"""
This module is imported from the pandas package __init__.py file
in order to ensure that the core.config options registered here will
be available as soon as the user loads the package. if register_option
is invoked inside specific modules, they will not be registered until that
module is imported, which may or may not be a problem.

If you need to make sure options are available even before a certain
module is imported, register them here rather than in the module.

"""
import os
import warnings

import pandas._config.config as cf
from pandas._config.config import (
    is_bool,
    is_callable,
    is_instance_factory,
    is_int,
    is_nonnegative_int,
    is_one_of_factory,
    is_text,
)

# compute

use_bottleneck_doc = """
: bool
    Use the bottleneck library to accelerate if it is installed,
    the default is True
    Valid values: False,True
"""


def use_bottleneck_cb(key):
    from pandas.core import nanops

    nanops.set_use_bottleneck(cf.get_option(key))


use_numexpr_doc = """
: bool
    Use the numexpr library to accelerate computation if it is installed,
    the default is True
    Valid values: False,True
"""


def use_numexpr_cb(key):
    from pandas.core.computation import expressions

    expressions.set_use_numexpr(cf.get_option(key))


use_numba_doc = """
: bool
    Use the numba engine option for select operations if it is installed,
    the default is False
    Valid values: False,True
"""


def use_numba_cb(key):
    from pandas.core.util import numba_

    numba_.set_use_numba(cf.get_option(key))


with cf.config_prefix("compute"):
    cf.register_option(
        "use_bottleneck",
        True,
        use_bottleneck_doc,
        validator=is_bool,
        cb=use_bottleneck_cb,
    )
    cf.register_option(
        "use_numexpr", True, use_numexpr_doc, validator=is_bool, cb=use_numexpr_cb
    )
    cf.register_option(
        "use_numba", False, use_numba_doc, validator=is_bool, cb=use_numba_cb
    )
#
# options from the "display" namespace

pc_precision_doc = """
: int
    Floating point output precision in terms of number of places after the
    decimal, for regular formatting as well as scientific notation. Similar
    to ``precision`` in :meth:`numpy.set_printoptions`.
"""

pc_colspace_doc = """
: int
    Default space for DataFrame columns.
"""

pc_max_rows_doc = """
: int
    If max_rows is exceeded, switch to truncate view. Depending on
    `large_repr`, objects are either centrally truncated or printed as
    a summary view. 'None' value means unlimited.

    In case python/IPython is running in a terminal and `large_repr`
    equals 'truncate' this can be set to 0 and pandas will auto-detect
    the height of the terminal and print a truncated object which fits
    the screen height. The IPython notebook, IPython qtconsole, or
    IDLE do not run in a terminal and hence it is not possible to do
    correct auto-detection.
"""

pc_min_rows_doc = """
: int
    The numbers of rows to show in a truncated view (when `max_rows` is
    exceeded). Ignored when `max_rows` is set to None or 0. When set to
    None, follows the value of `max_rows`.
"""

pc_max_cols_doc = """
: int
    If max_cols is exceeded, switch to truncate view. Depending on
    `large_repr`, objects are either centrally truncated or printed as
    a summary view. 'None' value means unlimited.

    In case python/IPython is running in a terminal and `large_repr`
    equals 'truncate' this can be set to 0 and pandas will auto-detect
    the width of the terminal and print a truncated object which fits
    the screen width. The IPython notebook, IPython qtconsole, or IDLE
    do not run in a terminal and hence it is not possible to do
    correct auto-detection.
"""

pc_max_categories_doc = """
: int
    This sets the maximum number of categories pandas should output when
    printing out a `Categorical` or a Series of dtype "category".
"""

pc_max_info_cols_doc = """
: int
    max_info_columns is used in DataFrame.info method to decide if
    per column information will be printed.
"""

pc_nb_repr_h_doc = """
: boolean
    When True, IPython notebook will use html representation for
    pandas objects (if it is available).
"""

pc_pprint_nest_depth = """
: int
    Controls the number of nested levels to process when pretty-printing
"""

pc_multi_sparse_doc = """
: boolean
    "sparsify" MultiIndex display (don't display repeated
    elements in outer levels within groups)
"""

float_format_doc = """
: callable
    The callable should accept a floating point number and return
    a string with the desired format of the number. This is used
    in some places like SeriesFormatter.
    See formats.format.EngFormatter for an example.
"""

