I'd like to a run a case where I calculate the power of a small farm for a range of TI values, do I need to edit the JSON input file to do this?
import matplotlib.pyplot as plt
import floris.tools as wfct
# Initialize the FLORIS interface fi (using the example input)
fi = wfct.floris_utilities.FlorisInterface("example_input.json")
# Would now like to loop over TI from 6-15% here and collect powers...
You can access any of the parameters in the input file programmatically after the input file is loaded. There isn't a one-stop object in the FLORIS framework where all of the parameters live, but the floris.simulation API docs should give you some guidance.
With that in mind, the best method for iterating over a parameter within FLORIS is the FlorisInterface object. Once you know where you want to change a parameter in the simulation modules, you can see if there's a helper function in the FlorisInterface. Typically, you'll end up modifying one of the inputs to FlorisInterface.reinitialize_flow_field and using that function. This script provides a simple example. I think this is the solution in your case since you can change turbulence intensity through that function call.
When you want to change parameters in other objects like Wake, you can extract it from the FLORIS object, make changes, and then supply it back to reinitialize_flow_field.
I hope this answers you question!
Related
I have been following the QML Oscilloscope example to create a dynamically updated graph of points, so far I managed to update the points with a QVector<QPointF> points being passed into the replace() function on my QXYSeries * xySeries object:
xySeries->replace(points);
However, I would like to use a QQueue<QPointF> if possible, so I can update the values at the beginning of the queue and remove old ones from the end of the queue faster than then vector. I want to avoid using append()since it's less efficient.
Does anyone know if this is possible?
While trying to use the pso or hydroPSO package from R-CRAN, I have a need to use/access the counts (current iteration, current number function evaluation, and current restart) to work with the function I've been writing. However, I can't seem to wrap my head around this. Any suggestions on figuring out how to call the current iteration/function/restarts within the objective function would be great. A piece of example code would be appreciated as I seem to fail to fully understand the documentation.
Background:
My function requires the iteration number as it is a wrapper to some code written in FORTRAN where the input files are generated and the output files are read back in to R. I want the iteration number surviving so that I can return back to the previous output files for further analysis. An example of this would be:
~/runs/<restart #>/<iteration>/<particle>/input/
~/runs/<restart #>/<iteration>/<particle>/output/
The wrapper function accepts the parameters, automatically generates the input files, runs the FORTRAN model, then parses in the output and post-processes them (e.g. performance index calculations).
I've bumped my head on the walls trying to create an alias (aka a pointer, or a new short nickname designating the same object in memory without copying that object) to a subpart of a complex object. Let's say I am working with an object of class SpatialPolygonsDataFrame (package "sp"), and I want to perform operations on an part thereof, deep down in the hierarchical representation of that object. Instead of writing repeatedly things like
myBigMap#polygons[FRA][[1]]#Polygons[[1]]
I want to be able to write simply
mypolygon
so that
myBigMap#polygons[FRA][[1]]#Polygons[[1]]#coords
can be abbreviated
mypolygon#coords
etc. I've seen that I should maybe use environments as a replacement to the former .Alias defunct function, but can't find out how to tell R that I want to consider a subpart of a complex object as an environment. Thanks!
assignment:
mypolygon=myBigMap#polygons[FRA][[1]]#Polygons[[1]]
doesn't create a copy until you modify something in it. So if its just shorthand for accessing the data to make some code more readable then that will be fine:
mypolygon#coords
mean(mypolygon#coords[,1])
neither of those will make a copy.
However, if you do modify mypolygon, eg by changing #coords, you need to put the modified value back in the structure since a copy is made:
mypolygon#coords = mypolygon#coords * 1000
myBigMap#polygons[FRA][[1]]#Polygons[[1]] = mypolygon
I think that's a preferred solution, since its just as efficient as any kind of magic aliasing scheme and its explicit since there's no magic action-at-a-distance happening.
I don't think there's any way to alias parts of an object like the way you want to do.
I have 10+ files that I want to add to ArcMap then do some spatial analysis in an automated fashion. The files are in csv format which are located in one folder and named in order as "TTS11_path_points_1" to "TTS11_path_points_13". The steps are as follows:
Make XY event layer
Export the XY table to a point shapefile using the feature class to feature class tool
Project the shapefiles
Snap the points to another line shapfile
Make a Route layer - network analyst
Add locations to stops using the output of step 4
Solve to get routes between points based on a RouteName field
I tried to attach a snapshot of the model builder to show the steps visually but I don't have enough points to do so.
