expected_rhs_values, expected_rhs_names,

expected_lhs_values, expected_lhs_names): # pragma: no cover

assert np.allclose(rhs, expected_rhs_values)

assert rhs.design_info.column_names == expected_rhs_names

if lhs is not None:

assert np.allclose(lhs, expected_lhs_values)

assert lhs.design_info.column_names == expected_lhs_names

else:

assert expected_lhs_values is None

assert expected_lhs_names is None

if expect_builders:

if lhs is None:

new_rhs, = build_design_matrices([rhs.design_info.builder], data)

else:

new_lhs, new_rhs = build_design_matrices([lhs.design_info.builder,

rhs.design_info.builder],

data)

assert np.allclose(new_lhs, lhs)

assert new_lhs.design_info.column_names == expected_lhs_names

assert np.allclose(new_rhs, rhs)

assert new_rhs.design_info.column_names == expected_rhs_names

else:

assert rhs.design_info.builder is None

return_type = "dataframe"

if isinstance(depth, int):

depth += 1

return_type = "dataframe"

if isinstance(depth, int):

depth += 1

expect_builders,

expected_rhs_values, expected_rhs_names,

expected_lhs_values=None, expected_lhs_names=None): # pragma: no cover

if isinstance(depth, int):

depth += 1

def data_iter_maker():

return iter([data])

if (isinstance(formula_like, (str, ModelDesc, DesignMatrixBuilder))

or (isinstance(formula_like, tuple)

and isinstance(formula_like[0], DesignMatrixBuilder))

or hasattr(formula_like, "__patsy_get_model_desc__")):

if expected_lhs_values is None:

builder = incr_dbuilder(formula_like, data_iter_maker, depth)

lhs = None

(rhs,) = build_design_matrices([builder], data)

else:

builders = incr_dbuilders(formula_like, data_iter_maker, depth)

lhs, rhs = build_design_matrices(builders, data)

check_result(expect_builders, lhs, rhs, data,

expected_rhs_values, expected_rhs_names,

expected_lhs_values, expected_lhs_names)

else:

assert_raises(PatsyError, incr_dbuilders,

formula_like, data_iter_maker)

assert_raises(PatsyError, incr_dbuilder,

formula_like, data_iter_maker)

one_mat_fs = [dmatrix]

two_mat_fs = [dmatrices]

if have_pandas:

one_mat_fs.append(dmatrix_pandas)

two_mat_fs.append(dmatrices_pandas)

if expected_lhs_values is None:

for f in one_mat_fs:

rhs = f(formula_like, data, depth)

check_result(expect_builders, None, rhs, data,

expected_rhs_values, expected_rhs_names,

expected_lhs_values, expected_lhs_names)

# We inline assert_raises here to avoid complications with the

# depth argument.

for f in two_mat_fs:

try:

f(formula_like, data, depth)

except PatsyError:

pass

else:

raise AssertionError

else:

for f in one_mat_fs:

try:

f(formula_like, data, depth)

except PatsyError:

pass

else:

raise AssertionError

for f in two_mat_fs:

(lhs, rhs) = f(formula_like, data, depth)

check_result(expect_builders, lhs, rhs, data,

expected_rhs_values, expected_rhs_names,

if isinstance(depth, int):

depth += 1

fs = [dmatrix, dmatrices]

if have_pandas:

fs += [dmatrix_pandas, dmatrices_pandas]

for f in fs:

try:

f(formula_like, data, depth)

except exc:

pass

else:

# Plain array-like, rhs only

t([[1, 2, 3], [4, 5, 6]], {}, 0,

False,

[[1, 2, 3], [4, 5, 6]], ["x0", "x1", "x2"])

t((None, [[1, 2, 3], [4, 5, 6]]), {}, 0,

False,

[[1, 2, 3], [4, 5, 6]], ["x0", "x1", "x2"])

t(np.asarray([[1, 2, 3], [4, 5, 6]]), {}, 0,

False,

[[1, 2, 3], [4, 5, 6]], ["x0", "x1", "x2"])

t((None, np.asarray([[1, 2, 3], [4, 5, 6]])), {}, 0,

False,

[[1, 2, 3], [4, 5, 6]], ["x0", "x1", "x2"])

dm = DesignMatrix([[1, 2, 3], [4, 5, 6]], default_column_prefix="foo")

t(dm, {}, 0,

False,

[[1, 2, 3], [4, 5, 6]], ["foo0", "foo1", "foo2"])

t((None, dm), {}, 0,

False,

[[1, 2, 3], [4, 5, 6]], ["foo0", "foo1", "foo2"])

