# viewer

Interactive analysis of time-series data.

Interactively analyze time-series data ...

... even with small windows sizes.

## Functions

- `viewer.ts()` ... Viewer for time-series data

## Details

Interactive viewer for time-series data. Replaces the older “ui.viewer”. Although it is a user interface utility, it is large enough to make up its own module.

Variable types that can in principle be plotted are:
  - np.ndarray
  - pd.core.frame.DataFrame
  - pd.core.series.Series

Viewer can be used to inspect a single variable, or to select one from the current workspace.

Notable aspects:
  - Based on Tkinter, to ensure that it runs on all Python installations.
  - Resizable window.
  - Keyboard-based interaction.
  - Logging of marked events.

`viewer.``ts`(data=None)

  Show the given time-series data. In addition to the (obvious) GUI-interactions, the following options are available:

  Keyboard interaction:
    - f ... forward (+ 1/2 frame)
    - n ... next (+ 1 frame)
    - b ... back ( -1/2 frame)
    - p ... previous (-1 frame)
    - z ... zoom (x-frame = 10% of total length)
    - a ... all (adjust x- and y-limits)
    - x ... exit
  Optimized y-scale:
    Often one wants to see data symmetrically about the zero-axis. To facilitate this display, adjusting the “Upper Limit” automatically sets the lower limit to the corresponding negative value.
  Logging:
    When “Log” is activated, right-mouse clicks are indicated with vertical bars, and the corresponding x-values are stored into the users home-directory, in the file “[varName].log”. Since the name of the first value is unknown the first events are stored into “data.log”.
  Load:

    Pushing the “Load”-button shows you all the plottable variables in your namespace. Plottable variables are:

    - ndarrays
    - Pandas DataFrames
    - Pandas Series
  Examples:
    To view a single plottable variable:

      >>> x = np.random.randn(100,3)
      >>> viewer.ts(x)

  > To select a plottable variable from the workspace

      >>> x = np.random.randn(100,3)
      >>> t = np.arange(0,10,0.1)
      >>> y = np.sin(x)
      >>> viewer.ts(locals)