max_colwidth_doc = """
: int or None
    The maximum width in characters of a column in the repr of
    a pandas data structure. When the column overflows, a "..."
    placeholder is embedded in the output. A 'None' value means unlimited.
"""

colheader_justify_doc = """
: 'left'/'right'
    Controls the justification of column headers. used by DataFrameFormatter.
"""

pc_expand_repr_doc = """
: boolean
    Whether to print out the full DataFrame repr for wide DataFrames across
    multiple lines, `max_columns` is still respected, but the output will
    wrap-around across multiple "pages" if its width exceeds `display.width`.
"""

pc_show_dimensions_doc = """
: boolean or 'truncate'
    Whether to print out dimensions at the end of DataFrame repr.
    If 'truncate' is specified, only print out the dimensions if the
    frame is truncated (e.g. not display all rows and/or columns)
"""

pc_east_asian_width_doc = """
: boolean
    Whether to use the Unicode East Asian Width to calculate the display text
    width.
    Enabling this may affect to the performance (default: False)
"""

pc_ambiguous_as_wide_doc = """
: boolean
    Whether to handle Unicode characters belong to Ambiguous as Wide (width=2)
    (default: False)
"""

pc_latex_repr_doc = """
: boolean
    Whether to produce a latex DataFrame representation for jupyter
    environments that support it.
    (default: False)
"""

pc_table_schema_doc = """
: boolean
    Whether to publish a Table Schema representation for frontends
    that support it.
    (default: False)
"""

pc_html_border_doc = """
: int
    A ``border=value`` attribute is inserted in the ``<table>`` tag
    for the DataFrame HTML repr.
"""

pc_html_use_mathjax_doc = """\
: boolean
    When True, Jupyter notebook will process table contents using MathJax,
    rendering mathematical expressions enclosed by the dollar symbol.
    (default: True)
"""

pc_width_doc = """
: int
    Width of the display in characters. In case python/IPython is running in
    a terminal this can be set to None and pandas will correctly auto-detect
    the width.
    Note that the IPython notebook, IPython qtconsole, or IDLE do not run in a
    terminal and hence it is not possible to correctly detect the width.
"""

pc_chop_threshold_doc = """
: float or None
    if set to a float value, all float values smaller then the given threshold
    will be displayed as exactly 0 by repr and friends.
"""

pc_max_seq_items = """
: int or None
    When pretty-printing a long sequence, no more then `max_seq_items`
    will be printed. If items are omitted, they will be denoted by the
    addition of "..." to the resulting string.

    If set to None, the number of items to be printed is unlimited.
"""

pc_max_info_rows_doc = """
: int or None
    df.info() will usually show null-counts for each column.
    For large frames this can be quite slow. max_info_rows and max_info_cols
    limit this null check only to frames with smaller dimensions than
    specified.
"""

pc_large_repr_doc = """
: 'truncate'/'info'
    For DataFrames exceeding max_rows/max_cols, the repr (and HTML repr) can
    show a truncated table (the default from 0.13), or switch to the view from
    df.info() (the behaviour in earlier versions of pandas).
"""

pc_memory_usage_doc = """
: bool, string or None
    This specifies if the memory usage of a DataFrame should be displayed when
    df.info() is called. Valid values True,False,'deep'
"""

pc_latex_escape = """
: bool
    This specifies if the to_latex method of a Dataframe uses escapes special
    characters.
    Valid values: False,True
"""

pc_latex_longtable = """
:bool
    This specifies if the to_latex method of a Dataframe uses the longtable
    format.
    Valid values: False,True
"""

pc_latex_multicolumn = """
: bool
    This specifies if the to_latex method of a Dataframe uses multicolumns
    to pretty-print MultiIndex columns.
    Valid values: False,True
"""

pc_latex_multicolumn_format = """
: string
    This specifies the format for multicolumn headers.
    Can be surrounded with '|'.
    Valid values: 'l', 'c', 'r', 'p{<width>}'
"""

pc_latex_multirow = """
: bool
    This specifies if the to_latex method of a Dataframe uses multirows
    to pretty-print MultiIndex rows.
    Valid values: False,True
"""


def table_schema_cb(key):
    from pandas.io.formats.printing import enable_data_resource_formatter

    enable_data_resource_formatter(cf.get_option(key))


def is_terminal() -> bool:
    """
    Detect if Python is running in a terminal.

