#!/usr/bin/env python
# encoding: utf-8
"""
Refraction Manager
------------------

This example shows how to use the Refraction manager to generate the response
of a three-layered sloping model and to invert the synthetic noisified data."""

# sphinx_gallery_thumbnail_number = 2
import numpy as np

import pygimli as pg
import pygimli.meshtools as mt
from pygimli.physics import TravelTimeManager

###############################################################################
# We start by creating a three-layered slope (The model is taken from the BSc
# thesis of Constanze Reinken (University of Bonn).
layer1 = mt.createPolygon([[0.0, 137], [117.5, 164], [117.5, 162], [0.0, 135]],
                          isClosed=True, marker=1, area=1)
layer2 = mt.createPolygon([[0.0, 126], [0.0, 135], [117.5, 162], [117.5, 153]],
                          isClosed=True, marker=2)
layer3 = mt.createPolygon([[0.0, 110], [0.0, 126], [117.5, 153], [117.5, 110]],
                          isClosed=True, marker=3)

slope = (164 - 137) / 117.5

geom = layer1 + layer2 + layer3
mesh = mt.createMesh(geom, quality=34.3, area=3, smooth=[1, 10])
pg.show(mesh)

###############################################################################
# Next we define geophone positions and a measurement scheme, which consists of
# shot and receiver indices.
numberGeophones = 48
sensors = np.linspace(0., 117.5, numberGeophones)
scheme = pg.physics.traveltime.createRAData(sensors)

# Adapt sensor positions to slope
pos = np.array(scheme.sensors())
for x in pos[:, 0]:
    i = np.where(pos[:, 0] == x)
    new_y = x * slope + 137
    pos[i, 1] = new_y

scheme.setSensors(pos)

###############################################################################
# Now we initialize the TravelTime manager and asssign P-wave velocities to the
# layers. To this end, we create a map from cell markers 0 through 3 to
# velocities (in m/s) and generate a velocity vector. To check whether the
# model looks correct, we plot it along with the sensor positions.

mgr = TravelTimeManager()
vp = np.array(mesh.cellMarkers())
vp[vp == 1] = 250
vp[vp == 2] = 500
vp[vp == 3] = 1300

ax, _ = pg.show(mesh, vp, colorBar=True, logScale=False, label='v in m/s')
pg.viewer.mpl.drawSensors(ax, scheme.sensors(), diam=0.5,
                         facecolor='white', edgecolor='black')

###############################################################################
# We use this model to create noisified synthetic data and look at the
# traveltime data matrix. Note, we force a specific noise seed as we want
# reproducable results for testing purposes.
# TODO: show first arrival traveltime curves.
data = mgr.simulate(slowness=1.0 / vp, scheme=scheme, mesh=mesh,
                    noiseLevel=0.001, noiseAbs=0.001, seed=1337,
                    verbose=True)
mgr.showData(data)
###############################################################################
# Now we invert the synthetic data. We need a new independent mesh without
# information about the layered structure. This mesh can be created manual or
# guessd automatic from the data sensor positions (in this example). We
# tune the maximum cell size in the parametric domain to 15m²
vest = mgr.invert(data, secNodes=2, paraMaxCellSize=15.0,
                  maxIter=10, verbose=True)
np.testing.assert_array_less(mgr.inv.inv.chi2(), 1)

###############################################################################
# The manager also holds the method showResult that is used to plot the result.
# Note that only covered cells are shown by default.
# For comparison we plot the geometry on top.

ax, _ = mgr.showResult(cMin=min(vp), cMax=max(vp), logScale=False)
pg.show(geom, ax=ax, fillRegion=False, regionMarker=False)

###############################################################################
# Note that internally the following is called
#
# .. code-block:: python
#
#    ax, _ = pg.show(ra.mesh, vest, label="Velocity [m/s]", **kwargs)
#

###############################################################################
# Another useful method is to show the model along with its response
# on the data.
mgr.showResultAndFit(cMin=min(vp), cMax=max(vp))

###############################################################################
# .. note:: Takeaway message

#     A default data inversion with checking of the data consists of only few
#     lines. Check out :ref:`ex:koenigsee`.