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Refactor meca to use virtualfile_from_data #1613

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Refactor meca to use virtualfile_from_data #1613

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3f981e1
Refactor meca to use virtualfile_from_data
weiji14 Nov 7, 2021
9945684
Put checks for unequal sized lists near top and add some unit tests
weiji14 Nov 8, 2021
e223057
Refactor to process dictionary spec like a pandas.DataFrame
weiji14 Nov 8, 2021
a7c158e
Merge branch 'main' into virtualfile_from_data/meca
weiji14 Jan 1, 2022
dbbd836
Merge branch 'main' into virtualfile_from_data/meca
weiji14 Mar 4, 2022
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164 changes: 46 additions & 118 deletions pygmt/src/meca.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,14 +6,7 @@
import pandas as pd
from pygmt.clib import Session
from pygmt.exceptions import GMTError, GMTInvalidInput
from pygmt.helpers import (
build_arg_string,
data_kind,
dummy_context,
fmt_docstring,
kwargs_to_strings,
use_alias,
)
from pygmt.helpers import build_arg_string, fmt_docstring, kwargs_to_strings, use_alias


def data_format_code(convention, component="full"):
Expand Down Expand Up @@ -136,7 +129,7 @@ def meca(

Parameters
----------
spec: dict, 1D array, 2D array, pd.DataFrame, or str
spec : str or dict or numpy.ndarray or pandas.DataFrame
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
Expand Down Expand Up @@ -247,6 +240,29 @@ def update_pointers(data_pointers):
):
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Lines 237-241 are unnecessary because line 236 already checks spec is dict or pd.DataFrame. Am I right?

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You might be right, let me recheck the logic.

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@weiji14 Please see my new PR #1784.

raise GMTError("Location not fully specified.")

# 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.")

if isinstance(spec, dict) and any(
isinstance(s, (list, np.ndarray)) for s in spec.values()
):
# 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'."
)

param_conventions = {
"AKI": ["strike", "dip", "rake", "magnitude"],
"GCMT": [
Expand Down Expand Up @@ -313,95 +329,20 @@ def update_pointers(data_pointers):

# 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 "A" not in kwargs:
kwargs["A"] = 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 "A" not in kwargs:
kwargs["A"] = 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):
# Convert single int, float data to List[int, float] data
_spec = {
"longitude": np.atleast_1d(longitude),
"latitude": np.atleast_1d(latitude),
"depth": np.atleast_1d(depth),
}
_spec.update({key: np.atleast_1d(val) for key, val in spec.items()})
spec = pd.DataFrame.from_dict(_spec)

assert isinstance(spec, pd.DataFrame)
dict_type_pointer = spec.values

# Assemble the array for the case of pd.DataFrames
if isinstance(dict_type_pointer, np.ndarray):
Comment on lines -403 to +345
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Just to explain this change (clearer if you look at commit e223057), previously the spec could be formatted in 3 different paths depending on the type:

  1. if isinstance(dict_type_pointer, (int, float)): (i.e. a dictionary of int/float like {"depth": 0}
  2. elif isinstance(dict_type_pointer, list): (i.e. a dictionary of lists like {"depth": [0, 1, 2]}
  3. elif isinstance(dict_type_pointer, np.ndarray): (i.e. a pandas.DataFrame with np.ndarray columns)

There was a lot of duplication in these 3 code paths, so I've removed paths 1 and 2 by converting any spec provided by the user in dictionary format into pandas.DataFrame. So now, everything happens via path 3 only.

Another benefit of handling everything using pandas.DataFrame is that columns can have different data types, so this sets things up for the ability to pass in string labels later in a follow up PR.

# update pointers
set_pointer(data_pointers, spec)
# look for optional parameters in the right place
Expand All @@ -413,19 +354,7 @@ def update_pointers(data_pointers):
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
# 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
Expand All @@ -449,20 +378,19 @@ def update_pointers(data_pointers):
else:
raise GMTError("Parameter 'spec' contains values of an unsupported type.")

