savu.plugins.plugin_tools.PluginCitations._set_plugin_citations() - Code Metrics - DiamondLightSource/Savu - Measure and Improve Code Quality continuously with Scrutinizer

PluginCitations._set_plugin_citations() A
last analyzed 2022-08-17 15:44 UTC

↳ Parent: savu.plugins.plugin_tools
Complexity

Conditions
Size

Total Lines	10
Code Lines	8
Duplication

Lines	0
Ratio	0 %
Importance

Changes
Metric	Value
cc	4
eloc	8
nop	2
dl	0
loc	10
rs	10
c	0
b	0
f	0
# 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_tools
   :platform: Unix
   :synopsis: Plugin tools

.. moduleauthor:: Jessica Verschoyle <[email protected]>

"""
import os
import copy
import json
import logging

from colorama import Fore
from collections import OrderedDict

import savu.plugins.utils as pu
from savu.data.meta_data import MetaData
import savu.plugins.docstring_parser as doc
import scripts.config_generator.parameter_utils as param_u
from savu.data.plugin_list import CitationInformation

logger = logging.getLogger("documentationLog")


class PluginParameters(object):
    """Save the parameters for the plugin and base classes to a
    dictionary. The parameters are in yaml format inside the
    define_parameter function. These are read and checked for problems.
    """

    def __init__(self):
        super(PluginParameters, self).__init__()
        self.param = MetaData(ordered=True)
        self.docstring_info = {}
        self.parameters = {}
        self.multi_params_dict = {}
        self.extra_dims = []        

    def populate_parameters(self, tools_list):
        """ Set parameter definitions and default parameter values """
        # set the parameter definitions
        # populates the dictionary returned by self.get_param_definitions()
        list(map(lambda tool_class: 
                 self._set_parameter_definitions(tool_class), tools_list))
        # set the default parameter values
        # populates the dictionary returned by self.get_param_values()
        self._populate_default_parameters()

    def initialise(self, params):
        # Override default parameter values with plugin list entries
        self.set_plugin_list_parameters(copy.deepcopy(params))
        self._get_plugin().set_parameters(self.parameters)

    def _populate_default_parameters(self):
        """
        This method should populate all the required parameters with
        default values. It is used for checking to see if parameter
        values are appropriate
        """
        p_defs = self.get_param_definitions()
        self.set_docstring(self.get_doc())
        self.parameters = \
            OrderedDict([(k, v['default']) for k, v in p_defs.items()])
        # parameters holds current values, this is edited outside of the
        # tools class so default and dependency display values are updated here
        self.update_dependent_defaults()
        self.check_dependencies(self.parameters)
        self._get_plugin().set_parameters(self.parameters)

    def set_docstring(self, doc_str):
        self.docstring_info['info'] = doc_str.get('verbose')
        self.docstring_info['warn'] = doc_str.get('warn')
        self.docstring_info['documentation_link'] = doc_str.get('documentation_link')
        self.docstring_info['synopsis'] = doc.find_synopsis(self._get_plugin())

    def _set_parameters_this_instance(self, indices):
        """ Determines the parameters for this instance of the plugin, in the
        case of parameter tuning.

        param np.ndarray indices: the index of the current value in the
            parameter tuning list.
        """
        dims = set(self.multi_params_dict.keys())
        count = 0
        for dim in dims:
            info = self.multi_params_dict[dim]
            name = info['label'].split('_param')[0]
            self.parameters[name] = info['values'][indices[count]]
            count += 1

    def _set_parameter_definitions(self, tool_class):
        """Load the parameters for each base class, c, check the
        dataset visibility, check data types, set dictionary values.
        """
        param_info_dict = self._load_param_from_doc(tool_class)
        if param_info_dict:
            for p_name, p_value in param_info_dict.items():
                if p_name in self.param.get_dictionary():
                    for k,v in p_value.items():
                        self.param[p_name][k] = v
                else:
                    self.param.set(p_name, p_value)
        self._check_param_defs(tool_class)

    def _check_param_defs(self, tool_class):
        """Check the parameter definitions for errors