# Plain array-likes, lhs and rhs

t(([1, 2], [[1, 2, 3], [4, 5, 6]]), {}, 0,

False,

[[1, 2, 3], [4, 5, 6]], ["x0", "x1", "x2"],

[[1], [2]], ["y0"])

t(([[1], [2]], [[1, 2, 3], [4, 5, 6]]), {}, 0,

False,

[[1, 2, 3], [4, 5, 6]], ["x0", "x1", "x2"],

[[1], [2]], ["y0"])

t((np.asarray([1, 2]), np.asarray([[1, 2, 3], [4, 5, 6]])), {}, 0,

False,

[[1, 2, 3], [4, 5, 6]], ["x0", "x1", "x2"],

[[1], [2]], ["y0"])

t((np.asarray([[1], [2]]), np.asarray([[1, 2, 3], [4, 5, 6]])), {}, 0,

False,

[[1, 2, 3], [4, 5, 6]], ["x0", "x1", "x2"],

[[1], [2]], ["y0"])

x_dm = DesignMatrix([[1, 2, 3], [4, 5, 6]], default_column_prefix="foo")

y_dm = DesignMatrix([1, 2], default_column_prefix="bar")

t((y_dm, x_dm), {}, 0,

False,

[[1, 2, 3], [4, 5, 6]], ["foo0", "foo1", "foo2"],

[[1], [2]], ["bar0"])

# number of rows must match

t_invalid(([1, 2, 3], [[1, 2, 3], [4, 5, 6]]), {}, 0)

# tuples must have the right size

t_invalid(([[1, 2, 3]],), {}, 0)

t_invalid(([[1, 2, 3]], [[1, 2, 3]], [[1, 2, 3]]), {}, 0)

# plain Series and DataFrames

if have_pandas:

# Names are extracted

t(pandas.DataFrame({"x": [1, 2, 3]}), {}, 0,

False,

[[1], [2], [3]], ["x"])

t(pandas.Series([1, 2, 3], name="asdf"), {}, 0,

False,

[[1], [2], [3]], ["asdf"])

t((pandas.DataFrame({"y": [4, 5, 6]}),

pandas.DataFrame({"x": [1, 2, 3]})), {}, 0,

False,

[[1], [2], [3]], ["x"],

[[4], [5], [6]], ["y"])

t((pandas.Series([4, 5, 6], name="y"),

pandas.Series([1, 2, 3], name="x")), {}, 0,

False,

[[1], [2], [3]], ["x"],

[[4], [5], [6]], ["y"])

# Or invented

t((pandas.DataFrame([[4, 5, 6]]),

pandas.DataFrame([[1, 2, 3]], columns=[7, 8, 9])), {}, 0,

False,

[[1, 2, 3]], ["x7", "x8", "x9"],

[[4, 5, 6]], ["y0", "y1", "y2"])

t(pandas.Series([1, 2, 3]), {}, 0,

False,

[[1], [2], [3]], ["x0"])

# indices must match

t_invalid((pandas.DataFrame([[1]], index=[1]),

pandas.DataFrame([[1]], index=[2])),

{}, 0)

# Foreign ModelDesc factories

class ForeignModelSource(object):

def __patsy_get_model_desc__(self, data):

return ModelDesc([Term([LookupFactor("Y")])],

[Term([LookupFactor("X")])])

foreign_model = ForeignModelSource()

t(foreign_model,

{"Y": [1, 2],

"X": [[1, 2], [3, 4]]},

0,

True,

[[1, 2], [3, 4]], ["X[0]", "X[1]"],

[[1], [2]], ["Y"])

class BadForeignModelSource(object):

def __patsy_get_model_desc__(self, data):

return data

t_invalid(BadForeignModelSource(), {}, 0)

# string formulas

t("y ~ x", {"y": [1, 2], "x": [3, 4]}, 0,

True,

[[1, 3], [1, 4]], ["Intercept", "x"],

[[1], [2]], ["y"])

t("~ x", {"y": [1, 2], "x": [3, 4]}, 0,

True,

[[1, 3], [1, 4]], ["Intercept", "x"])

t("x + y", {"y": [1, 2], "x": [3, 4]}, 0,

True,

[[1, 3, 1], [1, 4, 2]], ["Intercept", "x", "y"])

