Add opencv, numpy (for screen with confidence), start to create Screen module

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/cv2/LICENSE.txt

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+MIT License
+
+Copyright (c) Olli-Pekka Heinisuo
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/cv2/__init__.py

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+'''
+OpenCV Python binary extension loader
+'''
+import os
+import importlib
+import sys
+
+__all__ = []
+
+try:
+    import numpy
+    import numpy.core.multiarray
+except ImportError:
+    print('OpenCV bindings requires "numpy" package.')
+    print('Install it via command:')
+    print('    pip install numpy')
+    raise
+
+# TODO
+# is_x64 = sys.maxsize > 2**32
+
+
+def __load_extra_py_code_for_module(base, name, enable_debug_print=False):
+    module_name = "{}.{}".format(__name__, name)
+    export_module_name = "{}.{}".format(base, name)
+    native_module = sys.modules.pop(module_name, None)
+    try:
+        py_module = importlib.import_module(module_name)
+    except ImportError as err:
+        if enable_debug_print:
+            print("Can't load Python code for module:", module_name,
+                  ". Reason:", err)
+        # Extension doesn't contain extra py code
+        return False
+
+    if not hasattr(base, name):
+        setattr(sys.modules[base], name, py_module)
+    sys.modules[export_module_name] = py_module
+    # If it is C extension module it is already loaded by cv2 package
+    if native_module:
+        setattr(py_module, "_native", native_module)
+        for k, v in filter(lambda kv: not hasattr(py_module, kv[0]),
+                           native_module.__dict__.items()):
+            if enable_debug_print: print('    symbol({}): {} = {}'.format(name, k, v))
+            setattr(py_module, k, v)
+    return True
+
+
+def __collect_extra_submodules(enable_debug_print=False):
+    def modules_filter(module):
+        return all((
+             # module is not internal
+             not module.startswith("_"),
+             not module.startswith("python-"),
+             # it is not a file
+             os.path.isdir(os.path.join(_extra_submodules_init_path, module))
+        ))
+    if sys.version_info[0] < 3:
+        if enable_debug_print:
+            print("Extra submodules is loaded only for Python 3")
+        return []
+
+    __INIT_FILE_PATH = os.path.abspath(__file__)
+    _extra_submodules_init_path = os.path.dirname(__INIT_FILE_PATH)
+    return filter(modules_filter, os.listdir(_extra_submodules_init_path))
+
+
+def bootstrap():
+    import sys
+
+    import copy
+    save_sys_path = copy.copy(sys.path)
+
+    if hasattr(sys, 'OpenCV_LOADER'):
+        print(sys.path)
+        raise ImportError('ERROR: recursion is detected during loading of "cv2" binary extensions. Check OpenCV installation.')
+    sys.OpenCV_LOADER = True
+
+    DEBUG = False
+    if hasattr(sys, 'OpenCV_LOADER_DEBUG'):
+        DEBUG = True
+
+    import platform
+    if DEBUG: print('OpenCV loader: os.name="{}"  platform.system()="{}"'.format(os.name, str(platform.system())))
+
+    LOADER_DIR = os.path.dirname(os.path.abspath(os.path.realpath(__file__)))
+
+    PYTHON_EXTENSIONS_PATHS = []
+    BINARIES_PATHS = []
+
+    g_vars = globals()
+    l_vars = locals()
+
+    if sys.version_info[:2] < (3, 0):
+        from . load_config_py2 import exec_file_wrapper
+    else:
+        from . load_config_py3 import exec_file_wrapper
+
+    def load_first_config(fnames, required=True):
+        for fname in fnames:
+            fpath = os.path.join(LOADER_DIR, fname)
+            if not os.path.exists(fpath):
+                if DEBUG: print('OpenCV loader: config not found, skip: {}'.format(fpath))
+                continue
+            if DEBUG: print('OpenCV loader: loading config: {}'.format(fpath))
+            exec_file_wrapper(fpath, g_vars, l_vars)
+            return True
+        if required:
+            raise ImportError('OpenCV loader: missing configuration file: {}. Check OpenCV installation.'.format(fnames))
+
+    load_first_config(['config.py'], True)
+    load_first_config([
+        'config-{}.{}.py'.format(sys.version_info[0], sys.version_info[1]),
+        'config-{}.py'.format(sys.version_info[0])
+    ], True)
+
+    if DEBUG: print('OpenCV loader: PYTHON_EXTENSIONS_PATHS={}'.format(str(l_vars['PYTHON_EXTENSIONS_PATHS'])))
+    if DEBUG: print('OpenCV loader: BINARIES_PATHS={}'.format(str(l_vars['BINARIES_PATHS'])))
+
+    applySysPathWorkaround = False
+    if hasattr(sys, 'OpenCV_REPLACE_SYS_PATH_0'):
+        applySysPathWorkaround = True
+    else:
+        try:
+            BASE_DIR = os.path.dirname(LOADER_DIR)
+            if sys.path[0] == BASE_DIR or os.path.realpath(sys.path[0]) == BASE_DIR:
+                applySysPathWorkaround = True
+        except:
+            if DEBUG: print('OpenCV loader: exception during checking workaround for sys.path[0]')
+            pass  # applySysPathWorkaround is False
+
+    for p in reversed(l_vars['PYTHON_EXTENSIONS_PATHS']):
+        sys.path.insert(1 if not applySysPathWorkaround else 0, p)
+
+    if os.name == 'nt':
+        if sys.version_info[:2] >= (3, 8):  # https://github.com/python/cpython/pull/12302
+            for p in l_vars['BINARIES_PATHS']:
+                try:
+                    os.add_dll_directory(p)
+                except Exception as e:
+                    if DEBUG: print('Failed os.add_dll_directory(): '+ str(e))
+                    pass
+        os.environ['PATH'] = ';'.join(l_vars['BINARIES_PATHS']) + ';' + os.environ.get('PATH', '')
+        if DEBUG: print('OpenCV loader: PATH={}'.format(str(os.environ['PATH'])))
+    else:
+        # amending of LD_LIBRARY_PATH works for sub-processes only
+        os.environ['LD_LIBRARY_PATH'] = ':'.join(l_vars['BINARIES_PATHS']) + ':' + os.environ.get('LD_LIBRARY_PATH', '')
+
+    if DEBUG: print("Relink everything from native cv2 module to cv2 package")
+
+    py_module = sys.modules.pop("cv2")
+
+    native_module = importlib.import_module("cv2")
+
+    sys.modules["cv2"] = py_module
+    setattr(py_module, "_native", native_module)
+
+    for item_name, item in filter(lambda kv: kv[0] not in ("__file__", "__loader__", "__spec__",
+                                                           "__name__", "__package__"),
+                                  native_module.__dict__.items()):
+        if item_name not in g_vars:
+            g_vars[item_name] = item
+
+    sys.path = save_sys_path  # multiprocessing should start from bootstrap code (https://github.com/opencv/opencv/issues/18502)
+
+    try:
+        del sys.OpenCV_LOADER
+    except Exception as e:
+        if DEBUG:
+            print("Exception during delete OpenCV_LOADER:", e)
+
+    if DEBUG: print('OpenCV loader: binary extension... OK')
+
+    for submodule in __collect_extra_submodules(DEBUG):
+        if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
+            if DEBUG: print("Extra Python code for", submodule, "is loaded")
+
+    if DEBUG: print('OpenCV loader: DONE')
+
+
+bootstrap()

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/cv2/config-3.py

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+PYTHON_EXTENSIONS_PATHS = [
+    LOADER_DIR
+] + PYTHON_EXTENSIONS_PATHS
+
+ci_and_not_headless = False
+
+try:
+    from .version import ci_build, headless
+
+    ci_and_not_headless = ci_build and not headless
+except:
+    pass
+
+# the Qt plugin is included currently only in the pre-built wheels
+if sys.platform.startswith("linux") and ci_and_not_headless:
+    os.environ["QT_QPA_PLATFORM_PLUGIN_PATH"] = os.path.join(
+        os.path.dirname(os.path.abspath(__file__)), "qt", "plugins"
+    )
+
+# Qt will throw warning on Linux if fonts are not found
+if sys.platform.startswith("linux") and ci_and_not_headless:
+    os.environ["QT_QPA_FONTDIR"] = os.path.join(
+        os.path.dirname(os.path.abspath(__file__)), "qt", "fonts"
+    )

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/cv2/config.py

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+import os
+
+BINARIES_PATHS = [
+    os.path.join(os.path.join(LOADER_DIR, '../../'), 'x64/vc14/bin')
+] + BINARIES_PATHS

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/cv2/data/__init__.py

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+import os
+
+haarcascades = os.path.join(os.path.dirname(__file__), "")

