Power colours

Project.toml

@@ -4,19 +4,28 @@ authors = ["Aman Pandey"]
 version = "0.1.0"
 
 [deps]
-FFTW = "7a1cc6ca-52ef-59f5-83cd-3a7055c09341"
-Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
-ResumableFunctions = "c5292f4c-5179-55e1-98c5-05642aab7184"
-StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
-Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
-ProgressBars = "49802e3a-d2f1-5c88-81d8-b72133a6f568"
+AbstractFFTs = "621f4979-c628-5d54-868e-fcf4e3e8185c"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
+FFTW = "7a1cc6ca-52ef-59f5-83cd-3a7055c09341"
 HDF5 = "f67ccb44-e63f-5c2f-98bd-6dc0ccc4ba2f"
 NaNMath = "77ba4419-2d1f-58cd-9bb1-8ffee604a2e3"
 FITSIO = "525bcba6-941b-5504-bd06-fd0dc1a4d2eb"
+HDF5 = "f67ccb44-e63f-5c2f-98bd-6dc0ccc4ba2f"
 Intervals = "d8418881-c3e1-53bb-8760-2df7ec849ed5"
+JuliaFormatter = "98e50ef6-434e-11e9-1051-2b60c6c9e899"
+NaNMath = "77ba4419-2d1f-58cd-9bb1-8ffee604a2e3"
+Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+ProgressBars = "49802e3a-d2f1-5c88-81d8-b72133a6f568"
+ResumableFunctions = "c5292f4c-5179-55e1-98c5-05642aab7184"
+Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
+StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
+Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [compat]
+DataFrames = "1.3"
+Distributions = "0.25"
 julia = "1.11"
 FFTW = "1.4"
 Distributions = "0.25"
@@ -26,11 +35,21 @@ ProgressBars = "1.4"
 DataFrames = "1.3"
 HDF5 = "0.16"
 FITSIO = "0.16"
+HDF5 = "0.16"
 Intervals = "1.8"
+JuliaFormatter = "1.0.62"
 NaNMath = "0.3, 1"
+Plots = "1.8"
+ProgressBars = "1.4"
+ResumableFunctions = "0.6"
+StatsBase = "0.33"
+Test = "1.11.0"
+julia = "1.7"
 
 [extras]
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+ResumableFunctions = "c5292f4c-5179-55e1-98c5-05642aab7184"
 
 [targets]
-test = ["Test"]
+test = ["Test", "Random","ResumableFunctions"]

docs/src/index.md

@@ -12,3 +12,29 @@ Documentation for [Stingray](https://github.com/matteobachetti/Stingray.jl).
 ```@autodocs
 Modules = [Stingray]
 ```
+## Overview
+**Stingray.jl** is a Julia package for **X-ray spectral timing analysis**, designed for high-performance astrophysical data processing.
+
+
+## Get Started
+🚀 **New to Stingray.jl?** Follow these steps:
+
+1. **[Installation Guide](installation.md)** → Set up Stingray.jl.
+2. **[Usage Guide](usage.md)** → Learn how to analyze time-series data.
+
+## Quick Example
+Here's a simple example to analyze an X-ray light curve:
+```julia
+using Stingray, Random, Plots
+
+# Generate Simulated Light Curve
+N = 1024  # Number of data points
+t = collect(0:0.1:(N-1)*0.1)  # Time array
+Random.seed!(42)
+light_curve = sin.(2π * 0.5 .* t) + 0.3 * randn(N)  # Sine wave + noise
+
+# Plot Light Curve
+plot(t, light_curve, label="Simulated Light Curve", xlabel="Time (s)", ylabel="Intensity", title="X-ray Light Curve", legend=:topright)
+```
+
+🚀 **Start exploring Stingray.jl today!**

