More nitpicky updates

docs/conf.py

@@ -72,19 +72,20 @@
 
 nitpicky = True
 nitpick_ignore = [('py:class', 'M'), ('py:class', 'N'), ('py:class', 'optional'),
-                  ('py:class', 'array_like'), ('py:class', 'array-like'),
-                  ('py:class', 'ndarray'), ('py:class', 'sequence of arrays'),
+                  ('py:class', 'array-like'), ('py:obj', 'pyproj'),
+                  ('py:class', 'sequence of arrays'), ('py:obj', 'pyproj.Geod'),
                   ('py:class', 'sequence of floats'), ('py:class', 'sequence'),
                   ('py:class', 'array-like of strings'), ('py:class', 'K'), ('py:class', '2'),
                   ('py:class', 'P'), ('py:class', 'D'), ('py:class', 'file-like object'),
                   ('py:class', 'compatible'), ('py:class', 'callable'),
                   ('py:data', 'wx_symbol_font'), ('py:class', 'default: None'),
-                  ('py:class', 'default: All'), ('py:class:', "default: 'change'"),
+                  ('py:class', 'default: All'), ('py:class:', 'default: change'),
                   ('py:class', 'array_like of strings'), ('py:class', 'pint.Unit'),
                   ('py:mod', 'metpy.plots.wx_symbols'), ('py:class', 'Datetime instance'),
                   ('py:class', 'namedtuple'), ('py:class', 'list of tuples'),
                   ('py:class', 'Mapper'), ('py:class', 'All'), ('py:class', 'points'),
-                  ('py:obj', '__all__'), ('py:class', 'sequence of ints')]
+                  ('py:obj', '__all__'), ('py:class', 'sequence of ints'),
+                  ('py:obj', 'cartopy.crs')]
 
 # Tweak how docs are formatted
 napoleon_use_rtype = False

metpy/calc/basic.py

@@ -587,7 +587,7 @@ def coriolis_parameter(latitude):
 
     Parameters
     ----------
-    latitude : array_like
+    latitude : array-like
         Latitude at each point
 
     Returns
@@ -666,7 +666,7 @@ def sigma_to_pressure(sigma, psfc, ptop):
 
     Parameters
     ----------
-    sigma : ndarray
+    sigma : numpy.ndarray
         The sigma levels to be converted to pressure levels.
 
     psfc : `pint.Quantity`

metpy/calc/kinematics.py

@@ -298,14 +298,14 @@ def advection(scalar, wind, deltas):
 
     Parameters
     ----------
-    scalar : ndarray
+    scalar : numpy.ndarray
         Array (with N-dimensions) with the quantity to be advected.
     wind : sequence of arrays
         Length M sequence of N-dimensional arrays.  Represents the flow,
         with a component of the wind in each dimension.  For example, for
         horizontal advection, this could be a list: [u, v], where u and v
         are each a 2-dimensional array.
-    deltas : sequence of floats or ndarray
+    deltas : sequence of floats or numpy.ndarray
         A (length M) sequence containing the grid spacing(s) in each dimension. If using
         arrays, in each array there should be one item less than the size of `scalar` along the
         applicable axis.
@@ -418,7 +418,7 @@ def geostrophic_wind(heights, f, dx, dy):
     heights : (M, N) `pint.Quantity`
         The height field, with either leading dimensions of (x, y) or trailing dimensions
         of (y, x), depending on the value of ``dim_order``.
-    f : array_like
+    f : array-like
         The coriolis parameter.  This can be a scalar to be applied
         everywhere or an array of values.
     dx : `pint.Quantity`
@@ -456,9 +456,9 @@ def ageostrophic_wind(heights, f, dx, dy, u, v, dim_order='yx'):
 
     Parameters
     ----------
-    heights : (M, N) ndarray
+    heights : (M, N) numpy.ndarray
         The height or geopotential field.
-    f : array_like
+    f : array-like
         The coriolis parameter.  This can be a scalar to be applied
         everywhere or an array of values.
     dx : `pint.Quantity`
@@ -616,7 +616,7 @@ def absolute_vorticity(u, v, dx, dy, lats, dim_order='yx'):
     dy : `pint.Quantity`
         The grid spacing(s) in the y-direction. If an array, there should be one item less than
         the size of `u` along the applicable axis.
-    lats : (M, N) ndarray
+    lats : (M, N) numpy.ndarray
         latitudes of the wind data in radians or with appropriate unit information attached
 
     Returns
@@ -664,7 +664,7 @@ def potential_vorticity_baroclinic(potential_temperature, pressure, u, v, dx, dy
     dy : `pint.Quantity`
         The grid spacing(s) in the y-direction. If an array, there should be one item less than
         the size of `u` along the applicable axis.
-    lats : (M, N) ndarray
+    lats : (M, N) numpy.ndarray
         latitudes of the wind data in radians or with appropriate unit information attached
 
