni čas ponudite nižje plačano delo kot poskusno obdobje?

Kako prevesti "biti takšen"?

Ali lahko dodiplomskemu svetuje profesor, ki je zelo daleč?

Ali je v redu razmisliti o založništvu v prvem letniku doktorata?

Spremenljivka z narekovaji "$ ()"

Kateri je najučinkovitejši način za shranjevanje številčnega obsega?

"toliko podrobnosti, kot se spomnite"

Kerning za indekse sigme?

Ponarejanje v matematiki vs znanosti

Ali je imel kateri koli prenosni računalnik vgrajen 5 1/4-palčni disketni pogon?

Če dosežem kritični zadetek pri 18 ali več, kakšne so moje možnosti za kritični zadetek, če zavrtim 3d20?

Ali lahko obstajajo ženske Beli sprehajalci?

Zakaj lahko indeks seznama uporabim kot indeksirno spremenljivko v zanki for?

Matematika slikanja črne luknje

Ženska tat se ne proda za povračilo - kaj se potem zgodi?

Gumb spreminja besedilo in dejanje # 38. Dobro ali grozno?

Kakšen bi lahko bil pravi moči za 15 sekund življenjske dobe velikanske motorne žage za enkratno uporabo?

Ohranite retro slog znanstvenofantastičnih vesoljskih ladij?

Uporaba ModelBuilder in Inline nadomestitve v ArcGIS Pro?

Rezultati ankete razvijalcev za stack overflow 2019 so InKako skrajšati niz% Name% za vstavljeno zamenjavo spremenljivke Ali bo ArcGIS Pro (boljši) ModelBuilder? Omejitve izbire ponovitve v ArcGIS ModelBuilder? Uporaba samo imena spremenljivke (ne celotne poti do datoteke) za inline Zamenjava spremenljivk v Model BuilderUmestna zamenjava spremenljivk v Model Builder z uporabo python kode Uporaba samo dela vstavljenega nadomestnega niza spremenljivk v ModelBuilder? Uporaba iteratorjev v ArcGIS Pro ModelBuilder? Uporaba vstavljene zamenjave spremenljivk in problem vnosa imena za združitev dveh orodij skripta Python skupaj v ModelBuilder? Zamenjava inline spremenljivke v ArcGIS ModelBuilder? Inline zamenjava, ki povzroča prazen izbor v ArcGIS ModelBuilder?

Ustvarjam model in se spraševal, ali je mogoče uporabiti vstavljeno zamenjavo s spremenljivkami, ki so tabela vrednosti ali več vrednosti, prikazana spodaj.

Da. Toda orodje, s katerim ga hranite, mora pričakovati mizo.

@Hornbydd Predvidevam, da orodje CalculateField s tem ne bi delovalo. Sem prav?

Če preberete stran s pomočjo za to orodje, natančneje razdelek o skladnji, lahko vidite, kaj je podatkovni tipi so za vsak parameter, to vam sporoča. Kot boste odkrili, ni parametrov, ki bi bili vrsta "tabele vrednosti", zato bi morali ugotoviti, da je odgovor na vaše vprašanje ne.

Ustvarjam model in se spraševal, ali je mogoče uporabiti vstavljeno zamenjavo s spremenljivkami, ki so tabela vrednosti ali več vrednosti, prikazana spodaj.

Da. Toda orodje, s katerim ga hranite, mora pričakovati mizo.

@Hornbydd Predvidevam, da orodje CalculateField s tem ne bi delovalo. Sem prav?

If you read the Help page for that tool, specifically the syntax section you can see what the data types are for each parameter, this is what it is telling you. As you will discover there are no parameters that are a "table of values" type so you should deduce that the answer to your question is no.

I am creating a model and was wondering whether it is possible to use inline substitution with variables that are a table of values or multiple values, shown below.

I am creating a model and was wondering whether it is possible to use inline substitution with variables that are a table of values or multiple values, shown below.

Da. But the tool that you are feeding it into must expect a table .

@Hornbydd I'm assuming that the CalculateField tool wouldn't work with this. Am I correct?