# Convert 1d array types into 2d arrays
if isinstance(spec, np.ndarray) and spec.ndim == 1:
spec = np.atleast_2d(spec)

# determine data_foramt from convection and component
data_format = data_format_code(convention=convention, component=component)

# 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(f"Unrecognized data type: {type(spec)}")
# Choose how data will be passed into the module
file_context = lib.virtualfile_from_data(check_kind="vector", data=spec)
with file_context as fname:
arg_str = " ".join([fname, build_arg_string(kwargs)])
lib.call_module("meca", arg_str)
46 changes: 41 additions & 5 deletions pygmt/tests/test_meca.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,7 @@
import pandas as pd
import pytest
from pygmt import Figure
from pygmt.exceptions import GMTError
from pygmt.helpers import GMTTempFile


Expand All @@ -17,7 +18,7 @@ def test_meca_spec_dictionary():
fig = Figure()
# Right lateral strike slip focal mechanism
fig.meca(
dict(strike=0, dip=90, rake=0, magnitude=5),
spec=dict(strike=0, dip=90, rake=0, magnitude=5),
longitude=0,
latitude=5,
depth=0,
Expand All @@ -41,7 +42,7 @@ def test_meca_spec_dict_list():
strike=[330, 350], dip=[30, 50], rake=[90, 90], magnitude=[3, 2]
)
fig.meca(
focal_mechanisms,
spec=focal_mechanisms,
longitude=[-124.3, -124.4],
latitude=[48.1, 48.2],
depth=[12.0, 11.0],
Expand All @@ -52,6 +53,39 @@ def test_meca_spec_dict_list():
return fig


def test_meca_spec_unequal_sized_lists_fails():
"""
Test that supplying a dictionary containing unequal sized lists of
coordinates (longitude/latitude/depth) or focal mechanisms
(strike/dip/rake/magnitude) to the spec parameter fails.
"""
fig = Figure()

# Unequal sized coordinates (longitude/latitude/depth)
focal_mechanisms = dict(
strike=[330, 350], dip=[30, 50], rake=[90, 90], magnitude=[3, 2]
)
with pytest.raises(GMTError):
fig.meca(
spec=focal_mechanisms,
longitude=[-124.3],
latitude=[48.1, 48.2],
depth=[12.0],
scale="2c",
)

# Unequal sized focal mechanisms (strike/dip/rake/magnitude)
focal_mechanisms = dict(strike=[330], dip=[30, 50], rake=[90], magnitude=[3, 2])
with pytest.raises(GMTError):
fig.meca(
spec=focal_mechanisms,
longitude=[-124.3, -124.4],
latitude=[48.1, 48.2],
depth=[12.0, 11.0],
scale="2c",
)


@pytest.mark.mpl_image_compare
def test_meca_spec_dataframe():
"""
Expand All @@ -72,7 +106,9 @@ def test_meca_spec_dataframe():
depth=[12, 11.0],
)
spec_dataframe = pd.DataFrame(data=focal_mechanisms)
fig.meca(spec_dataframe, region=[-125, -122, 47, 49], scale="2c", projection="M14c")
fig.meca(
spec=spec_dataframe, region=[-125, -122, 47, 49], scale="2c", projection="M14c"
)
return fig


Expand Down Expand Up @@ -104,7 +140,7 @@ def test_meca_spec_1d_array():
]
focal_mech_array = np.asarray(focal_mechanism)
fig.meca(
focal_mech_array,
spec=focal_mech_array,
convention="mt",
component="full",
region=[-128, -127, 40, 41],
Expand Down Expand Up @@ -145,7 +181,7 @@ def test_meca_spec_2d_array():
]
focal_mechs_array = np.asarray(focal_mechanisms)
fig.meca(
focal_mechs_array,
spec=focal_mechs_array,
convention="gcmt",
region=[-128, -127, 40, 41],
scale="2c",
Expand Down