     Returns
@@ -737,7 +737,7 @@ def potential_vorticity_barotropic(heights, u, v, dx, dy, lats, dim_order='yx'):
     dy : `pint.Quantity`
         The grid spacing(s) in the y-direction. If an array, there should be one item less than
         the size of `u` along the applicable axis.
-    lats : (M, N) ndarray
+    lats : (M, N) numpy.ndarray
         latitudes of the wind data in radians or with appropriate unit information attached
 
     Returns
@@ -791,7 +791,7 @@ def inertial_advective_wind(u, v, u_geostrophic, v_geostrophic, dx, dy, lats):
     dy : `pint.Quantity`
         The grid spacing(s) in the y-direction. If an array, there should be one item less than
         the size of `u` along the applicable axis.
-    lats : (M, N) ndarray
+    lats : (M, N) numpy.ndarray
         latitudes of the wind data in radians or with appropriate unit information attached
 
     Returns

metpy/calc/thermo.py

@@ -1183,7 +1183,7 @@ def mixing_ratio_from_relative_humidity(relative_humidity, temperature, pressure
 
     Parameters
     ----------
-    relative_humidity: array_like
+    relative_humidity: array-like
         The relative humidity expressed as a unitless ratio in the range [0, 1]. Can also pass
         a percentage if proper units are attached.
     temperature: `pint.Quantity`
@@ -1559,13 +1559,13 @@ def isentropic_interpolation(theta_levels, pressure, temperature, *args, **kwarg
 
     Parameters
     ----------
-    theta_levels : array_like
+    theta_levels : array-like
         One-dimensional array of desired theta surfaces
-    pressure : array_like
+    pressure : array-like
         One-dimensional array of pressure levels
-    temperature : array_like
+    temperature : array-like
         Array of temperature
-    args : array_like, optional
+    args : array-like, optional
         Any additional variables will be interpolated to each isentropic level.
 
     Returns

metpy/calc/tools.py

@@ -802,12 +802,12 @@ def lat_lon_grid_deltas(longitude, latitude, **kwargs):
 
     Parameters
     ----------
-    longitude : array_like
+    longitude : array-like
         array of longitudes defining the grid
-    latitude : array_like
+    latitude : array-like
         array of latitudes defining the grid
     kwargs
-        Other keyword arguments to pass to :class:`~pyproj.Geod`
+        Other keyword arguments to pass to `~pyproj.Geod`
 
     Returns
     -------

metpy/calc/turbulence.py

@@ -18,13 +18,13 @@ def get_perturbation(ts, axis=-1):
 
     Parameters
     ----------
-    ts : array_like
+    ts : array-like
          The time series from which you wish to find the perturbation
          time series (perturbation from the mean).
 
     Returns
     -------
-    array_like
+    array-like
         The perturbation time series.
 
     Other Parameters
@@ -56,11 +56,11 @@ def tke(u, v, w, perturbation=False, axis=-1):
 
     Parameters
     ----------
-    u : array_like
+    u : array-like
         The wind component along the x-axis
-    v : array_like
+    v : array-like
         The wind component along the y-axis
-    w : array_like
+    w : array-like
         The wind component along the z-axis
     perturbation : bool, optional
                    True if the `u`, `v`, and `w` components of wind speed
@@ -70,7 +70,7 @@ def tke(u, v, w, perturbation=False, axis=-1):
 
     Returns
     -------
-    array_like
+    array-like
         The corresponding turbulence kinetic energy value
 
     Other Parameters
@@ -122,10 +122,10 @@ def kinematic_flux(vel, b, perturbation=False, axis=-1):
 
     Parameters
     ----------
-    vel : array_like
+    vel : array-like
         A component of velocity
 
-    b : array_like
+    b : array-like
         May be a component of velocity or a scalar variable (e.g. Temperature)
 
     perturbation : bool, optional
@@ -134,7 +134,7 @@ def kinematic_flux(vel, b, perturbation=False, axis=-1):
 
     Returns
     -------
-    array_like
+    array-like
         The corresponding kinematic flux
 
     Other Parameters
@@ -190,11 +190,11 @@ def friction_velocity(u, w, v=None, perturbation=False, axis=-1):
 
     Parameters
     ----------
-    u : array_like
+    u : array-like
         The wind component along the x-axis
-    w : array_like
+    w : array-like
         The wind component along the z-axis
-    v : array_like, optional
+    v : array-like, optional
         The wind component along the y-axis.
 
     perturbation : bool, optional
@@ -205,7 +205,7 @@ def friction_velocity(u, w, v=None, perturbation=False, axis=-1):
 
     Returns
     -------
-    array_like
+    array-like
         The corresponding friction velocity
 
     Other Parameters

metpy/interpolate/grid.py

@@ -30,9 +30,9 @@ def generate_grid(horiz_dim, bbox):
 
     Returns
     -------
-    grid_x: (X, Y) ndarray
+    grid_x: (X, Y) numpy.ndarray
         X dimension meshgrid defined by given bounding box
-    grid_y: (X, Y) ndarray
+    grid_y: (X, Y) numpy.ndarray
         Y dimension meshgrid defined by given bounding box
 
     """
@@ -58,7 +58,7 @@ def generate_grid_coords(gx, gy):
 
     Returns
     -------
-    (X, Y) ndarray
+    (X, Y) numpy.ndarray
         List of coordinates in meshgrid
 
     """
@@ -95,9 +95,9 @@ def get_xy_steps(bbox, h_dim):
 
     Returns
     -------
-    x_steps, (X, ) ndarray
+    x_steps, (X, ) numpy.ndarray
         Number of grids in x dimension.
-    y_steps: (Y, ) ndarray
+    y_steps: (Y, ) numpy.ndarray
         Number of grids in y dimension.
 
