import numpy as np


def histeq(im, num_bins=256):
    """
    Performs an histogram equalization on ``img``.
    This was taken from:
    http://www.janeriksolem.net/2009/06/histogram-equalization-with-python-and.html

    Parameters
    ----------
    im : ndarray
        Image on which to perform histogram equalization.
    num_bins : int
        Number of bins used to construct the histogram.

    Returns
    -------
    result : ndarray
        Histogram equalized image.
    """
    #get image histogram
    histo, bins = np.histogram(im.flatten(), num_bins, normed=True)
    cdf = histo.cumsum()
    cdf = 255 * cdf / cdf[-1]

    #use linear interpolation of cdf to find new pixel values
    result = np.interp(im.flatten(), bins[:-1], cdf)

    return result.reshape(im.shape)

import numpy as np


def histeq(arr, num_bins=256):
    """ Performs an histogram equalization on ``arr``.
    This was taken from:
    http://www.janeriksolem.net/2009/06/histogram-equalization-with-python-and.html

    Parameters
    ----------
    arr : ndarray
        Image on which to perform histogram equalization.
    num_bins : int
        Number of bins used to construct the histogram.

    Returns
    -------
    result : ndarray
        Histogram equalized image.
    """
    #get image histogram
    histo, bins = np.histogram(arr.flatten(), num_bins, normed=True)
    cdf = histo.cumsum()
    cdf = 255 * cdf / cdf[-1]

    #use linear interpolation of cdf to find new pixel values
    result = np.interp(arr.flatten(), bins[:-1], cdf)

    return result.reshape(arr.shape)

Fix comment format and input var name.
Fix comment format and input var name.

  import numpy as np
  
  
- def histeq(im, num_bins=256):
+ def histeq(arr, num_bins=256):
-     """
-     Performs an histogram equalization on ``img``.
+     """ Performs an histogram equalization on ``arr``.
      This was taken from:
      http://www.janeriksolem.net/2009/06/histogram-equalization-with-python-and.html
  
      Parameters
      ----------
-     im : ndarray
+     arr : ndarray
          Image on which to perform histogram equalization.
      num_bins : int
          Number of bins used to construct the histogram.
  
      Returns
      -------
      result : ndarray
          Histogram equalized image.
      """
      #get image histogram
-     histo, bins = np.histogram(im.flatten(), num_bins, normed=True)
+     histo, bins = np.histogram(arr.flatten(), num_bins, normed=True)
      cdf = histo.cumsum()
      cdf = 255 * cdf / cdf[-1]
  
      #use linear interpolation of cdf to find new pixel values
-     result = np.interp(im.flatten(), bins[:-1], cdf)
+     result = np.interp(arr.flatten(), bins[:-1], cdf)
  
-     return result.reshape(im.shape)
+     return result.reshape(arr.shape)
  
Fix comment format and input var name.
## Code Before:
import numpy as np


def histeq(im, num_bins=256):
    """
    Performs an histogram equalization on ``img``.
    This was taken from:
    http://www.janeriksolem.net/2009/06/histogram-equalization-with-python-and.html

    Parameters
    ----------
    im : ndarray
        Image on which to perform histogram equalization.
    num_bins : int
        Number of bins used to construct the histogram.

    Returns
    -------
    result : ndarray
        Histogram equalized image.
    """
    #get image histogram
    histo, bins = np.histogram(im.flatten(), num_bins, normed=True)
    cdf = histo.cumsum()
    cdf = 255 * cdf / cdf[-1]

    #use linear interpolation of cdf to find new pixel values
    result = np.interp(im.flatten(), bins[:-1], cdf)

    return result.reshape(im.shape)

## Instruction:
Fix comment format and input var name.
## Code After:
import numpy as np


def histeq(arr, num_bins=256):
    """ Performs an histogram equalization on ``arr``.
    This was taken from:
    http://www.janeriksolem.net/2009/06/histogram-equalization-with-python-and.html

    Parameters
    ----------
    arr : ndarray
        Image on which to perform histogram equalization.
    num_bins : int
        Number of bins used to construct the histogram.

    Returns
    -------
    result : ndarray
        Histogram equalized image.
    """
    #get image histogram
    histo, bins = np.histogram(arr.flatten(), num_bins, normed=True)
    cdf = histo.cumsum()
    cdf = 255 * cdf / cdf[-1]

    #use linear interpolation of cdf to find new pixel values
    result = np.interp(arr.flatten(), bins[:-1], cdf)

    return result.reshape(arr.shape)
