Coerce an EML file or object into an emld object.
as_emld(x, from = c("guess", "xml", "json", "list"))
| x | path to an EML file |
|---|---|
| from | explicit type for the input format. By default, will attempt to guess the format, but it always safer to specify the input format. This is essential for literal text strings or raw vectors where the type cannot be guessed by the R object class or file extension of the input. |
an emld object
#> access: #> authSystem: knb #> order: allowFirst #> scope: document #> allow: #> - principal: uid=HFR,o=lter,dc=ecoinformatics,dc=org #> permission: all #> - principal: public #> permission: read #> additionalMetadata: #> - metadata: #> additionalClassifications: #> status: ongoing #> researchTopic: community #> studyType: #> - short-term measurement #> - modeling #> - metadata: #> additionalLinks: #> url: #> - name: Ecophysiology of Carnivorous Plants Worldwide 1980-2011 #> url: http://harvardforest.fas.harvard.edu:8080/exist/xquery/data.xq?id=hf168 #> - name: Effects of Prey Availability on Sarracenia Physiology at Harvard Forest #> 2005 #> url: http://harvardforest.fas.harvard.edu:8080/exist/xquery/data.xq?id=hf109 #> - name: Food Web of Sarracenia Purpurea in United States and Canada since 1999 #> url: http://harvardforest.fas.harvard.edu:8080/exist/xquery/data.xq?id=hf193 #> - name: Thresholds and Regime Shifts at Four LTER Sites (CCE, JRN, PAL, SBC) #> 1951-2009 #> url: http://harvardforest.fas.harvard.edu:8080/exist/xquery/data.xq?id=hf170 #> - name: Nitrogen Cycling Dynamics in Sarracenia Purpurea at Harvard Forest 2004-2005 #> url: http://harvardforest.fas.harvard.edu:8080/exist/xquery/data.xq?id=hf096 #> - name: Organic and Inorganic Nitrogen Uptake by Sarracenia Purpurea at Harvard #> Forest and Fort Albany ON 2007 #> url: http://harvardforest.fas.harvard.edu:8080/exist/xquery/data.xq?id=hf146 #> - name: Prey Capture by Carnivorous Plants Worldwide 1923-2007 #> url: http://harvardforest.fas.harvard.edu:8080/exist/xquery/data.xq?id=hf111 #> - name: Sarracenia Purpurea Prey Capture at Harvard Forest 2008 #> url: http://harvardforest.fas.harvard.edu:8080/exist/xquery/data.xq?id=hf114 #> - name: Allochthonous Nutrients in the Sarracenia Microecosystem at Harvard #> Forest 2005-2007 #> url: http://harvardforest.fas.harvard.edu:8080/exist/xquery/data.xq?id=hf098 #> dataset: #> id: HF205 #> alternateIdentifier: HF205 #> title: Thresholds and Tipping Points in a Sarracenia Microecosystem at Harvard Forest #> since 2012 #> pubDate: '2012' #> abstract: #> section: '<para>The primary goal of this project is to determine experimentally #> the amount of lead time required to prevent a state change. To achieve this #> goal, we will (1) experimentally induce state changes in a natural aquatic ecosystem #> - the Sarracenia microecosystem; (2) use proteomic analysis to identify potential #> indicators of states and state changes; and (3) test whether we can forestall #> state changes by experimentally intervening in the system. This work uses state-of-the #> art molecular tools to identify early warning indicators in the field of aerobic #> to anaerobic state changes driven by nutrient enrichment in an aquatic ecosystem. #> The study tests two general hypotheses: (1) proteomic biomarkers can function #> as reliable indicators of impending state changes and may give early warning #> before increasing variances and statistical flickering of monitored variables; #> and (2) well-timed intervention based on proteomic biomarkers can avert future #> state changes in ecological systems.</para>' #> intellectualRights: #> section: '<para>This dataset is released to the public and may be freely downloaded. #> Please keep the designated Contact person informed of any plans to use the dataset. #> Consultation or collaboration with the original investigators is strongly encouraged. #> Publications and data products that make use of the dataset must include proper #> acknowledgement.  Field methods. All experiments #> will be carried out in the greenhouse at Harvard Forest. We have developed #> an instrumentation system that allows us to collect continuous dissolved #> [O2] measurements: dedicated micro-probes (DO-166MT; Lazar Research Laboratories: #> http://www.shelfscientific.com/) connected to multiplexers and data loggers #> (AM16/32B multiplexer, CR-1000 datalogger and control system [Campbell Scientific: #> http://www.cambellsci.com]). The initial ecosystem composition in all experimental #> plants will be standardized by seeding each pitcher with a 10-ml inoculum #> of liquid collected from pitchers growing at Tom Swamp. In all experiments, #> prey will be supplied to