def show(self, dpi=300, width=500, method="static"):
"""
Display a preview of the figure.
Inserts the preview in the Jupyter notebook output. You will need to
have IPython installed for this to work. You should have it if you are
using the notebook.
If ``method='external'``, makes PDF preview instead and opens it in the
default viewer for your operating system (falls back to the default web
browser). Note that the external viewer does not block the current
process, so this won't work in a script.
Parameters
----------
dpi : int
The image resolution (dots per inch).
width : int
Width of the figure shown in the notebook in pixels. Ignored if
``method='external'``.
method : str
How the figure will be displayed. Options are (1) ``'static'``: PNG
preview (default); (2) ``'external'``: PDF preview in an external
program.
Returns
-------
img : IPython.display.Image
Only if ``method != 'external'``.
"""
# Module level variable to know which figures had their show method
# called. Needed for the sphinx-gallery scraper.
SHOWED_FIGURES.append(self)
if method not in ["static", "external"]:
raise GMTInvalidInput("Invalid show method '{}'.".format(method))
if method == "external":
pdf = self._preview(fmt="pdf",
dpi=dpi,
anti_alias=False,
as_bytes=False)
launch_external_viewer(pdf)
img = None
elif method == "static":
png = self._preview(fmt="png",
dpi=dpi,
anti_alias=True,
as_bytes=True,
transparent=True)
if Image is None:
raise GMTError(" ".join([
"Cannot find IPython.",
"Make sure you have it installed",
"or use 'method=\"external\"' to open in an external viewer.",
]))
img = Image(data=png, width=width)
return img
def show(self, dpi=300, width=500, method=None, waiting=0.5):
"""
Display a preview of the figure.
Inserts the preview in the Jupyter notebook output if available,
otherwise opens it in the default viewer for your operating system
(falls back to the default web browser).
:func:`pygmt.set_display` can select the default display method
(**notebook**, **external**, or **none**).
The ``method`` parameter can also override the default display method
for the current figure. Parameters ``dpi`` and ``width`` can be used
to control the resolution and dimension of the figure in the notebook.
Note: The external viewer can be disabled by setting the
PYGMT_USE_EXTERNAL_DISPLAY environment variable to **false**.
This is useful when running unit tests and building the documentation
in consoles without a Graphical User Interface.
Note that the external viewer does not block the current process, thus
it's necessary to suspend the execution of the current process for a
short while after launching the external viewer, so that the preview
image won't be deleted before the external viewer tries to open it. Set
the ``waiting`` parameter to a larger number if your computer is slow.
Parameters
----------
dpi : int
The image resolution (dots per inch) in Jupyter notebooks.
width : int
The image width (in pixels) in Jupyter notebooks.
method : str
How the current figure will be displayed. Options are
- **external**: PDF preview in an external program [default]
- **notebook**: PNG preview [default in Jupyter notebooks]
- **none**: Disable image preview
waiting : float
Suspend the execution of the current process for a given number of
seconds after launching an external viewer.
Only works if ``method="external"``.
"""
# Module level variable to know which figures had their show method
# called. Needed for the sphinx-gallery scraper.
SHOWED_FIGURES.append(self)
# Set the display method
if method is None:
method = SHOW_CONFIG["method"]
if method not in ["external", "notebook", "none"]:
raise GMTInvalidInput(
(f"Invalid display method '{method}', "
"should be either 'notebook', 'external', or 'none'."))
if method in ["notebook", "none"]:
if IPython is None:
raise GMTError((
"Notebook display is selected, but IPython is not available. "
"Make sure you have IPython installed, "
"or run the script in a Jupyter notebook."))
png = self._preview(fmt="png",
dpi=dpi,
anti_alias=True,
as_bytes=True)
IPython.display.display(
IPython.display.Image(data=png, width=width))
if method == "external":
pdf = self._preview(fmt="pdf",
dpi=dpi,
anti_alias=False,
as_bytes=False)
launch_external_viewer(pdf, waiting=waiting)
def meca(
self, # pylint: disable=unused-argument
spec,
scale,
longitude=None,
latitude=None,
depth=None,
convention=None,
component="full",
plot_longitude=None,
plot_latitude=None,
**kwargs,
):
"""
Plot focal mechanisms.
