8. Saving Results

In this chapter, we will introduce various ways of saving visualization results in ParaView. Results generated throughout the visualization process not only include the images and the rendering results, but also include the datasets generated by filters, the scene representations that will be imported into other rendering applications, and the movies generated from animations.

8.1. Saving datasets

You can save the dataset produced by any pipeline module in ParaView, including sources, readers, and filters. To save the dataset in paraview, begin by selecting the pipeline module in the Pipeline Browser to make it the active source. For modules with multiple output ports, select the output port producing the dataset of interest. To save the dataset, use the File > Save Data menu or the pqSave button in the Main Controls toolbar. You can also use the keyboard shortcut CTRL + S (or + S). The Save File dialog (Fig. 8.1) will allow you to select the filename and the file format. The available list of file formats depends on the type of the dataset you are trying to save.


Fig. 8.1 Save File dialog in paraview.

On accepting a filename and file format to use, paraview may show the Configure Writer dialog (Fig. 8.2). This dialog allows you to further customize the writing process. The properties shown in this dialog depend on the selected file format and range from enabling you to Write All Time Steps , to selecting the attributes to write in the output file.


Fig. 8.2 Configure Writer dialog in paraview shown when saving a dataset as a csv file.

In pvpython too, you can save the datasets as follows:

# Saving the data using the default properties for
# the used writer, if any.
>>> SaveData("sample.csv", source)

# the second argument is optional, and refers to the pipeline module
# to write the data from. If none is specified the active source is used.

# To pass parameters to configure the writer
>>> SaveData("sample.csv", source,

pvpython will pick a writer based on the file extension and the dataset type selected for writing, similar to what it does in paraview. Admittedly, it can be tricky to figure out what options are available for the writer. The best way is to use the Python tracing capabilities in paraview and to use the generated sample script as a reference ( Section 1.6.2). Make sure you use a similar type of dataset and the same file format as you want to use in your Python script, when tracing, to avoid runtime issues.

8.2. Saving rendered results

Views that render results (this includes almost all of the views, except SpreadSheet View ) support saving images (or screenshots) in one of the standard image formats (PNG, JPEG, TIFF, BMP, PPM). Certain views also support exportings the results in several formats such as PDF, X3D, and VRML.

8.2.1. Saving screenshots

To save the render image from a view in paraview, use the File > Save Screenshot menu option. When selected, a file dialog will appear where you can select the file path and format to which the screenshot should be saved. After selecting the image file, the Save Screenshot Options dialog (Fig. 8.3) will be shown. This dialog allows you to select various parameters that controls what image is saved out and how.


Fig. 8.3 The Save Screenshot Options dialog, which is used to customize saving screenshots in paraview.

If your visualization setup only has one view the active tab, then you’ll be presented with options shown in (Fig. 8.3). The available options are as follows.

  • Image Resolution : This is the target image resolution in pixels. By default, it is set to the current view dimensions. You can change it as needed. If the resolution larger than the current resolution, then ParaView will use tiling to render the full image in multiple stages. For reliable results, you may want to present the current aspect ratio. You can use Tools > Lock View Size Custom to lock the view size to a suitable aspect ratio.

  • Font Scaling : When a resolution larger than the current resolution is specified, this allows you to control how the fonts are to be scaled. Default Scale fonts proportionally tries to achieve WYSIWYG as long as the aspect ratio is maintained. This is suitable for saving images targeted for higher DPI (or PPI) display than your screen. Do not scale fonts may be used to avoid font scaling and keep their size in pixels the same as what is currently on the screen. This is suitable for saving images targeted for a larger display with the same pixel resolution.

  • Override Color Palette : Optionally change the color palette just for saving the screenshot using this drop-down.

  • Stereo Mode : This option lets you save the image using one of the supported stereo modes.

  • Transparent Background : If the file format supports it, you can check this option to save the images with a transparent background rather than the current background color.

  • Format : This shows the file format selected in the file save dialog.