docs/src/installation.md

@@ -0,0 +1,196 @@
+```@meta
+CurrentModule = Stingray
+```
+
+# Stingray
+
+Documentation for [Stingray](https://github.com/matteobachetti/Stingray.jl).
+
+```@index
+```
+
+```@autodocs
+Modules = [Stingray]
+```
+# Stingray.jl
+
+Stingray.jl is a spectral-timing software package for time series analysis of astronomical data, with a particular focus on X-ray astronomy.
+
+## Installation
+
+### Prerequisites
+
+Before installing Stingray.jl, ensure you have Julia v1.7 or later installed:
+
+1. Download Julia from the [official website](https://julialang.org/downloads/)
+2. Follow the installation instructions for your operating system
+3. Verify your installation by running `julia --version` in your terminal
+
+### Installing from the Julia General Registry
+
+To install the stable version of Stingray.jl:
+
+```julia
+using Pkg
+Pkg.add("Stingray")
+```
+
+### Installing the Development Version
+
+For the latest features and improvements, you can install directly from the GitHub repository:
+
+```julia
+using Pkg
+Pkg.add(url="https://github.com/matteobachetti/Stingray.jl")
+```
+
+Or switch to development mode if you plan to contribute:
+
+```julia
+using Pkg
+Pkg.develop("Stingray")
+```
+
+### Verifying Installation
+
+Verify your installation works correctly:
+
+```julia
+using Stingray
+@info "Stingray.jl loaded successfully!"
+```
+
+## Testing
+
+Stingray.jl includes a comprehensive test suite to ensure functionality. To run the tests:
+
+```julia
+using Pkg
+Pkg.test("Stingray")
+```
+
+For contributors working on a local development version:
+
+```julia
+# Navigate to the package directory
+cd(joinpath(Pkg.devdir(), "Stingray"))
+
+# Run tests
+using Pkg
+Pkg.test()
+```
+
+### Writing New Tests
+
+When adding features or fixing bugs, please include appropriate tests:
+
+1. Tests should be placed in the `test/` directory
+2. Use the `@testset` macro to organize related tests
+3. Follow existing naming conventions (e.g., `test_[module_name].jl` or `test_[feature].jl`)
+4. Run tests locally before submitting a pull request
+
+Example test structure:
+
+```julia
+@testset "Module Name Tests" begin
+    @test function_to_test(input) == expected_output
+    @test_throws ErrorType function_to_test(invalid_input)
+end
+```
+
+## Documentation
+
+### Building Documentation Locally
+
+To build and preview the documentation locally:
+
+1. Install required documentation packages:
+
+```julia
+using Pkg
+Pkg.add(["Documenter", "DocumenterTools"])
+```
+
+2. Navigate to the docs directory and build:
+
+```julia
+cd("docs")
+julia --project=. make.jl
+```
+
+3. Open `docs/build/index.html` in your browser
+
+### Writing Documentation
+
+Documentation uses [Documenter.jl](https://juliadocs.github.io/Documenter.jl/stable/). When adding new features:
+
+1. Add docstrings to functions, types, and modules following Julia's documentation guidelines
+2. Use `"""..."""` for multi-line docstrings
+3. Include Examples section with working code examples
+4. For complex features, add a dedicated page in `docs/src/`
+
+Example docstring:
+
+```julia
+"""
+    power_color(frequency, power; kwargs...)
+
+Calculate two power colors from a power spectrum.
+
+# Arguments
+- `frequency::Vector{Number}`: The frequencies of the power spectrum
+- `power::Vector{Number}`: The power at each frequency
+
+# Keywords
+- `power_err=nothing`: Optional error measurements for power
+- `freq_edges=[1/256, 1/32, 0.25, 2.0, 16.0]`: Frequency edges for calculations
+- `return_log=false`: Return the base-10 logarithm of results
+
+# Returns
+- `pc0::Number`: The first power color
+- `pc0_err::Number`: Error on the first power color
+- `pc1::Number`: The second power color
+- `pc1_err::Number`: Error on the second power color
+
+# Examples
+```julia
+freq = collect(0.01:0.01:20)
+power = 1 ./ freq
+pc0, pc0_err, pc1, pc1_err = power_color(freq, power)
+```
+
+See Heil et al. 2015, MNRAS, 448, 3348 for details.
+"""
+function power_color(frequency, power; kwargs...)
+    # Implementation
+end
+```
+
+## Example Notebooks
+
+Example Jupyter notebooks demonstrating Stingray.jl functionality are available in the [notebooks repository](https://github.com/kashish2210/notebooks).
+
+These examples cover:
+- Basic time series analysis
+- Power spectral calculations
+- Cross-spectral techniques
+- Specialized techniques for X-ray astronomy
+
+## Contributing
+
+We welcome contributions! Please check the [CONTRIBUTING.md](CONTRIBUTING.md) file for guidelines.
+
+1. Fork the repository
+2. Create a feature branch (`git checkout -b feature/amazing-feature`)
+3. Implement your changes with tests and documentation
+4. Commit your changes (`git commit -m 'Add amazing feature'`)
+5. Push to your branch (`git push origin feature/amazing-feature`)
+6. Open a Pull Request
+
+## License
+
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+
+## Acknowledgments
+
+Stingray.jl is a port of the Python [Stingray](https://github.com/StingraySoftware/stingray) package with optimizations for Julia.