Use consistently 1-D, 2-D, or 3-D (with hyphen and capitalized)

examples/gallery/3d_plots/scatter3d.py

@@ -1,11 +1,11 @@
 """
-3D Scatter plots
-----------------
+3-D Scatter plots
+-----------------
 
-The :meth:`pygmt.Figure.plot3d` method can be used to plot symbols in 3D.
+The :meth:`pygmt.Figure.plot3d` method can be used to plot symbols in 3-D.
 In the example below, we show how the
 `Iris flower dataset <https://en.wikipedia.org/wiki/Iris_flower_data_set>`__
-can be visualized using a perspective 3D plot. The ``region``
+can be visualized using a perspective 3-D plot. The ``region``
 parameter has to include the :math:`x`, :math:`y`, :math:`z` axis limits in the
 form of (xmin, xmax, ymin, ymax, zmin, zmax), which can be done automatically
 using :func:`pygmt.info`. To plot the z-axis frame, set ``frame`` as a
@@ -42,7 +42,7 @@
     spacing=(1, 2, 0.5),
 )
 
-# Make a 3D scatter plot, coloring each of the 3 species differently
+# Make a 3-D scatter plot, coloring each of the 3 species differently
 fig = pygmt.Figure()
 
 # Define a colormap to be used for three categories, define the range of the
@@ -69,7 +69,7 @@
     # Vary each symbol size according to another feature (sepal width, scaled
     # by 0.1)
     size=0.1 * df.sepal_width,
-    # Use 3D cubes ("u") as symbols, with size in centimeter units ("c")
+    # Use 3-D cubes ("u") as symbols, with size in centimeter units ("c")
     style="uc",
     # Points colored by categorical number code
     fill=df.species.cat.codes.astype(int),

examples/gallery/images/contours.py

@@ -5,16 +5,16 @@
 points by direct triangulation. The data for the triangulation can be provided
 using one of three methods:
 
-#. ``x``, ``y``, ``z`` 1d :class:`numpy.ndarray` data columns.
-#. ``data`` 2d :class:`numpy.ndarray` data matrix with 3 columns corresponding
+#. ``x``, ``y``, ``z`` 1-D :class:`numpy.ndarray` data columns.
+#. ``data`` 2-D :class:`numpy.ndarray` data matrix with 3 columns corresponding
    to ``x``, ``y``, ``z``.
 #. ``data`` path string to a file containing the ``x``, ``y``, ``z`` in a
    tabular format.
 
 The parameters ``levels`` and ``annotation`` set the intervals of the contours
 and the annotation on the contours respectively.
 
-In this example we supply the data as  1d :class:`numpy.ndarray` with the
+In this example we supply the data as  1-D :class:`numpy.ndarray` with the
 ``x``, ``y``, and ``z`` parameters and draw the contours using a 0.5p pen with
 contours every 10 ``z`` values and annotations every 20 ``z`` values.
 """
@@ -35,7 +35,7 @@
     projection="X10c/10c",
     frame="ag",
     pen="0.5p",
-    # pass the data as 3 1d data columns
+    # pass the data as 3 1-D data columns
     x=x,
     y=y,
     z=z,

examples/gallery/images/track_sampling.py

@@ -3,11 +3,12 @@
 ---------------------
 
 The :func:`pygmt.grdtrack` function samples a raster grid's value along
-specified points. We will need to input a 2D raster to ``grid`` which can be an
-:class:`xarray.DataArray`. The argument passed to the ``points`` parameter can
-be a :class:`pandas.DataFrame` table where the first two columns are x and y
-(or longitude and latitude). Note also that there is a ``newcolname`` parameter
-that will be used to name the new column of values sampled from the grid.
+specified points. We will need to input a 2-D raster to ``grid`` which can be
+an :class:`xarray.DataArray`. The argument passed to the ``points`` parameter
+can be a :class:`pandas.DataFrame` table where the first two columns are
+x and y (or longitude and latitude). Note also that there is a ``newcolname``
+parameter that will be used to name the new column of values sampled from the
+grid.
 