        :param tool_class: tool_class to use for error message
        """
        pdefs = self.param.get_dictionary()
        # Remove ignored parameters
        self._remove_ignored_params(pdefs)
        # Check that unknown keys aren't present
        self._check_all_keys(pdefs, tool_class)
        # Check if the required keys are included
        self._check_required_keys(pdefs, tool_class)
        # Check that option values are valid
        self._check_options(pdefs, tool_class)
        # Check that the visibility is valid
        self._check_visibility(pdefs, tool_class)
        # Check that the dtype is valid
        self._check_dtype(pdefs, tool_class)
        # Use a display option to apply to dependent parameters later.
        self._set_display(pdefs)
        for k,v in pdefs.items():
            # Change empty OrderedDict to dict (due to yaml loader)
            if isinstance(v['default'], OrderedDict):
                v['default'] = json.loads(json.dumps(v['default']))
            # Change the string to an integer, float, list, str, dict
            if not self.default_dependency_dict_exists(v):
                v['default'] = pu._dumps(v['default'])

    def _load_param_from_doc(self, tool_class):
        """Find the parameter information from the method docstring.
        This is provided in a yaml format.
        """
        # *** TO DO turn the dtype entry into a string

        param_info_dict = None
        if hasattr(tool_class, "define_parameters"):
            yaml_text = tool_class.define_parameters.__doc__
            # If yaml_text is not None and not empty or consisting of spaces
            if yaml_text and yaml_text.strip():
                # When loading yaml parameter information, convert dtype value to a string
                docsting = doc.change_dtype_to_str(yaml_text)
                param_info_dict = doc.load_yaml_doc(docsting)
                if param_info_dict:
                    if not isinstance(param_info_dict, OrderedDict):
                        error_msg = (
                            f"The parameters have not been read "
                            f"in correctly for {tool_class.__name__}"
                        )
                        raise Exception(error_msg)

        return param_info_dict

    def get_default_value(self, mod_param):
        """If the '--default' argument is passed, then get the original
        default value. If the default contains a dictionary, then search
        for the correct value
        """
        param_info_dict = self.param.get_dictionary()
        if self.default_dependency_dict_exists(param_info_dict[mod_param]):
            mod_value = self.get_dependent_default(param_info_dict[mod_param])
        else:
            mod_value = param_info_dict[mod_param]["default"]
        return mod_value

    def _check_required_keys(self, param_info_dict, tool_class):
        """Check the four keys ['dtype', 'description', 'visibility',
        'default'] are included inside the dictionary given for each
        parameter.
        """
        required_keys = ["dtype", "description", "visibility", "default"]
        missing_keys = False
        missing_key_dict = {}

        for p_key, p in param_info_dict.items():
            all_keys = p.keys()
            if p.get("visibility"):
                if p.get("visibility") == "hidden":
                    # For hidden keys, only require a default value key
                    required_keys = ["default"]
            else:
                required_keys = ["visibility"]

            if not all(d in all_keys for d in required_keys):
                missing_key_dict[p_key] = set(required_keys) - set(all_keys)
                missing_keys = True

        if missing_keys:
            self._print_incorrect_key_str(missing_key_dict, "missing required", tool_class)

    def _check_all_keys(self, param_info_dict, tool_class):
        allowed_keys = ["dtype", "description", "visibility", "default",
                        "options", "display", "example", "dependency",
                        "warning"]
        unknown_keys = False
        unknown_keys_dict = {}

        for p_key, p in param_info_dict.items():
            all_keys = p.keys()
            if any(d not in allowed_keys for d in all_keys):
                unknown_keys_dict[p_key] = set(all_keys) - set(allowed_keys)
                unknown_keys = True

        if unknown_keys:
            self._print_incorrect_key_str(unknown_keys_dict, "unknown",
                                        tool_class)

    def _print_incorrect_key_str(self, incorrect_key_dict, key_str, tool_class):
        """Create and print an error message and log message when certain keys are
        invalid or missing

