o Uݢg3 @srUdZddlmZddlZddlZddlZddlmZmZm Z m Z m Z ddl m Z ddlmZmZmZddlZedeeeedZdZd ed <d Zd ed <d Zd ed<edZded<edZded<dTddZdUddZ dVd d!Z!e"ej#e$e%ej&fdWd'd(Z'dXd*d+Z(dYdZd/d0Z)d[d1d2Z*eee+eee,e-e%e.e*e e!e'f d\dFdGZ/GdHdIdIej0Z1d]dLdMZ2d,dNd^dRdSZ3dS)_a,Methods for safely encoding values to json. The json standard does not permit encoding NaN, but Python will still happily do it, which can cause problems for other programs. This module contains code to fix those values up, as well as convert numpy objects to things which encode properly in json. ) annotationsN)Callable GeneratorIterableIteratorMapping)PurePath)IOAnyTypeVar_T0NaNstr NAN_STRINGinfPOS_INF_STRINGz-infNEG_INF_STRINGz-Inffloat_NEG_INFz+Inf_POS_INFxreturn float | _T0cCs,|tkrttS|tkrtS|tkrtS|S)zConverts back a special numeric value encoded by json_sanitize. Args: x: a string encoded value or float Returns: a float or the original value )rrrrrr)rr_/oak/stanford/groups/akundaje/marinovg/programs/cellranger-9.0.1/lib/python/tenkit/safe_json.pydesanitize_value#s rkr cCs>t|tr|St|tr|St|tr|rdSdSt|S)Ntruefalse) isinstancerbytesdecodebool)rrrr _sanitize_key6s    r#data3bytes | PurePath | float | np.integer | np.floatingNone | str | int | floatcCst|ttBtBr |St|tr|St|trt|St|tjr't|St|t tj BrFt |r6t S|tkrgs z_dict_items..keys iteritemsitems __getitem__c3s|] }||fVqdSr'r)r6keyr.rrr:qs)rr=r<r;)r$r0r2r9hasattr is_dataclassrr8r _dict_itemsXs     rB5None | dict[str, Any] | int | str | float | list[Any]cCst|r|St|ttBtBtjBtjBrt|St |}dur(dd|DSt |dr4dd|DSt |drD|j dkrDt| S|S) aConvert data into a form that will correctly serialize to json. Yuck yuck yuck yuck yuck! The default JSON encoders do bad things if you try to encode NaN/Infinity/-Infinity as JSON. This code takes a nested data structure composed of: atoms, dicts, or array-like iteratables and finds all of the not-really-a-number floats and converts them to an appropriate string for subsequent jsonification. Note: Don't call this and then pass the result to `json.dump`. It's considerably more efficient to use `dump_numpy`, which avoids constructing the entire sanitized object in memory. NcSsi|] \}}t|t|qSr)r# json_sanitize)r6rvaluerrr sz!json_sanitize..__iter__cSsg|]}t|qSr)rDr6itemrrr sz!json_sanitize..shaper) _is_saferr rrr)r+r*r/rBr@rKrI)r$ dict_itemsrrrrDus  rDFprettyr"cKs.|dd|r t|tj|fdti|S)aDump an object to a string as json, after sanitizing it. Note: Rather than writing the result of this call to a file, it's considerably more efficient to use `dump_numpy`, which doesn't build the entire string up in memory. Args: data: The object to encode. pretty: If true, set various defaults for pretty-printed json. **kwargs: Additional arguments to pass to the json encoder. allow_nanFcls) setdefault_pretty_kwargsjsondumpsNumpyAwareJSONEncoder)r$rNkwargsrrr safe_jsonifys rWcCsv|dus t|ttBtBrdSt|trt|St|tr)tdd| DSt|t t Br9tdd|DSdS)a Returns True if we can use the default encoder. This allows us to short-circuit all of the custom logic to use the native implementation instead. In the case of json_sanitize, this can save quite a bit of ram copying objects which don't need to be copied. In the case of NumpyAwareJSONEncoder, it's less important but still means using the native C implementation. Args: data: The data to check Returns: True if the object