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How to use hook_spec method in localstack

Best Python code snippet using localstack_python

opts.py

Source:opts.py

1from copy import copy2from functools import partial, reduce3from typing import Any, Union4import panel as pn5import pandas as pd6import param7import proplot as pplt8from matplotlib.colors import Colormap, Normalize9from lumflux.support import apply_cmap10# todo baseclass widget generating thingy11class OptsBase(param.Parameterized):12    _type = None13    updated = param.Event()14    def __init__(self, **params):15        super().__init__(**params)16        self._excluded_from_opts = ["name"]  # todo remove this and opts property17        self.widgets = self.generate_widgets()18    @property19    def panel(self) -> pn.Column:20        return pn.Column(*self.widgets.values())21    @property22    def opts(self) -> dict:23        opts = {24            name: self.param[name]25            for name in self.param26            if name not in self._excluded_from_opts27        }28        return opts29    def generate_widgets(self, **kwargs) -> dict:30        """returns a dict with keys parameter names and values default mapped widgets"""31        # todo base class?32        names = [33            p34            for p in self.param35            if self.param[p].precedence is None or self.param[p].precedence > 136        ]37        widgets = pn.Param(38            self.param, show_name=False, show_labels=True, widgets=kwargs39        )40        return {k: v for k, v in zip(names[1:], widgets)}41class GenericOpts(OptsBase):42    _type = "generic"43    hooks = param.List()44    def __init__(self, **params):45        self.kwargs = {k: v for k, v in params.items() if k not in self.param}46        super().__init__(**{k: v for k, v in params.items() if k in self.param})47    def hooks_factory(self):48        def hook(hooks, plot, element):49            for hook_spec in hooks:50                handle = plot.handles[hook_spec["handle"]]51                rsetattr(handle, hook_spec["attr"], hook_spec["value"])52        f = partial(hook, self.hooks)53        return f54    @property55    def opts(self) -> dict:56        """Returns final opts dict which is passed to holoviews.57        Returns:58        """59        return {"hooks": [self.hooks_factory()], **self._parse_kwargs(self.kwargs)}60    @staticmethod61    def _parse_kwargs(kwargs):62        out = {}63        for k, v in kwargs.items():64            if k in ["xlim", "ylim"] and isinstance(v, list):65                out[k] = tuple(v)66            elif k in ["padding"] and isinstance(v, list):67                out[k] = to_tuple(v)68            else:69                out[k] = v70        return out71# https://stackoverflow.com/questions/27049998/convert-a-mixed-nested-list-to-a-nested-tuple/27050037#2705003772def to_tuple(lst):73    return tuple(to_tuple(i) if isinstance(i, list) else i for i in lst)74# https://stackoverflow.com/questions/31174295/getattr-and-setattr-on-nested-subobjects-chained-properties75def rsetattr(obj: Any, attr: str, val: Any):76    """Recursive set attr77    Args:78        obj:79        attr:80        val:81    Returns:82    """83    pre, _, post = attr.rpartition(".")84    return setattr(rgetattr(obj, pre) if pre else obj, post, val)85def rgetattr(obj, attr, *args):86    def _getattr(obj, attr):87        return getattr(obj, attr, *args)88    return reduce(_getattr, [obj] + attr.split("."))89class HooksOpts(OptsBase):90    _type = "hooks"91    hooks = param.List()92    #TODO: this code is duplicate with GenericOpts93    def hooks_factory(self):94        def hook(hooks, plot, element):95            for hook_spec in hooks:96                handle = plot.handles[hook_spec["handle"]]97                rsetattr(handle, hook_spec["attr"], hook_spec["value"])98        f = partial(hook, self.hooks)99        return f100    @property101    def opts(self):102        opts = {"hooks": [self.hooks_factory()]}103        return opts104class CmapOpts(OptsBase):105    _type = "cmap"106    cmap = param.ClassSelector(default=None, class_=Colormap)107    norm = param.ClassSelector(default=None, class_=Normalize)108    # the stored norm here is the unscaled one109    # scale factor is applied to clim and norm_scaled110    clim = param.Tuple((0.0, 1.0), length=2)111    sclf = param.Number(1.0, doc="scaling factor to apply")  # curent: 1e+3112    field = param.String(doc="field on which cmap works")113    def __init__(self, rename=True, invert=False, **params):114        # todo from_spec constructor method for this kind of logic115        cmap = params.pop("cmap", None)116        cmap = pplt.Colormap(cmap) if cmap else cmap117        params["cmap"] = cmap118        super().__init__(**params)119        self._excluded_from_opts += [120            "norm",121            "sclf",122        ]  # perhaps use leading underscore to exclude?123        self.norm = self.norm or pplt.Norm("linear", 0.0, 1.0)124        self._cmap = self.cmap or pplt.Colormap("viridis")  # unreversed cmap125        if rename:126            cmap.name = self.field + "_default"127        if invert:128            cmap = cmap.reversed()129        self.cmap = cmap130    @property131    def opts(self):132        names = ["cmap", "clim"]133        opts = {name: self.param[name] for name in names}134        return opts135    @property136    def norm_scaled(self):137        norm = copy(self.norm)138        norm.vmin *= self.sclf139        norm.vmax *= self.sclf140        return norm141    @norm_scaled.setter142    def norm_scaled(self, norm):143        _norm = copy(norm)144        _norm.vmin /= self.sclf145        _norm.vmax /= self.sclf146        self.norm = _norm147    @param.depends("norm", watch=True)148    def _norm_updated(self):149        self.clim = self.norm.vmin * self.sclf, self.norm.vmax * self.sclf150        # todo invert bool?151        # self.clim = self.norm.vmax*self.sclf, self.norm.vmin*self.sclf,152    def apply(self, data: Union[pd.Series, pd.DataFrame]) -> Union[pd.Series, pd.DataFrame]:153        """apply cmap / norm to data (pd series or df)"""154        # norm = copy(self.norm)155        # norm.vmin *= self.sclf156        # norm.vmax *= self.sclf157        return apply_cmap(data, self.cmap, self.norm)158    @param.depends("norm", "cmap", watch=True)159    def update(self):...

