/usr/lib/python2.7/dist-packages/tables/tests/test_numpy.py is in python-tables 3.3.0-5.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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from __future__ import print_function
from __future__ import absolute_import
import os
import sys
import tempfile
import numpy as np
import tables
from tables import (
StringCol, BoolCol, FloatCol, ComplexCol, EnumCol,
Int8Col, UInt8Col, Int16Col, UInt16Col, Int32Col, UInt32Col,
Int64Col, Float32Col, Float64Col, Time64Col
)
from tables.tests import common
from tables.tests.common import allequal
from tables.tests.common import unittest
from tables.tests.common import PyTablesTestCase as TestCase
from six.moves import range
typecodes = ['b', 'h', 'i', 'l', 'q', 'f', 'd']
# UInt64 checking disabled on win platforms
# because this type is not supported
if sys.platform != 'win32':
typecodes += ['B', 'H', 'I', 'L', 'Q', 'F', 'D']
else:
typecodes += ['B', 'H', 'I', 'L', 'F', 'D']
typecodes += ['b1'] # boolean
if hasattr(tables, 'Float16Atom'):
typecodes.append('e')
if hasattr(tables, 'Float96Atom') or hasattr(tables, 'Float128Atom'):
typecodes.append('g')
if hasattr(tables, 'Complex192Atom') or hasattr(tables, 'Conplex256Atom'):
typecodes.append('G')
byteorder = {'little': '<', 'big': '>'}[sys.byteorder]
class BasicTestCase(TestCase):
"""Basic test for all the supported typecodes present in NumPy.
All of them are included on PyTables.
"""
endiancheck = 0
def WriteRead(self, testArray):
if common.verbose:
print('\n', '-=' * 30)
print("Running test for array with typecode '%s'" %
testArray.dtype.char, end=' ')
print("for class check:", self.title)
# Create an instance of HDF5 Table
self.h5fname = tempfile.mktemp(".h5")
try:
with tables.open_file(self.h5fname, mode="w") as self.h5file:
self.root = self.h5file.root
# Create the array under root and name 'somearray'
a = testArray
self.h5file.create_array(self.root, 'somearray', a,
"Some array")
# Re-open the file in read-only mode
with tables.open_file(self.h5fname, mode="r") as self.h5file:
self.root = self.h5file.root
# Read the saved array
b = self.root.somearray.read()
# For cases that read returns a python type instead of a
# numpy type
if not hasattr(b, "shape"):
b = np.np.array(b, dtype=a.dtype.str)
# Compare them. They should be equal.
# if not allequal(a,b, "numpy") and common.verbose:
if common.verbose:
print("Array written:", a)
print("Array written shape:", a.shape)
print("Array written itemsize:", a.itemsize)
print("Array written type:", a.dtype.char)
print("Array read:", b)
print("Array read shape:", b.shape)
print("Array read itemsize:", b.itemsize)
print("Array read type:", b.dtype.char)
type_ = self.root.somearray.atom.type
# Check strictly the array equality
self.assertEqual(type(a), type(b))
self.assertEqual(a.shape, b.shape)
self.assertEqual(a.shape, self.root.somearray.shape)
self.assertEqual(a.dtype, b.dtype)
if a.dtype.char[0] == "S":
self.assertEqual(type_, "string")
else:
self.assertEqual(a.dtype.base.name, type_)
self.assertTrue(allequal(a, b, "numpy"))
finally:
# Then, delete the file
if os.path.exists(self.h5fname):
os.remove(self.h5fname)
def test00_char(self):
"""Data integrity during recovery (character objects)"""
a = np.array(self.tupleChar, 'S'+str(len(self.tupleChar)))
self.WriteRead(a)
def test01_char_nc(self):
"""Data integrity during recovery (non-contiguous character objects)"""
a = np.array(self.tupleChar, 'S'+str(len(self.tupleChar)))
if a.shape == ():
b = a # We cannot use the indexing notation
else:
b = a[::2]
# Ensure that this numpy string is non-contiguous
if a.shape[0] > 2:
self.assertEqual(b.flags['CONTIGUOUS'], False)
self.WriteRead(b)
def test02_types(self):
"""Data integrity during recovery (numerical types)"""
for typecode in typecodes:
if self.tupleInt.shape:
a = self.tupleInt.astype(typecode)
else:
# shape is the empty tuple ()
a = np.array(self.tupleInt, dtype=typecode)
self.WriteRead(a)
def test03_types_nc(self):
"""Data integrity during recovery (non-contiguous numerical types)"""
for typecode in typecodes:
if self.tupleInt.shape:
a = self.tupleInt.astype(typecode)
else:
# shape is the empty tuple ()
a = np.array(self.tupleInt, dtype=typecode)
# This should not be tested for the rank-0 case
if len(a.shape) == 0:
raise unittest.SkipTest
b = a[::2]
# Ensure that this array is non-contiguous (for non-trivial case)
if a.shape[0] > 2:
self.assertEqual(b.flags['CONTIGUOUS'], False)
self.WriteRead(b)
class Basic0DOneTestCase(BasicTestCase):
# Rank-0 case
title = "Rank-0 case 1"
tupleInt = np.array(3)
tupleChar = "4"
class Basic0DTwoTestCase(BasicTestCase):
# Rank-0 case
title = "Rank-0 case 2"
tupleInt = np.array(33)
tupleChar = "44"
class Basic1DOneTestCase(BasicTestCase):
# 1D case
title = "Rank-1 case 1"
tupleInt = np.array((3,))
tupleChar = ("a",)
class Basic1DTwoTestCase(BasicTestCase):
# 1D case
title = "Rank-1 case 2"
tupleInt = np.array((0, 4))
tupleChar = ("aaa",)
class Basic1DThreeTestCase(BasicTestCase):
# 1D case
title = "Rank-1 case 3"
tupleInt = np.array((3, 4, 5))
tupleChar = ("aaaa", "bbb",)
class Basic2DTestCase(BasicTestCase):
# 2D case
title = "Rank-2 case 1"
# tupleInt = reshape(np.array(np.arange((4)**2)), (4,)*2)
tupleInt = np.ones((4,)*2)
tupleChar = [["aaa", "ddddd"], ["d", "ss"], ["s", "tt"]]
class Basic10DTestCase(BasicTestCase):
# 10D case
title = "Rank-10 case 1"
# tupleInt = reshape(np.array(np.arange((2)**10)), (2,)*10)
tupleInt = np.ones((2,)*10)
