On Wed, Apr 26, 2017 at 8:20 PM, Carlton Banks <noflaco@gmail.com> wrote:

Is it possible to convert a data set stored as a numpy.ndarray -> plot

the data with a colormap , possibly cm.jet. -> and then store the image pixels to a numpy.ndarray.

The data array of the image should have the same shape of the data array

The image conversion is only used for getting a visual representation of

used to make the array. the data, and normalise it using the cm.jet.

So I actually just interested in the RGB values of the colormap normalised input data.

IS this possible?

Sure, you can use matplotlib's colormap objects as functions to convert arrays with values in the interval [0.0, 1.0] to arrays of colors. [~] |6> cm.viridis(np.linspace(0.0, 1.0, 5)) array([[ 0.267004, 0.004874, 0.329415, 1. ], [ 0.229739, 0.322361, 0.545706, 1. ], [ 0.127568, 0.566949, 0.550556, 1. ], [ 0.369214, 0.788888, 0.382914, 1. ], [ 0.993248, 0.906157, 0.143936, 1. ]]) [~] |7> cm.viridis? ... Call signature: cm.viridis(X, alpha=None, bytes=False) Call docstring: Parameters ---------- X : scalar, ndarray The data value(s) to convert to RGBA. For floats, X should be in the interval ``[0.0, 1.0]`` to return the RGBA values ``X*100`` percent along the Colormap line. For integers, X should be in the interval ``[0, Colormap.N)`` to return RGBA values *indexed* from the Colormap with index ``X``. alpha : float, None Alpha must be a scalar between 0 and 1, or None. bytes : bool If False (default), the returned RGBA values will be floats in the interval ``[0, 1]`` otherwise they will be uint8s in the interval ``[0, 255]``. Returns ------- Tuple of RGBA values if X is scalar, othewise an array of RGBA values with a shape of ``X.shape + (4, )``. -- Robert Kern