Rename Gradient to Derivative

doc/api/index.rst

@@ -36,6 +36,7 @@ Composite Estimators
 
  Chain
  Vector
+ Derivative
 
 Model Selection
 ---------------

examples/gradient.py

@@ -1,5 +1,5 @@
 """
-Gradient calculations
+Derivative calculations
 =====================
 
 
@@ -15,8 +15,8 @@
 ).scatter(size=700, random_state=0)
 
 spline = vd.Spline().fit((data.easting, data.northing), data.scalars)
-east_deriv = vd.Gradient(spline, step=10, direction=(1, 0)).grid(spacing=50)
-north_deriv = vd.Gradient(spline, step=10, direction=(0, 1)).grid(spacing=50)
+east_deriv = vd.Derivative(spline, step=10, direction=(1, 0)).grid(spacing=50)
+north_deriv = vd.Derivative(spline, step=10, direction=(0, 1)).grid(spacing=50)
 
 fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(9, 10), sharex=True)
 

examples/divergent_curl.py → examples/vector_derivatives.py

@@ -1,6 +1,6 @@
 """
-Divergence and curl of vector fields
-====================================
+Derivatives of vector fields
+============================
 """
 import matplotlib.pyplot as plt
 import cartopy.crs as ccrs
@@ -11,78 +11,63 @@
 
 # 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.
+# 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,
+ coordinates=projection(*coordinates),
+ data=(data.wind_speed_east_knots, data.wind_speed_north_knots),
+ test_size=0.1,
+ random_state=1,
 )
 
-# Chain together a blocked mean to avoid aliasing, a polynomial trend (Spline usually
-# requires de-trended data), and finally a Spline for each component. Notice that
-# BlockReduce can work on multicomponent data without the use of Vector.
-chain = vd.Chain(
- [
- ("trend", vd.Vector([vd.Trend(degree=1) for i in range(2)])),
- (
- "spline",
- vd.Vector([vd.Spline(damping=1e-10, mindist=500e3) for i in range(2)]),
- ),
- ]
-)
-print(chain)
+estimator = vd.Vector([vd.Spline(damping=1e-6, mindist=500e3) for i in range(2)])
 
 # Fit on the training data
-chain.fit(*train)
+estimator.fit(*train)
 # And score on the testing data. The best possible score is 1, meaning a perfect
 # prediction of the test data.
-score = chain.score(*test)
-print("Cross-validation R^2 score: {:.2f}".format(score))
+score = estimator.score(*test)
+print("Validation score (R²): {:.2f}".format(score))
 
 # Interpolate the wind speed onto a regular geographic grid and mask the data
 # that are outside of the convex hull of the data points.
 dims = ["latitude", "longitude"]
-grid_full = chain.grid(region, spacing=20 / 60, projection=projection, dims=dims)
-grid = vd.convexhull_mask(coordinates, grid=grid_full, projection=projection)
-
+grid = estimator.grid(region, spacing=20 / 60, projection=projection, dims=dims)
+grid = vd.convexhull_mask(coordinates, grid=grid, projection=projection)
 
 spacing = 10 / 60
 step = 0.5 * spacing * 100e3
 
-east_derivs = vd.Gradient(chain, step=step, direction=(1, 0)).grid(
+east_derivs = vd.Derivative(estimator, step=step, direction=(1, 0)).grid(
  region, spacing=spacing, projection=projection, dims=dims
 )
-north_derivs = vd.Gradient(chain, step=step, direction=(0, 1)).grid(
+north_derivs = vd.Derivative(estimator, step=step, direction=(0, 1)).grid(
  region, spacing=spacing, projection=projection, dims=dims
 )
 
 divergence = east_derivs.east_component + north_derivs.north_component
-curl = north_derivs.east_component - east_derivs.north_component
-
 
 # Make maps of the original and gridded wind speed
 plt.figure(figsize=(6, 6))
 ax = plt.axes(projection=ccrs.Mercator())
 
-curl.plot(ax=ax, transform=ccrs.PlateCarree())
+divergence.plot(ax=ax, transform=ccrs.PlateCarree())
 
 tmp = ax.quiver(
- grid.longitude.values,
- grid.latitude.values,
- grid.east_component.values,
- grid.north_component.values,
- width=0.0015,
+ data.longitude.values,
+ data.latitude.values,
+ data.wind_speed_east_knots.values,
+ data.wind_speed_north_knots.values,
+ width=0.003,
  scale=100,
- color="tab:blue",
+ color="black",
  transform=ccrs.PlateCarree(),
 )
 

verde/__init__.py

@@ -14,7 +14,7 @@
  pad_region,
  longitude_continuity,
 )
-from .gradient import Gradient
+from .derivative import Derivative
 from .mask import distance_mask, convexhull_mask
 from .utils import variance_to_weights, maxabs, grid_to_table
 from .io import load_surfer

verde/gradient.py → verde/derivative.py

@@ -7,7 +7,7 @@
 from .base.utils import check_data
 
 
-class Gradient(BaseGridder):
+class Derivative(BaseGridder):
  """
  """
 

verde/tests/test_gradient.py → verde/tests/test_derivative.py

@@ -1,12 +1,12 @@
 # pylint: disable=redefined-outer-name
 """
-Test the Gradient estimator
+Test the Derivative estimator
 """
 import numpy as np
 import numpy.testing as npt
 import pytest
 
