Getting started
===============

The easiest way to get started with GeoProfile is in the **Jupyter Notebook**.

Installation
------------
To install this package, including the `map` and `gef` reading functionality, run:

.. code-block::

    pip install geoprofile[map, gef]


To skip the installation of the `GeoProfile` library, in case you do not need it (e.g. only use pure plotting), run:

.. code-block::

    pip install geoprofile

Than you can import proficore as follows:

.. ipython:: python

    from geoprofile.column import Column
    from geoprofile.profile import Section

    import numpy as np
    import plotly.io as pio
    from shapely.geometry import LineString

or any equivalent :code:`import` statement.

Create Columns
---------------

The first thing to do is to create column classes. This class holds the information of the for
example a CPT or borehole. For more information on the :func:`geoprofile.column.Column` class go
the the reference.


.. ipython:: python

    columns = [
        Column(
            classify={
                "depth": [5, -10, -20, -30],
                "thickness": [15, 10, 10, 10],
                "geotechnicalSoilName": [
                    "sterkGrindigZand",
                    "zwakZandigSiltMetGrind",
                    "sterkZandigeDetritus",
                    "grind",
                ],
            },
            x=0,
            y=0,
            z=-1,
            name="A",
        ),
        Column(
            classify={
                "depth": [0, -10, -20, -30],
                "thickness": [10, 10, 10, 10],
                "geotechnicalSoilName": [
                    "sterkGrindigZand",
                    "zwakZandigSiltMetGrind",
                    "sterkZandigeDetritus",
                    "grind",
                ],
            },
            x=-3,
            y=50,
            z=4,
            name="B",
            data={
                "depth": np.linspace(0, -40).tolist(),
                "qc": np.random.random(50).tolist(),
                "abc": np.random.random(50).tolist(),
            },
        ),
        Column(
            classify={
                "depth": [0, -10, -20, -30],
                "thickness": [10, 10, 10, 10],
                "geotechnicalSoilName": [
                    "sterkGrindigZand",
                    "zwakZandigSiltMetGrind",
                    "sterkZandigeDetritus",
                    "grind",
                ],
            },
            x=4,
            y=10,
            z=3,
            name="C",
            data={
                "depth": np.linspace(0, -40).tolist(),
                "qc": np.random.random(50).tolist(),
                "abc": np.random.random(50).tolist(),
            },
        ),
        Column(
            classify={
                "depth": [0, -10, -20, -30],
                "thickness": [10, 10, 10, 10],
                "geotechnicalSoilName": [
                    "sterkGrindigZand",
                    "zwakZandigSiltMetGrind",
                    "sterkZandigeDetritus",
                    "grind",
                ],
            },
            x=-30,
            y=30,
            z=-5,
            name="D",
            data={
                "depth": np.linspace(0, -40).tolist(),
                "qc": np.random.random(50).tolist(),
                "abc": np.random.random(50).tolist(),
            },
        ),
        Column(
            classify={
                "depth": [0, -10, -20, -30],
                "thickness": [10, 10, 10, 10],
                "geotechnicalSoilName": [
                    "sterkGrindigZand",
                    "zwakZandigSiltMetGrind",
                    "sterkZandigeDetritus",
                    "grind",
                ],
            },
            x=-5,
            y=30,
            z=1,
            name="E",
            data={
                "depth": np.linspace(0, -40).tolist(),
                "qc": np.random.random(50).tolist(),
                "abc": np.random.random(50).tolist(),
            },
        ),
    ]


To plot the column as a standalone you can call the plot method of the class. Be default only the
data in the classify dictionary is shown. To include data from the data dictionary use the `plot_kwargs`
argument.

.. ipython:: python

    fig = columns[1].plot(
        plot_kwargs={"qc": {"line_color":"black"}, "abc": {"line_color": "red"}}
    )


.. ipython:: python
    :suppress:

    import os

    filename = os.path.join(os.environ["DOC_PATH"], "savefig", "plot.html")
    fig.write_html(filename)


.. raw:: html
   :file: ../savefig/plot.html


Create Profile
----------------

The next step is to create a Section class. This class holds the information of all Columns.
For more information on the :func:`geoprofile.column.Section` class go
the the reference.

.. ipython:: python

    profile = Section(
        columns,
        profile_line=LineString(((-1, 0), (3, 30), (1, 51))),
        sorting_algorithm="tsp",
        reproject=True,
    )

Different sorting algorithms can be used to sort the list of columns to the profile line. It is also
possible to project the location of the column onto the profile line. To have an overview of al the column
location, profile line and the section use the map plot.


.. ipython:: python

    @savefig map.png
    profile.plot_map()


If you happy with your profile line you can plot the section as follows:


.. ipython:: python

    fig = profile.plot(
        plot_kwargs={"qc": {}, "abc": {}},
        hue="uniform",
        fillpattern=False,
        surface_level=True,
        groundwater_level=True,
    )


.. ipython:: python
    :suppress:

    import os

    filename = os.path.join(os.environ["DOC_PATH"], "savefig", "profile.html")
    fig.write_html(filename)


.. raw:: html
   :file: ../savefig/profile.html