If you read the Help page for that tool, specifically the syntax section you can see what the data types are for each parameter, this is what it is telling you. As you will discover there are no parameters that are a "table of values" type so you should deduce that the answer to your question is no.

Da. But the tool that you are feeding it into must expect a table .

@Hornbydd I'm assuming that the CalculateField tool wouldn't work with this. Am I correct?

If you read the Help page for that tool, specifically the syntax section you can see what the data types are for each parameter, this is what it is telling you. As you will discover there are no parameters that are a "table of values" type so you should deduce that the answer to your question is no.

Da. But the tool that you are feeding it into must expect a table .

Da. But the tool that you are feeding it into must expect a table .

@Hornbydd I'm assuming that the CalculateField tool wouldn't work with this. Am I correct?

@Hornbydd I'm assuming that the CalculateField tool wouldn't work with this. Am I correct?

If you read the Help page for that tool, specifically the syntax section you can see what the data types are for each parameter, this is what it is telling you. As you will discover there are no parameters that are a "table of values" type so you should deduce that the answer to your question is no.

If you read the Help page for that tool, specifically the syntax section you can see what the data types are for each parameter, this is what it is telling you. As you will discover there are no parameters that are a "table of values" type so you should deduce that the answer to your question is no.


Process: Remove Join

All the tables have the same name aside from the FIPS number.

I have often found modelbuilder fails to run as expected under this scenario of making and breaking joins because parameters seem to hang on to previous values/settings. Thinks like field maps are a nightmare and never work! If this was me I would move to a python scripting environment where you have total control of parameter settings over complex loops. You are calling all the same tools just in a scripting environment.

Thanks for the reply. I have moved to a scripting environment but am getting this error: ExecuteError: Failed to execute. Parametri niso veljavni. The value cannot be a feature class ERROR 000840: The value is not a Raster Layer. ERROR 000840: The value is not a Raster Catalog Layer. ERROR 000840: The value is not a Mosaic Layer.

Can't really comment without seeing the code, suggest you amend your question and include the code?

I have a series of counties I need to update some information for. I have four tables, each with the FIPS# at the end of the table (a dbf). Na primer:
1. TabA007
2. TabB007
3. TabC007
4. TabD007

These tables get joined to a layer and a field calculator tool is used to grab the field values. There are roughly 25 steps in this whole process, and when I move on to a new county (say, 008), I have to change the 007 to 008 in every step, sometimes twice in the step.

For example, joined field "TableA007.FieldName007" now needs to become "TableA008.FieldName008". Setting the different steps as parameters doesn't work for a number of reasons, one being that each join needs to be indexed before it can move on. This tool has only worked in Edit mode.

Is there a way to tell the model to make anything that is of one number into another?

Here's some code with one table and one process (the one I am trying to work has 4 tables, 4 joins, and twenty field calculations. simple but elaborate)

workspace = r"C:UserscJonesOneDrive - ABCDocumentsNRCS_SoilsMasterNY115spatial"


Ključne besede

Dr. Jo Ellen Brandmeyer is an environmental engineer in the Environmental Programs group at MCNC–North Carolina Supercomputing Center. This work was part of her research program for obtaining a PhD in Environmental Sciences and Engineering at The University of North Carolina at Chapel Hill. Her current research includes coupling environmental models with geospatial data processing and development of emission processing tools.

Dr. Hassan A. Karimi has an interdisciplinary background and experience in geomatics engineering and computing science. He is an assistant professor at the University of Pittsburgh, where he is pursuing research on geomatics, uncertainty modeling and management in GISs, and computational geometry.


Field parameter in Modelbuilder - empty? - Geografski informacijski sistemi

Rob Baldwin Ryan Scherzinger Don Lipscomb Miranda Mockrin Susan Stein

Recent advances in planning and ecological software make it possible to conduct highly technical analyses to prioritize conservation investments and inform local land use planning. We review these tools, termed conservation planning tools, and assess the knowledge of a key set of potential users: the land use planning community. We grouped several conservation software .

