### Navigation

- index
- modules |
- PySptools 0.12.1 beta documentation »

# Abundance maps classes

## FCLS

class `abundance_maps.``FCLS`

  Performs fully constrained least squares. Fully constrained least squares is least squares with the abundance sum-to-one constraint (ASC) and the abundance nonnegative constraint (ANC).

  `display`(colorMap='jet', suffix=None)

    Plot the abundance maps on a IPython Notebook.

    Parameters:
      colorMap: string [default jet]
        A matplotlib color map.
      suffix: string [default None]
        Suffix to add to the title.
  `map`(M, U, normalize=False)

    Performs fully constrained least squares of each pixel in M using the endmember signatures of U.

    Parameters:
      M: numpy array
        A HSI cube (m x n x p).
      U: numpy array
        A spectral library of endmembers (q x p).
      normalize: boolean [default False]
        If True, M and U are normalized before doing the signals mapping.
    Returns: numpy array
      An abundance maps (m x n x q).
  `plot`(path, colorMap='jet', suffix=None)

    Plot the abundance maps.

    Parameters:
      path: string
        The path where to put the plot.
      colorMap: string [default jet]
        A matplotlib color map.
      suffix: string [default None]
        Suffix to add to the file name.

## NNLS

class `abundance_maps.``NNLS`

  NNLS performs non-negative constrained least squares with the abundance nonnegative constraint (ANC). Utilizes the method of Bro.

  `display`(colorMap='jet', suffix=None)

    Plot the abundance maps on a IPython Notebook.

    Parameters:
      colorMap: string [default jet]
        A matplotlib color map.
      suffix: string [default None]
        Suffix to add to the title.
  `map`(M, U, normalize=False)

    NNLS performs non-negative constrained least squares of each pixel in M using the endmember signatures of U.

    Parameters:
      M: numpy array
        A HSI cube (m x n x p).
      U: numpy array
        A spectral library of endmembers (q x p).
      normalize: boolean [default False]
        If True, M and U are normalized before doing the signals mapping.
    Returns: numpy array
      An abundance maps (m x n x q).
  `plot`(path, colorMap='jet', suffix=None)

    Plot the abundance maps.

    Parameters:
      path: string
        The path where to put the plot.
      colorMap: string [default jet]
        A matplotlib color map.
      suffix: string [default None]
        Suffix to add to the file name.

## UCLS

class `abundance_maps.``UCLS`

  Performs unconstrained least squares abundance estimation.

  `display`(colorMap='jet', suffix=None)

    Plot the abundance maps on a IPython Notebook.

    Parameters:
      colorMap: string [default jet]
        A matplotlib color map.
      suffix: string [default None]
        Suffix to add to the title.
  `map`(M, U, normalize=False)

    Performs unconstrained least squares abundance estimation on the HSI cube M using the signals library U.

    Parameters:
      M: numpy array
        A HSI cube (m x n x p).
      U: numpy array
        A spectral library of endmembers (q x p).
      normalize: boolean [default False]
        If True, M and U are normalized before doing the signals mapping.
    Returns: numpy array
      An abundance maps (m x n x q).
  `plot`(path, colorMap='jet', suffix=None)

    Plot the abundance maps.

    Parameters:
      path: string
        The path where to put the plot.
      colorMap: string [default jet]
        A matplotlib color map.
      suffix: string [default None]
        Suffix to add to the file name.

### Navigation

- index
- modules |
- PySptools 0.12.1 beta documentation »