pitchers as standardized aliquots of dried and #> finely ground bald-faced hornets (Dolichovespula maculata; Hymenoptera: #> Vespidae), which we collect in quantity throughout New England. Both hornets #> and ants (the latter are the dominant prey of S. purpurea) are hymenoptera, #> and have nearly identical C:N ratios (hornets: 3.97; common bog-dwelling #> ants [Tapinoma sessile and Myrmica lobifrons]: 3.37), but on average hornets #> have greater than 100 times the dry mass of these ants, and are easier to #> collect and process as a standardized food source. Additions of prey, either #> as large "pulses" or chronic "presses" are analogous to the enrichment and #> eutrophication that occur in aquatic "green" food webs in which phytoplankton #> abundance is boosted through addition of limiting nutrients. In "brown" #> food webs such as the Sarracenia microecosystem, detritus - not primary #> production - is at the base of the web, and our treatments boost this material #> as would happen through increases in arthropod prey capture78 or through #> nitrogen-enriched precipitation.</para><para>2. Proteomic analysis. Proteomic #> profiles of microbial communities are determined after separating the microbial #> fraction from the pitcher fluid, prey, and other detritus. The microbial #> "pellet" is subjected to SDS-PAGE (sodium dodecyl sulfate polyacrylamide #> gel) electrophoresis; bands are cut out and digested in-gel with trypsin. #> Tryptic peptides are subjected to LC-MS/MS (liquid chromatography tandem #> mass spectrometry) for peptide and protein identification. Absolute abundance #> of peptides and proteins are quantified using AQUA (Absolute QUAntification) #> analysis109.</para>' #> - '<title>Specific Experiments</title><para>Experiment #1. Effects of nutrient #> enrichment on state changes and [O2] profiles. This experiment alters nutrient #> enrichment rates to characterize the [O2] profile and the transition to #> the anaerobic state. The experimental design is a one-way layout with 5 #> treatment groups: one control (no enrichment) and 4 enrichment levels (0.125, #> 0.25, 0.5, 1.0 mg prey added ml-1 d-1). One plant is assigned to each treatment #> group, and the entire set is replicated 6 times over successive weeks. [O2] #> is monitored continuously for 4 days to characterize state changes and tipping #> points under different enrichment rates. This experiment tracks a continuous #> [O2] profile but does not include proteomic analysis. The purpose of Experiment #> #1 is to identify an enrichment rate E that generates a long pre-tipping #> period before transition time T to the anaerobic state. This enrichment #> rate will be used in Experiments #2 - #4.</para><para>Experiment #2. Identification #> of early intervention time and characterization of aerobic and anaerobic #> proteomes. This experiment will use the single enrichment rate E determined #> from Experiment #1 and impose different intervention times I at which nutrient #> enrichment will be terminated. Thus, this experiment will identify the latest #> time I* at which it is possible to intervene and stop the transition to #> the anaerobic state by halting enrichment. The [O2] profile will again be #> monitored continuously over 10 days to measure the state of the system. #> From Experiment #1, the transition time T to the anaerobic state with no #> intervention will be known. We will use one control group (no prey addition) #> and ten levels of intervention time (all with the same enrichment rate E) #> as a proportion of T (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0). #> Six plants will be assigned randomly to each of the 11 treatments in a randomized #> one-way layout and [O2] profiles will be monitored continuously. In addition #> to the [O2] profiles, we will also characterize the protein profiles of #> aerobic and anaerobic pitchers in all 11 treatment groups at the end of #> the experiment.</para><para>After the plants are harvested, we will create #> proteomic profiles of the predominantly bacterial portion (centrifuged pellet) #> of the pitcher fluid from each plant, as described in General Protocols. #> Thus, 66 separate pellet-fraction samples will be analyzed by SDS-PAGE. #> After examining the SDS-PAGE profiles, approximately ten proteins that show #> dynamic patterns consistent with state change and five that do not change #> will be cut from the gel, subjected to in-gel tryptic digestion and a portion #> of the tryptic peptides will be analyzed by LC-MS/MS. Using these data, #> we will choose three identified peptides from each protein for peptide synthesis #> such that each synthesized peptide contains stable isotope labels (AQUA #> peptides) to distinguish them by mass from the native peptides. We will #> then quantify all 45 of the native peptides from the original samples using #> a known amount of each AQUA peptide spiked into the tryptic digest. The #> AQUA analysis of proteins that do not show changes will be used for normalization #> between samples. These data will be used to independently identify the current #> state of the system and forecast the time-to-transition.