87 lines
2.5 KiB
Julia
87 lines
2.5 KiB
Julia
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module Utils
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import TOML: parsefile
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import DelimitedFiles: readdlm
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import DSP
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function load_init_params(filename::String)
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meta = parsefile(filename)["metadata"]
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params = NamedTuple(Dict(Symbol(p["symbol"]) => p["initial_value"] for p in meta["parameters"]))
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lower_bounds = [p["min"] for p in meta["parameters"]]
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upper_bounds = [p["max"] for p in meta["parameters"]]
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return (meta=meta, params=params, lower_bounds=lower_bounds, upper_bounds=upper_bounds)
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end
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function select_columns(QIE_array, qmin, qmax, bin, err_mul)
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#""Select q / I(q) / err(q) data - Change I(q) value from mm^-1 to nm^-1'
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Q = Float64[]
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IDATA = Float64[]
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ERR = Float64[]
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for i in axes(QIE_array, 1)
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# keep only the values of q within bounds
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if qmin <= QIE_array[i, 1] <= qmax
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# skip all but the (k*bin)-th values
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if i % bin == 0
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if QIE_array[i, 2] > 0
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push!(Q, QIE_array[i, 1])
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push!(IDATA, 1e-6 * QIE_array[i, 2])
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if err_mul == 0
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push!(ERR, 0.01 * 1e-6 * QIE_array[i, 2])
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elseif QIE_array[i, 3] * err_mul > 0.01 * QIE_array[i, 2]
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push!(ERR, 1e-6 * QIE_array[i, 3] * err_mul)
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else
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push!(ERR, 0.01 * 1e-6 * QIE_array[i, 2])
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end
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end
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end
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end
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end
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return (Q, IDATA, ERR)
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end
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"""
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compute_logscale_weights(q)
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Compute the weights W, which are proportional to the distance in the log space
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"""
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function compute_logscale_weights(q)
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diff_log_q = diff(log10.(q))
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left_diff_log_q = view(diff_log_q, [firstindex(diff_log_q); eachindex(diff_log_q)])
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right_diff_log_q = view(diff_log_q, [eachindex(diff_log_q); lastindex(diff_log_q)])
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return left_diff_log_q + right_diff_log_q
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end
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function load_config(filename::String)
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meta, params_init, lower_bounds, upper_bounds = load_init_params(filename)
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f = open(meta["data_file"], "r")
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qie_data = readdlm(f, '\t')
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close(f)
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return (meta=meta,
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params_init=params_init,
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lower_bounds=lower_bounds,
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upper_bounds=upper_bounds,
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qie_deta=qie_data)
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end
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function lowpass_filter(i; σ=1)
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half_w = 10
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x = range(-5, 5; length=2half_w + 1)
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i_padded = view(i, [fill(firstindex(i), half_w); eachindex(i); fill(lastindex(i), half_w)])
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k = exp.(-0.5 * ((x / σ) .^ 2)) / (2 * σ * sqrt(2π))
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k ./= sum(k)
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@show sum(k)
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r = DSP.conv(i_padded, k)[2half_w+1:end-2half_w]
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@show size(r)
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return r
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end
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function chi2(I_data, I_model, err)
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return sum((I_data .- I_model ./ err) .^ 2)
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end
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end # module Utils
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