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# Plotting

Plots can be performed as the results of pipelines by piping elements to stages corresponding to matplotlib.pyplot operations.

matplotlib.pyplot contains three types of relevant commands for pipelines:

- Graph attribute manipulation functions (e.g., `xlabel`, `title`)
- Plotting functions (e.g., `plot`, `scatter`, `pie`)
- Output manipulation functions (e.g., `savefig`, `show`).

The first type is modeled as filter stages that merely relay whatever is passed to them, and the other two types are modeled as sink stages. The stages are in the `dagpype.plot`, and correspond to pyplot functions: each has the same name as a pyplot command, and takes the same arguments.

## Connecting Stages Corresponding To Different pyplot Function Types

For example, to plot the exponential function between 0 and 3, we can do the following:

    a = numpy.arange(0, 3, 0.2)
    source(numpy.exp(a)) | plot.plot()

which uses a sink stage. This will create the plot:

To add labels and a title, we can use filter stages:

    a = numpy.arange(0, 3, 0.2)
    source(numpy.exp(a)) | plot.xlabel('x') | plot.ylabel('exp') | plot.title('Exponential functions') | plot.plot()

This will create the plot:

In order to manipulate the output, e.g., to save the graph to a file, we can use sink chaining, e.g.,

    source(numpy.exp(a)) | \
      plot.xlabel('x') | plot.ylabel('exp') | plot.title('Exponential functions') | \
      (plot.plot() | plot.savefig('foo.png'))

## Stages’ Similarity To pyplot Functions

As stated before, the stages closely resemble the corresponding pyplot functions.

The `dagpype.plot.plot()` stage, for example, can plot multiple lines simultaneously. For example,

    a = numpy.arange(0, 3, 0.2)
    source(a) + source(a) + source(a) + source(numpy.exp(a)) | plot.plot()

will create the plot

When using pyplot, the `figure()` function might be needed in order to clear what has been plotted, and start a new figure. The library contains a corresponding stage (which takes the same arguments):

    a = numpy.arange(0, 3, 0.2)
    source(a) | plot.figure() | plot.plot()

or

    a = numpy.arange(0, 3, 0.2)
    source(a) | plot.figure(8) | plot.plot()

pyplot supports other types of plots as well. The following code

    source(numpy.random.randn(1000)) + source(numpy.random.randn(1000) + 5) | plot.hexbin()

produces the plot:

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