can be safely encoded with the native encoder. NTcss$|] \}}t|o t|VqdSr'rL)r6r?rErrrr:s"z_is_safe..css|]}t|VqdSr'rX)r6rErrrr:sF) rrr(r"rr,isfiniter0allr=listtupler.rrrrLs   rLenc#Callable[[Any, int], Iterable[str]]indent str | None sort_keysitem_separator key_separator type[float]r( type[int] type[str] type[bool]r type[bytes]Callable[[Any], bool]Callable[[Any], str])Callable[[Any], None | str | int | float]1Callable[[Any], Iterable[tuple[Any, Any]] | None]0Callable[[Any, int], Generator[str, None, None]]cstj tjtj tjd fdd d f d d d fd d S)Nobj Iterable[Any]depthr(rGenerator[str, None, None]c3s|dr|sdVdS|d7}dVr"d|}|Vnd}d}|D]}|r/d}nV|r7|V||EdHq(|durN|d VdVdS) N__len__z[][ TF]r)rnrpnewline_indentfirstrE) _iterencoder@r_rblenrr_iterencode_iterables*  z._make_iterencode.._iterencode_iterableIterable[tuple[Any, Any]]c3s|dr|sdVdS|d7}dVr"d|}|Vnd}d}r3 fdd|D}|D]&\}}|r>d }nV|rF|V||EdHV||EdHq5|durj|d Vd VdS) Nrrz{}rs{ruTc3s |] \}}||fVqdSr'rr6rv)r#rrr:sz=_make_iterencode.._iterencode_dict..F}r)rnrprwrxr?rE) ryr#r]r@r_rbrcrzrasortedrr_iterencode_dicts2   z*_make_iterencode.._iterencode_dictr c3s|dus s |rn| fr||EdHdS|tBB BBr7||EdHdS|}durI||EdHdS|rS|jdksX| rf||EdHdS |dru||EdHdS||EdHdS)NrrG)rndimrI)rnrprM)rBrLrr{r/r"r r]rfloat64genericr@r_r(r*rndarrayrrrrys& z%_make_iterencode.._iterencode)rnrorpr(rrq)rnr|rpr(rrq)rnr rpr(rrq)r)rr*rr)r]r_rarbrcrr(rrr"r rzr@rrLr#r/rBr)rBrLryrr{r#r/r"r r]rrrr@r_r(r*rrbrcrzrrarrr_make_iterencodes 0rcs.eZdZdZd ddZddfd d ZZS)rUzThis encoder will convert 1D np.ndarrays to lists. For other numpy types, uses obj.item() to extract a python scalar. or rcstrStttBtBtjBtjBrtSttj r/j dkr( Stt Sttj r<SttrLfddDStdr]fddDSttrkfddDStjS)zConvert a 1D np.ndarray into something json-serializable. Returns: list, or a single-element array or matrix into its scalar value. rscs i|] \}}t||qSrr#defaultr~selfrrrF@ z1NumpyAwareJSONEncoder.default..r;cs i|] }t||qSrr)r6rrrrrrFCrcsg|]}|qSr)rrHrrrrJFsz1NumpyAwareJSONEncoder.default..)rLrr rrr)r+r*r/rrtolistZasscalarrrrIrr=r@r;rrS JSONEncoder)rrrrrr.s"      zNumpyAwareJSONEncoder.defaultF _one_shotr" Iterator[str]c st|r t||Sttj|jrind|j|j rtjj ntjj |j dus,t |j tr/|j nd|j }|j|j|j|j|j ||j|j|j|dS)zEncode the given object and yield each string as available. For example:: for chunk in JSONEncoder().iterencode(bigobject): mysocket.write(chunk) N r)rLsuper iterencoderrSencoderc_make_encodercheck_circularr ensure_asciic_encode_basestring_asciic_encode_basestringr_rrrcrbraskipkeysrO)rrrr_ __class__rrrIs4  z NumpyAwareJSONEncoder.iterencode)rr rr F)rr rr"rr)__name__ __module__ __qualname____doc__rr __classcell__rrrrrU(s rUrVdict[str, Any]cCs(|dd|dd|dddS)z7Update kwargs with defaults for "pretty printing" json.r_raT separators),:N)rQ)rVrrrrRos  rR)rNfpIO[str]NonecKs4|dd|r t|tj||fdti|dS)zDump object to json, converting numpy objects to reasonable JSON. Also handles converting bytes to str, dict-like objects such as h5 tables into objects, and non-list/tuple iterables into lists. rOFrPN)rQrRrSdumprU)r$rrNrVrrr dump_numpyvs r)rr rr)rr rr)r$r%rr&)r0r1r2r3rr4)rrCr)r$r rNr"rr)r$r rr")r]r^r_r`rar"rbrrcrrrdr(rerrfr"rgr rhrLrir#rjr/rkrBrlrrm)rVr)r$r rrrNr"rr)4r __future__r dataclassesrSr,collections.abcrrrrrpathlibrtypingr r r Znumpyr)rrr(r"r r__annotations__rrrrrr#r/r0r2r9r@rArBrDrWrLrr rzrrrrUrRrrrrrsZ          ! % c G