fix and tests for PY-8836 (false positive after ndarray and int multiplication) and PY-13422 (wrong return type in numpy.ones quickdoc)

Conflicts:
	python/testSrc/com/jetbrains/python/PyTypeTest.java

python/src/com/jetbrains/numpy/codeInsight/NumpyDocStringTypeProvider.java

@@ -168,4 +168,13 @@ public class NumpyDocStringTypeProvider extends PyTypeProviderBase {
     }
     return null;
   }
+
+  @Nullable
+  @Override
+  public PyType getReturnType(@NotNull Callable callable, @NotNull TypeEvalContext context) {
+    if (callable instanceof PyFunction) {
+      return getCallType((PyFunction)callable, null, context);
+    }
+    return null;
+  }
 }

python/testData/quickdoc/NumPyOnesDoc.html

@@ -0,0 +1,2 @@
+def ones(shape, dtype=None, order='C')
+Inferred type: (shape:&nbsp;<a href="psi_element://#typename#int">int</a>,&nbsp;dtype:&nbsp;<a href="psi_element://#typename#object">object</a>,&nbsp;order:&nbsp;<a href="psi_element://#typename#str">str</a>)&nbsp;-&gt;&nbsp;<a href="psi_element://#typename#ndarray">ndarray</a><br>

python/testData/quickdoc/NumPyOnesDoc.py

@@ -0,0 +1,4 @@
+import numpy as np
+
+x = np.<the_ref>ones(10)
+

python/testData/quickdoc/NumPyOnesDoc/numpy/__init__.py

@@ -0,0 +1,5 @@
+from . import core
+from .core import *
+
+__all__ = []
+__all__.extend(core.__all__)

python/testData/quickdoc/NumPyOnesDoc/numpy/core/__init__.py

@@ -0,0 +1,6 @@
+from . import multiarray
+from . import numeric
+from .numeric import *
+
+__all__ = []
+__all__ += numeric.__all__

python/testData/quickdoc/NumPyOnesDoc/numpy/core/multiarray.py

@@ -0,0 +1,120 @@
+class ndarray(object):
+    """
+    ndarray(shape, dtype=float, buffer=None, offset=0,
+                strides=None, order=None)
+
+        An array object represents a multidimensional, homogeneous array
+        of fixed-size items.  An associated data-type object describes the
+        format of each element in the array (its byte-order, how many bytes it
+        occupies in memory, whether it is an integer, a floating point number,
+        or something else, etc.)
+
+        Arrays should be constructed using `array`, `zeros` or `empty` (refer
+        to the See Also section below).  The parameters given here refer to
+        a low-level method (`ndarray(...)`) for instantiating an array.
+
+        For more information, refer to the `numpy` module and examine the
+        the methods and attributes of an array.
+
+        Parameters
+        ----------
+        (for the __new__ method; see Notes below)
+
+        shape : tuple of ints
+            Shape of created array.
+        dtype : data-type, optional
+            Any object that can be interpreted as a numpy data type.
+        buffer : object exposing buffer interface, optional
+            Used to fill the array with data.
+        offset : int, optional
+            Offset of array data in buffer.
+        strides : tuple of ints, optional
+            Strides of data in memory.
+        order : {'C', 'F'}, optional
+            Row-major or column-major order.
+
+        Attributes
+        ----------
+        T : ndarray
+            Transpose of the array.
+        data : buffer
+            The array's elements, in memory.
+        dtype : dtype object
+            Describes the format of the elements in the array.
+        flags : dict
+            Dictionary containing information related to memory use, e.g.,
+            'C_CONTIGUOUS', 'OWNDATA', 'WRITEABLE', etc.
+        flat : numpy.flatiter object
+            Flattened version of the array as an iterator.  The iterator
+            allows assignments, e.g., ``x.flat = 3`` (See `ndarray.flat` for
+            assignment examples; TODO).
+        imag : ndarray
+            Imaginary part of the array.
+        real : ndarray
+            Real part of the array.
+        size : int
+            Number of elements in the array.
+        itemsize : int
+            The memory use of each array element in bytes.
+        nbytes : int
+            The total number of bytes required to store the array data,
+            i.e., ``itemsize * size``.
+        ndim : int
+            The array's number of dimensions.
+        shape : tuple of ints
+            Shape of the array.
+        strides : tuple of ints
+            The step-size required to move from one element to the next in
+            memory. For example, a contiguous ``(3, 4)`` array of type
+            ``int16`` in C-order has strides ``(8, 2)``.  This implies that
+            to move from element to element in memory requires jumps of 2 bytes.
+            To move from row-to-row, one needs to jump 8 bytes at a time
+            (``2 * 4``).
+        ctypes : ctypes object
+            Class containing properties of the array needed for interaction
+            with ctypes.
+        base : ndarray
+            If the array is a view into another array, that array is its `base`
+            (unless that array is also a view).  The `base` array is where the
+            array data is actually stored.
+
+        See Also
+        --------
+        array : Construct an array.
+        zeros : Create an array, each element of which is zero.
+        empty : Create an array, but leave its allocated memory unchanged (i.e.,
+                it contains "garbage").
+        dtype : Create a data-type.
+
+        Notes
+        -----
+        There are two modes of creating an array using ``__new__``:
+
+        1. If `buffer` is None, then only `shape`, `dtype`, and `order`
+           are used.
+        2. If `buffer` is an object exposing the buffer interface, then
+           all keywords are interpreted.
+
+        No ``__init__`` method is needed because the array is fully initialized
+        after the ``__new__`` method.
+
+        Examples
+        --------
+        These examples illustrate the low-level `ndarray` constructor.  Refer
+        to the `See Also` section above for easier ways of constructing an
+        ndarray.
+
+        First mode, `buffer` is None:
+
+        >>> np.ndarray(shape=(2,2), dtype=float, order='F')
+        array([[ -1.13698227e+002,   4.25087011e-303],
+               [  2.88528414e-306,   3.27025015e-309]])         #random
+
+        Second mode:
+
+        >>> np.ndarray((2,), buffer=np.array([1,2,3]),
+        ...            offset=np.int_().itemsize,
+        ...            dtype=int) # offset = 1*itemsize, i.e. skip first element
+        array([2, 3])
+    """
+    pass