I have two problems:
How do I iterate this procedure over the number of files that I have?
How to make sure that every time the output has a different name so it doesn't overwrite the one form the previous iteration?
Your help is much appreciated.
Once you're satisfied with the way the model works on a single input CSV, you can batch the operation 10+ times, manually adjusting the input/output files. This easily addresses your second problem, since you're controlling the output name.
You can use an iterator in your ModelBuilder model -- specifically, Iterate Files. The iterator would be the first input to the model, and has two outputs: File (which you link to other tools), and Name. The latter is a variable which you can use in other tools to control their output -- for example, you can set the final output to C:\temp\out%Name% instead of just C:\temp\output. This can be a little trickier, but once it's in place it tends to work well.
For future reference, gis.stackexchange.com is likely to get you a faster response.
I have built an R package that runs a complex Bayesian model (Dirichlet Process Mixture model on spatial data) including an MCMC, thinning and validation and interface with Googlemaps. I'm very happy with performance and it runs without problems. The only issue is I would like to get it up on CRAN and it will be rejected because I extensively use global variables.
The package is built around the use of 8 core functions (which the user interacts with):
1) LoadData: Loads in data, extracts key information and sets up a series of global matrices as well as other small list objects.
2) ModelParameters: Sets model parameters, option to plot prior on parameter sigma on Googlemap. Calculates a hyper-prior at this point and saves a large matrix to the global environment
3) GraphicParameters: Sets graphic parameters of maps and plots (see code below)
4) CreateMaps: Creates the prior surface on source location tau and plots the data on a Google map. Keeps a number of global objects saved for repeated plotting of this map.
5) RunMCMC: Runs the bulk of the analysis using MCMC (a time intensive step), creates many global objects.
6) ThinandAnalsye: Thins the posterior samples and constructs the geoprofile (a time intensive step)
7) PlotGP: Plots the data and overlays the geoprofile onto a Google map
8) reporthitscores: OPTIONAL if source data is imported, calculates the hit scores of potential sources
Each one is run in turn before the next, and I pass global variables out which are used by one or more of the other functions.
I built it this way for a reason, as the user must stop and evaluate the results of these functions before rushing ahead to the future ones.
Each of these functions passes not just fixed parameters, but also large map objects, lists and matrices as global objects. I thought it was a nice simple solution with a smooth workflow (you can check the results in your main working environment before moving on, possibly applying transformations etc) and I have given all the objects unique and informative names.
How do I get around this, and pass the checks of CRAN whilst keeping my user friendly workflow of a series of interacting functions?
I dont want to post up a lot of code (as just the MCMC part is several hundred lines long)
But I will include one of the simple examples. GraphicParameters is one of my simple parameter setting functions, that comes with the default values set. This is a simple example, there are much more complex ones in the package. There is a model parameters function that pulls many of the variables from an existing data loading function for example.
GraphicParameters <-
function(Guardrail=0.05, nring=20,transp=0.4,gridsize=640,gridsize2=300,MapType= "roadmap",Location=getwd(),pointcol="black") {
Guardrail<<-Guardrail
nring<<-nring
transp<<-transp
gridsize<<-gridsize
gridsize2<<-gridsize2
MapType<<-MapType
Location<<-Location
pointcol<<-pointcol
}
Most of the material I have seen concerning avoiding global objects resolves around a single function that will do all the work. I want to keep my step by step multi-function approach, but loose the global objects.
Any help would be greatly appreciated.
I understand this may be a major reworking of the code (which is several 1000 lines currently), so I would also love solutions that minimally affect the overall structure of the package.
P.S. I wish I had known about CRANs displeasure with global objects before I started!!!
Your problem is very amenable to OOP-style design. You can use reference classes or S4 to export a single global, e.g., a MapAnalysis class generator. The idea is then that someone creates this using
ma <- new('MapAnalysis', option1 = ..., option2 = ..., ...) # S4
# or
ma <- MapAnalysis$new(option1 = ..., ...) # refClass
and can then call your methods with
ma$loadData(...)
ma$setParameters(...)
with the object doing any bookkeeping of options and auxiliary objects internally. It should not be that much work to refactor. If you read the page I linked to at the top of this post, you should see it's probably possible to just wrap all your functions with a refClass('MapAnalysis', fields = (...), methods = (...)) with few further modifications. (Although it would do you a lot of good down the road to re-think the architecture in OOP terms.)