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# Matrix Plots

class `sage.plot.matrix_plot.``MatrixPlot`(xy_data_array, xrange, yrange, options)

  Bases: `sage.plot.primitive.GraphicPrimitive`

  Primitive class for the matrix plot graphics type. See `matrix_plot?` for help actually doing matrix plots.

  INPUT:

  - `xy_data_array` - list of lists giving matrix values corresponding to the grid
  - `xrange` - tuple of 2 floats indicating range for horizontal direction (number of columns in the matrix)
  - `yrange` - tuple of 2 floats indicating range for vertical direction (number of rows in the matrix)
  - `options` - dict of valid plot options to pass to constructor

  EXAMPLES:

  Note this should normally be used indirectly via `matrix_plot()`:

      sage: from sage.plot.matrix_plot import MatrixPlot
      sage: M = MatrixPlot([[1,3],[2,4]],(1,2),(2,3),options={'cmap':'winter'})
      sage: M
      MatrixPlot defined by a 2 x 2 data grid
      sage: M.yrange
      (2, 3)
      sage: M.xy_data_array
      [[1, 3], [2, 4]]
      sage: M.options()
      {'cmap': 'winter'}

  Extra options will get passed on to `show()`, as long as they are valid:

      sage: matrix_plot([[1, 0], [0, 1]], fontsize=10)
      Graphics object consisting of 1 graphics primitive
      sage: matrix_plot([[1, 0], [0, 1]]).show(fontsize=10) # These are equivalent

  TESTS:

  We test creating a matrix plot:

      sage: matrix_plot([[mod(i,5)^j for i in range(5)] for j in range(1,6)])
      Graphics object consisting of 1 graphics primitive

  `get_minmax_data`()

    Returns a dictionary with the bounding box data.

    EXAMPLES:

        sage: m = matrix_plot(matrix([[1,3,5,1],[2,4,5,6],[1,3,5,7]]))[0]
        sage: list(sorted(m.get_minmax_data().items()))
        [('xmax', 3.5), ('xmin', -0.5), ('ymax', -0.5), ('ymin', 2.5)]

`sage.plot.matrix_plot.``matrix_plot`(mat, origin='upper', vmin=None, frame=True, axes=False, colorbar=False, aspect_ratio=1, subdivisions=False, cmap='gray', ticks_integer=True, marker='.', vmax=None, norm=None, subdivision_boundaries=None, subdivision_style=None, colorbar_orientation='vertical', colorbar_format=None, **options)

  A plot of a given matrix or 2D array.

  If the matrix is dense, each matrix element is given a different color value depending on its relative size compared to the other elements in the matrix. If the matrix is sparse, colors only indicate whether an element is nonzero or zero, so the plot represents the sparsity pattern of the matrix.

  The tick marks drawn on the frame axes denote the row numbers (vertical ticks) and the column numbers (horizontal ticks) of the matrix.

  INPUT:

  - `mat` - a 2D matrix or array

  The following input must all be passed in as named parameters, if default not used:

  - `cmap` - a colormap (default: ‘gray’), the name of a predefined colormap, a list of colors, or an instance of a matplotlib Colormap. Type: `import matplotlib.cm; matplotlib.cm.datad.keys()` for available colormap names.

  - `colorbar` – boolean (default: False) Show a colorbar or not (dense matrices only).

    The following options are used to adjust the style and placement of colorbars. They have no effect if a colorbar is not shown.

    - `colorbar_orientation` – string (default: ‘vertical’), controls placement of the colorbar, can be either ‘vertical’ or ‘horizontal’
    - `colorbar_format` – a format string, this is used to format the colorbar labels.
    - `colorbar_options` – a dictionary of options for the matplotlib colorbar API. Documentation for the `matplotlib.colorbar` module has details.

  - `norm` - If None (default), the value range is scaled to the interval [0,1]. If ‘value’, then the actual value is used with no scaling. A `matplotlib.colors.Normalize` instance may also passed.

  - `vmin` - The minimum value (values below this are set to this value)

  - `vmax` - The maximum value (values above this are set to this value)

  - `origin` - If ‘upper’ (default), the first row of the matrix is on the top of the graph. If ‘lower’, the first row is on the bottom of the graph.

  - `subdivisions` - If True, plot the subdivisions of the matrix as lines.

  - `subdivision_boundaries` - a list of lists in the form `[row_subdivisions, column_subdivisions]`, which specifies the row and column subdivisions to use. If not specified, defaults to the matrix subdivisions

  - `subdivision_style` - a dictionary of properties passed on to the `line2d()` command for plotting subdivisions. If this is a two-element list or tuple, then it specifies the styles of row and column divisions, respectively.

  EXAMPLES:

  A matrix over \(\ZZ\) colored with different grey levels:

      sage: matrix_plot(matrix([[1,3,5,1],[2,4,5,6],[1,3,5,7]]))
      Graphics object consisting of 1 graphics primitive

  Here we make a random matrix over \(\RR\) and use `cmap='hsv'` to color the matrix elements different RGB colors:

      sage: matrix_plot(random_matrix(RDF, 50), cmap='hsv')
      Graphics object consisting of 1 graphics primitive

  By default, entries are scaled to the interval [0,1] before determining colors from the color map. That means the two plots below are the same:

      sage: P = matrix_plot(matrix(2,[1,1,3,3]))
      sage: Q = matrix_plot(matrix(2,[2,2,3,3]))
      sage: P; Q
      Graphics object consisting of 1 graphics primitive
      Graphics object consisting of 1 graphics primitive

