DOC: Update docs to use consistently x-* and y-* (with hyphen) - 02 (#3966)

Author: yvonnefroehlich

examples/gallery/basemaps/double_y_axes.py

@@ -21,20 +21,20 @@ class can control which axes should be plotted and optionally show annotations,
 import numpy as np
 import pygmt
 
-# Generate two sample Y data from one common X data
+# Generate two sample Y-data from one common X-data
 x = np.linspace(1.0, 9.0, num=9)
 y1 = x
 y2 = x**2 + 110
 
 fig = pygmt.Figure()
 
-# Plot the common X axes
+# Plot the common X-axes
 # The bottom axis (S) is plotted with annotations and tick marks
 # The top axis (t) is plotted without annotations and tick marks
 # The left and right axes are not drawn
 fig.basemap(region=[0, 10, 0, 10], projection="X15c/15c", frame=["St", "xaf+lx"])
 
-# Plot the Y axis for y1 data
+# Plot the Y-axis for y1-data
 # The left axis (W) is plotted with blue annotations, ticks, and label
 with pygmt.config(
     MAP_FRAME_PEN="blue",
@@ -44,12 +44,12 @@ class can control which axes should be plotted and optionally show annotations,
 ):
     fig.basemap(frame=["W", "yaf+ly1"])
 
-# Plot the line for y1 data
+# Plot the line for y1-data
 fig.plot(x=x, y=y1, pen="1p,blue")
-# Plot points for y1 data
+# Plot points for y1-data
 fig.plot(x=x, y=y1, style="c0.2c", fill="blue", label="y1")
 
-# Plot the Y axis for y2 data
+# Plot the Y-axis for y2-data
 # The right axis (E) is plotted with red annotations, ticks, and label
 with pygmt.config(
     MAP_FRAME_PEN="red",
@@ -58,9 +58,9 @@ class can control which axes should be plotted and optionally show annotations,
     FONT_LABEL="red",
 ):
     fig.basemap(region=[0, 10, 100, 200], frame=["E", "yaf+ly2"])
-# Plot the line for y2 data
+# Plot the line for y2-data
 fig.plot(x=x, y=y2, pen="1p,red")
-# Plot points for y2 data
+# Plot points for y2-data
 fig.plot(x=x, y=y2, style="s0.28c", fill="red", label="y2")
 
 # Create a legend in the Top Left (TL) corner of the plot with an

examples/gallery/histograms/scatter_and_histograms.py

@@ -12,8 +12,8 @@
 import numpy as np
 import pygmt
 
-# Generate random x, y coordinates from a standard normal distribution.
-# x values are centered on 0 with a standard deviation of 1, and y values are centered
+# Generate random x-, y-coordinates from a standard normal distribution.
+# x-values are centered on 0 with a standard deviation of 1, and y-values are centered
 # on 30 with a standard deviation of 2.
 rng = np.random.default_rng()
 x = rng.normal(loc=0, scale=1, size=1000)

examples/gallery/images/contours.py

@@ -13,18 +13,18 @@
    tabular format.
 
 The parameters ``levels`` and ``annotation`` set the intervals of the contours
-and the annotation on the contours respectively.
+and the annotations on the contours, respectively.
 
-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.
+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 0.5-points pen with
+contours every 10 z-values and annotations every 20 z-values.
 """
 
 # %%
 import numpy as np
 import pygmt
 
-# build the contours underlying data with the function z = x^2 + y^2
+# Build the contours underlying data with the function z = x^2 + y^2
 X, Y = np.meshgrid(np.linspace(-10, 10, 50), np.linspace(-10, 10, 50))
 Z = X**2 + Y**2
 x, y, z = X.flatten(), Y.flatten(), Z.flatten()
@@ -36,13 +36,13 @@
     projection="X10c/10c",
     frame="ag",
     pen="0.5p",
-    # pass the data as 3 1-D data columns
+    # Pass the data as 3 1-D data columns
     x=x,
     y=y,
     z=z,
-    # set the contours z values intervals to 10
+    # Set the interval of the contours to 10
     levels=10,
-    # set the contours annotation intervals to 20
+    # Set the interval of the annotations to 20
     annotation=20,
 )
 fig.show()