Software deployment is needed to process and distribute scientific data throughout the data lifecycle. Developing software in-house can take software development teams away from other software development projects and can require efforts to maintain the software over time. Adopting and reusing software and system modules that have been previously developed by others can reduce in-house software development and maintenance costs and can contribute to the quality of the system being developed. A variety of models are available for reusing and deploying software and systems that have been developed by others. These deployment models include open source software , vendor-supported open source software , commercial software , and combinations of these approaches. Deployment in Earth science data processing and distribution has demonstrated the advantages and drawbacks of each model. Deploying open source software offers advantages for developing and maintaining scientific data processing systems and applications. By joining an open source community that is developing a particular system module or application, a scientific data processing team can contribute to aspects of the software development without having to commit to developing the software alone. Communities of interested developers can share the work while focusing on activities that utilize in-house expertise and addresses internal requirements. Maintenance is also shared by members of the community. Deploying vendor-supported open source software offers similar advantages to open source software . However, by procuring the services of a vendor, the in-house team can rely on the vendor to provide, install, and maintain the software over time. Vendor-supported open source software may be ideal for teams that recognize the value of an open source software component or application and would like to contribute to the effort, but do not have the time or expertise to contribute extensively. Vendor-supported software may

D’Souza, Malcolm J. Kashmar, Richard J. Hurst, Kent Fiedler, Frank Gross, Catherine E. Deol, Jasbir K. Wilson, Alora

Wesley College is a private, primarily undergraduate minority-serving institution located in the historic district of Dover, Delaware (DE). The College recently revised its baccalaureate biological chemistry program requirements to include a one-semester Physical Chemistry for the Life Sciences course and project-based experiential learning courses using instrumentation, data-collection, data-storage, statistical-modeling analysis, visualization, and computational techniques. In this revised curriculum, students begin with a traditional set of biology, chemistry, physics, and mathematics major core-requirements, a geographic information systems ( GIS ) course, a choice of an instrumental analysis course or a statistical analysis systems (SAS) programming course, and then, students can add major-electives that further add depth and value to their future post-graduate specialty areas. Open-sourced georeferenced census, health and health disparity data were coupled with GIS and SAS tools, in a public health surveillance system project, based on US county zip-codes, to develop use-cases for chronic adult obesity where income, poverty status, health insurance coverage, education, and age were categorical variables. Across the 48 contiguous states, obesity rates are found to be directly proportional to high poverty and inversely proportional to median income and educational achievement. For the State of Delaware, age and educational attainment were found to be limiting obesity risk-factors in its adult population. Furthermore, the 2004–2010 obesity trends showed that for two of the less densely populated Delaware counties Sussex and Kent, the rates of adult obesity were found to be progressing at much higher proportions when compared to the national average. PMID:26191337

D'Souza, Malcolm J Kashmar, Richard J Hurst, Kent Fiedler, Frank Gross, Catherine E Deol, Jasbir K Wilson, Alora

Wesley College is a private, primarily undergraduate minority-serving institution located in the historic district of Dover, Delaware (DE). The College recently revised its baccalaureate biological chemistry program requirements to include a one-semester Physical Chemistry for the Life Sciences course and project-based experiential learning courses using instrumentation, data-collection, data-storage, statistical-modeling analysis, visualization, and computational techniques. In this revised curriculum, students begin with a traditional set of biology, chemistry, physics, and mathematics major core-requirements, a geographic information systems ( GIS ) course, a choice of an instrumental analysis course or a statistical analysis systems (SAS) programming course, and then, students can add major-electives that further add depth and value to their future post-graduate specialty areas. Open-sourced georeferenced census, health and health disparity data were coupled with GIS and SAS tools, in a public health surveillance system project, based on US county zip-codes, to develop use-cases for chronic adult obesity where income, poverty status, health insurance coverage, education, and age were categorical variables. Across the 48 contiguous states, obesity rates are found to be directly proportional to high poverty and inversely proportional to median income and educational achievement. For the State of Delaware, age and educational attainment were found to be limiting obesity risk-factors in its adult population. Furthermore, the 2004-2010 obesity trends showed that for two of the less densely populated Delaware counties Sussex and Kent, the rates of adult obesity were found to be progressing at much higher proportions when compared to the national average.

