#!/usr/bin/env python3

"""
Parts of this file were taken from the pyzmq project
(https://github.com/zeromq/pyzmq) which have been permitted for use under the
BSD license. Parts are from lxml (https://github.com/lxml/lxml)
"""

import argparse
from distutils.sysconfig import get_config_vars
from distutils.version import LooseVersion
import multiprocessing
import os
from os.path import join as pjoin
import platform
import shutil
import sys

import pkg_resources
from setuptools import Command, find_packages, setup

# versioning
import versioneer

cmdclass = versioneer.get_cmdclass()


def is_platform_windows():
    return sys.platform == "win32" or sys.platform == "cygwin"


def is_platform_mac():
    return sys.platform == "darwin"


min_numpy_ver = "1.15.4"
min_cython_ver = "0.29.16"  # note: sync with pyproject.toml

try:
    import Cython

    _CYTHON_VERSION = Cython.__version__
    from Cython.Build import cythonize

    _CYTHON_INSTALLED = _CYTHON_VERSION >= LooseVersion(min_cython_ver)
except ImportError:
    _CYTHON_VERSION = None
    _CYTHON_INSTALLED = False
    cythonize = lambda x, *args, **kwargs: x  # dummy func

# The import of Extension must be after the import of Cython, otherwise
# we do not get the appropriately patched class.
# See https://cython.readthedocs.io/en/latest/src/userguide/source_files_and_compilation.html # noqa
from distutils.extension import Extension  # noqa: E402 isort:skip
from distutils.command.build import build  # noqa: E402 isort:skip

if _CYTHON_INSTALLED:
    from Cython.Distutils.old_build_ext import old_build_ext as _build_ext

    cython = True
    from Cython import Tempita as tempita
else:
    from distutils.command.build_ext import build_ext as _build_ext

    cython = False


_pxi_dep_template = {
    "algos": ["_libs/algos_common_helper.pxi.in", "_libs/algos_take_helper.pxi.in"],
    "hashtable": [
        "_libs/hashtable_class_helper.pxi.in",
        "_libs/hashtable_func_helper.pxi.in",
    ],
    "index": ["_libs/index_class_helper.pxi.in"],
    "sparse": ["_libs/sparse_op_helper.pxi.in"],
    "interval": ["_libs/intervaltree.pxi.in"],
}

_pxifiles = []
_pxi_dep = {}
for module, files in _pxi_dep_template.items():
    pxi_files = [pjoin("pandas", x) for x in files]
    _pxifiles.extend(pxi_files)
    _pxi_dep[module] = pxi_files


class build_ext(_build_ext):
    @classmethod
    def render_templates(cls, pxifiles):
        for pxifile in pxifiles:
            # build pxifiles first, template extension must be .pxi.in
            assert pxifile.endswith(".pxi.in")
            outfile = pxifile[:-3]

            if (
                os.path.exists(outfile)
                and os.stat(pxifile).st_mtime < os.stat(outfile).st_mtime
            ):
                # if .pxi.in is not updated, no need to output .pxi
                continue

            with open(pxifile, "r") as f:
                tmpl = f.read()
            pyxcontent = tempita.sub(tmpl)

            with open(outfile, "w") as f:
                f.write(pyxcontent)

    def build_extensions(self):
        # if building from c files, don't need to
        # generate template output
        if cython:
            self.render_templates(_pxifiles)

        super().build_extensions()


DESCRIPTION = "Powerful data structures for data analysis, time series, and statistics"
LONG_DESCRIPTION = """
**pandas** is a Python package providing fast, flexible, and expressive data
structures designed to make working with structured (tabular, multidimensional,
potentially heterogeneous) and time series data both easy and intuitive. It
aims to be the fundamental high-level building block for doing practical,
**real world** data analysis in Python. Additionally, it has the broader goal
of becoming **the most powerful and flexible open source data analysis /
manipulation tool available in any language**. It is already well on its way
toward this goal.

