class_docstr: Calculates the Pearson product-moment correlation coefficient of the
  returns of the given asset with the returns of all other assets.
  
  Pearson correlation is what most people mean when they say "correlation
  coefficient" or "R-value".
  
  Parameters
  ----------
  target : catalyst.assets.Asset
      The asset to correlate with all other assets.
  returns_length : int >= 2
      Length of the lookback window over which to compute returns. Daily
      returns require a window length of 2.
  correlation_length : int >= 1
      Length of the lookback window over which to compute each correlation
      coefficient.
  mask : catalyst.pipeline.Filter, optional
      A Filter describing which assets should have their correlation with the
      target asset computed each day.
  
  Notes
  -----
  Computing this factor over many assets can be time consuming. It is
  recommended that a mask be used in order to limit the number of assets over
  which correlations are computed.
  
  Examples
  --------
  Let the following be example 10-day returns for three different assets::
  
                     SPY    MSFT     FB
      2017-03-13    -.03     .03    .04
      2017-03-14    -.02    -.03    .02
      2017-03-15    -.01     .02    .01
      2017-03-16       0    -.02    .01
      2017-03-17     .01     .04   -.01
      2017-03-20     .02    -.03   -.02
      2017-03-21     .03     .01   -.02
      2017-03-22     .04    -.02   -.02
  
  Suppose we are interested in SPY's rolling returns correlation with each
  stock from 2017-03-17 to 2017-03-22, using a 5-day look back window (that
  is, we calculate each correlation coefficient over 5 days of data). We can
  achieve this by doing::
  
      rolling_correlations = RollingPearsonOfReturns(
          target=sid(8554),
          returns_length=10,
          correlation_length=5,
      )
  
  The result of computing ``rolling_correlations`` from 2017-03-17 to
  2017-03-22 gives::
  
                     SPY   MSFT     FB
      2017-03-17       1    .15   -.96
      2017-03-20       1    .10   -.96
      2017-03-21       1   -.16   -.94
      2017-03-22       1   -.16   -.85
  
  Note that the column for SPY is all 1's, as the correlation of any data
  series with itself is always 1. To understand how each of the other values
  were calculated, take for example the .15 in MSFT's column. This is the
  correlation coefficient between SPY's returns looking back from 2017-03-17
  (-.03, -.02, -.01, 0, .01) and MSFT's returns (.03, -.03, .02, -.02, .04).
  
  See Also
  --------
  :class:`catalyst.pipeline.factors.RollingSpearmanOfReturns`
  :class:`catalyst.pipeline.factors.RollingLinearRegressionOfReturns`
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code: def __new__(
      cls, target, returns_length, correlation_length, mask=NotSpecified
  ):
      # Use the `SingleAsset` filter here because it protects against
      # inputting a non-existent target asset.
      returns = Returns(
          window_length=returns_length,
          mask=(AssetExists() | SingleAsset(asset=target)),
      )
      return super(RollingPearsonOfReturns, cls).__new__(
          cls,
          base_factor=returns,
          target=returns[target],
          correlation_length=correlation_length,
          mask=mask,
      )
  
imports: from numpy import broadcast_arrays
  from scipy.stats import linregress, pearsonr, spearmanr
  from catalyst.errors import IncompatibleTerms
  from catalyst.pipeline.factors import CustomFactor
  from catalyst.pipeline.filters import SingleAsset
  from catalyst.pipeline.mixins import SingleInputMixin
  from catalyst.pipeline.sentinels import NotSpecified
  from catalyst.pipeline.term import AssetExists
  from catalyst.utils.input_validation import expect_bounded, expect_dtypes
  from catalyst.utils.numpy_utils import float64_dtype, int64_dtype
  from .technical import Returns
label_desc: Use image node_id 1 for calling the RollingPearsonOfReturns obj's underlying member method code with example usage: obj.__new__(cls, target, returns_length, correlation_length, mask) and returns: super