# tupleChar = reshape(np.array([1],dtype="S1"),(1,)*10)
# The next tuple consumes far more time, so this
# test should be run in common.heavy mode.
tupleChar = np.array(tupleInt, dtype="S1")
# class Basic32DTestCase(BasicTestCase):
# # 32D case (maximum)
# tupleInt = reshape(np.array((22,)), (1,)*32)
# # Strings seems to be very slow with somewhat large dimensions
# # This should not be run unless the numarray people address this problem
# # F. Alted 2006-01-04
# tupleChar = np.array(tupleInt, dtype="S1")
class GroupsArrayTestCase(common.TempFileMixin, TestCase):
"""This test class checks combinations of arrays with groups.
It also uses arrays ranks which ranges until 10.
"""
def test00_iterativeGroups(self):
"""Checking combinations of arrays with groups
It also uses arrays ranks which ranges until 10.
"""
if common.verbose:
print('\n', '-=' * 30)
print("Running %s.test00_iterativeGroups..." %
self.__class__.__name__)
# Get the root group
group = self.h5file.root
i = 1
for typecode in typecodes:
# Create an array of typecode, with incrementally bigger ranges
a = np.ones((2,) * i, typecode)
# Save it on the HDF5 file
dsetname = 'array_' + typecode
if common.verbose:
print("Creating dataset:", group._g_join(dsetname))
self.h5file.create_array(group, dsetname, a, "Large array")
# Create a new group
group = self.h5file.create_group(group, 'group' + str(i))
# increment the range for next iteration
i += 1
self._reopen()
# Get the root group
group = self.h5file.root
# Get the metadata on the previosly saved arrays
for i in range(1, len(typecodes)):
# Create an array for later comparison
a = np.ones((2,) * i, typecodes[i - 1])
# Get the dset object hanging from group
dset = getattr(group, 'array_' + typecodes[i-1])
# Get the actual array
b = dset.read()
if not allequal(a, b, "numpy") and common.verbose:
print("Array a original. Shape: ==>", a.shape)
print("Array a original. Data: ==>", a)
print("Info from dataset:", dset._v_pathname)
print(" shape ==>", dset.shape, end=' ')
print(" dtype ==> %s" % dset.dtype)
print("Array b read from file. Shape: ==>", b.shape, end=' ')
print(". Type ==> %s" % b.dtype.char)
self.assertEqual(a.shape, b.shape)
if np.dtype('l').itemsize == 4:
if (a.dtype.char == "i" or a.dtype.char == "l"):
# Special expection. We have no way to distinguish between
# "l" and "i" typecode, and we can consider them the same
# to all practical effects
self.assertTrue(b.dtype.char == "l" or b.dtype.char == "i")
elif (a.dtype.char == "I" or a.dtype.char == "L"):
# Special expection. We have no way to distinguish between
# "L" and "I" typecode, and we can consider them the same
# to all practical effects
self.assertTrue(b.dtype.char == "L" or b.dtype.char == "I")
else:
self.assertTrue(allequal(a, b, "numpy"))
elif np.dtype('l').itemsize == 8:
if (a.dtype.char == "q" or a.dtype.char == "l"):
# Special expection. We have no way to distinguish between
# "q" and "l" typecode in 64-bit platforms, and we can
# consider them the same to all practical effects
self.assertTrue(b.dtype.char == "l" or b.dtype.char == "q")
elif (a.dtype.char == "Q" or a.dtype.char == "L"):
# Special expection. We have no way to distinguish between
# "Q" and "L" typecode in 64-bit platforms, and we can
# consider them the same to all practical effects
self.assertTrue(b.dtype.char == "L" or b.dtype.char == "Q")
else:
self.assertTrue(allequal(a, b, "numpy"))
# Iterate over the next group
group = getattr(group, 'group' + str(i))
def test01_largeRankArrays(self):
"""Checking creation of large rank arrays (0 < rank <= 32)
It also uses arrays ranks which ranges until maxrank.
"""