pandas is well suited for many different kinds of data:

  - Tabular data with heterogeneously-typed columns, as in an SQL table or
    Excel spreadsheet
  - Ordered and unordered (not necessarily fixed-frequency) time series data.
  - Arbitrary matrix data (homogeneously typed or heterogeneous) with row and
    column labels
  - Any other form of observational / statistical data sets. The data actually
    need not be labeled at all to be placed into a pandas data structure

The two primary data structures of pandas, Series (1-dimensional) and DataFrame
(2-dimensional), handle the vast majority of typical use cases in finance,
statistics, social science, and many areas of engineering. For R users,
DataFrame provides everything that R's ``data.frame`` provides and much
more. pandas is built on top of `NumPy <https://www.numpy.org>`__ and is
intended to integrate well within a scientific computing environment with many
other 3rd party libraries.

Here are just a few of the things that pandas does well:

  - Easy handling of **missing data** (represented as NaN) in floating point as
    well as non-floating point data
  - Size mutability: columns can be **inserted and deleted** from DataFrame and
    higher dimensional objects
  - Automatic and explicit **data alignment**: objects can be explicitly
    aligned to a set of labels, or the user can simply ignore the labels and
    let `Series`, `DataFrame`, etc. automatically align the data for you in
    computations
  - Powerful, flexible **group by** functionality to perform
    split-apply-combine operations on data sets, for both aggregating and
    transforming data
  - Make it **easy to convert** ragged, differently-indexed data in other
    Python and NumPy data structures into DataFrame objects
  - Intelligent label-based **slicing**, **fancy indexing**, and **subsetting**
    of large data sets
  - Intuitive **merging** and **joining** data sets
  - Flexible **reshaping** and pivoting of data sets
  - **Hierarchical** labeling of axes (possible to have multiple labels per
    tick)
  - Robust IO tools for loading data from **flat files** (CSV and delimited),
    Excel files, databases, and saving / loading data from the ultrafast **HDF5
    format**
  - **Time series**-specific functionality: date range generation and frequency
    conversion, moving window statistics, date shifting and lagging.

Many of these principles are here to address the shortcomings frequently
experienced using other languages / scientific research environments. For data
scientists, working with data is typically divided into multiple stages:
munging and cleaning data, analyzing / modeling it, then organizing the results
of the analysis into a form suitable for plotting or tabular display. pandas is
the ideal tool for all of these tasks.
"""

DISTNAME = "pandas"
LICENSE = "BSD"
AUTHOR = "The PyData Development Team"
EMAIL = "pydata@googlegroups.com"
URL = "https://pandas.pydata.org"
DOWNLOAD_URL = ""
PROJECT_URLS = {
    "Bug Tracker": "https://github.com/pandas-dev/pandas/issues",
    "Documentation": "https://pandas.pydata.org/pandas-docs/stable/",
    "Source Code": "https://github.com/pandas-dev/pandas",
}
CLASSIFIERS = [
    "Development Status :: 5 - Production/Stable",
    "Environment :: Console",
    "Operating System :: OS Independent",
    "Intended Audience :: Science/Research",
    "Programming Language :: Python",
    "Programming Language :: Python :: 3",
    "Programming Language :: Python :: 3.6",
    "Programming Language :: Python :: 3.7",
    "Programming Language :: Python :: 3.8",
    "Programming Language :: Cython",
    "Topic :: Scientific/Engineering",
]


class CleanCommand(Command):
    """Custom distutils command to clean the .so and .pyc files."""

