Vehicle-cpp/hkpc/software/pandas/tests/io/generate_legacy_storage_files.py

"""
self-contained to write legacy storage pickle files

To use this script. Create an environment where you want
generate pickles, say its for 0.20.3, with your pandas clone
in ~/pandas

. activate pandas_0.20.3
cd ~/pandas/pandas

$ python -m tests.io.generate_legacy_storage_files \
    tests/io/data/legacy_pickle/0.20.3/ pickle

This script generates a storage file for the current arch, system,
and python version
  pandas version: 0.20.3
  output dir    : pandas/pandas/tests/io/data/legacy_pickle/0.20.3/
  storage format: pickle
created pickle file: 0.20.3_x86_64_darwin_3.5.2.pickle

The idea here is you are using the *current* version of the
generate_legacy_storage_files with an *older* version of pandas to
generate a pickle file. We will then check this file into a current
branch, and test using test_pickle.py. This will load the *older*
pickles and test versus the current data that is generated
(with main). These are then compared.

If we have cases where we changed the signature (e.g. we renamed
offset -> freq in Timestamp). Then we have to conditionally execute
in the generate_legacy_storage_files.py to make it
run under the older AND the newer version.

"""

from datetime import timedelta
import os
import pickle
import platform as pl
import sys

import numpy as np

import pandas
from pandas import (
    Categorical,
    DataFrame,
    Index,
    MultiIndex,
    NaT,
    Period,
    RangeIndex,
    Series,
    Timestamp,
    bdate_range,
    date_range,
    interval_range,
    period_range,
    timedelta_range,
)
from pandas.arrays import SparseArray

from pandas.tseries.offsets import (
    FY5253,
    BusinessDay,
    BusinessHour,
    CustomBusinessDay,
    DateOffset,
    Day,
    Easter,
    Hour,
    LastWeekOfMonth,
    Minute,
    MonthBegin,
    MonthEnd,
    QuarterBegin,
    QuarterEnd,
    SemiMonthBegin,
    SemiMonthEnd,
    Week,
    WeekOfMonth,
    YearBegin,
    YearEnd,
)


def _create_sp_series():
    nan = np.nan

    # nan-based
    arr = np.arange(15, dtype=np.float64)
    arr[7:12] = nan
    arr[-1:] = nan

    bseries = Series(SparseArray(arr, kind="block"))
    bseries.name = "bseries"
    return bseries


def _create_sp_tsseries():
    nan = np.nan

    # nan-based
    arr = np.arange(15, dtype=np.float64)
    arr[7:12] = nan
    arr[-1:] = nan

    date_index = bdate_range("1/1/2011", periods=len(arr))
    bseries = Series(SparseArray(arr, kind="block"), index=date_index)
    bseries.name = "btsseries"
    return bseries


def _create_sp_frame():
    nan = np.nan

    data = {
        "A": [nan, nan, nan, 0, 1, 2, 3, 4, 5, 6],
        "B": [0, 1, 2, nan, nan, nan, 3, 4, 5, 6],
        "C": np.arange(10).astype(np.int64),
        "D": [0, 1, 2, 3, 4, 5, nan, nan, nan, nan],
    }

    dates = bdate_range("1/1/2011", periods=10)
    return DataFrame(data, index=dates).apply(SparseArray)


def create_data():
    """create the pickle data"""
    data = {
        "A": [0.0, 1.0, 2.0, 3.0, np.nan],
        "B": [0, 1, 0, 1, 0],
        "C": ["foo1", "foo2", "foo3", "foo4", "foo5"],
        "D": date_range("1/1/2009", periods=5),
        "E": [0.0, 1, Timestamp("20100101"), "foo", 2.0],
    }

    scalars = {"timestamp": Timestamp("20130101"), "period": Period("2012", "M")}

    index = {
        "int": Index(np.arange(10)),
        "date": date_range("20130101", periods=10),
        "period": period_range("2013-01-01", freq="M", periods=10),
        "float": Index(np.arange(10, dtype=np.float64)),
        "uint": Index(np.arange(10, dtype=np.uint64)),
        "timedelta": timedelta_range("00:00:00", freq="30T", periods=10),
    }

    index["range"] = RangeIndex(10)

    index["interval"] = interval_range(0, periods=10)

    mi = {
        "reg2": MultiIndex.from_tuples(
            tuple(
                zip(
                    *[
                        ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"],
                        ["one", "two", "one", "two", "one", "two", "one", "two"],
                    ]
                )
            ),
            names=["first", "second"],
        )
    }

    series = {
        "float": Series(data["A"]),
        "int": Series(data["B"]),
        "mixed": Series(data["E"]),
        "ts": Series(
            np.arange(10).astype(np.int64), index=date_range("20130101", periods=10)
        ),
        "mi": Series(
            np.arange(5).astype(np.float64),
            index=MultiIndex.from_tuples(
                tuple(zip(*[[1, 1, 2, 2, 2], [3, 4, 3, 4, 5]])), names=["one", "two"]
            ),
        ),
        "dup": Series(np.arange(5).astype(np.float64), index=["A", "B", "C", "D", "A"]),
        "cat": Series(Categorical(["foo", "bar", "baz"])),
        "dt": Series(date_range("20130101", periods=5)),
        "dt_tz": Series(date_range("20130101", periods=5, tz="US/Eastern")),
        "period": Series([Period("2000Q1")] * 5),
    }