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/cv2/gapi/__init__.py

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+__all__ = ['op', 'kernel']
+
+import sys
+import cv2 as cv
+
+# NB: Register function in specific module
+def register(mname):
+    def parameterized(func):
+        sys.modules[mname].__dict__[func.__name__] = func
+        return func
+    return parameterized
+
+
+@register('cv2.gapi')
+def networks(*args):
+    return cv.gapi_GNetPackage(list(map(cv.detail.strip, args)))
+
+
+@register('cv2.gapi')
+def compile_args(*args):
+    return list(map(cv.GCompileArg, args))
+
+
+@register('cv2')
+def GIn(*args):
+    return [*args]
+
+
+@register('cv2')
+def GOut(*args):
+    return [*args]
+
+
+@register('cv2')
+def gin(*args):
+    return [*args]
+
+
+@register('cv2.gapi')
+def descr_of(*args):
+    return [*args]
+
+
+@register('cv2')
+class GOpaque():
+    # NB: Inheritance from c++ class cause segfault.
+    # So just aggregate cv.GOpaqueT instead of inheritance
+    def __new__(cls, argtype):
+        return cv.GOpaqueT(argtype)
+
+    class Bool():
+        def __new__(self):
+            return cv.GOpaqueT(cv.gapi.CV_BOOL)
+
+    class Int():
+        def __new__(self):
+            return cv.GOpaqueT(cv.gapi.CV_INT)
+
+    class Double():
+        def __new__(self):
+            return cv.GOpaqueT(cv.gapi.CV_DOUBLE)
+
+    class Float():
+        def __new__(self):
+            return cv.GOpaqueT(cv.gapi.CV_FLOAT)
+
+    class String():
+        def __new__(self):
+            return cv.GOpaqueT(cv.gapi.CV_STRING)
+
+    class Point():
+        def __new__(self):
+            return cv.GOpaqueT(cv.gapi.CV_POINT)
+
+    class Point2f():
+        def __new__(self):
+            return cv.GOpaqueT(cv.gapi.CV_POINT2F)
+
+    class Size():
+        def __new__(self):
+            return cv.GOpaqueT(cv.gapi.CV_SIZE)
+
+    class Rect():
+        def __new__(self):
+            return cv.GOpaqueT(cv.gapi.CV_RECT)
+
+    class Prim():
+        def __new__(self):
+            return cv.GOpaqueT(cv.gapi.CV_DRAW_PRIM)
+
+    class Any():
+        def __new__(self):
+            return cv.GOpaqueT(cv.gapi.CV_ANY)
+
+@register('cv2')
+class GArray():
+    # NB: Inheritance from c++ class cause segfault.
+    # So just aggregate cv.GArrayT instead of inheritance
+    def __new__(cls, argtype):
+        return cv.GArrayT(argtype)
+
+    class Bool():
+        def __new__(self):
+            return cv.GArrayT(cv.gapi.CV_BOOL)
+
+    class Int():
+        def __new__(self):
+            return cv.GArrayT(cv.gapi.CV_INT)
+
+    class Double():
+        def __new__(self):
+            return cv.GArrayT(cv.gapi.CV_DOUBLE)
+
+    class Float():
+        def __new__(self):
+            return cv.GArrayT(cv.gapi.CV_FLOAT)
+
+    class String():
+        def __new__(self):
+            return cv.GArrayT(cv.gapi.CV_STRING)
+
+    class Point():
+        def __new__(self):
+            return cv.GArrayT(cv.gapi.CV_POINT)
+
+    class Point2f():
+        def __new__(self):
+            return cv.GArrayT(cv.gapi.CV_POINT2F)
+
+    class Size():
+        def __new__(self):
+            return cv.GArrayT(cv.gapi.CV_SIZE)
+
+    class Rect():
+        def __new__(self):
+            return cv.GArrayT(cv.gapi.CV_RECT)
+
+    class Scalar():
+        def __new__(self):
+            return cv.GArrayT(cv.gapi.CV_SCALAR)
+
+    class Mat():
+        def __new__(self):
+            return cv.GArrayT(cv.gapi.CV_MAT)
+
+    class GMat():
+        def __new__(self):
+            return cv.GArrayT(cv.gapi.CV_GMAT)
+
+    class Prim():
+        def __new__(self):
+            return cv.GArray(cv.gapi.CV_DRAW_PRIM)
+
+    class Any():
+        def __new__(self):
+            return cv.GArray(cv.gapi.CV_ANY)
+
+
+# NB: Top lvl decorator takes arguments
+def op(op_id, in_types, out_types):
+
+    garray_types= {
+            cv.GArray.Bool:    cv.gapi.CV_BOOL,
+            cv.GArray.Int:     cv.gapi.CV_INT,
+            cv.GArray.Double:  cv.gapi.CV_DOUBLE,
+            cv.GArray.Float:   cv.gapi.CV_FLOAT,
+            cv.GArray.String:  cv.gapi.CV_STRING,
+            cv.GArray.Point:   cv.gapi.CV_POINT,
+            cv.GArray.Point2f: cv.gapi.CV_POINT2F,
+            cv.GArray.Size:    cv.gapi.CV_SIZE,
+            cv.GArray.Rect:    cv.gapi.CV_RECT,
+            cv.GArray.Scalar:  cv.gapi.CV_SCALAR,
+            cv.GArray.Mat:     cv.gapi.CV_MAT,
+            cv.GArray.GMat:    cv.gapi.CV_GMAT,
+            cv.GArray.Prim:    cv.gapi.CV_DRAW_PRIM,
+            cv.GArray.Any:     cv.gapi.CV_ANY
+    }
+
+    gopaque_types= {
+            cv.GOpaque.Size:    cv.gapi.CV_SIZE,
+            cv.GOpaque.Rect:    cv.gapi.CV_RECT,
+            cv.GOpaque.Bool:    cv.gapi.CV_BOOL,
+            cv.GOpaque.Int:     cv.gapi.CV_INT,
+            cv.GOpaque.Double:  cv.gapi.CV_DOUBLE,
+            cv.GOpaque.Float:   cv.gapi.CV_FLOAT,
+            cv.GOpaque.String:  cv.gapi.CV_STRING,
+            cv.GOpaque.Point:   cv.gapi.CV_POINT,
+            cv.GOpaque.Point2f: cv.gapi.CV_POINT2F,
+            cv.GOpaque.Size:    cv.gapi.CV_SIZE,
+            cv.GOpaque.Rect:    cv.gapi.CV_RECT,
+            cv.GOpaque.Prim:    cv.gapi.CV_DRAW_PRIM,
+            cv.GOpaque.Any:     cv.gapi.CV_ANY
+    }
+
+    type2str = {
+        cv.gapi.CV_BOOL:      'cv.gapi.CV_BOOL' ,
+        cv.gapi.CV_INT:       'cv.gapi.CV_INT' ,
+        cv.gapi.CV_DOUBLE:    'cv.gapi.CV_DOUBLE' ,
+        cv.gapi.CV_FLOAT:     'cv.gapi.CV_FLOAT' ,
+        cv.gapi.CV_STRING:    'cv.gapi.CV_STRING' ,
+        cv.gapi.CV_POINT:     'cv.gapi.CV_POINT' ,
+        cv.gapi.CV_POINT2F:   'cv.gapi.CV_POINT2F' ,
+        cv.gapi.CV_SIZE:      'cv.gapi.CV_SIZE',
+        cv.gapi.CV_RECT:      'cv.gapi.CV_RECT',
+        cv.gapi.CV_SCALAR:    'cv.gapi.CV_SCALAR',
+        cv.gapi.CV_MAT:       'cv.gapi.CV_MAT',
+        cv.gapi.CV_GMAT:      'cv.gapi.CV_GMAT',
+        cv.gapi.CV_DRAW_PRIM: 'cv.gapi.CV_DRAW_PRIM'
+    }
+
+    # NB: Second lvl decorator takes class to decorate
+    def op_with_params(cls):
+        if not in_types:
+            raise Exception('{} operation should have at least one input!'.format(cls.__name__))
+
+        if not out_types:
+            raise Exception('{} operation should have at least one output!'.format(cls.__name__))
+
+        for i, t in enumerate(out_types):
+            if t not in [cv.GMat, cv.GScalar, *garray_types, *gopaque_types]:
+                   raise Exception('{} unsupported output type: {} in position: {}'
+                           .format(cls.__name__, t.__name__, i))
+
+        def on(*args):
+            if len(in_types) != len(args):
+                raise Exception('Invalid number of input elements!\nExpected: {}, Actual: {}'
+                        .format(len(in_types), len(args)))
+
+            for i, (t, a) in enumerate(zip(in_types, args)):
+                if t in garray_types:
+                    if not isinstance(a, cv.GArrayT):
+                        raise Exception("{} invalid type for argument {}.\nExpected: {}, Actual: {}"
+                                .format(cls.__name__, i, cv.GArrayT.__name__, type(a).__name__))
+
+                    elif a.type() != garray_types[t]:
+                        raise Exception("{} invalid GArrayT type for argument {}.\nExpected: {}, Actual: {}"
+                                .format(cls.__name__, i, type2str[garray_types[t]], type2str[a.type()]))
+
+                elif t in gopaque_types:
+                    if not isinstance(a, cv.GOpaqueT):
+                        raise Exception("{} invalid type for argument {}.\nExpected: {}, Actual: {}"
+                                .format(cls.__name__, i, cv.GOpaqueT.__name__, type(a).__name__))
+
+                    elif a.type() != gopaque_types[t]:
+                        raise Exception("{} invalid GOpaque type for argument {}.\nExpected: {}, Actual: {}"
+                                .format(cls.__name__, i, type2str[gopaque_types[t]], type2str[a.type()]))
+
+                else:
+                    if t != type(a):
+                        raise Exception('{} invalid input type for argument {}.\nExpected: {}, Actual: {}'
+                                .format(cls.__name__, i, t.__name__, type(a).__name__))
+
+            op = cv.gapi.__op(op_id, cls.outMeta, *args)
+
+            out_protos = []
+            for i, out_type in enumerate(out_types):
+                if out_type == cv.GMat:
+                    out_protos.append(op.getGMat())
+                elif out_type == cv.GScalar:
+                    out_protos.append(op.getGScalar())
+                elif out_type in gopaque_types:
+                    out_protos.append(op.getGOpaque(gopaque_types[out_type]))
+                elif out_type in garray_types:
+                    out_protos.append(op.getGArray(garray_types[out_type]))
+                else:
+                    raise Exception("""In {}: G-API operation can't produce the output with type: {} in position: {}"""
+                            .format(cls.__name__, out_type.__name__, i))
+
+            return tuple(out_protos) if len(out_protos) != 1 else out_protos[0]
+
+        # NB: Extend operation class
+        cls.id = op_id
+        cls.on = staticmethod(on)
+        return cls
+
+    return op_with_params
+
+
+def kernel(op_cls):
+    # NB: Second lvl decorator takes class to decorate
+    def kernel_with_params(cls):
+        # NB: Add new members to kernel class
+        cls.id      = op_cls.id
+        cls.outMeta = op_cls.outMeta
+        return cls
+
+    return kernel_with_params
+
+
+cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/cv2/load_config_py2.py