 Alternatively, a NetCDF file path can be passed to ``grid``. An ASCII file path
 can also be accepted for ``points``. To save an output ASCII file, a file name

examples/gallery/lines/wiggle.py

@@ -3,10 +3,10 @@
 -------------------
 
 The :meth:`pygmt.Figure.wiggle` method can plot z = f(x,y) anomalies along
-tracks. ``x``, ``y``, ``z`` can be specified as 1d arrays or within a specified
-file. The ``scale`` parameter can be used to set the scale of the anomaly in
-data/distance units. The positive and/or negative areas can be filled with
-color by setting the ``color`` parameter.
+tracks. ``x``, ``y``, ``z`` can be specified as 1-D arrays or within a
+specified file. The ``scale`` parameter can be used to set the scale of the
+anomaly in data/distance units. The positive and/or negative areas can be
+filled with color by setting the ``color`` parameter.
 """
 
 import numpy as np

examples/gallery/seismology/meca.py

@@ -4,9 +4,9 @@
 
 The :meth:`pygmt.Figure.meca` method can plot focal mechanisms or beachballs.
 We can specify the focal mechanism nodal planes or moment tensor components as
-a dictionary using the ``spec`` parameter (or they can be specified as a 1d or
-2d array, or within a specified file). The size of plotted beachballs can be
-specified using the ``scale`` parameter.
+a dictionary using the ``spec`` parameter (or they can be specified as a 1-D
+or 2-D array, or within a specified file). The size of plotted beachballs can
+be specified using the ``scale`` parameter.
 """
 
 import pygmt

examples/gallery/symbols/multi_parameter_symbols.py

@@ -21,7 +21,7 @@
 ###############################################################################
 # We can plot multi-parameter symbols using the same symbol style. We need to
 # define locations (lon, lat) via the ``x`` and ``y`` parameters (scalar for
-# a single symbol or 1d list for several ones) and two or three symbol
+# a single symbol or 1-D list for several ones) and two or three symbol
 # parameters after those shortcuts via the ``style`` parameter.
 #
 # The multi-parameter symbols in the ``style`` parameter are defined as:
@@ -54,11 +54,11 @@
 
 ###############################################################################
 # We can also plot symbols with varying parameters via defining those values in
-# a 2d list or numpy array (``[[parameters]]`` for a single symbol or
+# a 2-D list or numpy array (``[[parameters]]`` for a single symbol or
 # ``[[parameters_1],[parameters_2],[parameters_i]]`` for several ones) or using
 # an appropriately formatted input file and passing it to ``data``.
 #
-# The symbol parameters in the 2d list or numpy array are defined as:
+# The symbol parameters in the 2-D list or numpy array are defined as:
 #
 # - **e**: ellipse, ``[[lon, lat, direction, major_axis, minor_axis]]``
 # - **j**: rotated rectangle, ``[[lon, lat, direction, width, height]]``

examples/gallery/symbols/points_categorical.py

@@ -39,7 +39,7 @@
     spacing=(3, 2),
 )
 
-# Make a 2D categorical scatter plot, coloring each of the 3 species
+# Make a 2-D categorical scatter plot, coloring each of the 3 species
 # differently
 fig = pygmt.Figure()
 

examples/tutorials/advanced/3d_perspective_image.py

@@ -1,6 +1,6 @@
 """
-Creating a 3D perspective image
-===============================
+Creating a 3-D perspective image
+================================
 
 Create 3-D perspective image or surface mesh from a grid
 using :meth:`pygmt.Figure.grdview`.

examples/tutorials/advanced/vectors.py

@@ -18,7 +18,7 @@
 # On the shown figure, the plot is projected on a 10cm X 10cm region,
 # which is specified by the ``projection`` parameter.
 # The direction is specified
-# by a list of two 1d arrays structured as ``[[angle_in_degrees], [length]]``.
+# by a list of two 1-D arrays structured as ``[[angle_in_degrees], [length]]``.
 # The angle is measured in degrees and moves counter-clockwise from the
 # horizontal.
 # The length of the vector uses centimeters by default but
@@ -107,7 +107,7 @@
 ###############################################################################
 # Vectors can also be plotted by including all the information
 # about a vector in a single list. However, this requires creating
-# a 2D list or numpy array containing all vectors.
+# a 2-D list or numpy array containing all vectors.
 # Each vector list contains the information structured as:
 # ``[x_start, y_start, direction_degrees, length]``.
 #
@@ -132,7 +132,7 @@
 ###############################################################################
 # Using the functionality mentioned in the previous example,
 # multiple vectors can be plotted at the same time. Another
-# vector could be simply added to the 2D list or numpy
+# vector could be simply added to the 2-D list or numpy
 # array object and passed using ``data`` parameter.
 