        :param missing_key_dict: Dictionary of parameter option keys which are invalid/missing
        :param key_str: Error message string for this
        :param tool_class:
        :return:
        """
        print(
            f"{tool_class.__name__} has {key_str} keys."
        )
        for param, incorrect_values in incorrect_key_dict.items():
            print(f"The {key_str} keys for '{param}' are:")
            print(*incorrect_values, sep=", ")
        logger.error(f"ERROR: {key_str} keys inside {tool_class.__name__}")
        raise Exception(f"Please edit {tool_class.__name__}")

    def _check_dtype(self, param_info_dict, tool_class):
        """
        Make sure that the dtype input is valid and that the default value is
        compatible
        """
        plugin_error_str = f"There was an error with {tool_class.__name__}"
        for p_key, p_dict in param_info_dict.items():
            dtype = p_dict.get("dtype")
            if dtype:
                dtype = dtype.replace(" ", "")
                try:
                    pvalid, error_str = param_u.is_valid_dtype(dtype)
                    if not pvalid:
                        raise Exception("Invalid parameter definition %s:\n %s"
                                        % (p_key, error_str))
                except IndexError:
                    print(plugin_error_str)
                if not self.default_dependency_dict_exists(p_dict):
                    default_value = pu._dumps(p_dict["default"])
                    pvalid, error_str = param_u.is_valid(p_key, default_value,
                                                 p_dict, check=True)
                    if not pvalid:
                        raise Exception(f"{plugin_error_str}: {error_str}")

    def _check_visibility(self, param_info_dict, tool_class):
        """Make sure that the visibility choice is valid"""
        visibility_levels = [
            "basic",
            "intermediate",
            "advanced",
            "datasets",
            "hidden",
        ]
        visibility_valid = True
        for p_key, p in param_info_dict.items():
            # Check dataset visibility level is correct
            self._check_data_keys(p_key, p)
            # Check that the data types are valid choices
            if p["visibility"] not in visibility_levels:
                print(
                    f"Inside {tool_class.__name__} the {p_key}"
                    f" parameter is assigned an invalid visibility "
                    f"level '{p['visibility']}'"
                )
                print("Valid choices are:")
                print(*visibility_levels, sep=", ")
                visibility_valid = False

        if not visibility_valid:
            raise Exception(
                f"Please change the file for {tool_class.__name__}"
            )

    def _check_data_keys(self, p_key, p):
        """Make sure that the visibility of dataset parameters is 'datasets'
        so that the display order is unchanged.
        """
        datasets = ["in_datasets", "out_datasets"]
        exceptions = ["hidden"]
        if p_key in datasets:
            if p["visibility"] != "datasets" \
                    and p["visibility"] not in exceptions:
                p["visibility"] = "datasets"

    def _check_options(self, param_info_dict, tool_class):
        """Make sure that option verbose descriptions match the actual
        options
        """
        options_valid = True
        for p_key, p in param_info_dict.items():
            desc = param_info_dict[p_key].get("description")
            # desc not present for hidden keys
            if desc and isinstance(desc, dict):
                options = param_info_dict[p_key].get("options")
                option_desc = desc.get("options")
                if options and option_desc:
                    # Check that there is not an invalid option description
                    # inside the option list.
                    invalid_option = [
                        opt for opt in option_desc if opt not in options
                    ]
                    if invalid_option:
                        options_valid = False
                        break

        if options_valid is False:
            raise Exception(
                f"Please check the parameter options for {tool_class.__name__}"
            )

    def _remove_ignored_params(self, param_info_dict):
        """Remove any parameters with visibility = ignore"""
        p_dict_copy = param_info_dict.copy()
        for p_key, p in p_dict_copy.items():
            visibility = param_info_dict[p_key].get("visibility")
            if visibility == "ignore":
                del param_info_dict[p_key]

    def _set_display(self, param_info_dict):
        """Initially, set all of the parameters to display 'on'
        This is later altered when dependent parameters need to be shown
        or hidden
        """
        for k, v in param_info_dict.items():
            v["display"] = "on"

    def update_dependent_defaults(self):
        """
        Fix default values for parameters that have a dependency on the value 
        of another parameter, and are in dictionary form.
        """
        for name, pdict in self.get_param_definitions().items():
            if self.default_dependency_dict_exists(pdict):
                self.parameters[name] = self.get_dependent_default(pdict)

    def default_dependency_dict_exists(self, pdict):
        """ Check that the parameter default value is in a format with
        the parent parameter string and the dependent value
        e.g. default:
                algorithm: FGP
        and not an actual default value to be set
        e.g. default: {'2':5}

