lly it should not make much of a difference - random number generation is not affected, mutation also is not. crossover is a bit more tricky, I use Simulated Binary Crossover (SBX-20) which was introduced already in 1194:
Deb K., Agrawal R. B.: Simulated Binary Crossover for Continuous Search Space, inIITK/ME/SMD-94027, Convenor, Technical Reports, Indian Institue of Technology, Kanpur, India,November 1994
Abst ract. The success of binary-coded gene t ic algorithms (GA s) inproblems having discrete sear ch sp ace largely depends on the codingused to represent the prob lem variables and on the crossover ope ratorthat propagates buildin g blocks from pare nt strings to childrenst rings . In solving optimization problems having continuous searchspace, binary-co ded GAs discr et ize the search space by using a codingof the problem var iables in binary st rings. However , t he coding of realvaluedvari ables in finit e-length st rings causes a number of difficulties:inability to achieve arbit rary pr ecision in the obtained solution , fixedmapping of problem var iab les, inh eren t Hamming cliff problem associatedwit h binary coding, and processing of Holland 's schemata incont inuous search space. Although a number of real-coded GAs aredevelop ed to solve optimization problems having a cont inuous searchspace, the search powers of these crossover operators are not adequate .In t his paper , t he search power of a crossover operator is defined int erms of the probability of creating an arbitrary child solut ion froma given pair of parent solutions . Motivated by t he success of binarycodedGAs in discret e search space problems , we develop a real-codedcrossover (which we call the simulated binar y crossover , or SBX) operatorwhose search power is similar to that of the single-point crossoverused in binary-coded GAs . Simulation results on a number of realvaluedt est problems of varying difficulty and dimensionality suggestt hat the real-cod ed GAs with t he SBX operator ar e ab le to perform asgood or bet t er than binary-cod ed GAs wit h t he single-po int crossover.SBX is found to be particularly useful in problems having mult ip le optimalsolutions with a narrow global basin an d in prob lems where thelower and upper bo unds of the global optimum are not known a priori.Further , a simulation on a two-var iable blocked function showsthat the real-coded GA with SBX work s as suggested by Goldberg
and in most cases t he performance of real-coded GA with SBX is similarto that of binary GAs with a single-point crossover. Based onth ese encouraging results, this paper suggests a number of extensionsto the present study.
7. ConclusionsIn this paper, a real-coded crossover operator has been develop ed bas ed ont he search characte rist ics of a single-point crossover used in binary -codedGAs. In ord er to define the search power of a crossover operator, a spreadfactor has been introduced as the ratio of the absolute differences of thechildren points to that of the parent points. Thereaft er , the probabilityof creat ing a child point for two given parent points has been derived forthe single-point crossover. Motivat ed by the success of binary-coded GAsin problems wit h discrete sear ch space, a simul ated bin ary crossover (SBX)operator has been develop ed to solve problems having cont inuous searchspace. The SBX operator has search power similar to that of the single-po intcrossover.On a number of t est fun ctions, including De Jong's five te st fun ct ions, ithas been found that real-coded GAs with the SBX operator can overcome anumb er of difficult ies inherent with binary-coded GAs in solving cont inuoussearch space problems-Hamming cliff problem, arbitrary pr ecision problem,and fixed mapped coding problem. In the comparison of real-coded GAs wit ha SBX operator and binary-coded GAs with a single-point crossover ope rat or ,it has been observed that the performance of the former is better than thelatt er on continuous functions and the performance of the former is similarto the lat ter in solving discret e and difficult functions. In comparison withanother real-coded crossover operator (i.e. , BLX-0 .5) suggested elsewhere ,SBX performs better in difficult test functions. It has also been observedthat SBX is particularly useful in problems where the bounds of the optimum
point is not known a priori and wher e there are multi ple optima, of whichone is global.Real-coded GAs wit h t he SBX op erator have also been tried in solvinga two-variab le blocked function (the concept of blocked fun ctions was introducedin [10]). Blocked fun ct ions are difficult for real-coded GAs , becauselocal optimal points block t he progress of search to continue towards t heglobal optimal point . The simulat ion results on t he two-var iable blockedfunction have shown that in most occasions , the sea rch proceeds the way aspr edicted in [10]. Most importantly, it has been observed that the real-codedGAs wit h SBX work similar to that of t he binary-coded GAs wit h single-pointcrossover in overcoming t he barrier of the local peaks and converging to t heglobal bas in. However , it is premature to conclude whether real-coded GAswit h SBX op erator can overcome t he local barriers in higher-dimensionalblocked fun ct ions.These results are encour aging and suggest avenues for further research.Because the SBX ope rat or uses a probability distribut ion for choosing a childpo int , the real-coded GAs wit h SBX are one st ep ahead of the binary-codedGAs in te rms of ach ieving a convergence proof for GAs. With a direct probabilist ic relationship between children and parent points used in t his paper,cues from t he clas sical stochast ic optimization methods can be borrowed toachieve a convergence proof of GAs , or a much closer tie between the classicaloptimization methods and GAs is on t he horizon.
