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import copy

from libertem.analysis.base import Analysis
from .template import TemplateBase

class Literal:
    def __init__(self, value):
        self.value = value

    def __repr__(self) -> str:
        return self.value

class CodeTemplate(TemplateBase):
    Generate code using template from :class:`TemplateBase` and parameters
    from GUI.

    new code segments can be generated by adding templates in :class:`TemplateBase`
    and combining parameters accordingly.

    code for dataset, dependencies, connection are generated here. Analysis
    specific code are handled in `libertem.analysis.helper`.

    def __init__(self, connection, dataset, compound_analysis):
        self.conn = connection['connection']
        self.ds = dataset
        self.compound_analysis = compound_analysis

        self.analysis_helper = {}
        for analysis in self.compound_analysis:

            type = analysis['analysisType']
            params = analysis['parameters']
            cls = Analysis.get_analysis_by_type(type)
            helperCls = cls.get_template_helper()
            helper = helperCls(params)
            self.analysis_helper[type] = helper

    def dataset(self):
        ds_type = self.ds['type']
        ds_params = copy.copy(self.ds['params'])
        data = {
            'type': ds_type,
            'params': ds_params,
        result = []
        # because the io_backend parameter is not trivially serializable
        # like the others, we special case it here:
        if "io_backend" in ds_params:
            ds_params["io_backend"] = Literal("io_backend")
            data['backend_cls'] = self.ds["params"]["io_backend"].__class__.__name__
                self.code_formatter(self.format_template(self.temp_ds_backend, data))
            self.code_formatter(self.format_template(self.temp_ds, data))
        return "\n".join(result)

    def dependency(self):
        dependencies required for generated script.

        Common dependencies are available in :class:`TemplateBase`.
        Any additional dependencies can be added through
        corresponding analysis helper.
        extra_dep = []

        if self.conn['type'].lower() == "tcp":

        if "io_backend" in self.ds["params"]:
            data = {
                "backend_cls": self.ds["params"]["io_backend"].__class__.__name__
                self.code_formatter(self.format_template(self.temp_dep_ds, data))

        for helper in self.analysis_helper.values():
            analysis_dep = helper.get_dependency()
            if analysis_dep is not None:
        dep = self.temp_dep + extra_dep
        return self.code_formatter('\n'.join(dep))

    def initial_setup(self):
        return "%matplotlib nbagg"

    def connection(self):
        docs = ["# Connection"]
        if self.conn['type'].lower() == "tcp":
            link = ""
            more_info = f"[For more info]({link})"
            docs.append(f"Connecting to dask cluster, {more_info}")
            data = {'conn_url': self.conn['address']}
            ctx = self.format_template(self.temp_conn, data)
            docs = '\n'.join(docs)
            return ctx, docs
        elif self.conn['type'].lower() == "local":
            docs.append("This starts a local cluster that is accessible through ctx.")
            ctx = "ctx = lt.Context()"
            docs = '\n'.join(docs)
            return ctx, docs
            raise ValueError("unknown connection type")

    def analysis(self):
        form_analysis = []

        for helper in self.analysis_helper.values():

            plot_ = list(map(self.code_formatter, helper.get_plot()))
            analy_ = self.code_formatter(helper.get_analysis())
            docs_ = self.code_formatter(helper.get_docs())
            save_ = self.code_formatter(helper.get_save())

            form_analysis.append((docs_, analy_, plot_, save_))

        return form_analysis