@@ -0,0 +1,6 @@
+# flake8: noqa
+import sys
+
+if sys.version_info[:2] < (3, 0):
+    def exec_file_wrapper(fpath, g_vars, l_vars):
+        execfile(fpath, g_vars, l_vars)

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/cv2/load_config_py3.py

@@ -0,0 +1,9 @@
+# flake8: noqa
+import os
+import sys
+
+if sys.version_info[:2] >= (3, 0):
+    def exec_file_wrapper(fpath, g_vars, l_vars):
+        with open(fpath) as f:
+            code = compile(f.read(), os.path.basename(fpath), 'exec')
+            exec(code, g_vars, l_vars)

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/cv2/mat_wrapper/__init__.py

@@ -0,0 +1,33 @@
+__all__ = []
+
+import sys
+import numpy as np
+import cv2 as cv
+
+# NumPy documentation: https://numpy.org/doc/stable/user/basics.subclassing.html
+
+class Mat(np.ndarray):
+    '''
+    cv.Mat wrapper for numpy array.
+
+    Stores extra metadata information how to interpret and process of numpy array for underlying C++ code.
+    '''
+
+    def __new__(cls, arr, **kwargs):
+        obj = arr.view(Mat)
+        return obj
+
+    def __init__(self, arr, **kwargs):
+        self.wrap_channels = kwargs.pop('wrap_channels', getattr(arr, 'wrap_channels', False))
+        if len(kwargs) > 0:
+            raise TypeError('Unknown parameters: {}'.format(repr(kwargs)))
+
+    def __array_finalize__(self, obj):
+        if obj is None:
+            return
+        self.wrap_channels = getattr(obj, 'wrap_channels', None)
+
+
+Mat.__module__ = cv.__name__
+cv.Mat = Mat
+cv._registerMatType(Mat)

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/cv2/misc/__init__.py

@@ -0,0 +1 @@
+from .version import get_ocv_version

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/cv2/misc/version.py

@@ -0,0 +1,5 @@
+import cv2
+
+
+def get_ocv_version():
+    return getattr(cv2, "__version__", "unavailable")

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/cv2/utils/__init__.py

@@ -0,0 +1,14 @@
+from collections import namedtuple
+
+import cv2
+
+
+NativeMethodPatchedResult = namedtuple("NativeMethodPatchedResult",
+                                       ("py", "native"))
+
+
+def testOverwriteNativeMethod(arg):
+    return NativeMethodPatchedResult(
+        arg + 1,
+        cv2.utils._native.testOverwriteNativeMethod(arg)
+    )

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/cv2/version.py

@@ -0,0 +1,4 @@
+opencv_version = "4.6.0.66"
+contrib = False
+headless = False
+ci_build = True

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy-1.21.6.dist-info/INSTALLER

@@ -0,0 +1 @@
+pip

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy-1.21.6.dist-info/LICENSE.txt

@@ -0,0 +1,968 @@
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Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy-1.21.6.dist-info/LICENSES_bundled.txt

@@ -0,0 +1,22 @@
+The NumPy repository and source distributions bundle several libraries that are
+compatibly licensed.  We list these here.
+
+Name: lapack-lite
+Files: numpy/linalg/lapack_lite/*
+License: BSD-3-Clause
+  For details, see numpy/linalg/lapack_lite/LICENSE.txt
+
+Name: tempita
+Files: tools/npy_tempita/*
+License: MIT
+  For details, see tools/npy_tempita/license.txt
+
+Name: dragon4
+Files: numpy/core/src/multiarray/dragon4.c
+License: MIT
+  For license text, see numpy/core/src/multiarray/dragon4.c
+
+Name: libdivide
+Files: numpy/core/include/numpy/libdivide/*
+License: Zlib
+  For license text, see numpy/core/include/numpy/libdivide/LICENSE.txt

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy-1.21.6.dist-info/METADATA

@@ -0,0 +1,59 @@
+Metadata-Version: 2.1
+Name: numpy
+Version: 1.21.6
+Summary: NumPy is the fundamental package for array computing with Python.
+Home-page: https://www.numpy.org
+Author: Travis E. Oliphant et al.
+Maintainer: NumPy Developers
+Maintainer-email: numpy-discussion@python.org
+License: BSD
+Download-URL: https://pypi.python.org/pypi/numpy
+Project-URL: Bug Tracker, https://github.com/numpy/numpy/issues
+Project-URL: Documentation, https://numpy.org/doc/1.21
+Project-URL: Source Code, https://github.com/numpy/numpy
+Platform: Windows
+Platform: Linux
+Platform: Solaris
+Platform: Mac OS-X
+Platform: Unix
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Science/Research
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: BSD License
+Classifier: Programming Language :: C
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Programming Language :: Python :: Implementation :: CPython
+Classifier: Topic :: Software Development
+Classifier: Topic :: Scientific/Engineering
+Classifier: Typing :: Typed
+Classifier: Operating System :: Microsoft :: Windows
+Classifier: Operating System :: POSIX
+Classifier: Operating System :: Unix
+Classifier: Operating System :: MacOS
+Requires-Python: >=3.7,<3.11
+License-File: LICENSE.txt
+License-File: LICENSES_bundled.txt
+
+It provides:
+
+- a powerful N-dimensional array object
+- sophisticated (broadcasting) functions
+- tools for integrating C/C++ and Fortran code
+- useful linear algebra, Fourier transform, and random number capabilities
+- and much more
+
+Besides its obvious scientific uses, NumPy can also be used as an efficient
+multi-dimensional container of generic data. Arbitrary data-types can be
+defined. This allows NumPy to seamlessly and speedily integrate with a wide
+variety of databases.
+
+All NumPy wheels distributed on PyPI are BSD licensed.
+
+
+

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy-1.21.6.dist-info/WHEEL

@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: bdist_wheel (0.37.0)
+Root-Is-Purelib: false
+Tag: cp37-cp37m-win_amd64
+

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy-1.21.6.dist-info/entry_points.txt

@@ -0,0 +1,3 @@
+[console_scripts]
+f2py = numpy.f2py.f2py2e:main
+

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy-1.21.6.dist-info/top_level.txt