In short, according to the authors my SBX operator using real gene values is as good as older ones specially designed for discrete searches, and better in continuous searches. SBX as far as i know meanwhile is a standard general crossover operator.
But:
- there might be better ones out there i just havent seen yet. please tell me.
- besides tournament selection and mutation, crossover is just one part of the breeding pipeline. also there is the elite management for MOEA which is AT LEAST as important as the breeding itself.
- depending on the problem, there are almost always better specific ways of how to code the mutation and the crossover operators. but octopus is meant to keep it general for the moment - maybe there's a way for an interface to code those things yourself..!?
2) elite size = SPEA-2 archive size, yes. the rate depends on your convergence behaviour i would say. i usually start off with at least half the size of the population, but mostly the same size (as it is hard-coded in the new version, i just realize) is big enough.
4) the non-dominated front is always put into the archive first. if the archive size is exceeded, the least important individual (the significant strategy in SPEA-2) are truncated one by one until the size is reached. if it is smaller, the fittest dominated individuals are put into the elite. the latter happens in the beginning of the run, when the front wasn't discovered well yet.
3) yes it is. this is a custom implementation i figured out myself. however i'm close to have the HypE algorithm working in the new version, which natively has got the possibility to articulate perference relations on sets of solutions.
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Introduction to Grasshopper Videos by David Rutten.
Wondering how to get started with Grasshopper? Look no further. Spend an some time with the creator of Grasshopper, David Rutten, to learn the
This blog post is a rough approximation of the lecture I gave at the AAG10 conference in Vienna on September 21st 2010. Naturally it will be quite a different experience as the medium is quite…
Added by David Rutten at 3:27pm on September 24, 2010
ng is deciding how and where to store your data. If you're writing textual code using any one of a huge number of programming languages there are a lot of different options, each with its own benefits and drawbacks. Sometimes you just need to store a single data point. At other times you may need a list of exactly one hundred data points. At other times still circumstances may demand a list of a variable number of data points.
In programming jargon, lists and arrays are typically used to store an ordered collection of data points, where each item is directly accessible. Bags and hash sets are examples of unordered data storage. These storage mechanisms do not have a concept of which data comes first and which next, but they are much better at searching the data set for specific values. Stacks and queues are ordered data structures where only the youngest or oldest data points are accessible respectively. These are popular structures for code designed to create and execute schedules. Linked lists are chains of consecutive data points, where each point knows only about its direct neighbours. As a result, it's a lot of work to find the one-millionth point in a linked list, but it's incredibly efficient to insert or remove points from the middle of the chain. Dictionaries store data in the form of key-value pairs, allowing one to index complicated data points using simple lookup codes.
The above is a just a small sampling of popular data storage mechanisms, there are many, many others. From multidimensional arrays to SQL databases. From readonly collections to concurrent k-dTrees. It takes a fair amount of knowledge and practice to be able to navigate this bewildering sea of options and pick the best suited storage mechanism for any particular problem. We did not wish to confront our users with this plethora of programmatic principles, and instead decided to offer only a single data storage mechanism.*
Data storage in Grasshopper
In order to see what mechanism would be optimal for Grasshopper, it is necessary to first list the different possible ways in which components may wish to access and store data, and also how families of data points flow through a Grasshopper network, often acquiring more complexity over time.
A lot of components operate on individual values and also output individual values as results. This is the simplest category, let's call it 1:1 (pronounced as "one to one", indicating a mapping from single inputs to single outputs). Two examples of 1:1 components are Subtraction and Construct Point. Subtraction takes two arguments on the left (A and B), and outputs the difference (A-B) to the right. Even when the component is called upon to calculate the difference between two collections of 12 million values each, at any one time it only cares about three values; A, B and the difference between the two. Similarly, Construct Point takes three separate numbers as input arguments and combines them to form a single xyz point.
