Creates fixed size histogram by adding compression to accumulated state.

  This routine transforms a histogram at a particular step by interpolating its
  variable number of buckets to represent their cumulative weight at a constant
  number of compression points. This significantly reduces the size of the
  histogram and makes it suitable for a two-dimensional area plot where the
  output of this routine constitutes the ranges for a single x coordinate.

  Args:
    histo: A HistogramProto object.
    bps: Compression points represented in basis points, 1/100ths of a percent.
        Defaults to normal distribution.

  Returns:
    List of values for each basis point.

def compress_histogram_proto(histo, bps=NORMAL_HISTOGRAM_BPS):
  """Creates fixed size histogram by adding compression to accumulated state.

  This routine transforms a histogram at a particular step by interpolating its
  variable number of buckets to represent their cumulative weight at a constant
  number of compression points. This significantly reduces the size of the
  histogram and makes it suitable for a two-dimensional area plot where the
  output of this routine constitutes the ranges for a single x coordinate.

  Args:
    histo: A HistogramProto object.
    bps: Compression points represented in basis points, 1/100ths of a percent.
        Defaults to normal distribution.

  Returns:
    List of values for each basis point.
  """
  # See also: Histogram::Percentile() in core/lib/histogram/histogram.cc
  if not histo.num:
    return [CompressedHistogramValue(b, 0.0) for b in bps]
  bucket = np.array(histo.bucket)
  bucket_limit = list(histo.bucket_limit)
  weights = (bucket * bps[-1] / (bucket.sum() or 1.0)).cumsum()
  values = []
  j = 0
  while j < len(bps):
    i = np.searchsorted(weights, bps[j], side='right')
    while i < len(weights):
      cumsum = weights[i]
      cumsum_prev = weights[i - 1] if i > 0 else 0.0
      if cumsum == cumsum_prev:  # prevent lerp divide by zero
        i += 1
        continue
      if not i or not cumsum_prev:
        lhs = histo.min
      else:
        lhs = max(bucket_limit[i - 1], histo.min)
      rhs = min(bucket_limit[i], histo.max)
      weight = _lerp(bps[j], cumsum_prev, cumsum, lhs, rhs)
      values.append(CompressedHistogramValue(bps[j], weight))
      j += 1
      break
    else:
      break
  while j < len(bps):
    values.append(CompressedHistogramValue(bps[j], histo.max))
    j += 1
  return values