</para><para>We #> will use Sequest searches for initial identification of peptides; relevant #> scores including Xcorr and Delta Cn values will be given for each peptide. #> Other peptides will be identified by de novo sequencing using PepNovo; all #> PepNovo scores will likewise be given including any N- or C-terminal gaps. #> Mass error in ppm will be reported for each precursor ion. We will use standard #> multivariate analysis to search for distinctive proteomic profiles114 that #> characterize aerobic and anaerobic ecosystems, as well as ecosystems that #> developed with and without inputs of photosynthetic O2 and plant metabolites.</para><para>Experiment #> #3. Identification of diagnostic proteins. Using Experiments #1 and 2, we #> will have identified an enrichment rate E with a long pre-tipping period #> and an intervention time I* before which mitigation and termination of enrichment #> will prevent eutrophication. Experiment #3 will characterize the mean and #> variance of the protein profile before and after I*. We are especially interested #> in identifying proteins that increase rapidly in abundance (or variance) #> well before the onset of flickering in [O2] and before the transition time #> T from the aerobic to the anaerobic state.</para><para>A cohort of 100 plants #> all will be fed at rate E (determined from Experiment #1), with intervention #> time I* determined from Experiment #2, although no intervention will be #> used in this "press" experiment so that we can contrast proteins before #> and after the state change. At seven times before I* and three times after #> I*, we will harvest 10 randomly chosen plants. At each prescribed harvest #> time, we will measure [O2] and collect samples from each plant for proteomic #> screening using both SDS-PAGE and AQUA analysis. This experiment will identify #> proteins that rise quickly in abundance during the pre- I* period and can #> be used as early indicators of a future tipping point. Because different #> plants will be harvested at each time period, this is a one-way ANOVA design, #> with pre-and post- I* a priori contrasts. A randomization test will be used #> to determine whether variances in protein expression differ through time. #> During these analyses we will use the data from the AQUA peptides and from #> known amounts of protein standards, such as bovine serum albumin, to approximate #> the amount of protein in a given coomassie-stained SDS-PAGE gel band. The #> reason for doing this is to provide a fast "real-time" assay based just #> on expression in the SDS-PAGE. This rapid assay will be used in Experiment #> #4.</para><para>Experiment # 4. Proof-of-application. This experiment will #> provide a benchmark test of our methods and their ability to correctly identify #> tipping points. A cohort of 100 plants will each be fitted with [O2] probes #> and started on the enrichment regime. Two times per day, we will collect #> 3 plants each, pool their contents, and conduct a rapid screen in the lab #> with SDS-PAGE for the diagnostic proteins that were identified in Experiment #> #3. We will use the protein expression in the gel to delineate an "early" #> and a "late" mitigation strategy. As soon as diagnostic proteins measured #> in the SDS-gels are at abundances that signal we are at 0.5×I* - approximately #> one-half of the way to the latest intervention time - we will randomly select #> one third of the remaining plants for mitigation and termination of enrichment #> (the "early" mitigation strategy). We will continue to harvest plants from #> the remainder of the cohort and monitor proteins. As soon as diagnostic #> proteins signal we are at 0.75 times I*, we will randomly select one half #> of the remaining plants for mitigation and termination of enrichment (the #> "late" mitigation strategy). The remaining plants (approximately one sixth #> to one third of the original cohort) will continue to be enriched. We will #> monitor [O2] in all 3 groups (no-mitigation control, early mitigation, late #> mitigation) until all plants reach a new [O2] equilibrium. If the protein #> markers are successful, the proportion of food webs that remain aerobic #> will be significantly higher in the two mitigation treatments than in the #> no-mitigation control.