    Returns True if Python is running in a terminal or False if not.
    """
    try:
        # error: Name 'get_ipython' is not defined
        ip = get_ipython()  # type: ignore[name-defined]
    except NameError:  # assume standard Python interpreter in a terminal
        return True
    else:
        if hasattr(ip, "kernel"):  # IPython as a Jupyter kernel
            return False
        else:  # IPython in a terminal
            return True


with cf.config_prefix("display"):
    cf.register_option("precision", 6, pc_precision_doc, validator=is_nonnegative_int)
    cf.register_option(
        "float_format",
        None,
        float_format_doc,
        validator=is_one_of_factory([None, is_callable]),
    )
    cf.register_option("column_space", 12, validator=is_int)
    cf.register_option(
        "max_info_rows",
        1690785,
        pc_max_info_rows_doc,
        validator=is_instance_factory((int, type(None))),
    )
    cf.register_option("max_rows", 60, pc_max_rows_doc, validator=is_nonnegative_int)
    cf.register_option(
        "min_rows",
        10,
        pc_min_rows_doc,
        validator=is_instance_factory([type(None), int]),
    )
    cf.register_option("max_categories", 8, pc_max_categories_doc, validator=is_int)

    def _deprecate_negative_int_max_colwidth(key):
        value = cf.get_option(key)
        if value is not None and value < 0:
            warnings.warn(
                "Passing a negative integer is deprecated in version 1.0 and "
                "will not be supported in future version. Instead, use None "
                "to not limit the column width.",
                FutureWarning,
                stacklevel=4,
            )

    cf.register_option(
        # TODO(2.0): change `validator=is_nonnegative_int` see GH#31569
        "max_colwidth",
        50,
        max_colwidth_doc,
        validator=is_instance_factory([type(None), int]),
        cb=_deprecate_negative_int_max_colwidth,
    )
    if is_terminal():
        max_cols = 0  # automatically determine optimal number of columns
    else:
        max_cols = 20  # cannot determine optimal number of columns
    cf.register_option(
        "max_columns", max_cols, pc_max_cols_doc, validator=is_nonnegative_int
    )
    cf.register_option(
        "large_repr",
        "truncate",
        pc_large_repr_doc,
        validator=is_one_of_factory(["truncate", "info"]),
    )
    cf.register_option("max_info_columns", 100, pc_max_info_cols_doc, validator=is_int)
    cf.register_option(
        "colheader_justify", "right", colheader_justify_doc, validator=is_text
    )
    cf.register_option("notebook_repr_html", True, pc_nb_repr_h_doc, validator=is_bool)
    cf.register_option("pprint_nest_depth", 3, pc_pprint_nest_depth, validator=is_int)
    cf.register_option("multi_sparse", True, pc_multi_sparse_doc, validator=is_bool)
    cf.register_option("expand_frame_repr", True, pc_expand_repr_doc)
    cf.register_option(
        "show_dimensions",
        "truncate",
        pc_show_dimensions_doc,
        validator=is_one_of_factory([True, False, "truncate"]),
    )
    cf.register_option("chop_threshold", None, pc_chop_threshold_doc)
    cf.register_option("max_seq_items", 100, pc_max_seq_items)
    cf.register_option(
        "width", 80, pc_width_doc, validator=is_instance_factory([type(None), int])
    )
    cf.register_option(
        "memory_usage",
        True,
        pc_memory_usage_doc,
        validator=is_one_of_factory([None, True, False, "deep"]),
    )
    cf.register_option(
        "unicode.east_asian_width", False, pc_east_asian_width_doc, validator=is_bool
    )
    cf.register_option(
        "unicode.ambiguous_as_wide", False, pc_east_asian_width_doc, validator=is_bool
    )
    cf.register_option("latex.repr", False, pc_latex_repr_doc, validator=is_bool)
    cf.register_option("latex.escape", True, pc_latex_escape, validator=is_bool)
    cf.register_option("latex.longtable", False, pc_latex_longtable, validator=is_bool)
    cf.register_option(
        "latex.multicolumn", True, pc_latex_multicolumn, validator=is_bool
    )
    cf.register_option(
        "latex.multicolumn_format", "l", pc_latex_multicolumn, validator=is_text
    )
    cf.register_option("latex.multirow", False, pc_latex_multirow, validator=is_bool)
    cf.register_option(
        "html.table_schema",
        False,
        pc_table_schema_doc,
        validator=is_bool,
        cb=table_schema_cb,
    )
    cf.register_option("html.border", 1, pc_html_border_doc, validator=is_int)
    cf.register_option(
        "html.use_mathjax", True, pc_html_use_mathjax_doc, validator=is_bool
    )

tc_sim_interactive_doc = """
: boolean
    Whether to simulate interactive mode for purposes of testing
"""

with cf.config_prefix("mode"):
    cf.register_option("sim_interactive", False, tc_sim_interactive_doc)

use_inf_as_null_doc = """
: boolean
    use_inf_as_null had been deprecated and will be removed in a future
    version. Use `use_inf_as_na` instead.
"""

use_inf_as_na_doc = """
: boolean
    True means treat None, NaN, INF, -INF as NA (old way),
    False means None and NaN are null, but INF, -INF are not NA
    (new way).
"""

