I have multiples files .mat that are organized at the same way. They look like that:

# file name : Case_1
# Created by Octave 4.2.0
# matrix
# other comments
1 2 3
4 5 6
# vector
0.2
0.3
0.4

And I’d like to get these values and group them in two other files. Let’s assume the second file it’s like that:

# file name : Case_2
# Created by Octave 4.2.0
# matrix
# other comments
7 8 9
10 11 12
# vector
0.5
0.6
0.7

So, the final files would look like that :

# file name :Merge Matrix Case_1 Case_2
# Created by Octave 4.2.0
# matrix
# other comments
1 2 3 7 8 9
4 5 6 10 11 12

AND this other one:

# file name :Merge Vector Case_1 Case_2
# Created by Octave 4.2.0
# vector
0.2
0.3
0.4
0.5
0.6
0.7

Anyone know if it’s really possible to do it in Julia ? Thanks in advance!
p.s : I have no access to the code source in octave, I only have the output data.

using DelimitedFiles
function quick_and_dirty_read(filename)
m = readdlm(filename; comments = true, comment_char = '#')
vector_rows = isempty.(m[:, 2])
Int.(m[.!vector_rows, :]), Float64.(m[ vector_rows, 1]) # modify types or do something else if necessary
end

reads the file, do the concatenation, then write it out similarly.

I have tried to change the type of my data, but I got this message:

ERROR: LoadError: MethodError: no method matching zero(::Type{Any})
Closest candidates are:
zero(::Type{Union{Missings.Missing, T}}) where T at C:\JuliaPro-0.6.2.2\pkgs-0.6.2.2\v0.6\Missings\src\Missings
.jl:97
zero(::Type{Base.LibGit2.GitHash}) at libgit2\oid.jl:106
zero(::Type{Base.Pkg.Resolve.VersionWeights.VWPreBuildItem}) at pkg\resolve\versionweight.jl:82

My code is

for file in list_file
data = readdlm(file; comments = true, comment_char = '#')
vector_rows = isempty.(data[:, 2])
float(data[vector_rows, :])
end

I have also tried by using convert(Float64,data) but I got another error:

ERROR: LoadError: MethodError: Cannot `convert` an object of type Array{Any,2} to an object of type Float64
This may have arisen from a call to the constructor Float64(...),
since type constructors fall back to convert methods.

Do you have any idea how I could convert my data? Thanks in advance.

The error ERROR: LoadError: MethodError: no method matching zero(::Type{Any}) has been produced by the following code, using float():

clearconsole()
# this code aims to get the values of a .mat file, then save it in dataframe
# each file represents a period, so the goal is to concanet the files in order to have all the periods into a SAME dataframe
# in order to respect the standard model, the lignes and columns might be transposed.
using DataFrames
list_file = readdir() # getting file names
key = "1"
nb_hours = 24
nb_points = 23
list_file_real = [file for file in list_file if contains(file,key)] # excluding unnecessary files
periods= ["1-1_31-3","1-4_30-6","1-7_30-9","1-10_31-12"]
# Getting the real order
# WORKAROUND https://stackoverflow.com/questions/45013963/julia-sort-string-array-by-substring-containment
sort!(list_file_real,by=t->first(filter(x->contains(t,x[2]),enumerate([periods;""])))[1])
# the information contained in the following lines and columns:[nb_hours+1:nb_hours + nb_points,1 ] don't need to be repeted so
# they can placed OUT of the loop
file = list_file_real[1]
data_temp = readdlm(file; comments = true, comment_char = '#')
vector_final_case1_ProbPower = data_temp[nb_hours+1:nb_hours + nb_points,1]
# list of arrays that will keep the values of each file
matrix_test_case1 = []
vector_average_test_case1 =[]
matrix_test_case2 = []
vector_average_test_case2 = []
for file in list_file_real
data = readdlm(file; comments = true, comment_char = '#')
vector_rows = isempty.(data[:, 2])
float(data[vector_rows, :])
# case 1 data
probabilities_case1_data =transpose(data[1:nb_hours,1:nb_points])
average_power_case1_data = data[nb_hours + nb_points+1:nb_hours + nb_points+nb_hours,1]
push!(matrix_test_case1, probabilities_case1_data)
push!(vector_average_test_case1,average_power_case1_data)
# case 2 data
probabilities_case2_data = data[nb_hours + nb_points+nb_hours + 1:nb_hours + nb_points+nb_hours+nb_hours,1:nb_points]
average_power_case2_data = data[nb_hours + nb_points+nb_hours+nb_hours+nb_points+1:nb_hours + nb_points+nb_hours+nb_hours+nb_points+nb_hours,1]
#
push!(matrix_test_case2, probabilities_case2_data)
push!(vector_average_test_case2, average_power_case2_data)
end