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Frk DUMMY_KEYrmzTCache params for function %s are incompatible with current cache storage manager: %sN)rrtru check_contextr#rqerror)r>rYr1r2r4rnrxes r@rZz DataCaches.validate_cache_paramss %SUCCC 99$'## :     $ $ & & 4 4] C C C C C)    MM,      s'A B A==BrQrn float | Nonerc*t|||||S)N)rQfunction_display_namernr2r1)r)r>rQrYr1rnr2s r@rtz'DataCaches.create_cache_storage_contexts*#%"/##     rAr"ctjrtjjStdt S)Nz1No runtime found, using MemoryCacheStorageManager)r exists get_instanceryrqwarningr$rFs r@ruzDataCaches.get_storage_manager%sE >   /'))? ? OOO P P P,.. .rAN)rSrKr1r*r2r3r4rhrPrKr6r7rBrir\rBr) rYrKr1r*r2r3r4rhrBrW) rQrKrYrKr1r*rnrr2r3rBr)rBr") r[rNrOr]r<rUr{rrZrtrurAr@rbrbs))999FFFFP ' ' ' '    """"H    //////rArbrBr(ctS)z:Return the StatsProvider for all @st.cache_data functions.)rTrrAr@get_data_cache_stats_providerr3s rAceZdZdZ d'd(dZeded ef Ze d)d Z e dddddddd*dZ d'dddddddd+d Z d'dd!d+d"Z e d#d,d%Z d&ZdS)- CacheDataAPIzjImplements the public st.cache_data API: the @st.cache_data decorator, and st.cache_data.clear(). Ndecorator_metric_namerKdeprecation_warning str | NonecHt||j|_||_dS)aCreate a CacheDataAPI instance. Parameters ---------- decorator_metric_name The metric name to record for decorator usage. `@st.experimental_memo` is deprecated, but we're still supporting it and tracking its usage separately from `@st.cache_data`. deprecation_warning An optional deprecation warning to show when the API is accessed. N)r% _decorator_deprecation_warning)r>rrs r@r<zCacheDataAPI.__init__=s,$) !4?  %8!!!rAF.)boundr-rBcdSrDr)r>r-s r@__call__zCacheDataAPI.__call__Ys rATF)r4r2r/r1experimental_allow_widgetsr8r4r5r2r3r/r0r1CachePersistType | boolrr7r8r9Callable[[F], F]cdSrDr)r>r4r2r/r1rr8s r@rzCacheDataAPI.__call__^s  rAF | Nonec :||||||||S)N)r4r2r1r/rr8)r)r>r-r4r2r/r1rr8s r@rzCacheDataAPI.__call__ks4 #%'A!   rA)r8c  |durd n |durd n| dvrtd|d| fd}||Sttt t j|  S) aFDecorator to cache functions that return data (e.g. dataframe transforms, database queries, ML inference). Cached objects are stored in "pickled" form, which means that the return value of a cached function must be pickleable. Each caller of the cached function gets its own copy of the cached data. You can clear a function's cache with ``func.clear()`` or clear the entire cache with ``st.cache_data.clear()``. To cache global resources, use ``st.cache_resource`` instead. Learn more about caching at https://docs.streamlit.io/library/advanced-features/caching. Parameters ---------- func : callable The function to cache. Streamlit hashes the function's source code. ttl : float, timedelta, str, or None The maximum time to keep an entry in the cache. Can be one of: * ``None`` if cache entries should never expire (default). * A number specifying the time in seconds. * A string specifying the time in a format supported by `Pandas's Timedelta constructor `_, e.g. ``"1d"``, ``"1.5 days"``, or ``"1h23s"``. * A ``timedelta`` object from `Python's built-in datetime library `_, e.g. ``timedelta(days=1)``. Note that ``ttl`` will be ignored if ``persist="disk"`` or ``persist=True``. max_entries : int or None The maximum number of entries to keep in the cache, or None for an unbounded cache. When a new entry is added to a full cache, the oldest cached entry will be removed. Defaults to None. show_spinner : bool or str Enable the spinner. Default is True to show a spinner when there is a "cache miss" and the cached data is being created. If string, value of show_spinner param will be used for spinner text. persist : "disk", bool, or None Optional location to persist cached data to. Passing "disk" (or True) will persist the cached data to the local disk. None (or False) will disable