savu.plugins.plugin

Complexity

Total Complexity

58

Size/Duplication

Total Lines	335
Duplicated Lines	0 %

Importance

Changes

0

38 Methods

Rating	Name	Duplication	Size	Complexity
A	Plugin.set_parameters()	0	2	1
A	Plugin.initialise()	0	5	1
A	Plugin.__init__()	0	12	1
A	Plugin.nFrames()	0	4	1
A	Plugin.executive_summary()	0	10	1
A	Plugin.setup()	0	8	1
A	Plugin.__clean_up_plugin_data()	0	8	2
A	Plugin._set_plugin_tools()	0	4	1
A	Plugin.base_pre_process()	0	5	1
A	Plugin.base_process_frames_before()	0	3	1
A	Plugin.base_process_frames_after()	0	4	1
A	Plugin.get_process_frames_counter()	0	2	1
A	Plugin.set_filter_padding()	0	6	1
A	Plugin.get_plugin_tools()	0	2	1
A	Plugin._reset_process_frames_counter()	0	2	1
A	Plugin.get_parameters()	0	8	1
A	Plugin.get_global_frame_index()	0	3	1
A	Plugin.get_current_slice_list()	0	3	1
A	Plugin.nClone_datasets()	0	5	1
A	Plugin.pre_process()	0	3	1
A	Plugin.final_parameter_updates()	0	4	1
A	Plugin.__remove_axis_data()	0	17	3
A	Plugin.delete_parameter_entry()	0	3	2
A	Plugin.get_slice_dir_reps()	0	10	2
A	Plugin.set_global_frame_index()	0	2	1
A	Plugin.nOutput_datasets()	0	8	1
A	Plugin.nInput_datasets()	0	8	1
A	Plugin._revert_preview()	0	8	3
A	Plugin.__set_previous_patterns()	0	4	2
A	Plugin.set_current_slice_list()	0	2	1
A	Plugin._main_setup()	0	15	1
A	Plugin.set_preview()	0	15	3
B	Plugin.__copy_meta_data()	0	21	6
A	Plugin.post_process()	0	10	1
A	Plugin.process_frames()	0	11	1
A	Plugin.base_post_process()	0	9	4
A	Plugin.plugin_process_frames()	0	9	3
A	Plugin._clean_up()	0	7	1

# Copyright 2014 Diamond Light Source Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""
.. module:: plugin
   :platform: Unix
   :synopsis: Base class for all plugins used by Savu

.. moduleauthor:: Mark Basham <scientificsoftware@diamond.ac.uk>

"""

import copy
import logging
import numpy as np

import savu.plugins.utils as pu
from savu.plugins.plugin_datasets import PluginDatasets
from savu.plugins.stats.statistics import Statistics


class Plugin(PluginDatasets):

    def __init__(self, name="Plugin"):
        super(Plugin, self).__init__(name)
        self.name = name
        self.chunk = False
        self.slice_list = None
        self.global_index = None
        self.pcount = 0
        self.exp = None
        self.check = False
        self.fixed_length = True
        self.parameters = {}
        self.tools = self._set_plugin_tools()

    def set_parameters(self, params):
        self.parameters = params

    def initialise(self, params, exp, check=False):
        self.check = check
        self.exp = exp
        self.get_plugin_tools().initialise(params)
        self._main_setup()

    def _main_setup(self):
        """ Performs all the required plugin setup.

        It sets the experiment, then the parameters and replaces the
        in/out_dataset strings in ``self.parameters`` with the relevant data
        objects. It then creates PluginData objects for each of these datasets.
        """
        self._set_plugin_datasets()
        self._reset_process_frames_counter()
        self.stats_obj = Statistics()
        self.setup()
        self.stats_obj.setup(self)
        self.set_filter_padding(*(self.get_plugin_datasets()))
        self._finalise_plugin_datasets()
        self._finalise_datasets()


    def _reset_process_frames_counter(self):
        self.pcount = 0

    def get_process_frames_counter(self):
        return self.pcount

    def set_filter_padding(self, in_data, out_data):
        """
        Should be overridden to define how wide the frame should be for each
        input data set
        """
        return {}

    def setup(self):
        """
        This method is first to be called after the plugin has been created.
        It determines input/output datasets and plugin specific dataset
        information such as the pattern (e.g. sinogram/projection).
        """
        logging.error("set_up needs to be implemented")
        raise NotImplementedError("setup needs to be implemented")

    def get_plugin_tools(self):
        return self.tools

    def _set_plugin_tools(self):
        plugin_tools_id = self.__class__.__module__ + '_tools'
        tool_class = pu.get_tools_class(plugin_tools_id, self)
        return tool_class

    def delete_parameter_entry(self, param):
        if param in list(self.parameters.keys()):
            del self.parameters[param]

    def get_parameters(self, name):
        """ Return a plugin parameter

        :params str name: parameter name (dictionary key)
        :returns: the associated value in ``self.parameters``
        :rtype: dict value
        """
        return self.parameters[name]

    def base_pre_process(self):
        """ This method is called after the plugin has been created by the
        pipeline framework as a pre-processing step.
        """
        pass

    def pre_process(self):
        """ This method is called immediately after base_pre_process(). """
        pass

    def base_process_frames_before(self, data):
        """ This method is called before each call to process frames """
        return data

    def base_process_frames_after(self, data):
        """ This method is called directly after each call to process frames \
        and before returning the data to file."""
        return data

    def plugin_process_frames(self, data):
        data_copy = data.copy()  # is it ok to copy every frame like this? Enough memory?
        frames = self.base_process_frames_after(self.process_frames(
                self.base_process_frames_before(data)))

        if self.stats_obj.calc_stats and self.stats_obj._stats_flag:
            self.stats_obj.set_slice_stats(frames, data_copy)
        self.pcount += 1
        return frames

    def process_frames(self, data):
        """
        This method is called after the plugin has been created by the
        pipeline framework and forms the main processing step

        :param data: A list of numpy arrays for each input dataset.
        :type data: list(np.array)
        """

        logging.error("process frames needs to be implemented")
        raise NotImplementedError("process needs to be implemented")

    def post_process(self):
        """
        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.