Lee, Gong Hee Bang, Young Seok Woo, Sweng Woong Kim, Do Hyeong Kang, Min Ku

As the computer hardware technology develops the license applicants for nuclear power plant use the commercial CFD software with the aim of reducing the excessive conservatism associated with using simplified and conservative analysis tools. Even if some of CFD software developer and its user think that a state of the art CFD software can be used to solve reasonably at least the single-phase nuclear reactor problems, there is still limitation and uncertainty in the calculation result. From a regulatory perspective, Korea Institute of Nuclear Safety (KINS) is presently conducting the performance assessment of the commercial CFD software for nuclear reactor problems. In this study, in order to examine the validity of the results of 1/5 scaled APR+ (Advanced Power Reactor Plus) flow distribution tests and the applicability of CFD in the analysis of reactor internal flow, the simulation was conducted with the two commercial CFD software (ANSYS CFX V.14 and FLUENT V.14) among the numerous commercial CFD software and was compared with the measurement. In addition, what needs to be improved in CFD for the accurate simulation of reactor core inlet flow was discussed.

Jalali, M. Ali Ierodiaconou, Daniel Gorfine, Harry Monk, Jacquomo Rattray, Alex

Assessing patterns of fisheries activity at a scale related to resource exploitation has received particular attention in recent times. However, acquiring data about the distribution and spatiotemporal allocation of catch and fishing effort in small scale benthic fisheries remains challenging. Here, we used GIS -based spatio-statistical models to investigate the footprint of commercial diving events on blacklip abalone (Haliotis rubra) stocks along the south-west coast of Victoria, Australia from 2008 to 2011. Using abalone catch data matched with GPS location we found catch per unit of fishing effort (CPUE) was not uniformly spatially and temporally distributed across the study area. Spatial autocorrelation and hotspot analysis revealed significant spatiotemporal clusters of CPUE (with distance thresholds of 100’s of meters) among years, indicating the presence of CPUE hotspots focused on specific reefs. Cumulative hotspot maps indicated that certain reef complexes were consistently targeted across years but with varying intensity, however often a relatively small proportion of the full reef extent was targeted. Integrating CPUE with remotely-sensed light detection and ranging (LiDAR) derived bathymetry data using generalized additive mixed model corroborated that fishing pressure primarily coincided with shallow, rugose and complex components of reef structures. This study demonstrates that a geospatial approach is efficient in detecting patterns and trends in commercial fishing effort and its association with seafloor characteristics. PMID:25992800

Guo, Jiao Zhong, Ruofei Zeng, Fanyang

There is a general study on panoramic images which are presented along with appearance of the Google street map. Despite 360 degree viewing of street, we can realize more applications over panoramic images. This paper developed a toolkits plugged in Arc GIS , which can view panoramic photographs at street level directly from ArcMap and measure and capture all visible elements as frontages, trees and bridges. We use a series of panoramic images adjoined with absolute coordinate through GPS and IMU. There are two methods in this paper to measure object from these panoramic images: one is to intersect object position through a stereogram the other one is multichip matching involved more than three images which all cover the object. While someone wants to measure objects from these panoramic images, each two panoramic images which both contain the object can be chosen to display on ArcMap. Then we calculate correlation coefficient of the two chosen panoramic images so as to calculate the coordinate of object. Our study test different patterns of panoramic pairs and compare the results of measurement to the real value of objects so as to offer the best choosing suggestion. The article has mainly elaborated the principles of calculating correlation coefficient and multichip matching.

Kenow, K.P. Wright, R.G. Samuel, M.D. Rasmussen, P.W.

