I'm trying to download precipitation data for a list of latitude-longitude coordinates in R. I've came across this question which gets me most of the way there, but over half of the weather stations don't have precipitation data. I've pasted code below up to this point.
I'm now trying to figure out how to only get data from the closest station with precipitation data, or run a second function on the sites with missing data to get data from the second closest station. However, I haven't been able to figure out how to do this. Any suggestions or resources that might help?
`
library(rnoaa)
# load station data - takes some minutes
station_data <- ghcnd_stations() %>% filter(element == "PRCP")
# add id column for each location (necessary for next function)
sites_df$id <- 1:nrow(sites_df)
# retrieve all stations in radius (e.g. 20km) using lapply
stations <- lapply(1:nrow(sites_df),
function(i) meteo_nearby_stations(sites_df[i,],lat_colname = 'Lattitude',lon_colname = 'Longitude',radius = 20,station_data = station_data)[[1]])
# pull data for nearest stations - x$id[1] selects ID of closest station
stations_data <- lapply(stations,function(x) meteo_pull_monitors(x$id[1], date_min = "2022-05-01", date_max = "2022-05-31", var = c("prcp")))
stations_data`
# poor attempt its making me include- trying to rerun subset for second closest station. I know this isn't working but don't know how to get lapply to run for a subset of a list, or understand exactly how the function is running to code it another way
for (i in c(1,2,3,7,9,10,11,14,16,17,19,20)){
stations_data[[i]] <- lapply(stations,function(x) meteo_pull_monitors(x$id[2], date_min = "2022-05-01", date_max = "2022-05-31", var = c("prcp")))
}
I'm a beginner in Python and I'm working with the Ansys Customization Tool (ACT) to add my own extension.
Is there a direct way to fill a file with every node's coordinates after deformation?
hopefully in 3 lines or columns: x , y , z
So far I only found the GetNodeValue object but it only gives me the displacement and I need the deformed coordinates for the entire model.
My first idea was to add the displacements to the initial coordinates but I didn't manage to do it.
Many thanks for your help
Lara
APDL Snippet
Add an APDL Snippet in the solution part of the tree:
/prep7
UPGEOM,1,1,1,file,rst ! adds the displacements to the nodal coordinates.
cdwrite,geom,nodesAndelements,geom ! Writes node and element data to nodesAndelement.geom
I'm not sure if you can work with the output format from cdwrite, but this is the quickest solution i can think of.
If you want to automate you have to insert the command snippet via
solution = ExtAPI.DataModel.Project.Model.Analyses[0].Solution
fullPath = "path//to//snippet"
snippet = solution.AddCommandSnippet()
snippet.ImportTextFile(fullPath)
ACT
If you want to stay in ACT it could be done like this:
global nodeResults
import units
analysis = ExtAPI.DataModel.Project.Model.Analyses[0]
mesh = analysis.MeshData
# Get nodes
allNodes = mesh.Nodes
# get the result data
reader = analysis.GetResultsData()
# get the deformation result
myDeformation = reader.GetResult("U")
nodeResultsTemp = []
result_unit = myDeformation.GetComponentInfo("X").Unit
for node in allNodes:
# get node deformation and convert values in meter
deformationNode1 = myDeformation.GetNodeValues(node.Id)
deformationNode1[0] = units.ConvertUnit(deformationNode1[0],result_unit,"m","Length")
deformationNode1[1] = units.ConvertUnit(deformationNode1[1],result_unit,"m","Length")
deformationNode1[2] = units.ConvertUnit(deformationNode1[2],result_unit,"m","Length")
# add node coordinates (in meter) to the displacement
mesh_unit = mesh.Unit
node1 = mesh.NodeById(node.Id)
node1CoorX = units.ConvertUnit(node1.X,mesh_unit,"m","Length")
node1CoorY = units.ConvertUnit(node1.Y,mesh_unit,"m","Length")
node1CoorZ = units.ConvertUnit(node1.Z,mesh_unit,"m","Length")
deformationNode1[0] = deformationNode1[0]+node1CoorX
deformationNode1[1] = deformationNode1[1]+node1CoorY
deformationNode1[2] = deformationNode1[2]+node1CoorZ
nodeResultsTemp.append([node1.X,node1.Y,node1.Z,deformationNode1[0],deformationNode1[1],deformationNode1[2]])
nodeResults = nodeResultsTemp
I'm using the geocoder gem in rails to get latitude & longitude for addresses and then display them in OpenStreetMap.
When I search for my old address:
>> results = Geocoder.search("1000 Mount Curve Ave E, Altadena, CA 91001")
I get:
>> results.first.coordinates
=> [34.1976645, -118.1278219]
Mount Curve Address Discrepancy
Those coordinates are perhaps a thousand feet off. (See image.) The resulting accurate coordinates from Google Maps are [34.200503,-118.1310407].
