`silx.sx`: Using silx from Python Interpreter#

This is a convenient package to use from Python or IPython interpreter. It loads the main features of silx and provides high-level functions.

>>> from silx import sx

When used in an interpreter is sets-up Qt and loads some silx widgets. In a jupyter / IPython notebook, to set-up Qt and loads silx widgets, you must then call:

>>> sx.enable_gui()

When used in IPython, it also runs %pylab, thus importing numpy and matplotlib.

Plot functions#

The following functions plot curves and images with silx widgets:

plot() for curves
imshow() for images
scatter() for scatter plot

The ginput() function handles user selection on those widgets.

Note

Those functions are not available from a notebook.

`plot()`#

plot(*args, **kwargs)[source]#

Plot curves in a Plot1D widget.

How to use:

>>> from silx import sx
>>> import numpy

Plot a single curve given some values:

>>> values = numpy.random.random(100)
>>> plot_1curve = sx.plot(values, title='Random data')

Plot a single curve given the x and y values:

>>> angles = numpy.linspace(0, numpy.pi, 100)
>>> sin_a = numpy.sin(angles)
>>> plot_sinus = sx.plot(angles, sin_a, xlabel='angle (radian)', ylabel='sin(a)')

Plot many curves by giving a 2D array, provided xn, yn arrays:

>>> plot_curves = sx.plot(x0, y0, x1, y1, x2, y2, ...)

Plot curve with style giving a style string:

>>> plot_styled = sx.plot(x0, y0, 'ro-', x1, y1, 'b.')

Supported symbols:

‘o’ circle

‘.’ point

‘,’ pixel

‘+’ cross

‘x’ x-cross

‘d’ diamond

‘s’ square

Supported types of line:

‘ ‘ no line

‘-’ solid line

‘–’ dashed line

‘-.’ dash-dot line

‘:’ dotted line

If provided, the names arguments color, linestyle, linewidth and marker override any style provided to a curve.

This function supports a subset of matplotlib.pyplot.plot arguments.

Parameters:

color (str) – Color to use for all curves (default: None)
linestyle (str) – Type of line to use for all curves (default: None)
linewidth (float) – With of all the curves (default: 1)
marker (str) – Symbol to use for all the curves (default: None)
title (str) – The title of the Plot widget (default: None)
xlabel (str) – The label of the X axis (default: None)
ylabel (str) – The label of the Y axis (default: None)

Returns:

The widget plotting the curve(s)

Return type:

silx.gui.plot.Plot1D

`imshow()`#

imshow(data=None, cmap=None, norm='linear', vmin=None, vmax=None, aspect=False, origin='upper', scale=(1.0, 1.0), title='', xlabel='X', ylabel='Y')[source]#

Plot an image in a Plot2D widget.

How to use:

>>> from silx import sx
>>> import numpy

>>> data = numpy.random.random(1024 * 1024).reshape(1024, 1024)
>>> plt = sx.imshow(data, title='Random data')

By default, the image origin is displayed in the upper left corner of the plot. To invert the Y axis, and place the image origin in the lower left corner of the plot, use the origin parameter:

>>> plt = sx.imshow(data, origin='lower')

This function supports a subset of matplotlib.pyplot.imshow arguments.

Parameters:

data (numpy.ndarray-like with 2 dimensions) – data to plot as an image
cmap (str) – The name of the colormap to use for the plot. It also supports a numpy array containing a RGB LUT, or a colors.Colormap instance.
norm (str) – The normalization of the colormap: ‘linear’ (default) or ‘log’
vmin (float) – The value to use for the min of the colormap
vmax (float) – The value to use for the max of the colormap
aspect (bool) – True to keep aspect ratio (Default: False)
origin (str or 2-tuple of floats) – Either image origin as the Y axis orientation: ‘upper’ (default) or ‘lower’ or the coordinates (ox, oy) of the image origin in the plot.
scale (2-tuple of floats) – (sx, sy) The scale of the image in the plot (i.e., the size of the image’s pixel in plot coordinates)
title (str) – The title of the Plot widget
xlabel (str) – The label of the X axis
ylabel (str) – The label of the Y axis

Returns:

The widget plotting the image

Return type:

silx.gui.plot.Plot2D

`scatter()`#

scatter(x=None, y=None, value=None, size=None, marker=None, cmap=None, norm='linear', vmin=None, vmax=None)[source]#

Plot scattered data in a ScatterView widget.

How to use:

>>> from silx import sx
>>> import numpy

>>> x = numpy.random.random(100)
>>> y = numpy.random.random(100)
>>> values = numpy.random.random(100)
>>> plt = sx.scatter(x, y, values, cmap='viridis')

Supported symbols:

‘o’ circle

‘.’ point

‘,’ pixel

‘+’ cross

‘x’ x-cross

‘d’ diamond

‘s’ square

This function supports a subset of matplotlib.pyplot.scatter arguments.

