Use Bordado in code, tests and docs

.github/environment.yml

@@ -16,6 +16,7 @@ dependencies:
   - xrft>=1.0
   - choclo>=0.1
   - boule>=0.6.0
+  - bordado>=0.4.0
   # Build
   - python-build
   - twine
@@ -30,7 +31,6 @@ dependencies:
   - ipykernel
   - boule
   - pyproj
-  - bordado>=0.4.0
   - ensaio>=0.5.*
   - netcdf4
   - matplotlib

doc/conf.py

@@ -53,6 +53,7 @@
     "pooch": ("https://www.fatiando.org/pooch/latest/", None),
     "ensaio": ("https://www.fatiando.org/ensaio/latest/", None),
     "verde": ("https://www.fatiando.org/verde/latest/", None),
+    "bordado": ("https://www.fatiando.org/bordado/latest/", None),
     "boule": ("https://www.fatiando.org/boule/latest/", None),
     "matplotlib": ("https://matplotlib.org/", None),
     "pyproj": ("https://pyproj4.github.io/pyproj/stable/", None),

doc/install.rst

@@ -74,6 +74,8 @@ Required:
 * `scikit-learn <https://scikit-learn.org>`__
 * `pooch <http://www.fatiando.org/pooch/>`__
 * `verde <http://www.fatiando.org/verde/>`__
+* `bordado <http://www.fatiando.org/bordado/>`__
+* `boule <http://www.fatiando.org/boule/>`__
 * `xrft <https://xrft.readthedocs.io/>`__
 
 Optional:
@@ -92,7 +94,6 @@ Optional:
 
 The examples in the :ref:`gallery` also use:
 
-* `boule <http://www.fatiando.org/boule/>`__
 * `ensaio <http://www.fatiando.org/ensaio/>`__ for downloading sample datasets
 * `pygmt <https://www.pygmt.org/>`__ for plotting maps
 * `pyproj <https://jswhit.github.io/pyproj/>`__ for cartographic projections

doc/user_guide/coordinate_systems.rst

@@ -33,7 +33,7 @@ height:
 
 .. jupyter-execute::
 
-    import verde as vd
+    import bordado as bd
 
     # Define boundaries of the rectangular prism (in meters)
     west, east, south, north = -20, 20, -20, 20
@@ -45,8 +45,8 @@ Or a regular grid of points at 100 meters above the zeroth plane:
 .. jupyter-execute::
 
     # Define a regular grid of observation points (coordinates in meters)
-    coordinates = vd.grid_coordinates(
-        region=(-40, 40, -40, 40), shape=(5, 5), extra_coords=100
+    coordinates = bd.grid_coordinates(
+        region=(-40, 40, -40, 40), shape=(5, 5), non_dimensional_coords=100
     )
     easting, northing, upward = coordinates[:]
 
@@ -103,12 +103,12 @@ Spatial data are usually given in geodetic coordinates, along with the
 reference ellipsoid on which they are defined.
 
 For example, let's define a regular grid of points (separated by equal
-angles) at 2km above the ellipsoid using :mod:`verde`.
+angles) at 2km above the ellipsoid using :mod:`bordado`.
 
 .. jupyter-execute::
 
-    coordinates = vd.grid_coordinates(
-        region=(-70, -65, -35, -30), shape=(6, 6), extra_coords=2e3
+    coordinates = bd.grid_coordinates(
+        region=(-70, -65, -35, -30), shape=(6, 6), non_dimensional_coords=2e3
     )
     longitude, latitude, height = coordinates[:]
     print("longitude:", longitude)
@@ -199,10 +199,10 @@ the same radius equal to the *mean radius of the Earth*.
 
 .. jupyter-execute::
 
-    coordinates = vd.grid_coordinates(
+    coordinates = bd.grid_coordinates(
         region=(-70, -65, -35, -30),
         shape=(6, 6),
-        extra_coords=ellipsoid.mean_radius,
+        non_dimensional_coords=ellipsoid.mean_radius,
     )
     longitude, sph_latitude, radius = coordinates[:]
     print("longitude:", longitude)

doc/user_guide/equivalent_sources/block-averaged-eqs.rst

@@ -44,10 +44,10 @@ the data:
 
 .. jupyter-execute::
 
-    import verde as vd
+    import bordado as bd
 
     region = (-5.5, -4.7, 57.8, 58.5)
-    inside = vd.inside((data.longitude, data.latitude), region)
+    inside = bd.inside((data.longitude, data.latitude), region)
     data = data[inside]
     data
 
@@ -60,7 +60,7 @@ And project the geographic coordinates to plain Cartesian ones:
     projection = pyproj.Proj(proj="merc", lat_ts=data.latitude.mean())
     easting, northing = projection(data.longitude.values, data.latitude.values)
     coordinates = (easting, northing, data.height_m)
-    xy_region=vd.get_region(coordinates)
+    xy_region = bd.get_region((easting, northing))
 
