o ;Di@sddlmZmZmZddlZddlZddlZddlm Z ddl m Z ddl m Z mZddlmZddZdd d ZGd ddeZd de ejdd fde ejdfdejffddfddZGdddeZdS))absolute_importprint_functiondivisionN) OrderedDict)partial)xrange text_type)TablecCs"tt}t|}|S)z^ Obtain the hex digest of a sha256 hash of the current epoch time in nanoseconds. )strtimeencodehashlibsha256 hexdigest)time_ns hash_timerI/var/www/Datamplify/venv/lib/python3.10/site-packages/petl/util/random.py randomseed srdcCt||||dS)an Construct a table with random numerical data. Use `numflds` and `numrows` to specify the number of fields and rows respectively. Set `wait` to a float greater than zero to simulate a delay on each row generation (number of seconds per row). E.g.:: >>> import petl as etl >>> table = etl.randomtable(3, 100, seed=42) >>> table +----------------------+----------------------+---------------------+ | f0 | f1 | f2 | +======================+======================+=====================+ | 0.6394267984578837 | 0.025010755222666936 | 0.27502931836911926 | +----------------------+----------------------+---------------------+ | 0.22321073814882275 | 0.7364712141640124 | 0.6766994874229113 | +----------------------+----------------------+---------------------+ | 0.8921795677048454 | 0.08693883262941615 | 0.4219218196852704 | +----------------------+----------------------+---------------------+ | 0.029797219438070344 | 0.21863797480360336 | 0.5053552881033624 | +----------------------+----------------------+---------------------+ | 0.026535969683863625 | 0.1988376506866485 | 0.6498844377795232 | +----------------------+----------------------+---------------------+ ... Note that the data are generated on the fly and are not stored in memory, so this function can be used to simulate very large tables. The only supported seed types are: None, int, float, str, bytes, and bytearray. )waitseed) RandomTable)numfldsnumrowsrrrrr randomtables rc@s&eZdZd ddZddZd d ZdS) rrrrNcCs0||_||_||_|durt|_dS||_dSN)rrrrr)selfrrrrrrr__init__<s   zRandomTable.__init__ccsv|j}|j}|j}t|ddt|D}t|Vt|D]}|jr,t |jtddt|DVq!dS)NcSsg|]}d|qS)zf%sr.0nrrr Nsz(RandomTable.__iter__..css|]}tVqdSr)pyrandomrandomr!rrr Vz'RandomTable.__iter__..) rrrr%rangetuplerrr sleep)rnfnrrflds_rrr__iter__Es    zRandomTable.__iter__cC t|_dSrrrrrrrreseedX zRandomTable.reseedrrrN)__name__ __module__ __qualname__r r0r4rrrrr;s  rfoobar)applespearsbananasorangesbazcCr)a Construct a table with dummy data. Use `numrows` to specify the number of rows. Set `wait` to a float greater than zero to simulate a delay on each row generation (number of seconds per row). E.g.:: >>> import petl as etl >>> table1 = etl.dummytable(100, seed=42) >>> table1 +-----+----------+----------------------+ | foo | bar | baz | +=====+==========+======================+ | 81 | 'apples' | 0.025010755222666936 | +-----+----------+----------------------+ | 35 | 'pears' | 0.22321073814882275 | +-----+----------+----------------------+ | 94 | 'apples' | 0.6766994874229113 | +-----+----------+----------------------+ | 69 | 'apples' | 0.5904925124490397 | +-----+----------+----------------------+ | 4 | 'apples' | 0.09369523986159245 | +-----+----------+----------------------+ ... >>> import random as pyrandom >>> from functools import partial >>> fields = [('foo', pyrandom.random), ... ('bar', partial(pyrandom.randint, 0, 500)), ... ('baz', partial(pyrandom.choice, ['chocolate', 'strawberry', 'vanilla']))] >>> table2 = etl.dummytable(100, fields=fields, seed=42) >>> table2 +---------------------+-----+-------------+ | foo | bar | baz | +=====================+=====+=============+ | 0.6394267984578837 | 12 | 'vanilla' | +---------------------+-----+-------------+ | 0.27502931836911926 | 114 | 'chocolate' | +---------------------+-----+-------------+ | 0.7364712141640124 | 346 | 'vanilla' | +---------------------+-----+-------------+ | 0.8921795677048454 | 44 | 'vanilla' | +---------------------+-----+-------------+ | 0.4219218196852704 | 15 | 'chocolate' | +---------------------+-----+-------------+ ... >>> table3_1 = etl.dummytable(50) >>> table3_2 = etl.dummytable(100) >>> table3_1[5] == table3_2[5] False Data generation functions can be specified via the `fields` keyword argument. Note that the data are generated on the fly and are not stored in memory, so this function can be used to simulate very large tables. The only supported seed types are: None, int, float, str, bytes, and bytearray. rfieldsrr) DummyTablerArrr dummytable\sGrDc@s.eZdZd ddZddZdd Zd d ZdS) rCrNrcCsF||_||_|durt|_nt||_|durt|_dS||_dSr)rrrrBrr)rrrBrrrrrr s    zDummyTable.__init__cCs||jt|<dSr)rBr)ritemvaluerrr __setitem__szDummyTable.__setitem__c#sz|j}|j}|jt|tddD}|Vt|D]}|jr.t |jtfddDVq#dS)Ncss|]}t|VqdSr)rr"frrrr'r(z&DummyTable.__iter__..c3s|]}|VqdSrrrHrBrrr's) rrrBcopyr%r*keysrrr r+)rr-rhdrr/rrJrr0s    zDummyTable.__iter__cCr1rr2r3rrrr4r5zDummyTable.reseed)rNrN)r7r8r9r rGr0r4rrrrrCs    rCr6) __future__rrrr r&r%r collectionsr functoolsr petl.compatrrpetl.util.baser rrrrandintchoicerDrCrrrrs(    #" J