</para>' #> dataTable: #> id: hf205-01 #> entityName: hf205-01-TPexp1.csv #> entityDescription: tipping point experiment 1 #> physical: #> id: '1354212701790' #> objectName: hf205-01-TPexp1.csv #> dataFormat: #> textFormat: #> numHeaderLines: '1' #> recordDelimiter: \r\n #> attributeOrientation: column #> simpleDelimited: #> fieldDelimiter: ',' #> distribution: #> online: #> url: #> function: download #> url: http://harvardforest.fas.harvard.edu/data/p20/hf205/hf205-01-TPexp1.csv #> attributeList: #> attribute: #> - id: '1354213311470' #> attributeName: run.num #> attributeDefinition: 'which run number (=block). Range: 1 – 6. (integer)' #> measurementScale: #> nominal: #> nonNumericDomain: #> textDomain: #> definition: which run number #> - id: '1354213311471' #> attributeName: year #> attributeDefinition: year, 2012 #> measurementScale: #> dateTime: #> formatString: YYYY #> - id: '1354213311472' #> attributeName: day #> attributeDefinition: 'Julian day. Range: 170 – 209.' #> measurementScale: #> dateTime: #> formatString: DDD #> - id: '1354213311473' #> attributeName: hour.min #> attributeDefinition: hour and minute of observation. Range 1 – 2400 (integer) #> measurementScale: #> dateTime: #> formatString: hhmm #> - id: '1354213311474' #> attributeName: i.flag #> attributeDefinition: is variable Real, Interpolated or Bad (character/factor) #> measurementScale: #> nominal: #> nonNumericDomain: #> enumeratedDomain: #> codeDefinition: #> - code: R #> definition: real #> - code: I #> definition: interpolated #> - code: B #> definition: bad #> - id: '1354213311475' #> attributeName: variable #> attributeDefinition: what variable being measured in what treatment (character/factor). #> measurementScale: #> nominal: #> nonNumericDomain: #> enumeratedDomain: #> codeDefinition: #> - code: control #> definition: no prey added #> - code: low #> definition: 0.125 mg prey added ml-1 d-1 #> - code: med.low #> definition: 0,25 mg prey added ml-1 d-1 #> - code: med.high #> definition: 0.5 mg prey added ml-1 d-1 #> - code: high #> definition: 1.0 mg prey added ml-1 d-1 #> - code: air.temp #> definition: air temperature measured just above all plants (1 thermocouple) #> - code: water.temp #> definition: water temperature measured within each pitcher #> - code: par #> definition: photosynthetic active radiation (PAR) measured just #> above all plants (1 sensor) #> - id: '1354213311476' #> attributeName: value.i #> attributeDefinition: value of measured variable for run.num on year/day/hour.min. #> measurementScale: #> nominal: #> nonNumericDomain: #> enumeratedDomain: #> codeDefinition: #> - code: control #> definition: '% dissolved oxygen' #> - code: low #> definition: '% dissolved oxygen' #> - code: med.low #> definition: '% dissolved oxygen' #> - code: med.high #> definition: '% dissolved oxygen' #> - code: high #> definition: '% dissolved oxygen' #> - code: air.temp #> definition: degrees C #> - code: water.temp #> definition: degrees C #> - code: par #> definition: micromoles m-1 s-1 #> numberOfRecords: '9999' #> creator: #> - individualName: #> givenName: Aaron #> surName: Ellison #> - individualName: #> givenName: Nicholas #> surName: Gotelli #> associatedParty: #> - individualName: #> givenName: Benjamin #> surName: Baiser #> role: Researcher #> - individualName: #> givenName: Jennifer #> surName: Sirota #> role: Researcher #> keywordSet: #> - keywordThesaurus: LTER controlled vocabulary #> keyword: #> - bacteria #> - carnivorous plants #> - genetics #> - thresholds #> - keywordThesaurus: LTER core area #> keyword: #> - populations #> - inorganic nutrients #> - disturbance #> - keywordThesaurus: HFR default #> keyword: #> - Harvard Forest #> - HFR #> - LTER #> - USA #> otherEntity: #> - id: hf205-02 #> entityName: hf205-02-mathematica-oxygen.nb #> entityDescription: Mathematica code for oxygen model (nb) #> physical: #> objectName: hf205-02-mathematica-oxygen.nb #> dataFormat: #> externallyDefinedFormat: #> formatName: Mathematica notebook #> distribution: #> online: #> url: #> function: download #> url: http://harvardforest.fas.harvard.edu/data/p20/hf205/hf205-02-mathematica-oxygen.nb #> entityType: Mathematica code #> - id: hf205-03 #> entityName: hf205-03-mathematica-oxygen.pdf #> entityDescription: Mathematica code for oxygen model (pdf) #> physical: #> objectName: hf205-03-mathematica-oxygen.pdf #> dataFormat: #> externallyDefinedFormat: #> formatName: pdf #> distribution: #> online: #> url: #> function: download #> url: http://harvardforest.fas.harvard.edu/data/p20/hf205/hf205-03-mathematica-oxygen.pdf #> entityType: Mathematica code #> packageId: knb-lter-hfr.205.4 #> schemaLocation: eml://ecoinformatics.org/eml-2.1.0 http://nis.lternet.edu/schemas/EML/eml-2.1.0/eml.xsd #> scope: system #> system: hfr