For formats that have different options like compression levels, format-specific options are presented in the Save Screenshot Options dialog. The PNG format has a Compression Level option that ranges from 0 (no compression) to 9 (maximum compression). The JPEG format options are Quality , which ranges from 0 to 100, and Progressive , which enables saving the file as a progressive JPEG. The TIFF file format has a Compression option with possible values None , PackBits , and Deflate . The BMP file format has no options.

If the active tab has more than one view, then the Save Screenshot Options dialog has a few more options as shown in Fig. 8.4.


Fig. 8.4 Extra options in Save Screenshot Options dialog available when the active tab has more than 1 view.

  • Save All Views : Check this to save all the views in the active tab laid out exactly as in the UI. If unchecked, only the active view will be saved.

  • Separator Options : These control the separator drawn between the views in the generated image. You can specify the Separator Width in approximate pixels as well as the Separator Color .

To save a screenshot in pvpython, you use SaveScreenshot .

# Save a screenshot from a specific view.
>>> myview = GetActiveView()
>>> SaveScreenshot("aview.png", myview)

# Save all views in a tab
>>> layout = GetLayout()
>>> SaveScreenshot("allviews.png", layout)

# To save a specific target resolution, rather than using the
# the current view (or layout) size, and override the color palette.
>>> SaveScreenshot("aviewResolution.png", myview,
        ImageResolution=[1500, 1500],
        OverrideColorPalette="Black Background")

As always, you can use Python tracing in paraview to trace the exact form of the method to use to save a specific screenshot image.

8.2.2. Exporting scenes

When available, you can export a visualization in a view in several of the supported formats using the File > Export Scene… menu option in paraview. For a Render View (or similar), the available formats include Cinema Database, EPS, PDF, PS, SVG, POV, VRML, WebGL, X3D, and X3DB. On selecting a file as which to export, paraview may pop up an Export Options dialog that allows you to set up parameters for the exporter, similar to saving datasets ( Section 8.1).

In addition, from pvpython, exporting takes the following form (again, just use Python trace to figure out the proper form – that’s the easiest way).

>>> myview = GetActiveView()
>>> ExportView('/tmp/sample.svg', view=myview,
               Plottitle='ParaView GL2PS Export',
# the arguments after 'view' depend on the exporter selected.

8.3. Saving animation


Fig. 8.5 The Save Animation Options dialog in paraview, which is used to customize saving of animation.

To save an animation as a series of images or a video file, you use the File > Save Animation menu option. This raises a file save dialog where you choose where to save the file and which format to use. The available file formats include AVI (on Windows and Linux), MP4 (on Windows only), and Ogg video formats, as well as image formats such PNG, JPEG, and TIFF. If saving an animation as image frames, ParaView will generate a series of image files sequentially numbered using the frame number as a suffix to the specified filename.

After selecting the file and format, the Save Animation Options dialog (Fig. 8.5) is displayed. This dialog is nearly a clone of the Save Screenshot Options dialog (Fig. 8.3), including, optionally, the extra multiview options from Fig. 8.4, with additional format-specific compression options and a few animation-specific parameters. These are as follows:

  • Frame Rate: When saving the animation as a video file (AVI or Ogg) rather than a series of images, this lets you specify the frame rate for the generated video. It has no effect when saving as a series of images.

  • Frame Stride: Defines how many timesteps to jump forward after recording a screenshot of the current timestep.

  • Frame Window: If you didn’t want to save out the full animation, instead limit to a specific window, you can use this to specify the range of frames to save. If you are generating a animation from a temporal dataset with timesteps, the frame generally corresponds to the timestep number.

To save animations in pvpython, you use SaveAnimation . The arguments to this function are same as the SaveScreenshot with additional parameters for the animation specific options.

>>> SaveAnimation('animation.avi', GetActiveView(),
      FrameWindow = [1, 100],
      FrameRate = 1)

8.4. Saving state

Besides saving the results produced by your visualization setup, you can save the state of the visualization pipeline itself, including all the pipeline modules, views, their layout, and their properties. This is referred to as the Application State , or just State . In paraview, you can save the state using the File > Save State… menu option. Conversely, to load a saved state file, you can use File > Load State….