Radiotelemetry has been used commonly to remotely determine habitat use by a variety of wildlife species. However, habitat misclassification can occur because the true location of a radiomarked animal can only be estimated. Analytical methods that provide improved estimates of habitat use from radiotelemetry location data using a subsampling approach have been proposed previously. We developed software , based on these methods, to conduct improved habitat-use analyses. A Statistical Analysis System (SAS)-executable file generates a random subsample of points from the error distribution of an estimated animal location and formats the output into ARC/INFO-compatible coordinate and attribute files. An associated ARC/INFO Arc Macro Language (AML) creates a coverage of the random points, determines the habitat type at each random point from an existing habitat coverage, sums the number of subsample points by habitat type for each location, and outputs tile results in ASCII format. The proportion and precision of habitat types used is calculated from the subsample of points generated for each radiotelemetry location. We illustrate the method and software by analysis of radiotelemetry data for a female wild turkey (Meleagris gallopavo).

van Rooij, Shahron Williams

This exploratory study investigated the perceptions of technology and academic decision-makers about open source benefits and risks versus commercial software applications. The study also explored reactions to a concept for outsourcing campus-wide deployment and maintenance of open source. Data collected from telephone interviews were analyzed,…

Groleau, Nicolas Friedland, Peter (Technical Monitor)

In October 1993, the Astronaut Science Advisor (ASA) was on board the STS-58 flight of the space shuttle. ASA is an interactive system providing data acquisition and analysis, experiment step re-scheduling, and various other forms of reasoning. As fielded, the system runs on a single Macintosh PowerBook 170, which hosts the six ASA modules. There is one other piece of hardware, an external (GW Instruments, Sommerville, Massachusetts) analog-to-digital converter connected to the PowerBook's SCSI port. Three main software tools were used: LabVIEW, CLIPS, and HyperCard: First, a module written in LabVIEW (National Instruments, Austin, Texas) controls the A/D conversion and stores the resulting data in appropriate arrays. This module also analyzes the numerical data to produce a small set of characteristic numbers or symbols describing the results of an experiment trial. Second, a forward-chaining inference system written in CLIPS (NASA) uses the symbolic information provided by the first stage with a static rule base to infer decisions about the experiment. This expert system shell is used by the system for diagnosis. The third component of the system is the user interface, written in HyperCard (Claris Inc. and Apple Inc., both in Cupertino, California).

Hanzlick, R L Parrish, R G Ing, R

There are many ways of automating medical examiner and coroner offices, one of which is to purchase commercial software products specifically designed for death investigation. We surveyed four companies that offer such products and requested information regarding each company and its hardware, software , operating systems, peripheral devices, applications, networking options, programming language, querying capability, coding systems, prices, customer support, and number and size of offices using the product. Although the four products (CME2, ForenCIS, InQuest, and Medical Examiner's Software System) are similar in many respects and each can be installed on personal computers, there are differences among the products with regard to cost, applications, and the other features. Death investigators interested in office automation should explore these products to determine the usefulness of each in comparison with the others and in comparison with general-purpose, off-the-shelf databases and software adaptable to death investigation needs.

The use of commercially available digital computer systems and components in safety critical systems (nuclear power plant, military, and commercial applications) is increasing rapidly. While this paper focuses on the software aspects of the application most of these continents are applicable to the hardware aspects as well. Commercial dedication (the process of assuring that a commercial grade item will perform its intended safety function) has demonstrated benefits in cost savings and a wide base of user experience, however, care must be taken to avoid difficulties with some aspects of the dedication process such as access to vendor development information, configurationmore » management long term support, and system integration.« less

Castrogiovanni, E. M. La Loggia, G. Noto, L. V.

The aim of this work has been to implement a set of procedures useful to automatise the evaluation, the design storm prediction and the flood discharge associated with a selected risk level. For this purpose a Geographic Information System has been implemented using Grass 5.0. One of the main topics of such a system is a georeferenced database of the highest intensity rainfalls and their assigned duration recorded in Sicily. This database contains the main characteristics for more than 250 raingauges, as well as the values of intense rainfall events recorded by these raingauges. These data are managed through the combined use of the PostgreSQL and GRASS- GIS 5.0 databases. Some of the best-known probability distributions have been implemented within the Geographical Information System in order to determine the point and/or areal rain values once duration and return period have been defined. The system also includes a hydrological module necessary to compute the probable flow, for a selected risk level, at points chosen by the user. A peculiarity of the system is the possibility to querying the model using a web-interface. The assumption is that the rising needs of geographic information, and dealing with the rising importance of peoples participation in the decision process, requires new forms for the diffusion of territorial data. Furthermore, technicians as well as public administrators needs to get customized and specialist data to support planning, particularly in emergencies. In this perspective a Web-interface has been developed for the hydrologic system. The aim is to allow remote users to access a centralized database and processing-power to serve the needs of knowledge without complex hardware/ software infrastructures.