-from ..gradient import Gradient
+from ..derivative import Derivative
 from ..trend import Trend
 from ..vector import Vector
 from ..chain import Chain
@@ -35,8 +35,8 @@ def test_gradient(polynomial):
  coordinates, data, true_east_deriv, true_north_deriv = polynomial
 
  trend = Trend(degree=2).fit(coordinates, data)
- east_deriv = Gradient(trend, step=10, direction=(1, 0)).predict(coordinates)
- north_deriv = Gradient(trend, step=10, direction=(0, 1)).predict(coordinates)
+ east_deriv = Derivative(trend, step=10, direction=(1, 0)).predict(coordinates)
+ north_deriv = Derivative(trend, step=10, direction=(0, 1)).predict(coordinates)
 
  npt.assert_allclose(true_east_deriv, east_deriv, atol=1e-2)
  npt.assert_allclose(true_north_deriv, north_deriv, atol=1e-2)
@@ -48,17 +48,17 @@ def test_gradient_fails_wrong_dimensions(polynomial):
 
  trend = Trend(degree=2).fit(coordinates, data)
  with pytest.raises(ValueError):
- Gradient(trend, step=10, direction=(1, 0, 1)).predict(coordinates)
+ Derivative(trend, step=10, direction=(1, 0, 1)).predict(coordinates)
  with pytest.raises(ValueError):
- Gradient(trend, step=10, direction=(1, 0)).predict((coordinates[0],))
+ Derivative(trend, step=10, direction=(1, 0)).predict((coordinates[0],))
 
 
 def test_gradient_grid(polynomial):
  "Make sure the grid method works as expected"
  coordinates, data, true_east_deriv = polynomial[:3]
 
  trend = Trend(degree=2).fit(coordinates, data)
- deriv = Gradient(trend, step=10, direction=(1, 0)).grid(spacing=50)
+ deriv = Derivative(trend, step=10, direction=(1, 0)).grid(spacing=50)
 
  npt.assert_allclose(true_east_deriv, deriv.scalars.values, atol=1e-2)
 
@@ -70,22 +70,22 @@ def test_gradient_direction(polynomial):
  for azimuth in np.radians(np.linspace(0, 360, 60)):
  direction = (np.sin(azimuth), np.cos(azimuth))
  true_deriv = true_east_deriv * direction[0] + true_north_deriv * direction[1]
- deriv = Gradient(trend, step=10, direction=direction).predict(coordinates)
+ deriv = Derivative(trend, step=10, direction=direction).predict(coordinates)
  npt.assert_allclose(true_deriv, deriv, atol=1e-2)
 
 
 def test_gradient_fit(polynomial):
- "Check that calling fit on the Gradient works as expected"
+ "Check that calling fit on the Derivative works as expected"
  coordinates, data, true_east_deriv, true_north_deriv = polynomial
 
  trend = Trend(degree=2)
  east_deriv = (
- Gradient(trend, step=10, direction=(1, 0))
+ Derivative(trend, step=10, direction=(1, 0))
  .fit(coordinates, data)
  .predict(coordinates)
  )
  north_deriv = (
- Gradient(trend, step=10, direction=(0, 1))
+ Derivative(trend, step=10, direction=(0, 1))
  .fit(coordinates, data)
  .predict(coordinates)
  )
@@ -95,14 +95,14 @@ def test_gradient_fit(polynomial):
 
 
 def test_gradient_vector(polynomial):
- "Make sure putting Gradients in a Vector works"
+ "Make sure putting Derivatives in a Vector works"
  coordinates, data, true_east_deriv, true_north_deriv = polynomial
 
  trend = Trend(degree=2)
  gradient = Vector(
  [
- Gradient(trend, step=10, direction=(1, 0)),
- Gradient(trend, step=10, direction=(0, 1)),
+ Derivative(trend, step=10, direction=(1, 0)),
+ Derivative(trend, step=10, direction=(0, 1)),
  ]
  )
  # This is wasteful because it fits the same trend twice so should not
@@ -115,11 +115,11 @@ def test_gradient_vector(polynomial):
 
 
 def test_gradient_chain(polynomial):
- "Make sure putting Gradients in a Chain works"
+ "Make sure putting Derivatives in a Chain works"
  coordinates, data, true_east_deriv = polynomial[:3]
 
  trend = Trend(degree=2)
- gradient = Chain([("east", Gradient(trend, step=10, direction=(1, 0)))])
+ gradient = Chain([("east", Derivative(trend, step=10, direction=(1, 0)))])
  gradient.fit(coordinates, data)
  deriv = gradient.predict(coordinates)
  npt.assert_allclose(true_east_deriv, deriv, atol=1e-2)