n and Fabrication).
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her Ladybug components. In one sense, I can see that it might be confusing if an analysis period from 10 to 14 gave you 5 hours of data instead of 4 (since you might expect 14-10=4). However, all of the other ladybug components would give you the 5 hours of data for this type of analysis period and I feel that, for the sake of consistency with the other Ladybug components, you are right that you should get 5 sun vectors out for this type of analysis period. If Mostapha agrees, I will change this in the sunpath component.
-Chris…
erie. For example I need first branch 1st time to cull index 1, 2end time to cull index 2 and etc. And also apply to other other branches same opration.…
eir entire project was only three weeks. These guys have just over a year of architecture school under their belt.
Entirely scripted and prepared for fabrication with standard Grasshopper libraries.…
Added by Duane McLemore at 11:43pm on September 15, 2016
pt for my stadium, much work, much learned, first project using GH. You helped me a lot, thank you for that!!!
So i start my daily asking again :D :D - 8 Weeks until my Master's thesis must be finished
Thx again !!!
…
onent are experiential or location specific. For example: humidex has been derived and widely used in Canada.Also both humidex and discomfort index should be used in in-shade conditions.For universal applications and locations, you should concentrate on either PET or UTCI (this is what "Outdoor Comfort Calculator" component is based on).
I have found out, that for instance - OutdoorComfortCalculator - which considers temperatures of 9-26 and other factors, gives the % of comfortable time outdoor for instance in Kenya in Africa (high temperatures and humidity) 55%, whereas within the same .epw data and some additional factors added to the Thermal Indices component, the "humidex" or "Discomfort index" give a result drastically lower, I think it was even 1-5% comfortable.How is that?
Yes, this is one of the issues that I have with UTCI index: the authors wanted to make it as an index applicable in any type of climate. To create the UTCI comfort categories a number of data has been collected from different locations (for hot humid climate, it was the data from Madagascar. I may be wrong on this). This resulted in universal comfortable range of 9 to 26 C which you mentioned. How would the people in Madagascar perceive the feel like temperature of 9 degrees as comfortable is beyond my understanding.Thermophysiology of a human in Madagascar, and in Poland is the same. However their acclimatization is quite different, which raises the issue with the upper universal comfortable range. In general people who live in hotter climates have a bit higher tolerance to high temperature than those living in continental climates. And vice-versa: their tolerance to lower temperatures is lower than the tolerance of the people from the continental climates. Here is a comparison of the UTCI - PET stress categories:
UTCI
all climates stress category
above +46 extreme heat stress+38 to +46 very strong heat stress+32 to +38 strong heat stress+26 to +32 moderate heat stress+9 to +26 no thermal stress+9 to 0 slight cold stress0 to -13 moderate cold stress-13 to -27 strong cold stress-27 to -40 very strong cold stressbelow -40 extreme cold stress
PET
(sub)tropical humid climate temperate climate stress categoryabove +42 above +41 extreme heat stress+38 to +42 +35 to +41 strong heat stress+34 to +38 +29 to +35 moderate heat stress+30 to +34 +23 to +29 slight heat stress+26 to +30 +18 to +23 no thermal stress+22 to +26 +13 to +18 slight cold stress+18 to +22 +8 to +13 moderate cold stress+14 to +18 +4 to +8 strong cold stressbelow +14 below +4 extreme cold stress
I attached below an example of PET humid climate comparison with UTCI, for in-shade and out-shade conditions.As it can be seen UTCI shows the percent of time comfortable: two times higher than PET.
Thank you Pin, for the useful comment, on usage of "Analysis period" component.…
number of divisions on that curve as in the defintion (i.e. by 4). The offset in the def is slightly different and should cull two or three more curves as in the lists that show my aim below.
Basically I want to look into each branch of the groups of points from each closed curve . Marking in a list whether it contains a one or a zero (0= outside 1 = coincidents).
{0;0}0. 21. 22. 23. 2 {0;1} 0. 01. 22. 03. 2 {0;2}0. 01. 02. 03. 0 {0;3}0. 21. 22. 23. 2 {0;4}0. 21. 22. 23. 2 {0;5}0. 21. 22. 23. 2 {0;6}0. 01. 22. 23. 1 {0;7}0. 21. 22. 03. 0 {0;8}0. 21. 22. 23. 2 {0;9}0. 21. 22. 23. 2 {0;10}0. 21. 22. 23. 2 {0;11}0. 21. 22. 23. 2 {0;12}0. 21. 22. 23. 2 {0;13}0. 01. 22. 23. 0 {0;14}0. 21. 22. 23. 2
I want to create a list from these points. That marks each curve that pokes out, in a cull pattern as such:
20022210222202
Using a 1 where there are co-incidents in the curve points and the boundary. A 2 for true (outside points) and a 0 for containment. So I might be able to use the 1 in future developments - however if a true false list is easiest I can live with that.
So could I use F(x) function? - to look for 0 or 1's in each bunch of points and thus list as such for a cull pattern? or will Path mapper help me here? Or can I rely on simply grafting and splitting??
I am usure of the neatest solution and would love to learn. Hope you can direct me.rgrds
J.…