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Fix the _validate_data_input function after refactoring data_kind #3541

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Merged
merged 2 commits into from
Oct 23, 2024
Merged

Fix the _validate_data_input function after refactoring data_kind #3541

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8f21815
Fix the _validate_data_input function after refacting data_kind
seisman Oct 16, 2024
dfaad35
Merge branch 'main' into fix/validate-data-input
seisman Oct 22, 2024
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38 changes: 23 additions & 15 deletions pygmt/helpers/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -58,15 +58,15 @@ def _validate_data_input(
>>> _validate_data_input(
... data=pd.DataFrame(data, columns=["x", "y"]),
... required_z=True,
... kind="matrix",
... kind="vectors",
... )
Traceback (most recent call last):
...
pygmt.exceptions.GMTInvalidInput: data must provide x, y, and z columns.
>>> _validate_data_input(
... data=xr.Dataset(pd.DataFrame(data, columns=["x", "y"])),
... required_z=True,
... kind="matrix",
... kind="vectors",
... )
Traceback (most recent call last):
...
Expand Down Expand Up @@ -96,23 +96,31 @@ def _validate_data_input(
if data is None: # data is None
if x is None and y is None: # both x and y are None
if required_data: # data is not optional
raise GMTInvalidInput("No input data provided.")
msg = "No input data provided."
raise GMTInvalidInput(msg)
elif x is None or y is None: # either x or y is None
raise GMTInvalidInput("Must provide both x and y.")
msg = "Must provide both x and y."
raise GMTInvalidInput(msg)
if required_z and z is None: # both x and y are not None, now check z
raise GMTInvalidInput("Must provide x, y, and z.")
msg = "Must provide x, y, and z."
raise GMTInvalidInput(msg)
else: # data is not None
if x is not None or y is not None or z is not None:
raise GMTInvalidInput("Too much data. Use either data or x/y/z.")
# For 'matrix' kind, check if data has the required z column
if kind == "matrix" and required_z:
if hasattr(data, "shape"): # np.ndarray or pd.DataFrame
if len(data.shape) == 1 and data.shape[0] < 3:
raise GMTInvalidInput("data must provide x, y, and z columns.")
if len(data.shape) > 1 and data.shape[1] < 3:
raise GMTInvalidInput("data must provide x, y, and z columns.")
if hasattr(data, "data_vars") and len(data.data_vars) < 3: # xr.Dataset
raise GMTInvalidInput("data must provide x, y, and z columns.")
msg = "Too much data. Use either data or x/y/z."
raise GMTInvalidInput(msg)
# check if data has the required z column
if required_z:
msg = "data must provide x, y, and z columns."
if kind == "matrix" and data.shape[1] < 3:
raise GMTInvalidInput(msg)
if kind == "vectors":
if hasattr(data, "shape") and (
(len(data.shape) == 1 and data.shape[0] < 3)
or (len(data.shape) > 1 and data.shape[1] < 3)
): # np.ndarray or pd.DataFrame
raise GMTInvalidInput(msg)
if hasattr(data, "data_vars") and len(data.data_vars) < 3: # xr.Dataset
raise GMTInvalidInput(msg)


def _check_encoding(
Expand Down
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