Advancements in high-throughput nucleotide sequencing techniques have brought with them state-of-the-art bioinformatics programs and software packages. Given the importance of molecular sequence data in contemporary life science research, these software suites are becoming an essential component of many labs and classrooms, and as such are frequently designed for non-computer specialists and marketed as one-stop bioinformatics toolkits. Although beautifully designed and powerful, user-friendly bioinformatics packages can be expensive and, as more arrive on the market each year, it can be difficult for researchers, teachers and students to choose the right software for their needs, especially if they do not have a bioinformatics background. This review highlights some of the currently available and most popular commercial bioinformatics packages, discussing their prices, usability, features and suitability for teaching. Although several commercial bioinformatics programs are arguably overpriced and overhyped, many are well designed, sophisticated and, in my opinion, worth the investment. If you are just beginning your foray into molecular sequence analysis or an experienced genomicist, I encourage you to explore proprietary software bundles. They have the potential to streamline your research, increase your productivity, energize your classroom and, if anything, add a bit of zest to the often dry detached world of bioinformatics. © The Author 2014. Published by Oxford University Press.

Advancements in high-throughput nucleotide sequencing techniques have brought with them state-of-the-art bioinformatics programs and software packages. Given the importance of molecular sequence data in contemporary life science research, these software suites are becoming an essential component of many labs and classrooms, and as such are frequently designed for non-computer specialists and marketed as one-stop bioinformatics toolkits. Although beautifully designed and powerful, user-friendly bioinformatics packages can be expensive and, as more arrive on the market each year, it can be difficult for researchers, teachers and students to choose the right software for their needs, especially if they do not have a bioinformatics background. This review highlights some of the currently available and most popular commercial bioinformatics packages, discussing their prices, usability, features and suitability for teaching. Although several commercial bioinformatics programs are arguably overpriced and overhyped, many are well designed, sophisticated and, in my opinion, worth the investment. If you are just beginning your foray into molecular sequence analysis or an experienced genomicist, I encourage you to explore proprietary software bundles. They have the potential to streamline your research, increase your productivity, energize your classroom and, if anything, add a bit of zest to the often dry detached world of bioinformatics. PMID:25183247

Kadoya, Noriyuki, E-mail: [email protected] Nakajima, Yujiro Saito, Masahide

Purpose: To assess the accuracy of the commercially available deformable image registration (DIR) software for thoracic images at multiple institutions. Methods and Materials: Thoracic 4-dimensional (4D) CT images of 10 patients with esophageal or lung cancer were used. Datasets for these patients were provided by DIR-lab ( (dir-lab.com)) and included a coordinate list of anatomic landmarks (300 bronchial bifurcations) that had been manually identified. Deformable image registration was performed between the peak-inhale and -exhale images. Deformable image registration error was determined by calculating the difference at each landmark point between the displacement calculated by DIR software and that calculated bymore » the landmark. Results: Eleven institutions participated in this study: 4 used RayStation (RaySearch Laboratories, Stockholm, Sweden), 5 used MIM Software (Cleveland, OH), and 3 used Velocity (Varian Medical Systems, Palo Alto, CA). The ranges of the average absolute registration errors over all cases were as follows: 0.48 to 1.51 mm (right-left), 0.53 to 2.86 mm (anterior-posterior), 0.85 to 4.46 mm (superior-inferior), and 1.26 to 6.20 mm (3-dimensional). For each DIR software package, the average 3-dimensional registration error (range) was as follows: RayStation, 3.28 mm (1.26-3.91 mm) MIM Software , 3.29 mm (2.17-3.61 mm) and Velocity, 5.01 mm (4.02-6.20 mm). These results demonstrate that there was moderate variation among institutions, although the DIR software was the same. Conclusions: We evaluated the commercially available DIR software using thoracic 4D-CT images from multiple centers. Our results demonstrated that DIR accuracy differed among institutions because it was dependent on both the DIR software and procedure. Our results could be helpful for establishing prospective clinical trials and for the widespread use of DIR software . In addition, for clinical care, we should try to find the optimal DIR procedure using