Another common category of components create lists of data from single input values. We'll refer to these components as 1:N. Range and Divide Curve are oft used examples in this category. Range takes a single numeric domain and a single integer, but it outputs a list of numbers that divide the domain into the specified number of steps. Similarly, Divide Curve requires a single curve and a division count, but it outputs several lists of data, where the length of each list is a function of the division count.
The opposite behaviour also occurs. Common N:1 components are Polyline and Loft, both of which consume a list of points and curves respectively, yet output only a single curve or surface.
Lastly (in the list category), N:N components are also available. A fair number of components operate on lists of data and also output lists of data. Sort and Reverse List are examples of N:N components you will almost certainly encounter when using Grasshopper. It is true that N:N components mostly fall into the data management category, in the sense that they are mostly employed to change the way data is stored, rather than to create entirely new data, but they are common and important nonetheless.
A rare few components are even more complex than 1:N, N:1, or N:N, in that they are not content to operate on or output single lists of data points. The Divide Surface and Square Grid components want to output not just lists of points, but several lists of points, each of which represents a single row or column in a grid. We can refer to these components as 1:N' or N':1 or N:N' or ... depending on how the inputs and outputs are defined.
The above listing of data mapping categories encapsulate all components that ship with Grasshopper, though they do not necessarily minister to all imaginable mappings. However in the spirit of getting on with the software it was decided that a data structure that could handle individual values, lists of values, and lists of lists of values would solve at least 99% of the then existing problems and was thus considered to be a 'good thing'.
Data storage as the outcome of a process
If the problems of 1:N' mappings only occurred in those few components to do with grids, it would probably not warrant support for lists-of-lists in the core data structure. However, 1:N' or N:N' mappings can be the result of the concatenation of two or more 1:N components. Consider the following case: A collection of three polysurfaces (a box, a capped cylinder, and a triangular prism) is imported from Rhino into Grasshopper. The shapes are all exploded into their separate faces, resulting in 6 faces for the box, 3 for the cylinder, and 5 for the prism. Across each face, a collection of isocurves is drawn, resembling a hatching. Ultimately, each isocurve is divided into equally spaced points.
This is not an unreasonably elaborate case, but it already shows how shockingly quickly layers of complexity are introduced into the data as it flows from the left to the right side of the network.
It's no good ending up with a single huge list containing all the points. The data structure we use must be detailed enough to allow us to select from it any logical subset. This means that the ultimate data structure must contain a record of all the mappings that were applied from start to finish. It must be possible to select all the points that are associated with the second polysurface, but not the first or third. It must also be possible to select all points that are associated with the first face of each polysurface, but not any subsequent faces. Or a selection which includes only the fourth point of each division and no others.
The only way such selection sets can be defined, is if the data structure contains a record of the "history" of each data point. I.e. for every point we must be able to figure out which original shape it came from (the cube, the cylinder or the prism), which of the exploded faces it is associated with, which isocurve on that face was involved and the index of the point within the curve division family.
A flexible mechanism for variable history records.
The storage constraints mentioned so far (to wit, the requirement of storing individual values, lists of values, and lists of lists of values), combined with the relational constraints (to wit, the ability to measure the relatedness of various lists within the entire collection) lead us to Data Trees. The data structure we chose is certainly not the only imaginable solution to this problem, and due to its terse notation can appear fairly obtuse to the untrained eye. However since data trees only employ non-negative integers to identify both lists and items within lists, the structure is very amenable to simple arithmetic operations, which makes the structure very pliable from an algorithmic point of view.
A data tree is an ordered collection of lists. Each list is associated with a path, which serves as the identifier of that list. This means that two lists in the same tree cannot have the same path. A path is a collection of one or more non-negative integers. Path notation employs curly brackets and semi-colons as separators. The simplest path contains only the number zero and is written as: {0}. More complicated paths containing more elements are written as: {2;4;6}. Just as a path identifies a list within the tree, an index identifies a data point within a list. An index is always a single, non-negative integer. Indices are written inside square brackets and appended to path notation, in order to fully identify a single piece of data within an entire data tree: {2,4,6}[10].