python/testData/quickdoc/NumPyOnesDoc/numpy/core/numeric.py

@@ -0,0 +1,51 @@
+from . import multiarray
+
+__all__ = ['ndarray', 'ones']
+
+
+ndarray = multiarray.ndarray
+
+def ones(shape, dtype=None, order='C'):
+    """
+    **Test docstring**
+    Return a new array of given shape and type, filled with ones.
+
+    Parameters
+    ----------
+    shape : int or sequence of ints
+        Shape of the new array, e.g., ``(2, 3)`` or ``2``.
+    dtype : data-type, optional
+        The desired data-type for the array, e.g., `numpy.int8`.  Default is
+        `numpy.float64`.
+    order : {'C', 'F'}, optional
+        Whether to store multidimensional data in C- or Fortran-contiguous
+        (row- or column-wise) order in memory.
+
+    Returns
+    -------
+    out : ndarray
+        Array of ones with the given shape, dtype, and order.
+
+    See Also
+    --------
+    zeros, ones_like
+
+    Examples
+    --------
+    >>> np.ones(5)
+    array([ 1.,  1.,  1.,  1.,  1.])
+
+    >>> np.ones((5,), dtype=np.int)
+    array([1, 1, 1, 1, 1])
+
+    >>> np.ones((2, 1))
+    array([[ 1.],
+           [ 1.]])
+
+    >>> s = (2,2)
+    >>> np.ones(s)
+    array([[ 1.,  1.],
+           [ 1.,  1.]])
+
+    """
+    pass

python/testData/types/NumpyArrayIntMultiplicationType/numpy/__init__.py

@@ -0,0 +1,5 @@
+from . import core
+from .core import *
+
+__all__ = []
+__all__.extend(core.__all__)

python/testData/types/NumpyArrayIntMultiplicationType/numpy/core/__init__.py

@@ -0,0 +1,6 @@
+from . import multiarray
+from . import numeric
+from .numeric import *
+
+__all__ = []
+__all__ += numeric.__all__

