Johan,
I think there may be an issue with the Data Frames package as I get the
following:
julia> data = readtable("test.csv",header=false)
6x2 DataFrame:
x1 x2
[1,] 1 7
[2,] 2 8
[3,] 3 9
[4,] 4 10
[5,] 5 11
[6,] 6 12
julia> convert(Array,data)
MethodError(convert,(Array{T,N},6x2 DataFrame:
x1 x2
[1,] 1 7
[2,] 2 8
[3,] 3 9
[4,] 4 10
[5,] 5 11
[6,] 6 12
))
julia> dropna(data)
ErrorException("dropna not defined")
I read the documentation and they both say the same thing but it doesn't seem
to work in my case.
Thoughts?
Thanks,
Don
On Thursday, July 3, 2014 7:54:49 PM UTC-4, Johan Sigfrids wrote:
>
> You can use dropna() to convert a DataArray to a Array. This will
> obviously drop any missing values.
>
> On Friday, July 4, 2014 2:08:55 AM UTC+3, Donald Lacombe wrote:
>>
>> Patrick (and others),
>>
>> Another issue that has reared it's ugly head is that when I read the data
>> using the Data Frames package, I get the following:
>>
>> data = readtable("ct_coord_2.csv",header=false)
>>
>> 8x2 DataFrame:
>>
>> x1 x2
>>
>> [1,] -73.3712 41.225
>>
>> [2,] -72.1065 41.4667
>>
>> [3,] -73.2453 41.7925
>>
>> [4,] -71.9876 41.83
>>
>> [5,] -72.3365 41.855
>>
>> [6,] -72.7328 41.8064
>>
>> [7,] -72.5231 41.4354
>>
>> [8,] -72.8999 41.3488
>>
>>
>> julia> xc = data[:,1]
>>
>> 8-element DataArray{Float64,1}:
>>
>> -73.3712
>>
>> -72.1065
>>
>> -73.2453
>>
>> -71.9876
>>
>> -72.3365
>>
>> -72.7328
>>
>> -72.5231
>>
>> -72.8999
>>
>>
>> julia> yc = data[:,2]
>>
>> 8-element DataArray{Float64,1}:
>>
>> 41.225
>>
>> 41.4667
>>
>> 41.7925
>>
>> 41.83
>>
>> 41.855
>>
>> 41.8064
>>
>> 41.4354
>>
>> 41.3488
>>
>>
>> julia> xc=xc'
>>
>> 1x8 DataArray{Float64,2}:
>>
>> -73.3712 -72.1065 -73.2453 -71.9876 … -72.7328 -72.5231 -72.8999
>>
>>
>> julia> yc=yc'
>>
>> 1x8 DataArray{Float64,2}:
>>
>> 41.225 41.4667 41.7925 41.83 41.855 41.8064 41.4354 41.3488
>>
>>
>> julia> temp = [xc;yc]
>>
>> 2x8 DataArray{Float64,2}:
>>
>> -73.3712 -72.1065 -73.2453 -71.9876 … -72.7328 -72.5231 -72.8999
>>
>> 41.225 41.4667 41.7925 41.83 41.8064 41.4354 41.3488
>>
>>
>> julia> R = pairwise(Euclidean(),temp)
>>
>> MethodError(At_mul_B!,(
>>
>> 8x8 Array{Float64,2}:
>>
>> 2.7273e-316 2.7273e-316 2.67478e-315 … 2.7273e-316 2.7273e-316
>>
>> 2.67736e-315 2.67736e-315 2.67736e-315 2.72726e-316 2.72726e-316
>>
>> 2.67727e-315 2.67727e-315 2.67727e-315 2.67727e-315 2.67727e-315
>>
>> 2.67727e-315 2.67727e-315 2.67727e-315 2.67727e-315 2.67727e-315
>>
>> 4.94066e-324 4.94066e-324 4.94066e-324 9.88131e-324 4.94066e-324
>>
>> 2.76235e-318 2.76235e-318 2.76235e-318 … 2.76235e-318 2.76235e-318
>>
>> 4.94066e-324 4.94066e-324 4.94066e-324 9.88131e-324 4.94066e-324
>>
>> 4.94066e-324 4.94066e-324 4.94066e-324 9.88131e-324 4.94066e-324,
>>
>>
>> 2x8 DataArray{Float64,2}:
>>
>> -73.3712 -72.1065 -73.2453 -71.9876 … -72.7328 -72.5231 -72.8999
>>
>> 41.225 41.4667 41.7925 41.83 41.8064 41.4354 41.3488,
>>
>>
>> 2x8 DataArray{Float64,2}:
>>
