t ''Morph'' turns Red saying ''Cannot morph from a degenerate box'' (image 2),
that's because every curve generates a box (image 3).
After what i check the Option ''Union'' box to make only one box for all the curves (image 4).
However, the result is aleatory and not accurate at all ... :/ (see image 6).I know you are developing Pufferfish and not ''Morph'' component, but recently you publish on instagram a video where i believe you could morph and Twist with success a collection of curves (please see image 7 and 8)...If you could give me a hint how that can be achieved, it would be awesome.(Piping/Meshing the curves with very small diameter will perhaps work and help for visualisation purposes, but i actually just need morphing Raw curves for fabrication purposes).Hope to read you very soon...Ghali,…
to a rationalized integer format for use in the grasshopper definition . I don't want to convert the information permanently, but have been looking for an elegant method for converting the datetime string as needed so that it can interface within a grasshopper definition.
What I am trying to do is create a process for working with the timestamp included alongside sensor data I pull from Cosm. The data feed is formatted like this; <current_value at="2012-07-25T05:24:44.601988Z">77.68</current_value> and the Time paramater seems to easily understand the datetime string to read 2012-07-25T05:24:44.601988 as Wednesday, 25 July 2012 (05:24:44) so I plan on keeping the string as is when stored in a local mySQL database I have setup, but want to make sure I am not setting myself up for major headaches down the road.
I asked about converting the date time string to an integer to allow me to find time spans and generate a series of time stamps in an efficient way for creating a batch/ iterative process for requesting additional sets of data based on a query comparison of what gaps are left to fill in along a timeline of data points.
At the moment, I think a conversion to an integer is my best bet. (ref; Excel Date Time Code Id love to be able to feed in a date into a math component, add 08:00:00 and for it to result with a time 8 hours later than the first. I also have a string manipulation method working also, but have yet to really test it beyond what I have shown in the screenshot below.
I currently have these batches stream from Cosm.com into Grasshopper via a xml parser component (either the one found in Andy Payne's Firefly set, or the one included in gHowl.) From there I sort/ extract the data points and their respective time stamps and feed these data points into a mySQL database I've setup with Nathan Miller's Slingshot components. With the points in a local SQL database, I can then begin to integrate the use of database queries into definitions and retrieve data points based on sensor value Booleans rather than being restricted to time spans, (which is the only way to request historical data from Cosm's site)
I've been debating on if I should convert all the time information to an integer before writing it to the SQL database so GH can work with the data, or if I should keep the time code as a string and create a couple of conversation component clusters to translate the information each time it needs to be processed or manipulated in GH. I'd prefer to keep things in the date time string because the database can handle it without a problem, GH sort of handles it in one translation direction and allows me to avoid conversion that Id have to explain to others if they were to access the database in their own grasshopper definition or from another application.…
an run. GH2 still uses the Rhino SDK for the geometry functionality, so curve offsets, meshing, brep intersections etc. will run exactly as fast as they do now.
However even in the absence of a working version of GH2 which can be profiled, we can still discuss some of the major aspects of performance:
Preview display. Each GH solution involves a redraw of all the Rhino viewports at the end (unless is preview is switched off, which I imagine is exceedingly rare). For simple GH files, the viewport redraw takes far more time than the solution. Rhino6 has a completely rewritten display pipeline using more modern APIs so we should see a speed-up here in the future, be it GH1 or GH2 or GHx.
Canvas display. Each GH solution involves a redraw of the Grasshopper canvas. If the canvas shows a lot of bitmaps or intricate geometry (lots or text, dense graphs, etc.) this can take a significant amount of time. GH2 will use Eto instead of GDI+ as a UI platform. Eto can be both faster and slower than GDI, depending on what's being drawn. It is particularly fast when drawing images, not so much when drawing lots of lines. There is a little room for improvement here and I intend to take full advantage of that.