python/testData/types/NumpyArrayIntMultiplicationType/numpy/core/multiarray.py

@@ -0,0 +1,129 @@
+class ndarray(object):
+    """
+    ndarray(shape, dtype=float, buffer=None, offset=0,
+                strides=None, order=None)
+
+        An array object represents a multidimensional, homogeneous array
+        of fixed-size items.  An associated data-type object describes the
+        format of each element in the array (its byte-order, how many bytes it
+        occupies in memory, whether it is an integer, a floating point number,
+        or something else, etc.)
+
+        Arrays should be constructed using `array`, `zeros` or `empty` (refer
+        to the See Also section below).  The parameters given here refer to
+        a low-level method (`ndarray(...)`) for instantiating an array.
+
+        For more information, refer to the `numpy` module and examine the
+        the methods and attributes of an array.
+
+        Parameters
+        ----------
+        (for the __new__ method; see Notes below)
+
+        shape : tuple of ints
+            Shape of created array.
+        dtype : data-type, optional
+            Any object that can be interpreted as a numpy data type.
+        buffer : object exposing buffer interface, optional
+            Used to fill the array with data.
+        offset : int, optional
+            Offset of array data in buffer.
+        strides : tuple of ints, optional
+            Strides of data in memory.
+        order : {'C', 'F'}, optional
+            Row-major or column-major order.
+
+        Attributes
+        ----------
+        T : ndarray
+            Transpose of the array.
+        data : buffer
+            The array's elements, in memory.
+        dtype : dtype object
+            Describes the format of the elements in the array.
+        flags : dict
+            Dictionary containing information related to memory use, e.g.,
+            'C_CONTIGUOUS', 'OWNDATA', 'WRITEABLE', etc.
+        flat : numpy.flatiter object
+            Flattened version of the array as an iterator.  The iterator
+            allows assignments, e.g., ``x.flat = 3`` (See `ndarray.flat` for
+            assignment examples; TODO).
+        imag : ndarray
+            Imaginary part of the array.
+        real : ndarray
+            Real part of the array.
+        size : int
+            Number of elements in the array.
+        itemsize : int
+            The memory use of each array element in bytes.
+        nbytes : int
+            The total number of bytes required to store the array data,
+            i.e., ``itemsize * size``.
+        ndim : int
+            The array's number of dimensions.
+        shape : tuple of ints
+            Shape of the array.
+        strides : tuple of ints
+            The step-size required to move from one element to the next in
+            memory. For example, a contiguous ``(3, 4)`` array of type
+            ``int16`` in C-order has strides ``(8, 2)``.  This implies that
+            to move from element to element in memory requires jumps of 2 bytes.
+            To move from row-to-row, one needs to jump 8 bytes at a time
+            (``2 * 4``).
+        ctypes : ctypes object
+            Class containing properties of the array needed for interaction
+            with ctypes.
+        base : ndarray
+            If the array is a view into another array, that array is its `base`
+            (unless that array is also a view).  The `base` array is where the
+            array data is actually stored.
+
+        See Also
+        --------
+        array : Construct an array.
+        zeros : Create an array, each element of which is zero.
+        empty : Create an array, but leave its allocated memory unchanged (i.e.,
+                it contains "garbage").
+        dtype : Create a data-type.
+
+        Notes
+        -----
+        There are two modes of creating an array using ``__new__``:
+
+        1. If `buffer` is None, then only `shape`, `dtype`, and `order`
+           are used.
+        2. If `buffer` is an object exposing the buffer interface, then
+           all keywords are interpreted.
+
+        No ``__init__`` method is needed because the array is fully initialized
+        after the ``__new__`` method.
+
+        Examples
+        --------
+        These examples illustrate the low-level `ndarray` constructor.  Refer
+        to the `See Also` section above for easier ways of constructing an
+        ndarray.
+
+        First mode, `buffer` is None:
+
+        >>> np.ndarray(shape=(2,2), dtype=float, order='F')
+        array([[ -1.13698227e+002,   4.25087011e-303],
+               [  2.88528414e-306,   3.27025015e-309]])         #random
+
+        Second mode:
+
+        >>> np.ndarray((2,), buffer=np.array([1,2,3]),
+        ...            offset=np.int_().itemsize,
+        ...            dtype=int) # offset = 1*itemsize, i.e. skip first element
+        array([2, 3])
+    """
+    pass
+
+
+    def __mul__(self, y):
+        """ x.__mul__(y) <==> x*y """
+        pass
+
+    def __rmul__(self, y):
+        """ x.__rmul__(y) <==> x*y """
+        pass

python/testData/types/NumpyArrayIntMultiplicationType/numpy/core/numeric.py

@@ -0,0 +1,51 @@
+from . import multiarray
+
+__all__ = ['ndarray', 'ones']
+
+
+ndarray = multiarray.ndarray
+
+def ones(shape, dtype=None, order='C'):
+    """
+    **Test docstring**
+    Return a new array of given shape and type, filled with ones.
+
+    Parameters
+    ----------
+    shape : int or sequence of ints
+        Shape of the new array, e.g., ``(2, 3)`` or ``2``.
+    dtype : data-type, optional
+        The desired data-type for the array, e.g., `numpy.int8`.  Default is
+        `numpy.float64`.
+    order : {'C', 'F'}, optional
+        Whether to store multidimensional data in C- or Fortran-contiguous
+        (row- or column-wise) order in memory.
+
+    Returns
+    -------
+    out : ndarray
+        Array of ones with the given shape, dtype, and order.
+
+    See Also
+    --------
+    zeros, ones_like
+
+    Examples
+    --------
+    >>> np.ones(5)
+    array([ 1.,  1.,  1.,  1.,  1.])
+
+    >>> np.ones((5,), dtype=np.int)
+    array([1, 1, 1, 1, 1])
+
+    >>> np.ones((2, 1))
+    array([[ 1.],
+           [ 1.]])
+
+    >>> s = (2,2)
+    >>> np.ones(s)
+    array([[ 1.,  1.],
+           [ 1.,  1.]])
+
+    """
+    pass