# ModelDesc

desc = ModelDesc([], [Term([LookupFactor("x")])])

t(desc, {"x": [1.5, 2.5, 3.5]}, 0,

True,

[[1.5], [2.5], [3.5]], ["x"])

desc = ModelDesc([], [Term([]), Term([LookupFactor("x")])])

t(desc, {"x": [1.5, 2.5, 3.5]}, 0,

True,

[[1, 1.5], [1, 2.5], [1, 3.5]], ["Intercept", "x"])

desc = ModelDesc([Term([LookupFactor("y")])],

[Term([]), Term([LookupFactor("x")])])

t(desc, {"x": [1.5, 2.5, 3.5], "y": [10, 20, 30]}, 0,

True,

[[1, 1.5], [1, 2.5], [1, 3.5]], ["Intercept", "x"],

[[10], [20], [30]], ["y"])

# builders

termlists = ([],

[Term([LookupFactor("x")])],

[Term([]), Term([LookupFactor("x")])],

)

builders = design_matrix_builders(termlists,

lambda: iter([{"x": [1, 2, 3]}]))

# twople but with no LHS

t((builders[0], builders[2]), {"x": [10, 20, 30]}, 0,

True,

[[1, 10], [1, 20], [1, 30]], ["Intercept", "x"])

# single DesignMatrixBuilder

t(builders[2], {"x": [10, 20, 30]}, 0,

True,

[[1, 10], [1, 20], [1, 30]], ["Intercept", "x"])

# twople with LHS

t((builders[1], builders[2]), {"x": [10, 20, 30]}, 0,

True,

[[1, 10], [1, 20], [1, 30]], ["Intercept", "x"],

[[10], [20], [30]], ["x"])

# check depth arguments

x_in_env = [1, 2, 3]

t("~ x_in_env", {}, 0,

True,

[[1, 1], [1, 2], [1, 3]], ["Intercept", "x_in_env"])

t("~ x_in_env", {"x_in_env": [10, 20, 30]}, 0,

True,

[[1, 10], [1, 20], [1, 30]], ["Intercept", "x_in_env"])

# Trying to pull x_in_env out of our *caller* shouldn't work.

t_invalid("~ x_in_env", {}, 1, exc=(NameError, PatsyError))

# But then again it should, if called from one down on the stack:

def check_nested_call():

x_in_env = "asdf"

t("~ x_in_env", {}, 1,

True,

[[1, 1], [1, 2], [1, 3]], ["Intercept", "x_in_env"])

check_nested_call()

# passing in an explicit EvalEnvironment also works:

e = EvalEnvironment.capture(1)

t_invalid("~ x_in_env", {}, e, exc=(NameError, PatsyError))

e = EvalEnvironment.capture(0)

def check_nested_call_2():

x_in_env = "asdf"

t("~ x_in_env", {}, e,

True,

[[1, 1], [1, 2], [1, 3]], ["Intercept", "x_in_env"])

if not have_pandas:

return

# basic check of pulling a Series out of the environment

s1 = pandas.Series([1, 2, 3], name="AA", index=[10, 20, 30])

s2 = pandas.Series([4, 5, 6], name="BB", index=[10, 20, 30])

df1 = dmatrix("s1", return_type="dataframe")

assert np.allclose(df1, [[1, 1], [1, 2], [1, 3]])

assert np.array_equal(df1.columns, ["Intercept", "s1"])

assert df1.design_info.column_names == ["Intercept", "s1"]

assert np.array_equal(df1.index, [10, 20, 30])

df2, df3 = dmatrices("s2 ~ s1", return_type="dataframe")

assert np.allclose(df2, [[4], [5], [6]])

assert np.array_equal(df2.columns, ["s2"])

assert df2.design_info.column_names == ["s2"]

assert np.array_equal(df2.index, [10, 20, 30])

assert np.allclose(df3, [[1, 1], [1, 2], [1, 3]])

assert np.array_equal(df3.columns, ["Intercept", "s1"])

assert df3.design_info.column_names == ["Intercept", "s1"]

assert np.array_equal(df3.index, [10, 20, 30])