Kadoya, Noriyuki Nakajima, Yujiro Saito, Masahide Miyabe, Yuki Kurooka, Masahiko Kito, Satoshi Fujita, Yukio Sasaki, Motoharu Arai, Kazuhiro Tani, Kensuke Yagi, Masashi Wakita, Akihisa Tohyama, Naoki Jingu, Keiichi

To assess the accuracy of the commercially available deformable image registration (DIR) software for thoracic images at multiple institutions. Thoracic 4-dimensional (4D) CT images of 10 patients with esophageal or lung cancer were used. Datasets for these patients were provided by DIR-lab (dir-lab.com) and included a coordinate list of anatomic landmarks (300 bronchial bifurcations) that had been manually identified. Deformable image registration was performed between the peak-inhale and -exhale images. Deformable image registration error was determined by calculating the difference at each landmark point between the displacement calculated by DIR software and that calculated by the landmark. Eleven institutions participated in this study: 4 used RayStation (RaySearch Laboratories, Stockholm, Sweden), 5 used MIM Software (Cleveland, OH), and 3 used Velocity (Varian Medical Systems, Palo Alto, CA). The ranges of the average absolute registration errors over all cases were as follows: 0.48 to 1.51 mm (right-left), 0.53 to 2.86 mm (anterior-posterior), 0.85 to 4.46 mm (superior-inferior), and 1.26 to 6.20 mm (3-dimensional). For each DIR software package, the average 3-dimensional registration error (range) was as follows: RayStation, 3.28 mm (1.26-3.91 mm) MIM Software , 3.29 mm (2.17-3.61 mm) and Velocity, 5.01 mm (4.02-6.20 mm). These results demonstrate that there was moderate variation among institutions, although the DIR software was the same. We evaluated the commercially available DIR software using thoracic 4D-CT images from multiple centers. Our results demonstrated that DIR accuracy differed among institutions because it was dependent on both the DIR software and procedure. Our results could be helpful for establishing prospective clinical trials and for the widespread use of DIR software . In addition, for clinical care, we should try to find the optimal DIR procedure using thoracic 4D-CT data. Copyright © 2016 Elsevier Inc. All rights

Laubenthal, N. A. Bertsch, D. Lal, N. Etienne, A. Mcdonald, L. Mattox, J. Sreekumar, P. Nolan, P. Fierro, J.

The Energetic Gamma Ray Telescope Experiment (EGRET) on the Compton Gamma Ray Observatory has been in orbit for more than a year and is being used to map the full sky for gamma rays in a wide energy range from 30 to 20,000 MeV. Already these measurements have resulted in a wide range of exciting new information on quasars, pulsars, galactic sources, and diffuse gamma ray emission. The central part of the analysis is done with sky maps that typically cover an 80 x 80 degree section of the sky for an exposure time of several days. Specific software developed for this program generates the counts, exposure, and intensity maps. The analysis is done on a network of UNIX based workstations and takes full advantage of a custom-built user interface called X-dialog. The maps that are generated are stored in the FITS format for a collection of energies. These, along with similar diffuse emission background maps generated from a model calculation, serve as input to a maximum likelihood program that produces maps of likelihood with optional contours that are used to evaluate regions for sources. Likelihood also evaluates the background corrected intensity at each location for each energy interval from which spectra can be generated. Being in a standard FITS format permits all of the maps to be easily accessed by the full complement of tools available in several commercial astronomical analysis systems. In the EGRET case, IDL is used to produce graphics plots in two and three dimensions and to quickly implement any special evaluation that might be desired. Other custom-built software , such as the spectral and pulsar analyses, take advantage of the XView toolkit for display and Postscript output for the color hard copy. This poster paper outlines the data flow and provides examples of the user interfaces and output products. It stresses the advantages that are derived from the integration of the specific instrument-unique software and powerful commercial tools for graphics and


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