@@ -0,0 +1 @@
+numpy

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/LICENSE.txt

@@ -0,0 +1,968 @@
+Copyright (c) 2005-2021, NumPy Developers.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
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+       from this software without specific prior written permission.
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+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+----
+
+This binary distribution of NumPy also bundles the following software:
+
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+Files: extra-dll\libopenb*.dll
+Description: bundled as a dynamically linked library
+Availability: https://github.com/xianyi/OpenBLAS/
+License: 3-clause BSD
+  Copyright (c) 2011-2014, The OpenBLAS Project
+  All rights reserved.
+
+  Redistribution and use in source and binary forms, with or without
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+  met:
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+
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+Name: LAPACK
+Files: extra-dll\libopenb*.dll
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+Availability: https://github.com/xianyi/OpenBLAS/
+License 3-clause BSD
+  Copyright (c) 1992-2013 The University of Tennessee and The University
+                          of Tennessee Research Foundation.  All rights
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+Files: extra-dll\*.dll
+Description: statically linked, in DLL files compiled with gfortran only
+Availability: https://gcc.gnu.org/viewcvs/gcc/
+License: GPLv3 + runtime exception
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+  Libgfortran is distributed in the hope that it will be useful,
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+  Under Section 7 of GPL version 3, you are granted additional
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+  You should have received a copy of the GNU General Public License and
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+Files: extra-dll\msvcp140.dll
+License: MSVC
+  https://www.visualstudio.com/license-terms/distributable-code-microsoft-visual-studio-2015-rc-microsoft-visual-studio-2015-sdk-rc-includes-utilities-buildserver-files/#visual-c-runtime
+
+  Subject to the License Terms for the software, you may copy and
+  distribute with your program any of the files within the followng
+  folder and its subfolders except as noted below. You may not modify
+  these files.
+
+    C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\redist
+
+  You may not distribute the contents of the following folders:
+
+    C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\redist\debug_nonredist
+    C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\redist\onecore\debug_nonredist
+
+  Subject to the License Terms for the software, you may copy and
+  distribute the following files with your program in your program’s
+  application local folder or by deploying them into the Global
+  Assembly Cache (GAC):
+
+  VC\atlmfc\lib\mfcmifc80.dll
+  VC\atlmfc\lib\amd64\mfcmifc80.dll
+
+
+Name: Microsoft Visual C++ Runtime Files
+Files: extra-dll\msvc*90.dll, extra-dll\Microsoft.VC90.CRT.manifest
+License: MSVC
+  For your convenience, we have provided the following folders for
+  use when redistributing VC++ runtime files. Subject to the license
+  terms for the software, you may redistribute the folder
+  (unmodified) in the application local folder as a sub-folder with
+  no change to the folder name. You may also redistribute all the
+  files (*.dll and *.manifest) within a folder, listed below the
+  folder for your convenience, as an entire set.
+
+  \VC\redist\x86\Microsoft.VC90.ATL\
+   atl90.dll
+   Microsoft.VC90.ATL.manifest
+  \VC\redist\ia64\Microsoft.VC90.ATL\
+   atl90.dll
+   Microsoft.VC90.ATL.manifest
+  \VC\redist\amd64\Microsoft.VC90.ATL\
+   atl90.dll
+   Microsoft.VC90.ATL.manifest
+  \VC\redist\x86\Microsoft.VC90.CRT\
+   msvcm90.dll
+   msvcp90.dll
+   msvcr90.dll
+   Microsoft.VC90.CRT.manifest
+  \VC\redist\ia64\Microsoft.VC90.CRT\
+   msvcm90.dll
+   msvcp90.dll
+   msvcr90.dll
+   Microsoft.VC90.CRT.manifest
+
+----
+
+Full text of license texts referred to above follows (that they are
+listed below does not necessarily imply the conditions apply to the
+present binary release):
+
+----
+
+GCC RUNTIME LIBRARY EXCEPTION
+
+Version 3.1, 31 March 2009
+
+Copyright (C) 2009 Free Software Foundation, Inc. <http://fsf.org/>
+
+Everyone is permitted to copy and distribute verbatim copies of this
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+
+This GCC Runtime Library Exception ("Exception") is an additional
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+
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+
+The availability of this Exception does not imply any general
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+
+----
+
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
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+patent sublicenses in a manner consistent with the requirements of
+this License.
+
+  Each contributor grants you a non-exclusive, worldwide, royalty-free
+patent license under the contributor's essential patent claims, to
+make, use, sell, offer for sale, import and otherwise run, modify and
+propagate the contents of its contributor version.
+
+  In the following three paragraphs, a "patent license" is any express
+agreement or commitment, however denominated, not to enforce a patent
+(such as an express permission to practice a patent or covenant not to
+sue for patent infringement).  To "grant" such a patent license to a
+party means to make such an agreement or commitment not to enforce a
+patent against the party.
+
+  If you convey a covered work, knowingly relying on a patent license,
+and the Corresponding Source of the work is not available for anyone
+to copy, free of charge and under the terms of this License, through a
+publicly available network server or other readily accessible means,
+then you must either (1) cause the Corresponding Source to be so
+available, or (2) arrange to deprive yourself of the benefit of the
+patent license for this particular work, or (3) arrange, in a manner
+consistent with the requirements of this License, to extend the patent
+license to downstream recipients.  "Knowingly relying" means you have
+actual knowledge that, but for the patent license, your conveying the
+covered work in a country, or your recipient's use of the covered work
+in a country, would infringe one or more identifiable patents in that
+country that you have reason to believe are valid.
+
+  If, pursuant to or in connection with a single transaction or
+arrangement, you convey, or propagate by procuring conveyance of, a
+covered work, and grant a patent license to some of the parties
+receiving the covered work authorizing them to use, propagate, modify
+or convey a specific copy of the covered work, then the patent license
+you grant is automatically extended to all recipients of the covered
+work and works based on it.
+
+  A patent license is "discriminatory" if it does not include within
+the scope of its coverage, prohibits the exercise of, or is
+conditioned on the non-exercise of one or more of the rights that are
+specifically granted under this License.  You may not convey a covered
+work if you are a party to an arrangement with a third party that is
+in the business of distributing software, under which you make payment
+to the third party based on the extent of your activity of conveying
+the work, and under which the third party grants, to any of the
+parties who would receive the covered work from you, a discriminatory
+patent license (a) in connection with copies of the covered work
+conveyed by you (or copies made from those copies), or (b) primarily
+for and in connection with specific products or compilations that
+contain the covered work, unless you entered into that arrangement,
+or that patent license was granted, prior to 28 March 2007.
+
+  Nothing in this License shall be construed as excluding or limiting
+any implied license or other defenses to infringement that may
+otherwise be available to you under applicable patent law.
+
+  12. No Surrender of Others' Freedom.
+
+  If conditions are imposed on you (whether by court order, agreement or
+otherwise) that contradict the conditions of this License, they do not
+excuse you from the conditions of this License.  If you cannot convey a
+covered work so as to satisfy simultaneously your obligations under this
+License and any other pertinent obligations, then as a consequence you may
+not convey it at all.  For example, if you agree to terms that obligate you
+to collect a royalty for further conveying from those to whom you convey
+the Program, the only way you could satisfy both those terms and this
+License would be to refrain entirely from conveying the Program.
+
+  13. Use with the GNU Affero General Public License.
+
+  Notwithstanding any other provision of this License, you have
+permission to link or combine any covered work with a work licensed
+under version 3 of the GNU Affero General Public License into a single
+combined work, and to convey the resulting work.  The terms of this
+License will continue to apply to the part which is the covered work,
+but the special requirements of the GNU Affero General Public License,
+section 13, concerning interaction through a network will apply to the
+combination as such.
+
+  14. Revised Versions of this License.
+
+  The Free Software Foundation may publish revised and/or new versions of
+the GNU General Public License from time to time.  Such new versions will
+be similar in spirit to the present version, but may differ in detail to
+address new problems or concerns.
+
+  Each version is given a distinguishing version number.  If the
+Program specifies that a certain numbered version of the GNU General
+Public License "or any later version" applies to it, you have the
+option of following the terms and conditions either of that numbered
+version or of any later version published by the Free Software
+Foundation.  If the Program does not specify a version number of the
+GNU General Public License, you may choose any version ever published
+by the Free Software Foundation.
+
+  If the Program specifies that a proxy can decide which future
+versions of the GNU General Public License can be used, that proxy's
+public statement of acceptance of a version permanently authorizes you
+to choose that version for the Program.
+
+  Later license versions may give you additional or different
+permissions.  However, no additional obligations are imposed on any
+author or copyright holder as a result of your choosing to follow a
+later version.
+
+  15. Disclaimer of Warranty.
+
+  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
+APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
+HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
+OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
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+IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
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+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<http://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<http://www.gnu.org/philosophy/why-not-lgpl.html>.

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/__config__.py

@@ -0,0 +1,98 @@
+# This file is generated by numpy's setup.py
+# It contains system_info results at the time of building this package.
+__all__ = ["get_info","show"]
+
+
+import os
+import sys
+
+extra_dll_dir = os.path.join(os.path.dirname(__file__), '.libs')
+
+if sys.platform == 'win32' and os.path.isdir(extra_dll_dir):
+    if sys.version_info >= (3, 8):
+        os.add_dll_directory(extra_dll_dir)
+    else:
+        os.environ.setdefault('PATH', '')
+        os.environ['PATH'] += os.pathsep + extra_dll_dir
+
+blas_mkl_info={}
+blis_info={}
+openblas_info={'library_dirs': ['D:\\a\\1\\s\\numpy\\build\\openblas_info'], 'libraries': ['openblas_info'], 'language': 'f77', 'define_macros': [('HAVE_CBLAS', None)]}
+blas_opt_info={'library_dirs': ['D:\\a\\1\\s\\numpy\\build\\openblas_info'], 'libraries': ['openblas_info'], 'language': 'f77', 'define_macros': [('HAVE_CBLAS', None)]}
+lapack_mkl_info={}
+openblas_lapack_info={'library_dirs': ['D:\\a\\1\\s\\numpy\\build\\openblas_lapack_info'], 'libraries': ['openblas_lapack_info'], 'language': 'f77', 'define_macros': [('HAVE_CBLAS', None)]}
+lapack_opt_info={'library_dirs': ['D:\\a\\1\\s\\numpy\\build\\openblas_lapack_info'], 'libraries': ['openblas_lapack_info'], 'language': 'f77', 'define_macros': [('HAVE_CBLAS', None)]}
+
+def get_info(name):
+    g = globals()
+    return g.get(name, g.get(name + "_info", {}))
+
+def show():
+    """
+    Show libraries in the system on which NumPy was built.
+
+    Print information about various resources (libraries, library
+    directories, include directories, etc.) in the system on which
+    NumPy was built.
+
+    See Also
+    --------
+    get_include : Returns the directory containing NumPy C
+                  header files.
+
+    Notes
+    -----
+    Classes specifying the information to be printed are defined
+    in the `numpy.distutils.system_info` module.
+
+    Information may include:
+
+    * ``language``: language used to write the libraries (mostly
+      C or f77)
+    * ``libraries``: names of libraries found in the system
+    * ``library_dirs``: directories containing the libraries
+    * ``include_dirs``: directories containing library header files
+    * ``src_dirs``: directories containing library source files
+    * ``define_macros``: preprocessor macros used by
+      ``distutils.setup``
+    * ``baseline``: minimum CPU features required
+    * ``found``: dispatched features supported in the system
+    * ``not found``: dispatched features that are not supported
+      in the system
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.show_config()
+    blas_opt_info:
+        language = c
+        define_macros = [('HAVE_CBLAS', None)]
+        libraries = ['openblas', 'openblas']
+        library_dirs = ['/usr/local/lib']
+    """
+    from numpy.core._multiarray_umath import (
+        __cpu_features__, __cpu_baseline__, __cpu_dispatch__
+    )
+    for name,info_dict in globals().items():
+        if name[0] == "_" or type(info_dict) is not type({}): continue
+        print(name + ":")
+        if not info_dict:
+            print("  NOT AVAILABLE")
+        for k,v in info_dict.items():
+            v = str(v)
+            if k == "sources" and len(v) > 200:
+                v = v[:60] + " ...\n... " + v[-60:]
+            print("    %s = %s" % (k,v))
+
+    features_found, features_not_found = [], []
+    for feature in __cpu_dispatch__:
+        if __cpu_features__[feature]:
+            features_found.append(feature)
+        else:
+            features_not_found.append(feature)
+
+    print("Supported SIMD extensions in this NumPy install:")
+    print("    baseline = %s" % (','.join(__cpu_baseline__)))
+    print("    found = %s" % (','.join(features_found)))
+    print("    not found = %s" % (','.join(features_not_found)))
+

