Hdf5 Saver¶
Description¶
A class to save tomography data to a hdf5 file
Parameters
in_datasets:
visibility: datasets
dtype: "[list[],list[str]]"
description:
summary: A list of the dataset(s) to process.
verbose: A list of strings, where each string gives the name of a dataset that was either specified by a loader plugin or created as output to a previous plugin. The length of the list is the number of input datasets requested by the plugin. If there is only one dataset and the list is left empty it will default to that dataset.
default: "[]"
out_datasets:
visibility: hidden
dtype: "[list[],list[str]]"
description:
summary: A list of the dataset(s) to create.
verbose: A list of strings, where each string is a name to be assigned to a dataset output by the plugin. If there is only one input dataset and one output dataset and the list is left empty, the output will take the name of the input dataset. The length of the list is the number of output datasets created by the plugin.
default: "[]"
pattern:
visibility: basic
dtype: str
description: "Optimise data storage to this access pattern. 'optimum' will automate this process by choosing the output pattern from the previous plugin, if it exists, else the first pattern."
default: optimum
Key
visibility: The visibility level of the parameter
dtype: The type of data
description: A short explanation of the parameter
default: The default value
options: A list of permitted values
dependency: The name of the parameter and value which this parameter depends upon
range: A guide for the range of the parameter
Citations
No citations
API
-
class
Hdf5Saver
(name='Hdf5Saver')[source] -
get_pattern
()[source]
-
post_process
()[source] This method is called after the process function in the pipeline framework as a post-processing step. All processes will have finished performing the main processing at this stage.
- Parameters
exp (experiment class instance) – An experiment object, holding input and output datasets
-
pre_process
()[source] This method is called immediately after base_pre_process().
-
process_frames
(data)[source] This method is called after the plugin has been created by the pipeline framework and forms the main processing step
- Parameters
data (list(np.array)) – A list of numpy arrays for each input dataset.
-