examples/gallery/lines/vector_styles.py

@@ -24,8 +24,8 @@
 
 
 # Left: plot 12 Cartesian vectors with different lengths
-x = np.linspace(-116, -116, 12)  # x vector coordinates
-y = np.linspace(33.5, 42.5, 12)  # y vector coordinates
+x = np.linspace(-116, -116, 12)  # x-vector coordinates
+y = np.linspace(33.5, 42.5, 12)  # y-vector coordinates
 direction = np.zeros(x.shape)  # direction of vectors
 length = np.linspace(0.5, 2.4, 12)  # length of vectors
 # Cartesian vectors (v) with red fill and pen (+g, +p), vector head at the end (+e), and
@@ -37,8 +37,8 @@
 
 # Middle: plot 7 math angle arcs with different radii
 num = 7
-x = np.full(num, -95)  # x coordinates of the center
-y = np.full(num, 37)  # y coordinates of the center
+x = np.full(num, -95)  # x-coordinates of the center
+y = np.full(num, 37)  # y-coordinates of the center
 radius = 1.8 - 0.2 * np.arange(0, num)  # radius
 startdir = np.full(num, 90)  # start direction in degrees
 stopdir = 180 + 40 * np.arange(0, num)  # stop direction in degrees

examples/tutorials/advanced/vectors.py

@@ -46,7 +46,7 @@
 # %%
 # In this example, we apply the same concept shown previously to plot multiple
 # vectors. Notice that instead of passing int/float to ``x`` and ``y``, a list
-# of all x and y coordinates will be passed. Similarly, the length of direction
+# of all x- and y-coordinates will be passed. Similarly, the length of direction
 # list will increase accordingly.
 #
 # Additionally, we change the style of the vector to include a red
@@ -157,8 +157,8 @@
 
 # %%
 # In this example, Cartesian vectors are plotted over a Mercator
-# projection of the continental US. The x values represent the
-# longitude and y values represent the latitude where the vector starts.
+# projection of the continental US. The x-values represent the
+# longitude and y-values represent the latitude where the vector starts.
 #
 # This example also shows some of the styles a vector supports.
 # The beginning point of the vector (**+b**)
@@ -213,8 +213,8 @@
 # Transverse Mercator projection is used. Additionally, :func:`numpy.linspace`
 # is used to create 5 vectors with equal stops.
 
-x = np.linspace(36, 42, 5)  # x values = [36.  37.5 39.  40.5 42. ]
-y = np.linspace(39, 39, 5)  # y values = [39. 39. 39. 39.]
+x = np.linspace(36, 42, 5)  # x-values = [36.  37.5 39.  40.5 42.]
+y = np.linspace(39, 39, 5)  # y-values = [39. 39. 39. 39.]
 direction = np.linspace(-90, -90, 5)  # direction values = [-90. -90. -90. -90.]
 length = np.linspace(1.5, 1.5, 5)  # length values = [1.5 1.5 1.5 1.5]
 

examples/tutorials/basics/polygons.py

@@ -6,7 +6,7 @@
 
 This tutorial focuses on input data given as NumPy arrays. Besides NumPy arrays,
 array-like objects are supported. Here, a polygon is a closed shape defined by a series
-of data points with x and y coordinates, connected by line segments, with the start and
+of data points with x- and y-coordinates, connected by line segments, with the start and
 end points being identical. For plotting a :class:`geopandas.GeoDataFrame` object with
 polygon geometries, e.g., to create a choropleth map, see the gallery example
 :doc:`Choropleth map </gallery/maps/choropleth_map>`.
@@ -20,7 +20,7 @@
 # Plot polygons
 # -------------
 #
-# Set up sample data points as NumPy arrays for the x and y values.
+# Set up sample data points as NumPy arrays for the x- and y-values.
 
 x = np.array([-2, 1, 3, 0, -4, -2])
 y = np.array([-3, -1, 1, 3, 2, -3])
@@ -65,8 +65,8 @@
 # Close polygons
 # --------------
 #
-# Set up sample data points as NumPy arrays for the x and y values. Now, the data points
-# do not form a polygon.
+# Set up sample data points as NumPy arrays for the x- and y-values. Now, the data
+# points do not form a polygon.
 
 x = np.array([-2, 1, 3, 0, -4])
 y = np.array([-3, -1, 1, 3, 2])