    mixed_dup_df = DataFrame(data)
    mixed_dup_df.columns = list("ABCDA")
    frame = {
        "float": DataFrame({"A": series["float"], "B": series["float"] + 1}),
        "int": DataFrame({"A": series["int"], "B": series["int"] + 1}),
        "mixed": DataFrame({k: data[k] for k in ["A", "B", "C", "D"]}),
        "mi": DataFrame(
            {"A": np.arange(5).astype(np.float64), "B": np.arange(5).astype(np.int64)},
            index=MultiIndex.from_tuples(
                tuple(
                    zip(
                        *[
                            ["bar", "bar", "baz", "baz", "baz"],
                            ["one", "two", "one", "two", "three"],
                        ]
                    )
                ),
                names=["first", "second"],
            ),
        ),
        "dup": DataFrame(
            np.arange(15).reshape(5, 3).astype(np.float64), columns=["A", "B", "A"]
        ),
        "cat_onecol": DataFrame({"A": Categorical(["foo", "bar"])}),
        "cat_and_float": DataFrame(
            {
                "A": Categorical(["foo", "bar", "baz"]),
                "B": np.arange(3).astype(np.int64),
            }
        ),
        "mixed_dup": mixed_dup_df,
        "dt_mixed_tzs": DataFrame(
            {
                "A": Timestamp("20130102", tz="US/Eastern"),
                "B": Timestamp("20130603", tz="CET"),
            },
            index=range(5),
        ),
        "dt_mixed2_tzs": DataFrame(
            {
                "A": Timestamp("20130102", tz="US/Eastern"),
                "B": Timestamp("20130603", tz="CET"),
                "C": Timestamp("20130603", tz="UTC"),
            },
            index=range(5),
        ),
    }

    cat = {
        "int8": Categorical(list("abcdefg")),
        "int16": Categorical(np.arange(1000)),
        "int32": Categorical(np.arange(10000)),
    }

    timestamp = {
        "normal": Timestamp("2011-01-01"),
        "nat": NaT,
        "tz": Timestamp("2011-01-01", tz="US/Eastern"),
    }

    off = {
        "DateOffset": DateOffset(years=1),
        "DateOffset_h_ns": DateOffset(hour=6, nanoseconds=5824),
        "BusinessDay": BusinessDay(offset=timedelta(seconds=9)),
        "BusinessHour": BusinessHour(normalize=True, n=6, end="15:14"),
        "CustomBusinessDay": CustomBusinessDay(weekmask="Mon Fri"),
        "SemiMonthBegin": SemiMonthBegin(day_of_month=9),
        "SemiMonthEnd": SemiMonthEnd(day_of_month=24),
        "MonthBegin": MonthBegin(1),
        "MonthEnd": MonthEnd(1),
        "QuarterBegin": QuarterBegin(1),
        "QuarterEnd": QuarterEnd(1),
        "Day": Day(1),
        "YearBegin": YearBegin(1),
        "YearEnd": YearEnd(1),
        "Week": Week(1),
        "Week_Tues": Week(2, normalize=False, weekday=1),
        "WeekOfMonth": WeekOfMonth(week=3, weekday=4),
        "LastWeekOfMonth": LastWeekOfMonth(n=1, weekday=3),
        "FY5253": FY5253(n=2, weekday=6, startingMonth=7, variation="last"),
        "Easter": Easter(),
        "Hour": Hour(1),
        "Minute": Minute(1),
    }

    return {
        "series": series,
        "frame": frame,
        "index": index,
        "scalars": scalars,
        "mi": mi,
        "sp_series": {"float": _create_sp_series(), "ts": _create_sp_tsseries()},
        "sp_frame": {"float": _create_sp_frame()},
        "cat": cat,
        "timestamp": timestamp,
        "offsets": off,
    }


def create_pickle_data():
    data = create_data()

    return data


def platform_name():
    return "_".join(
        [
            str(pandas.__version__),
            str(pl.machine()),
            str(pl.system().lower()),
            str(pl.python_version()),
        ]
    )


def write_legacy_pickles(output_dir):
    version = pandas.__version__

    print(
        "This script generates a storage file for the current arch, system, "
        "and python version"
    )
    print(f"  pandas version: {version}")
    print(f"  output dir    : {output_dir}")
    print("  storage format: pickle")

    pth = f"{platform_name()}.pickle"

    with open(os.path.join(output_dir, pth), "wb") as fh:
        pickle.dump(create_pickle_data(), fh, pickle.DEFAULT_PROTOCOL)

    print(f"created pickle file: {pth}")


def write_legacy_file():
    # force our cwd to be the first searched
    sys.path.insert(0, ".")

    if not 3 <= len(sys.argv) <= 4:
        sys.exit(
            "Specify output directory and storage type: generate_legacy_"
            "storage_files.py <output_dir> <storage_type> "
        )

    output_dir = str(sys.argv[1])
    storage_type = str(sys.argv[2])

    if storage_type == "pickle":
        write_legacy_pickles(output_dir=output_dir)
    else:
        sys.exit("storage_type must be one of {'pickle'}")


if __name__ == "__main__":
    write_legacy_file()