I've tried another address and it was much farther off, perhaps a mile. (1346 E Woodbury Rd, Pasadena, CA 91104)
I've tried yet another address, and it was pretty much dead-accurate. (922 E Brockton Ave, Redlands, CA 92374)
Does anyone know what might be causing these inaccuracies and how to get accurate results consistently?
Because of the inaccuracies and/or limitations with OSM/Nominatim, I switched to Google maps service. At the top of my controller I added:
require 'google_maps_service'
And in my controller's show routine I ended up with this, which yields accurate results:
source = Property.find(params[:id])
#property = PropertyDecorator.new(source)
gmaps = GoogleMapsService::Client.new(key: '[removed, put in your own key here]')
results = gmaps.geocode("#{#property.address1} #{#property.address2}")
if results[0] == nil
#lat = 0
#lng = 0
else
#lat = results[0][:geometry][:location][:lat]
#lng = results[0][:geometry][:location][:lng]
end
The following example resembles a similar problem that I'm dealing with, although the code below is merely an example, it is structured in the same format as my actual data set.
clear all
England = struct('AirT',rand(320,1),'SolRad',rand(320,1),'Rain',rand(320,1));
Wales = struct('AirT',rand(320,1),'SolRad',rand(320,1),'Rain',rand(320,1));
Ireland = struct('AirT',rand(320,1),'SolRad',rand(320,1),'Rain',rand(320,1));
Scotland = struct('AirT',rand(320,1),'SolRad',rand(320,1),'Rain',rand(320,1));
Location = struct('England',England,'Wales', Wales, 'Ireland',Ireland,'Scotland',Scotland);
FieldName={'England','Wales','Scotland','Ireland'};
Data = {England.AirT,Wales.AirT,Scotland.AirT,Ireland.AirT};
Data = [FieldName;Data];
Data = struct(Data{:});
Data = cell2mat(struct2cell(Data)');
[R,P] = corrcoef(Data,'rows','pairwise');
R_Value= [FieldName(nchoosek(1:size(R,1),2)) num2cell(nonzeros(tril(R,-1)))];
So, this script would show the correlation between pairs of Air Temperature of 4 locations. I'm looking for a way of also looking at the correlation between 'SolRad' and 'Rain' between the locations (same process as for AirT) or any variables denoted in the structure. I could do this by replacing the inputs into 'Data' but this seems rather long winded especially when involving many different variables. Any ideas on how to do this? I've tried using a loop but it seems harder than I though to try and get the data into the same format as the example.
Let's see if this helps, or is what you are thinking:
clear all
England = struct('AirT',rand(320,1),'SolRad',rand(320,1),'Rain',rand(320,1));
Wales = struct('AirT',rand(320,1),'SolRad',rand(320,1),'Rain',rand(320,1));
Ireland = struct('AirT',rand(320,1),'SolRad',rand(320,1),'Rain',rand(320,1));
Scotland = struct('AirT',rand(320,1),'SolRad',rand(320,1),'Rain',rand(320,1));
Location = struct('England',England,'Wales', Wales, 'Ireland',Ireland,'Scotland',Scotland);
% get all the location fields
FieldName = transpose(fieldnames(Location));
% get the variables recorded at the first location
CorrData = fieldnames(Location.(FieldName{1}));
% get variables which were stored at all locations(just to be safe,
% we know that they are all the same)
for ii=2:length(FieldName)
CorrData = intersect(CorrData,fieldnames(Location.(FieldName{ii})));
end
% process each variable that was recorded
for ii=1:length(CorrData)
Data = cell(1,length(FieldName));
% get the variable data from each location and store in Data
for jj=1:length(FieldName)
Data{jj} = Location.(FieldName{jj}).(CorrData{ii});
end
% process the data
Data = [FieldName;Data];
Data = struct(Data{:});
Data = cell2mat(struct2cell(Data)');
[R,P] = corrcoef(Data,'rows','pairwise');
R_Value= [FieldName(nchoosek(1:size(R,1),2)) num2cell(nonzeros(tril(R,-1)))];
% display the data, sounds good right?
fprintf(1,'Correlation for %s\n',CorrData{ii});
for jj=1:size(R_Value,1)
fprintf(1,'%s\t%s\t%f\n',R_Value{jj,1},R_Value{jj,2},R_Value{jj,3});
end
end
Let me know if I misunderstood, or if this is more involved than what you were thinking. Thanks!
fieldnames(s) and dynamic field references are your friend.
What I would suggest is to make one structure in which 'name' is a field, and the other fields are whatever you'd like. Regardless of how you set up your structure s, you can use fn = fieldnames(s); to return a cell array of the fields. You can access the contents of your structure using these names by using parentheses around the variable containing the name.
fn = fieldnames(s);
for i=1:length(fn)
disp([fn{i} ':' s.(fn{i})]
end
Whatever you do with the values is up to you, of course!