Preview meshing. Grasshopper uses standard Rhino mesher to generate preview meshes. If a GH file generates lots of breps, a large amount of time will be required to create the preview meshes. The new display improvements in Rhino6 will allow us to get away with previewing some types of geometry without the need to mesh them first, and I imagine some effort will be spend in the near future to improve the Rhino mesher as well.
Data casting. Most component code operates on standard framework and RhinoCommon types (bool, int, string, Point3d, Curve, Brep, ...), however Grasshopper stores and transfers data wrapped up in IGH_Goo derived types. This means that every time a component 'does it's thing', data needs to be converted from one type into another, and then back again. This involves type-checking and often type instantiation. This stuff is fast, but it's overhead nonetheless and can take significant amount of processor cycles when there's lots of data. GH2 no longer does this, it stores and transfers the types directly as they are. There will still be some overhead left, but hopefully a lot less.
Computation. GH1 is a single-threaded application. When a component operates on a large collection of data, each iteration waits for the next. GH2 will be parallel, meaning components will be invoked on multiple threads, each thread focusing only on part of the data. Then all the results need to be merged back into a single data tree. On my 8-core machine (4 physical cores, each with 2 logical cores) I've been getting performance speed-ups of 4~6 times when using my multi-threading code. I wish it was 8, but clearly there is some overhead involved here as well.This will not help to speed up a single very complicated solid boolean operation, but if you're offsetting 800 curves, then each thread can be assigned 100 curves and the time it takes will set by whatever thread takes the longest.
Algorithms. If a specific component is slow, there may be things we can do to speed it up. Either improve the Rhino SDK, or improve the GH code. Depends on the component in question.
When all's said and done, I'd love to see a 10x speed increase for GH2 over GH1 for simplish stuff, and I shall get very cross if it's anything less than 5x.…
ino:
Go to "Windows Control Panel", then "Programs and Features", then find "Rhinoceros 5 (64-bit)" and "Rhinoceros", select and "Repaire".
Permalink Reply by Heath on August 14, 2013 at 1:13pm
I got it to work, thanks.
Permalink Reply by Akche MacEshwa on August 22, 2013 at 8:20pm
Right click the .rhi file and open it with rhino execution wizard which is located in Rhino directory. Good luck.
…
Added by Adam Donner at 5:38pm on September 19, 2013
ace Syntax." eCAADe 2013 18 (2013): 357.
http://www.sss9.or.kr/paperpdf/mmd/sss9_2013_ref048_p.pdf
The measure Entropy is newer. I hereby explain it (from my PhD dissertation):
Entropy values, as described in (Hillier & Hanson, The Social Logic of Space, 1984) and specified in (Turner A. , “Depthmap: A Program to Perform Visibility Graph Analysis, 2007), intuitively describe the difficulty of getting to other spaces from a certain space. In other words, the higher the entropy value, the more difficult it is to reach other spaces from that space and vice-versa. We compute the spatial entropy of the node as using the point depth set:
(11)
“The term is the maximum depth from vertex and is the frequency of point depth *d* from the vertex” (ibid). Technically, we compute it using the function below, which itself uses some outputs and by-products from previous calculations:
Algorithm 4: Entropy Computation
Given the graph (adjacency lists), Depths as List of List of integer, DepthMap as Dictionary of integer
Initialize Entropies as List(double)
For node as integer in range [0, |V|)
integer How_Many_of_D=0
double S_node=0
For depth as integer in range [1, Depths[node].Max()]
How_Many_of_D=DepthMap.Branch[(node,depth)].Count
double frequency= How_Many_of_D/|V|
S_node = S_node - frequency * Math.Log(frequency, 2)
Next
Entropies [node] = S_node
Next
…
t file** - ply file with just x,y,z locations. I got it from a 3d scanner. Here is how first few lines of file looks like - ply format ascii 1.0 comment VCGLIB generated element vertex 6183 property float x property float y property float z end_header -32.3271 -43.9859 11.5124 -32.0631 -43.983 11.4945 12.9266 -44.4913 28.2031 13.1701 -44.4918 28.2568 13.4138 -44.4892 28.2531 13.6581 -44.4834 28.1941 13.9012 -44.4851 28.2684 ... ... ... In case you need the data - please email me on **nisha.m234@gmail.com**. **Algorithm:** I am trying to find principal curvatures for extracting the ridges and valleys. The steps I am following is: 1. Take a point x 2. Find its k nearest neighbors. I used k from 3 to 20. 3. average the k nearest neighbors => gives (_x, _y, _z) 4. compute covariance matrix 5. Now I take eigen values and eigen vectors of this covariance matrix 6. I get u, v and n here from eigen vectors. u is a vector corresponding to largest eigen value v corresponding to 2nd largest n is 3rd smallest vector corresponding to smallest eigen value 7. Then for transforming the point(x,y,z) I compute matrix T T = [ui ] [u ] [x - _x] [vi ] = [v ] x [y - _y] [ni ] [n ] [z - _z] 8. for each i of the k nearest neighbors:<br> [ n1 ] [u1*u1 u1*v1 v1*v1] [ a ]<br> [ n2 ] = [u2*u2 u2*v2 v2*v2] [ b ] <br> [... ] [ ... ... ... ] [ c ] <br> [ nk ] [uk*uk uk*vk vk*vk]<br> Solve this for a, b and c with least squares 9. this equations will give me a,b,c 10. now I compute eigen values of matrix [a b b a ] 11. This will give me 2 eigen values. one is Kmin and another Kmax. **My Problem:** The output is no where close to finding the correct Ridges and Valleys. I am totally Stuck and frustrated. I am not sure where exactly I am getting it wrong. I think the normal's are not computed correctly. But I am not sure. I am very new to graphics programming and so this maths, normals, shaders go way above my head. Any help will be appreciated. **PLEASE PLEASE HELP!!** **Resources:** I am using Visual Studio 2010 + Eigen Library + ANN Library. **Other Options used** I tried using MeshLab. I used ball pivoting triangles remeshing in MeshLab and then applied the polkadot3d shader. If correctly identifies the ridges and valleys. But I am not able to code it. **My Function:** //the function outputs to ply file void getEigen() { int nPts; // actual number of data points ANNpointArray dataPts; // data points ANNpoint queryPt; // query point ANNidxArray nnIdx;// near neighbor indices ANNdistArray dists; // near neighbor distances ANNkd_tree* kdTree; // search structure //for k = 25 and esp = 2, seems to got few ridges queryPt = annAllocPt(dim); // allocate query point dataPts = annAllocPts(maxPts, dim); // allocate data points nnIdx = new ANNidx[k]; // allocate near neigh indices dists = new ANNdist[k]; // allocate near neighbor dists nPts = 0; // read data points ifstream dataStream; dataStream.open(inputFile, ios::in);// open data file dataIn = &dataStream; ifstream queryStream; queryStream.open("input/query.