# We don't want to start importing everything at the global context level
# or we'll hit circular deps.


def use_inf_as_na_cb(key):
    from pandas.core.dtypes.missing import _use_inf_as_na

    _use_inf_as_na(key)


with cf.config_prefix("mode"):
    cf.register_option("use_inf_as_na", False, use_inf_as_na_doc, cb=use_inf_as_na_cb)
    cf.register_option(
        "use_inf_as_null", False, use_inf_as_null_doc, cb=use_inf_as_na_cb
    )


cf.deprecate_option(
    "mode.use_inf_as_null", msg=use_inf_as_null_doc, rkey="mode.use_inf_as_na"
)


data_manager_doc = """
: string
    Internal data manager type; can be "block" or "array". Defaults to "block",
    unless overridden by the 'PANDAS_DATA_MANAGER' environment variable (needs
    to be set before pandas is imported).
"""


with cf.config_prefix("mode"):
    cf.register_option(
        "data_manager",
        # Get the default from an environment variable, if set, otherwise defaults
        # to "block". This environment variable can be set for testing.
        os.environ.get("PANDAS_DATA_MANAGER", "block"),
        data_manager_doc,
        validator=is_one_of_factory(["block", "array"]),
    )


# user warnings
chained_assignment = """
: string
    Raise an exception, warn, or no action if trying to use chained assignment,
    The default is warn
"""

with cf.config_prefix("mode"):
    cf.register_option(
        "chained_assignment",
        "warn",
        chained_assignment,
        validator=is_one_of_factory([None, "warn", "raise"]),
    )


string_storage_doc = """
: string
    The default storage for StringDtype.
"""

with cf.config_prefix("mode"):
    cf.register_option(
        "string_storage",
        "python",
        string_storage_doc,
        validator=is_one_of_factory(["python", "pyarrow"]),
    )

# Set up the io.excel specific reader configuration.
reader_engine_doc = """
: string
    The default Excel reader engine for '{ext}' files. Available options:
    auto, {others}.
"""

_xls_options = ["xlrd"]
_xlsm_options = ["xlrd", "openpyxl"]
_xlsx_options = ["xlrd", "openpyxl"]
_ods_options = ["odf"]
_xlsb_options = ["pyxlsb"]


with cf.config_prefix("io.excel.xls"):
    cf.register_option(
        "reader",
        "auto",
        reader_engine_doc.format(ext="xls", others=", ".join(_xls_options)),
        validator=is_one_of_factory(_xls_options + ["auto"]),
    )

with cf.config_prefix("io.excel.xlsm"):
    cf.register_option(
        "reader",
        "auto",
        reader_engine_doc.format(ext="xlsm", others=", ".join(_xlsm_options)),
        validator=is_one_of_factory(_xlsm_options + ["auto"]),
    )


with cf.config_prefix("io.excel.xlsx"):
    cf.register_option(
        "reader",
        "auto",
        reader_engine_doc.format(ext="xlsx", others=", ".join(_xlsx_options)),
        validator=is_one_of_factory(_xlsx_options + ["auto"]),
    )


with cf.config_prefix("io.excel.ods"):
    cf.register_option(
        "reader",
        "auto",
        reader_engine_doc.format(ext="ods", others=", ".join(_ods_options)),
        validator=is_one_of_factory(_ods_options + ["auto"]),
    )

with cf.config_prefix("io.excel.xlsb"):
    cf.register_option(
        "reader",
        "auto",
        reader_engine_doc.format(ext="xlsb", others=", ".join(_xlsb_options)),
        validator=is_one_of_factory(_xlsb_options + ["auto"]),
    )

# Set up the io.excel specific writer configuration.
writer_engine_doc = """
: string
    The default Excel writer engine for '{ext}' files. Available options:
    auto, {others}.
"""