Since both path elements and indices are zero-based (we start counting at zero, not one), there is a slight disconnect between the ordinality and the cardinality of numbers within data trees. The first element equals index 0, the second element can be found at index 1, the third element maps to index 2, and so on and so forth. This means that the "Eleventh point of the seventh isocurve of the fifth face of the third polysurface" will be written as {2;4;6}[10]. The first path element corresponds with the oldest mapping that occurred within the file, and each subsequent element represents a more recent operation. In this sense the path elements can be likened to taxonomic identifiers. The species {Animalia;Mammalia;Hominidea;Homo} and {Animalia;Mammalia;Hominidea;Pan} are more closely related to each other than to {Animalia;Mammalia; Cervidea;Rangifer}** because they share more codes at the start of their classification. Similarly, the paths {2;4;4} and {2;4;6} are more closely related to each other than they are to {2;3;5}.
The messy reality of data trees.
Although you may agree with me that in theory the data tree approach is solid, you may still get frustrated at the rate at which data trees grow more complex. Often Grasshopper will choose to add additional elements to the paths in a tree where none in fact is needed, resulting in paths that all share a lot of zeroes in certain places. For example a data tree might contain the paths:
{0;0;0;0;0}
{0;0;0;0;1}
{0;0;0;0;2}
{0;0;0;0;3}
{0;0;1;0;0}
{0;0;1;0;1}
{0;0;1;0;2}
{0;0;1;0;3}
instead of the far more economical:
{0;0}
{0;1}
{0;2}
{0;3}
{1;0}
{1;1}
{1;2}
{1;3}
The reason all these zeroes are added is because we value consistency over economics. It doesn't matter whether a component actually outputs more than one list, if the component belongs to the 1:N, 1:N', or N:N' groups, it will always add an extra integer to all the paths, because some day in the future, when the inputs change, it may need that extra integer to keep its lists untangled. We feel it's bad behaviour for the topology of a data tree to be subject to the topical values in that tree. Any component which relies on a specific topology will no longer work when that topology changes, and that should happen as seldom as possible.
Conclusion
Although data trees can be difficult to work with and probably cause more confusion than any other part of Grasshopper, they seem to work well in the majority of cases and we haven't been able to come up with a better solution. That's not to say we never will, but data trees are here to stay for the foreseeable future.
* This is not something we hit on immediately. The very first versions of Grasshopper only allowed for the storage of a single data point per parameter, making operations like [Loft] or [Divide Curve] impossible. Later versions allowed for a single list per parameter, which was still insufficient for all but the most simple algorithms.
** I'm skipping a lot of taxonometric classifications here to keep it simple.…
Added by David Rutten at 2:22pm on January 20, 2015
nd improvements. Many of the new features and components announced in the last release have become stable and have emerged from their WIP section. Additionally, after two years of work, we are happy to announce that we finally have full support of an OpenStudio connection within Honeybee, which has ushered in a whole host of new features, notably the modelling of detailed HVAC systems. As always you can download the new release from Food4Rhino. Make sure to remove the older version of Ladybug and Honeybee and update your scripts.
LADYBUG
1 - Solar Hot Water Components Out of WIP
After much beta-testing, bug-fixing, and general development, all of the Photovoltaic and Solar Hot Water components are now fully out of WIP! The main component is based on a Chengchu Yan's publication. Components have been added to Ladybug thanks to the efforts of Chengchu Yan and Djordje Spasic.. See Djorje’s original release post of the solar hot water components for more information on the components that just made it out of WIP.
2 - New Terrain Shading Mask Released in WIP
In addition to Djordje’s prolific addition of renewable energy components, he has also contributed a widely-useful component to generate terrain shading masks, which account for the shading of surrounding mountains/terrain in simulations. While initially added to assist the solar radiation radiation and renewable energy components, the component will undergo development to optimize it for energy and daylight simulations over the next few months. Another new component called Horizon Angles can be used to visualize and export horizon angles. You can test them out now by accessing them in the WIP section. For more information, see Djordje’s release post on the GH forum here.
3 - New Mesh Selector Component
After realizing that the Optimal Shade Creator component has applications to a whole range of analyses, it has now been re-branded as the Mesh Selector and has been optimized to work easily with these many analyses. Specifically, the component selects out the portion of a mesh that meets a given threshold. This can be the portion of a shade benefit analysis meeting a certain level of shade desirability, the portion of a radiation study meeting a certain level of fulx, the portion of a daylight analysis meeting a certain lux threshold, and much more!