     """
@@ -143,21 +143,21 @@ def natural_neighbor_to_grid(xp, yp, variable, grid_x, grid_y):
 
     Parameters
     ----------
-    xp: (N, ) ndarray
+    xp: (N, ) numpy.ndarray
         x-coordinates of observations
-    yp: (N, ) ndarray
+    yp: (N, ) numpy.ndarray
         y-coordinates of observations
-    variable: (N, ) ndarray
+    variable: (N, ) numpy.ndarray
         observation values associated with (xp, yp) pairs.
         IE, variable[i] is a unique observation at (xp[i], yp[i])
-    grid_x: (M, 2) ndarray
+    grid_x: (M, 2) numpy.ndarray
         Meshgrid associated with x dimension
-    grid_y: (M, 2) ndarray
+    grid_y: (M, 2) numpy.ndarray
         Meshgrid associated with y dimension
 
     Returns
     -------
-    img: (M, N) ndarray
+    img: (M, N) numpy.ndarray
         Interpolated values on a 2-dimensional grid
 
     See Also
@@ -196,16 +196,16 @@ def inverse_distance_to_grid(xp, yp, variable, grid_x, grid_y, r, gamma=None, ka
 
     Parameters
     ----------
-    xp: (N, ) ndarray
+    xp: (N, ) numpy.ndarray
         x-coordinates of observations.
-    yp: (N, ) ndarray
+    yp: (N, ) numpy.ndarray
         y-coordinates of observations.
-    variable: (N, ) ndarray
+    variable: (N, ) numpy.ndarray
         observation values associated with (xp, yp) pairs.
         IE, variable[i] is a unique observation at (xp[i], yp[i]).
-    grid_x: (M, 2) ndarray
+    grid_x: (M, 2) numpy.ndarray
         Meshgrid associated with x dimension.
-    grid_y: (M, 2) ndarray
+    grid_y: (M, 2) numpy.ndarray
         Meshgrid associated with y dimension.
     r: float
         Radius from grid center, within which observations
@@ -223,7 +223,7 @@ def inverse_distance_to_grid(xp, yp, variable, grid_x, grid_y, r, gamma=None, ka
 
     Returns
     -------
-    img: (M, N) ndarray
+    img: (M, N) numpy.ndarray
         Interpolated values on a 2-dimensional grid
 
     See Also
@@ -265,11 +265,11 @@ def interpolate_to_grid(x, y, z, interp_type='linear', hres=50000,
 
     Parameters
     ----------
-    x: array_like
+    x: array-like
         x coordinate, can have units of linear distance or degrees
-    y: array_like
+    y: array-like
         y coordinate, can have units of linear distance or degrees
-    z: array_like
+    z: array-like
         observation value
     interp_type: str
         What type of interpolation to use. Available options include:
@@ -304,11 +304,11 @@ def interpolate_to_grid(x, y, z, interp_type='linear', hres=50000,
 
     Returns
     -------
-    grid_x: (N, 2) ndarray
+    grid_x: (N, 2) numpy.ndarray
         Meshgrid for the resulting interpolation in the x dimension
-    grid_y: (N, 2) ndarray
-        Meshgrid for the resulting interpolation in the y dimension ndarray
-    img: (M, N) ndarray
+    grid_y: (N, 2) numpy.ndarray
+        Meshgrid for the resulting interpolation in the y dimension numpy.ndarray
+    img: (M, N) numpy.ndarray
         2-dimensional array representing the interpolated values for each grid.
 
     Notes
@@ -351,10 +351,10 @@ def interpolate_to_isosurface(level_var, interp_var, level, **kwargs):
 
     Parameters
     ----------
-    level_var: array_like (P, M, N)
+    level_var: array-like (P, M, N)
         Level values in 3D grid on common vertical coordinate (e.g., PV values on
         isobaric levels). Assumes height dimension is highest to lowest in atmosphere.
-    interp_var: array_like (P, M, N)
+    interp_var: array-like (P, M, N)
         Variable on 3D grid with same vertical coordinate as level_var to interpolate to
         given level (e.g., potential temperature on isobaric levels)
     level: int or float
@@ -368,7 +368,7 @@ def interpolate_to_isosurface(level_var, interp_var, level, **kwargs):
 
     Returns
     -------
-    interp_level: (M, N) ndarray
+    interp_level: (M, N) numpy.ndarray
         The interpolated variable (e.g., potential temperature) on the desired level (e.g.,
         2 PVU surface)