re is my problem... I need to arrange Boxes in the most efficient way within boundaries of object and follow the following constraints.
The Goal: To fit 125 boxes in the most efficient way inside the total area. Starting Variables:
(1) 40% of the Boxes need to be between 60 and 85MSQ. (2) 40% of the boxes need to be between 86 and 110MSQ.
(3) 20% of the boxes need to be between 111 and 125mSQ. The breakdown doesn’t have to be exact to give the script some flexibility. Meaning you can have 41% +39% +20% = 100%.
Constraints:
1. A total MAXIMUM area of approximately 1600M per layer.
2. A maximum of 8 layers for a total of 12,800M per layer. Optimization can make as little or as many as 8 layers vertical to accommodate all boxes. So if script can achieve with 3 levels great. If needed all 8 levels, that's fine too. However, pay attention to next constraint (#3).
3. Approximately 15% of that space on each layer is off limits. (internal area) (blue area in example script) and the shape of the boundary cannot be modified to accommodate box design resulting in jagged lines for the internal area.
4. All generated squares/rectangles must have at least 3m touching an outside border (The Green lines).
5. All boxes must also be touching minimum 1M of border of the blue line.
6. If the boxes generated go outside the green boundary, they must be fillet to maintain the straight lines of the green boundaries.
7. Get as many of the boxes as possible a view towards the dots.
Could any one provide me a method or a way to start, if there are any useful links, please share with me. Thank you!
…
ey eventually recover and you can continue to working normally. This however is not very practical...
(Additional information: We have a virtualized Windows SPS environment, might this be the problem? Locally - on my hard drive - it works fine.)
Futhermore we've discovered the following bug/feature:
We export a cluster and reference it back into our .gh file, then copy the .ghcluster file to a different location and rename the copy (without opening or changing it), then also reference the copied version back into the .gh file. Now Grasshopper shows two clusters with two different file paths, but claims that they both are the same ("this cluster occurs twice in this document"). If I double click one of them, make a change and save, both clusters get changed, even though they are separate .ghcluster files.
This would follow the logic that David laid out in this entry (http://www.grasshopper3d.com/page/clusters09), that GH identifies a cluster not by its file name or location but by its internal ID.
An addition we would very much appreciate for the next GH update, would be the option to right click a referenced cluster and then not only be able to "update" it but to also to "relink" it to a new or different source.
Right now you have to rename or delete the .ghcluster file in order to relink a cluster via the update option. You can also overwrite the old cluster und update. However, sometimes we want to keep the old version or disentangle one of a clusters many instances and relink just one, with out loosing its various inputs and outputs by referencing the new version and reconnecting it.
Thanks, BB.…
H are automated by using them as an ActiveX, the C# script object fails on the simplest tasks. That is, when initiating Rhino and GH externally (as by the following C# code):
Rhino5Application rhino_app = new Rhino5Application();
dynamic grasshopper = newRhino.rhino_app.GetPlugInObject("b45a29b1-4343-4035-989e-044e8580d9cf", "00000000-0000-0000-0000-000000000000") as dynamic;
The following very simple C# script component fails because it cant cast its input:
The c# code at the component is only:
Line 89 is simply casting of the input. Clearly, this makes the usage of C# component, under automation, impossible which is a major loss.
As said, when initiating Rhino and GH manually , all works well as in the following:
Any ideas why it misbehaves under automation (as an Active X ) ?
I added the gh file of this example.…
he concept, moving on to decision making and continuing with digital and generative design tools TO GET THE BEST SOLUTION for each problem.
WHY? The world is complex and ever-changing and we need to be able to handle the volume of information we receive and, of course, to find and choose the best solution. Therefore, we direct our ATTENTION TO THE CAUSE, and not only on the effects/solutions.
We will learn from NATURE, the only “company” that has not gone bankrupt in over 4000M years, and it’s GENERATIVE SOLUTIONS.
> OBJECTIVES <
The participants will work in multidisciplinary groups (ex. architect + designer + business manager + constructor + communication specialist etc.) applying knowledge management tools, different approaches and nature-based optimization methods.
Listed objectives:
1. Improving the generative way of TURNING AN IDEA INTO A PROJECT through problem-solving thinking
2. Discovering nature’s ways of shaping evolutionary solutions
3. Getting out from our comfort zone and working together with other professionals in groups in order to achieve better solutions: Multidisciplinary Design Optimization
4. Learning to use technology to manage information in the decision making process
& surviving the whole week
> ATTENDANCE & COSTS <
> Early bird – until 17th March 2013
Lecture – 15 euro (includes presentations, food& drinks)
Workshop – 100 euro (includes lecture, food& drinks)
> Late bird – until 6th April 2013
Lecture – 25 euro (includes presentations, food& drinks)
Workshop – 120 euro (includes lecture, food& drinks)
…
le with you.
I am trying to achieve the minimal path algorithm of Steiners tree in Python using the minimal path algorithm.The syntax would be as followsFirst I need to create a cube of any dimension.
Then I need to specify one origin say point A and destination point say B.
Now for this point A,B I need to create a machine based network which will automatically enroute A to B.