# indices are preserved if pandas is passed in directly

df4 = dmatrix(s1, return_type="dataframe")

assert np.allclose(df4, [[1], [2], [3]])

assert np.array_equal(df4.columns, ["AA"])

assert df4.design_info.column_names == ["AA"]

assert np.array_equal(df4.index, [10, 20, 30])

df5, df6 = dmatrices((s2, s1), return_type="dataframe")

assert np.allclose(df5, [[4], [5], [6]])

assert np.array_equal(df5.columns, ["BB"])

assert df5.design_info.column_names == ["BB"]

assert np.array_equal(df5.index, [10, 20, 30])

assert np.allclose(df6, [[1], [2], [3]])

assert np.array_equal(df6.columns, ["AA"])

assert df6.design_info.column_names == ["AA"]

assert np.array_equal(df6.index, [10, 20, 30])

# Both combinations of with-index and without-index

df7, df8 = dmatrices((s1, [10, 11, 12]), return_type="dataframe")

assert np.array_equal(df7.index, s1.index)

assert np.array_equal(df8.index, s1.index)

df9, df10 = dmatrices(([10, 11, 12], s1), return_type="dataframe")

assert np.array_equal(df9.index, s1.index)

assert np.array_equal(df10.index, s1.index)

# pandas must be available

import patsy.highlevel

had_pandas = patsy.highlevel.have_pandas

try:

patsy.highlevel.have_pandas = False

assert_raises(PatsyError,

dmatrix, "x", {"x": [1]}, 0, return_type="dataframe")

assert_raises(PatsyError,

dmatrices, "y ~ x", {"x": [1], "y": [2]}, 0,

return_type="dataframe")

finally:

data = balanced(a=2, b=2)

rhs = dmatrix("a:b", data)

assert rhs.design_info.column_names == ["Intercept", "b[T.b2]",

"a[T.a2]:b[b1]", "a[T.a2]:b[b2]"]

assert rhs.design_info.term_names == ["Intercept", "a:b"]

assert len(rhs.design_info.terms) == 2

data = {"a": [1, 2, 3],

"b": [1.0, 2.0, 3.0],

"c": np.asarray([1, 2, 3], dtype=np.float32),

"d": [True, False, True],

"e": ["foo", "bar", "baz"],

"f": C([1, 2, 3]),

"g": C(["foo", "bar", "baz"]),

"h": np.array(["foo", 1, (1, "hi")], dtype=object),

}

t("~ 0 + a", data, 0, True,

[[1], [2], [3]], ["a"])

t("~ 0 + b", data, 0, True,

[[1], [2], [3]], ["b"])

t("~ 0 + c", data, 0, True,

[[1], [2], [3]], ["c"])

t("~ 0 + d", data, 0, True,

[[0, 1], [1, 0], [0, 1]], ["d[False]", "d[True]"])

t("~ 0 + e", data, 0, True,

[[0, 0, 1], [1, 0, 0], [0, 1, 0]], ["e[bar]", "e[baz]", "e[foo]"])

t("~ 0 + f", data, 0, True,

[[1, 0, 0], [0, 1, 0], [0, 0, 1]], ["f[1]", "f[2]", "f[3]"])

t("~ 0 + g", data, 0, True,

[[0, 0, 1], [1, 0, 0], [0, 1, 0]], ["g[bar]", "g[baz]", "g[foo]"])

# This depends on Python's sorting behavior:

t("~ 0 + h", data, 0, True,

[[0, 1, 0], [1, 0, 0], [0, 0, 1]],

data = balanced(a=2, b=2)

# There are more exhaustive tests for all the different coding options in

# test_build; let's just make sure that C() and stuff works.

t("~ C(a)", data, 0,

True,

[[1, 0], [1, 0], [1, 1], [1, 1]], ["Intercept", "C(a)[T.a2]"])

t("~ C(a, levels=['a2', 'a1'])", data, 0,

True,

[[1, 1], [1, 1], [1, 0], [1, 0]],

["Intercept", "C(a, levels=['a2', 'a1'])[T.a1]"])

t("~ C(a, Treatment(reference=-1))", data, 0,

True,

[[1, 1], [1, 1], [1, 0], [1, 0]],

["Intercept", "C(a, Treatment(reference=-1))[T.a1]"])