 # Vector specifications structured as:
@@ -386,7 +386,7 @@
 # of one is the starting point of another. This can be done
 # by adding the coordinate lists together to create this structure:
 # ``[[start_latitude, start_longitude, end_latitude, end_longitude]]``.
-# Each list within the 2D list contains the start and end information
+# Each list within the 2-D list contains the start and end information
 # for each vector.
 
 # Coordinate pairs for all the locations used

pygmt/helpers/decorators.py

@@ -44,7 +44,7 @@
            (e.g., *color1*,\ *color2*,\ *color3*) to build a linear continuous
            CPT from those colors automatically.""",
     "color": """\
-        color : str or 1d array
+        color : str or 1-D array
             Select color or pattern for filling of symbols or polygons [Default
             is no fill].""",
     "fill": """\
@@ -227,13 +227,13 @@
 
             Blank lines and lines starting with \# are always skipped.""",
     "incols": r"""
-        incols : str or 1d array
+        incols : str or 1-D array
             Specify data columns for primary input in arbitrary order. Columns
             can be repeated and columns not listed will be skipped [Default
             reads all columns in order, starting with the first (i.e., column
             0)].
 
-            - For *1d array*: specify individual columns in input order (e.g.,
+            - For *1-D array*: specify individual columns in input order (e.g.,
               ``incols=[1,0]`` for the 2nd column followed by the 1st column).
             - For :py:class:`str`: specify individual columns or column
               ranges in the format *start*\ [:*inc*]:*stop*, where *inc*
@@ -285,14 +285,14 @@
             - **l** for bilinear
             - **n** for nearest-neighbor""",
     "outcols": r"""
-        outcols : str or 1d array
+        outcols : str or 1-D array
             *cols*\ [,...][,\ **t**\ [*word*]].
             Specify data columns for primary output in arbitrary order. Columns
             can be repeated and columns not listed will be skipped [Default
             writes all columns in order, starting with the first (i.e., column
             0)].
 
-            - For *1d array*: specify individual columns in output order (e.g.,
+            - For *1-D array*: specify individual columns in output order (e.g.,
               ``outcols=[1,0]`` for the 2nd column followed by the 1st column).
             - For :py:class:`str`: specify individual columns or column
               ranges in the format *start*\ [:*inc*]:*stop*, where *inc*
@@ -408,7 +408,7 @@ def fmt_docstring(module_func):
     ...     Parameters
     ...     ----------
     ...     data : str or {table-like}
-    ...         Pass in either a file name to an ASCII data table, a 2D
+    ...         Pass in either a file name to an ASCII data table, a 2-D
     ...         {table-classes}.
     ...     {region}
     ...     {projection}
@@ -424,9 +424,9 @@ def fmt_docstring(module_func):
     Parameters
     ----------
     data : str or numpy.ndarray or pandas.DataFrame or xarray.Dataset or geo...
-        Pass in either a file name to an ASCII data table, a 2D
+        Pass in either a file name to an ASCII data table, a 2-D
         :class:`numpy.ndarray`, a :class:`pandas.DataFrame`, an
-        :class:`xarray.Dataset` made up of 1D :class:`xarray.DataArray`
+        :class:`xarray.Dataset` made up of 1-D :class:`xarray.DataArray`
         data variables, or a :class:`geopandas.GeoDataFrame` containing the
         tabular data.
     region : str or list
@@ -461,7 +461,7 @@ def fmt_docstring(module_func):
     )
     filler_text["table-classes"] = (
         ":class:`numpy.ndarray`, a :class:`pandas.DataFrame`, an\n"
-        "    :class:`xarray.Dataset` made up of 1D :class:`xarray.DataArray`\n"
+        "    :class:`xarray.Dataset` made up of 1-D :class:`xarray.DataArray`\n"
         "    data variables, or a :class:`geopandas.GeoDataFrame` containing the\n"
         "    tabular data"
     )

pygmt/helpers/utils.py

@@ -25,7 +25,7 @@ def data_kind(data, x=None, y=None, z=None, required_z=False):
     * a pathlib.Path provided as 'data'
     * an xarray.DataArray provided as 'data'
     * a matrix provided as 'data'
-    * 1D arrays x and y (and z, optionally)
+    * 1-D arrays x and y (and z, optionally)
 
