def test_get_election0_xlsx(self):
        response = self.client.get(
            reverse('results-export'),
            { 'election': str(Election.objects.get(name='Election 0').id) }
        )

        self.assertEqual(response.status_code, 200)        

        self.assertEqual(
            response['Content-Disposition'],
            'attachment; filename="Election 0 Results.xlsx"'
        )

        wb = openpyxl.load_workbook(io.BytesIO(response.content))

        self.assertEqual(len(wb.worksheets), 1)

        # Check first worksheet.
        ws = wb.worksheets[0]

        self.assertEqual(wb.sheetnames[0], 'Election 0')

        row_count = ws.max_row
        col_count = ws.max_column
        self.assertEqual(row_count, 25)
        self.assertEqual(col_count, 5)

        self.assertEqual(str(ws.cell(1, 1).value), 'Election 0 Results')

        self.assertEqual(str(ws.cell(2, 1).value), 'Candidates')

        cellContents = [
            'Position 0',
            'Party 0',
            '0, 0',
            'Party 1',
            '3, 3',
            'Party 2',
            'None',
            'Position 1',
            'Party 0',
            '1, 1',
            'Party 1',
            '4, 4',
            'Party 2',
            'None',
            'Position 2',
            'Party 0',
            '2, 2',
            'Party 1',
            '5, 5',
            'Party 2',
            'None'
        ]
        for cellIndex, content in enumerate(cellContents, 5):
            self.assertEqual(str(ws.cell(cellIndex, 1).value), content) 

        self.assertEqual(str(ws.cell(2, 2).value), 'Number of Votes')

        self.assertEqual(str(ws.cell(3, 2).value), '0')

        self.assertEqual(str(ws.cell(4, 2).value), '0') # Section

        self.assertEqual(str(ws.cell(7, 2).value), '1')
        self.assertEqual(str(ws.cell(9, 2).value), '0')
        self.assertEqual(str(ws.cell(11, 2).value), 'N/A')
        self.assertEqual(str(ws.cell(14, 2).value), '2')
        self.assertEqual(str(ws.cell(16, 2).value), '0')
        self.assertEqual(str(ws.cell(18, 2).value), 'N/A')
        self.assertEqual(str(ws.cell(21, 2).value), '0')
        self.assertEqual(str(ws.cell(23, 2).value), '0')
        self.assertEqual(str(ws.cell(25, 2).value), 'N/A')

        self.assertEqual(str(ws.cell(4, 3).value), '1') # Section

        self.assertEqual(str(ws.cell(7, 3).value), '0')
        self.assertEqual(str(ws.cell(9, 3).value), '1')
        self.assertEqual(str(ws.cell(11, 2).value), 'N/A')
        self.assertEqual(str(ws.cell(14, 3).value), '0')
        self.assertEqual(str(ws.cell(16, 3).value), '0')
        self.assertEqual(str(ws.cell(18, 2).value), 'N/A')
        self.assertEqual(str(ws.cell(21, 3).value), '1')
        self.assertEqual(str(ws.cell(23, 3).value), '0')
        self.assertEqual(str(ws.cell(25, 2).value), 'N/A')

        self.assertEqual(str(ws.cell(3, 4).value), '1')

        self.assertEqual(str(ws.cell(4, 4).value), '2') # Section

        self.assertEqual(str(ws.cell(7, 4).value), '0')
        self.assertEqual(str(ws.cell(9, 4).value), '0')
        self.assertEqual(str(ws.cell(11, 2).value), 'N/A')
        self.assertEqual(str(ws.cell(14, 4).value), '0')
        self.assertEqual(str(ws.cell(16, 4).value), '1')
        self.assertEqual(str(ws.cell(18, 2).value), 'N/A')
        self.assertEqual(str(ws.cell(21, 4).value), '0')
        self.assertEqual(str(ws.cell(23, 4).value), '1')
        self.assertEqual(str(ws.cell(25, 2).value), 'N/A')

        self.assertEqual(str(ws.cell(3, 5).value), 'Total Votes')
        self.assertEqual(str(ws.cell(7, 5).value), '1')
        self.assertEqual(str(ws.cell(9, 5).value), '1')
        self.assertEqual(str(ws.cell(11, 2).value), 'N/A')
        self.assertEqual(str(ws.cell(14, 5).value), '2')
        self.assertEqual(str(ws.cell(16, 5).value), '1')
        self.assertEqual(str(ws.cell(18, 2).value), 'N/A')
        self.assertEqual(str(ws.cell(21, 5).value), '1')
        self.assertEqual(str(ws.cell(23, 5).value), '1')
        self.assertEqual(str(ws.cell(25, 2).value), 'N/A')
def _building_block_v2(inputs, filters, training, projection_shortcut, strides,
                       data_format, name):
  """A single block for ResNet v2, without a bottleneck.

  Batch normalization then ReLu then convolution as described by:
    Identity Mappings in Deep Residual Networks
    https://arxiv.org/pdf/1603.05027.pdf
    by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, Jul 2016.

  Args:
    inputs: A tensor of size [batch, channels, height_in, width_in] or [batch,
      height_in, width_in, channels] depending on data_format.
    filters: The number of filters for the convolutions.
    training: A Boolean for whether the model is in training or inference mode.
      Needed for batch normalization.
    projection_shortcut: The function to use for projection shortcuts (typically
      a 1x1 convolution when downsampling the input).
    strides: The block's stride. If greater than 1, this block will ultimately
      downsample the input.
    data_format: The input format ('channels_last' or 'channels_first').
    name: Block name.

  Returns:
    The output tensor of the block; shape should match inputs.
  """
  shortcut = inputs
  first_name = name + 'first'
  inputs = batch_norm(
      inputs, training, data_format, name=first_name + 'batch_norm')
  inputs = tf.nn.relu(inputs, name=first_name + 'relu')

  # The projection shortcut should come after the first batch norm and ReLU
  # since it performs a 1x1 convolution.
  if projection_shortcut is not None:
    shortcut = projection_shortcut(inputs, name=first_name + 'proj')

  second_name = name + 'second'
  inputs = conv2d_fixed_padding(
      inputs=inputs,
      filters=filters,
      kernel_size=3,
      strides=strides,
      data_format=data_format,
      name=second_name + 'input')

  inputs = batch_norm(
      inputs, training, data_format, name=second_name + 'batch_norm')
  inputs = tf.nn.relu(inputs, name=second_name + 'relu')

  third_name = name + 'third'
  inputs = conv2d_fixed_padding(
      inputs=inputs,
      filters=filters,
      kernel_size=3,
      strides=1,
      data_format=data_format,
      name=third_name + 'input')

  return inputs + shortcut