# maximum level of recursivity (deepest group level) achieved:
# maxrank = 32 (for a effective maximum rank of 32)
# This limit is due to a limit in the HDF5 library.
minrank = 1
maxrank = 32
if common.verbose:
print('\n', '-=' * 30)
print("Running %s.test01_largeRankArrays..." %
self.__class__.__name__)
print("Maximum rank for tested arrays:", maxrank)
group = self.h5file.root
if common.verbose:
print("Rank array writing progress: ", end=' ')
for rank in range(minrank, maxrank + 1):
# Create an array of integers, with incrementally bigger ranges
a = np.ones((1,) * rank, 'i')
if common.verbose:
print("%3d," % (rank), end=' ')
self.h5file.create_array(group, "array", a, "Rank: %s" % rank)
group = self.h5file.create_group(group, 'group' + str(rank))
# Flush the buffers
self.h5file.flush()
self._reopen()
group = self.h5file.root
if common.verbose:
print()
print("Rank array reading progress: ")
# Get the metadata on the previosly saved arrays
for rank in range(minrank, maxrank + 1):
# Create an array for later comparison
a = np.ones((1,) * rank, 'i')
# Get the actual array
b = group.array.read()
if common.verbose:
print("%3d," % (rank), end=' ')
if not a.tolist() == b.tolist() and common.verbose:
dset = group.array
print("Info from dataset:", dset._v_pathname)
print(" Shape: ==>", dset.shape, end=' ')
print(" typecode ==> %c" % dset.typecode)
print("Array b read from file. Shape: ==>", b.shape, end=' ')
print(". Type ==> %c" % b.dtype.char)
self.assertEqual(a.shape, b.shape)
if a.dtype.char == "i":
# Special expection. We have no way to distinguish between
# "l" and "i" typecode, and we can consider them the same
# to all practical effects
self.assertTrue(b.dtype.char == "l" or b.dtype.char == "i")
else:
self.assertEqual(a.dtype.char, b.dtype.char)
self.assertEqual(a, b)
# Iterate over the next group
group = self.h5file.get_node(group, 'group' + str(rank))
if common.verbose:
print() # This flush the stdout buffer
# Test Record class
class Record(tables.IsDescription):
var1 = StringCol(itemsize=4, dflt=b"abcd", pos=0)
var2 = StringCol(itemsize=1, dflt=b"a", pos=1)
var3 = BoolCol(dflt=1)
var4 = Int8Col(dflt=1)
var5 = UInt8Col(dflt=1)
var6 = Int16Col(dflt=1)
var7 = UInt16Col(dflt=1)
var8 = Int32Col(dflt=1)
var9 = UInt32Col(dflt=1)
var10 = Int64Col(dflt=1)
var11 = Float32Col(dflt=1.0)
var12 = Float64Col(dflt=1.0)
var13 = ComplexCol(itemsize=8, dflt=(1.+0.j))
var14 = ComplexCol(itemsize=16, dflt=(1.+0.j))
if hasattr(tables, 'Float16Col'):
var15 = tables.Float16Col(dflt=1.0)
if hasattr(tables, 'Float96Col'):
var16 = tables.Float96Col(dflt=1.0)
if hasattr(tables, 'Float128Col'):
var17 = tables.Float128Col(dflt=1.0)
if hasattr(tables, 'Complex196Col'):
var18 = tables.ComplexCol(itemsize=24, dflt=(1.+0.j))
if hasattr(tables, 'Complex256Col'):
var19 = tables.ComplexCol(itemsize=32, dflt=(1.+0.j))
class TableReadTestCase(common.TempFileMixin, TestCase):
nrows = 100
def setUp(self):
super(TableReadTestCase, self).setUp()
# Create an instance of an HDF5 Table
table = self.h5file.create_table(self.h5file.root, 'table', Record)
for i in range(self.nrows):
table.row.append() # Fill 100 rows with default values
self._reopen(mode='a')
def test01_readTableChar(self):
"""Checking column conversion into NumPy in read().
Char flavor
"""
table = self.h5file.root.table
table.flavor = "numpy"
for colname in table.colnames:
numcol = table.read(field=colname)
typecol = table.coltypes[colname]
itemsizecol = table.description._v_dtypes[colname].base.itemsize
nctypecode = numcol.dtype.char
if typecol == "string":
if itemsizecol > 1:
orignumcol = np.array(['abcd']*self.nrows, dtype='S4')
else:
orignumcol = np.array(['a']*self.nrows, dtype='S1')
if common.verbose:
print("Typecode of NumPy column read:", nctypecode)
print("Should look like:", 'c')
print("Itemsize of column:", itemsizecol)
print("Shape of NumPy column read:", numcol.shape)
print("Should look like:", orignumcol.shape)
print("First 3 elements of read col:", numcol[:3])
# Check that both NumPy objects are equal
self.assertTrue(allequal(numcol, orignumcol, "numpy"))
def test01_readTableNum(self):
"""Checking column conversion into NumPy in read().
NumPy flavor
"""
table = self.h5file.root.table
table.flavor = "numpy"
for colname in table.colnames:
numcol = table.read(field=colname)
typecol = table.coltypes[colname]
nctypecode = np.typeNA[numcol.dtype.char[0]]
if typecol != "string":
if common.verbose:
print("Typecode of NumPy column read:", nctypecode)
print("Should look like:", typecol)
orignumcol = np.ones(shape=self.nrows, dtype=numcol.dtype.char)
# Check that both NumPy objects are equal
self.assertTrue(allequal(numcol, orignumcol, "numpy"))
def test02_readCoordsChar(self):
"""Column conversion into NumPy in readCoords().
Chars
"""
table = self.h5file.root.table
table.flavor = "numpy"
coords = [1, 2, 3]
self.nrows = len(coords)
for colname in table.colnames:
numcol = table.read_coordinates(coords, field=colname)
typecol = table.coltypes[colname]
itemsizecol = table.description._v_dtypes[colname].base.itemsize
nctypecode = numcol.dtype.char
if typecol == "string":
if itemsizecol > 1:
orignumcol = np.array(['abcd']*self.nrows, dtype='S4')
else:
orignumcol = np.array(['a']*self.nrows, dtype='S1')
if common.verbose:
print("Typecode of NumPy column read:", nctypecode)
print("Should look like:", 'c')
print("Itemsize of column:", itemsizecol)
print("Shape of NumPy column read:", numcol.shape)
print("Should look like:", orignumcol.shape)
print("First 3 elements of read col:", numcol[:3])
# Check that both NumPy objects are equal
self.assertTrue(allequal(numcol, orignumcol, "numpy"))
def test02_readCoordsNum(self):
"""Column conversion into NumPy in read_coordinates().
NumPy.
