# Author: Avraam Marimpis <avraam.marimpis@gmail.com>

from .estimator import Estimator
from ..analytic_signal import analytic_signal

import numpy as np


def crosscorr(data, fb, fs, pairs=None):
    """

    Parameters
    ----------


    Returns
    -------

    """
    n_channels, _ = np.shape(data)
    filtered, _, _ = analytic_signal(data, fb, fs)

    r = np.zeros([n_channels, n_channels], dtype=np.float32)

    for i in range(n_channels):
        for ii in range(n_channels):
            r[i, ii] = np.correlate(filtered[i, ], filtered[ii, ], mode='valid')

    return r

# Author: Avraam Marimpis <avraam.marimpis@gmail.com>

from .estimator import Estimator
from ..analytic_signal import analytic_signal

import numpy as np


def crosscorr(data, fb, fs, pairs=None):
    """

    Parameters
    ----------


    Returns
    -------

    """
    n_channels, _ = np.shape(data)
    _, _, filtered = analytic_signal(data, fb, fs)

    r = np.zeros([n_channels, n_channels], dtype=np.float32)

    for i in range(n_channels):
        for ii in range(n_channels):
            r[i, ii] = np.correlate(filtered[i,], filtered[ii,], mode="valid")

    return r

Fix the order of the returned values from `analytic_signal`.
Fix the order of the returned values from `analytic_signal`.

  # Author: Avraam Marimpis <avraam.marimpis@gmail.com>
  
  from .estimator import Estimator
  from ..analytic_signal import analytic_signal
  
  import numpy as np
  
  
  def crosscorr(data, fb, fs, pairs=None):
      """
  
      Parameters
      ----------
  
  
      Returns
      -------
  
      """
      n_channels, _ = np.shape(data)
-     filtered, _, _ = analytic_signal(data, fb, fs)
+     _, _, filtered = analytic_signal(data, fb, fs)
  
      r = np.zeros([n_channels, n_channels], dtype=np.float32)
  
      for i in range(n_channels):
          for ii in range(n_channels):
-             r[i, ii] = np.correlate(filtered[i, ], filtered[ii, ], mode='valid')
+             r[i, ii] = np.correlate(filtered[i,], filtered[ii,], mode="valid")
  
      return r
  
Fix the order of the returned values from `analytic_signal`.
## Code Before:
# Author: Avraam Marimpis <avraam.marimpis@gmail.com>

from .estimator import Estimator
from ..analytic_signal import analytic_signal

import numpy as np


def crosscorr(data, fb, fs, pairs=None):
    """

    Parameters
    ----------


    Returns
    -------

    """
    n_channels, _ = np.shape(data)
    filtered, _, _ = analytic_signal(data, fb, fs)

    r = np.zeros([n_channels, n_channels], dtype=np.float32)

    for i in range(n_channels):
        for ii in range(n_channels):
            r[i, ii] = np.correlate(filtered[i, ], filtered[ii, ], mode='valid')

    return r

## Instruction:
Fix the order of the returned values from `analytic_signal`.
## Code After:
# Author: Avraam Marimpis <avraam.marimpis@gmail.com>

from .estimator import Estimator
from ..analytic_signal import analytic_signal

import numpy as np


def crosscorr(data, fb, fs, pairs=None):
    """

    Parameters
    ----------


    Returns
    -------

    """
    n_channels, _ = np.shape(data)
    _, _, filtered = analytic_signal(data, fb, fs)

    r = np.zeros([n_channels, n_channels], dtype=np.float32)

    for i in range(n_channels):
        for ii in range(n_channels):
            r[i, ii] = np.correlate(filtered[i,], filtered[ii,], mode="valid")

    return r
