Reputation: 21
I'm new to Julia and currently trying to run the following code:
Using DelimitedFiles
M=readdlm(data)
ts,A=M[:,1],M[:,2:end]
(nsweeps,N)=size(A)
dx=0.01;
x=[minimum(collect(A)):dx:maximum(collect(A))];
bx=[x-dx/2,x[end]+dx/2];
But, when I try to run the last line of code, it gives me the following error:
MethodError: no method
matching(::Array{StepRangeLen{Float64,Base.TwicePrecision
{Float64},Base.TwicePrecision{Float64}},1}, ::Float64)
Closest candidates are:
-(!Matched::BigFloat, ::Union{Float16, Float32, Float64}) at
mpfr.jl:437
-(!Matched::Complex{Bool}, ::Real) at complex.jl:307
-(!Matched::Missing, ::Number) at missing.jl:115
Can you please help me? Also, the data I'm using it's
30×6 Array{Float64,2}
UPDATE here's the whole function I'm trying to run is the following:
function mymain(filename,nsamples)
start_time=time()
M=readdlm(filename)
ts,A=M[:,1],M[:,2:end]
(nsweeps,N)=size(A)
dx=0.01;
x=[minimum(collect(A)):dx:maximum(collect(A))];
bx=[x-dx/2,x[end]+dx/2];
(bx,hA)=hist(A[:],bx);
f1=figure()
subplot(2,1,1); plot(ts,A,"-o"); xlabel("Time [ms]"); ylabel("Amps
[mV]");
subplot(2,1,2); plot(x,hA,"-"); xlabel("Amps [mV]");
ylabel("Density");draw()
nparams=8
Sx=Array(ASCIIString,1,nparams)
Rx=zeros(2,nparams)
nx=zeros(Int,1,nparams)
Sx[1,1]="p"; Rx[1:2,1]=[0.02,0.98]; nx[1]=49
Sx[1,2]="n"; Rx[1:2,2]=[1,20]; nx[2]=20
Sx[1,3]="tD"; Rx[1:2,3]=[50,200]; nx[3]=46
Sx[1,4]="a"; Rx[1:2,4]=[0.05,0.5]; nx[4]=46
Sx[1,5]="siga"; Rx[1:2,5]=[0.01,0.2]; nx[5]=39
Sx[1,6]="sigb"; Rx[1:2,6]=[0.01,0.1]; nx[6]=19
Sx[1,7]="tauf"; Rx[1:2,7]=[50,200]; nx[7]=46
Sx[1,8]="u1"; Rx[1:2,8]=Rx[1:2,1]; nx[8]=nx[1]
x=zeros(maximum(nx),nparams)
p=zeros(maximum(nx),nparams)
dx=zeros(1,nparams)
for j=1:nparams
x[1:nx[j],j]=linspace(Rx[1,j],Rx[2,j],nx[j])'
dx[j]=x[2,j]-x[1,j]
end
S=zeros(Int,nsamples,nparams)
sold=zeros(Int,1,nparams)
for j=1:nparams
sold[j]=rand(1:nx[j])
end
while x[sold[4],4]<=x[sold[5],5]
sold[4]=rand(1:nx[4])
sold[5]=rand(1:nx[5])
end
while x[sold[8],8]<=x[sold[1],1]
sold[1]=rand(1:nx[1])
sold[8]=rand(1:nx[8])
end
xold=zeros(1,nparams)
xnew=zeros(1,nparams)
for j=1:nparams
xold[j]=x[sold[j],j]
end
llold=myloglikelihood(xold,ts,A)
for k=1:nsamples
snew=sold+rand(-1:1,1,nparams)
if all(ones(1,nparams).<=snew.<=nx)
allowed2=x[snew[4],4]>x[snew[5],5]
allowed3=x[snew[8],8]>x[snew[1],1]
if allowed2&allowed3
for j=1:nparams
xnew[j]=x[snew[j],j]
end
llnew=myloglikelihood(xnew,ts,A)
if rand()<exp(llnew-llold)
sold,llold=snew,llnew
end
end
end
S[k,:]=sold
end
for k=1:nsamples
for j=1:nparams
p[S[k,j],j]+=1/(nsamples*dx[j])
end
end
f2=figure()
for j=1:nparams
subplot(2,4,j)
plot(x[1:nx[j],j],p[1:nx[j],j]);
xlabel(Sx[j])
end
diff_time=time()-start_time;
println("Total runtime
",round(diff_time,3),"s=",round(diff_time/60,1),"mins." );
return S
end
This goes in line with some other functions, but as you can see, this is the main function, so I really can't move forward without first runnning this one.
Upvotes: 0
Views: 76
Reputation: 18560
It isn't clear what outcome you are hoping for here. So I'll just give some pointers that hopefully help.
First, in this line:
x=[minimum(collect(A)):dx:maximum(collect(A))];
the calls to collect
are redundant. Also, I suspect you are trying to construct a StepRangeLen
, but by putting it in []
you actually are getting a Vector{StepRangeLen}
. I think what you want in this line is actually this:
x=minimum(A):dx:maximum(A);
Second, in this line:
bx=[x-dx/2,x[end]+dx/2];
note that dx/2
is a Float64
while x
is a StepRangeLen
. This is important because the latter is a collection so if you want to perform this operation element-wise across the collection you need to broadcast, that is, x .- dx/2
. Note, I suspect you may not be on the latest version of Julia, because when I run this the error message actually tells me explicitly I need to broadcast. Anyway, in contrast, x[end]+dx/2
is fine and does not need to be broadcast because x[end]
is Float64
. So I think you want:
bx=[x .- dx/2, x[end] + dx/2];
Having said that, it isn't clear to me why you want this bx
, which is why I said at the start I'm not sure what outcome you were hoping for.
Upvotes: 2