     Arguments should be ``None`` if not used. If doesn't fit any of these
     categories (or fits more than one), will raise an exception.
@@ -34,11 +34,11 @@ def data_kind(data, x=None, y=None, z=None, required_z=False):
     ----------
     data : str or pathlib.Path or xarray.DataArray or {table-like} or None
         Pass in either a file name or :class:`pathlib.Path` to an ASCII data
-        table, an :class:`xarray.DataArray`, a 1D/2D
+        table, an :class:`xarray.DataArray`, a 1-D/2-D
         {table-classes}.
-    x/y : 1d arrays or None
+    x/y : 1-D arrays or None
         x and y columns as numpy arrays.
-    z : 1d array or None
+    z : 1-D array or None
         z column as numpy array. To be used optionally when x and y are given.
     required_z : bool
         State whether the 'z' column is required.

pygmt/src/binstats.py

@@ -49,7 +49,7 @@ def binstats(data, **kwargs):
     Parameters
     ----------
     data : str or {table-like}
-        A file name of an ASCII data table or a 2D
+        A file name of an ASCII data table or a 2-D
         {table-classes}.
     outgrid : str or None
         The name of the output netCDF file with extension .nc to store the grid

pygmt/src/blockm.py

@@ -108,9 +108,9 @@ def blockmean(data=None, x=None, y=None, z=None, outfile=None, **kwargs):
     ----------
     data : str or {table-like}
         Pass in (x, y, z) or (longitude, latitude, elevation) values by
-        providing a file name to an ASCII data table, a 2D
+        providing a file name to an ASCII data table, a 2-D
         {table-classes}.
-    x/y/z : 1d arrays
+    x/y/z : 1-D arrays
         Arrays of x and y coordinates and values z of the data points.
 
     {spacing}
@@ -204,9 +204,9 @@ def blockmedian(data=None, x=None, y=None, z=None, outfile=None, **kwargs):
     ----------
     data : str or {table-like}
         Pass in (x, y, z) or (longitude, latitude, elevation) values by
-        providing a file name to an ASCII data table, a 2D
+        providing a file name to an ASCII data table, a 2-D
         {table-classes}.
-    x/y/z : 1d arrays
+    x/y/z : 1-D arrays
         Arrays of x and y coordinates and values z of the data points.
 
     {spacing}
@@ -291,9 +291,9 @@ def blockmode(data=None, x=None, y=None, z=None, outfile=None, **kwargs):
     ----------
     data : str or {table-like}
         Pass in (x, y, z) or (longitude, latitude, elevation) values by
-        providing a file name to an ASCII data table, a 2D
+        providing a file name to an ASCII data table, a 2-D
         {table-classes}.
-    x/y/z : 1d arrays
+    x/y/z : 1-D arrays
         Arrays of x and y coordinates and values z of the data points.
 
     {spacing}

pygmt/src/contour.py

@@ -49,9 +49,9 @@ def contour(self, data=None, x=None, y=None, z=None, **kwargs):
     ----------
     data : str or {table-like}
         Pass in (x, y, z) or (longitude, latitude, elevation) values by
-        providing a file name to an ASCII data table, a 2D
+        providing a file name to an ASCII data table, a 2-D
         {table-classes}.
-    x/y/z : 1d arrays
+    x/y/z : 1-D arrays
         Arrays of x and y coordinates and values z of the data points.
     {projection}
     {region}

pygmt/src/grdtrack.py

@@ -76,7 +76,7 @@ def grdtrack(grid, points=None, newcolname=None, outfile=None, **kwargs):
         format).
 
     points : str or {table-like}
-        Pass in either a file name to an ASCII data table, a 2D
+        Pass in either a file name to an ASCII data table, a 2-D
         {table-classes}.
 
     newcolname : str

pygmt/src/histogram.py

@@ -51,7 +51,7 @@ def histogram(self, data, **kwargs):
     Parameters
     ----------
     data : str or list or {table-like}
-        Pass in either a file name to an ASCII data table, a Python list, a 2D
+        Pass in either a file name to an ASCII data table, a Python list, a 2-D
         {table-classes}.
     {projection}
     {region}

pygmt/src/info.py

@@ -48,7 +48,7 @@ def info(data, **kwargs):
     Parameters
     ----------
     data : str or {table-like}
-        Pass in either a file name to an ASCII data table, a 1D/2D
+        Pass in either a file name to an ASCII data table, a 1-D/2-D
         {table-classes}.
     per_column : bool
         Report the min/max values per column in separate columns.