4 - Solar Adjusted Temperature Now Includes Long Wave Radiation
Thanks to a question asked by Aymeric and a number of clarifications made by Djordje Spasic, the Solar Adjusted Temperature component now includes the ability to account for long-wave radiative loss to the sky in addition to it original capability to account for short wave radiation from the sun. As such, the component now includes all capabilities of similar outdoor comfort tools such as RayMan. The addition of this capability is also paralleled by the addition of a new horizontalInfraredRadiation output on the ImportEPW component. See the updated solar adjusted example file hereto see how to use the component properly.
5 - Support for both Log and Power Law Wind Profiles
In preparation for the future release of the Butterfly CFD-modelling insect, the Ladybug Wind Profile component now includes the option of either power law or log law wind profiles, which are both used extensively in CFD studies. Thanks goes to Theodoros Galanos for providing the formulas!
6 - New Radiant Asymmetry Comfort Components
Prompted by a suggestion from Christian Kongsgaard, Ladybug now includes components to calculate radiant asymmetry discomfort! For examples of how to use the components see this example file for spatial analysis of radiant asymmetry discomfort and this example for temporal analysis.
7 - Pedestrian Wind Comfort Component Released in WIP
In preparation for the impending release of the butterfly CFD-modelling insect, Djordje Spasic with assistance from Liam Harrington has contributed a component to evaluate outdoor discomfort and pedestrian safety. The component identifies if certain areas around the building are suitable for sitting, building entrances-exits, window shopping... based on its wind microclimate. Dangerous areas due to high wind speeds are also identified.You can check it out now in the WIP section.
HONEYBEE
1 - New HVAC Systems and Full OpenStudio Support
After a significant amount of development on the part of the OpenStudio team and two years of effort on the part of LB+HB developers, we (finally!) have full support for an OpenStudio connection within Honeybee. By this, we mean that any energy simulation property that can be assigned to a HBZone will be taken into account in the simulation run by the OpenStudio component. The connection to OpenStudio has brought with it several new capabilities. Most notably, you can now assign full HVAC systems and receive energy results in units of electricity and fuel instead of simple heating and cooling loads. This Honeybee release includes 14 built-in HVAC template systems that can be assigned to the zones, each of which can be customized:
0. Ideal Air Loads 1. PTAC | Residential 2. PTHP | Residential 3. Packaged Single Zone - AC 4. Packaged Single Zone - HP 5. Packaged VAV w/ Reheat 6. Packaged VAV w/ PFP Boxes 7. VAV w/ Reheat 8. VAV w/ PFP Boxes 9. Warm Air Furnace - Gas Fired 10.Warm Air Furnace - Electric 11.Fan Coil Units + DOAS 12.Active Chilled Beams + DOAS 13.Radiant Floors + DOAS 14.VRF + DOAS
Systems 1-10 are ASHRAE Baseline systems that represent much of what has been added to building stock over the last few decades while systems 11-14 are systems that are commonly being installed today to reduce energy use. Here is an example file showing how to assign these systems in Honeybee and interpret the results and here is an example showing how to customize the HVAC system specifications to a wide variety of cases. To run the file, you will need to have OpenStudio installed and you can download and install OpenStudio from here.
In addition to these template systems within Honeybee, the OpenStudio interface includes hundreds of HVAC components to build your own custom HVAC systems. OpenStudio also has a growing number of user-contributed HVAC system templates that have been integrated into a set of scripts called "Measures" that you can apply to your OpenStudio model within the OpenStudio interface. You can find these system templates by searching for them in the building components library. Here is a good tutorial video on how to apply measures to your model within the OpenStudio interface. Honeybee includes a component that runs these measures from Grasshopper (without having to use the OpenStudio interface), which you can see a demo video of here. However, this component is currently in WIP as OpenStudio team is still tweaking the file structure of measures and it is fairly safe to estimate that, by the next stable release of Honeybee, we will have full support of OpenStudio measures within GH.
2 - Phasing Out IDF Exporter
With the connection to OpenStudio now fully established, this release marks the start of a transition away from exporting directly to EnergyPlus and the beginning of Honeybee development that capitalizes on OpenStudio’s development. As such THIS WILL BE THE LAST STABLE RELEASE THAT INCLUDES THE HONEYBEE_RUN ENERGY SIMULATION COMPONENT.
