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clib.Session.virtualfile_in: Refactor if-else statements into match-case statements #3521

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Oct 16, 2024
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clib.Session.virtualfile_in: Refactor if-else statements into match-case statements #3521

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75 changes: 37 additions & 38 deletions pygmt/clib/session.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,6 @@
import contextlib
import ctypes as ctp
import io
import pathlib
import sys
import warnings
from collections.abc import Callable, Generator, Sequence
Expand Down Expand Up @@ -1792,43 +1791,43 @@ def virtualfile_in( # noqa: PLR0912
"vectors": self.virtualfile_from_vectors,
}[kind]

# Ensure the data is an iterable (Python list or tuple)
if kind in {"geojson", "grid", "image", "file", "arg", "stringio"}:
if kind == "image" and data.dtype != "uint8":
msg = (
f"Input image has dtype: {data.dtype} which is unsupported, "
"and may result in an incorrect output. Please recast image "
"to a uint8 dtype and/or scale to 0-255 range, e.g. "
"using a histogram equalization function like "
"skimage.exposure.equalize_hist."
)
warnings.warn(message=msg, category=RuntimeWarning, stacklevel=2)
_data = (data,) if not isinstance(data, pathlib.PurePath) else (str(data),)
elif kind == "empty":
# data is None, so data must be given via x/y/z.
_data = [x, y]
if z is not None:
_data.append(z)
if extra_arrays:
_data.extend(extra_arrays)
elif kind == "vectors":
if hasattr(data, "items") and not hasattr(data, "to_frame"):
# pandas.DataFrame or xarray.Dataset types.
# pandas.Series will be handled below like a 1-D numpy.ndarray.
_data = [array for _, array in data.items()]
else:
# Python list, tuple, numpy.ndarray, and pandas.Series types
_data = np.atleast_2d(np.asanyarray(data).T)
elif kind == "matrix":
# GMT can only accept a 2-D matrix which are signed integer (i), unsigned
# integer (u) or floating point (f) types. For other data types, we need to
# use virtualfile_from_vectors instead, which turns the matrix into a list
# of vectors and allows for better handling of non-integer/float type inputs
# (e.g. for string or datetime data types).
_data = (data,)
if data.dtype.kind not in "iuf":
_virtualfile_from = self.virtualfile_from_vectors
_data = data.T
# Ensure the data is an iterable (Python list or tuple).
match kind:
case "arg" | "file" | "geojson" | "grid" | "image" | "stringio":
_data = (data,)
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_data = (data,) if not isinstance(data, pathlib.PurePath) else (str(data),) is changed to _data = (data,).

For reference, the code str(data) was initially added in #1382 to support passing pathlib.Path as filenames. It turns out it's no longer necessary because the build_arg_list function already handle with pathlib.Path objects correctly:

pygmt/pygmt/helpers/utils.py

Lines 516 to 520 in d41922c

if infile: # infile can be a single file or a list of files
if isinstance(infile, str | pathlib.PurePath):
gmt_args = [str(infile), *gmt_args]
else:
gmt_args = [str(_file) for _file in infile] + gmt_args
.

The support of pathlib.Path objects can even be tracked back to the old, deprecated build_arg_string function (xref: #1837).

We already have a lot of tests that takes a pathlib.Path object as input (e.g.,

pygmt/pygmt/tests/test_info.py

Line 20 in d41922c

def test_info():
). So the changes should be safe if all tests pass.

if kind == "image" and data.dtype != "uint8":
msg = (
f"Input image has dtype: {data.dtype} which is unsupported, "
"and may result in an incorrect output. Please recast image "
"to a uint8 dtype and/or scale to 0-255 range, e.g. "
"using a histogram equalization function like "
"skimage.exposure.equalize_hist."
)
warnings.warn(message=msg, category=RuntimeWarning, stacklevel=2)
case "empty": # data is None, so data must be given via x/y/z.
_data = [x, y]
if z is not None:
_data.append(z)
if extra_arrays:
_data.extend(extra_arrays)
case "vectors":
if hasattr(data, "items") and not hasattr(data, "to_frame"):
# pandas.DataFrame or xarray.Dataset types.
# pandas.Series will be handled below like a 1-D numpy.ndarray.
_data = [array for _, array in data.items()]
else:
# Python list, tuple, numpy.ndarray, and pandas.Series types
_data = np.atleast_2d(np.asanyarray(data).T)
case "matrix":
# GMT can only accept a 2-D matrix which are signed integer (i),
# unsigned integer (u) or floating point (f) types. For other data
# types, we need to use virtualfile_from_vectors instead, which turns
# the matrix into a list of vectors and allows for better handling of
# non-integer/float type inputs (e.g. for string or datetime data types)
_data = (data,)
if data.dtype.kind not in "iuf":
_virtualfile_from = self.virtualfile_from_vectors
_data = data.T

# Finally create the virtualfile from the data, to be passed into GMT
file_context = _virtualfile_from(*_data)
Expand Down
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