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/__init__.py

@@ -0,0 +1,429 @@
+"""
+NumPy
+=====
+
+Provides
+  1. An array object of arbitrary homogeneous items
+  2. Fast mathematical operations over arrays
+  3. Linear Algebra, Fourier Transforms, Random Number Generation
+
+How to use the documentation
+----------------------------
+Documentation is available in two forms: docstrings provided
+with the code, and a loose standing reference guide, available from
+`the NumPy homepage <https://www.scipy.org>`_.
+
+We recommend exploring the docstrings using
+`IPython <https://ipython.org>`_, an advanced Python shell with
+TAB-completion and introspection capabilities.  See below for further
+instructions.
+
+The docstring examples assume that `numpy` has been imported as `np`::
+
+  >>> import numpy as np
+
+Code snippets are indicated by three greater-than signs::
+
+  >>> x = 42
+  >>> x = x + 1
+
+Use the built-in ``help`` function to view a function's docstring::
+
+  >>> help(np.sort)
+  ... # doctest: +SKIP
+
+For some objects, ``np.info(obj)`` may provide additional help.  This is
+particularly true if you see the line "Help on ufunc object:" at the top
+of the help() page.  Ufuncs are implemented in C, not Python, for speed.
+The native Python help() does not know how to view their help, but our
+np.info() function does.
+
+To search for documents containing a keyword, do::
+
+  >>> np.lookfor('keyword')
+  ... # doctest: +SKIP
+
+General-purpose documents like a glossary and help on the basic concepts
+of numpy are available under the ``doc`` sub-module::
+
+  >>> from numpy import doc
+  >>> help(doc)
+  ... # doctest: +SKIP
+
+Available subpackages
+---------------------
+doc
+    Topical documentation on broadcasting, indexing, etc.
+lib
+    Basic functions used by several sub-packages.
+random
+    Core Random Tools
+linalg
+    Core Linear Algebra Tools
+fft
+    Core FFT routines
+polynomial
+    Polynomial tools
+testing
+    NumPy testing tools
+f2py
+    Fortran to Python Interface Generator.
+distutils
+    Enhancements to distutils with support for
+    Fortran compilers support and more.
+
+Utilities
+---------
+test
+    Run numpy unittests
+show_config
+    Show numpy build configuration
+dual
+    Overwrite certain functions with high-performance SciPy tools.
+    Note: `numpy.dual` is deprecated.  Use the functions from NumPy or Scipy
+    directly instead of importing them from `numpy.dual`.
+matlib
+    Make everything matrices.
+__version__
+    NumPy version string
+
+Viewing documentation using IPython
+-----------------------------------
+Start IPython with the NumPy profile (``ipython -p numpy``), which will
+import `numpy` under the alias `np`.  Then, use the ``cpaste`` command to
+paste examples into the shell.  To see which functions are available in
+`numpy`, type ``np.<TAB>`` (where ``<TAB>`` refers to the TAB key), or use
+``np.*cos*?<ENTER>`` (where ``<ENTER>`` refers to the ENTER key) to narrow
+down the list.  To view the docstring for a function, use
+``np.cos?<ENTER>`` (to view the docstring) and ``np.cos??<ENTER>`` (to view
+the source code).
+
+Copies vs. in-place operation
+-----------------------------
+Most of the functions in `numpy` return a copy of the array argument
+(e.g., `np.sort`).  In-place versions of these functions are often
+available as array methods, i.e. ``x = np.array([1,2,3]); x.sort()``.
+Exceptions to this rule are documented.
+
+"""
+import sys
+import warnings
+
+from ._globals import (
+    ModuleDeprecationWarning, VisibleDeprecationWarning, _NoValue
+)
+
+# We first need to detect if we're being called as part of the numpy setup
+# procedure itself in a reliable manner.
+try:
+    __NUMPY_SETUP__
+except NameError:
+    __NUMPY_SETUP__ = False
+
+if __NUMPY_SETUP__:
+    sys.stderr.write('Running from numpy source directory.\n')
+else:
+    try:
+        from numpy.__config__ import show as show_config
+    except ImportError as e:
+        msg = """Error importing numpy: you should not try to import numpy from
+        its source directory; please exit the numpy source tree, and relaunch
+        your python interpreter from there."""
+        raise ImportError(msg) from e
+
+    __all__ = ['ModuleDeprecationWarning',
+               'VisibleDeprecationWarning']
+
+    # get the version using versioneer
+    from ._version import get_versions
+    vinfo = get_versions()
+    __version__ = vinfo.get("closest-tag", vinfo["version"])
+    __git_version__ = vinfo.get("full-revisionid")
+    del get_versions, vinfo
+
+    # mapping of {name: (value, deprecation_msg)}
+    __deprecated_attrs__ = {}
+
+    # Allow distributors to run custom init code
+    from . import _distributor_init
+
+    from . import core
+    from .core import *
+    from . import compat
+    from . import lib
+    # NOTE: to be revisited following future namespace cleanup.
+    # See gh-14454 and gh-15672 for discussion.
+    from .lib import *
+
+    from . import linalg
+    from . import fft
+    from . import polynomial
+    from . import random
+    from . import ctypeslib
+    from . import ma
+    from . import matrixlib as _mat
+    from .matrixlib import *
+
+    # Deprecations introduced in NumPy 1.20.0, 2020-06-06
+    import builtins as _builtins
+
+    _msg = (
+        "`np.{n}` is a deprecated alias for the builtin `{n}`. "
+        "To silence this warning, use `{n}` by itself. Doing this will not "
+        "modify any behavior and is safe. {extended_msg}\n"
+        "Deprecated in NumPy 1.20; for more details and guidance: "
+        "https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations")
+
+    _specific_msg = (
+        "If you specifically wanted the numpy scalar type, use `np.{}` here.")
+
+    _int_extended_msg = (
+        "When replacing `np.{}`, you may wish to use e.g. `np.int64` "
+        "or `np.int32` to specify the precision. If you wish to review "
+        "your current use, check the release note link for "
+        "additional information.")
+
+    _type_info = [
+        ("object", ""),  # The NumPy scalar only exists by name.
+        ("bool", _specific_msg.format("bool_")),
+        ("float", _specific_msg.format("float64")),
+        ("complex", _specific_msg.format("complex128")),
+        ("str", _specific_msg.format("str_")),
+        ("int", _int_extended_msg.format("int"))]
+
+    __deprecated_attrs__.update({
+        n: (getattr(_builtins, n), _msg.format(n=n, extended_msg=extended_msg))
+        for n, extended_msg in _type_info
+    })
+    # Numpy 1.20.0, 2020-10-19
+    __deprecated_attrs__["typeDict"] = (
+        core.numerictypes.typeDict,
+        "`np.typeDict` is a deprecated alias for `np.sctypeDict`."
+    )
+
+    _msg = (
+        "`np.{n}` is a deprecated alias for `np.compat.{n}`. "
+        "To silence this warning, use `np.compat.{n}` by itself. "
+        "In the likely event your code does not need to work on Python 2 "
+        "you can use the builtin `{n2}` for which `np.compat.{n}` is itself "
+        "an alias. Doing this will not modify any behaviour and is safe. "
+        "{extended_msg}\n"
+        "Deprecated in NumPy 1.20; for more details and guidance: "
+        "https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations")
+
+    __deprecated_attrs__["long"] = (
+        getattr(compat, "long"),
+        _msg.format(n="long", n2="int",
+                    extended_msg=_int_extended_msg.format("long")))
+
+    __deprecated_attrs__["unicode"] = (
+        getattr(compat, "unicode"),
+        _msg.format(n="unicode", n2="str",
+                    extended_msg=_specific_msg.format("str_")))
+
+    del _msg, _specific_msg, _int_extended_msg, _type_info, _builtins
+
+    from .core import round, abs, max, min
+    # now that numpy modules are imported, can initialize limits
+    core.getlimits._register_known_types()
+
+    __all__.extend(['__version__', 'show_config'])
+    __all__.extend(core.__all__)
+    __all__.extend(_mat.__all__)
+    __all__.extend(lib.__all__)
+    __all__.extend(['linalg', 'fft', 'random', 'ctypeslib', 'ma'])
+
+    # These are exported by np.core, but are replaced by the builtins below
+    # remove them to ensure that we don't end up with `np.long == np.int_`,
+    # which would be a breaking change.
+    del long, unicode
+    __all__.remove('long')
+    __all__.remove('unicode')
+
+    # Remove things that are in the numpy.lib but not in the numpy namespace
+    # Note that there is a test (numpy/tests/test_public_api.py:test_numpy_namespace)
+    # that prevents adding more things to the main namespace by accident.
+    # The list below will grow until the `from .lib import *` fixme above is
+    # taken care of
+    __all__.remove('Arrayterator')
+    del Arrayterator
+
+    # These names were removed in NumPy 1.20.  For at least one release,