 
 .. jupyter-execute::
@@ -77,9 +77,10 @@ And project the geographic coordinates to plain Cartesian ones:
 
 .. jupyter-execute::
 
+    import verde as vd
     import pygmt
 
-    maxabs = vd.maxabs(data.total_field_anomaly_nt)*.8
+    maxabs = vd.maxabs(data.total_field_anomaly_nt) * .8
 
     # Set figure properties
     w, e, s, n = xy_region
@@ -167,10 +168,10 @@ we are efectivelly upward continuing the data.
 
 .. jupyter-execute::
 
-    grid_coords = vd.grid_coordinates(
-        region=vd.get_region(coordinates),
+    grid_coords = bd.grid_coordinates(
+        region=bd.get_region((easting, northing)),
         spacing=500,
-        extra_coords=1500,
+        non_dimensional_coords=1500,
     )
     grid = eqs.grid(grid_coords, data_names=["magnetic_anomaly"])
     grid

doc/user_guide/equivalent_sources/eq-sources-spherical.rst

@@ -98,15 +98,17 @@ of 6 arcminutes.
 
 .. jupyter-execute::
 
+    import bordado as bd
+
     # Get the bounding region of the data in geodetic coordinates
-    region = vd.get_region((data.longitude, data.latitude))
+    region = bd.get_region((data.longitude, data.latitude))
 
     # Get the maximum height of the data coordinates
     max_height = data.height_sea_level_m.max()
 
     # Define a regular grid of points in geodetic coordinates
-    grid_coords = vd.grid_coordinates(
-        region=region, spacing=6 / 60, extra_coords=max_height
+    grid_coords = bd.grid_coordinates(
+        region=region, spacing=6 / 60, non_dimensional_coords=max_height
     )
 
 But before we can tell the equivalent sources to predict the

doc/user_guide/equivalent_sources/eqs-parameters-estimation.rst

@@ -175,12 +175,14 @@ And grid the data using the two equivalent sources:
 
 .. jupyter-execute::
 
+   import bordado as bd
+
    # Define grid coordinates
-   region = vd.get_region(coordinates)
-   grid_coords = vd.grid_coordinates(
+   region = bd.get_region((easting, northing))
+   grid_coords = bd.grid_coordinates(
        region=region,
        spacing=2e3,
-       extra_coords=2.5e3,
+       non_dimensional_coords=2.5e3,
    )
 
    grid_first_guess = eqs_first_guess.grid(grid_coords)

doc/user_guide/equivalent_sources/gradient-boosted-eqs.rst

@@ -125,12 +125,13 @@ And then predict the field on a regular grid of computation points:
 
 .. jupyter-execute::
 
-    import verde as vd
-    region = vd.get_region(coordinates)
-    grid_coords = vd.grid_coordinates(
+    import bordado as bd
+
+    region = bd.get_region((easting, northing))
+    grid_coords = bd.grid_coordinates(
         region=region,
         spacing=5e3,
-        extra_coords=2.5e3,
+        non_dimensional_coords=2.5e3,
     )
     grid = eqs.grid(grid_coords, data_names=["gravity_disturbance"])
     grid
@@ -141,6 +142,8 @@ point. We can do so through the :func:`verde.distance_mask` function.
 
 .. jupyter-execute::
 
+    import verde as vd
+
     grid_masked = vd.distance_mask(coordinates, maxdist=50e3, grid=grid)
 
 And plot it:

doc/user_guide/equivalent_sources/index.rst

@@ -59,11 +59,11 @@ coordinates, so we need to project these gravity observations:
 .. jupyter-execute::
 
    import pyproj
-   import verde as vd
+   import bordado as bd
 
    projection = pyproj.Proj(proj="merc", lat_ts=data.latitude.values.mean())
    easting, northing = projection(data.longitude.values, data.latitude.values)
-   region = vd.get_region((easting, northing))
+   region = bd.get_region((easting, northing))
 
 Now we can initialize the :class:`harmonica.EquivalentSources` class.
 
@@ -131,6 +131,7 @@ And plot it:
 .. jupyter-execute::
 
    import pygmt
+   import verde as vd
 
    # Get max absolute value for the observed gravity disturbance
    maxabs = vd.maxabs(data.gravity_disturbance_mgal)
@@ -176,7 +177,7 @@ And plot it:
 
 We can also *grid* and *upper continue* the field by predicting its values on
 a regular grid at a constant height higher than the observations. To do so we
-can use the :func:`verde.grid_coordinates` function to create the coordinates
+can use the :func:`bordado.grid_coordinates` function to create the coordinates
 of the grid and then use the :meth:`harmonica.EquivalentSources.grid` method.
 