  However, we can specify which values scale to 0 or 1 with the `vmin` and `vmax` parameters (values outside the range are clipped). The two plots below are now distinguished:

      sage: P = matrix_plot(matrix(2,[1,1,3,3]), vmin=0, vmax=3, colorbar=True)
      sage: Q = matrix_plot(matrix(2,[2,2,3,3]), vmin=0, vmax=3, colorbar=True)
      sage: P; Q
      Graphics object consisting of 1 graphics primitive
      Graphics object consisting of 1 graphics primitive

  We can also specify a norm function of ‘value’, which means that there is no scaling performed:

      sage: matrix_plot(random_matrix(ZZ,10)*.05, norm='value', colorbar=True)
      Graphics object consisting of 1 graphics primitive

  Matrix subdivisions can be plotted as well:

      sage: m=random_matrix(RR,10)
      sage: m.subdivide([2,4],[6,8])
      sage: matrix_plot(m, subdivisions=True, subdivision_style=dict(color='red',thickness=3))
      Graphics object consisting of 1 graphics primitive

  You can also specify your own subdivisions and separate styles for row or column subdivisions:

      sage: m=random_matrix(RR,10)
      sage: matrix_plot(m, subdivisions=True, subdivision_boundaries=[[2,4],[6,8]], subdivision_style=[dict(color='red',thickness=3),dict(linestyle='--',thickness=6)])
      Graphics object consisting of 1 graphics primitive

  Generally matrices are plotted with the (0,0) entry in the upper left. However, sometimes if we are plotting an image, we’d like the (0,0) entry to be in the lower left. We can do that with the `origin` argument:

      sage: matrix_plot(identity_matrix(100), origin='lower')
      Graphics object consisting of 1 graphics primitive

  Another random plot, but over \(\GF{389}\):

      sage: m = random_matrix(GF(389), 10)
      sage: matrix_plot(m, cmap='Oranges')
      Graphics object consisting of 1 graphics primitive

  It also works if you lift it to the polynomial ring:

      sage: matrix_plot(m.change_ring(GF(389)['x']), cmap='Oranges')
      Graphics object consisting of 1 graphics primitive

  We have several options for colorbars:

      sage: matrix_plot(random_matrix(RDF, 50), colorbar=True, colorbar_orientation='horizontal')
      Graphics object consisting of 1 graphics primitive

      sage: matrix_plot(random_matrix(RDF, 50), colorbar=True, colorbar_format='%.3f')
      Graphics object consisting of 1 graphics primitive

  The length of a color bar and the length of the adjacent matrix plot dimension may be quite different. This example shows how to adjust the length of the colorbar by passing a dictionary of options to the matplotlib colorbar routines.

      sage: m = random_matrix(ZZ, 40, 80, x=-10, y=10)
      sage: m.plot(colorbar=True, colorbar_orientation='vertical',
      ....:        colorbar_options={'shrink':0.50})
      Graphics object consisting of 1 graphics primitive

  Here we plot a random sparse matrix:

      sage: sparse = matrix(dict([((randint(0, 10), randint(0, 10)), 1) for i in xrange(100)]))
      sage: matrix_plot(sparse)
      Graphics object consisting of 1 graphics primitive

      sage: A=random_matrix(ZZ,100000,density=.00001,sparse=True)
      sage: matrix_plot(A,marker=',')
      Graphics object consisting of 1 graphics primitive

  As with dense matrices, sparse matrix entries are automatically converted to floating point numbers before plotting. Thus the following works:

      sage: b=random_matrix(GF(2),200,sparse=True,density=0.01)
      sage: matrix_plot(b)
      Graphics object consisting of 1 graphics primitive

  While this returns an error:

      sage: b=random_matrix(CDF,200,sparse=True,density=0.01)
      sage: matrix_plot(b)
      Traceback (most recent call last):
      ...
      ValueError: can not convert entries to floating point numbers

  To plot the absolute value of a complex matrix, use the `apply_map` method:

      sage: b=random_matrix(CDF,200,sparse=True,density=0.01)
      sage: matrix_plot(b.apply_map(abs))
      Graphics object consisting of 1 graphics primitive

  Plotting lists of lists also works:

      sage: matrix_plot([[1,3,5,1],[2,4,5,6],[1,3,5,7]])
      Graphics object consisting of 1 graphics primitive

  As does plotting of NumPy arrays:

      sage: import numpy
      sage: matrix_plot(numpy.random.rand(10, 10))
      Graphics object consisting of 1 graphics primitive

  A plot title can be added to the matrix plot.:

      sage: matrix_plot(identity_matrix(50), origin='lower', title='not identity')
      Graphics object consisting of 1 graphics primitive

  The title position is adjusted upwards if the `origin` keyword is set to `"upper"` (this is the default).:

      sage: matrix_plot(identity_matrix(50), title='identity')
      Graphics object consisting of 1 graphics primitive

  TESTS:

      sage: P.<t> = RR[]
      sage: matrix_plot(random_matrix(P, 3, 3))
      Traceback (most recent call last):
      ...
      TypeError: cannot coerce nonconstant polynomial to float

      sage: matrix_plot([1,2,3])
      Traceback (most recent call last):
      ...
      TypeError: mat must be a Matrix or a two dimensional array

      sage: matrix_plot([[sin(x), cos(x)], [1, 0]])
      Traceback (most recent call last):
      ...
      TypeError: mat must be a Matrix or a two dimensional array

  Test that sparse matrices also work with subdivisions:

      sage: matrix_plot(sparse, subdivisions=True, subdivision_boundaries=[[2,4],[6,8]])
      Graphics object consisting of 1 graphics primitive

  Test that matrix plots have aspect ratio one (see trac ticket #15315):

      sage: P = matrix_plot(random_matrix(RDF, 5))
      sage: P.aspect_ratio()
      1

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