_xls_options = ["xlwt"]
_xlsm_options = ["openpyxl"]
_xlsx_options = ["openpyxl", "xlsxwriter"]
_ods_options = ["odf"]


with cf.config_prefix("io.excel.xls"):
    cf.register_option(
        "writer",
        "auto",
        writer_engine_doc.format(ext="xls", others=", ".join(_xls_options)),
        validator=str,
    )
cf.deprecate_option(
    "io.excel.xls.writer",
    msg="As the xlwt package is no longer maintained, the xlwt engine will be "
    "removed in a future version of pandas. This is the only engine in pandas that "
    "supports writing in the xls format. Install openpyxl and write to an "
    "xlsx file instead.",
)

with cf.config_prefix("io.excel.xlsm"):
    cf.register_option(
        "writer",
        "auto",
        writer_engine_doc.format(ext="xlsm", others=", ".join(_xlsm_options)),
        validator=str,
    )


with cf.config_prefix("io.excel.xlsx"):
    cf.register_option(
        "writer",
        "auto",
        writer_engine_doc.format(ext="xlsx", others=", ".join(_xlsx_options)),
        validator=str,
    )


with cf.config_prefix("io.excel.ods"):
    cf.register_option(
        "writer",
        "auto",
        writer_engine_doc.format(ext="ods", others=", ".join(_ods_options)),
        validator=str,
    )


# Set up the io.parquet specific configuration.
parquet_engine_doc = """
: string
    The default parquet reader/writer engine. Available options:
    'auto', 'pyarrow', 'fastparquet', the default is 'auto'
"""

with cf.config_prefix("io.parquet"):
    cf.register_option(
        "engine",
        "auto",
        parquet_engine_doc,
        validator=is_one_of_factory(["auto", "pyarrow", "fastparquet"]),
    )


# Set up the io.sql specific configuration.
sql_engine_doc = """
: string
    The default sql reader/writer engine. Available options:
    'auto', 'sqlalchemy', the default is 'auto'
"""

with cf.config_prefix("io.sql"):
    cf.register_option(
        "engine",
        "auto",
        sql_engine_doc,
        validator=is_one_of_factory(["auto", "sqlalchemy"]),
    )

# --------
# Plotting
# ---------

plotting_backend_doc = """
: str
    The plotting backend to use. The default value is "matplotlib", the
    backend provided with pandas. Other backends can be specified by
    providing the name of the module that implements the backend.
"""


def register_plotting_backend_cb(key):
    if key == "matplotlib":
        # We defer matplotlib validation, since it's the default
        return
    from pandas.plotting._core import _get_plot_backend

    _get_plot_backend(key)


with cf.config_prefix("plotting"):
    cf.register_option(
        "backend",
        defval="matplotlib",
        doc=plotting_backend_doc,
        validator=register_plotting_backend_cb,
    )


register_converter_doc = """
: bool or 'auto'.
    Whether to register converters with matplotlib's units registry for
    dates, times, datetimes, and Periods. Toggling to False will remove
    the converters, restoring any converters that pandas overwrote.
"""


def register_converter_cb(key):
    from pandas.plotting import (
        deregister_matplotlib_converters,
        register_matplotlib_converters,
    )

    if cf.get_option(key):
        register_matplotlib_converters()
    else:
        deregister_matplotlib_converters()


with cf.config_prefix("plotting.matplotlib"):
    cf.register_option(
        "register_converters",
        "auto",
        register_converter_doc,
        validator=is_one_of_factory(["auto", True, False]),
        cb=register_converter_cb,
    )

# ------
# Styler
# ------

styler_sparse_index_doc = """
: bool
    Whether to sparsify the display of a hierarchical index. Setting to False will
    display each explicit level element in a hierarchical key for each row.
"""

styler_sparse_columns_doc = """
: bool
    Whether to sparsify the display of hierarchical columns. Setting to False will
    display each explicit level element in a hierarchical key for each column.
"""

styler_max_elements = """
: int
    The maximum number of data-cell (<td>) elements that will be rendered before
    trimming will occur over columns, rows or both if needed.
"""

with cf.config_prefix("styler"):
    cf.register_option("sparse.index", True, styler_sparse_index_doc, validator=bool)

    cf.register_option(
        "sparse.columns", True, styler_sparse_columns_doc, validator=bool
    )

    cf.register_option(
        "render.max_elements",
        2 ** 18,
        styler_max_elements,
        validator=is_nonnegative_int,
    )