"""
table = self.h5file.root.table
table.flavor = "numpy"
coords = [1, 2, 3]
self.nrows = len(coords)
for colname in table.colnames:
numcol = table.read_coordinates(coords, field=colname)
typecol = table.coltypes[colname]
type_ = numcol.dtype.type
if typecol != "string":
if typecol == "int64":
return
if common.verbose:
print("Type of read NumPy column:", type_)
print("Should look like:", typecol)
orignumcol = np.ones(shape=self.nrows, dtype=numcol.dtype.char)
# Check that both NumPy objects are equal
self.assertTrue(allequal(numcol, orignumcol, "numpy"))
def test03_getIndexNumPy(self):
"""Getting table rows specifyied as NumPy scalar integers."""
table = self.h5file.root.table
coords = np.array([1, 2, 3], dtype='int8')
for colname in table.colnames:
numcol = [table[coord][colname] for coord in coords]
typecol = table.coltypes[colname]
if typecol != "string":
if typecol == "int64":
return
numcol = np.array(numcol, typecol)
if common.verbose:
type_ = numcol.dtype.type
print("Type of read NumPy column:", type_)
print("Should look like:", typecol)
orignumcol = np.ones(shape=len(numcol),
dtype=numcol.dtype.char)
# Check that both NumPy objects are equal
self.assertTrue(allequal(numcol, orignumcol, "numpy"))
def test04_setIndexNumPy(self):
"""Setting table rows specifyied as NumPy integers."""
self._reopen(mode='a')
table = self.h5file.root.table
table.flavor = "numpy"
coords = np.array([1, 2, 3], dtype='int8')
# Modify row 1
# From PyTables 2.0 on, assignments to records can be done
# only as tuples (see http://projects.scipy.org/scipy/numpy/ticket/315)
# table[coords[0]] = ["aasa","x"]+[232]*12
n = len(Record.columns) - 2
table[coords[0]] = tuple(["aasa", "x"]+[232]*n) # XXX
# record = list(table[coords[0]])
record = table.read(coords[0], coords[0] + 1)
if common.verbose:
print("Original row:\n"
"['aasa', 'x', True, -24, 232, 232, 232, 232, 232L, "
"232, 232.0, 232.0, (232 + 0j), (232+0j), 232.0, "
"(232+0j)]\n")
print("Read row:\n", record)
self.assertEqual(record['var1'], b'aasa')
self.assertEqual(record['var2'], b'x')
self.assertEqual(record['var3'], True)
self.assertEqual(record['var4'], -24)
self.assertEqual(record['var7'], 232)
# The declaration of the nested table:
class Info(tables.IsDescription):
_v_pos = 3
Name = StringCol(itemsize=2)
Value = ComplexCol(itemsize=16)
class TestTDescr(tables.IsDescription):
"""A description that has several nested columns."""
x = Int32Col(dflt=0, shape=2, pos=0) # 0
y = FloatCol(dflt=1, shape=(2, 2))
z = UInt8Col(dflt=1)
z3 = EnumCol({'r': 4, 'g': 2, 'b': 1}, 'r', 'int32', shape=2)
color = StringCol(itemsize=4, dflt=b"ab", pos=2)
info = Info()
class Info(tables.IsDescription): # 1
_v_pos = 1
name = StringCol(itemsize=2)
value = ComplexCol(itemsize=16, pos=0) # 0
y2 = FloatCol(pos=1) # 1
z2 = UInt8Col()
class Info2(tables.IsDescription):
y3 = Time64Col(shape=2)
name = StringCol(itemsize=2)
value = ComplexCol(itemsize=16, shape=2)
class TableNativeFlavorTestCase(common.TempFileMixin, TestCase):
nrows = 100
def setUp(self):
super(TableNativeFlavorTestCase, self).setUp()
# Create an instance of an HDF5 Table
table = self.h5file.create_table(self.h5file.root, 'table', TestTDescr,
expectedrows=self.nrows)
table.flavor = "numpy"
for i in range(self.nrows):
table.row.append() # Fill 100 rows with default values
table.flush()
def test01a_basicTableRead(self):
"""Checking the return of a NumPy in read()."""
if self.close:
self._reopen(mode='a')
table = self.h5file.root.table
data = table[:]
if common.verbose:
print("Type of read:", type(data))
print("Description of the record:", data.dtype.descr)
print("First 3 elements of read:", data[:3])
# Check that both NumPy objects are equal
self.assertTrue(isinstance(data, np.ndarray))
# Check the value of some columns
# A flat column
col = table.cols.x[:3]
self.assertTrue(isinstance(col, np.ndarray))
npcol = np.zeros((3, 2), dtype="int32")
self.assertTrue(allequal(col, npcol, "numpy"))
# A nested column
col = table.cols.Info[:3]
self.assertTrue(isinstance(col, np.ndarray))
dtype = [('value', 'c16'),
('y2', 'f8'),
('Info2',
[('name', 'S2'),
('value', 'c16', (2,)),
('y3', 'f8', (2,))]),
('name', 'S2'),
('z2', 'u1')]
npcol = np.zeros((3,), dtype=dtype)
self.assertEqual(col.dtype.descr, npcol.dtype.descr)
if common.verbose:
print("col-->", col)
print("npcol-->", npcol)
# A copy() is needed in case the buffer can be in different segments
self.assertEqual(bytes(col.copy().data), bytes(npcol.data))
def test01b_basicTableRead(self):
"""Checking the return of a NumPy in read() (strided version)."""