        :param pdict: The parameter definition dictionary
        :return: True if the default dictionary contains the
                correct format
        """
        if pdict["default"] and isinstance(pdict["default"], dict):
            if "dict" not in pdict["dtype"]:
                return True
            else:
                parent_name = list(pdict['default'].keys())[0]
                if parent_name in self.get_param_definitions():
                    return True
        return False

    def does_exist(self, key, ddict):
        if not key in ddict:
            raise Exception("The dependency %s does not exist" % key)
        return ddict[key]

    def get_dependent_default(self, child):
        """
        Recursive function to replace a dictionary of default parameters with
        a single value.

        Parameters
        ----------
        child : dict
            The parameter definition dictionary of the dependent parameter.

        Returns1
        -------
        value
            The correct default value based on the current value of the
            dependency, or parent, parameter.

        """
        pdefs = self.get_param_definitions()
        parent_name = list(child['default'].keys())[0]
        parent = self.does_exist(parent_name, pdefs)

        # if the parent default is a dictionary then apply the function
        # recursively
        if isinstance(parent['default'], dict):
            self.parameters[parent_name] = \
                self.get_dependent_default(parent['default'])
        return child['default'][parent_name][self.parameters[parent_name]]

    def warn_dependents(self, mod_param, mod_value): 
        """
        Find dependents of a modified parameter # complete the docstring
        """
        # find dependents
        for name, pdict in self.get_param_definitions().items():
            if self.default_dependency_dict_exists(pdict):
                default = pdict['default']
                parent_name = list(default.keys())[0]
                if parent_name == mod_param:
                    if mod_value in default[parent_name].keys():
                        value = default[parent_name][mod_value]
                        desc = pdict['description']


    def make_recommendation(self, child_name, desc, parent_name, value): 
        if isinstance(desc, dict):
            desc["range"] = f"The recommended value with the chosen " \
                            f"{parent_name} would be {value}"
        recommendation = f"It's recommended that you update {child_name}"\
                         f" to {value}"
        print(recommendation)

        
    def check_dependencies(self, parameters):
        """Determine which parameter values are dependent on a parent
        value and whether they should be hidden or shown
        """
        param_info_dict = self.param.get_dictionary()
        dep_list = {
            k: v["dependency"]
            for k, v in param_info_dict.items()
            if "dependency" in v
        }
        for p_name, dependency in dep_list.items():
            if isinstance(dependency, OrderedDict):
                # There is a dictionary of dependency values
                parent_param_name = list(dependency.keys())[0]
                # The choices which must be in the parent value
                parent_choice_list = dependency[parent_param_name]

                if parent_param_name in parameters:
                    """Check that the parameter is in the current plug in
                    This is relevant for base classes which have several
                    dependent classes
                    """
                    parent_value = parameters[parent_param_name]

                    if str(parent_value) in parent_choice_list:
                        param_info_dict[p_name]["display"] = "on"
                    else:
                        param_info_dict[p_name]["display"] = "off"
            else:
                if dependency in parameters:
                    parent_value = parameters[dependency]
                    if parent_value is None or str(parent_value) == "None":
                        param_info_dict[p_name]["display"] = "off"
                    else:
                        param_info_dict[p_name]["display"] = "on"


    def set_plugin_list_parameters(self, input_parameters):
        """
        This method is called after the plugin has been created by the
        pipeline framework.  It replaces ``self.parameters``
        default values with those given in the input process list. It
        checks for multi parameter strings, eg. 57;68;56;

        :param dict input_parameters: A dictionary of the input parameters
        for this plugin, or None if no customisation is required.
        """        
        for key in input_parameters.keys():
            if key in self.parameters.keys():
                new_value = input_parameters[key]
                self.__check_multi_params(
                    self.parameters, new_value, key
                )
            else:
                error = (
                    f"Parameter '{key}' is not valid for plugin "
                    f"{self.plugin_class.name}. \nTry opening and re-saving "
                    f"the process list in the configurator to auto remove "
                    f"\nobsolete parameters."
                )
                raise ValueError(error)

    def __check_multi_params(self, parameters, value, key):
        """
        Convert parameter value to a list if it uses parameter tuning
        and set associated parameters, so the framework knows the new size
        of the data and which plugins to re-run.