Image2ASCII is a Python library I wrote which is capable of approximating images with text, creating ASCII art from any source. To make the tool more accessible, I also wrapped the library in a simple app using Pythonista.
Image2ASCII generates artwork by looking at the source image and matching brightness levels
in different parts of the image with characters that have an approximately appropriate
"visual weight" on the page. Characters that fill more of their area with black, like W
or $
are considered to have a high visual weight, while characters like .
and '
, which leave most of the space they occupy blank, are considered to have
low visual weight.
In order to apply this principle, the first step that the program takes is to generate a map between characters and their visual weights. The program renders each ASCII glyph onto a canvas the size of one character, and computes the average brightness of the resulting image. Lower brightnesses indicate characters with more visual weight, because more of the image is black.
After building the map between each character and its visual weight, image2ASCII assigns characters to different portions of the image. The image is converted to black and white, its contrast increased to improve clarity of the ASCII art, and then the image is downscaled to a size where each pixel could be reasonably represented by a single character. Then, pixel-by-pixel the character with the visual weight most similar to the brightness of the pixel in question is selected and added to the output string. The result is the entire artwork represented as text.
Image2ASCII provides functions for rendering artwork onto an image for display, but the example to the left uses the raw text output and dynamically renders it onto a canvas.


|
BTK Python wrapper
0.3dev.0
Python bindings for the Biomechanical ToolKit library (BTK)
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High level classes and functions to open, edit and modify biomechanical acquisition. More...
Classes | |
| class | _object |
| Abstract interface for the Python wrapping of BTK. | |
| class | btkAcquisition |
| Contains the data related to a biomechanical acquisition. More... | |
| class | btkAcquisitionFileIO |
| Interface to read/write acquisition's files. More... | |
| class | btkAcquisitionFileReader |
| Reader for files which contain acquisition data (C3D, TRC, ...). More... | |
| class | btkAcquisitionFileWriter |
| Writer for files which contain acquisition data (C3D, TRC, ...). More... | |
| class | btkAcquisitionUnitConverter |
| Convert the units of the given input for points and analog channels. More... | |
| class | btkAnalog |
| Analog channel data along the time. More... | |
| class | btkAnalogCollection |
| Collection of analog channels stored in a list. More... | |
| class | btkAnalogCollectionIterator |
| Iterator to pass trough the collection of analog channels. More... | |
| class | btkAnalogData |
| Class storing the measures for one analog channel. More... | |
| class | btkAnalogOffsetRemover |
| Remove a possible analog offset to the selected analog channels. More... | |
| class | btkANBFileIO |
| Class to read/write ANB files. More... | |
| class | btkANCFileIO |
| Class to read/write ANC files. More... | |
| class | btkANGFileIO |
| Class to read ANG files. More... | |
| class | btkC3DFileIO |
| Class to read/write C3D files. More... | |
| class | btkCALForcePlateFileIO |
| Class to read/write force plate CAL files. More... | |
| class | btkCLBFileIO |
| Class to read CLB Contec Inc file. More... | |
| class | btkDelsysEMGFileIO |
| Class to read Delsys EMG files. More... | |
| class | btkDoubleArray |
| Array of doubles stored in a btkMetaDataInfo object. More... | |
| class | btkEMFFileIO |
| Class to read EMF files. More... | |
| class | btkEMxFileIO |
| Class to read EMG files. More... | |
| class | btkEvent |
| Label a specific time/frame during an acquisition. More... | |
| class | btkEventCollection |
| Collection of events stored in a list. More... | |
| class | btkEventCollectionIterator |
| Iterator to pass trough the collection of events. More... | |
| class | btkForcePlatform |
| Base class for all type of force platform. More... | |
| class | btkForcePlatformCollection |
| Collection of force platform stored in a list. More... | |
| class | btkForcePlatformCollectionIterator |
| Iterator to pass trough the collection of force platform. More... | |