pts", ios::in);// open data file queryIn = &queryStream; while (nPts < maxPts && readPt(*dataIn, dataPts[nPts])) nPts++; kdTree = new ANNkd_tree( // build search structure dataPts, // the data points nPts, // number of points dim); // dimension of space while (readPt(*queryIn, queryPt)) // read query points { kdTree->annkSearch( // search queryPt, // query point k, // number of near neighbors nnIdx, // nearest neighbors (returned) dists, // distance (returned) eps); // error bound double x = queryPt[0]; double y = queryPt[1]; double z = queryPt[2]; double _x = 0.0; double _y = 0.0; double _z = 0.0; #pragma region Compute covariance matrix for (int i = 0; i < k; i++) { _x += dataPts[nnIdx[i]][0]; _y += dataPts[nnIdx[i]][1]; _z += dataPts[nnIdx[i]][2]; } _x = _x/k; _y = _y/k; _z = _z/k; double A[3][3] = {0,0,0,0,0,0,0,0,0}; for (int i = 0; i < k; i++) { double X = dataPts[nnIdx[i]][0]; double Y = dataPts[nnIdx[i]][1]; double Z = dataPts[nnIdx[i]][2]; A[0][0] += (X-_x) * (X-_x); A[0][1] += (X-_x) * (Y-_y); A[0][2] += (X-_x) * (Z-_z); A[1][0] += (Y-_y) * (X-_x); A[1][1] += (Y-_y) * (Y-_y); A[1][2] += (Y-_y) * (Z-_z); A[2][0] += (Z-_z) * (X-_x); A[2][1] += (Z-_z) * (Y-_y); A[2][2] += (Z-_z) * (Z-_z); } MatrixXd C(3,3); C <<A[0][0]/k, A[0][1]/k, A[0][2]/k, A[1][0]/k, A[1][1]/k, A[1][2]/k, A[2][0]/k, A[2][1]/k, A[2][2]/k; #pragma endregion EigenSolver<MatrixXd> es(C); MatrixXd Eval = es.eigenvalues().real().asDiagonal(); MatrixXd Evec = es.eigenvectors().real(); MatrixXd u,v,n; double a = Eval.row(0).col(0).value(); double b = Eval.row(1).col(1).value(); double c = Eval.row(2).col(2).value(); #pragma region SET U V N if(a>b && a>c) { u = Evec.row(0); if(b>c) { v = Eval.row(1); n = Eval.row(2);} else { v = Eval.row(2); n = Eval.row(1);} } else if(b>a && b>c) { u = Evec.row(1); if(a>c) { v = Eval.row(0); n = Eval.row(2);} else { v = Eval.row(2); n = Eval.row(0);} } else { u = Eval.row(2); if(a>b) { v = Eval.row(0); n = Eval.row(1);} else { v = Eval.row(1); n = Eval.row(0);} } #pragma endregion MatrixXd O(3,3); O <<u, v, n; MatrixXd UV(k,3); VectorXd N(k,1); for( int i=0; i<k; i++) { double x = dataPts[nnIdx[i]][0];; double y = dataPts[nnIdx[i]][1];; double z = dataPts[nnIdx[i]][2];; MatrixXd X(3,1); X << x-_x, y-_y, z-_z; MatrixXd T = O * X; double ui = T.row(0).col(0).value(); double vi = T.row(1).col(0).value(); double ni = T.row(2).col(0).value(); UV.row(i) << ui * ui, ui * vi, vi * vi; N.row(i) << ni; } Vector3d S = UV.colPivHouseholderQr().solve(N); MatrixXd II(2,2); II << S.row(0).value(), S.row(1).value(), S.row(1).value(), S.row(2).value(); EigenSolver<MatrixXd> es2(II); MatrixXd Eval2 = es2.eigenvalues().real().asDiagonal(); MatrixXd Evec2 = es2.eigenvectors().real(); double kmin, kmax; if(Eval2.row(0).col(0).value() < Eval2.row(1).col(1).value()) { kmin = Eval2.row(0).col(0).value(); kmax = Eval2.row(1).col(1).value(); } else { kmax = Eval2.row(0).col(0).value(); kmin = Eval2.row(1).col(1).value(); } double thresh = 0.0020078; if (kmin < thresh && kmax > thresh ) cout << x << " " << y << " " << z << " " << 255 << " " << 0 << " " << 0 << endl; else cout << x << " " << y << " " << z << " " << 255 << " " << 255 << " " << 255 << endl; } delete [] nnIdx; delete [] dists; delete kdTree; annClose(); } Thanks, NISHA…
2013 | mayo 30, 31 y 1 de junio. 15 Hrs.
Horario: 18:00 – 22.00 Jueves, Viernes y Sábado de 8:00 a 16:00 Hrs. Instructor_ Arch. David Hernández Melgarejo.
http://bioarchitecturestudio.wordpress.com
Objetivos:
El curso está dirigido a cada diseñador, ingeniero o arquitecto que quiere obtener una sólida base en modelado generativo y paramétrico dentro del flujo de trabajo en Rhinoceros.
