Fix format and update index.rst

Author: noorbuchi

doc/index.rst

@@ -35,6 +35,7 @@
     tutorials/plot.rst
     tutorials/lines.rst
     tutorials/vectors.rst
+    tutorials/date_time_charts.rst
     tutorials/text.rst
     tutorials/contour_map.rst
     tutorials/earth_relief.rst

examples/tutorials/date_time_charts.py

@@ -1,8 +1,8 @@
 """
-Plotting Datetime Charts
-========================
+Plotting Date Time Charts
+================
 
-Plotting datetime charts is handled by :meth:`pygmt.Figure.basemap`.
+Plotting vectors is handled by :meth:`pygmt.Figure.basemap`.
 
 .. note::
 
@@ -13,12 +13,12 @@
     To save the figure, use ``fig.savefig("figname.pdf")`` where ``"figname.pdf"``
     is the desired name and file extension for the saved figure.
 """
-# sphinx_gallery_thumbnail_number = 0
+# sphinx_gallery_thumbnail_number = 6
 
 import numpy as np
 import pygmt
 
-########################################################################################
+#########################################################################################
 # Plot Cartesian Vectors
 # ----------------------
 #
@@ -51,200 +51,3 @@
     direction=[[-45], [6]],
 )
 fig.show()
-
-########################################################################################
-# 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
-# list will increase accordingly.
-#
-# Additionally, we change the style of the vector to include a red
-# arrow head at the end (**+e**) of the vector and increase the
-# thickness (``pen="2p"``) of the vector stem. A list of different
-# styling attributes can be found in
-# :doc:`Vector heads and tails </gallery/lines/vector_heads_tails>`.
-
-fig = pygmt.Figure()
-fig.plot(
-    region=[0, 10, 0, 10],
-    projection="X10c/10c",
-    frame="ag",
-    x=[2, 4],
-    y=[8, 1],
-    style="v0.6c+e",
-    direction=[[-45, 23], [6, 3]],
-    pen="2p",
-    color="red3",
-)
-fig.show()
-
-########################################################################################
-# The default unit of vector length is centimeters,
-# however, this can be changed to inches or points. Note that, in PyGMT,
-# one point is defined as 1/72 inch.
-#
-# In this example, the graphed region is 5in X 5in, but
-# the length of the first vector is still graphed in centimeters.
-# Using ``pygmt.config(PROJ_LENGTH_UNIT="i")``, the default unit
-# can be changed to inches in the second plotted vector.
-
-fig = pygmt.Figure()
-# Vector 1 with default unit as cm
-fig.plot(
-    region=[0, 10, 0, 10],
-    projection="X5i/5i",
-    frame="ag",
-    x=2,
-    y=8,
-    style="v1c+e",
-    direction=[[0], [3]],
-    pen="2p",
-    color="red3",
-)
-# Vector 2 after changing default unit to inch
-with pygmt.config(PROJ_LENGTH_UNIT="i"):
-    fig.plot(
-        x=2,
-        y=7,
-        direction=[[0], [3]],
-        style="v1c+e",
-        pen="2p",
-        color="red3",
-    )
-fig.show()
-
-########################################################################################
-# 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.
-# Each vector list contains the information structured as:
-# ``[x_start, y_start, direction_degrees, length]``.
-#
-# If this approach is chosen, the ``data`` parameter must be
-# used instead of ``x``, ``y`` and  ``direction``.
-
-# Create a list of lists that include each vector information
-vectors = [[2, 3, 45, 4]]
-
-fig = pygmt.Figure()
-fig.plot(
-    region=[0, 10, 0, 10],
-    projection="X10c/10c",
-    frame="ag",
-    data=vectors,
-    style="v0.6c+e",
-    pen="2p",
-    color="red3",
-)
-fig.show()
-
-########################################################################################
-# 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
-# array object and passed using ``data`` parameter.
-
-# Vector specifications structured as: [x_start, y_start, direction_degrees, length]
-vector_1 = [2, 3, 45, 4]
-vector_2 = [7.5, 8.3, -120.5, 7.2]
-# Create a list of lists that include each vector information
-vectors = [vector_1, vector_2]
-# Vectors structure: [[2, 3, 45, 4], [7.5, 8.3, -120.5, 7.2]]
-
-fig = pygmt.Figure()
-fig.plot(
-    region=[0, 10, 0, 10],
-    projection="X10c/10c",
-    frame="ag",
-    data=vectors,
-    style="v0.6c+e",
-    pen="2p",
-    color="red3",
-)
-fig.show()
-
-########################################################################################
-# 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.
-#
-# This example also shows some of the styles a vector supports.
-# The beginning point of the vector (**+b**)
-# should take the shape of a circle (**c**). Similarly, the end
-# point of the vector (**+e**) should have an arrow shape (**a**)
-# (to draw a plain arrow, use **A** instead). Lastly, the **+a**
-# specifies the angle of the vector head apex (30 degrees in
-# this example).
-
-# Create a plot with coast, Mercator projection (M) over the continental US
-fig = pygmt.Figure()
-fig.coast(
-    region=[-127, -64, 24, 53],
-    projection="M10c",
-    frame="ag",
-    borders=1,
-    shorelines="0.25p,black",
-    area_thresh=4000,
-    land="grey",
-    water="lightblue",
-)
-
-# Plot a vector using the x, y, direction parameters
-style = "v0.4c+bc+ea+a30"
-fig.plot(
-    x=-110,
-    y=40,
-    style=style,
-    direction=[[-25], [3]],
-    pen="1p",
-    color="red3",
-)
-
-# vector specifications structured as: [x_start, y_start, direction_degrees, length]
-vector_2 = [-82, 40.5, 138, 2.5]
-vector_3 = [-71.2, 45, -115.7, 4]
-# Create a list of lists that include each vector information
-vectors = [vector_2, vector_3]
-
-# Plot vectors using the data parameter.
-fig.plot(
-    data=vectors,
-    style=style,
-    pen="1p",
-    color="yellow",
-)
-fig.show()
-
-########################################################################################
-# Another example of plotting cartesian vectors over a coast plot. This time
-# a 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.]
-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]
-
-# Create a plot with coast, Mercator projection (M) over the continental US
-fig = pygmt.Figure()
-fig.coast(
-    region=[20, 50, 30, 45],
-    projection="T35/10c",
-    frame=True,
-    borders=1,
-    shorelines="0.25p,black",
-    area_thresh=4000,
-    land="lightbrown",
-    water="lightblue",
-)
-
-fig.plot(
-    x=x,
-    y=y,
-    style="v0.4c+ea+bc",
-    direction=[direction, length],
-    pen="0.6p",
-    color="red3",
-)
-
-fig.show()