Hi YT users, over the past week I've spoken to a couple YT developers and got some great feedback and suggestions, but I just want to outline my plans/strategies/troubles to see if I can get more help from the community, I'm currently trying to use YT to analyze data on ionized clumps of gas from the FLD Radiation runs of Enzo I made. My plan is to (if possible): 1) Create a hierarchy of clumps, based on their level of ionization instead of topology like in the cookbook find_clumps() 2) Calculate a global clumping factor from regions inside these clumps 3) Find the location(x,y,z) of the peaks of HI_Density and HI/H (ionized fraction) and the value of Density in those regions 4) Find the volume inside each clump region 5) From the hierarchy of clumps, create their merger history 6) Save the clump information in such a way that I can come back to it if I find something else that's interesting to analyze about them 7) Volume render the clumps separately and/or together on the same picture my strategy and the troubles I am running into for the corresponding points are: 1) I want to avoid re-inventing the wheel and use the current machinery inside YT to create the hierarchy of clumps. I think I can use the Clump() to find a master clump like in the cookbook, but instead of calling on find_clump(), call find_children(). Inside find_children, I can supply it a different minimum value for the amount of ionization I want, because they may not be equally spaced or equal multiples of each other. So maybe for level 0 I can do find_children( min[0], max), then level 1 do find_children(min[1], max) etc. The problems I'm running into are - I haven't played around with this enough to see if it'll work for just 1 level down from the master clump - If I don't use find_clumps then I'll need another way to make this process recursive, it might be as simple as copying what's done in find_clumps() without the checking if it is valid, but I am not sure. 2) I think an logical way of doing this is to calculate the local clumping factor for each clumps individually and get the global one from the ionizing clumps by doing a volume weighted average. - Should it be weighted by mass or something else? 3) I can get the index of the peak region by something like for child in master_clump.children: HI_peak_index = na.argmax(child["HI_Density"]) HI_peak_x = child["x"][HI_peak_index] finding the Density value would be Density_at_Peak_HI = child["Density"][HI_peak_index] - Don't think I'll run into trouble here. 4) I know the regular Clump's write_info() writes the number of cells from set_default_clump_info(), but can it return the physical volume maybe in mpc or co-moving mpc? - It would be easy for unigrid simulations that I do now, but if there's a more general way of getting this information for the AMR case I'd like to know. Right now I can just take the cell count and multiply it by the volume per cell... harder for the AMR. 5) I know Stephen was able to do create a Halo merger tree, I think it was done with the SQLite database or he displayed the tree from a SQlite database. I was wondering if something similar can be done for ionized clumps. I think Matt mentioned it was doable with just using the YT object without the need of the database. There seems to be more attention at sqlite lately, saw an email from Irina couple days ago, but not sure if that issue was resolved. I myself didn't encounter that problem at all I just had to "import sqlite3" and everything worked, on my mac OSX 10.6.4, Kraken, and Triton. my question: - Is it more straight forward one way or the other? (w/ or w/o database) - I'm having a hard time coming up with an unique identifier that keeps track of a clump. Do I define it having the peak at a certain cell? Dave suggested this, but I've considered this previously and thought I'd get into trouble if the peak moved from one data output to another, maybe he has a workaround. Or do I identify a clump by containing a certain star particle? Because the star particles are all uniquely defined with a number, so this way there is no ambiguity. But this is assuming that the star particle will not move beyond the specific clump, which may or may not be an valid assumption for all levels of ionization thoughout the entire simulation. Or is there a much simpler build in way that python identify each object that I don't know about? 6) There's the write_info() for the Clump(), but I don't think that is adequate for what I need. Dave and Matt suggested cPickle() where I save the location of the object, which I can later access if I have the data at its original location. An alternative is to save the data I want in a database as mentioned before. pros of cPickle: little data is duplicated, everything is in python cons of cPickle: original simulation data has to be available, when scratch disk fills up, big simulation data are usually the first to go. pros of database: can do a lot of type of retrieval that's already pre-programmed, can access the clump data that's saved even if the big simulation is on archive only. cons of database: Do not have any information about data that's not previously saved, and duplicate some data redundantly. 7) I see that for slices of data, I can do a callback of .clump() to plot the contour, but I was wondering if it's just as simple to plot clumps in volume rendering. Maybe sometimes have contour of different ionization on the same picture, sometimes only the specific ionization I want. I apologize for the email being kind of long and wordy, but any help/suggestions on any of the points is appreicated, thanks :-) From G.S.