| class | btkForcePlatformsExtractor |
| Extracts force platform data from a btk::Acquisition object. More... | |
| class | btkForcePlatformWrenchFilter |
| Calcule the wrench of the center of the force platform data, expressed in the global frame (by default). More... | |
| class | btkGroundReactionWrenchFilter |
| Transform force platform data into ground reaction wrenches. More... | |
| class | btkGRxFileIO |
| Class to read GR* files. More... | |
| class | btkIMU |
| Container of analog channels representing an inertial measurement unit (IMU). More... | |
| class | btkIMUCollection |
| Collection of IMUs stored in a list. More... | |
| class | btkIMUCollectionIterator |
| Iterator to pass trough the collection of IMUs. More... | |
| class | btkIMUsExtractor |
| Extracts inertial measurement units (IMUs) from a btkAcquisition object. More... | |
| class | btkIntArray |
| Array of integers stored in a btkMetaDataInfo object. More... | |
| class | btkMDFFileIO |
| Class to read MDF files (Codamotion). More... | |
| class | btkMergeAcquisitionFilter |
| Merges or concatenates two or more btk::Acquisition objects into a single new one. More... | |
| class | btkMetaData |
| Store data which cannot be embedded within timeseries (calibration matrix, subject informations, ...). More... | |
| class | btkMetaDataInfo |
| Container class to store data of a btkMetaData object. More... | |
| class | btkMetaDataIterator |
| Iterator to pass trough a level of metadata. More... | |
| class | btkMOMFileIO |
| Class to read MOM files. More... | |
| class | btkPoint |
| Coordinates of a point in a 3D space along the time. More... | |
| class | btkPointCollection |
| Collection of points stored in a list. More... | |
| class | btkPointCollectionIterator |
| Iterator to pass trough the collection of points. More... | |
| class | btkPointData |
| Class storing the measures for a point (3D values and residuals along the time). More... | |
| class | btkPWRFileIO |
| Class to read PWR files. More... | |
| class | btkRAxFileIO |
| Class to read RAH, RAW files. More... | |
| class | btkRICFileIO |
| Class to read RIC files. More... | |
| class | btkSeparateKnownVirtualMarkersFilter |
| Separate a collection of points in five categories to be able to distinguish real markers from the others. More... | |
| class | btkSpecializedPointsExtractor |
| Extracts only the points corresponding to the given type (without cloning the extracted points). More... | |
| class | btkStringArray |
| Array of strings stored in a btkMetaDataInfo object. More... | |
| class | btkStringAxes |
| Workaround to represent the C++ class btk::SeparateKnownVirtualMarkersFilter::StringAxes with SWIG. More... | |
| class | btkStringAxesList |
| Convenient class to store a list of btkStringAxes object. More... | |
| class | btkStringList |
| Convenient class to store a list of string. More... | |
| class | btkSubAcquisitionFilter |
| Extract a subpart of the acquisition. More... | |
| class | btkTDFFileIO |
| Class to read TDF files. More... | |
| class | btkTRBFileIO |
| Class to read/write TRB files. More... | |
| class | btkTRCFileIO |
| Class to read/write TRC files. More... | |
| class | btkVerticalGroundReactionForceGaitEventDetector |
| Detect heel strike and toe-off events during gait from vertical ground reaction wrench. More... | |
| class | btkWrench |
| Represents a wrench as position, force and moment varying during the time. More... | |
| class | btkWrenchCollection |
| Collection of wrenches stored in a list. More... | |
| class | btkWrenchCollectionDownsampleFilter |
| Downsample a collection of wrenches. More... | |
| class | btkWrenchCollectionIterator |
| Iterator to pass trough the collection of wrenches. More... | |
| class | btkWrenchDirectionAngleFilter |
| Calcule the direction angle of the force vector for each wrench. More... | |
| class | btkWrenchDownsampleFilter |
| Downsample data stored in a wrench. More... | |
| class | btkXLSOrthoTrakFileIO |
| Class to read XLS (Orthotrak) files. More... | |
| class | btkXMOVEFileIO |
| Class to read XMOVE files (Codamotion). More... | |
| class | SwigPyIterator |
Functions | |
| def | btkForcePlatformType1 |
| def | btkForcePlatformType2 |
| def | btkForcePlatformType3 |
| def | btkForcePlatformType4 |
| def | btkForcePlatformType5 |
| def | btkForcePlatformType6 |
| def | btkIMUType1 |
| def | btkIMUType2 |
| def | btkMetaDataCollapseChildrenValuesDouble |
| def | btkMetaDataCollapseChildrenValuesInt |
| def | btkMetaDataCollapseChildrenValuesString |
| def | btkMetaDataCreateChild |
| def | Iterate |
High level classes and functions to open, edit and modify biomechanical acquisition.
