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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def subsampleCorrelatedData(A_t, g=None, fast=False, conservative=False, verbose=False):
 """Determine the indices of an uncorrelated subsample of the data. Parameters A_t : np.ndarray A_t[t] is the t-th value of timeseries A(t). Length is deduced from vector. g : float, optional if provided, the statistical inefficiency g is used to subsample the timeseries -- otherwise it will be computed (default: None) fast : bool, optional, default=False fast can be set to True to give a less accurate but very quick estimate (default: False) conservative : bool, optional, default=False if set to True, uniformly-spaced indices are chosen with interval ceil(g), where g is the statistical inefficiency. Otherwise, indices are chosen non-uniformly with interval of approximately g in order to end up with approximately T/g total indices verbose : bool, optional, default=False if True, some output is printed Returns ------- indices : list of int the indices of an uncorrelated subsample of the data Notes ----- The statistical inefficiency is computed with the function computeStatisticalInefficiency(). ToDo ---- Instead of using regular stride, use irregular stride so more data can be fit in when g is non-integral. Examples -------- Subsample a correlated timeseries to extract an effectively uncorrelated dataset. Extract uncorrelated samples from multiple timeseries data from the same process. """


    # Create np copy of arrays.
    A_t = np.array(A_t)

    # Get the length of the timeseries.
    T = A_t.size

    # Compute the statistical inefficiency for the timeseries.
    if not g:
        if verbose:
            print("Computing statistical inefficiency...")
        g = statisticalInefficiency(A_t, A_t, fast=fast)
        if verbose:
            print("g = %f" % g)

    if conservative:
        # Round g up to determine the stride we can use to pick out regularly-spaced uncorrelated samples.
        stride = int(math.ceil(g))
        if verbose:
            print("conservative subsampling: using stride of %d" % stride)

        # Assemble list of indices of uncorrelated snapshots.
        indices = range(0, T, stride)
    else:
        # Choose indices as floor(n*g), with n = 0,1,2,..., until we run out of data.
        indices = []
        n = 0
        while int(round(n * g)) < T:
            t = int(round(n * g))
            # ensure we don't sample the same point twice
            if (n == 0) or (t != indices[n - 1]):
                indices.append(t)
            n += 1
        if verbose:
            print("standard subsampling: using average stride of %f" % g)

    # Number of samples in subsampled timeseries.
    N = len(indices)

    if verbose:
        print("The resulting subsampled set has %d samples (original timeseries had %d)." % (N, T))

    # Return the list of indices of uncorrelated snapshots.
    return indices