Parameters:

x (numpy.ndarray) – 1D array-like of x coordinates
y (numpy.ndarray) – 1D array-like of y coordinates
value (numpy.ndarray) – 1D array-like of data values
size (float) – Size^2 of the markers
marker (str) – Symbol used to represent the points
cmap (str) – The name of the colormap to use for the plot
norm (str) – The normalization of the colormap: ‘linear’ (default) or ‘log’
vmin (float) – The value to use for the min of the colormap
vmax (float) – The value to use for the max of the colormap

Returns:

The widget plotting the scatter plot

Return type:

silx.gui.plot.ScatterView.ScatterView

`ginput()`#

ginput(n=1, timeout=30, plot=None)[source]#

Get input points on a plot.

If no plot is provided, it uses a plot widget created with either silx.sx.plot() or silx.sx.imshow().

How to use:

>>> from silx import sx

>>> sx.imshow(image)  # Plot the image
>>> sx.ginput(1)  # Request selection on the image plot
((0.598, 1.234))

How to get more information about the selected positions:

>>> positions = sx.ginput(1)

>>> positions[0].getData()  # Returns value(s) at selected position

>>> positions[0].getIndices()  # Returns data indices at selected position

>>> positions[0].getItem()  # Returns plot item at selected position

Parameters:

n (int) – Number of points the user need to select
timeout (float) – Timeout in seconds before ginput returns event if selection is not completed
plot (silx.gui.plot.PlotWidget.PlotWidget) – An optional PlotWidget from which to get input

Returns:

List of clicked points coordinates (x, y) in plot

Raises:

ValueError – If provided plot is not a PlotWidget

3D plot functions#

The following functions plot 3D data with silx widgets (it requires OpenGL):

contour3d() for isosurfaces (and cut plane) in a 3D scalar field
points3d() for 2D/3D scatter plots

Note

Those functions are not available from a notebook.

`contour3d()`#

contour3d(scalars, contours=1, copy=True, color=None, colormap='viridis', vmin=None, vmax=None, opacity=1.0)[source]#

Plot isosurfaces of a 3D scalar field in a ScalarFieldView widget.

How to use:

>>> from silx import sx

Provided data, a 3D scalar field as a numpy array of float32:

>>> plot3d_window = sx.contour3d(data)

Alternatively you can provide the level of the isosurfaces:

>>> plot3d_window = sx.contour3d(data, contours=[0.2, 0.4])

This function provides a subset of mayavi.mlab.contour3d arguments.

Parameters:

scalars (numpy.ndarray of float32 with 3 dimensions) – The 3D scalar field to visualize
contours (Union[int, float, List[float]]) – Either the number of isosurfaces to draw (as an int) or the isosurface level (as a float) or a list of isosurface levels (as a list of float)
copy (bool) – True (default) to make a copy of scalars. False to avoid this copy (do not modify provided data afterwards)
color – Color.s to use for isosurfaces. Either a single color or a list of colors (one for each isosurface). A color can be defined by its name (as a str) or as RGB(A) as float or uint8.
colormap (str) – If color is not provided, this colormap is used for coloring isosurfaces.
vmin (Union[float, None]) – Minimum value of the colormap
vmax (Union[float, None]) – Maximum value of the colormap
opacity (float) – Transparency of the isosurfaces as a float in [0., 1.]

Returns:

The widget used to visualize the data

Return type:

ScalarFieldView

`points3d()`#

points3d(x, y, z=None, values=0.0, copy=True, colormap='viridis', vmin=None, vmax=None, mode=None)[source]#

Plot a 3D scatter plot in a SceneWindow widget.

How to use:

>>> from silx import sx

Provided x, y, z, values, 4 numpy array of float32:

>>> plot3d_window = sx.points3d(x, y, z)

>>> plot3d_window = sx.points3d(x, y, z, values)

This function provides a subset of mayavi.mlab.points3d arguments.

Parameters:

x (numpy.ndarray) – X coordinates of the points
y (numpy.ndarray) – Y coordinates of the points
z (numpy.ndarray) – Z coordinates of the points (optional)
values (numpy.ndarray) – Values at each point (optional)
copy (bool) – True (default) to make a copy of scalars. False to avoid this copy (do not modify provided data afterwards)
colormap (str) – Colormap to use for coding points as colors.
vmin (Union[float, None]) – Minimum value of the colormap
vmax (Union[float, None]) – Maximum value of the colormap
mode (str) –
The type of marker to use
- Circle: ‘o’, ‘2dcircle’
- Diamond: ‘d’, ‘2ddiamond’
- Square: ‘s’, ‘2dsquare’
- Plus: ‘+’
- Cross: ‘x’, ‘2dcross’
- Star: ‘*’
- Vertical line: ‘|’
- Horizontal line: ‘_’, ‘2ddash’
- Point: ‘.’
- Pixel: ‘,’

Returns:

The widget used to visualize the data

Return type:

SceneWindow

Widgets#

The widgets of the silx.gui.plot package are also exposed in this package. See silx.gui.plot for documentation.

Input/Output#

The content of the silx.io package is also exposed in this package. See silx.io for documentation.

Math#

The following classes from silx.math are exposed in this package:

silx.sx: Using silx from Python Interpreter#

Plot functions#

plot()#

imshow()#

scatter()#

ginput()#

3D plot functions#

contour3d()#

points3d()#

Widgets#

Input/Output#

Math#

`silx.sx`: Using silx from Python Interpreter#

`plot()`#

`imshow()`#

`scatter()`#

`ginput()`#

`contour3d()`#

`points3d()`#