    user_options = [("all", "a", "")]

    def initialize_options(self):
        self.all = True
        self._clean_me = []
        self._clean_trees = []

        base = pjoin("pandas", "_libs", "src")
        tsbase = pjoin("pandas", "_libs", "tslibs", "src")
        dt = pjoin(tsbase, "datetime")
        util = pjoin("pandas", "util")
        parser = pjoin(base, "parser")
        ujson_python = pjoin(base, "ujson", "python")
        ujson_lib = pjoin(base, "ujson", "lib")
        self._clean_exclude = [
            pjoin(dt, "np_datetime.c"),
            pjoin(dt, "np_datetime_strings.c"),
            pjoin(parser, "tokenizer.c"),
            pjoin(parser, "io.c"),
            pjoin(ujson_python, "ujson.c"),
            pjoin(ujson_python, "objToJSON.c"),
            pjoin(ujson_python, "JSONtoObj.c"),
            pjoin(ujson_python, "date_conversions.c"),
            pjoin(ujson_lib, "ultrajsonenc.c"),
            pjoin(ujson_lib, "ultrajsondec.c"),
            pjoin(util, "move.c"),
        ]

        for root, dirs, files in os.walk("pandas"):
            for f in files:
                filepath = pjoin(root, f)
                if filepath in self._clean_exclude:
                    continue

                if os.path.splitext(f)[-1] in (
                    ".pyc",
                    ".so",
                    ".o",
                    ".pyo",
                    ".pyd",
                    ".c",
                    ".cpp",
                    ".orig",
                ):
                    self._clean_me.append(filepath)
            for d in dirs:
                if d == "__pycache__":
                    self._clean_trees.append(pjoin(root, d))

        # clean the generated pxi files
        for pxifile in _pxifiles:
            pxifile = pxifile.replace(".pxi.in", ".pxi")
            self._clean_me.append(pxifile)

        for d in ("build", "dist"):
            if os.path.exists(d):
                self._clean_trees.append(d)

    def finalize_options(self):
        pass

    def run(self):
        for clean_me in self._clean_me:
            try:
                os.unlink(clean_me)
            except OSError:
                pass
        for clean_tree in self._clean_trees:
            try:
                shutil.rmtree(clean_tree)
            except OSError:
                pass


# we need to inherit from the versioneer
# class as it encodes the version info
sdist_class = cmdclass["sdist"]


class CheckSDist(sdist_class):
    """Custom sdist that ensures Cython has compiled all pyx files to c."""

    _pyxfiles = [
        "pandas/_libs/lib.pyx",
        "pandas/_libs/hashtable.pyx",
        "pandas/_libs/tslib.pyx",
        "pandas/_libs/index.pyx",
        "pandas/_libs/internals.pyx",
        "pandas/_libs/algos.pyx",
        "pandas/_libs/join.pyx",
        "pandas/_libs/indexing.pyx",
        "pandas/_libs/interval.pyx",
        "pandas/_libs/hashing.pyx",
        "pandas/_libs/missing.pyx",
        "pandas/_libs/reduction.pyx",
        "pandas/_libs/testing.pyx",
        "pandas/_libs/sparse.pyx",
        "pandas/_libs/ops.pyx",
        "pandas/_libs/parsers.pyx",
        "pandas/_libs/tslibs/base.pyx",
        "pandas/_libs/tslibs/ccalendar.pyx",
        "pandas/_libs/tslibs/dtypes.pyx",
        "pandas/_libs/tslibs/period.pyx",
        "pandas/_libs/tslibs/strptime.pyx",
        "pandas/_libs/tslibs/np_datetime.pyx",
        "pandas/_libs/tslibs/timedeltas.pyx",
        "pandas/_libs/tslibs/timestamps.pyx",
        "pandas/_libs/tslibs/timezones.pyx",
        "pandas/_libs/tslibs/conversion.pyx",
        "pandas/_libs/tslibs/fields.pyx",
        "pandas/_libs/tslibs/offsets.pyx",
        "pandas/_libs/tslibs/frequencies.pyx",
        "pandas/_libs/tslibs/resolution.pyx",
        "pandas/_libs/tslibs/parsing.pyx",
        "pandas/_libs/tslibs/tzconversion.pyx",
        "pandas/_libs/window/indexers.pyx",
        "pandas/_libs/writers.pyx",
        "pandas/io/sas/sas.pyx",
    ]

