How to use the verde.datasets.fetch_texas_wind function in verde

Polynomial trend
================

Verde offers the :class:`verde.Trend` class to fit a 2D polynomial trend to your data.
This can be useful for isolating a regional component of your data, for example, which
is a common operation for gravity and magnetic data. Let's look at how we can use Verde
to remove the clear trend from our Texas temperature dataset
(:func:`verde.datasets.fetch_texas_wind`).
"""
import numpy as np
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import verde as vd

# Load the Texas wind and temperature data as a pandas.DataFrame
data = vd.datasets.fetch_texas_wind()
print("Original data:")
print(data.head())

# Fit a 1st degree 2D polynomial to the data
coordinates = (data.longitude, data.latitude)
trend = vd.Trend(degree=1).fit(coordinates, data.air_temperature_c)
print("\nTrend estimator:", trend)

# Add the estimated trend and the residual data to the DataFrame
data["trend"] = trend.predict(coordinates)
data["residual"] = data.air_temperature_c - data.trend
print("\nUpdated DataFrame:")
print(data.head())


# Make a function to plot the data using the same colorbar

components of vector data. Each component is processed and gridded separately
(see `Erizo `__ for an elastically coupled
alternative) but we have the convenience of dealing with a single estimator.
:class:`verde.Vector` can be combined with :class:`verde.Trend`,
:class:`verde.Spline`, and :class:`verde.Chain` to create a full processing
pipeline.
"""
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import numpy as np
import pyproj
import verde as vd


# Fetch the wind speed data from Texas.
data = vd.datasets.fetch_texas_wind()
print(data.head())

# Separate out some of the data into utility variables
coordinates = (data.longitude.values, data.latitude.values)
region = vd.get_region(coordinates)
# Use a Mercator projection because Spline is a Cartesian gridder
projection = pyproj.Proj(proj="merc", lat_ts=data.latitude.mean())

# Split the data into a training and testing set. We'll fit the gridder on the training
# set and use the testing set to evaluate how well the gridder is performing.
train, test = vd.train_test_split(
    projection(*coordinates),
    (data.wind_speed_east_knots, data.wind_speed_north_knots),
    random_state=2,
)

"""
Wind speed data from Texas
==========================

This is average wind speed and air temperature for data for the state of Texas, USA, on
February 26 2018. The original data was downloaded from `Iowa State University
`__.
"""
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import verde as vd


# The data are in a pandas.DataFrame
data = vd.datasets.fetch_texas_wind()
print(data.head())

# Make a Mercator map of the data using Cartopy
plt.figure(figsize=(8, 6))
ax = plt.axes(projection=ccrs.Mercator())
ax.set_title("Wind speed and air temperature for Texas")
# Plot the air temperature as colored circles and the wind speed as vectors.
plt.scatter(
    data.longitude,
    data.latitude,
    c=data.air_temperature_c,
    s=100,
    cmap="plasma",
    transform=ccrs.PlateCarree(),
)
plt.colorbar().set_label("Air temperature (C)")

In :ref:`model_evaluation`, we saw how to check the performance of an interpolator using
cross-validation. We found that the default parameters for :class:`verde.Spline` are not
good for predicting our sample air temperature data. Now, let's see how we can tune the
:class:`~verde.Spline` to improve the cross-validation performance.

Once again, we'll start by importing the required packages and loading our sample data.
"""
import numpy as np
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import itertools
import pyproj
import verde as vd

data = vd.datasets.fetch_texas_wind()

# Use Mercator projection because Spline is a Cartesian gridder
projection = pyproj.Proj(proj="merc", lat_ts=data.latitude.mean())
proj_coords = projection(data.longitude.values, data.latitude.values)

region = vd.get_region((data.longitude, data.latitude))
# The desired grid spacing in degrees (converted to meters using 1 degree approx. 111km)
spacing = 15 / 60

########################################################################################
# Before we begin tuning, let's reiterate what the results were with the default
# parameters.

spline_default = vd.Spline()
score_default = np.mean(
    vd.cross_val_score(spline_default, proj_coords, data.air_temperature_c)

In :ref:`model_evaluation`, we saw how to check the performance of an interpolator using
cross-validation. We found that the default parameters for :class:`verde.Spline` are not
good for predicting our sample air temperature data. Now, let's see how we can tune the
:class:`~verde.Spline` to improve the cross-validation performance.

Once again, we'll start by importing the required packages and loading our sample data.
"""
import numpy as np
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import itertools
import pyproj
import verde as vd

data = vd.datasets.fetch_texas_wind()

# Use Mercator projection because Spline is a Cartesian gridder
projection = pyproj.Proj(proj="merc", lat_ts=data.latitude.mean())
proj_coords = projection(data.longitude.values, data.latitude.values)

region = vd.get_region((data.longitude, data.latitude))
# The desired grid spacing in degrees (converted to meters using 1 degree approx. 111km)
spacing = 15 / 60

########################################################################################
# Before we begin tuning, let's reiterate what the results were with the default
# parameters.

spline_default = vd.Spline()
score_default = np.mean(
    vd.cross_val_score(spline_default, proj_coords, data.air_temperature_c)

Trend estimation and removal is a common operation, particularly when dealing
with geophysical data. Moreover, some of the interpolation methods, like
:class:`verde.Spline`, can struggle with long-wavelength trends in the data.
The :class:`verde.Trend` class fits a 2D polynomial trend of arbitrary degree
to the data and can be used to remove it.
"""
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import numpy as np
import verde as vd

########################################################################################
# Our sample air temperature data from Texas has a clear trend from land to the ocean:

data = vd.datasets.fetch_texas_wind()
coordinates = (data.longitude, data.latitude)

plt.figure(figsize=(8, 6))
ax = plt.axes(projection=ccrs.Mercator())
plt.scatter(
    data.longitude,
    data.latitude,
    c=data.air_temperature_c,
    s=100,
    cmap="plasma",
    transform=ccrs.PlateCarree(),
)
plt.colorbar().set_label("Air temperature (C)")
vd.datasets.setup_texas_wind_map(ax)
plt.show()

:mod:`sklearn.model_selection` to evaluate our interpolator's performance. Once we have
a quantified measure of the quality of a given fitted gridder, we can use it to tune the
gridder's parameters, like ``damping`` for a :class:`~verde.Spline` (see
:ref:`model_selection`).

Verde provides adaptations of common scikit-learn tools to work better with spatial
data. Let's use these tools to evaluate the performance of a :class:`~verde.Spline` on
our sample air temperature data.
"""
import numpy as np
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import pyproj
import verde as vd

data = vd.datasets.fetch_texas_wind()

# Use Mercator projection because Spline is a Cartesian gridder
projection = pyproj.Proj(proj="merc", lat_ts=data.latitude.mean())
proj_coords = projection(data.longitude.values, data.latitude.values)

region = vd.get_region((data.longitude, data.latitude))
# For this data, we'll generate a grid with 15 arc-minute spacing
spacing = 15 / 60

########################################################################################
# Splitting the data
# ------------------
#
# We can't evaluate a gridder on the data that went into fitting it. The true test of a
# model is if it can correctly predict data that it hasn't seen before. scikit-learn has
# the :func:`sklearn.model_selection.train_test_split` function to separate a dataset