.. _quick_overview:

==============
Quick Overview
==============

Here is a quick overview of PyWAsP. To begin with, lets import ``pywasp``, ``windkit``. We will use ``pathlib.Path`` to point to the data location.

.. ipython:: python

    from pathlib import Path
    import pywasp as pw
    import windkit as wk

    path = Path("../modules/examples/tutorial_4/data")


Read in a observed wind climate
===============================


You can use ``windkit`` to open a WAsP ``.omwc`` file, which is a binned observed wind climate file, and reproject it to
UTM32 coordinates

.. ipython:: python

    bwc = wk.read_bwc(path / "SerraSantaLuzia.omwc", crs="EPSG:4326")
    bwc = wk.spatial.reproject(bwc, to_crs="EPSG:32629")
    print(bwc)


Read in topography data
===============================

Secondly, let's also read in some elevation and landcover data and combine it into a :py:class:`pywasp.wasp.TopographyMap`

.. ipython:: python

    elev_map = wk.read_vector_map(path / "SerraSantaLuzia.map", crs="EPSG:32629", map_type="elevation")
    lc_map, lc_tbl = wk.read_vector_map(path / "SerraSantaLuzia.map", crs="EPSG:32629", map_type="roughness")
    topo_map = pw.wasp.TopographyMap(elev_map, lc_map, lc_tbl)



Calculate Generalized Wind climate
==================================

A observed wind climate can be generalized with WAsP, using the `pywasp.wasp.generalize` function

.. ipython:: python

    gwc = pw.wasp.generalize(bwc, topo_map)
    print(gwc)


Downscale to wind turbine locations
===================================

The generalized wind climate can be used with the topography data to predict the wind climate at nearby locations, like the hub-height of planned wind turbines

.. ipython:: python

    import pandas as pd

    wtg_locs = pd.read_csv(path / 'turbine_positions.csv')

    output_locs =  wk.create_dataset(wtg_locs.Easting.values,
                                     wtg_locs.Northing.values,
                                     wtg_locs['Hub height'].values,
                                     crs="EPSG:32629")

    pwc = pw.wasp.downscale(gwc, topo_map, output_locs=output_locs, genwc_interp="nearest")
    print(pwc)


Estimate the gross AEP
======================

The predicted wind climates can be used to estimate the Annual Energy Production in GWh for a specific turbine model

.. ipython:: python

    wtg = wk.read_wtg(path / "Bonus_1_MW.wtg")

    gross_aep = pw.wasp.gross_aep(pwc, wtg)

    print(gross_aep)


In the :ref:`user_guide` we go into more much greater detail with each component in PyWAsP.