        :param parameters: Dictionary of parameters and current values
        :param value: Value to set parameter to
        :param key: Parameter name
        :return:
        """
        if param_u.is_multi_param(key, value):
            value, error_str = pu.convert_multi_params(key, value)
            if not error_str:
                parameters[key] = value
                label = key + "_params." + type(value[0]).__name__
                self.alter_multi_params_dict(
                    len(self.get_multi_params_dict()),
                    {"label": label, "values": value},
                )
                self.append_extra_dims(len(value))
        else:
            parameters[key] = value

    def get_expand_dict(self, preview, expand_dim):
        """Create dict for expand syntax

        :param preview: Preview parameter value
        :param expand_dim: Number of dimensions to return dict for
        :return: dict
        """
        expand_dict = {}
        preview_val = pu._dumps(preview)
        if not preview_val:
            # In the case that there is an empty dict, display the default
            preview_val = []
        if isinstance( preview_val, dict):
            for key, prev_list in preview_val.items():
                expand_dict[key] = self.get_expand_dict(prev_list, expand_dim)
            return expand_dict
        elif isinstance(preview_val, list):
            if expand_dim == "all":
                expand_dict = \
                    self._output_all_dimensions(preview_val,
                         self._get_dimensions(preview_val))
            else:
                pu.check_valid_dimension(expand_dim, preview_val)
                dim_key = f"dim{expand_dim}"
                expand_dict[dim_key] = \
                    self._dim_slice_output(preview_val, expand_dim)
            return expand_dict
        else:
            raise ValueError("This preview value was not a recognised list "
                             "or dictionary. This expand command currenty "
                             "only works with those two data types.")

    def _get_dimensions(self, preview_list):
        """
        :param preview_list: The preview parameter list
        :return: Dimensions to display
        """
        return 1 if not preview_list else len(preview_list)

    def _output_all_dimensions(self, preview_list, dims):
        """Compile output string lines for all dimensions

        :param preview_list: The preview parameter list
        :param dims: Number of dimensions to display
        :return: dict
        """
        prev_dict = {}
        for dim in range(1, dims + 1):
            dim_key = f"dim{dim}"
            prev_dict[dim_key] = self._dim_slice_output(preview_list, dim)
        return prev_dict

    def _dim_slice_output(self, preview_list, dim):
        """If there are multiple values in list format
        Only save the values for the dimensions chosen

        :param preview_list: The preview parameter list
        :param dim: dimension to return the slice notation dictionary for
        :return slice notation dictionary
        """
        if not preview_list:
            # If empty
            preview_display_value = ":"
        else:
            preview_display_value = preview_list[dim - 1]
        prev_val = self._set_all_syntax(preview_display_value)
        return self._get_slice_notation_dict(prev_val)

    def _get_slice_notation_dict(self, val):
        """Create a dict for slice notation information,
        start:stop:step (and chunk if provided)

        :param val: The list value in slice notation
        :return: dictionary of slice notation
        """
        import itertools

        basic_slice_keys = ["start", "stop", "step"]
        all_slice_keys = [*basic_slice_keys, "chunk"]
        slice_dict = {}

        if pu.is_slice_notation(val):
            val_list = val.split(":")
            if len(val_list) < 3:
                # Make sure the start stop step slice keys are always shown,
                # even when blank
                val_list.append("")
            for slice_name, v in zip(all_slice_keys, val_list):
                # Only print up to the shortest list.
                # (Only show the chunk value if it is in val_list)
                slice_dict[slice_name] = v
        else:
            val_list = [val]
            for slice_name, v in itertools.zip_longest(
                    basic_slice_keys, val_list, fillvalue=""
            ):
                slice_dict[slice_name] = v
        return slice_dict

    def _set_all_syntax(self, val, replacement_str=""):
        """Remove additional spaces from val, replace colon when 'all'
        data is selected

