#===============================================================================
# Copyright 2014 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#===============================================================================

# daal4py low order moments example for streaming on shared memory systems

import daal4py as d4p
import numpy as np

# let's try to use pandas' fast csv reader
try:
    import pandas

    def read_csv(f, c, s=0, n=None, t=np.float64):
        return pandas.read_csv(f, usecols=c, delimiter=',', header=None,
                               skiprows=s, nrows=n, dtype=t)
except:
    # fall back to numpy genfromtxt
    def read_csv(f, c, s=0, n=np.iinfo(np.int64).max):
        a = np.genfromtxt(f, usecols=c, delimiter=',', skip_header=s, max_rows=n)
        if a.shape[0] == 0:
            raise Exception("done")
        if a.ndim == 1:
            return a[:, np.newaxis]
        return a


def main(readcsv=read_csv, method='defaultDense'):
    # read data from file
    file = "./data/batch/covcormoments_dense.csv"

    # Configure a low order moments object for streaming
    algo = d4p.low_order_moments(streaming=True)

    chunk_size = 55
    lines_read = 0
    # read and feed chunk by chunk
    while True:
        # Read data in chunks
        try:
            data = readcsv(file, range(10), lines_read, chunk_size)
        except:
            break
        # Now feed chunk
        algo.compute(data)
        lines_read += data.shape[0]

    # All files are done, now finalize the computation
    result = algo.finalize()

    # result provides minimum, maximum, sum, sumSquares, sumSquaresCentered,
    # mean, secondOrderRawMoment, variance, standardDeviation, variation
    return result


if __name__ == "__main__":
    res = main()
    # print results
    print("\nMinimum:\n", res.minimum)
    print("\nMaximum:\n", res.maximum)
    print("\nSum:\n", res.sum)
    print("\nSum of squares:\n", res.sumSquares)
    print("\nSum of squared difference from the means:\n", res.sumSquaresCentered)
    print("\nMean:\n", res.mean)
    print("\nSecond order raw moment:\n", res.secondOrderRawMoment)
    print("\nVariance:\n", res.variance)
    print("\nStandard deviation:\n", res.standardDeviation)
    print("\nVariation:\n", res.variation)
    print('All looks good!')