En el curso se explorarán y construirán estructuras en el espacio paramétrico, incorporando entidades geométricas (Curvas, Superficies, Puntos, etc…) y usando patrones algorítmicos.
Cada paso será soportado con ejercicios que gradualmente incrementarán su complejidad.
El alumno aprenderá cómo trabajar con asociación geométrica y parámetros. Para perfeccionar asociación geométrica – asociación entre partes, asociación dinámica – las formas geométricas son generadas al seguir la conexión lógica entre la parte geométrica y su restricción, dimensión paramétrica y él proceso dinámico del diseño: en pocas palabras estimulamos el pensamiento relacional.
Resultados:
Los participantes con éste entrenamiento obtendrán las siguientes fundamentos.
· Construir, modificar, depurar y correr aplicaciones de Grasshopper.
· Comprender el editor gráfico algorítmico y sus patrones usando grupos y cables conectores.
· Trabajar con dimensiones dinámicas, parámetros y listas.
· Generar aplicaciones orientadas a la documentación del diseño y la fabricación.
Palabras clave:
Diseño Computacional, Scripting, Rhinoceros 5.0 + Grasshopper, Parametrización, Análisis, Fabricación Digital.
Para mayor información:
MArch. Kathrin Schröter. E-mail: kschroter@itesm.mx
Dirección de Arquitectura. Oficinas de Aulas 1, segundo piso.…
st variety of papers (mostly related with LIDAR airborne sampled clouds) ... but ... hmm ... no code (other than some "abstract" algos that may (or may not) work). Reason? A very hot cake that one these days: from reverse engineering to DARPA founded future defense systems and up to cruse missiles pattern recognition algos.
The solution (obviously doable only via code) is the so called flat hard clustering ... were points are sampled into clusters based on the coPlanarity "rule". For large amounts recursive octTrees (an oriented box divided in 8 "partitions") subdivisions are used and then pts are processed in parallel (and then clusters are re-evaluated in order to "absorb" other clusters with same plane A,B,C,D vars etc etc).
See what's happening in a very carefully made test point collection:
3.7 ms and the "ideal" clustering (7 search loops VS the max 42M theoretical threshold):
Depending on the pts "preparation" ... a considerable more time/search loops is required ... and ... well ... also "valid" clusters (4 points and up) made:
So "ideally" speaking in your case:
1. Mesh faces center points (or alternatively: mesh vertices) are sampled into a pts collection .
2. Hard flat coPlanarity clustering is attempted yielding pts/planes in equivalent DataTrees.
3. Planar Breps are made with respect the planes (like the black things captured above) and sampled, say, into a breps List.
4. The method Brep[] solids = Brep.CreateSolid(breps); is used for attempting to create your desired "engulfing" brep. This method is very slow mind (other waaaay faster approaches also available).
…
that both the ASHRAE and European Adaptive models were derived from surveys of awake occupants. While the topic has not been investigated as well as it should be, the few adaptive-style surveys of sleeping occupants that have been conducted show that people tend to desire significantly cooler temperatures when they are sleeping as opposed to when they are awake.
Notably, Chapter 8 of Humphrey's recently-published book on Adaptive Comfort (https://books.google.com/books?id=lOZzCgAAQBAJ&printsec=frontcover&dq=Adaptive+Thermal+Comfort+Foundations+and+analysis&hl=en&sa=X&ved=0ahUKEwi6npqSi__KAhUJMj4KHf7SCXMQ6AEIKjAA#v=onepage&q=Adaptive%20Thermal%20Comfort%20Foundations%20and%20analysis&f=false) provides some interesting insights into this. In a 1973 survey, Humphreys found that the quality of sleep started to deteriorate at temperatures above 24-26C regardless of the time of year and that there was no clearly-determinable lower limit to comfortable sleeping temperatures (in other words, people were fine at 12C if they were given enough blankets). He surveyed only British occupants who were sleeping in traditional beds with mattresses and a wide range of blankets. This is important because the nature of the findings is such that the comfort temperatures would be very different if the survey participants had been sleeping in a hammock or in closer contact with the ground (both popular practices for a number of cultures living in warmer climates). Traditional mattresses cut the ability to radiate body heat in half as compared to a standing human body and I would venture a guess that this is a big reason why much cooler temperatures are desired while sleeping on mattresses as opposed to standing awake/uptight.