Full option list at :gmt-docs:`supplements/seis/meca.html`
Note
----
Currently, labeling of beachballs with text strings is only supported
via providing a file to `spec` as input.
{aliases}
Parameters
----------
spec: dict, 1D array, 2D array, pd.DataFrame, or str
Either a filename containing focal mechanism parameters as columns, a
1- or 2-D array with the same, or a dictionary. If a filename or array,
`convention` is required so we know how to interpret the
columns/entries. If a dictionary, the following combinations of keys
are supported; these determine the convention. Dictionary may contain
values for a single focal mechanism or lists of values for many focal
mechanisms. A Pandas DataFrame may optionally contain columns latitude,
longitude, depth, plot_longitude, and/or plot_latitude instead of
passing them to the meca method.
- ``"aki"`` — *strike, dip, rake, magnitude*
- ``"gcmt"`` — *strike1, dip1, rake1, strike2, dip2, rake2, mantissa,
exponent*
- ``"mt"`` — *mrr, mtt, mff, mrt, mrf, mtf, exponent*
- ``"partial"`` — *strike1, dip1, strike2, fault_type, magnitude*
- ``"principal_axis"`` — *t_exponent, t_azimuth, t_plunge, n_exponent,
n_azimuth, n_plunge, p_exponent, p_azimuth, p_plunge, exponent*
scale: str
Adjusts the scaling of the radius of the beachball, which is
proportional to the magnitude. Scale defines the size for magnitude = 5
(i.e. scalar seismic moment M0 = 4.0E23 dynes-cm)
longitude: int, float, list, or 1d numpy array
Longitude(s) of event location. Ignored if `spec` is not a dictionary.
List must be the length of the number of events. Ignored if `spec` is a
DataFrame and contains a 'longitude' column.
latitude: int, float, list, or 1d numpy array
Latitude(s) of event location. Ignored if `spec` is not a dictionary.
List must be the length of the number of events. Ignored if `spec` is a
DataFrame and contains a 'latitude' column.
depth: int, float, list, or 1d numpy array
Depth(s) of event location in kilometers. Ignored if `spec` is not a
dictionary. List must be the length of the number of events. Ignored if
`spec` is a DataFrame and contains a 'depth' column.
convention: str
``"aki"`` (Aki & Richards), ``"gcmt"`` (global CMT), ``"mt"`` (seismic
moment tensor), ``"partial"`` (partial focal mechanism), or
``"principal_axis"`` (principal axis). Ignored if `spec` is a
dictionary or dataframe.
component: str
The component of the seismic moment tensor to plot. ``"full"`` (the
full seismic moment tensor), ``"dc"`` (the closest double couple with
zero trace and zero determinant), ``"deviatoric"`` (zero trace)
plot_longitude: int, float, list, or 1d numpy array
Longitude(s) at which to place beachball, only used if `spec` is a
dictionary. List must be the length of the number of events. Ignored if
`spec` is a DataFrame and contains a 'plot_longitude' column.
plot_latitude: int, float, list, or 1d numpy array
Latitude(s) at which to place beachball, only used if `spec` is a
dictionary. List must be the length of the number of events. Ignored if
`spec` is a DataFrame and contains a 'plot_latitude' column.
offset: bool or str
Offsets beachballs to the longitude, latitude specified in the last two
columns of the input file or array, or by `plot_longitude` and
`plot_latitude` if provided. A small circle is plotted at the initial
location and a line connects the beachball to the circle. Specify pen
and optionally append ``+ssize`` to change the line style and/or size
of the circle.
no_clip : bool
Does NOT skip symbols that fall outside frame boundary specified by
*region* [Default is False, i.e. plot symbols inside map frame only].