        :param exp: An experiment object, holding input and output datasets
        :type exp: experiment class instance
        """
        pass

    def base_post_process(self):
        """ This method is called immediately after post_process(). """
        if self.stats_obj.calc_stats and self.stats_obj._stats_flag:
            if not self.stats_obj._already_called:
                self.stats_obj.set_volume_stats()
            self.stats_obj._already_called = False
        #else:
        #    self.stats_obj._delete_stats_metadata(self)
        pass

    def set_preview(self, data, params):
        if not params:
            return True
        preview = data.get_preview()
        orig_indices = preview.get_starts_stops_steps()
        nDims = len(orig_indices[0])
        no_preview = [[0]*nDims, data.get_shape(), [1]*nDims, [1]*nDims]

        # Set previewing params if previewing has not already been applied to
        # the dataset.
        if no_preview == orig_indices:
            data.get_preview().revert_shape = data.get_shape()
            data.get_preview().set_preview(params)
            return True
        return False

    def _clean_up(self):
        """ Perform necessary plugin clean up after the plugin has completed.
        """
        self._clone_datasets()
        self.__copy_meta_data()
        self.__set_previous_patterns()
        self.__clean_up_plugin_data()

    def __copy_meta_data(self):
        """
        Copy all metadata from input datasets to output datasets, except axis
        data and statistics that is no longer valid.
        """
        remove_keys = self.__remove_axis_data()
        for i in range(len(remove_keys)):
            remove_keys[i].add("stats")
        in_meta_data, out_meta_data = self.get()
        copy_dict = {}
        for mData in in_meta_data:
            temp = copy.deepcopy(mData.get_dictionary())
            copy_dict.update(temp)

        for i in range(len(out_meta_data)):
            temp = copy_dict.copy()
            for key in remove_keys[i]:
                if temp.get(key, None) is not None:
                    del temp[key]
            temp.update(out_meta_data[i].get_dictionary())
            out_meta_data[i]._set_dictionary(temp)

    def __set_previous_patterns(self):
        for data in self.get_out_datasets():
            data._set_previous_pattern(
                copy.deepcopy(data._get_plugin_data().get_pattern()))

    def __remove_axis_data(self):
        """
        Returns a list of meta_data entries corresponding to axis labels that
        are not copied over to the output datasets
        """
        in_datasets, out_datasets = self.get_datasets()
        all_in_labels = []
        for data in in_datasets:
            axis_keys = data.get_axis_label_keys()
            all_in_labels = all_in_labels + axis_keys

        remove_keys = []
        for data in out_datasets:
            axis_keys = data.get_axis_label_keys()
            remove_keys.append(set(all_in_labels).difference(set(axis_keys)))

        return remove_keys

    def __clean_up_plugin_data(self):
        """ Remove pluginData object encapsulated in a dataset after plugin
        completion.
        """
        in_data, out_data = self.get_datasets()
        data_object_list = in_data + out_data
        for data in data_object_list:
            data._clear_plugin_data()

    def _revert_preview(self, in_data):
        """ Revert dataset back to original shape if previewing was used in a
        plugin to reduce the data shape but the original data shape should be
        used thereafter. Remove previewing if it was added in the plugin.
        """
        for data in in_data:
            if data.get_preview().revert_shape:
                data.get_preview()._unset_preview()

    def set_global_frame_index(self, frame_idx):
        self.global_index = frame_idx

    def get_global_frame_index(self):
        """ Get the global frame index. """
        return self.global_index

    def set_current_slice_list(self, sl):
        self.slice_list = sl

    def get_current_slice_list(self):
        """ Get the slice list of the current frame being processed. """
        return self.slice_list

    def get_slice_dir_reps(self, nData):
        """ Return the periodicity of the main slice direction.

        :params int nData: The number of the dataset in the list.
        """
        slice_dir = \
            self.get_plugin_in_datasets()[nData].get_slice_directions()[0]
        sl = [sl[slice_dir] for sl in self.slice_list]
        reps = [i for i in range(len(sl)) if sl[i] == sl[0]]
        return np.diff(reps)[0] if len(reps) > 1 else 1

    def nInput_datasets(self):
        """
        The number of datasets required as input to the plugin

        :returns:  Number of input datasets

        """
        return 1

    def nOutput_datasets(self):
        """
        The number of datasets created by the plugin

        :returns:  Number of output datasets

        """
        return 1

    def nClone_datasets(self):
        """ The number of output datasets that have an clone - i.e. they take\
        it in turns to be used as output in an iterative plugin.
        """
        return 0

    def nFrames(self):
        """ The number of frames to process during each call to process_frames.
        """
        return 'single'

    def final_parameter_updates(self):
        """ An opportunity to update the parameters after they have been set.
        """
        pass

    def executive_summary(self):
        """ Provide a summary to the user for the result of the plugin.

        e.g.
         - Warning, the sample may have shifted during data collection
         - Filter operated normally

        :returns:  A list of string summaries
        """
        return ["Nothing to Report"]