content: """
  Provides a collection of utilities for comparing (image) results.
  
  """
  from __future__ import (absolute_import, division, print_function,
                          unicode_literals)
  
  import six
  
  import hashlib
  import os
  import shutil
  
  import numpy as np
  
  import matplotlib
  from matplotlib.compat import subprocess
  from matplotlib.testing.noseclasses import ImageComparisonFailure
  from matplotlib import _png
  from matplotlib import _get_cachedir
  from matplotlib import cbook
  from distutils import version
  
  __all__ = ['compare_float', 'compare_images', 'comparable_formats']
  
  
  def make_test_filename(fname, purpose):
      """
      Make a new filename by inserting `purpose` before the file's
      extension.
      """
      base, ext = os.path.splitext(fname)
      return '%s-%s%s' % (base, purpose, ext)
  
  
  def compare_float(expected, actual, relTol=None, absTol=None):
      """
      Fail if the floating point values are not close enough, with
      the given message.
  
      You can specify a relative tolerance, absolute tolerance, or both.
  
      """
      if relTol is None and absTol is None:
          raise ValueError("You haven't specified a 'relTol' relative "
                           "tolerance or a 'absTol' absolute tolerance "
                           "function argument. You must specify one.")
      msg = ""
  
      if absTol is not None:
          absDiff = abs(expected - actual)
          if absTol < absDiff:
              template = ['',
                          'Expected: {expected}',
                          'Actual:   {actual}',
                          'Abs diff: {absDiff}',
                          'Abs tol:  {absTol}']
              msg += '\n  '.join([line.format(**locals()) for line in template])
  
      if relTol is not None:
          # The relative difference of the two values.  If the expected value is
          # zero, then return the absolute value of the difference.
          relDiff = abs(expected - actual)
          if expected:
              relDiff = relDiff / abs(expected)
  
          if relTol < relDiff:
              # The relative difference is a ratio, so it's always unit-less.
              template = ['',
                          'Expected: {expected}',
                          'Actual:   {actual}',
                          'Rel diff: {relDiff}',
                          'Rel tol:  {relTol}']
              msg += '\n  '.join([line.format(**locals()) for line in template])
  
      return msg or None
  
  
  def get_cache_dir():
      cachedir = _get_cachedir()
      if cachedir is None:
          raise RuntimeError('Could not find a suitable configuration directory')
      cache_dir = os.path.join(cachedir, 'test_cache')
      if not os.path.exists(cache_dir):
          try:
              cbook.mkdirs(cache_dir)
          except IOError:
              return None
      if not os.access(cache_dir, os.W_OK):
          return None
      return cache_dir
  
  
  def get_file_hash(path, block_size=2 ** 20):
      md5 = hashlib.md5()
      with open(path, 'rb') as fd:
          while True:
              data = fd.read(block_size)
              if not data:
                  break
              md5.update(data)
      return md5.hexdigest()
  
  
  def make_external_conversion_command(cmd):
      def convert(old, new):
          cmdline = cmd(old, new)
          pipe = subprocess.Popen(
              cmdline, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
          stdout, stderr = pipe.communicate()
          errcode = pipe.wait()
          if not os.path.exists(new) or errcode:
              msg = "Conversion command failed:\n%s\n" % ' '.join(cmdline)
              if stdout:
                  msg += "Standard output:\n%s\n" % stdout
              if stderr:
                  msg += "Standard error:\n%s\n" % stderr
              raise IOError(msg)
  
      return convert
  
  
  def _update_converter():
      gs, gs_v = matplotlib.checkdep_ghostscript()
      if gs_v is not None:
          cmd = lambda old, new: \
              [gs, '-q', '-sDEVICE=png16m', '-dNOPAUSE', '-dBATCH',
               '-sOutputFile=' + new, old]
          converter['pdf'] = make_external_conversion_command(cmd)
          converter['eps'] = make_external_conversion_command(cmd)
  
      if matplotlib.checkdep_inkscape() is not None:
          cmd = lambda old, new: \
              ['inkscape', '-z', old, '--export-png', new]
          converter['svg'] = make_external_conversion_command(cmd)
  
  
  #: A dictionary that maps filename extensions to functions which
  #: themselves map arguments `old` and `new` (filenames) to a list of strings.
  #: The list can then be passed to Popen to convert files with that
  #: extension to png format.
  converter = {}
  _update_converter()
  
  
  def comparable_formats():
      """
      Returns the list of file formats that compare_images can compare
      on this system.
  