hooks.py

Source:hooks.py

...14    def wrapper(fn):15        fn.hook_priority = priority16        return plugin(namespace=namespace, **kwargs)(fn)17    return wrapper18def hook_spec(namespace: str):19    """20    Creates a new hook decorator bound to a namespace.21    on_infra_start = hook_spec("localstack.hooks.on_infra_start")22    @on_infra_start()23    def foo():24        pass25    # run all hooks in order26    on_infra_start.run()27    """28    fn = functools.partial(hook, namespace=namespace)29    # attach hook manager and run method to decorator for convenience calls30    fn.manager = HookManager(namespace)31    fn.run = fn.manager.run_in_order32    return fn33class HookManager(PluginManager):34    def load_all_sorted(self, propagate_exceptions=False):35        """36        Loads all hook plugins and sorts them by their hook_priority attribute.37        """38        plugins = self.load_all(propagate_exceptions)39        # the hook_priority attribute is part of the function wrapped in the FunctionPlugin40        plugins.sort(41            key=lambda _fn_plugin: getattr(_fn_plugin.fn, "hook_priority", 0), reverse=True42        )43        return plugins44    def run_in_order(self, *args, **kwargs):45        """46        Loads and runs all plugins in order them with the given arguments.47        """48        for fn_plugin in self.load_all_sorted():49            fn_plugin(*args, **kwargs)50    def __str__(self):51        return "HookManager(%s)" % self.namespace52    def __repr__(self):53        return self.__str__()54configure_localstack_container = hook_spec(HOOKS_CONFIGURE_LOCALSTACK_CONTAINER)55"""Hooks to configure the LocalStack container before it starts. Executed on the host when invoking the CLI."""56install = hook_spec(HOOKS_INSTALL)57"""Additional programmatic installers."""58prepare_host = hook_spec(HOOKS_PREPARE_HOST)59"""Hooks to prepare the host that's starting LocalStack. Executed on the host when invoking the CLI."""60on_infra_start = hook_spec(HOOKS_ON_INFRA_START)61"""Hooks that are executed right before starting the LocalStack infrastructure."""62on_infra_ready = hook_spec(HOOKS_ON_INFRA_READY)63"""Hooks that are execute after all startup hooks have been executed, and the LocalStack infrastructure has become...

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