>> -73.3712 -72.1065 -73.2453 -71.9876 … -72.7328 -72.5231 -72.8999
>>
>> 41.225 41.4667 41.7925 41.83 41.8064 41.4354 41.3488))
>>
>>
>> I do not think that the Distance package likes the types that is input into
>> the function, i.e. the vectors are DataArrays instead of Arrays. It works
>> just fine when I used Tony's idea:
>>
>>
>> julia> data = readcsv("ct_coord_2.csv",Float64)
>>
>> 8x2 Array{Float64,2}:
>>
>> -73.3712 41.225
>>
>> -72.1065 41.4667
>>
>> -73.2453 41.7925
>>
>> -71.9876 41.83
>>
>> -72.3365 41.855
>>
>> -72.7328 41.8064
>>
>> -72.5231 41.4354
>>
>> -72.8999 41.3488
>>
>>
>> julia> xc = data[:,1]
>>
>> 8-element Array{Float64,1}:
>>
>> -73.3712
>>
>> -72.1065
>>
>> -73.2453
>>
>> -71.9876
>>
>> -72.3365
>>
>> -72.7328
>>
>> -72.5231
>>
>> -72.8999
>>
>>
>> julia> yc = data[:,2]
>>
>> 8-element Array{Float64,1}:
>>
>> 41.225
>>
>> 41.4667
>>
>> 41.7925
>>
>> 41.83
>>
>> 41.855
>>
>> 41.8064
>>
>> 41.4354
>>
>> 41.3488
>>
>>
>> julia> xc=xc'
>>
>> 1x8 Array{Float64,2}:
>>
>> -73.3712 -72.1065 -73.2453 -71.9876 … -72.7328 -72.5231 -72.8999
>>
>>
>> julia> yc=yc'
>>
>> 1x8 Array{Float64,2}:
>>
>> 41.225 41.4667 41.7925 41.83 41.855 41.8064 41.4354 41.3488
>>
>>
>> julia> temp = [xc;yc]
>>
>> 2x8 Array{Float64,2}:
>>
>> -73.3712 -72.1065 -73.2453 -71.9876 … -72.7328 -72.5231 -72.8999
>>
>> 41.225 41.4667 41.7925 41.83 41.8064 41.4354 41.3488
>>
>>
>> julia> R = pairwise(Euclidean(),temp)
>>
>> 8x8 Array{Float64,2}:
>>
>> 0.0 1.28762 0.581327 1.51014 … 0.863479 0.873799 0.487347
>>
>> 1.28762 0.0 1.18451 0.382214 0.712542 0.417808 0.802085
>>
>> 0.581327 1.18451 0.0 1.25833 0.512668 0.805673 0.562309
>>
>> 1.51014 0.382214 1.25833 0.0 0.745667 0.665227 1.03141
>>
>> 1.21144 0.451294 0.910982 0.349837 0.399323 0.459258 0.757372
>>
>> 0.863479 0.712542 0.512668 0.745667 … 0.0 0.426208 0.487124
>>
>> 0.873799 0.417808 0.805673 0.665227 0.426208 0.0 0.386557
>>
>> 0.487347 0.802085 0.562309 1.03141 0.487124 0.386557 0.0
>>
>>
>> There seems to be some issue with the Distance package not accepting Data
>> Frames. Of course, the readcsv works fine but this might be an issue for
>> others as well.
>>
>>
>> Thanks,
>>
>> Don
>>
>>
>>
>> On Thursday, July 3, 2014 6:49:18 PM UTC-4, Patrick O'Leary wrote:
>>>
>>> On Thursday, July 3, 2014 5:36:23 PM UTC-5, Donald Lacombe wrote:
>>>>
>>>> I'm no GIS expert (I'm an applied econometrician) and the code I've
>>>> written seems to work. The Distance package also works with my "real" data
>>>> which are the centroids of the counties in Connecticut and I tested it
>>>> with
>>>> Euclidean, Cityblock, and SqEuclidean.
>>>>
>>>
>>> Glad you got something working. Whether those distances are accurate
>>> enough depends on how the points are arranged and what you plan to do with
>>> it--I can see where it wouldn't make much difference in this case. I can't
>>> let the statisticians and image processing folks have all the technical
>>> conversation fun in this mailing list, though!
>>>
>>
