"""
Plotting vectors
================
Plotting vectors is handled by :meth:`pygmt.Figure.plot`.
"""
# %%
import numpy as np
import pygmt
# %%
# Plot Cartesian Vectors
# ----------------------
#
# Create a simple Cartesian vector using a start point through
# ``x``, ``y``, and ``direction`` parameters.
# 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 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
# could be changed using :class:`pygmt.config`
# (Check the next examples for unit changes).
#
# Notice that the ``v`` in the ``style`` parameter stands for
# vector; it distinguishes it from regular lines and allows for
# different customization. ``0c`` is used to specify the size
# of the arrow head which explains why there is no arrow on either
# side of the vector.
fig = pygmt.Figure()
fig.plot(
region=[0, 10, 0, 10],
projection="X10c/10c",
frame="ag",
x=2,
y=8,
style="v0c",
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 `.
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",
fill="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",
fill="red3",
)
# Vector 2 after changing default unit to inches
with pygmt.config(PROJ_LENGTH_UNIT="i"):
fig.plot(
x=2,
y=7,
direction=[[0], [3]],
style="v1c+e",
pen="2p",
fill="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 2-D 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",
fill="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 2-D 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",
fill="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",
fill="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",
fill="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,
# Transverse Mercator projection (T) over Turkey and Syria
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",
fill="red3",
)
fig.show()
# %%
# Plot Circular Vectors
# ---------------------
#
# When plotting circular vectors, all of the information for a single vector is
# to be stored in a list. Each circular vector list is structured as:
# ``[x_start, y_start, radius, degree_start, degree_stop]``. The first two
# values in the vector list represent the origin of the circle that will be
# plotted. The next value is the radius which is represented on the plot in cm.
#
# The last two values in the vector list represent the degree at which the plot
# will start and stop. These values are measured counter-clockwise from the
# horizontal axis. In this example, the result shown is the left half of a
# circle as the plot starts at 90 degrees and goes until 270. Notice that the
# ``m`` in the ``style`` parameter stands for circular vectors.
fig = pygmt.Figure()
circular_vector_1 = [0, 0, 2, 90, 270]
data = [circular_vector_1]
fig.plot(
region=[-5, 5, -5, 5],
projection="X10c",
frame="ag",
data=data,
style="m0.5c+ea",
pen="2p",
fill="red3",
)
# Another example using np.array()
circular_vector_2 = [0, 0, 4, -90, 90]
data = np.array([circular_vector_2])
fig.plot(
data=data,
style="m0.5c+ea",
pen="2p",
fill="red3",
)
fig.show()
# %%
# When plotting multiple circular vectors, a two dimensional array or numpy
# array object should be passed as the ``data`` parameter. In this example,
# :func:`numpy.column_stack` is used to generate this two dimensional array.
# Other numpy objects are used to generate linear values for the ``radius``
# parameter and random values for the ``degree_stop`` parameter discussed in
# the previous example. This is the reason in which each vector has a different
# appearance on the projection.
vector_num = 5
radius = 3 - (0.5 * np.arange(0, vector_num))
startdir = np.full(vector_num, 90)
stopdir = 180 + (50 * np.arange(0, vector_num))
data = np.column_stack(
[np.full(vector_num, 0), np.full(vector_num, 0), radius, startdir, stopdir]
)
fig = pygmt.Figure()
fig.plot(
region=[-5, 5, -5, 5],
projection="X10c",
frame="ag",
data=data,
style="m0.5c+ea",
pen="2p",
fill="red3",
)
fig.show()
# %%
# Much like when plotting Cartesian vectors, the default unit used is
# centimeters. When this is changed to inches, the size of the plot appears
# larger when the projection units do not change. Below is an example of two
# circular vectors. One is plotted using the default unit, and the second is
# plotted using inches. Despite using the same list to plot the vectors, a
# different measurement unit causes one to be larger than the other.
circular_vector = [6, 5, 1, 90, 270]
fig = pygmt.Figure()
fig.plot(
region=[0, 10, 0, 10],
projection="X10c",
frame="ag",
data=[circular_vector],
style="m0.5c+ea",
pen="2p",
fill="red3",
)
with pygmt.config(PROJ_LENGTH_UNIT="i"):
fig.plot(
data=[circular_vector],
style="m0.5c+ea",
pen="2p",
fill="red3",
)
fig.show()
# %%
# Plot Geographic Vectors
# -----------------------
#
# On this map,
# ``point_1`` and ``point_2`` are coordinate pairs used to set the
# start and end points of the geographic vector.
# The geographical vector is going from Idaho to
# Chicago. To style geographic
# vectors, use ``=`` at the beginning of the ``style`` parameter.
# Other styling features such as vector stem thickness and head color
# can be passed into the ``pen`` and ``fill`` parameters.
#
# Note that the **+s** is added to use a start point and an end point
# to represent the vector instead of input angle and length.
point_1 = [-114.7420, 44.0682]
point_2 = [-87.6298, 41.8781]
data = np.array([point_1 + point_2])
fig = pygmt.Figure()
fig.coast(
region=[-127, -64, 24, 53],
projection="M10c",
frame=True,
borders=1,
shorelines="0.25p,black",
area_thresh=4000,
)
fig.plot(
data=data,
style="=0.5c+ea+s",
pen="2p",
fill="red3",
)
fig.show()
# %%
# Using the same technique shown in the previous example,
# multiple vectors can be plotted in a chain where the end point
# of one is the start 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 2-D list contains the start and end information
# for each vector.
# Coordinate pairs for all the locations used
ME = [-69.4455, 45.2538]
CHI = [-87.6298, 41.8781]
SEA = [-122.3321, 47.6062]
NO = [-90.0715, 29.9511]
KC = [-94.5786, 39.0997]
CA = [-119.4179, 36.7783]
# Add array to piece together the vectors
data = [ME + CHI, CHI + SEA, SEA + KC, KC + NO, NO + CA]
fig = pygmt.Figure()
fig.coast(
region=[-127, -64, 24, 53],
projection="M10c",
frame=True,
borders=1,
shorelines="0.25p,black",
area_thresh=4000,
)
fig.plot(
data=data,
style="=0.5c+ea+s",
pen="2p",
fill="red3",
)
fig.show()
# %%
# This example plots vectors over a Mercator projection. The start points
# are located at SA which is South Africa and going to four different
# locations.
SA = [22.9375, -30.5595]
EUR = [15.2551, 54.5260]
ME = [-69.4455, 45.2538]
AS = [100.6197, 34.0479]
NM = [-105.8701, 34.5199]
data = np.array([SA + EUR, SA + ME, SA + AS, SA + NM])
fig = pygmt.Figure()
fig.coast(
region=[-180, 180, -80, 80],
projection="M0/0/12c",
frame="afg",
land="lightbrown",
water="lightblue",
)
fig.plot(
data=data,
style="=0.5c+ea+s",
pen="2p",
fill="red3",
)
fig.show()
# sphinx_gallery_thumbnail_number = 6