There are two types of state files that you can save in paraview: ParaView state file (*.pvsm) and Python state file (*.py). The PVSM files are XML-based text files that are human and machine readable, although not necessarily human friendly for a novice user. However, if you don’t plan to read and make sense of the state files, PVSM is the most robust and reliable way to save the application state. For those who want to save the state and then modify it manually, using Python state files may be better, as using Python trace simply traces the actions that you perform in the UI as a Python script. Python state files, on the other hand, save the entire current state of the application as a Python script that you can use in paraview or the Python Shell .


Fig. 8.6 The Save State File dialog in paraview.

To load a state file, you use the File > Load State… menu. Note that loading a state file will affect the current visualization state.


Fig. 8.7 The Load State Options dialog in paraview showing the options for where to find data files.

If you load a PVSM file this way you will be asked where to search for the data files. There are three available options: Use File Names From State , Search files under specified directory and Choose File Names . If you select Use File Names From State then ParaView will look for the data at the absolute paths saved in the state file. If you select Search files under specified directory then you will see an option to browse for a directory that ParaView will search for the files before looking for them in the absolute path in the state file. This defaults to the location of the state file to make sharing state files between computers easier. If you select Choose File Names then you will be given a list of file names in the state file and can override each one individually.


Fig. 8.8 The Load State Options dialog in paraview showing the Search files under specified directory option.

You can save/load the PVSM state file in pvpython as follows:

>>> from paraview.simple import *

# Save the PVSM state file. Currently, this doesn't support
# saving Python state files.
>>> SaveState("sample.pvsm")

# To load a PVSM state file.
>>> LoadState("sample.pvsm")

To replace all data files used by state with those under a specific directory, you use the following form:

>>> LoadState("sample.pvsm",
              data_directory="[directory path]",

              # optionally, restrict to specified directory

The function signature can become a little more complex if you want to explicitly override filenames used in the state file. It may be easier to use the Python trace capabilities to generate the function call for specific state files. It takes the following form:

>>> LoadState("sample.pvsm",
              filenames = [

                # a `dict` object for each reader in statefile to update.
                  "name": "[reader name as shown in the pipeline browser]",

                  # if multiple readers have the same name, 'id' may be used
                  # instead of 'name' where the value is "id" used in the
                  # state file for this reader.

                  # filename properties and their overridden values for this
                  # reader, for example:
                  "FileName" : "foo.vtk",

                # multiple such `dict`s can be specified.

# here's an example
>>> LoadState(statefile,
                      'name' : 'can.ex2',
                      'FileName' : data_dir + 'can.ex2',
                      'name' : 'dataset',
                      'FileName' : data_dir + 'disk_out_ref.ex2',
                      'name' : 'timeseries',
                      'FileName' : [ data_dir + 'dualSphereAnimation/dualSphereAnimation_P00T0000.vtp',
                                     data_dir + 'dualSphereAnimation/dualSphereAnimation_P00T0001.vtp',
                                     data_dir + 'dualSphereAnimation/dualSphereAnimation_P00T0002.vtp',
                                     data_dir + 'dualSphereAnimation/dualSphereAnimation_P00T0003.vtp',
                                     data_dir + 'dualSphereAnimation/dualSphereAnimation_P00T0004.vtp',
                                     data_dir + 'dualSphereAnimation/dualSphereAnimation_P00T0005.vtp',
                                     data_dir + 'dualSphereAnimation/dualSphereAnimation_P00T0006.vtp',
                                     data_dir + 'dualSphereAnimation/dualSphereAnimation_P00T0007.vtp',
                                     data_dir + 'dualSphereAnimation/dualSphereAnimation_P00T0008.vtp',
                                     data_dir + 'dualSphereAnimation/dualSphereAnimation_P00T0009.vtp',
                                     data_dir + 'dualSphereAnimation/dualSphereAnimation_P00T0010.vtp']

Did you know? :class: tip

Not all user interface settings are saved in ParaView state files. For example, the Show Plane and Show Outline and Intersection Edges settings in the Plane widget in the Slice and Clip filters are not saved because they are temporary interface state, not part of the data pipeline or display properties. end{didyouknow}

8.5. Extractors

Section 8.1 and Section 8.2 are two ways of saving datasets and images using actions, i.e., you click a button (or in Python, invoke a function) and the results are saved out immediately. If, for example, you now want to generate the results for another timestep, you have to repeat all the actions. One way to avoid this is to put together a Python script to generate the data and image files and then use that as a macro. An easier way is to use extractors . Extractors are a type of pipeline module, similar to sources and filters, but behave more like writers. Similar to filters, they have inputs. Unlike sources or filters, however, they produce no output that can be consumed by another pipeline module. Instead, when activated, they generate files – which we call extracts.