+    # attempts to access these names in the numpy namespace will trigger
+    # a warning, and calling the function will raise an exception.
+    _financial_names = ['fv', 'ipmt', 'irr', 'mirr', 'nper', 'npv', 'pmt',
+                        'ppmt', 'pv', 'rate']
+    __expired_functions__ = {
+        name: (f'In accordance with NEP 32, the function {name} was removed '
+               'from NumPy version 1.20.  A replacement for this function '
+               'is available in the numpy_financial library: '
+               'https://pypi.org/project/numpy-financial')
+        for name in _financial_names}
+
+    # Filter out Cython harmless warnings
+    warnings.filterwarnings("ignore", message="numpy.dtype size changed")
+    warnings.filterwarnings("ignore", message="numpy.ufunc size changed")
+    warnings.filterwarnings("ignore", message="numpy.ndarray size changed")
+
+    # oldnumeric and numarray were removed in 1.9. In case some packages import
+    # but do not use them, we define them here for backward compatibility.
+    oldnumeric = 'removed'
+    numarray = 'removed'
+
+    if sys.version_info[:2] >= (3, 7):
+        # module level getattr is only supported in 3.7 onwards
+        # https://www.python.org/dev/peps/pep-0562/
+        def __getattr__(attr):
+            # Warn for expired attributes, and return a dummy function
+            # that always raises an exception.
+            try:
+                msg = __expired_functions__[attr]
+            except KeyError:
+                pass
+            else:
+                warnings.warn(msg, DeprecationWarning, stacklevel=2)
+
+                def _expired(*args, **kwds):
+                    raise RuntimeError(msg)
+
+                return _expired
+
+            # Emit warnings for deprecated attributes
+            try:
+                val, msg = __deprecated_attrs__[attr]
+            except KeyError:
+                pass
+            else:
+                warnings.warn(msg, DeprecationWarning, stacklevel=2)
+                return val
+
+            # Importing Tester requires importing all of UnitTest which is not a
+            # cheap import Since it is mainly used in test suits, we lazy import it
+            # here to save on the order of 10 ms of import time for most users
+            #
+            # The previous way Tester was imported also had a side effect of adding
+            # the full `numpy.testing` namespace
+            if attr == 'testing':
+                import numpy.testing as testing
+                return testing
+            elif attr == 'Tester':
+                from .testing import Tester
+                return Tester
+
+            raise AttributeError("module {!r} has no attribute "
+                                 "{!r}".format(__name__, attr))
+
+        def __dir__():
+            return list(globals().keys() | {'Tester', 'testing'})
+
+    else:
+        # We don't actually use this ourselves anymore, but I'm not 100% sure that
+        # no-one else in the world is using it (though I hope not)
+        from .testing import Tester
+
+        # We weren't able to emit a warning about these, so keep them around
+        globals().update({
+            k: v
+            for k, (v, msg) in __deprecated_attrs__.items()
+        })
+
+
+    # Pytest testing
+    from numpy._pytesttester import PytestTester
+    test = PytestTester(__name__)
+    del PytestTester
+
+
+    def _sanity_check():
+        """
+        Quick sanity checks for common bugs caused by environment.
+        There are some cases e.g. with wrong BLAS ABI that cause wrong
+        results under specific runtime conditions that are not necessarily
+        achieved during test suite runs, and it is useful to catch those early.
+
+        See https://github.com/numpy/numpy/issues/8577 and other
+        similar bug reports.
+
+        """
+        try:
+            x = ones(2, dtype=float32)
+            if not abs(x.dot(x) - 2.0) < 1e-5:
+                raise AssertionError()
+        except AssertionError:
+            msg = ("The current Numpy installation ({!r}) fails to "
+                   "pass simple sanity checks. This can be caused for example "
+                   "by incorrect BLAS library being linked in, or by mixing "
+                   "package managers (pip, conda, apt, ...). Search closed "
+                   "numpy issues for similar problems.")
+            raise RuntimeError(msg.format(__file__)) from None
+
+    _sanity_check()
+    del _sanity_check
+
+    def _mac_os_check():
+        """
+        Quick Sanity check for Mac OS look for accelerate build bugs.
+        Testing numpy polyfit calls init_dgelsd(LAPACK)
+        """
+        try:
+            c = array([3., 2., 1.])
+            x = linspace(0, 2, 5)
+            y = polyval(c, x)
+            _ = polyfit(x, y, 2, cov=True)
+        except ValueError:
+            pass
+
+    import sys
+    if sys.platform == "darwin":
+        with warnings.catch_warnings(record=True) as w:
+            _mac_os_check()
+            # Throw runtime error, if the test failed Check for warning and error_message
+            error_message = ""
+            if len(w) > 0:
+                error_message = "{}: {}".format(w[-1].category.__name__, str(w[-1].message))
+                msg = (
+                    "Polyfit sanity test emitted a warning, most likely due "
+                    "to using a buggy Accelerate backend. If you compiled "
+                    "yourself, more information is available at "
+                    "https://numpy.org/doc/stable/user/building.html#accelerated-blas-lapack-libraries "
+                    "Otherwise report this to the vendor "
+                    "that provided NumPy.\n{}\n".format(error_message))
+                raise RuntimeError(msg)
+    del _mac_os_check
+
+    # We usually use madvise hugepages support, but on some old kernels it
+    # is slow and thus better avoided.
+    # Specifically kernel version 4.6 had a bug fix which probably fixed this:
+    # https://github.com/torvalds/linux/commit/7cf91a98e607c2f935dbcc177d70011e95b8faff
+    import os
+    use_hugepage = os.environ.get("NUMPY_MADVISE_HUGEPAGE", None)
+    if sys.platform == "linux" and use_hugepage is None:
+        # If there is an issue with parsing the kernel version,
+        # set use_hugepages to 0. Usage of LooseVersion will handle
+        # the kernel version parsing better, but avoided since it
+        # will increase the import time. See: #16679 for related discussion.
+        try:
+            use_hugepage = 1
+            kernel_version = os.uname().release.split(".")[:2]
+            kernel_version = tuple(int(v) for v in kernel_version)
+            if kernel_version < (4, 6):
+                use_hugepage = 0
+        except ValueError:
+            use_hugepages = 0
+    elif use_hugepage is None:
+        # This is not Linux, so it should not matter, just enable anyway
+        use_hugepage = 1
+    else:
+        use_hugepage = int(use_hugepage)
+
+    # Note that this will currently only make a difference on Linux
+    core.multiarray._set_madvise_hugepage(use_hugepage)
+
+    # Give a warning if NumPy is reloaded or imported on a sub-interpreter
+    # We do this from python, since the C-module may not be reloaded and
+    # it is tidier organized.
+    core.multiarray._multiarray_umath._reload_guard()
+
+from ._version import get_versions
+__version__ = get_versions()['version']
+del get_versions

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/_distributor_init.py

@@ -0,0 +1,32 @@
+
+'''
+Helper to preload windows dlls to prevent dll not found errors.
+Once a DLL is preloaded, its namespace is made available to any
+subsequent DLL. This file originated in the numpy-wheels repo,
+and is created as part of the scripts that build the wheel.
+'''
+import os
+import glob
+if os.name == 'nt':
+    # convention for storing / loading the DLL from
+    # numpy/.libs/, if present
+    try:
+        from ctypes import WinDLL
+        basedir = os.path.dirname(__file__)
+    except:
+        pass
+    else:
+        libs_dir = os.path.abspath(os.path.join(basedir, '.libs'))
+        DLL_filenames = []
+        if os.path.isdir(libs_dir):
+            for filename in glob.glob(os.path.join(libs_dir,
+                                                   '*openblas*dll')):
+                # NOTE: would it change behavior to load ALL
+                # DLLs at this path vs. the name restriction?
+                WinDLL(os.path.abspath(filename))
+                DLL_filenames.append(filename)
+        if len(DLL_filenames) > 1:
+            import warnings
+            warnings.warn("loaded more than 1 DLL from .libs:"
+                          "\n%s" % "\n".join(DLL_filenames),
+                          stacklevel=1)