    _cpp_pyxfiles = [
        "pandas/_libs/window/aggregations.pyx",
    ]

    def initialize_options(self):
        sdist_class.initialize_options(self)

    def run(self):
        if "cython" in cmdclass:
            self.run_command("cython")
        else:
            # If we are not running cython then
            # compile the extensions correctly
            pyx_files = [(self._pyxfiles, "c"), (self._cpp_pyxfiles, "cpp")]

            for pyxfiles, extension in pyx_files:
                for pyxfile in pyxfiles:
                    sourcefile = pyxfile[:-3] + extension
                    msg = (
                        f"{extension}-source file '{sourcefile}' not found.\n"
                        "Run 'setup.py cython' before sdist."
                    )
                    assert os.path.isfile(sourcefile), msg
        sdist_class.run(self)


class CheckingBuildExt(build_ext):
    """
    Subclass build_ext to get clearer report if Cython is necessary.
    """

    def check_cython_extensions(self, extensions):
        for ext in extensions:
            for src in ext.sources:
                if not os.path.exists(src):
                    print(f"{ext.name}: -> [{ext.sources}]")
                    raise Exception(
                        f"""Cython-generated file '{src}' not found.
                Cython is required to compile pandas from a development branch.
                Please install Cython or download a release package of pandas.
                """
                    )

    def build_extensions(self):
        self.check_cython_extensions(self.extensions)
        build_ext.build_extensions(self)


class CythonCommand(build_ext):
    """
    Custom distutils command subclassed from Cython.Distutils.build_ext
    to compile pyx->c, and stop there. All this does is override the
    C-compile method build_extension() with a no-op.
    """

    def build_extension(self, ext):
        pass


class DummyBuildSrc(Command):
    """ numpy's build_src command interferes with Cython's build_ext.
    """

    user_options = []

    def initialize_options(self):
        self.py_modules_dict = {}

    def finalize_options(self):
        pass

    def run(self):
        pass


cmdclass.update({"clean": CleanCommand, "build": build})
cmdclass["build_ext"] = CheckingBuildExt

if cython:
    suffix = ".pyx"
    cmdclass["cython"] = CythonCommand
else:
    suffix = ".c"
    cmdclass["build_src"] = DummyBuildSrc

# ----------------------------------------------------------------------
# Preparation of compiler arguments

debugging_symbols_requested = "--with-debugging-symbols" in sys.argv
if debugging_symbols_requested:
    sys.argv.remove("--with-debugging-symbols")


if sys.byteorder == "big":
    endian_macro = [("__BIG_ENDIAN__", "1")]
else:
    endian_macro = [("__LITTLE_ENDIAN__", "1")]


if is_platform_windows():
    extra_compile_args = []
    extra_link_args = []
    if debugging_symbols_requested:
        extra_compile_args.append("/Z7")
        extra_link_args.append("/DEBUG")
else:
    extra_compile_args = ["-Werror"]
    extra_link_args = []
    if debugging_symbols_requested:
        extra_compile_args.append("-g")

# Build for at least macOS 10.9 when compiling on a 10.9 system or above,
# overriding CPython distuitls behaviour which is to target the version that
# python was built for. This may be overridden by setting
# MACOSX_DEPLOYMENT_TARGET before calling setup.py
if is_platform_mac():
    if "MACOSX_DEPLOYMENT_TARGET" not in os.environ:
        current_system = platform.mac_ver()[0]
        python_target = get_config_vars().get(
            "MACOSX_DEPLOYMENT_TARGET", current_system
        )
        if (
            LooseVersion(python_target) < "10.9"
            and LooseVersion(current_system) >= "10.9"
        ):
            os.environ["MACOSX_DEPLOYMENT_TARGET"] = "10.9"

    if sys.version_info[:2] == (3, 8):  # GH 33239
        extra_compile_args.append("-Wno-error=deprecated-declarations")