Since they are just another pipeline module, you use similar mechanisms as sources and filters for creating and configuring these. You use the Extractors menu to create them. The Pipeline Browser shows all the extractors present in the visualization. You select one of them by clicking on it in the Pipeline Browser at which point the Properties panel will update to show parameters on the selected extractor.

There are two types of extractors: data extractors and image extractors. The former generate files from datasets produced by sources and filters, while the latter save out rendering results from views. When created, a data extractor by default uses the active source as the input (similar to filters) which an image extractor uses the active view instead.

8.5.1. Extractor Properties


Fig. 8.9 Properties of an image extractor for PNG files.

You use the Properties panel view and change extractor properties. The available properties can be grouped into two major groups: first are Trigger properties which are common to all extractors, and the second are the Writer properties which are parameters specific to type of writer the extractor uses.

Trigger properties define when the extractor is activated i.e. under what conditions does the extractor produce extracts. Currently, we support time-based controls. You can select the Start Time Step, End Time Step or the Frequency at which to generate the results. Frequency is the number of timesteps per activation. To write every other timestep, set the Frequency to 2, to write every 3rd timestep, set it to 3, and so on.

Writer properties are specific to the writer. For data extractors, these will be similar to the writer properties shown in the Configure Writer dialog for the writer described in Section Section 8.1. For image extractors, they are similar to the Save Screenshot Options dialog described in Section Section 8.2.1. The Writer properties also lets you set a File Name . This is the file name to use to save the extracts. Since extractors are designed to generate a new extract every time they are activated, the File Name supports patterns that let you make the filename unique per activation. {timestep} or {time} in the filename are replaced by the timestep index and the time value for each activation. You add leading zeros (or other prefixes) to the numbers using a form such as {timestep:06d}. There the timestep will be padded with zeros if the number of digits is less than 6. You should not use absolute paths for specifying the filenames here. We will see how to select prefix to store these extracts under in the next section.

8.5.2. Saving Extracts


Fig. 8.10 Save Extracts Options dialog shown on File > Save Extracts….

Once the extractors have been setup, you can trigger the saving of extract using File > Save Extracts…. This will pop up the Save Extract Options dialog which lets you configure the extract generation. Extracts Output Directory specifies the root directories under which all extracts are saved. Check Generate Cinema Specification to generate a data.csv file under the chosen extracts output directory that summarizes the generated extracts. This can be then used with viewers provided by the Cinema Science project to explore the generated extracts. Frame Stride and Frame Window control the timesteps for which extracts are generated.

Hit Ok and ParaView will animate through all timesteps (similar to using the VCR Controls), activating extractors based on their trigger criteria and then generating extracts. On successful completion you should have files under the chosen root directories.


Fig. 8.11 Python State Options dialog shown when saving a Python state file using File > Save State.

In addition to generating extracts using the GUI, you can use pvpython or pvbatch to generate extract offline. Thus is especially handy for HPC use-cases; you can setup your state using an interactive session and once done save out the state and schedule a non-interactive job for the potentially time-consuming extract generation stage. To do so, setup your visualization pipeline including the extractors as normal. Then, instead of using Save Extracts , use File > Save State… and save out a Python state file. The Python State Options dialog has a section similar to Save Extracts Options dialog for choosing Extracts Output Directory and Generate Cinema Specification. Click Ok to save the Python script. The Python script has a section near the end of the end as follows:

if __name__ == '__main__':
    # generate extracts

This is what causes the Python script to save the extracts when the script is executed using pvbatch or pvpython.