Starting the LiberTEM server
The LiberTEM GUI is based on a client-server architecture. To use the LiberTEM GUI, you need to have the server running on the machine where your data is available. For using LiberTEM from Python scripts, this is not necessary, see Python API.
After installing LiberTEM, activate the virtualenv or conda environment.
You can then start the LiberTEM server by running:
(libertem) $ libertem-server
By default, this starts the server on http://localhost:9000, which you can verify by the log output:
[2018-08-08 13:57:58,266] INFO [libertem.web.server.main:886] listening on localhost:9000
It will then open your default web browser to this URL.
There are a few command line options available:
Usage: libertem-server [OPTIONS] Options: -h, --host TEXT host on which the server should listen on -p, --port INTEGER port on which the server should listen on -d, --local-directory TEXT local directory to manage dask-worker-space files -b, --browser / -n, --no-browser enable/disable opening the browser -l, --log-level TEXT set logging level. Default is 'info'. Allowed values are 'critical', 'error', 'warning', 'info', 'debug'. -t, --token-path PATH path to a file containing a token for authenticating API requests --help Show this message and exit.
New in version 0.4.0:
--no-browser option was added, open browser by default.
New in version 0.6.0:
-l, --log-level was added.
New in version 0.8.0:
-t, --token-path was added and
-h, --host was re-enabled.
The GUI is tested to work on Firefox and Chromium-based browsers for now. If you cannot use a compatible browser for some reason, please file an issue!
After starting the server, you can open the GUI in your browser. If it didn’t open automatically, you can access it by default at http://localhost:9000 . At the beginning, the GUI shows a prompt to create a local cluster or connect to a running one. The number of workers is preset with a number that will likely give optimal performance on the given machine. You can also select which CUDA devices to use, if you have any (needs to have a working cupy installation).
Starting a custom cluster
LiberTEM can connect to a running Dask cluster. To start a cluster on
localhost, first run a scheduler:
(libertem) $ dask-scheduler --host localhost
GPU support in LiberTEM requires specific resource tags and environment settings on the dask workers. The easiest way to start workers with the appropriate settings is
(libertem) $ libertem-worker tcp://localhost:8786
There are a few command line options available:
Usage: libertem-worker [OPTIONS] [SCHEDULER] Options: -k, --kind TEXT Worker kind. Currently only "dask" is implemented. -d, --local-directory TEXT local directory to manage temporary files -c, --n-cpus INTEGER Number of CPUs to use, defaults to number of CPU cores without hyperthreading. -u, --cudas TEXT List of CUDA device IDs to use, defaults to all detected CUDA devices. Use "" to deactivate CUDA. -n, --name TEXT Name of the cluster node, defaults to host name -l, --log-level TEXT set logging level. Default is 'info'. Allowed values are 'critical', 'error', 'warning', 'info', 'debug'. --help Show this message and exit.
New in version 0.6.0.
For a cluster setup, you can run the scheduler on the appropriate network interface and run workers on all cluster nodes to connect to the scheduler.
You can then connect to the cluster’s scheduler URL in the LiberTEM web GUI.
After connection to a cluster, LiberTEM shows a button to start browsing for available files. On a local cluster that’s simply the local filesystem.
See Sample Datasets for publicly available datasets.
This opens the file browser dialogue. On top it shows the current directory, below it lists all files and subdirectories in that directory. You select an entry by clicking once on it. You can move up one directory with the “..” entry on top of the list. The file browser is still very basic. Possible improvements are discussed in Issue #83. Contributions are highly appreciated! This example opens an HDF5 file [ZMB+19].
You can also bookmark locations you frequently need to access, using the star icon. The bookmarks are then found under “Go to…”.
After selecting a file, you set the type in the drop-down menu at the top of the dialogue above the file name. After that you set the appropriate parameters that depend on the file type. Clicking on “Load Dataset” will open the file with the selected parameters. The interface and internal logic to find good presets based on file type and available metadata, validate the inputs and display helpful error messages is still work in progress. Contributions are highly appreciated!
See Loading using the GUI for more detailed instructions and format-specific information.
Once a dataset is loaded, you can add analyses to it. As an example we choose a “Ring” analysis, which implements a ring-shaped virtual detector.
This analysis shows two views on your data: the two detector dimensions on the left, the scanning dimensions on the right, assuming a 4D-STEM dataset. For the general case, we also call the detector dimensions the signal dimensions, and the scanning dimensions the navigation dimensions. See also Concepts for more information on axes and coordinate system.
Directly after adding the analysis, LiberTEM starts calculating an average of all the detector frames. The average is overlaid with the mask representing the virtual detector. The view on the right will later show the result of applying the mask to the data. In the beginning it is empty. The first processing might take a while depending on file size and I/O performance. Fast SSDs and enough RAM to keep the working files in the file system cache are highly recommended for a good user experience.
You can adjust the virtual detector by dragging the handles in the GUI. Below it shows the parameters in numerical form. This is useful to extract positions, for example for scripting.
After clicking “Apply”, LiberTEM performs the calculation and shows the result in scan coordinates on the right side.
Instead of average, you can select “Standard Deviation”. This calculates standard deviation of all detector frames.
If you are interested in individual frames rather than the average, you can switch to “Pick” mode in the “Mode” drop-down menu directly below the detector window.
In “Pick” mode, a selector appears in the result frame on the right. You can drag it around with the mouse to see the frames live in the left window. The picked coordinates are displayed along with the virtual detector parameters below the frame window on the left.
If you are interested in a limited region, the ROI dropdown provides the option to select a rectangular region. For example if you select “Rect”, the average/standard deviation is calculated over all images that lie inside selected rectangle. You can adjust the rectangle by dragging the handles in the GUI.
Some analyses, such as the Center of Mass (COM) analysis, can render the result in different ways. You can select different result channels in the “Channel” drop-down menu below the right window.
After an analysis has finished running, you can download the results. Clicking the download button below the analysis will open a dialog:
In the download dialog, you can choose between different file formats, and separately download the available results.
You can also download a Jupyter notebook corresponding to the analysis and continue working with the same parameters using scripting.
It’s also possible to copy individual cells of Jupyter notebook directly from GUI, with an option to copy the complete source code.
You can use arrow keys to change the coordinate parameters of any analysis. To do this, click on the handle you want to modify, and then use the arrow keys to move the handle. Hold shift to move in larger steps.
For more applications, like strain mapping and crystallinity analysis, please see the Applications section.