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Deletions are marked like this. Additions are marked like this.
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{{{

 {{{#!python
a = EMData("bdb:raw_data#something",0)
b = EMData()
b.read_image("bdb:raw_data#something",0)
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The particles directory is used by e2ctf, e2boxer and e2workflow for storing boxed particle data that has optionally been filtered. In general e2boxer will be the first program that stores data here, and it can be done in a number of ways. People can save directly to the particles directory from e2boxer using the 'write box particle images' button (name may be different) and by making sure the output format is 'bdb'. e2boxer can also be used from the command line for writing output (output format is a command line argument). This is precisely what occurs from the context of e2workflow, which spawns output writing processes using e2boxer.

Note that a naming convention is employed by e2boxer - if the input image names is something.mrc or "bdb:raw_data#something" then the output particles will be written using the filename "something_ptcls". You can access these images from Python using any of these approaches:

{{{#!python
a = EMData.read_images("bdb:raw_data#something_ptcls",[])
n = EMUtil.get_image_count("bdb:raw_data#something_ptcls")
images = []
for i in range(n):
    images.append(EMData("bdb:raw_data#something_ptcls",i))
}}}

e2ctf also stores information in the particles directory

Contents

  1. Parameters/Metadata for applications stored in the project
    1. These parameters can be found in bdb:project
    2. These parameters are application specific and subject to change
      1. e2ctf.parms
      2. e2ctf.misc
      3. e2boxer.project
      4. e2boxer.cache
      5. e2classaverage.indices
    3. These parameters can be found in bdb:raw_data#
    4. These parameters can be found in bdb:particles#

Parameters/Metadata for applications stored in the project

These are metadata parameters associated with a 'Project', like a single particle reconstruction, and may be application specific or global. If an application wishes to make parameters available for other applications to see, it should put them in 'bdb:project' in the local directory and name them as described below. Documentation for application specific parameters not really intended for use by other applications appears here as well. This does NOT document parameters stored in the header of individual images, though we strive to use the same names whereever possible.

These parameters can be found in bdb:project

Parameters starting with 'global.' are those which could be used equally by any application, other applications wishing to publish parameters for use by others should prefix them with 'appname.'

global.apix

float

Default A/pix value for a project

global.microscope_voltage

float

Default microscope voltage in kV for a project

global.microscope_cs

float

Default microscope Cs in mm for a project

global.micrograph_ccd_filenames

list of strings

All micrograph and/or CCD data file names that are associated with a project

global.num_cpus

int

Number of cpus available to this project

global.memory_available

int

Maximum memory usage for this project

workflow.process_ids

list of ints

A list of process ids that is managed by the workflow system

These parameters are application specific and subject to change

Dictionary names should begin with the application name, the contents of each dictionary can be freely defined by the application.

e2ctf.parms

filename

EMAN2Ctf object

An EMAN2Ctf object associated with each filename (without extension)

e2ctf.misc

envelope

list of (x,y) tuples

Computed Envelope*StrucFact curve from CTF maxima from all sets processed during one run

working_ac

float

Amplitude contrast constant used to launch e2ctf from within the project/workflow setting

working_oversamp

float

Oversample factor used to launch e2ctf from within the project/workflow setting

e2boxer.project

working_boxsize

int

The box size that is used to launch e2boxer from within the project/workflow setting

e2boxer.cache

This is the cache associated with e2boxer.py.

filename_DD

Python dictionary

Image Database Dictionary

filename_DD.auto_boxer_state_TS

string

Greenwich Mean time stamp of the associated Autoboxer when it was last used to autobox this image. This information can be used to force reboxing if the associated autoboxer has been updated

filename_DD.auto_boxer_unique_id

string

stores the unique ID of an associated Autoboxer instance which is also stored in the boxer_cache database

filename_DD.auto_boxes

boxertools.TrimBox list

stores the current set of automatically selected boxes by the associated Autoboxer instance

filename_DD.coarse_flat_image

EMData

stores the image generated by the boxertools.CoarsenedFlattenedImage object that is associated with this image

