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from __future__ import absolute_import, print_function, division
import hashlib
import random as pyrandom
import time
from collections import OrderedDict
from functools import partial
from petl.compat import xrange, text_type
from petl.util.base import Table
def randomseed():
"""
Obtain the hex digest of a sha256 hash of the
current epoch time in nanoseconds.
"""
time_ns = str(time.time()).encode()
hash_time = hashlib.sha256(time_ns).hexdigest()
return hash_time
def randomtable(numflds=5, numrows=100, wait=0, seed=None):
"""
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 |
+----------------------+----------------------+---------------------+
...
<BLANKLINE>
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.
"""
return RandomTable(numflds, numrows, wait=wait, seed=seed)
class RandomTable(Table):
def __init__(self, numflds=5, numrows=100, wait=0, seed=None):
self.numflds = numflds
self.numrows = numrows
self.wait = wait
if seed is None:
self.seed = randomseed()
else:
self.seed = seed
def __iter__(self):
nf = self.numflds
nr = self.numrows
seed = self.seed
# N.B., we want this to be stable, i.e., same data each time
pyrandom.seed(seed)
# construct fields
flds = ["f%s" % n for n in range(nf)]
yield tuple(flds)
# construct data rows
for _ in xrange(nr):
# artificial delay
if self.wait:
time.sleep(self.wait)
yield tuple(pyrandom.random() for n in range(nf))
def reseed(self):
self.seed = randomseed()
def dummytable(
numrows=100,
fields=(
('foo', partial(pyrandom.randint, 0, 100)),
('bar', partial(pyrandom.choice, ('apples', 'pears', 'bananas', 'oranges'))),
('baz', pyrandom.random),
),
wait=0,
seed=None,
):
"""
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 |
+-----+----------+----------------------+
...
<BLANKLINE>
>>> 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' |
+---------------------+-----+-------------+
...
<BLANKLINE>
>>> 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.
"""
return DummyTable(numrows=numrows, fields=fields, wait=wait, seed=seed)
class DummyTable(Table):
def __init__(self, numrows=100, fields=None, wait=0, seed=None):
self.numrows = numrows
self.wait = wait
if fields is None:
self.fields = OrderedDict()
else:
self.fields = OrderedDict(fields)
if seed is None:
self.seed = randomseed()
else:
self.seed = seed
def __setitem__(self, item, value):
self.fields[text_type(item)] = value
def __iter__(self):
nr = self.numrows
seed = self.seed
fields = self.fields.copy()
# N.B., we want this to be stable, i.e., same data each time
pyrandom.seed(seed)
# construct header row
hdr = tuple(text_type(f) for f in fields.keys())
yield hdr
# construct data rows
for _ in xrange(nr):
# artificial delay
if self.wait:
time.sleep(self.wait)
yield tuple(fields[f]() for f in fields)
def reseed(self):
self.seed = randomseed()
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