The Export to OpenStudio component currently does everything that the Run Energy Simulation component does and, as such, it is intended that all GH definitions using the Run Energy Simulation component should replace it with the OpenStudio component. You can use the same Read EP Result components to import the results from the OpenStudio component and you can also use the same Energy Sim Par/Generate EP Output components to customize the parameters of the simulation. The only effective difference between the two components is that the OpenStudio component enables the modeling of HVAC and exports the HBZones to an .osm file before converting it to an EnergyPlus .idf.
For the sake of complete clarity, we should state that OpenStudio is simply an interface for EnergyPlus and, as such, the same calculation engine is under the hood of both the Export to OpenStudio component and the Run Energy Simulation component. At present, you should get matching energy simulation results between the Run Energy Simulation component and a run of the same zones with the OpenStudio component (using an ideal air system HVAC).
All of this is to say that you should convert your GH definitions that use the Run Energy Simulation component to have the OpenStudio component and this release is the best time to do it (while the two components are supported equally). Additionally, with this version of Honeybee you will no longer need to install EnergyPlus before using Honeybee and you will only need to install OpenStudio (which includes EnergyPlus in the install).
3 - New Schedule Generation Components
Thanks to the efforts of Antonello Di Nunzio, we now have 2 new components that ease the creation of schedule-generation in Honeybee. The new components make use of the native Grasshopper “Gener Pool” component to give a set of sliders for each hour of the day. Additionally, Antonello has included an annual schedule component that contains a dictionary of all holidays of every nearly every nation (phew!). Finally, this annual schedule component can output schedules in the text format recognized by EnergyPlus, which allows them to be written directly into the IDF instead of a separate CSV file. This will significantly reduce the size of files needed to run simulations and can even reduce the number of components on your canvas that are needed to add custom schedules. For more information, see Antonello’s explanatory images here and Antonello's example file here. You can also see a full example file of how to apply the schedules to energy simulations here.
4 - EnergyPlus Lookup Folder, Re-run OSM/IDF, and Read Result Dictionary
With the new capabilities of OpenStudio, we have also added a number of components to assist with managing all of the files that you get from the simulation. In particular, Abraham Yezioro has added a Lookup EnergyPlus Folder component that functions very similarly to the Lookup Daylight Folder component. This way, you can run an Energy simulation once and explore the results separately. Furthermore, we have added components to Re-Run OpenStudio .osm files or EnergyPlus .idf files within Grasshopper. These components are particularly useful if you edit these .osm or .idf files outside of Honeybee and want to re-run them to analyze their results in Grasshopper. Lastly, a component has been added to parse the .rdd (or Result Data Dictionary) file that EnergyPlus produces, enabling you to see all of the possible outputs that you can request from a given simulation.
5 - Electric Lighting Components Out of WIP
After Sarith Subramaniam’s initial components to model electric lights with Radiance in the last release, we are happy to report that they have been fully tested and are out of WIP. Improvements include support for all types of light fixture geometries and the ability to use the components in a more “Grasshoppery” list-like fashion. See Sarith’s original release post for more information and several example files showing how to use the components can be found here. 1 , 2 , 3 .
6 - Improvements to THERM Components
A number of bug fixes and improvements have been made to the THERM components in order to make their application more flexible and smooth. Special thanks is due to Derin Yilmaz , Mel King , Farnaz , Ben (@benmo1) , and Abraham Yezioro for all of the great feedback in the process of improving these components.
7 - HBObject Transform Components
After some demand for components that can ease the generation of buildings with modular zone types, two components to transform HBObjects with all of their properties have been added to the 00 | Honeybee section. The components allow you to produce copies of zones that are translated or rotated from the original position.
8 - Comfort Maps Supports PET and Integration of CFD Results
Thanks to the addition of the ‘Physiological Equivalent Temperature’ (PET) component by Djordje Spasic in the last stable release, it is now possible to make comfort maps of PET with Honeybee. PET is particularly helpful for evaluating OUTDOOR comfort with detailed wind fields at a high spatial resolution. As such, the new PET recipe has also been optimized for integration with CFD results. The windSpeed_ input can now accept the file path to a .csv file that is organized with 8760 values in each column and a number of columns that correspond to the number of test points. Components to generate this csv from Butterfly CFD results will be coming in later releases. Stay tuned!
As always let us know your comments and suggestions.
Enjoy!Ladybug Analysis Tools Development Team
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