      """
      return ['png'] + list(six.iterkeys(converter))
  
  
  def convert(filename, cache):
      """
      Convert the named file into a png file.  Returns the name of the
      created file.
  
      If *cache* is True, the result of the conversion is cached in
      `matplotlib._get_cachedir() + '/test_cache/'`.  The caching is based
      on a hash of the exact contents of the input file.  The is no limit
      on the size of the cache, so it may need to be manually cleared
      periodically.
  
      """
      base, extension = filename.rsplit('.', 1)
      if extension not in converter:
          raise ImageComparisonFailure(
              "Don't know how to convert %s files to png" % extension)
      newname = base + '_' + extension + '.png'
      if not os.path.exists(filename):
          raise IOError("'%s' does not exist" % filename)
  
      # Only convert the file if the destination doesn't already exist or
      # is out of date.
      if (not os.path.exists(newname) or
              os.stat(newname).st_mtime < os.stat(filename).st_mtime):
          if cache:
              cache_dir = get_cache_dir()
          else:
              cache_dir = None
  
          if cache_dir is not None:
              hash_value = get_file_hash(filename)
              new_ext = os.path.splitext(newname)[1]
              cached_file = os.path.join(cache_dir, hash_value + new_ext)
              if os.path.exists(cached_file):
                  shutil.copyfile(cached_file, newname)
                  return newname
  
          converter[extension](filename, newname)
  
          if cache_dir is not None:
              shutil.copyfile(newname, cached_file)
  
      return newname
  
  #: Maps file extensions to a function which takes a filename as its
  #: only argument to return a list suitable for execution with Popen.
  #: The purpose of this is so that the result file (with the given
  #: extension) can be verified with tools such as xmllint for svg.
  verifiers = {}
  
  # Turning this off, because it seems to cause multiprocessing issues
  if matplotlib.checkdep_xmllint() and False:
      verifiers['svg'] = lambda filename: [
          'xmllint', '--valid', '--nowarning', '--noout', filename]
  
  
  def verify(filename):
      """Verify the file through some sort of verification tool."""
      if not os.path.exists(filename):
          raise IOError("'%s' does not exist" % filename)
      base, extension = filename.rsplit('.', 1)
      verifier = verifiers.get(extension, None)
      if verifier is not None:
          cmd = verifier(filename)
          pipe = subprocess.Popen(
              cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
          stdout, stderr = pipe.communicate()
          errcode = pipe.wait()
          if errcode != 0:
              msg = "File verification command failed:\n%s\n" % ' '.join(cmd)
              if stdout:
                  msg += "Standard output:\n%s\n" % stdout
              if stderr:
                  msg += "Standard error:\n%s\n" % stderr
              raise IOError(msg)
  
  
  def crop_to_same(actual_path, actual_image, expected_path, expected_image):
      # clip the images to the same size -- this is useful only when
      # comparing eps to pdf
      if actual_path[-7:-4] == 'eps' and expected_path[-7:-4] == 'pdf':
          aw, ah = actual_image.shape
          ew, eh = expected_image.shape
          actual_image = actual_image[int(aw / 2 - ew / 2):int(
              aw / 2 + ew / 2), int(ah / 2 - eh / 2):int(ah / 2 + eh / 2)]
      return actual_image, expected_image
  
  
  def calculate_rms(expectedImage, actualImage):
      "Calculate the per-pixel errors, then compute the root mean square error."
      num_values = np.prod(expectedImage.shape)
      abs_diff_image = abs(expectedImage - actualImage)
  
      # On Numpy 1.6, we can use bincount with minlength, which is much
      # faster than using histogram
      expected_version = version.LooseVersion("1.6")
      found_version = version.LooseVersion(np.__version__)
      if found_version >= expected_version:
          histogram = np.bincount(abs_diff_image.ravel(), minlength=256)
      else:
          histogram = np.histogram(abs_diff_image, bins=np.arange(257))[0]
  
      sum_of_squares = np.sum(histogram * np.arange(len(histogram)) ** 2)
      rms = np.sqrt(float(sum_of_squares) / num_values)
  
      return rms
  
  
  def compare_images(expected, actual, tol, in_decorator=False):
      """
      Compare two "image" files checking differences within a tolerance.
  