if self.close:
self._reopen(mode='a')
table = self.h5file.root.table
data = table[::3]
if common.verbose:
print("Type of read:", type(data))
print("Description of the record:", data.dtype.descr)
print("First 3 elements of read:", data[:3])
# Check that both NumPy objects are equal
self.assertTrue(isinstance(data, np.ndarray))
# Check the value of some columns
# A flat column
col = table.cols.x[:9:3]
self.assertTrue(isinstance(col, np.ndarray))
npcol = np.zeros((3, 2), dtype="int32")
self.assertTrue(allequal(col, npcol, "numpy"))
# A nested column
col = table.cols.Info[:9:3]
self.assertTrue(isinstance(col, np.ndarray))
dtype = [('value', '%sc16' % byteorder),
('y2', '%sf8' % byteorder),
('Info2',
[('name', '|S2'),
('value', '%sc16' % byteorder, (2,)),
('y3', '%sf8' % byteorder, (2,))]),
('name', '|S2'),
('z2', '|u1')]
npcol = np.zeros((3,), dtype=dtype)
self.assertEqual(col.dtype.descr, npcol.dtype.descr)
if common.verbose:
print("col-->", col)
print("npcol-->", npcol)
# A copy() is needed in case the buffer can be in different segments
self.assertEqual(bytes(col.copy().data), bytes(npcol.data))
def test02_getWhereList(self):
"""Checking the return of NumPy in get_where_list method."""
if self.close:
self._reopen(mode='a')
table = self.h5file.root.table
data = table.get_where_list('z == 1')
if common.verbose:
print("Type of read:", type(data))
print("Description of the record:", data.dtype.descr)
print("First 3 elements of read:", data[:3])
# Check that both NumPy objects are equal
self.assertTrue(isinstance(data, np.ndarray))
# Check that all columns have been selected
self.assertEqual(len(data), 100)
# Finally, check that the contents are ok
self.assertTrue(allequal(data, np.arange(100, dtype="i8"), "numpy"))
def test03a_readWhere(self):
"""Checking the return of NumPy in read_where method (strings)."""
table = self.h5file.root.table
table.cols.color.create_index()
if self.close:
self._reopen(mode='a')
table = self.h5file.root.table
data = table.read_where('color == b"ab"')
if common.verbose:
print("Type of read:", type(data))
print("Length of the data read:", len(data))
# Check that both NumPy objects are equal
self.assertTrue(isinstance(data, np.ndarray))
# Check that all columns have been selected
self.assertEqual(len(data), self.nrows)
def test03b_readWhere(self):
"""Checking the return of NumPy in read_where method (numeric)."""
table = self.h5file.root.table
table.cols.z.create_index()
if self.close:
self._reopen(mode='a')
table = self.h5file.root.table
data = table.read_where('z == 0')
if common.verbose:
print("Type of read:", type(data))
print("Length of the data read:", len(data))
# Check that both NumPy objects are equal
self.assertTrue(isinstance(data, np.ndarray))
# Check that all columns have been selected
self.assertEqual(len(data), 0)
def test04a_createTable(self):
"""Checking the Table creation from a numpy recarray."""
dtype = [('value', '%sc16' % byteorder),
('y2', '%sf8' % byteorder),
('Info2',
[('name', '|S2'),
('value', '%sc16' % byteorder, (2,)),
('y3', '%sf8' % byteorder, (2,))]),
('name', '|S2'),
('z2', '|u1')]
npdata = np.zeros((3,), dtype=dtype)
table = self.h5file.create_table(self.h5file.root, 'table2', npdata)
if self.close:
self._reopen(mode='a')
table = self.h5file.root.table2
data = table[:]
if common.verbose:
print("Type of read:", type(data))
print("Description of the record:", data.dtype.descr)
print("First 3 elements of read:", data[:3])
print("Length of the data read:", len(data))
# Check that both NumPy objects are equal
self.assertTrue(isinstance(data, np.ndarray))
# Check the type
self.assertEqual(data.dtype.descr, npdata.dtype.descr)
if common.verbose:
print("npdata-->", npdata)
print("data-->", data)
# A copy() is needed in case the buffer would be in different segments
self.assertEqual(bytes(data.copy().data), bytes(npdata.data))
def test04b_appendTable(self):
"""Checking appending a numpy recarray."""
table = self.h5file.root.table
npdata = table[3:6]
table.append(npdata)
if self.close:
self._reopen(mode='a')
table = self.h5file.root.table
data = table[-3:]
if common.verbose:
print("Type of read:", type(data))
print("Description of the record:", data.dtype.descr)
print("Last 3 elements of read:", data[-3:])
print("Length of the data read:", len(data))
# Check that both NumPy objects are equal
self.assertTrue(isinstance(data, np.ndarray))
# Check the type
self.assertEqual(data.dtype.descr, npdata.dtype.descr)
if common.verbose:
print("npdata-->", npdata)
print("data-->", data)
# A copy() is needed in case the buffer would be in different segments
self.assertEqual(bytes(data.copy().data), bytes(npdata.data))
def test05a_assignColumn(self):
"""Checking assigning to a column."""
table = self.h5file.root.table
table.cols.z[:] = np.zeros((100,), dtype='u1')
if self.close:
self._reopen(mode='a')
table = self.h5file.root.table
data = table.cols.z[:]
if common.verbose:
print("Type of read:", type(data))
print("Description of the record:", data.dtype.descr)
print("First 3 elements of read:", data[:3])
print("Length of the data read:", len(data))
# Check that both NumPy objects are equal
self.assertTrue(isinstance(data, np.ndarray))
# Check that all columns have been selected
self.assertEqual(len(data), 100)
# Finally, check that the contents are ok
self.assertTrue(allequal(data, np.zeros((100,), dtype="u1"), "numpy"))
def test05b_modifyingColumns(self):
"""Checking modifying several columns at once."""
