![]() To accurately localize these electron puddles they must be spatiotemporally well separated (by increasing the frame rate or reducing the incident electron flux), thereby reducing the so-called coincidence loss 2. While the size and shape of secondary electron puddles contain some information 1, localizing the entry point of the incident electron from its electron cloud already noticeably improves the spatial resolution of the image. Nearly all the useful information in a single raw detector image is contained within “secondary electron puddles”, each of which is digitized from the cloud of secondary charged particles formed in the wake of individual high energy electrons passing through the detector’s sensor. Fortunately, the useful information on these raw data are typically sparse, hence a suitable data reduction and compression scheme should allow us to fully reap the advantages offered by these detectors. Whereas the first two factors have received considerable attention, it remains impractical for many existing algorithms to process the very large raw output produced by these movie-mode detectors. These transformations are driven by three key factors: (1) improved detection efficiency, (2) shorter detector readout times to better resolve individual electron events, and (3) algorithms that translate these advances into improved spatial and temporal resolution. These detectors ushered in a “resolution revolution” for electron cryo-microscopy (cryo-EM), and the prospect of seeing sub-millisecond dynamics for in-situ electron microscopy. ![]() We discuss calibration techniques that support electron detection and counting (e.g., estimate electron backscattering rates, false positive rates, and data compressibility), and novel data analysis methods enabled by ReCoDe (e.g., recalibration of data post acquisition, and accurate estimation of coincidence loss).įast, back-thinned direct electron detectors are rapidly transforming electron microscopy. The output was 100-fold smaller than the raw data and saved directly onto network-attached storage drives over a 10 GbE connection. Running ReCoDe on a workstation we demonstrate on-the-fly reduction and compression of raw data streaming off a detector at 3 GB/s, for hours of uninterrupted data collection. Here, we describe an efficient and flexible data reduction and compression scheme (ReCoDe) that retains both spatial and temporal resolution by preserving individual electron events. Preserving both spatial and temporal resolution in extended observations, however, requires storing prohibitively large amounts of data. We hope this helps you look at the inputs and outputs of MapReduce jobs, Hive queries, and Pig scripts.Fast, direct electron detectors have significantly improved the spatio-temporal resolution of electron microscopy movies. PopupException: Failed to read Avro file. Raise PopupException(_("Failed to read Avro file.")) Read_contents(compression, path, request.fs, offset, length)įile "/usr/lib//lib/hue/apps/filebrowser/src/filebrowser/views.py", line 663, in read_contentsĬontents = _read_avro(fhandle, path, offset, length, stats)įile "/usr/lib//lib/hue/apps/filebrowser/src/filebrowser/views.py", line 716, in _read_avro Response = callback(request, \*callback_args, \**callback_kwargs)įile "/usr/lib//lib/hue/apps/filebrowser/src/filebrowser/views.py", line 168, in viewįile "/usr/lib//lib/hue/apps/filebrowser/src/filebrowser/views.py", line 573, in display middleware INFO Processing exception: Failed to read Avro file.: Traceback (most recent call last):įile "/usr/lib//lib/hue/build/env/lib/python2.6/site-packages/Django-1.4.5-py2.6.egg/django/core/handlers/base.py", line 111, in get_response Raise DataFileException('Unknown codec: %s.' % dec)ĭataFileException: Unknown codec: snappy. views WARNING Could not read avro file at //user/cconner/test_snappy.avroįile "/usr/lib//lib/hue/apps/filebrowser/src/filebrowser/views.py", line 701, in _read_avroĭata_file_reader = datafile.DataFileReader(fhandle, io.DatumReader())įile "/usr/lib//lib/hue/build/env/lib/python2.6/site-packages/avro-1.7.6-py2.6.egg/avro/datafile.py", line 240, in _init_ It turns out that python-snappy is not compatible with the python library called snappy. Note: In this demo, we are using Avro files found in this github (1).
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