def tab_join(ToMerge,
keycols=None,
nullvals=None,
renamer=None,
returnrenaming=False,
Names=None):
'''
Database-join for tabular arrays.
Wrapper for :func:`tabular.spreadsheet.join` that deals with the coloring
and returns the result as a tabarray.
Method calls::
data = tabular.spreadsheet.join
'''
[Result, Renaming] = spreadsheet.join(ToMerge,
keycols=keycols,
nullvals=nullvals,
renamer=renamer,
returnrenaming=True,
Names=Names)
if isinstance(ToMerge, dict):
Names = ToMerge.keys()
else:
Names = range(len(ToMerge))
Colorings = dict([
(k, ToMerge[k].coloring) if 'coloring' in dir(ToMerge[k]) else {}
for k in Names
])
for k in Names:
if k in Renaming.keys():
l = ToMerge[k]
Colorings[k] = \
dict([(g, [n if not n in Renaming[k].keys() else Renaming[k][n]
for n in l.coloring[g]]) for g in Colorings[k].keys()])
Coloring = {}
for k in Colorings.keys():
for j in Colorings[k].keys():
if j in Coloring.keys():
Coloring[j] = utils.uniqify(Coloring[j] + Colorings[k][j])
else:
Coloring[j] = utils.uniqify(Colorings[k][j])
Result = Result.view(tabarray)
Result.coloring = Coloring
if returnrenaming:
return [Result, Renaming]
else:
return Result
def tab_rowstack(ListOfTabArrays, mode='nulls'):
"""
"Vertical stacking" of tabarrays, e.g. adding rows.
Wrapper for :func:`tabular.spreadsheet.rowstack` that deals with the
coloring and returns the result as a tabarray.
Method calls::
data = tabular.spreadsheet.rowstack(ListOfTabArrays, mode=mode)
"""
data = spreadsheet.rowstack(ListOfTabArrays, mode=mode)
coloring = {}
for a in ListOfTabArrays:
for k in a.coloring:
if k in coloring.keys():
coloring[k] = utils.uniqify(coloring[k] + a.coloring[k])
else:
coloring[k] = a.coloring[k]
for k in coloring.keys():
s = [x for x in coloring[k] if x in data.dtype.names]
if len(s) > 0:
coloring[k] = s
else:
coloring.pop(k)
data = data.view(tabarray)
data.coloring = coloring
return data
def tab_colstack(ListOfTabArrays, mode='abort'):
'''
"Horizontal stacking" of tabarrays, e.g. adding columns.
Wrapper for :func:`tabular.spreadsheet.colstack` that deals with the
coloring and returns the result as a tabarray.
Method calls::
data = tabular.spreadsheet.colstack(ListOfTabArrays, mode=mode)
'''
data = spreadsheet.colstack(ListOfTabArrays, mode=mode)
coloring = {}
for a in ListOfTabArrays:
for k in a.coloring:
if k in coloring.keys():
coloring[k] = utils.uniqify(coloring[k] + a.coloring[k])
else:
coloring[k] = a.coloring[k]
for k in coloring.keys():
s = [x for x in coloring[k] if x in data.dtype.names]
if len(s) > 0:
coloring[k] = s
else:
coloring.pop(k)
data = data.view(tabarray)
data.coloring = coloring
return data
def tab_colstack(ListOfTabArrays, mode='abort'):
"""
"Horizontal stacking" of tabarrays, e.g. adding columns.
Wrapper for :func:`tabular.spreadsheet.colstack` that deals with the
coloring and returns the result as a tabarray.