| def btk.btkForcePlatformType1 | ( | ) |
btkForcePlatformType1() -> btkForcePlatform Force platform composed of 6 channels: - FX: Horizontal forces on the axis X of the platform; - FY: Horizontal forces on the axis Y of the platform; - FZ: Vertical forces on the axis Z of the platform; - PX: Position of the centre of pressure (CoP) on the axis X of the platform; - PY: Position of the centre of pressure (CoP) on the axis Y of the platform; - MZ: Vertical moment on the axis Z of the platform.
| def btk.btkForcePlatformType2 | ( | ) |
btkForcePlatformType2() -> btkForcePlatform Force platform composed of 6 channels: - FX: Horizontal forces on the axis X of the platform; - FY: Horizontal forces on the axis Y of the platform; - FZ: Vertical forces on the axis Z of the platform; - MX: Horizontal moment on the axis X of the platform; - MY: Horizontal moment on the axis Y of the platform; - MZ: Vertical moment on the axis Z of the platform.
| def btk.btkForcePlatformType3 | ( | ) |
btkForcePlatformType3() -> btkForcePlatform Force platform composed of 8 channels: - FZ1: Vertical forces measured by the sensor on the corner 1; - FZ2: Vertical forces measured by the sensor on the corner 2; - FZ3: Vertical forces measured by the sensor on the corner 3; - FZ4: Vertical forces measured by the sensor on the corner 4; - FX12: Horizontal forces measured by the sensors 1 & 2; - FX34: Horizontal forces measured by the sensors 3 & 4; - FY14: Horizontal forces measured by the sensors 1 & 4; - FY23: Horizontal forces measured by the sensors 2 & 3.
| def btk.btkForcePlatformType4 | ( | ) |
btkForcePlatformType4() -> btkForcePlatform Force platform composed of 6 channels and a 6 columns by 6 rows calibration matrix. The definition of the channels are the same than the force platform Type 2.
| def btk.btkForcePlatformType5 | ( | ) |
btkForcePlatformType5() -> btkForcePlatform Force platform composed of 8 channels and a 8 columns by 6 rows calibration matrix. The definition of the channels are the same than the force platform Type 3.
| def btk.btkForcePlatformType6 | ( | ) |
btkForcePlatformType6() -> btkForcePlatform Force platform composed of 12 channels and a 12 columns by 12 rows calibration matrix: - FX1: Horizontal forces measured by the sensor on the corner 1; - FX2: Horizontal forces measured by the sensor on the corner 2; - FX3: Horizontal forces measured by the sensor on the corner 3; - FX4: Horizontal forces measured by the sensor on the corner 4; - FY1: Horizontal forces measured by the sensor on the corner 1; - FY2: Horizontal forces measured by the sensor on the corner 2; - FY3: Horizontal forces measured by the sensor on the corner 3; - FY4: Horizontal forces measured by the sensor on the corner 4; - FZ1: Vertical forces measured by the sensor on the corner 1; - FZ2: Vertical forces measured by the sensor on the corner 2; - FZ3: Vertical forces measured by the sensor on the corner 3; - FZ4: Vertical forces measured by the sensor on the corner 4.
