<jupyter_start><jupyter_text>Installing and Configuring Holoviews HoloViews can be installed on any platform where [NumPy](http://numpy.org) and Python 2.7 or 3 are available.That said, HoloViews is designed to work closely with many other libraries, which can make installation and configuration more complicated.  This user guide page describes some of these less-common or not-required options that may be helpful for some users.  Other installation optionsThe main [installation instructions](http://holoviews.org/installation) should be sufficient for most users, but you may also want the [Matplotlib](http://matplotlib.org) and [Plotly](https://plot.ly/python/) backends, which are required for some of the examples:    conda install matplotlib plotlyHoloViews can also be installed using one of these `pip` commands:    pip install holoviews    pip install 'holoviews[recommended]'    pip install 'holoviews[extras]'    pip install 'holoviews[all]'The first option installs just the bare library and the [NumPy](http://numpy.org) and [Param](http://ioam.github.com/param) libraries, which is all you need on your system to generate and work with HoloViews objects without visualizing them. The other options install additional libraries that are often useful, with the `recommended` option being similar to the `conda` install command above.Between releases, development snapshots are made available as conda packages:    conda install -c pyviz/label/dev holoviewsTo get the very latest development version you can clone our gitrepository and put it on the Python path:    git clone git://github.com/ioam/holoviews.git    cd holoviews    pip install -e .  Jupyter notebook configurationIn version 5.0 of the Jupyter notebook, limits on data rates were added that unfortunately also prevent image output from showing up in many cases.  For now, the most effective [fix](https://stackoverflow.com/questions/43288550/iopub-data-rate-exceeded-when-viewing-image-in-jupyter-notebook) is to generate a Jupyter config file:```jupyter notebook --generate-config```and then edit the resulting file (most likely, ~/.jupyter/jupyter_notebook_config.py) with code that changes the limit:```c = get_config()c.NotebookApp.iopub_data_rate_limit=100000000```  JupyterLab configurationTo work with JupyterLab you will also need the HoloViews JupyterLabextension:```conda install -c conda-forge jupyterlabjupyter labextension install @pyviz/jupyterlab_pyviz```Once you have installed JupyterLab and the extension launch it with:```jupyter-lab```  ``hv.config`` settingsThe default HoloViews installation will use the latest defaults and options available, which is appropriate for new users.  If you want to work with code written for older HoloViews versions, you can use the top-level ``hv.config`` object to control various backwards-compatibility options:* ``future_deprecations``: Enables warnings about future deprecations (introduced in 1.11).* ``warn_options_call``: Warn when using the to-be-deprecated ``__call__`` syntax for specifying options, instead of the recommended ``.opts`` method.It is recommended you set ``warn_options_call`` to ``True`` in your holoviews.rc file (see section below).It is possible to set the configuration using `hv.config` directly:<jupyter_code>import holoviews as hv
hv.config(future_deprecations=True)<jupyter_output><empty_output><jupyter_text>However, because in some cases this configuration needs to be declared before the plotting extensions are imported, the recommended way of setting configuration options is:<jupyter_code>hv.extension('bokeh', config=dict(future_deprecations=True))<jupyter_output><empty_output><jupyter_text>In addition to backwards-compatibility options, ``hv.config`` holds some global options:* ``image_rtol``: The tolerance used to enforce regular sampling for regular, gridded data. Used to validate ``Image`` data.This option allows you to set the ``rtol`` parameter of [``Image``](../reference/elements/bokeh/Image.ipynb) elements globally.  Improved tab-completionBoth ``Layout`` and ``Overlay`` are designed around convenient tab-completion, with the expectation of upper-case names being listed first. In recent versions of Jupyter/IPython there has been a regression whereby the tab-completion is no longer case-sensitive. This can be fixed with:<jupyter_code>import holoviews as hv
hv.extension(case_sensitive_completion=True)<jupyter_output><empty_output>
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