So for your case, if you want to account for a time of the day that occupants are sleeping on mattresses, I would change the comfort temperature for this these hours down to 24C. Otherwise, if you are trying to show the comfortable hours of awake people in your space, your current 100% comfortable nighttime hours are a better estimate. I have also noticed that nighttime temperatures become comfortable in extreme weeks of hot/dry climates. This is what is happening in this extreme week simulation of Los Angeles' San Fernando Valley here:
https://www.youtube.com/watch?v=WJz1Eojph8E&index=3&list=PLruLh1AdY-Sj3ehUTSfKa1IHPSiuJU52A
I will put in the ability to set custom values for comfort temperatures into the Adaptive Comfort Recipe soon so that you can test out a 'sleeping comfort temperature' if you would like. I have created a github issue for it here:
https://github.com/mostaphaRoudsari/Honeybee/issues/486
I was not so convinced by Nicol's argument about humidity on those pages as I was when I saw the correlations of both operative temperature and effective temperature to surveyed comfort votes in real buildings. Humphreys shows these correlations on page 106 of the book I linked to above. Notably, the correlation of Effective Temperature to comfort votes (0.257) is slightly worse than the correlation of just Operative Temperature (0.265). In other words, trying to account for humidity actually weakened the predictive power of the metric. This difference in correlation is not so great as for me to discount an Adaptive comfort model based on Effective temperature (as deDear once proposed). However, the correlations of PMV (0.213) and SET (0.185) to comfort votes are so poor that I now use the PMV model only with great caution.
This reason for the decreased importance of humidity may be multi-faceted, whether it's Nicol's explanation or another. Still, the data suggests that we are probably better off ignoring humidity when forecasting comfort and should only consider it when evaluating conditions of extreme heat stress where people's primary loss of heat is through sweating.
-Chris…
deform into rhombic dedocahedrons when they reach equilibrium.
http://mathworld.wolfram.com/CubicClosePacking.html
I was trying to model sphere lattice constrained within a boundary box. When inflated, they would not intersect with each other; they would stay in place; and would be malleable just enough to expand and fill in the gaps in between the spheres.
I started off with the help of this thread here(Thanks for those contributed!). As I understood, there was a bug in Kangaroo2. Solver can't handle more than one item plugged in. So I tried to understand David's Stasiuk's Script and adopted it with a few variations, please see gh file attached.
In the first 5 - I've used David Stasiuk's C# component-variable pressure (posted on June 9, 2015 at 12:25am): 'No. 4.5' being the most successful simulation so far(inflation value is kept very low so that they would not intersect);
although I realised I made some math mistake in setting the close packing grid.(could be checked by plugging voronoi3D to see if the area of the rhombic faces are regular)
No. 6-7 I tried with Kangaroo2 components.
After consulting my tutor(Andrei Jipa)'s help, I realised the following changes could be made:
- The definition posted by David on June 8, 2015 at 4:47pm with constant pressure would've worked better.
- Icosahedrons with WbCatmull(Quad divisions) would result in more even load distribution. With wbloop, vertices more concentrated at poles.
- Load in dir Z could be omitted. Andrei has suggested to use lengths(line) in Kangaroo 2 as 'pressure' instead. And I am trying to improve the grid; and maybe try with David's constant pressure definition. I will keep you guys posted of the progress!
I am new to the parametric world, comments/advice very much appreciated! :) Zhini
…