table = self.h5file.root.table
xcol = np.ones((3, 2), 'int32')
ycol = np.zeros((3, 2, 2), 'float64')
zcol = np.zeros((3,), 'uint8')
table.modify_columns(3, 6, 1, [xcol, ycol, zcol], ['x', 'y', 'z'])
if self.close:
self._reopen(mode='a')
table = self.h5file.root.table
data = table.cols.y[3:6]
if common.verbose:
print("Type of read:", type(data))
print("Description of the record:", data.dtype.descr)
print("First 3 elements of read:", data[:3])
print("Length of the data read:", len(data))
# Check that both NumPy objects are equal
self.assertTrue(isinstance(data, np.ndarray))
# Check the type
self.assertEqual(data.dtype.descr, ycol.dtype.descr)
if common.verbose:
print("ycol-->", ycol)
print("data-->", data)
# A copy() is needed in case the buffer would be in different segments
self.assertEqual(data.copy().data, ycol.data)
def test05c_modifyingColumns(self):
"""Checking modifying several columns using a single numpy buffer."""
table = self.h5file.root.table
dtype = [('x', 'i4', (2,)), ('y', 'f8', (2, 2)), ('z', 'u1')]
nparray = np.zeros((3,), dtype=dtype)
table.modify_columns(3, 6, 1, nparray, ['x', 'y', 'z'])
if self.close:
self._reopen(mode='a')
table = self.h5file.root.table
ycol = np.zeros((3, 2, 2), 'float64')
data = table.cols.y[3:6]
if common.verbose:
print("Type of read:", type(data))
print("Description of the record:", data.dtype.descr)
print("First 3 elements of read:", data[:3])
print("Length of the data read:", len(data))
# Check that both NumPy objects are equal
self.assertTrue(isinstance(data, np.ndarray))
# Check the type
self.assertEqual(data.dtype.descr, ycol.dtype.descr)
if common.verbose:
print("ycol-->", ycol)
print("data-->", data)
# A copy() is needed in case the buffer would be in different segments
self.assertEqual(data.copy().data, ycol.data)
def test06a_assignNestedColumn(self):
"""Checking assigning a nested column (using modify_column)."""
table = self.h5file.root.table
dtype = [('value', '%sc16' % byteorder),
('y2', '%sf8' % byteorder),
('Info2',
[('name', '|S2'),
('value', '%sc16' % byteorder, (2,)),
('y3', '%sf8' % byteorder, (2,))]),
('name', '|S2'),
('z2', '|u1')]
npdata = np.zeros((3,), dtype=dtype)
data = table.cols.Info[3:6]
table.modify_column(3, 6, 1, column=npdata, colname='Info')
if self.close:
self._reopen(mode='a')
table = self.h5file.root.table
data = table.cols.Info[3:6]
if common.verbose:
print("Type of read:", type(data))
print("Description of the record:", data.dtype.descr)
print("First 3 elements of read:", data[:3])
print("Length of the data read:", len(data))
# Check that both NumPy objects are equal
self.assertTrue(isinstance(data, np.ndarray))
# Check the type
self.assertEqual(data.dtype.descr, npdata.dtype.descr)
if common.verbose:
print("npdata-->", npdata)
print("data-->", data)
# A copy() is needed in case the buffer would be in different segments
self.assertEqual(bytes(data.copy().data), bytes(npdata.data))
def test06b_assignNestedColumn(self):
"""Checking assigning a nested column (using the .cols accessor)."""
table = self.h5file.root.table
dtype = [('value', '%sc16' % byteorder),
('y2', '%sf8' % byteorder),
('Info2',
[('name', '|S2'),
('value', '%sc16' % byteorder, (2,)),
('y3', '%sf8' % byteorder, (2,))]),
('name', '|S2'),
('z2', '|u1')]
npdata = np.zeros((3,), dtype=dtype)
#self.assertRaises(NotImplementedError,
# table.cols.Info.__setitem__, slice(3,6,1), npdata)
table.cols.Info[3:6] = npdata
if self.close:
self._reopen(mode='a')
table = self.h5file.root.table
data = table.cols.Info[3:6]
if common.verbose:
print("Type of read:", type(data))
print("Description of the record:", data.dtype.descr)
print("First 3 elements of read:", data[:3])
print("Length of the data read:", len(data))
# Check that both NumPy objects are equal
self.assertTrue(isinstance(data, np.ndarray))
# Check the type
self.assertEqual(data.dtype.descr, npdata.dtype.descr)
if common.verbose:
print("npdata-->", npdata)
print("data-->", data)
# A copy() is needed in case the buffer would be in different segments
self.assertEqual(bytes(data.copy().data), bytes(npdata.data))
def test07a_modifyingRows(self):
"""Checking modifying several rows at once (using modify_rows)."""
table = self.h5file.root.table
# Read a chunk of the table
chunk = table[0:3]
# Modify it somewhat
chunk['y'][:] = -1
table.modify_rows(3, 6, 1, rows=chunk)
if self.close:
self._reopen(mode='a')
table = self.h5file.root.table
ycol = np.zeros((3, 2, 2), 'float64')-1
data = table.cols.y[3:6]
if common.verbose:
print("Type of read:", type(data))
print("Description of the record:", data.dtype.descr)
print("First 3 elements of read:", data[:3])
print("Length of the data read:", len(data))
# Check that both NumPy objects are equal
self.assertTrue(isinstance(data, np.ndarray))
# Check the type
self.assertEqual(data.dtype.descr, ycol.dtype.descr)
if common.verbose:
print("ycol-->", ycol)
print("data-->", data)
self.assertTrue(allequal(ycol, data, "numpy"))
def test07b_modifyingRows(self):
"""Checking modifying several rows at once (using cols accessor)."""