python/testSrc/com/jetbrains/python/PyQuickDocTest.java

@@ -27,6 +27,8 @@ import com.jetbrains.python.fixtures.LightMarkedTestCase;
 import com.jetbrains.python.fixtures.PyTestCase;
 import com.jetbrains.python.psi.*;
 import com.jetbrains.python.psi.impl.PythonLanguageLevelPusher;
+import com.jetbrains.python.psi.types.PyType;
+import com.jetbrains.python.psi.types.TypeEvalContext;
 import junit.framework.Assert;
 
 import java.io.IOException;
@@ -240,7 +242,7 @@ public class PyQuickDocTest extends LightMarkedTestCase {
   public void testHoverOverMethod() {
     checkHover();
   }
-  
+
   public void testHoverOverParameter() {
     checkHover();
   }
@@ -248,4 +250,10 @@ public class PyQuickDocTest extends LightMarkedTestCase {
   public void testHoverOverControlFlowUnion() {
     checkHover();
   }
+
+  // PY-13422
+  public void testNumPyOnesDoc() {
+    myFixture.copyDirectoryToProject("/quickdoc/" + getTestName(false), "");
+    checkHover();
+  }
 }

python/testSrc/com/jetbrains/python/PyTypeTest.java

@@ -194,14 +194,16 @@ public class PyTypeTest extends PyTestCase {
            "def foo(*args):\n" +
            "    '''@rtype: C{str}'''\n" +
            "    return args[0]" +
-           "expr = foo('')");
+           "expr = foo('')"
+    );
   }
 
   public void testEpydocParamType() {
     doTest("str",
            "def foo(s):\n" +
            "    '''@type s: C{str}'''\n" +
-           "    expr = s");
+           "    expr = s"
+    );
   }
 
   public void testEpydocIvarType() {
@@ -385,7 +387,8 @@ public class PyTypeTest extends PyTestCase {
            "    return 1\n" +
            "g = f\n" +
            "h = g\n" +
-           "expr = h()\n");
+           "expr = h()\n"
+    );
   }
 
   public void testPropertyOfUnionType() {
@@ -454,7 +457,8 @@ public class PyTypeTest extends PyTestCase {
            "\n" +
            "    def __init__(self):\n" +
            "        self.foo = 3\n" +
-           "        expr = self.foo\n");
+           "        expr = self.foo\n"
+    );
   }
 
   // PY-7215
@@ -465,7 +469,8 @@ public class PyTypeTest extends PyTestCase {
            "        yield 10\n" +
            "    return list(g())\n" +
            "\n" +
-           "expr = f()\n");
+           "expr = f()\n"
+    );
   }
 
   public void testGeneratorNextType() {
@@ -508,7 +513,8 @@ public class PyTypeTest extends PyTestCase {
     doTest("list[list]",
            "def f():\n" +
            "    return [f()]\n" +
-           "expr = f()\n");
+           "expr = f()\n"
+    );
   }
 
   // PY-5084
@@ -519,7 +525,8 @@ public class PyTypeTest extends PyTestCase {
            "    if isinstance(x, int):\n" +
            "        print(x)\n" +
            "    else:\n" +
-           "        expr = x\n");
+           "        expr = x\n"
+    );
   }
 
   // PY-5614
@@ -606,7 +613,8 @@ public class PyTypeTest extends PyTestCase {
            "    :rtype: T\n" +
            "    '''\n" +
            "def bar(x):\n" +
-           "    expr = foo(x)\n");
+           "    expr = foo(x)\n"
+    );
   }
 
   public void testIterationTypeFromGetItem() {
@@ -866,6 +874,13 @@ public class PyTypeTest extends PyTestCase {
            "expr = C(10).foo()\n");
   }
 
+  // PY-8836
+  public void testNumpyArrayIntMultiplicationType() {
+    doMultiFileTest("ndarray",
+                    "import numpy as np\n" +
+                    "expr = np.ones(10) * 2\n");
+  }
+
   private static TypeEvalContext getTypeEvalContext(@NotNull PyExpression element) {
     return TypeEvalContext.userInitiated(element.getContainingFile()).withTracing();
   }
@@ -882,4 +897,16 @@ public class PyTypeTest extends PyTestCase {
     final String actualType = PythonDocumentationProvider.getTypeName(actual, context);
     assertEquals(expectedType, actualType);
   }
+
+  public static final String TEST_DIRECTORY = "/types/";
+
+  private void doMultiFileTest(final String expectedType, final String text) {
+    final String testName = getTestName(false);
+    myFixture.copyDirectoryToProject(TEST_DIRECTORY + testName, "");
+    PyExpression expr = parseExpr(text);
+    TypeEvalContext context = getTypeEvalContext(expr);
+    PyType actual = context.getType(expr);
+    final String actualType = PythonDocumentationProvider.getTypeName(actual, context);
+    assertEquals(expectedType, actualType);
+  }
 }