      The two given filenames may point to files which are convertible to
      PNG via the `.converter` dictionary. The underlying RMS is calculated
      with the `.calculate_rms` function.
  
      Parameters
      ----------
      expected : str
          The filename of the expected image.
      actual :str
          The filename of the actual image.
      tol : float
          The tolerance (a color value difference, where 255 is the
          maximal difference).  The test fails if the average pixel
          difference is greater than this value.
      in_decorator : bool
          If called from image_comparison decorator, this should be
          True. (default=False)
  
      Example
      -------
      img1 = "./baseline/plot.png"
      img2 = "./output/plot.png"
      compare_images( img1, img2, 0.001 ):
  
      """
      if not os.path.exists(actual):
          msg = "Output image %s does not exist." % actual
          raise Exception(msg)
  
      if os.stat(actual).st_size == 0:
          msg = "Output image file %s is empty." % actual
          raise Exception(msg)
  
      verify(actual)
  
      # Convert the image to png
      extension = expected.split('.')[-1]
  
      if not os.path.exists(expected):
          raise IOError('Baseline image %r does not exist.' % expected)
  
      if extension != 'png':
          actual = convert(actual, False)
          expected = convert(expected, True)
  
      # open the image files and remove the alpha channel (if it exists)
      expectedImage = _png.read_png_int(expected)
      actualImage = _png.read_png_int(actual)
      expectedImage = expectedImage[:, :, :3]
      actualImage = actualImage[:, :, :3]
  
      actualImage, expectedImage = crop_to_same(
          actual, actualImage, expected, expectedImage)
  
      # convert to signed integers, so that the images can be subtracted without
      # overflow
      expectedImage = expectedImage.astype(np.int16)
      actualImage = actualImage.astype(np.int16)
  
      rms = calculate_rms(expectedImage, actualImage)
  
      diff_image = make_test_filename(actual, 'failed-diff')
  
      if rms <= tol:
          if os.path.exists(diff_image):
              os.unlink(diff_image)
          return None
  
      save_diff_image(expected, actual, diff_image)
  
      results = dict(rms=rms, expected=str(expected),
                     actual=str(actual), diff=str(diff_image), tol=tol)
  
      if not in_decorator:
          # Then the results should be a string suitable for stdout.
          template = ['Error: Image files did not match.',
                      'RMS Value: {rms}',
                      'Expected:  \n    {expected}',
                      'Actual:    \n    {actual}',
                      'Difference:\n    {diff}',
                      'Tolerance: \n    {tol}', ]
          results = '\n  '.join([line.format(**results) for line in template])
      return results
  
  
  def save_diff_image(expected, actual, output):
      expectedImage = _png.read_png(expected)
      actualImage = _png.read_png(actual)
      actualImage, expectedImage = crop_to_same(
          actual, actualImage, expected, expectedImage)
      expectedImage = np.array(expectedImage).astype(np.float)
      actualImage = np.array(actualImage).astype(np.float)
      assert expectedImage.ndim == actualImage.ndim
      assert expectedImage.shape == actualImage.shape
      absDiffImage = abs(expectedImage - actualImage)
  
      # expand differences in luminance domain
      absDiffImage *= 255 * 10
      save_image_np = np.clip(absDiffImage, 0, 255).astype(np.uint8)
      height, width, depth = save_image_np.shape
  
      # The PDF renderer doesn't produce an alpha channel, but the
      # matplotlib PNG writer requires one, so expand the array
      if depth == 3:
          with_alpha = np.empty((height, width, 4), dtype=np.uint8)
          with_alpha[:, :, 0:3] = save_image_np
          save_image_np = with_alpha
  
      # Hard-code the alpha channel to fully solid
      save_image_np[:, :, 3] = 255
  
      _png.write_png(save_image_np.tostring(), width, height, output)
  