python
xrview
xrview provides several utilities for automatically creating interactive bokeh plots from xarray data types.
xrview.plot will create a plot of an xarray.DataArray or Dataset given the name of the dimension that represents the x coordinate in the plot.
All examples in this section assume the following imports:
>>> import numpy as np >>> import xarray as xr >>> import xrview
The following code will open a browser tab with the figure shown below.
../examples/html/minimal_example.py
>>> x = np.linspace(0, 1, 100) >>> y = np.sqrt(x) >>> da = xr.DataArray(y, {'x': x}, 'x') >>> plot = xrview.plot(da, x='x') >>> plot.show() # doctest:+SKIP
When passing a Dataset, each variable will be plotted in a separate figure. If the variable has a second dimension, each element along this dimension will be plotted as a separate line and a legend will be automatically created based on the coordinates of this dimension.
../examples/html/overlay_dims.py
>>> x = np.linspace(0, 1, 100) >>> y = np.vstack([np.sqrt(x), x, x ** 2]).T >>> ds = xr.Dataset({'Clean': (['x', 'f'], y), ... 'Noisy': (['x', 'f'], y + 0.01*np.random.randn(100, 3))}, ... {'x': x, 'f': ['sqrt(x)', 'x', 'x^2']}) >>> plot = xrview.plot(ds, x='x', ncols=2) >>> plot.show() # doctest:+SKIP
Alternatively, you can show the elements of the dimension in separate figures and overlay the variables by specifying overlay='data_vars':
../examples/html/overlay_data_vars.py
>>> plot = xrview.plot(ds, x='x', ncols=2, overlay='data_vars') >>> plot.show() # doctest:+SKIP
You can add additional figures to the plot with the :pyadd_figure method by providing data as a DataArray. The DataArray has to contain a coordinate with the same name as the x coordinate, but they do not need to have the same values.
../examples/html/add_figure.py
>>> da = xr.DataArray(np.ones(20), {'x': np.linspace(0, 1, 20)}, 'x') >>> plot = xrview.plot(ds, x='x', ncols=2) >>> plot.add_figure(da) >>> plot.show() # doctest:+SKIP
Data can also be overlaid onto existing figures with the :pyadd_overlay method. With the onto parameter, you can select a figure by index or title on which to overlay the data. By default, the data will be overlaid onto all figures.
../examples/html/add_overlay.py
>>> plot = xrview.plot(ds, x='x', ncols=2) >>> plot.add_overlay(da, onto='Clean') >>> plot.show() # doctest:+SKIP
xrview supports plotting with many of the standard bokeh glyphs as well as some custom composite glyphs such as error bars and boxes for box plots. Glyphs can be passed to :pyplot, :pyadd_overlay and :pyadd_figure as strings, instances of a glyph class or iterables of any combination of both. When you pass a glyph instance, you can specify additional keyword arguments.
In this example, one array is plotted with circles and a second with a blue line and red squares:
../examples/html/basic_glyphs.py
>>> from xrview.glyphs import Square >>> x = np.linspace(0, 1, 100) >>> y = np.sqrt(x) >>> da_sqrt = xr.DataArray(y, {'x': x}, 'x') >>> da_const = xr.DataArray(np.ones(20), {'x': x[::5]}, 'x') >>> plot = xrview.plot(da_sqrt, x='x', glyphs='circle') >>> plot.add_overlay(da_const, glyphs=['line', Square(color='red')]) >>> plot.show() # doctest:+SKIP
xrview also provides a straightforward way of
Note
See API/Glyphs for a list of available glyphs.
Note
Interactive plotting is so far only supported in jupyter notebooks.