table = self.h5file.root.table
# Read a chunk of the table
chunk = table[0:3]
# Modify it somewhat
chunk['y'][:] = -1
table.cols[3:6] = chunk
if self.close:
self._reopen(mode='a')
table = self.h5file.root.table
# Check that some column has been actually modified
ycol = np.zeros((3, 2, 2), 'float64')-1
data = table.cols.y[3:6]
if common.verbose:
print("Type of read:", type(data))
print("Description of the record:", data.dtype.descr)
print("First 3 elements of read:", data[:3])
print("Length of the data read:", len(data))
# Check that both NumPy objects are equal
self.assertTrue(isinstance(data, np.ndarray))
# Check the type
self.assertEqual(data.dtype.descr, ycol.dtype.descr)
if common.verbose:
print("ycol-->", ycol)
print("data-->", data)
self.assertTrue(allequal(ycol, data, "numpy"))
def test08a_modifyingRows(self):
"""Checking modifying just one row at once (using modify_rows)."""
table = self.h5file.root.table
# Read a chunk of the table
chunk = table[3:4]
# Modify it somewhat
chunk['y'][:] = -1
table.modify_rows(6, 7, 1, chunk)
if self.close:
self._reopen(mode='a')
table = self.h5file.root.table
# Check that some column has been actually modified
ycol = np.zeros((2, 2), 'float64')-1
data = table.cols.y[6]
if common.verbose:
print("Type of read:", type(data))
print("Description of the record:", data.dtype.descr)
print("First 3 elements of read:", data[:3])
print("Length of the data read:", len(data))
# Check that both NumPy objects are equal
self.assertTrue(isinstance(data, np.ndarray))
# Check the type
self.assertEqual(data.dtype.descr, ycol.dtype.descr)
if common.verbose:
print("ycol-->", ycol)
print("data-->", data)
self.assertTrue(allequal(ycol, data, "numpy"))
def test08b_modifyingRows(self):
"""Checking modifying just one row at once (using cols accessor)."""
table = self.h5file.root.table
# Read a chunk of the table
chunk = table[3:4]
# Modify it somewhat
chunk['y'][:] = -1
table.cols[6] = chunk
if self.close:
self._reopen(mode='a')
table = self.h5file.root.table
# Check that some column has been actually modified
ycol = np.zeros((2, 2), 'float64')-1
data = table.cols.y[6]
if common.verbose:
print("Type of read:", type(data))
print("Description of the record:", data.dtype.descr)
print("First 3 elements of read:", data[:3])
print("Length of the data read:", len(data))
# Check that both NumPy objects are equal
self.assertTrue(isinstance(data, np.ndarray))
# Check the type
self.assertEqual(data.dtype.descr, ycol.dtype.descr)
if common.verbose:
print("ycol-->", ycol)
print("data-->", data)
self.assertTrue(allequal(ycol, data, "numpy"))
def test09a_getStrings(self):
"""Checking the return of string columns with spaces."""
if self.close:
self._reopen(mode='a')
table = self.h5file.root.table
rdata = table.get_where_list('color == b"ab"')
data = table.read_coordinates(rdata)
if common.verbose:
print("Type of read:", type(data))
print("Description of the record:", data.dtype.descr)
print("First 3 elements of read:", data[:3])
# Check that both NumPy objects are equal
self.assertTrue(isinstance(data, np.ndarray))
# Check that all columns have been selected
self.assertEqual(len(data), 100)
# Finally, check that the contents are ok
for idata in data['color']:
self.assertEqual(idata, np.array("ab", dtype="|S4"))
def test09b_getStrings(self):
"""Checking the return of string columns with spaces.
(modify)
"""
if self.close:
self._reopen(mode='a')
table = self.h5file.root.table
for i in range(50):
table.cols.color[i] = "a "
table.flush()
data = table[:]
if common.verbose:
print("Type of read:", type(data))
print("Description of the record:", data.dtype.descr)
print("First 3 elements of read:", data[:3])
# Check that both NumPy objects are equal
self.assertTrue(isinstance(data, np.ndarray))
# Check that all columns have been selected
self.assertEqual(len(data), 100)
# Finally, check that the contents are ok
for i in range(100):
idata = data['color'][i]
if i >= 50:
self.assertEqual(idata, np.array("ab", dtype="|S4"))
else:
self.assertEqual(idata, np.array("a ", dtype="|S4"))
def test09c_getStrings(self):
"""Checking the return of string columns with spaces.
(append)
"""
if self.close:
self._reopen(mode='a')
table = self.h5file.root.table
row = table.row
for i in range(50):
row["color"] = "a " # note the trailing spaces
row.append()
table.flush()
if self.close:
self.h5file.close()
self.h5file = tables.open_file(self.h5fname, "a")
data = self.h5file.root.table[:]
if common.verbose:
print("Type of read:", type(data))
print("Description of the record:", data.dtype.descr)
print("First 3 elements of read:", data[:3])
# Check that both NumPy objects are equal
self.assertTrue(isinstance(data, np.ndarray))
# Check that all columns have been selected
self.assertEqual(len(data), 150)
# Finally, check that the contents are ok
for i in range(150):
idata = data['color'][i]
if i < 100:
self.assertEqual(idata, np.array("ab", dtype="|S4"))
else:
self.assertEqual(idata, np.array("a ", dtype="|S4"))
class TableNativeFlavorOpenTestCase(TableNativeFlavorTestCase):
close = 0
class TableNativeFlavorCloseTestCase(TableNativeFlavorTestCase):
close = 1
class AttributesTestCase(common.TempFileMixin, TestCase):
def setUp(self):
super(AttributesTestCase, self).setUp()
# Create an instance of an HDF5 Table
self.h5file.create_group(self.h5file.root, 'group')
def test01_writeAttribute(self):
"""Checking the creation of a numpy attribute."""
