.. figure:: ../platecurie/examples/picture/platecurie_logo.png
   :align: center

Documentation
=============

Crustal magnetic anomalies carry information on the distribution of remanent magnetization in the Earth's crust (`Blakely, 1995 <https://www.cambridge.org/core/books/potential-theory-in-gravity-and-magnetic-applications/348880F23008E16E663D6AD14A41D8DE>`_). The Curie point corresponds to the depth at which crustal rocks become paramagnetic where they reach their Curie temperature, and is obtained by fitting the power spectral density (PSD) of magnetic anomaly data with a model where magnetic anomalies are confined within a buried layer (`Bouligand et al., 2009 <https://doi.org/10.1029/2009JB006494>`_ `Audet and Gosselin, 2019 <https://doi.org/10.1093/gji/ggz166>`_ `Mather and Fullea, 2019 <https://doi.org/10.5194/se-10-839-2019>`_). Mapping the Curie point provides important information on geothermal gradients in the Earth; however, this is a spatio-spectral localization problem because the PSD needs to be calculated within moving windows at wavelengths long enough to capture the greatest possible depth to the bottom of the magnetic layer. The wavelet transform is particularly well suited to overcome this problem because it avoids splitting the grids into small windows and can therefore produce PSD functions at each point of the input grid (`Gaudreau et al., 2019 <https://doi.org/10.1029/>`_).

PlateCurie is a software that extends the package ``PlateFlex``, which contains Python and Fortran modules to calculate the wavelet transform and scalogram of 2D gridded data, by providing new classes :class:`~platecurie.classes.MagGrid` and :class:`~platecurie.classes.ZtGrid` that inherit from the ``PlateFlex`` :class:`~plateflex.classes.Grid` class, and a new class :class:`~platecurie.classes.Project` with methods to estimate the properties of the magnetic layer (depth to top of layer ``zt``, its thickness ``dz``, and power-law exponent of fractal magnetization ``β``) using either non-linear least-squares or Bayesian inference. The software is bundled with ``Jupyter`` notebooks that describe the 
processing steps required for the estimation of model parameters. 

This software was inspired by a sister software `PyCurious <https://github.com/brmather/pycurious>`_ by `Mather and Delhaye <https://joss.theoj.org/papers/10.21105/joss.01544>`_. The two softwares differ by their use of different spectral estimators (wavelet transform for ``platecurie`` and periodogram for ``pycurious``) as well as the estimation process (either non-linear least-squares or ``pymc3`` bayesian estimation for ``platecurie``, and a Bayesian framework for ``pycurious``). 


.. toctree::
   :maxdepth: 1
   :caption: Quick Links

   links

.. toctree::
   :maxdepth: 1
   :caption: Getting Started

   api

.. toctree::
   :maxdepth: 1
   :caption: API Documentation

   classes
   estimate
   plotting

.. toctree::
   :maxdepth: 1
   :caption: Jupyter Notebooks

   Example 1: Making grids <https://github.com/paudetseis/PlateCurie/blob/master/platecurie/examples/Notebooks/Ex1_making_grids.ipynb>
   Example 2: Wavelet analysis <https://github.com/paudetseis/PlateCurie/blob/master/platecurie/examples/Notebooks/Ex2_wavelet_analysis.ipynb>
   Example 3: Magnetic parameters at single grid cell <https://github.com/paudetseis/PlateCurie/blob/master/platecurie/examples/Notebooks/Ex3_estimate_curie_parameters_cell.ipynb>
   Example 4: Magnetic parameters mapped over grid <https://github.com/paudetseis/PlateCurie/blob/master/platecurie/examples/Notebooks/Ex4_estimate_curie_parameters_grid.ipynb>
   Example 5: Magnetic parameters with a priori constraints <https://github.com/paudetseis/PlateCurie/blob/master/platecurie/examples/Notebooks/Ex5_estimate_curie_variable_zt.ipynb>



.. Indices and tables
.. """"""""""""""""""

.. * :ref:`genindex`
.. * :ref:`modindex`
.. * :ref:`search`