 First, lets get the maximum height of the observations:
@@ -191,7 +192,9 @@ and use the equivalent sources to generate a gravity disturbance grid.
 .. jupyter-execute::
 
    # Build the grid coordinates
-   grid_coords = vd.grid_coordinates(region=region, spacing=2e3, extra_coords=2.2e3)
+   grid_coords = bd.grid_coordinates(
+       region=region, spacing=2e3, non_dimensional_coords=2.2e3
+   )
 
    # Grid the gravity disturbances
    grid = equivalent_sources.grid(grid_coords, data_names=["gravity_disturbance"])

doc/user_guide/forward_modelling/dipole.rst

@@ -60,13 +60,13 @@ Let's define a regular grid of observation points:
 
 .. jupyter-execute::
 
-   import verde as vd
+   import bordado as bd
 
    region = (-100, 100, -100, 100)
    spacing = 1
    height = 0
-   coordinates = vd.grid_coordinates(
-      region=region, spacing=spacing, extra_coords=height
+   coordinates = bd.grid_coordinates(
+      region=region, spacing=spacing, non_dimensional_coords=height
    )
 
 And a set of dipoles with their magnetic moments:

doc/user_guide/forward_modelling/point.rst

@@ -70,10 +70,10 @@ height:
 
 .. jupyter-execute::
 
-   import verde as vd
+   import bordado as bd
 
-   coordinates = vd.grid_coordinates(
-       region=(-250, 1250, -250, 1250), shape=(40, 40), extra_coords=0
+   coordinates = bd.grid_coordinates(
+       region=(-250, 1250, -250, 1250), shape=(40, 40), non_dimensional_coords=0
    )
 
 And finally calculate the vertical component of the gravitational acceleration
@@ -106,6 +106,7 @@ Lets plot this gravitational field:
 .. jupyter-execute::
 
    import pygmt
+   import verde as vd
 
    grid = vd.make_xarray_grid(
       coordinates, g_z, data_names="g_z", extra_coords_names="extra")
@@ -200,10 +201,10 @@ coordinates and located at 1km above the ellipsoid:
 
 .. jupyter-execute::
 
-   coordinates = vd.grid_coordinates(
+   coordinates = bd.grid_coordinates(
        region=(-72, -68, -46, -42),
        shape=(101, 101),
-       extra_coords=20e3,
+       non_dimensional_coords=20e3,
    )
 
 Before we can start forward modelling these sources, we need to convert them to

doc/user_guide/forward_modelling/prism.rst

@@ -61,12 +61,14 @@ Build a regular grid of computation points located 10m above the zero height:
 
 .. jupyter-execute::
 
-   import verde as vd
+   import bordado as bd
 
    region = (-10e3, 10e3, -10e3, 10e3)
    shape = (51, 51)
    height = 10
-   coordinates = vd.grid_coordinates(region, shape=shape, extra_coords=height)
+   coordinates = bd.grid_coordinates(
+       region, shape=shape, non_dimensional_coords=height
+   )
 
 Define a single prism:
 
@@ -164,10 +166,10 @@ height:
 
 .. jupyter-execute::
 
-   import verde as vd
+   import bordado as bd
 
-   coordinates = vd.grid_coordinates(
-       region=(0, 10e3, 0, 10e3), shape=(40, 40), extra_coords=0
+   coordinates = bd.grid_coordinates(
+       region=(0, 10e3, 0, 10e3), shape=(40, 40), non_dimensional_coords=0
    )
 
 And finally calculate the vertical component of the gravitational acceleration
@@ -200,6 +202,8 @@ Lets plot this gravitational field:
 .. jupyter-execute::
 
    import pygmt
+   import verde as vd
+
    grid = vd.make_xarray_grid(
       coordinates, g_z, data_names="g_z", extra_coords_names="extra")
    fig = pygmt.Figure()
@@ -253,7 +257,9 @@ points by choosing ``field="b"``:
    region = (-10e3, 10e3, -10e3, 10e3)
    shape = (51, 51)
    height = 10
-   coordinates = vd.grid_coordinates(region, shape=shape, extra_coords=height)
+   coordinates = bd.grid_coordinates(
+       region, shape=shape, non_dimensional_coords=height
+   )
 
 .. jupyter-execute::
 
@@ -364,7 +370,7 @@ Then we can define a regular grid where the centers of the prisms will fall:
 
 .. jupyter-execute::
 
-   easting, northing = vd.grid_coordinates(region=region, spacing=spacing)
+   easting, northing = bd.grid_coordinates(region=region, spacing=spacing)
 
 We need to define a 2D array for the uppermost *surface* of the layer. We will
 sample a trigonometric function for this simple example:
@@ -396,9 +402,9 @@ Let's define a grid of observation points at 1 km above the zeroth height:
 
 .. jupyter-execute::
 
-   region_pad = vd.pad_region(region, 10e3)
-   coordinates = vd.grid_coordinates(
-       region_pad, spacing=spacing, extra_coords=1e3
+   region_pad = bd.pad_region(region, 10e3)
+   coordinates = bd.grid_coordinates(
+       region_pad, spacing=spacing, non_dimensional_coords=1e3
    )