group = self.h5file.root.group
g_attrs = group._v_attrs
g_attrs.numpy1 = np.zeros((1, 1), dtype='int16')
if self.close:
self._reopen(mode='a')
group = self.h5file.root.group
g_attrs = group._v_attrs
# Check that we can retrieve a numpy object
data = g_attrs.numpy1
npcomp = np.zeros((1, 1), dtype='int16')
# Check that both NumPy objects are equal
self.assertTrue(isinstance(data, np.ndarray))
# Check the type
self.assertEqual(data.dtype.descr, npcomp.dtype.descr)
if common.verbose:
print("npcomp-->", npcomp)
print("data-->", data)
self.assertTrue(allequal(npcomp, data, "numpy"))
def test02_updateAttribute(self):
"""Checking the modification of a numpy attribute."""
group = self.h5file.root.group
g_attrs = group._v_attrs
g_attrs.numpy1 = np.zeros((1, 2), dtype='int16')
if self.close:
self._reopen(mode='a')
group = self.h5file.root.group
g_attrs = group._v_attrs
# Update this attribute
g_attrs.numpy1 = np.ones((1, 2), dtype='int16')
# Check that we can retrieve a numpy object
data = g_attrs.numpy1
npcomp = np.ones((1, 2), dtype='int16')
# Check that both NumPy objects are equal
self.assertTrue(isinstance(data, np.ndarray))
# Check the type
self.assertEqual(data.dtype.descr, npcomp.dtype.descr)
if common.verbose:
print("npcomp-->", npcomp)
print("data-->", data)
self.assertTrue(allequal(npcomp, data, "numpy"))
class AttributesOpenTestCase(AttributesTestCase):
close = 0
class AttributesCloseTestCase(AttributesTestCase):
close = 1
class StrlenTestCase(common.TempFileMixin, TestCase):
def setUp(self):
super(StrlenTestCase, self).setUp()
# Create an instance of an HDF5 Table
group = self.h5file.create_group(self.h5file.root, 'group')
tablelayout = {'Text': StringCol(itemsize=1000), }
self.table = self.h5file.create_table(group, 'table', tablelayout)
self.table.flavor = 'numpy'
row = self.table.row
row['Text'] = 'Hello Francesc!' # XXX: check unicode --> bytes
row.append()
row['Text'] = 'Hola Francesc!' # XXX: check unicode --> bytes
row.append()
self.table.flush()
def test01(self):
"""Checking the lengths of strings (read field)."""
if self.close:
self._reopen(mode='a')
self.table = self.h5file.root.group.table
# Get both strings
str1 = self.table.col('Text')[0]
str2 = self.table.col('Text')[1]
if common.verbose:
print("string1-->", str1)
print("string2-->", str2)
# Check that both NumPy objects are equal
self.assertEqual(len(str1), len(b'Hello Francesc!'))
self.assertEqual(len(str2), len(b'Hola Francesc!'))
self.assertEqual(str1, b'Hello Francesc!')
self.assertEqual(str2, b'Hola Francesc!')
def test02(self):
"""Checking the lengths of strings (read recarray)."""
if self.close:
self._reopen(mode='a')
self.table = self.h5file.root.group.table
# Get both strings
str1 = self.table[:]['Text'][0]
str2 = self.table[:]['Text'][1]
# Check that both NumPy objects are equal
self.assertEqual(len(str1), len(b'Hello Francesc!'))
self.assertEqual(len(str2), len(b'Hola Francesc!'))
self.assertEqual(str1, b'Hello Francesc!')
self.assertEqual(str2, b'Hola Francesc!')
def test03(self):
"""Checking the lengths of strings (read recarray, row by row)."""
if self.close:
self._reopen(mode='a')
self.table = self.h5file.root.group.table
# Get both strings
str1 = self.table[0]['Text']
str2 = self.table[1]['Text']
# Check that both NumPy objects are equal
self.assertEqual(len(str1), len(b'Hello Francesc!'))
self.assertEqual(len(str2), len(b'Hola Francesc!'))
self.assertEqual(str1, b'Hello Francesc!')
self.assertEqual(str2, b'Hola Francesc!')
class StrlenOpenTestCase(StrlenTestCase):
close = 0
class StrlenCloseTestCase(StrlenTestCase):
close = 1
def suite():
theSuite = unittest.TestSuite()
niter = 1
# theSuite.addTest(unittest.makeSuite(StrlenOpenTestCase))
# theSuite.addTest(unittest.makeSuite(Basic0DOneTestCase))
# theSuite.addTest(unittest.makeSuite(GroupsArrayTestCase))
for i in range(niter):
theSuite.addTest(unittest.makeSuite(Basic0DOneTestCase))
theSuite.addTest(unittest.makeSuite(Basic0DTwoTestCase))
theSuite.addTest(unittest.makeSuite(Basic1DOneTestCase))
theSuite.addTest(unittest.makeSuite(Basic1DTwoTestCase))
theSuite.addTest(unittest.makeSuite(Basic1DThreeTestCase))
theSuite.addTest(unittest.makeSuite(Basic2DTestCase))
theSuite.addTest(unittest.makeSuite(GroupsArrayTestCase))
theSuite.addTest(unittest.makeSuite(TableReadTestCase))
theSuite.addTest(unittest.makeSuite(TableNativeFlavorOpenTestCase))
theSuite.addTest(unittest.makeSuite(TableNativeFlavorCloseTestCase))
theSuite.addTest(unittest.makeSuite(AttributesOpenTestCase))
theSuite.addTest(unittest.makeSuite(AttributesCloseTestCase))
theSuite.addTest(unittest.makeSuite(StrlenOpenTestCase))
theSuite.addTest(unittest.makeSuite(StrlenCloseTestCase))
if common.heavy:
theSuite.addTest(unittest.makeSuite(Basic10DTestCase))
# The 32 dimensions case takes forever to run!!
# theSuite.addTest(unittest.makeSuite(Basic32DTestCase))
return theSuite
if __name__ == '__main__':
common.parse_argv(sys.argv)
common.print_versions()
unittest.main(defaultTest='suite')
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