# enable coverage by building cython files by setting the environment variable
# "PANDAS_CYTHON_COVERAGE" (with a Truthy value) or by running build_ext
# with `--with-cython-coverage`enabled
linetrace = os.environ.get("PANDAS_CYTHON_COVERAGE", False)
if "--with-cython-coverage" in sys.argv:
    linetrace = True
    sys.argv.remove("--with-cython-coverage")

# Note: if not using `cythonize`, coverage can be enabled by
# pinning `ext.cython_directives = directives` to each ext in extensions.
# github.com/cython/cython/wiki/enhancements-compilerdirectives#in-setuppy
directives = {"linetrace": False, "language_level": 3}
macros = []
if linetrace:
    # https://pypkg.com/pypi/pytest-cython/f/tests/example-project/setup.py
    directives["linetrace"] = True
    macros = [("CYTHON_TRACE", "1"), ("CYTHON_TRACE_NOGIL", "1")]

# in numpy>=1.16.0, silence build warnings about deprecated API usage
#  we can't do anything about these warnings because they stem from
#  cython+numpy version mismatches.
macros.append(("NPY_NO_DEPRECATED_API", "0"))
if "-Werror" in extra_compile_args:
    try:
        import numpy as np
    except ImportError:
        pass
    else:
        if np.__version__ < LooseVersion("1.16.0"):
            extra_compile_args.remove("-Werror")


# ----------------------------------------------------------------------
# Specification of Dependencies

# TODO: Need to check to see if e.g. `linetrace` has changed and possibly
# re-compile.
def maybe_cythonize(extensions, *args, **kwargs):
    """
    Render tempita templates before calling cythonize. This is skipped for

    * clean
    * sdist
    """
    if "clean" in sys.argv or "sdist" in sys.argv:
        # See https://github.com/cython/cython/issues/1495
        return extensions

    elif not cython:
        # GH#28836 raise a helfpul error message
        if _CYTHON_VERSION:
            raise RuntimeError(
                f"Cannot cythonize with old Cython version ({_CYTHON_VERSION} "
                f"installed, needs {min_cython_ver})"
            )
        raise RuntimeError("Cannot cythonize without Cython installed.")

    numpy_incl = pkg_resources.resource_filename("numpy", "core/include")
    # TODO: Is this really necessary here?
    for ext in extensions:
        if hasattr(ext, "include_dirs") and numpy_incl not in ext.include_dirs:
            ext.include_dirs.append(numpy_incl)

    # reuse any parallel arguments provided for compilation to cythonize
    parser = argparse.ArgumentParser()
    parser.add_argument("-j", type=int)
    parser.add_argument("--parallel", type=int)
    parsed, _ = parser.parse_known_args()

    nthreads = 0
    if parsed.parallel:
        nthreads = parsed.parallel
    elif parsed.j:
        nthreads = parsed.j

    kwargs["nthreads"] = nthreads
    build_ext.render_templates(_pxifiles)
    return cythonize(extensions, *args, **kwargs)


def srcpath(name=None, suffix=".pyx", subdir="src"):
    return pjoin("pandas", subdir, name + suffix)


lib_depends = ["pandas/_libs/src/parse_helper.h"]

klib_include = ["pandas/_libs/src/klib"]

tseries_depends = [
    "pandas/_libs/tslibs/src/datetime/np_datetime.h",
    "pandas/_libs/tslibs/src/datetime/np_datetime_strings.h",
]