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/_globals.py

@@ -0,0 +1,91 @@
+"""
+Module defining global singleton classes.
+
+This module raises a RuntimeError if an attempt to reload it is made. In that
+way the identities of the classes defined here are fixed and will remain so
+even if numpy itself is reloaded. In particular, a function like the following
+will still work correctly after numpy is reloaded::
+
+    def foo(arg=np._NoValue):
+        if arg is np._NoValue:
+            ...
+
+That was not the case when the singleton classes were defined in the numpy
+``__init__.py`` file. See gh-7844 for a discussion of the reload problem that
+motivated this module.
+
+"""
+__ALL__ = [
+    'ModuleDeprecationWarning', 'VisibleDeprecationWarning', '_NoValue'
+    ]
+
+
+# Disallow reloading this module so as to preserve the identities of the
+# classes defined here.
+if '_is_loaded' in globals():
+    raise RuntimeError('Reloading numpy._globals is not allowed')
+_is_loaded = True
+
+
+class ModuleDeprecationWarning(DeprecationWarning):
+    """Module deprecation warning.
+
+    The nose tester turns ordinary Deprecation warnings into test failures.
+    That makes it hard to deprecate whole modules, because they get
+    imported by default. So this is a special Deprecation warning that the
+    nose tester will let pass without making tests fail.
+
+    """
+
+
+ModuleDeprecationWarning.__module__ = 'numpy'
+
+
+class VisibleDeprecationWarning(UserWarning):
+    """Visible deprecation warning.
+
+    By default, python will not show deprecation warnings, so this class
+    can be used when a very visible warning is helpful, for example because
+    the usage is most likely a user bug.
+
+    """
+
+
+VisibleDeprecationWarning.__module__ = 'numpy'
+
+
+class _NoValueType:
+    """Special keyword value.
+
+    The instance of this class may be used as the default value assigned to a
+    keyword if no other obvious default (e.g., `None`) is suitable,
+
+    Common reasons for using this keyword are:
+
+    - A new keyword is added to a function, and that function forwards its
+      inputs to another function or method which can be defined outside of
+      NumPy. For example, ``np.std(x)`` calls ``x.std``, so when a ``keepdims``
+      keyword was added that could only be forwarded if the user explicitly
+      specified ``keepdims``; downstream array libraries may not have added
+      the same keyword, so adding ``x.std(..., keepdims=keepdims)``
+      unconditionally could have broken previously working code.
+    - A keyword is being deprecated, and a deprecation warning must only be
+      emitted when the keyword is used.
+
+    """
+    __instance = None
+    def __new__(cls):
+        # ensure that only one instance exists
+        if not cls.__instance:
+            cls.__instance = super().__new__(cls)
+        return cls.__instance
+
+    # needed for python 2 to preserve identity through a pickle
+    def __reduce__(self):
+        return (self.__class__, ())
+
+    def __repr__(self):
+        return "<no value>"
+
+
+_NoValue = _NoValueType()

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/_pytesttester.py

@@ -0,0 +1,201 @@
+"""
+Pytest test running.
+
+This module implements the ``test()`` function for NumPy modules. The usual
+boiler plate for doing that is to put the following in the module
+``__init__.py`` file::
+
+    from numpy._pytesttester import PytestTester
+    test = PytestTester(__name__)
+    del PytestTester
+
+
+Warnings filtering and other runtime settings should be dealt with in the
+``pytest.ini`` file in the numpy repo root. The behavior of the test depends on
+whether or not that file is found as follows:
+
+* ``pytest.ini`` is present (develop mode)
+    All warnings except those explicitly filtered out are raised as error.
+* ``pytest.ini`` is absent (release mode)
+    DeprecationWarnings and PendingDeprecationWarnings are ignored, other
+    warnings are passed through.
+
+In practice, tests run from the numpy repo are run in develop mode. That
+includes the standard ``python runtests.py`` invocation.
+
+This module is imported by every numpy subpackage, so lies at the top level to
+simplify circular import issues. For the same reason, it contains no numpy
+imports at module scope, instead importing numpy within function calls.
+"""
+import sys
+import os
+
+__all__ = ['PytestTester']
+
+
+
+def _show_numpy_info():
+    import numpy as np
+
+    print("NumPy version %s" % np.__version__)
+    relaxed_strides = np.ones((10, 1), order="C").flags.f_contiguous
+    print("NumPy relaxed strides checking option:", relaxed_strides)
+    info = np.lib.utils._opt_info()
+    print("NumPy CPU features: ", (info if info else 'nothing enabled'))
+
+
+
+class PytestTester:
+    """
+    Pytest test runner.
+
+    A test function is typically added to a package's __init__.py like so::
+
+      from numpy._pytesttester import PytestTester
+      test = PytestTester(__name__).test
+      del PytestTester
+
+    Calling this test function finds and runs all tests associated with the
+    module and all its sub-modules.
+
+    Attributes
+    ----------
+    module_name : str
+        Full path to the package to test.
+
+    Parameters
+    ----------
+    module_name : module name
+        The name of the module to test.
+
+    Notes
+    -----
+    Unlike the previous ``nose``-based implementation, this class is not
+    publicly exposed as it performs some ``numpy``-specific warning
+    suppression.
+
+    """
+    def __init__(self, module_name):
+        self.module_name = module_name
+
+    def __call__(self, label='fast', verbose=1, extra_argv=None,
+                 doctests=False, coverage=False, durations=-1, tests=None):
+        """
+        Run tests for module using pytest.
+
+        Parameters
+        ----------
+        label : {'fast', 'full'}, optional
+            Identifies the tests to run. When set to 'fast', tests decorated
+            with `pytest.mark.slow` are skipped, when 'full', the slow marker
+            is ignored.
+        verbose : int, optional
+            Verbosity value for test outputs, in the range 1-3. Default is 1.
+        extra_argv : list, optional
+            List with any extra arguments to pass to pytests.
+        doctests : bool, optional
+            .. note:: Not supported
+        coverage : bool, optional
+            If True, report coverage of NumPy code. Default is False.
+            Requires installation of (pip) pytest-cov.
+        durations : int, optional
+            If < 0, do nothing, If 0, report time of all tests, if > 0,
+            report the time of the slowest `timer` tests. Default is -1.
+        tests : test or list of tests
+            Tests to be executed with pytest '--pyargs'
+
+        Returns
+        -------
+        result : bool
+            Return True on success, false otherwise.
+
+        Notes
+        -----
+        Each NumPy module exposes `test` in its namespace to run all tests for
+        it. For example, to run all tests for numpy.lib:
+
+        >>> np.lib.test() #doctest: +SKIP
+
+        Examples
+        --------
+        >>> result = np.lib.test() #doctest: +SKIP
+        ...
+        1023 passed, 2 skipped, 6 deselected, 1 xfailed in 10.39 seconds
+        >>> result
+        True
+
+        """
+        import pytest
+        import warnings
+
+        module = sys.modules[self.module_name]
+        module_path = os.path.abspath(module.__path__[0])
+
+        # setup the pytest arguments
+        pytest_args = ["-l"]
+
+        # offset verbosity. The "-q" cancels a "-v".
+        pytest_args += ["-q"]
+
+        # Filter out distutils cpu warnings (could be localized to
+        # distutils tests). ASV has problems with top level import,
+        # so fetch module for suppression here.
+        with warnings.catch_warnings():
+            warnings.simplefilter("always")
+            from numpy.distutils import cpuinfo
+
+        # Filter out annoying import messages. Want these in both develop and
+        # release mode.
+        pytest_args += [
+            "-W ignore:Not importing directory",
+            "-W ignore:numpy.dtype size changed",
+            "-W ignore:numpy.ufunc size changed",
+            "-W ignore::UserWarning:cpuinfo",
+            ]
+
+        # When testing matrices, ignore their PendingDeprecationWarnings
+        pytest_args += [
+            "-W ignore:the matrix subclass is not",
+            "-W ignore:Importing from numpy.matlib is",
+            ]
+
+        if doctests:
+            raise ValueError("Doctests not supported")
+
+        if extra_argv:
+            pytest_args += list(extra_argv)
+
+        if verbose > 1:
+            pytest_args += ["-" + "v"*(verbose - 1)]
+
+        if coverage:
+            pytest_args += ["--cov=" + module_path]
+
+        if label == "fast":
+            # not importing at the top level to avoid circular import of module
+            from numpy.testing import IS_PYPY
+            if IS_PYPY:
+                pytest_args += ["-m", "not slow and not slow_pypy"]
+            else:
+                pytest_args += ["-m", "not slow"]
+
+        elif label != "full":
+            pytest_args += ["-m", label]
+
+        if durations >= 0:
+            pytest_args += ["--durations=%s" % durations]
+
+        if tests is None:
+            tests = [self.module_name]
+
+        pytest_args += ["--pyargs"] + list(tests)
+
+        # run tests.
+        _show_numpy_info()
+
+        try:
+            code = pytest.main(pytest_args)
+        except SystemExit as exc:
+            code = exc.code
+
+        return code == 0