{J}
{R}
{B}
{V}
{XY}
{p}
{t}
"""
# pylint warnings that need to be fixed
# pylint: disable=too-many-locals
# pylint: disable=too-many-nested-blocks
# pylint: disable=too-many-branches
# pylint: disable=too-many-statements
def set_pointer(data_pointers, spec):
"""
Set optional parameter pointers based on DataFrame or dict, if those
parameters are present in the DataFrame or dict.
"""
for param in list(data_pointers.keys()):
if param in spec:
# set pointer based on param name
data_pointers[param] = spec[param]
def update_pointers(data_pointers):
"""
Updates variables based on the location of data, as the following data
can be passed as parameters or it can be contained in `spec`.
"""
# update all pointers
longitude = data_pointers["longitude"]
latitude = data_pointers["latitude"]
depth = data_pointers["depth"]
plot_longitude = data_pointers["plot_longitude"]
plot_latitude = data_pointers["plot_latitude"]
return (longitude, latitude, depth, plot_longitude, plot_latitude)
# Check the spec and parse the data according to the specified
# convention
if isinstance(spec, (dict, pd.DataFrame)):
# dicts and DataFrames are handed similarly but not identically
if (longitude is None or latitude is None
or depth is None) and not isinstance(spec,
(dict, pd.DataFrame)):
raise GMTError("Location not fully specified.")
param_conventions = {
"AKI": ["strike", "dip", "rake", "magnitude"],
"GCMT":
["strike1", "dip1", "dip2", "rake2", "mantissa", "exponent"],
"MT": ["mrr", "mtt", "mff", "mrt", "mrf", "mtf", "exponent"],
"PARTIAL":
["strike1", "dip1", "strike2", "fault_type", "magnitude"],
"PRINCIPAL_AXIS": [
"t_exponent",
"t_azimuth",
"t_plunge",
"n_exponent",
"n_azimuth",
"n_plunge",
"p_exponent",
"p_azimuth",
"p_plunge",
"exponent",
],
}
# to keep track of where optional parameters exist
data_pointers = {
"longitude": longitude,
"latitude": latitude,
"depth": depth,
"plot_longitude": plot_longitude,
"plot_latitude": plot_latitude,
}
# make a DataFrame copy to check convention if it contains
# other parameters
if isinstance(spec, (dict, pd.DataFrame)):
# check if a copy is necessary
copy = False
drop_list = []
for pointer in data_pointers:
if pointer in spec:
copy = True
drop_list.append(pointer)
if copy:
spec_conv = spec.copy()
# delete optional parameters from copy for convention check
for item in drop_list:
del spec_conv[item]
else:
spec_conv = spec
# set convention and focal parameters based on spec convention
convention_assigned = False
for conv in param_conventions:
if set(spec_conv.keys()) == set(param_conventions[conv]):
convention = conv.lower()
foc_params = param_conventions[conv]
convention_assigned = True
break
if not convention_assigned:
raise GMTError("Parameters in spec dictionary do not match known "
"conventions.")
# create a dict type pointer for easier to read code
if isinstance(spec, dict):
dict_type_pointer = list(spec.values())[0]
elif isinstance(spec, pd.DataFrame):
# use df.values as pointer for DataFrame behavior
dict_type_pointer = spec.values
# assemble the 1D array for the case of floats and ints as values
if isinstance(dict_type_pointer, (int, float)):
# update pointers
set_pointer(data_pointers, spec)
# look for optional parameters in the right place
(
longitude,
latitude,
depth,
plot_longitude,
plot_latitude,
) = update_pointers(data_pointers)
# Construct the array (order matters)
spec = [longitude, latitude, depth
] + [spec[key] for key in foc_params]
# Add in plotting options, if given, otherwise add 0s
for arg in plot_longitude, plot_latitude:
if arg is None:
spec.append(0)
else:
if "C" not in kwargs:
kwargs["C"] = True
spec.append(arg)
# or assemble the 2D array for the case of lists as values
elif isinstance(dict_type_pointer, list):
# update pointers
set_pointer(data_pointers, spec)
# look for optional parameters in the right place
(
longitude,
latitude,
depth,
plot_longitude,
plot_latitude,
) = update_pointers(data_pointers)
# before constructing the 2D array lets check that each key
# of the dict has the same quantity of values to avoid bugs
list_length = len(list(spec.values())[0])
for value in list(spec.values()):
if len(value) != list_length:
raise GMTError("Unequal number of focal mechanism "
"parameters supplied in 'spec'.")