Method calls::
data = tabular.spreadsheet.colstack(ListOfTabArrays, mode=mode)
"""
(data, naming) = spreadsheet.colstack(ListOfTabArrays, mode=mode,
returnnaming=True)
coloring = {}
for (i, a) in enumerate(ListOfTabArrays):
namedict = dict([(x,y) for (j,x,y) in naming if i == j])
for k in a.coloring:
s = [namedict[kk] for kk in a.coloring[k]]
if k in coloring.keys():
coloring[k] = utils.uniqify(coloring[k] + s)
else:
coloring[k] = s
for k in coloring.keys():
s = [x for x in coloring[k] if x in data.dtype.names]
if len(s) > 0:
coloring[k] = s
else:
coloring.pop(k)
data = data.view(tabarray)
data.coloring = coloring
return data
def tab_join(ToMerge, keycols=None, nullvals=None, renamer=None,
returnrenaming=False, Names=None):
'''
Database-join for tabular arrays.
Wrapper for :func:`tabular.spreadsheet.join` that deals with the coloring
and returns the result as a tabarray.
Method calls::
data = tabular.spreadsheet.join
'''
[Result,Renaming] = spreadsheet.join(ToMerge, keycols=keycols,
nullvals=nullvals, renamer=renamer, returnrenaming=True, Names=Names)
if isinstance(ToMerge,dict):
Names = ToMerge.keys()
else:
Names = range(len(ToMerge))
Colorings = dict([(k,ToMerge[k].coloring) if 'coloring' in dir(ToMerge[k])
else {} for k in Names])
for k in Names:
if k in Renaming.keys():
l = ToMerge[k]
Colorings[k] = \
dict([(g, [n if not n in Renaming[k].keys() else Renaming[k][n]
for n in l.coloring[g]]) for g in Colorings[k].keys()])
Coloring = {}
for k in Colorings.keys():
for j in Colorings[k].keys():
if j in Coloring.keys():
Coloring[j] = utils.uniqify(Coloring[j] + Colorings[k][j])
else:
Coloring[j] = utils.uniqify(Colorings[k][j])
Result = Result.view(tabarray)
Result.coloring = Coloring
if returnrenaming:
return [Result,Renaming]
else:
return Result
def deletecols(self, cols):
"""
Delete columns and/or colors.
Method wraps::
tabular.spreadsheet.deletecols(self, cols)
"""
deletenames = utils.uniqify(utils.listunion([[c] if c in
self.dtype.names else self.coloring[c] for c in cols]))
return spreadsheet.deletecols(self,deletenames)
def GroupByLevel(NTree, sdict):
#Levels = [NTree.subtrees.keys()]
Levels = EqualLevels(NTree.subtrees.keys(), sdict)
LowerLevels = [GroupByLevel(t, sdict) for t in NTree.subtrees.values()]
if len(LowerLevels) > 0:
h = max([len(l) for l in LowerLevels])
for i in range(h):
New = utils.uniqify(
utils.listunion([l[i] for l in LowerLevels if len(l) > i]))
if len(New) > 0:
Levels += [New]
return Levels
def GroupByLevel(NTree, sdict):
#Levels = [NTree.subtrees.keys()]
Levels = EqualLevels(NTree.subtrees.keys(), sdict)
LowerLevels = [GroupByLevel(t, sdict) for t in NTree.subtrees.values()]
if len(LowerLevels) > 0:
h = max([len(l) for l in LowerLevels])
for i in range(h):
New = utils.uniqify(utils.listunion([l[i] for l in LowerLevels
if len(l) > i]))
if len(New) > 0:
Levels += [New]
return Levels
def __getitem__(self, ind):
"""
Returns a subrectangle of the table.
The representation of the subrectangle depends on `type(ind)`. Also,
whether the returned object represents a new independent copy of the
subrectangle, or a "view" into this self object, depends on
`type(ind)`.
* If you pass the name of an existing coloring, you get a tabarray
consisting of copies of columns in that coloring.
* If you pass a list of existing coloring names and/or column names,
you get a tabarray consisting of copies of columns in the list
(name of coloring is equivalent to list of names of columns in that
coloring; duplicate columns are deleted).
