Updated conclusion of Operations tutorial
"source": [
"Using a few additional lines we have now evaluated the operation over a number of different parameters values, allowing us to process the data with different smoothing parameters. In addition, by interacting with this visualization we can gain a better understanding of the operation parameters as well as gain insights into the structure of the underlying data.\n",
"\n",
- "## Benefits of using operations\n",
+ "## Benefits of ``Operations``\n",
"\n",
- "Now that we have seen some operations in action we can get some appreciation of what makes them useful. When working with data interactively you often end up doing a lot of data wrangling, which provides maximum flexibility but is neither reproducible nor maintainable. Operations allow you to encapsulate analysis code using a well defined interface that is well suited towards building complex analysis pipelines:\n",
+ "Now that we have seen some operations in action we can get some appreciation of what makes them useful. When working with data interactively you often end up applying a lot of ad-hoc data transforms, which provides maximum flexibility but is neither reproducible nor maintainable. Operations allow you to encapsulate analysis code using a well defined interface that is well suited for building complex analysis pipelines:\n",
"\n",
- "1. Their parameters are well defined by declaring parameters on the class. These parameters also perform validation on the types and ranges of the inputs.\n",
+ "1. ``Operation`` parameters are well defined by declaring parameters on the class. These parameters can be easily documented and automatically carry out validation on the types and ranges of the inputs.\n",
"\n",
- "2. Both the inputs and outputs of operations are visualizable, because the data **is** the visualization. This means you're not constantly context switching between data processing and visualization---you essentially get the visualization for free.\n",
+ "2. Both inputs and outputs of an operation are instantly visualizable, because the data **is** the visualization. This means you're not constantly context switching between data processing and visualization --- visualization comes for free as you build your data processing pipeline.\n",
"\n",
- "3. Operations understand HoloViews datastructures and can be applied to many Elements at once, allowing you to evaluate the operation with permutations of parameter values. This flexibility makes it easier to assess what the parameters of the operation are actually doing and how they shape your data.\n",
+ "3. Operations understand HoloViews datastructures and can be immediately applied to any appropriate collection of elements, allowing you to evaluate the operation with permutations of parameter values. This flexibility makes it easy to assess the effect of operation parameters and their effect on your data.\n",
"\n",
- "4. As we will discover in the [Dynamic Operation Tutorial](Dynamic_Operations.ipynb), operations can be applied lazily to build up complex deferred data-processing pipelines, which can aid your data exploration and drive your interactive visualizations and dashboards."
+ "4. As we will discover in the [Dynamic Operation Tutorial](Dynamic_Operations.ipynb), operations can be applied lazily to build up complex deferred data-processing pipelines, which can aid your data exploration and drive interactive visualizations and dashboards."
]
}
],