        :param val: Slice notation value
        :param replacement_str: String to replace ':' with
        :return:
        """
        if isinstance(val, str):
            if pu.is_slice_notation(val):
                if val == ":":
                    val = replacement_str
                else:
                    val = val.strip()
            else:
                val = val.strip()
        return val

    def get_multi_params_dict(self):
        """ Get the multi parameter dictionary. """
        return self.multi_params_dict

    def alter_multi_params_dict(self, key, value):
        self.multi_params_dict[key] = value

    def get_extra_dims(self):
        """ Get the extra dimensions. """
        return self.extra_dims

    def set_extra_dims(self, value):
        self.extra_dims = value

    def append_extra_dims(self, value):
        self.extra_dims.append(value)

    def define_parameters(self):
        pass

    """
    @dataclass
    class Parameter:
        ''' Descriptor of Parameter Information for plugins
        '''
        visibility: int
        datatype: specific_type
        description: str
        default: int
        Options: Optional[[str]]
        dependency: Optional[]

        def _get_param(self):
            param_dict = {}
            param_dict['visibility'] = self.visibility
            param_dict['type'] = self.dtype
            param_dict['description'] = self.description
            # and the remaining keys
            return param_dict
    """


class PluginCitations(object):
    """Get this citation dictionary so get_dictionary of the metadata type
    should return a dictionary of all the citation info as taken from
    docstring
    """

    def __init__(self):
        super(PluginCitations, self).__init__()
        self.cite = MetaData(ordered=True)

    def set_cite(self, tools_list):
        """Set the citations for each of the tools classes
        :param tools_list: List containing tool classes of parent plugins
        """
        list(
            map(
                lambda tool_class: self._set_plugin_citations(tool_class),
                tools_list
            )
        )

    def _set_plugin_citations(self, tool_class):
        """ Load the parameters for each base class and set values"""
        citations = self._load_cite_from_doc(tool_class)
        if citations:
            for citation in citations.values():
                if self._citation_keys_valid(citation, tool_class):
                    new_citation = CitationInformation(**citation)
                    self.cite.set(new_citation.name, new_citation)
                else:
                    print(f"The citation for {tool_class.__name__} "
                          f"was not saved.")

    def _citation_keys_valid(self, new_citation, tool_class):
        """Check that required citation keys are present. Return false if
        required keys are missing
        """
        required_keys = ["description"]
        # Inside the fresnel filter there is only a description
        citation_keys = [k for k in new_citation.keys()]
        # Check that all of the required keys are contained inside the
        # citation definition
        check_keys = all(item in citation_keys for item in required_keys)
        citation_keys_valid = False if check_keys is False else True

        all_keys = [
            "short_name_article",
            "description",
            "bibtex",
            "endnote",
            "doi",
            "dependency",
        ]
        # Keys which are not used
        additional_keys = [k for k in citation_keys if k not in all_keys]
        if additional_keys:
            print(f"Please only use the following keys inside the citation"
                  f" definition for {tool_class.__name__}:")
            print(*all_keys, sep=", ")
            print("The incorrect keys used:", additional_keys)

        return citation_keys_valid

    def _load_cite_from_doc(self, tool_class):
        """Find the citation information from the method docstring.
        This is provided in a yaml format.

        :param tool_class: Tool to retrieve citation docstring from
        :return: All citations from this tool class
        """
        all_c = OrderedDict()
        # Seperate the citation methods. __dict__ returns instance attributes.
        citation_methods = {key: value
                            for key, value in tool_class.__dict__.items()
                            if key.startswith('citation')}
        for c_method_name, c_method in citation_methods.items():
            yaml_text = c_method.__doc__
            if yaml_text is not None:
                yaml_text = self.seperate_description(yaml_text)
                current_citation = doc.load_yaml_doc(yaml_text)
                if not isinstance(current_citation, OrderedDict):
                    print(f"The citation information has not been read in "
                          f"correctly for {tool_class.__name__}.")
                else:
                    all_c[c_method_name] = current_citation
        return all_c

    def seperate_description(self, yaml_text):
        """Change the format of the docstring to retain new lines for the
        endnote and bibtex and create a key for the description so that
        it be read as a yaml file