* If you pass a :class:`numpy.ndarray`, you get a tabarray consisting
a subrectangle of the tabarray, as handled by
:func:`numpy.ndarray.__getitem__`:
* if you pass a 1D NumPy ndarray of booleans of `len(self)`,
the rectangle contains copies of the rows for which the
corresponding entry is `True`.
* if you pass a list of row numbers, you get a tabarray
containing copies of these rows.
"""
if ind in self.coloring.keys():
return self[self.coloring[ind]]
elif isinstance(ind, list) and self.dtype.names and \
all([a in self.dtype.names or a in self.coloring.keys()
for a in ind]) and \
set(self.coloring.keys()).intersection(ind):
ns = utils.uniqify(
utils.listunion(
[[a] if a in self.dtype.names else self.coloring[a]
for a in ind]))
return self[ns]
else:
D = np.ndarray.__getitem__(self, ind)
if isinstance(D, np.ndarray) and not (D.dtype.names is None):
D = D.view(tabarray)
D.coloring = dict([(
k,
list(
set(self.coloring[k]).intersection(set(D.dtype.names)))
) for k in self.coloring.keys() if len(
set(self.coloring[k]).intersection(set(D.dtype.names))) > 0
])
return D
def test_aggregate_AggFunc(self):
AggFunc=np.mean
[D1,s] = self.D[['a', 'b', 'e']].aggregate(
On=['e'], AggFunc=AggFunc,returnsort=True)
e = utils.uniqify(self.D['e'][s])
a = []
b = []
for i in e:
boolvec = self.D['e'][s] == i
a += [AggFunc(self.D['a'][s][boolvec])]
b += [AggFunc(self.D['b'][s][boolvec])]
D2 = tb.tabarray(columns=[e,a,b], names=['e','a','b'], coloring=D1.coloring)
self.assert_(eq(D1,D2))
def TestIsIn():
Y = np.random.randint(0, 10000, size=(100,))
X = np.arange(10000)
Z = isin(X, Y)
D = np.array(uniqify(Y))
D.sort()
T1 = (X[Z] == D).all()
X = np.array(range(10000) + range(10000))
Z = isin(X, Y)
T2 = (X[Z] == np.append(D, D.copy())).all()
assert T1 & T2
def test_aggregate1(self):
AggFuncDict = {'d': ','.join}
[D1,s] = self.D[['a', 'b', 'd']].aggregate(
On=['a'], AggFuncDict=AggFuncDict,returnsort=True)
a = utils.uniqify(self.D['a'][s])
AggFuncDict.update({'b': sum})
b = []
d = []
for i in a:
boolvec = self.D['a'][s] == i
b += [AggFuncDict['b'](self.D['b'][s][boolvec])]
d += [AggFuncDict['d'](self.D['d'][s][boolvec])]
D2 = tb.tabarray(columns=[a, b, d], names=['a', 'b', 'd'], coloring=D1.coloring)
self.assert_(eq(D1, D2))
def TestPivot3():
V1 = ['NorthAmerica', 'SouthAmerica', 'Europe', 'Asia', 'Australia',
'Africa', 'Antarctica']
V1.sort()
V2 = ['House', 'Car', 'Boat', 'Savings', 'Food', 'Entertainment', 'Taxes']
V2.sort()
Recs = [(a, b, 100 * np.random.rand()) for a in V1 for b in V2]
X = tb.tabarray(records=Recs, names=['Region', 'Source', 'Amount'])
Y = X.pivot('Region', 'Source')
Z = utils.uniqify(X['Source'])
Z.sort()
Cols = [[y['Amount'] for y in X if y['Source'] == b] for b in Z]
W = tb.tabarray(columns=[V1] + Cols,
names=['Region'] + [b + '_Amount' for b in Z])
assert (W == Y).all()
def __getitem__(self, ind):
"""
Returns a subrectangle of the table.
