dgbpy.hdf5

Attributes

`hdf5ext`
`modeloutstr`

Classes

`StorageType`	Create a collection of name/value pairs.
`Scaler`	Create a collection of name/value pairs.

Functions

`dictAddIfNew`(newset, toadd)
`getCubeLetNames`(info)
`getCubeLetNamesByGroup`(info, groupnm, example)
`getCubeLetNamesByGroupByItem`(info, groupnm, collnm, idx)
`getGroupSize`(filenm, groupnm)
`getNrAttribs`(info)
`getNrGroupInputs`(info)
`getNrOutputs`(info)
`getSeed`(info)
`get_np_shape`(shape[, nrpts, nrattribs])
`getTrainingConfig`(h5file)
`isRegression`(info)
`isClassification`(info)
`isSegmentation`(info)
`isSeisClass`(info)
`hasUnlabeled`(info)
`isLogInput`(info)
`isLogOutput`(info)
`isImg2Img`(info)
`isZipModel`(info)
`isCrossValidation`(info)
`unscaleOutput`(info)
`applyGlobalStd`(info)
`applyLocalStd`(info)
`applyNormalization`(info)
`applyMinMaxScaling`(info)
`applyRangeScaling`(info)
`applyArrTranspose`(info)
`isDefaultScaler`(scaler, info[, uselearntype])
`updateScaleInfo`(scaler, info)
`getScalerStr`(info)
`doOutputScaling`(info)
`isModel`(info)
`isMultiLabelRegression`(info)
`hasboto3`([auth])
`isS3Uri`(uri)
`shouldUseS3`(modelfnm[, params, relaxed, kwargs])	Check if the model file should be stored or retrieved from S3.
`rm_tree`(pth)
`getLogDir`(withtensorboard, examplenm, platform, ...)
`getOutdType`(classinfo, hasunlabels)
`getCubeLets_img2img_multitarget`(infos, collection, groupnm)
`getCubeLets`(infos, collection, groupnm)
`getDatasets_`(infos, datasets, fortrain)
`getDatasets`(infos[, dsetsel, train, validation])
`validInfo`(info)
`getInfo`(filenm, quick)
`getAttribInfo`(info, filenm)
`getWellInfo`(info, filenm)
`getNrClasses`(info)
`arroneitemsize`(dtype)
`getTotalSize`(info)
`modelIdxStr`(idx)
`odsetBoolValue`(value)
`addInfo`(inpfile, plfnm, filenm, infos, clssnm)
`getClassIndices`(info[, filternms])
`getClassIndicesFromData`(info)
`getMainOutputs`(info)
`getOutputs`(info)
`getOutputNames`(filenm, indices)
`translateFnm`(modfnm, modelfnm)

Module Contents

dgbpy.hdf5.hdf5ext = 'h5'

dgbpy.hdf5.dictAddIfNew(newset, toadd)

dgbpy.hdf5.getCubeLetNames(info)

dgbpy.hdf5.getCubeLetNamesByGroup(info, groupnm, example)

dgbpy.hdf5.getCubeLetNamesByGroupByItem(info, groupnm, collnm, idx)

dgbpy.hdf5.getGroupSize(filenm, groupnm)

dgbpy.hdf5.getNrAttribs(info)

dgbpy.hdf5.getNrGroupInputs(info)

dgbpy.hdf5.getNrOutputs(info)

dgbpy.hdf5.getSeed(info)

dgbpy.hdf5.get_np_shape(shape, nrpts=None, nrattribs=None)

dgbpy.hdf5.getTrainingConfig(h5file)

dgbpy.hdf5.isRegression(info)

dgbpy.hdf5.isClassification(info)

dgbpy.hdf5.isSegmentation(info)

dgbpy.hdf5.isSeisClass(info)

dgbpy.hdf5.hasUnlabeled(info)

dgbpy.hdf5.isLogInput(info)

dgbpy.hdf5.isLogOutput(info)

dgbpy.hdf5.isImg2Img(info)

dgbpy.hdf5.isZipModel(info)

dgbpy.hdf5.isCrossValidation(info)

dgbpy.hdf5.unscaleOutput(info)

dgbpy.hdf5.applyGlobalStd(info)

dgbpy.hdf5.applyLocalStd(info)

dgbpy.hdf5.applyNormalization(info)

dgbpy.hdf5.applyMinMaxScaling(info)

dgbpy.hdf5.applyRangeScaling(info)

dgbpy.hdf5.applyArrTranspose(info)

class dgbpy.hdf5.StorageType(*args, **kwds)

Bases: enum.Enum

Create a collection of name/value pairs.

Example enumeration:

>>> class Color(Enum):
...     RED = 1
...     BLUE = 2
...     GREEN = 3

Access them by:

attribute access:
```
>>> Color.RED
<Color.RED: 1>
```
value lookup:
```
>>> Color(1)
<Color.RED: 1>
```
name lookup:
```
>>> Color['RED']
<Color.RED: 1>
```

Enumerations can be iterated over, and know how many members they have:

>>> len(Color)
3

>>> list(Color)
[<Color.RED: 1>, <Color.BLUE: 2>, <Color.GREEN: 3>]

Methods can be added to enumerations, and members can have their own attributes – see the documentation for details.

AWS = 'AWS'

LOCAL = 'LOCAL'

class dgbpy.hdf5.Scaler(*args, **kwds)