# Different interactions

t("a*b", data, 0,

True,

[[1, 0, 0, 0],

[1, 0, 1, 0],

[1, 1, 0, 0],

[1, 1, 1, 1]],

["Intercept", "a[T.a2]", "b[T.b2]", "a[T.a2]:b[T.b2]"])

t("0 + a:b", data, 0,

True,

[[1, 0, 0, 0],

[0, 0, 1, 0],

[0, 1, 0, 0],

[0, 0, 0, 1]],

["a[a1]:b[b1]", "a[a2]:b[b1]", "a[a1]:b[b2]", "a[a2]:b[b2]"])

t("1 + a + a:b", data, 0,

True,

[[1, 0, 0, 0],

[1, 0, 1, 0],

[1, 1, 0, 0],

[1, 1, 0, 1]],

["Intercept", "a[T.a2]", "a[a1]:b[T.b2]", "a[a2]:b[T.b2]"])

# Changing contrast with C()

data["a"] = C(data["a"], Helmert)

t("a", data, 0,

True,

[[1, -1], [1, -1], [1, 1], [1, 1]], ["Intercept", "a[H.a2]"])

t("C(a, Treatment)", data, 0,

True,

[[1, 0], [1, 0], [1, 1], [1, 1]], ["Intercept", "C(a, Treatment)[T.a2]"])

# That didn't affect the original object

t("a", data, 0,

True,

data = {"x": [1, 2, 3],

"y": [4, 5, 6],

"a b c": [10, 20, 30]}

t("0 + I(x + y)", data, 0,

True,

[[1], [2], [3], [4], [5], [6]], ["I(x + y)"])

t("Q('a b c')", data, 0,

True,

[[1, 10], [1, 20], [1, 30]], ["Intercept", "Q('a b c')"])

t("center(x)", data, 0,

True,

# incr_dbuilder(s)

# stateful transformations

datas = [

{"a": ["a2", "a2", "a2"],

"x": [1, 2, 3]},

{"a": ["a2", "a2", "a1"],

"x": [4, 5, 6]},

]

x = np.asarray([1, 2, 3, 4, 5, 6])

sin_center_x = np.sin(x - np.mean(x))

x_col = sin_center_x - np.mean(sin_center_x)

def data_iter_maker():

return iter(datas)

builders = incr_dbuilders("1 ~ a + center(np.sin(center(x)))",

data_iter_maker)

lhs, rhs = build_design_matrices(builders, datas[1])

assert lhs.design_info.column_names == ["Intercept"]

assert rhs.design_info.column_names == ["Intercept",

"a[T.a2]",

"center(np.sin(center(x)))"]

assert np.allclose(lhs, [[1], [1], [1]])

assert np.allclose(rhs, np.column_stack(([1, 1, 1],

[1, 1, 0],

x_col[3:])))

builder = incr_dbuilder("~ a + center(np.sin(center(x)))",

data_iter_maker)

(rhs,) = build_design_matrices([builder], datas[1])

assert rhs.design_info.column_names == ["Intercept",

"a[T.a2]",

"center(np.sin(center(x)))"]

assert np.allclose(lhs, [[1], [1], [1]])

assert np.allclose(rhs, np.column_stack(([1, 1, 1],

[1, 1, 0],

x_col[3:])))

assert_raises(PatsyError, incr_dbuilder, "x ~ x", data_iter_maker)

t("~ np.sin(x)", {"x": [1, 2, 3]}, 0,

True,

[[1, np.sin(1)], [1, np.sin(2)], [1, np.sin(3)]],

data = balanced(a=2, b=2)

data["x1"] = np.linspace(0, 1, 4)

data["x2"] = data["x1"] ** 2

def t_terms(formula, order):

m = dmatrix(formula, data)

assert m.design_info.term_names == order

t_terms("a + b + x1 + x2", ["Intercept", "a", "b", "x1", "x2"])

t_terms("b + a + x2 + x1", ["Intercept", "b", "a", "x2", "x1"])

t_terms("0 + x1 + a + x2 + b + 1", ["Intercept", "a", "b", "x1", "x2"])

t_terms("0 + a:b + a + b + 1", ["Intercept", "a", "b", "a:b"])

t_terms("a + a:x1 + x2 + x1 + b",

["Intercept", "a", "b", "x1", "a:x1", "x2"])

t_terms("0 + a:x1:x2 + a + x2:x1:b + x2 + x1 + a:x1 + x1:x2 + x1:a:x2:a:b",

["a",

"x1:x2", "a:x1:x2", "x2:x1:b", "x1:a:x2:b",

"x2",

"x1",

# We evaluate the formula "I(x / y)" in our *caller's* scope, so the

# result depends on whether our caller has done 'from __future__ import

# division'.

data = {"x": 5, "y": 2}

m = dmatrix("0 + I(x / y)", data, 1)

if expect_true_division:

assert np.allclose(m, [[2.5]])

else:

if __future__.division.getMandatoryRelease() < sys.version_info:

# This is Python 3, where division is already default

return

# no __future__.division in this module's scope

_check_division(False)

# create an execution context where __future__.division is in effect

exec ("from __future__ import division\n"

data = {

"a": ["a1", "a2"],

"X": [[1, 2], [3, 4]],

"Y": [[1, 3], [2, 4]],

}

t("X*Y", data, 0,

True,

[[1, 1, 2, 1, 3, 1 * 1, 2 * 1, 1 * 3, 2 * 3],

[1, 3, 4, 2, 4, 3 * 2, 4 * 2, 3 * 4, 4 * 4]],

["Intercept", "X[0]", "X[1]", "Y[0]", "Y[1]",

"X[0]:Y[0]", "X[1]:Y[0]", "X[0]:Y[1]", "X[1]:Y[1]"])

t("a:X + Y", data, 0,

True,

[[1, 1, 0, 2, 0, 1, 3],

[1, 0, 3, 0, 4, 2, 4]],

["Intercept",

"a[a1]:X[0]", "a[a2]:X[0]", "a[a1]:X[1]", "a[a2]:X[1]",

x = [1, 2, 3]

y = [4, 5, 6]

assert np.allclose(dmatrix("x"), [[1, 1], [1, 2], [1, 3]])

lhs, rhs = dmatrices("y ~ x")

assert np.allclose(lhs, [[4], [5], [6]])

lhs, rhs = dmatrices("y ~ x + a", {"y": [1, 2, 3],

"x": [4, 5, 6],

"a": ["a1", "a2", "a3"]})

assert lhs.design_info.describe() == "y"

# This will produce a PatsyError, but buried inside the factor evaluation,

# so the original code has no way to give it an appropriate origin=

# attribute. EvalFactor should notice this, and add a useful origin:

def raise_patsy_error(x):

raise PatsyError("WHEEEEEE")

formula = "raise_patsy_error(X) + Y"

try:

dmatrix(formula, {"X": [1, 2, 3], "Y": [4, 5, 6]})

except PatsyError as e:

assert e.origin == Origin(formula, 0, formula.index(" "))

else:

assert False

# This will produce a KeyError, which on Python 3 we can do wrap without

# destroying the traceback, so we do so. On Python 2 we let the original

# exception escape.

try:

dmatrix("1 + x[1]", {"x": {}})

except Exception as e:

if sys.version_info[0] >= 3:

assert isinstance(e, PatsyError)

assert e.origin == Origin("1 + x[1]", 4, 8)

else:

assert isinstance(e, KeyError)

else:

data = {"x": [1, 2, 3, np.nan], "y": [np.nan, 20, 30, 40]}

return_types = ["matrix"]

if have_pandas:

return_types.append("dataframe")

for return_type in return_types:

mat = dmatrix("x + y", data=data, return_type=return_type)

assert np.array_equal(mat, [[1, 2, 20],

[1, 3, 30]])

if return_type == "dataframe":

assert mat.index.equals([1, 2])

assert_raises(PatsyError, dmatrix, "x + y", data=data,

return_type=return_type,

NA_action="raise")

lmat, rmat = dmatrices("y ~ x", data=data, return_type=return_type)

assert np.array_equal(lmat, [[20], [30]])

assert np.array_equal(rmat, [[1, 2], [1, 3]])

if return_type == "dataframe":

assert lmat.index.equals([1, 2])

assert rmat.index.equals([1, 2])

assert_raises(PatsyError,

dmatrices, "y ~ x", data=data, return_type=return_type,

NA_action="raise")

# Initial release for the NA handling code had problems with

# non-data-dependent matrices like "~ 1".

lmat, rmat = dmatrices("y ~ 1", data=data, return_type=return_type)

assert np.array_equal(lmat, [[20], [30], [40]])

assert np.array_equal(rmat, [[1], [1], [1]])

if return_type == "dataframe":

assert lmat.index.equals([1, 2, 3])

assert rmat.index.equals([1, 2, 3])

assert_raises(PatsyError,

dmatrices, "y ~ 1", data=data, return_type=return_type,