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# simple_norm

astropy.visualization.simple_norm(data, stretch='linear', power=1.0, asinh_a=0.1, vmin=None, vmax=None, min_percent=None, max_percent=None, percent=None, clip=False, log_a=1000, invalid=-1.0, sinh_a=0.3)

  Return a Normalization class that can be used for displaying images with Matplotlib.

  This function enables only a subset of image stretching functions available in `ImageNormalize`.

  This function is used by the `astropy.visualization.scripts.fits2bitmap` script.

  Parameters:
    data `ndarray`

      The image array.

    stretch{‘linear’, ‘sqrt’, :ref: ‘power’, ‘log’, ‘asinh’, ‘sinh’}, optional

      The stretch function to apply to the image. The default is ‘linear’.

    power `python:float`, optional

      The power index for `stretch='power'`. The default is 1.0.

    asinh_a `python:float`, optional

      For `stretch='asinh'`, the value where the asinh curve transitions from linear to logarithmic behavior, expressed as a fraction of the normalized image. Must be in the range between 0 and 1. The default is 0.1.

    vmin `python:float`, optional

      The pixel value of the minimum cut level. Data values less than `vmin` will set to `vmin` before stretching the image. The default is the image minimum. `vmin` overrides `min_percent`.

    vmax `python:float`, optional

      The pixel value of the maximum cut level. Data values greater than `vmax` will set to `vmax` before stretching the image. The default is the image maximum. `vmax` overrides

    min_percent `python:float`, optional

      The percentile value used to determine the pixel value of minimum cut level. The default is 0.0. `min_percent` overrides `percent`.

    max_percent `python:float`, optional

      The percentile value used to determine the pixel value of maximum cut level. The default is 100.0. `max_percent` overrides `percent`.

    percent `python:float`, optional

      The percentage of the image values used to determine the pixel values of the minimum and maximum cut levels. The lower cut level will set at the `(100 - percent) / 2` percentile, while the upper cut level will be set at the `(100 + percent) / 2` percentile. The default is 100.0. `percent` is ignored if either `min_percent` or `max_percent` is input.

    clip bool, optional

      If `True`, data values outside the [0:1] range are clipped to the [0:1] range.

    log_a `python:float`, optional

      The log index for `stretch='log'`. The default is 1000.

    invalid `python:None` or `python:float`, optional

      Value to assign NaN values generated by the normalization. NaNs in the input `data` array are not changed. For matplotlib normalization, the `invalid` value should map to the matplotlib colormap “under” value (i.e., any finite value < 0). If `None`, then NaN values are not replaced. This keyword has no effect if `clip=True`.

    sinh_a `python:float`, optional

      The scaling parameter for `stretch='sinh'`. The default is 0.3.

  Returns:
    result `ImageNormalize` instance

      An `ImageNormalize` instance that can be used for displaying images with Matplotlib.

  See also

  `SimpleNorm`