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Signature Description Parameters 
template<comparable T>
StlVecType<char>
peaks(const char *col_name,
      size_type n = 1) const;
This function determines if each item in the named column is a peak. A peak data point is bigger than n data points before and after it.
It returns a vector of chars with the same size as the named column. A 0 value means the data point is not a peak. 1 means it is. The first and last n values of the returned vector are always 0;

NOTE: StlVecType is just a std::vector with the applicable allocator for the DataFrame. For example, if you have declared the DataFrame to allocate memory on a particular boundary, then StlVecType is std::vector with an allocator that does so. Otherwise it is just a default std::vector.
 T: Type of the named column T must have the comparison operators (== != > < >= <=) well defined
col_name: Name of the column
n: Number of periods to account for before and after. It is defaulted to 1
 
template<comparable T>
StlVecType<char>
valleys(const char *col_name,
        size_type n = 1) const;
This function determines if each item in the named column is a valley. A valley data point is smaller than n data points before and after it.
It returns a vector of chars with the same size as the named column. A 0 value means the data point is not a valley. 1 means it is. The first and last n values of the returned vector are always 0;

NOTE: StlVecType is just a std::vector with the applicable allocator for the DataFrame. For example, if you have declared the DataFrame to allocate memory on a particular boundary, then StlVecType is std::vector with an allocator that does so. Otherwise it is just a default std::vector.
 T: Type of the named column T must have the comparison operators (== != > < >= <=) well defined
col_name: Name of the column
n: Number of periods to account for before and after. It is defaulted to 1
 
static void test_peaks()  {

    std::cout << "\nTesting peaks( ) ..." << std::endl;

    MyDataFrame                df;
    StlVecType<unsigned long>  idxvec = { 1UL, 2UL, 3UL, 10UL, 5UL, 7UL, 8UL, 12UL, 9UL, 12UL, 10UL, 13UL, 10UL, 15UL, 14UL };
    StlVecType<double>         dblvec = { 0.0, 15.0, -14.0, 2.0, 1.0, 12.0, 11.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 9.0, 10.0};
    StlVecType<double>         dblvec2 = { 100.0, 101.0, 102.0, 103.0, 104.0, 103.9, 106.55, 106.34, 1.8, 111.0, 112.0, 111.5, 114.0, 115.0, 116.0};
    StlVecType<std::string>    strvec = { "zz", "bb", "cc", "ww", "ee", "ff", "gg", "hh", "ii", "jj", "kk", "ll", "mm", "nn", "oo" };

    df.load_data(std::move(idxvec),
                 std::make_pair("dbl_col", dblvec),
                 std::make_pair("dbl_col_2", dblvec2),
                 std::make_pair("str_col", strvec));

    const auto  res1 = df.peaks<double>("dbl_col_2");

    {
        StlVecType<char>    out_res = { 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0 };

        assert(res1 == out_res);
    }

    const auto  res2 = df.peaks<unsigned long>(DF_INDEX_COL_NAME);

    {
        StlVecType<char>    out_res = { 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0 };

        assert(res2 == out_res);
    }
}

// ----------------------------------------------------------------------------

static void test_valleys()  {

    std::cout << "\nTesting valleys( ) ..." << std::endl;

    MyDataFrame                df;
    StlVecType<unsigned long>  idxvec = { 1UL, 2UL, 3UL, 10UL, 5UL, 7UL, 8UL, 12UL, 9UL, 12UL, 10UL, 13UL, 10UL, 15UL, 14UL };
    StlVecType<double>         dblvec = { 0.0, 15.0, -14.0, 2.0, 1.0, 12.0, 11.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 9.0, 10.0};
    StlVecType<double>         dblvec2 = { 100.0, 101.0, 102.0, 103.0, 104.0, 103.9, 106.55, 106.34, 1.8, 111.0, 112.0, 111.5, 114.0, 115.0, 116.0};
    StlVecType<std::string>    strvec = { "zz", "bb", "cc", "ww", "ee", "ff", "gg", "hh", "ii", "jj", "kk", "ll", "mm", "nn", "oo" };

    df.load_data(std::move(idxvec),
                 std::make_pair("dbl_col", dblvec),
                 std::make_pair("dbl_col_2", dblvec2),
                 std::make_pair("str_col", strvec));
    df.load_column("dbl_col_2_mask_1", df.valleys<double>("dbl_col_2"));
    df.load_column("dbl_col_2_mask_2", df.valleys<double>("dbl_col_2", 2));
    df.load_column("dbl_col_2_mask_3", df.valleys<double>("dbl_col_2", 3));

    {
        StlVecType<char>    out_res1 = { 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0 };
        StlVecType<char>    out_res2 = { 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0 };
        StlVecType<char>    out_res3 = { 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0 };

        assert((df.get_column<char>("dbl_col_2_mask_1") == out_res1));
        assert((df.get_column<char>("dbl_col_2_mask_2") == out_res2));
        assert((df.get_column<char>("dbl_col_2_mask_3") == out_res3));
    }

    df.load_column("index_mask", df.valleys<double>(DF_INDEX_COL_NAME));
    {
        StlVecType<char>    out_res = { 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0 };

        assert((df.get_column<char>("index_mask") == out_res));
    }
}

C++ DataFrame