# lets also check the inputs for longitude, latitude,
# and depth if it is a list or array
if (isinstance(longitude, (list, np.ndarray))
or isinstance(latitude, (list, np.ndarray))
or isinstance(depth, (list, np.ndarray))):
if (len(longitude) != len(latitude)) or (len(longitude) !=
len(depth)):
raise GMTError("Unequal number of focal mechanism "
"locations supplied.")
# values are ok, so build the 2D array
spec_array = []
for index in range(list_length):
# Construct the array one row at a time (note that order
# matters here, hence the list comprehension!)
row = [longitude[index], latitude[index], depth[index]
] + [spec[key][index] for key in foc_params]
# Add in plotting options, if given, otherwise add 0s as
# required by GMT
for arg in plot_longitude, plot_latitude:
if arg is None:
row.append(0)
else:
if "C" not in kwargs:
kwargs["C"] = True
row.append(arg[index])
spec_array.append(row)
spec = spec_array
# or assemble the array for the case of pd.DataFrames
elif isinstance(dict_type_pointer, np.ndarray):
# update pointers
set_pointer(data_pointers, spec)
# look for optional parameters in the right place
(
longitude,
latitude,
depth,
plot_longitude,
plot_latitude,
) = update_pointers(data_pointers)
# lets also check the inputs for longitude, latitude, and depth
# just in case the user entered different length lists
if (isinstance(longitude, (list, np.ndarray))
or isinstance(latitude, (list, np.ndarray))
or isinstance(depth, (list, np.ndarray))):
if (len(longitude) != len(latitude)) or (len(longitude) !=
len(depth)):
raise GMTError(
"Unequal number of focal mechanism locations supplied."
)
# values are ok, so build the 2D array in the correct order
spec_array = []
for index in range(len(spec)):
# Construct the array one row at a time (note that order
# matters here, hence the list comprehension!)
row = [longitude[index], latitude[index], depth[index]
] + [spec[key][index] for key in foc_params]
# Add in plotting options, if given, otherwise add 0s as
# required by GMT
for arg in plot_longitude, plot_latitude:
if arg is None:
row.append(0)
else:
if "C" not in kwargs:
kwargs["C"] = True
row.append(arg[index])
spec_array.append(row)
spec = spec_array
else:
raise GMTError(
"Parameter 'spec' contains values of an unsupported type.")
# Add condition and scale to kwargs
if convention == "aki":
data_format = "a"
elif convention == "gcmt":
data_format = "c"
elif convention == "mt":
# Check which component of mechanism the user wants plotted
if component == "deviatoric":
data_format = "z"
elif component == "dc":
data_format = "d"
else: # component == 'full'
data_format = "m"
elif convention == "partial":
data_format = "p"
elif convention == "principal_axis":
# Check which component of mechanism the user wants plotted
if component == "deviatoric":
data_format = "t"
elif component == "dc":
data_format = "y"
else: # component == 'full'
data_format = "x"
# Support old-school GMT format options
elif convention in ["a", "c", "m", "d", "z", "p", "x", "y", "t"]:
data_format = convention
else:
raise GMTError("Convention not recognized.")
# Assemble -S flag
kwargs["S"] = data_format + scale
kind = data_kind(spec)
with Session() as lib:
if kind == "matrix":
file_context = lib.virtualfile_from_matrix(np.atleast_2d(spec))
elif kind == "file":
file_context = dummy_context(spec)
else:
raise GMTInvalidInput("Unrecognized data type: {}".format(
type(spec)))
with file_context as fname:
arg_str = " ".join([fname, build_arg_string(kwargs)])
lib.call_module("meca", arg_str)