filename_DD.exclusion_image

EMData

stores the binary exclusion image that is associated with this image

filename_DD.flcf_image

EMData

stores the image generated by the boxertools.FLCFImage object that is associated with this image

filename_DD.frozen_state

boolean

indicates whether or not the image is frozen. If the image is frozen then no changes can be made to the currently associated boxes

filename_DD.image_tag

string

stores the tag of the image (e.g. hdf, mrc, dm3). This information can be used to check that the user is not trying to box images that have the same file name (minus the tag). This feature should become deprecated

filename_DD.manual_boxes

boxertools.TrimBox list

stores boxes that have been manually placed in this image

filename_DD.moved_boxes

Python list of 2D coordinates

effectively a memorization of all the box movements enforced by the user. Can be used to perform collision detection and automatically correct the results of automatic boxing

filename_DD.quality

integer [0-4]

in the special case of the quality being equal to zero this means the image is excluded from boxing and that no boxes should ever be generated for it. Otherwise this is just metadata that may be acted upon autonomously by the user

filename_DD.reference_boxes

boxertools.TrimBox list

stores the reference boxes located in this image that are also being used by the associated Autoboxer instance

filename_DD.subsampled_image

EMData

stores the image generated by the boxertools.SincBlackmanSubsampledImage object that is associated with this image

filename_DD.template_ts

string

Greenwich Mean time stamp that records when the associated template (that was used to generate the correlation image, which is potentially stored in the database also) was created

autoboxer_ts

Python dictionary

the key here is literally the string "autobxer_" followed the a unique time stamp as a string. This dictionary object stores the essential parameters of a an Autoboxer instance

autoboxer_ts.autoboxer

boxertools.AutoBoxer or boxertools.TrimAutoBoxer

the Autoboxer instance itself (or similar)

autoboxer_ts.autoboxer_type

string

this is the unique associated with the Autoboxer type, for example "Swarm" or "Gauss"

autoboxer_ts.convenience_name

string

the user may optionally supply a convenience name for this autoboxer in the e2boxer interface, otherwise the name is generated automatically

current_autoboxer

boxertools.AutoBoxer or boxertools.TrimAutoBoxer

the most recently use Autoboxer instance

current_autoboxer_type

string

the type of the most recently used Autoboxer instance. This should become deprecated.

e2classaverage.indices

str(class number)

list of integers

The indexes of particles that went into the class

These parameters can be found in bdb:raw_data#

The raw_directory is used by e2workflow.py for storing imported micrograph/CCD data. The image file tag (e.g. the file tag of /home/somewhere/something.mrc is "something") may be used to access the image, for example 

   1 a = EMData("bdb:raw_data#something",0)
   2 b = EMData()
   3 b.read_image("bdb:raw_data#something",0)

These parameters can be found in bdb:particles#

The particles directory is used by e2ctf, e2boxer and e2workflow for storing boxed particle data that has optionally been filtered. In general e2boxer will be the first program that stores data here, and it can be done in a number of ways. People can save directly to the particles directory from e2boxer using the 'write box particle images' button (name may be different) and by making sure the output format is 'bdb'. e2boxer can also be used from the command line for writing output (output format is a command line argument). This is precisely what occurs from the context of e2workflow, which spawns output writing processes using e2boxer.

Note that a naming convention is employed by e2boxer - if the input image names is something.mrc or "bdb:raw_data#something" then the output particles will be written using the filename "something_ptcls". You can access these images from Python using any of these approaches: 

   1 a = EMData.read_images("bdb:raw_data#something_ptcls",[])
   2 n = EMUtil.get_image_count("bdb:raw_data#something_ptcls")
   3 images = []
   4 for i in range(n):
   5     images.append(EMData("bdb:raw_data#something_ptcls",i))

e2ctf also stores information in the particles directory

filename_ptcls

EMAN database image matrix

Particle files generally written to disk by e2boxer but potentially also placed by the particle import mechanism in the workflow, or manually

filename_ctf_

Eman2AppMetadata (last edited 2013-08-11 18:07:28 by SteveLudtke)