ext_data = {
    "_libs.algos": {
        "pyxfile": "_libs/algos",
        "include": klib_include,
        "depends": _pxi_dep["algos"],
    },
    "_libs.groupby": {"pyxfile": "_libs/groupby"},
    "_libs.hashing": {"pyxfile": "_libs/hashing", "depends": []},
    "_libs.hashtable": {
        "pyxfile": "_libs/hashtable",
        "include": klib_include,
        "depends": (["pandas/_libs/src/klib/khash_python.h"] + _pxi_dep["hashtable"]),
    },
    "_libs.index": {
        "pyxfile": "_libs/index",
        "include": klib_include,
        "depends": _pxi_dep["index"],
    },
    "_libs.indexing": {"pyxfile": "_libs/indexing"},
    "_libs.internals": {"pyxfile": "_libs/internals"},
    "_libs.interval": {
        "pyxfile": "_libs/interval",
        "include": klib_include,
        "depends": _pxi_dep["interval"],
    },
    "_libs.join": {"pyxfile": "_libs/join", "include": klib_include},
    "_libs.lib": {
        "pyxfile": "_libs/lib",
        "depends": lib_depends + tseries_depends,
        "include": klib_include,  # due to tokenizer import
        "sources": ["pandas/_libs/src/parser/tokenizer.c"],
    },
    "_libs.missing": {"pyxfile": "_libs/missing", "depends": tseries_depends},
    "_libs.parsers": {
        "pyxfile": "_libs/parsers",
        "include": klib_include + ["pandas/_libs/src"],
        "depends": [
            "pandas/_libs/src/parser/tokenizer.h",
            "pandas/_libs/src/parser/io.h",
        ],
        "sources": [
            "pandas/_libs/src/parser/tokenizer.c",
            "pandas/_libs/src/parser/io.c",
        ],
    },
    "_libs.reduction": {"pyxfile": "_libs/reduction"},
    "_libs.ops": {"pyxfile": "_libs/ops"},
    "_libs.ops_dispatch": {"pyxfile": "_libs/ops_dispatch"},
    "_libs.properties": {"pyxfile": "_libs/properties"},
    "_libs.reshape": {"pyxfile": "_libs/reshape", "depends": []},
    "_libs.sparse": {"pyxfile": "_libs/sparse", "depends": _pxi_dep["sparse"]},
    "_libs.tslib": {"pyxfile": "_libs/tslib", "depends": tseries_depends},
    "_libs.tslibs.base": {"pyxfile": "_libs/tslibs/base"},
    "_libs.tslibs.ccalendar": {"pyxfile": "_libs/tslibs/ccalendar"},
    "_libs.tslibs.dtypes": {"pyxfile": "_libs/tslibs/dtypes"},
    "_libs.tslibs.conversion": {
        "pyxfile": "_libs/tslibs/conversion",
        "depends": tseries_depends,
        "sources": ["pandas/_libs/tslibs/src/datetime/np_datetime.c"],
    },
    "_libs.tslibs.fields": {
        "pyxfile": "_libs/tslibs/fields",
        "depends": tseries_depends,
    },
    "_libs.tslibs.frequencies": {"pyxfile": "_libs/tslibs/frequencies"},
    "_libs.tslibs.nattype": {"pyxfile": "_libs/tslibs/nattype"},
    "_libs.tslibs.np_datetime": {
        "pyxfile": "_libs/tslibs/np_datetime",
        "depends": tseries_depends,
        "sources": [
            "pandas/_libs/tslibs/src/datetime/np_datetime.c",
            "pandas/_libs/tslibs/src/datetime/np_datetime_strings.c",
        ],
    },
    "_libs.tslibs.offsets": {
        "pyxfile": "_libs/tslibs/offsets",
        "depends": tseries_depends,
    },
    "_libs.tslibs.parsing": {
        "pyxfile": "_libs/tslibs/parsing",
        "include": klib_include,
        "depends": ["pandas/_libs/src/parser/tokenizer.h"],
        "sources": ["pandas/_libs/src/parser/tokenizer.c"],
    },
    "_libs.tslibs.period": {
        "pyxfile": "_libs/tslibs/period",
        "depends": tseries_depends,
        "sources": ["pandas/_libs/tslibs/src/datetime/np_datetime.c"],
    },