The representation of the subrectangle depends on `type(ind)`. Also,
whether the returned object represents a new independent copy of the
subrectangle, or a "view" into this self object, depends on
`type(ind)`.
* If you pass the name of an existing coloring, you get a tabarray
consisting of copies of columns in that coloring.
* If you pass a list of existing coloring names and/or column names,
you get a tabarray consisting of copies of columns in the list
(name of coloring is equivalent to list of names of columns in that
coloring; duplicate columns are deleted).
* If you pass a :class:`numpy.ndarray`, you get a tabarray consisting
a subrectangle of the tabarray, as handled by
:func:`numpy.ndarray.__getitem__`:
* if you pass a 1D NumPy ndarray of booleans of `len(self)`,
the rectangle contains copies of the rows for which the
corresponding entry is `True`.
* if you pass a list of row numbers, you get a tabarray
containing copies of these rows.
"""
if ind in self.coloring.keys():
return self[self.coloring[ind]]
elif isinstance(ind,list) and \
all([a in self.dtype.names or a in self.coloring.keys()
for a in ind]) and \
set(self.coloring.keys()).intersection(ind):
ns = utils.uniqify(utils.listunion([[a] if a in self.dtype.names
else self.coloring[a] for a in ind]))
return self[ns]
else:
D = np.ndarray.__getitem__(self,ind)
if isinstance(D,np.ndarray) and not D.dtype.names is None:
D = D.view(tabarray)
D.coloring = dict([(k,
list(set(self.coloring[k]).intersection(set(D.dtype.names))))
for k in self.coloring.keys() if
len(set(self.coloring[k]).intersection(set(D.dtype.names))) > 0
and len(set(D.dtype.names).difference(self.coloring[k])) > 0])
return D
def test_aggregate2(self):
AggFuncDict = {'c': '+'.join, 'd': ','.join}
[D1,s] = self.D[['a', 'c', 'b', 'd']].aggregate(
On=['a', 'b'], AggFuncDict=AggFuncDict,returnsort=True)
ab = utils.uniqify(zip(self.D['a'][s], self.D['b'][s]))
c = []
d = []
for i in ab:
boolvec = np.array([tuple(self.D[['a', 'b']][s][ind])==i
for ind in range(len(self.D))])
c += [AggFuncDict['c'](self.D['c'][s][boolvec])]
d += [AggFuncDict['d'](self.D['d'][s][boolvec])]
D2 = tb.tabarray(
columns=[[x[0] for x in ab],[x[1] for x in ab], c,d],
names=['a', 'b','c', 'd'], coloring=D1.coloring)
self.assert_(eq(D1, D2))
def tab_colstack(ListOfTabArrays, mode='abort'):
"""
"Horizontal stacking" of tabarrays, e.g. adding columns.
Wrapper for :func:`tabular.spreadsheet.colstack` that deals with the
coloring and returns the result as a tabarray.
Method calls::
data = tabular.spreadsheet.colstack(ListOfTabArrays, mode=mode)
"""
(data, naming) = spreadsheet.colstack(ListOfTabArrays,
mode=mode,
returnnaming=True)
coloring = {}
for (i, a) in enumerate(ListOfTabArrays):
namedict = dict([(x, y) for (j, x, y) in naming if i == j])
for k in a.coloring:
s = [namedict[kk] for kk in a.coloring[k]]
if k in coloring.keys():
coloring[k] = utils.uniqify(coloring[k] + s)
else:
coloring[k] = s
for k in coloring.keys():
s = [x for x in coloring[k] if x in data.dtype.names]
if len(s) > 0:
coloring[k] = s
else:
coloring.pop(k)
data = data.view(tabarray)
data.coloring = coloring
return data
def TestUniqify2():
Input = [2, 3, 4, 4, 4, 5, 5, 1, 1, 2, 3, 6, 6, 5]
Output = [2, 3, 4, 5, 1, 6]
assert (Output == uniqify(Input))