| def btk.btkIMUType1 | ( | label = "IMU" , |
|
desc = "" , |
|||
init = True |
|||
| ) |
btkIMUType1(label="IMU", desc="", init=True) -> btkIMU btkIMUType1(label="IMU", desc="") -> btkIMU btkIMUType1(label="IMU") -> btkIMU btkIMUType1() -> btkIMU IMU 6D (3 accelerometers and 3 gyroscopes).
| def btk.btkIMUType2 | ( | label = "IMU" , |
|
desc = "" , |
|||
init = True |
|||
| ) |
btkIMUType2(label="IMU", desc="", init=True) -> btkIMU btkIMUType2(label="IMU", desc="") -> btkIMU btkIMUType2(label="IMU") -> btkIMU btkIMUType2() -> btkIMU IMU 6D (same as Type 1) with a calibration matrix used to align sensors (accelerometers and gyroscopes) on the same inertial reference frame.
| def btk.btkMetaDataCollapseChildrenValuesDouble | ( | args | ) |
btkMetaDataCollapseChildrenValuesDouble(parent, baselabel, targetFinalSize=-1, blankReplacement=0.0) -> btkDoubleArray btkMetaDataCollapseChildrenValuesDouble(parent, baselabel, targetFinalSize=-1) -> btkDoubleArray btkMetaDataCollapseChildrenValuesDouble(parent, baselabel) -> btkDoubleArray Convenient method to extract the values of a metadata as an array of doubles. Collapse the parent children entries' values starting with the string 'baselabel' and incrementing (for example: LABELS, LABELS2, LABELS3).
| def btk.btkMetaDataCollapseChildrenValuesInt | ( | args | ) |
btkMetaDataCollapseChildrenValuesInt(parent, baselabel, targetFinalSize=-1, blankReplacement=0) -> btkIntArray btkMetaDataCollapseChildrenValuesInt(parent, baselabel, targetFinalSize=-1) -> btkIntArray btkMetaDataCollapseChildrenValuesInt(parent, baselabel) -> btkIntArray Convenient method to extract the values of a metadata as an array of integers. Collapse the parent children entries' values starting with the string 'baselabel' and incrementing (for example: LABELS, LABELS2, LABELS3).
| def btk.btkMetaDataCollapseChildrenValuesString | ( | args | ) |
btkMetaDataCollapseChildrenValuesString(parent, baselabel, targetFinalSize=-1, blankReplacement="") -> btkStringArray btkMetaDataCollapseChildrenValuesString(parent, baselabel, targetFinalSize=-1) -> btkStringArray btkMetaDataCollapseChildrenValuesString(parent, baselabel) -> btkStringArray Convenient method to extract the values of a metadata as an array of strings. Collapse the parent children entries' values starting with the string 'baselabel' and incrementing (for example: LABELS, LABELS2, LABELS3).
| def btk.btkMetaDataCreateChild | ( | args | ) |
btkMetaDataCreateChild(parent, label) -> btkMetaData btkMetaDataCreateChild(parent , label , int ) btkMetaDataCreateChild(parent , label , double ) btkMetaDataCreateChild(parent , label , string ) btkMetaDataCreateChild(parent , label , btkIntArray) btkMetaDataCreateChild(parent , label , btkDoubleArray) btkMetaDataCreateChild(parent , label , btkStringArray) btkMetaDataCreateChild(parent , label , btkIntArray , numCol) btkMetaDataCreateChild(parent , label , btkDoubleArray , numCol) btkMetaDataCreateChild(parent , label , btkStringArray , numCol) Function to create/set easily a metadata containing a 2D array as value, from a given label into a given parent.
| def btk.Iterate | ( | c | ) |
Python generator to iterate through a collection of BTK objects (Points, Analogs, Events and Metadata). For example: for i in btk.Iterate(acq.GetPoints()): print i.GetLabel()


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AWS Access and Secret keys to use for this deployment. At minimum the keys must be allowed to execute
codedeploy:*
and s3:Put*
. It's a best practice to have these keys be from an IAM role
with limited scope.
If your Jenkins install is running on an EC2 instance with an associate IAM role, you can leave these fields blank. You will just need to ensure that the role has the correct policies.