        :param yaml_text:
        :return: Reformatted yaml text
        """
        description = doc.remove_new_lines(yaml_text.partition("bibtex:")[0])
        desc_str = "        description:" + description

        bibtex_text = \
            yaml_text.partition("bibtex:")[2].partition("endnote:")[0]
        end_text = \
            yaml_text.partition("bibtex:")[2].partition("endnote:")[2]

        if bibtex_text and end_text:
            final_str = desc_str + '\n        bibtex: |' + bibtex_text \
                      + 'endnote: |' + end_text
        elif end_text:
            final_str = desc_str + '\n        endnote: |' + end_text
        elif bibtex_text:
            final_str = desc_str + '\n        bibtex: |' + bibtex_text
        else:
            final_str = desc_str

        return final_str


class PluginDocumentation(object):
    """Get this documentation dictionary so get_dictionary of
    the metadata type should return a dictionary of all the
    documentation details taken from docstring
    """

    def __init__(self):
        super(PluginDocumentation, self).__init__()
        self.doc = MetaData()

    def set_doc(self, tools_list):
        # Use the tools class at the 'top'
        doc_lines = tools_list[-1].__doc__.splitlines()
        doc_lines = [line.strip() for line in doc_lines if line]
        docstring = " ".join(doc_lines)
        self.doc.set("verbose", docstring)
        self.doc.set("warn", self.set_warn(tools_list))
        self.set_doc_link()

    def set_warn(self, tools_list):
        """Remove new lines and save config warnings for the child tools
        class only.
        """
        config_str = tools_list[-1].config_warn.__doc__
        if config_str and "\n\n" in config_str:
            # Separate multiple warnings with two new lines \n\n
            config_warn_list = [doc.remove_new_lines(l)
                                for l in config_str.split("\n\n")]
            config_str = '\n'.join(config_warn_list)
        return config_str

    def set_doc_link(self):
        """If there is a restructured text documentation file inside the
        doc/source/plugin_guides/plugins folder, then save the link to the page.

        """
        # determine Savu base path
        savu_base_path = os.path.join(
            os.path.dirname(os.path.realpath(__file__)), '../../')

        # Locate documentation file
        doc_folder = savu_base_path + "doc/source/plugin_guides"
        module_path = \
            self.plugin_class.__module__.replace(".", "/").replace("savu", "")
        file_ = module_path + "_doc"
        file_name = file_ + ".rst"
        file_path = doc_folder + file_name
        sphinx_link = f"https://savu.readthedocs.io/en/latest/" \
                      f"plugin_guides{file_}.html"
        if os.path.isfile(file_path):
            self.doc.set("documentation_link", sphinx_link)

    def config_warn(self):
        pass


class PluginTools(PluginParameters, PluginCitations, PluginDocumentation):
    """Holds all of the parameter, citation and documentation information
    for one plugin class - cls"""

    def __init__(self, cls):
        super(PluginTools, self).__init__()
        self.plugin_class = cls
        self.tools_list = self._find_tools()
        self._set_tools_data()

    def _get_plugin(self):
        return self.plugin_class

    def _find_tools(self):
        """Using the method resolution order, find base class tools.
        Add tools information in reverse order, to build up on base parameters
        """
        tool_list = []
        for tool_class in reversed(self.plugin_class.__class__.__mro__[:-1]):
            plugin_tools_id = tool_class.__module__ + "_tools"
            p_tools = pu.get_tools_class(plugin_tools_id)
            if p_tools:
                tool_list.append(p_tools)
        return tool_list

    def _set_tools_data(self):
        """Populate the parameters, citations and documentation
        with information from all of the tools classes
        """
        self.populate_parameters(self.tools_list)
        self.set_cite(self.tools_list)
        self.set_doc(self.tools_list)

    def get_param_definitions(self):
        """
        Returns
        -------
        dict
            Original parameter definitions read from tools file.
        """
        return self.param.get_dictionary()

    def get_param_values(self):
        """
        Returns
        -------
        dict
            Plugin parameter values for this instance.

        """
        return self.parameters

    def get_citations(self):
        return self.cite.get_dictionary()

    def get_doc(self):
        return self.doc.get_dictionary()

DiamondLightSource / Savu

PluginCitations._set_plugin_citations() A last analyzed 2022-08-17 15:44 UTC

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last analyzed 2022-08-17 15:44 UTC