Where the angle will be constant i.e 120, length can be a variable, triangular node(steiners tree)using these constraints it will create a network.
Now, I should iterate the program in such a way that I should specify the further points say like A1 and B1 so on.The program will contain a limit constraint where it will come out of iteration loop and start a new loop,forming the network.
By this I will get a dense network of 120 deg branches.
The branching gets denser the moment I add source and destination points.
There can be 100 iterations to reach from A to B but the algorithm chooses the one following the minimal path.
I would be highly thankful to you if you would please share the python syntax and grasshopper definitionCapture.JPG for the same
Thank you for your time in advance
I would be highly grateful if you help me through
warm regards
Arya
12.gifShortest%20path%20algorithm.gh
min-paths.jpgcc.henn.studyimagesminimalpaths.jpg …
o, presso la sede Eurac e il TIS, nei giorni 21,22 e 23 maggio 2015.
Il processo di progettazione integrata è riconosciuto come metodo per ottenere gli elevati livelli di qualità oggi richiesti agli edifici. Con questo approccio diventano sempre più rilevanti il comfort visivo e la gestione dell’illuminazione naturale in relazione al risparmio energetico. Di fatto, il nuovo protocollo Leed v4 riconosce crediti ad hoc e conferma l’importanza della progettazione daylighting per “collegare gli occupanti con lo spazio esterno, rinforzare i ritmi circadiani, ridurre l’uso dell’illuminazione elettrica con l’introduzione della luce naturale negli spazi”.
Una progettazione robusta richiede l’uso di strumenti di simulazione efficaci e Radiance è riconosciuto come uno dei software con le capacità di fornire risultati affidabili. Radiance è utilizzato sia a livello di ricerca che tra i progettisti, ed è tra i più accurati per la simulazione professionale della luce naturale ed artificiale. Non ha limiti di complessità geometrica ed è adatto a essere integrato in altri software di calcolo e interfacce grafiche. Le principali e più versatili tra queste (DIVA4Rhino, plug-ins per Grasshopper e Rhinoceros3D), essendo in grado di facilitare notevolmente le procedure di programmazione, saranno oggetto del corso.
Il corso è rivolto a progettisti e ricercatori che vogliano acquisire strumenti pratici per la simulazione con Radiance al fine di mettere a punto e verificare le soluzioni più adatte alle proprie esigenze. Sono previste lezioni di teoria e pratica con esempi ed esercitazioni volte a coprire in modo dimostrativo ed interattivo i concetti trattati.
Il corso viene riconosciuto con 15 crediti dall’Ordine degli Architetti.
Le domande di iscrizione devono essere presentate entro il 27 aprile 2015.
Scarica la brochure con tutte le informazioni Corso Radiance - EURAC.pdf
Il corso è sponsorizzato da Pellinindustrie.…
hop innovativo sulle prospettive e sfide future del design computazionale.
INFO ED ISCRIZIONI
PLUG IT | Rhino + Grasshopper | Livello Base | Modellazione parametrica e controllo di forme complesse
Plug it, primo step del percorso formativo in tre fasi “AAD Workshop Series“. Plug it fornirà ai partecipanti un’effettiva padronanza delle più avanzate tecniche di modellazione digitale, approfondendo le metodologie della modellazione algoritmica e parametrica nel campo dell’architettura e del design del prodotto. Il corso è rivolto a studenti e professionisti dei settori della progettazione architettonica, design, moda e gioielleria, con esperienza minima nel disegno CAD bidimensionale (acquisita su qualsiasi piattaforma software) e si articolerà in lezioni teoriche frontali ed esercitazioni guidate
FORM FINDING STRATEGIES | Livello Intermedio | Analisi ambientale ed ottimizzazione della forma
Form Finding Strategies è il secondo step del percorso formativo in tre fasi “AAD Workshop Series“. Il workshop intende esplorare le possibilità di generazione di forme efficienti in relazione ad influenze esterne ed alle caratteristiche intrinseche della materia stessa. Analisi ambientale (input solari, termici ed acustici) ed analisi/ottimizzazione strutturale FEM saranno le principali metodologie utilizzate per raggiungere gli obiettivi di ricerca della forma. Saranno introdotti numerosi plug-ins tra cui: Weaverbird, Kangaroo, Geco/Ecotect, Ladybug, Millipede. Il corso si rivolge a studenti e professionisti con conoscenza base di Rhino e Grasshopper.
PERSPECTIVES | Livello Avanzato | Python coding e modellazione algoritmica avanzata
Il nuovo corso Perspectives proposto per la prima volta nel 2019 (ed ultimo step del percorso formativo in tre fasi “AAD Workshop Series) introdurrà gli studenti alla programmazione Python ed alla sua integrazione con Grasshopper. Verranno inoltre esplorate tecniche avanzate di generazione formale basate su iterazioni. Tra i principali plugins utilizzati: GhPython, Anemone, Hoopsnake, Plankton, MeshMachine, Pufferfish. Pensato come workshop innovativo sulle prospettive e sfide future del design computazionale, è rivolto a studenti e professionisti con esperienza in modellazione algoritmica con Grasshopper.…