clib.conversion: Remove the array_to_datetime function, which is no longer needed

[seisman]

After a series of PRs (especially #3760), the _to_numpy function takes the responsibility to convert any array-like objects into numpy arrays with recognized dtypes. So, if _to_numpy returns a np.object dtype string, it means the original array cannot to converted to a numpy array that can be passed to GMT.

After #3760, the array_to_datetime function is no longer used and can be removed.

[seisman]

pd.api.types.is_string_dtype was added in PR #684. Before PR #684, we used np.issubdtype(array.dtype, np.str_), which was added in #520. Any specific reason that we should use np.issubdtype(array.dtype, np.str_), not array.dtype.type == np.str_?

[weiji14]

I think we'll need to check if this can handle pandas.StringDtype and pyarrow.StringArray (xref #2933).

[seisman]

Both can be converted to the numpy string dtype by the vectors_to_arrays method, so in virtualfile_from_vectors, we only need to check np.str_:

In [1]: import numpy as np

In [2]: import pandas as pd

In [3]: import pyarrow as pa

In [4]: x = pd.Series(["abc", "defghi"], dtype="string")

In [5]: np.asarray(x)
Out[5]: array(['abc', 'defghi'], dtype=object)

In [6]: np.asarray(x, dtype=str)
Out[6]: array(['abc', 'defghi'], dtype='<U6')

In [7]: y = pa.array(["abc", "defghi"])

In [8]: np.asarray(y)
Out[8]: array(['abc', 'defghi'], dtype=object)

In [9]: np.asarray(y, dtype=str)
Out[9]: array(['abc', 'defghi'], dtype='<U6')

[seisman]

The main idea of this PR is to let vectors_to_arrays do the conversion work from any dtypes (including pd.StringDtype and pa.StringArray) into the numpy dtypes, so that the virtualfile_from_vectors only need to care about how to map numpy dtypes into GMT data types.

For any special dtypes that we know how to convert it to numpy dtype, we can maintain a mapping dictionary, just like what you did to support pyarrow's date32[day] and date64[ms] in #2845:

pygmt/pygmt/clib/conversion.py

Lines 208 to 211 in c2e429c

	dtypes = {
	"date32[day][pyarrow]": np.datetime64,
	"date64[ms][pyarrow]": np.datetime64,
	}

[seisman]

In 83673cf, I've moved most of the doctests into a separate test file pygmt/tests/test_clib_vectors_to_arrays.py. A test test_vectors_to_arrays_pandas_string is added to check that the function can handle pd.StringDtype correctly.

[seisman]

For any special dtypes that we know how to convert it to numpy dtype, we can maintain a mapping dictionary, just like what you did to support pyarrow's date32[day] and date64[ms] in #2845:

pygmt/pygmt/clib/conversion.py

Lines 208 to 211 in c2e429c

	dtypes = {
	"date32[day][pyarrow]": np.datetime64,
	"date64[ms][pyarrow]": np.datetime64,
	}

Based on the tests below, I think we should add the entry "string": np.str_ to the dictionary:

In [1]: import pandas as pd

In [2]: x = pd.Series(["abc", "12345"])

In [3]: x.dtype
Out[3]: dtype('O')

In [4]: str(x.dtype)
Out[4]: 'object'

In [5]: x = pd.Series(["abc", "12345"], dtype="string")

In [6]: x.dtype
Out[6]: string[python]

In [7]: str(x.dtype)
Out[7]: 'string'

In [8]: x = pd.Series(["abc", "12345"], dtype="string[pyarrow]")

In [9]: x.dtype
Out[9]: string[pyarrow]

In [10]: str(x.dtype)
Out[10]: 'string'

In [11]: import pyarrow as pa

In [12]: x = pa.array(["abc", "defghi"])

In [13]: x.type
Out[13]: DataType(string)

In [14]: str(x.type)
Out[14]: 'string'

[seisman]

In PR #2774 and #2774, we only checked if PyGMT supports pandas with the pyarrow backend, but didn't check if the original pyarrow arrays works. For example, for a pyarrow date32 array, we need to check array.type rather than array.dtype:

In [1]: import datetime

In [2]: import pyarrow as pa

In [3]: x = pa.array([datetime.date(2020, 1, 1), datetime.date(2021, 12, 31)])

In [4]: str(x.type)
Out[4]: 'date32[day]'

[weiji14]

For example, for a pyarrow date32 array, we need to check array.type

Yes, raw pyarrow arrays of date32/date64 type are not supported yet. That's why it's marked 🚧 in #2800 (I was planning to modify array_to_datetime to handle it).

[seisman]

Will revisit changes in this PR after #3583, #3584, #3585.

[seisman]

Moved to the _to_numpy function in PR #3760.

[seisman]

These doctests are already covered by the tests in test_clib_to_numpy.py