persistence. The default is None. experimental_allow_widgets : bool Allow widgets to be used in the cached function. Defaults to False. Support for widgets in cached functions is currently experimental. Setting this parameter to True may lead to excessive memory use since the widget value is treated as an additional input parameter to the cache. We may remove support for this option at any time without notice. hash_funcs : dict or None Mapping of types or fully qualified names to hash functions. This is used to override the behavior of the hasher inside Streamlit's caching mechanism: when the hasher encounters an object, it will first check to see if its type matches a key in this dict and, if so, will use the provided function to generate a hash for it. See below for an example of how this can be used. Example ------- >>> import streamlit as st >>> >>> @st.cache_data ... def fetch_and_clean_data(url): ... # Fetch data from URL here, and then clean it up. ... return data ... >>> d1 = fetch_and_clean_data(DATA_URL_1) >>> # Actually executes the function, since this is the first time it was >>> # encountered. >>> >>> d2 = fetch_and_clean_data(DATA_URL_1) >>> # Does not execute the function. Instead, returns its previously computed >>> # value. This means that now the data in d1 is the same as in d2. >>> >>> d3 = fetch_and_clean_data(DATA_URL_2) >>> # This is a different URL, so the function executes. To set the ``persist`` parameter, use this command as follows: >>> import streamlit as st >>> >>> @st.cache_data(persist="disk") ... def fetch_and_clean_data(url): ... # Fetch data from URL here, and then clean it up. ... return data By default, all parameters to a cached function must be hashable. Any parameter whose name begins with ``_`` will not be hashed. You can use this as an "escape hatch" for parameters that are not hashable: >>> import streamlit as st >>> >>> @st.cache_data ... def fetch_and_clean_data(_db_connection, num_rows): ... # Fetch data from _db_connection here, and then clean it up. ... return data ... >>> connection = make_database_connection() >>> d1 = fetch_and_clean_data(connection, num_rows=10) >>> # Actually executes the function, since this is the first time it was >>> # encountered. >>> >>> another_connection = make_database_connection() >>> d2 = fetch_and_clean_data(another_connection, num_rows=10) >>> # Does not execute the function. Instead, returns its previously computed >>> # value - even though the _database_connection parameter was different >>> # in both calls. A cached function's cache can be procedurally cleared: >>> import streamlit as st >>> >>> @st.cache_data ... def fetch_and_clean_data(_db_connection, num_rows): ... # Fetch data from _db_connection here, and then clean it up. ... return data ... >>> fetch_and_clean_data.clear() >>> # Clear all cached entries for this function. To override the default hashing behavior, pass a custom hash function. You can do that by mapping a type (e.g. ``datetime.datetime``) to a hash function (``lambda dt: dt.isoformat()``) like this: >>> import streamlit as st >>> import datetime >>> >>> @st.cache_data(hash_funcs={datetime.datetime: lambda dt: dt.isoformat()}) ... def convert_to_utc(dt: datetime.datetime): ... return dt.astimezone(datetime.timezone.utc) Alternatively, you can map the type's fully-qualified name (e.g. ``"datetime.datetime"``) to the hash function instead: >>> import streamlit as st >>> import datetime >>> >>> @st.cache_data(hash_funcs={"datetime.datetime": lambda dt: dt.isoformat()}) ... def convert_to_utc(dt: datetime.datetime): ... return dt.astimezone(datetime.timezone.utc) Tr)FN)Nr)zUnsupported persist option 'z#'. 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