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/_version.py

@@ -0,0 +1,21 @@
+
+# This file was generated by 'versioneer.py' (0.19) from
+# revision-control system data, or from the parent directory name of an
+# unpacked source archive. Distribution tarballs contain a pre-generated copy
+# of this file.
+
+import json
+
+version_json = '''
+{
+ "date": "2022-04-11T17:43:10-0600",
+ "dirty": false,
+ "error": null,
+ "full-revisionid": "ef0ec786fd4c7622ad2fa0e54d3881f3b9bbd792",
+ "version": "1.21.6"
+}
+'''  # END VERSION_JSON
+
+
+def get_versions():
+    return json.loads(version_json)

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/char.pyi

@@ -0,0 +1,59 @@
+from typing import Any, List
+
+from numpy import (
+    chararray as chararray,
+)
+
+__all__: List[str]
+
+def equal(x1, x2): ...
+def not_equal(x1, x2): ...
+def greater_equal(x1, x2): ...
+def less_equal(x1, x2): ...
+def greater(x1, x2): ...
+def less(x1, x2): ...
+def str_len(a): ...
+def add(x1, x2): ...
+def multiply(a, i): ...
+def mod(a, values): ...
+def capitalize(a): ...
+def center(a, width, fillchar=...): ...
+def count(a, sub, start=..., end=...): ...
+def decode(a, encoding=..., errors=...): ...
+def encode(a, encoding=..., errors=...): ...
+def endswith(a, suffix, start=..., end=...): ...
+def expandtabs(a, tabsize=...): ...
+def find(a, sub, start=..., end=...): ...
+def index(a, sub, start=..., end=...): ...
+def isalnum(a): ...
+def isalpha(a): ...
+def isdigit(a): ...
+def islower(a): ...
+def isspace(a): ...
+def istitle(a): ...
+def isupper(a): ...
+def join(sep, seq): ...
+def ljust(a, width, fillchar=...): ...
+def lower(a): ...
+def lstrip(a, chars=...): ...
+def partition(a, sep): ...
+def replace(a, old, new, count=...): ...
+def rfind(a, sub, start=..., end=...): ...
+def rindex(a, sub, start=..., end=...): ...
+def rjust(a, width, fillchar=...): ...
+def rpartition(a, sep): ...
+def rsplit(a, sep=..., maxsplit=...): ...
+def rstrip(a, chars=...): ...
+def split(a, sep=..., maxsplit=...): ...
+def splitlines(a, keepends=...): ...
+def startswith(a, prefix, start=..., end=...): ...
+def strip(a, chars=...): ...
+def swapcase(a): ...
+def title(a): ...
+def translate(a, table, deletechars=...): ...
+def upper(a): ...
+def zfill(a, width): ...
+def isnumeric(a): ...
+def isdecimal(a): ...
+def array(obj, itemsize=..., copy=..., unicode=..., order=...): ...
+def asarray(obj, itemsize=..., unicode=..., order=...): ...

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/compat/__init__.py

@@ -0,0 +1,18 @@
+"""
+Compatibility module.
+
+This module contains duplicated code from Python itself or 3rd party
+extensions, which may be included for the following reasons:
+
+  * compatibility
+  * we may only need a small subset of the copied library/module
+
+"""
+from . import _inspect
+from . import py3k
+from ._inspect import getargspec, formatargspec
+from .py3k import *
+
+__all__ = []
+__all__.extend(_inspect.__all__)
+__all__.extend(py3k.__all__)

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/compat/_inspect.py

@@ -0,0 +1,191 @@
+"""Subset of inspect module from upstream python
+
+We use this instead of upstream because upstream inspect is slow to import, and
+significantly contributes to numpy import times. Importing this copy has almost
+no overhead.
+
+"""
+import types
+
+__all__ = ['getargspec', 'formatargspec']
+
+# ----------------------------------------------------------- type-checking
+def ismethod(object):
+    """Return true if the object is an instance method.
+
+    Instance method objects provide these attributes:
+        __doc__         documentation string
+        __name__        name with which this method was defined
+        im_class        class object in which this method belongs
+        im_func         function object containing implementation of method
+        im_self         instance to which this method is bound, or None
+
+    """
+    return isinstance(object, types.MethodType)
+
+def isfunction(object):
+    """Return true if the object is a user-defined function.
+
+    Function objects provide these attributes:
+        __doc__         documentation string
+        __name__        name with which this function was defined
+        func_code       code object containing compiled function bytecode
+        func_defaults   tuple of any default values for arguments
+        func_doc        (same as __doc__)
+        func_globals    global namespace in which this function was defined
+        func_name       (same as __name__)
+
+    """
+    return isinstance(object, types.FunctionType)
+
+def iscode(object):
+    """Return true if the object is a code object.
+
+    Code objects provide these attributes:
+        co_argcount     number of arguments (not including * or ** args)
+        co_code         string of raw compiled bytecode
+        co_consts       tuple of constants used in the bytecode
+        co_filename     name of file in which this code object was created
+        co_firstlineno  number of first line in Python source code
+        co_flags        bitmap: 1=optimized | 2=newlocals | 4=*arg | 8=**arg
+        co_lnotab       encoded mapping of line numbers to bytecode indices
+        co_name         name with which this code object was defined
+        co_names        tuple of names of local variables
+        co_nlocals      number of local variables
+        co_stacksize    virtual machine stack space required
+        co_varnames     tuple of names of arguments and local variables
+        
+    """
+    return isinstance(object, types.CodeType)
+
+# ------------------------------------------------ argument list extraction
+# These constants are from Python's compile.h.
+CO_OPTIMIZED, CO_NEWLOCALS, CO_VARARGS, CO_VARKEYWORDS = 1, 2, 4, 8
+
+def getargs(co):
+    """Get information about the arguments accepted by a code object.
+
+    Three things are returned: (args, varargs, varkw), where 'args' is
+    a list of argument names (possibly containing nested lists), and
+    'varargs' and 'varkw' are the names of the * and ** arguments or None.
+
+    """
+
+    if not iscode(co):
+        raise TypeError('arg is not a code object')
+
+    nargs = co.co_argcount
+    names = co.co_varnames
+    args = list(names[:nargs])
+
+    # The following acrobatics are for anonymous (tuple) arguments.
+    # Which we do not need to support, so remove to avoid importing
+    # the dis module.
+    for i in range(nargs):
+        if args[i][:1] in ['', '.']:
+            raise TypeError("tuple function arguments are not supported")
+    varargs = None
+    if co.co_flags & CO_VARARGS:
+        varargs = co.co_varnames[nargs]
+        nargs = nargs + 1
+    varkw = None
+    if co.co_flags & CO_VARKEYWORDS:
+        varkw = co.co_varnames[nargs]
+    return args, varargs, varkw
+
+def getargspec(func):
+    """Get the names and default values of a function's arguments.
+
+    A tuple of four things is returned: (args, varargs, varkw, defaults).
+    'args' is a list of the argument names (it may contain nested lists).
+    'varargs' and 'varkw' are the names of the * and ** arguments or None.
+    'defaults' is an n-tuple of the default values of the last n arguments.
+
+    """
+
+    if ismethod(func):
+        func = func.__func__
+    if not isfunction(func):
+        raise TypeError('arg is not a Python function')
+    args, varargs, varkw = getargs(func.__code__)
+    return args, varargs, varkw, func.__defaults__
+
+def getargvalues(frame):
+    """Get information about arguments passed into a particular frame.
+
+    A tuple of four things is returned: (args, varargs, varkw, locals).
+    'args' is a list of the argument names (it may contain nested lists).
+    'varargs' and 'varkw' are the names of the * and ** arguments or None.
+    'locals' is the locals dictionary of the given frame.
+    
+    """
+    args, varargs, varkw = getargs(frame.f_code)
+    return args, varargs, varkw, frame.f_locals
+
+def joinseq(seq):
+    if len(seq) == 1:
+        return '(' + seq[0] + ',)'
+    else:
+        return '(' + ', '.join(seq) + ')'
+
+def strseq(object, convert, join=joinseq):
+    """Recursively walk a sequence, stringifying each element.
+
+    """
+    if type(object) in [list, tuple]:
+        return join([strseq(_o, convert, join) for _o in object])
+    else:
+        return convert(object)
+
+def formatargspec(args, varargs=None, varkw=None, defaults=None,
+                  formatarg=str,
+                  formatvarargs=lambda name: '*' + name,
+                  formatvarkw=lambda name: '**' + name,
+                  formatvalue=lambda value: '=' + repr(value),
+                  join=joinseq):
+    """Format an argument spec from the 4 values returned by getargspec.
+
+    The first four arguments are (args, varargs, varkw, defaults).  The
+    other four arguments are the corresponding optional formatting functions
+    that are called to turn names and values into strings.  The ninth
+    argument is an optional function to format the sequence of arguments.
+
+    """
+    specs = []
+    if defaults:
+        firstdefault = len(args) - len(defaults)
+    for i in range(len(args)):
+        spec = strseq(args[i], formatarg, join)
+        if defaults and i >= firstdefault:
+            spec = spec + formatvalue(defaults[i - firstdefault])
+        specs.append(spec)
+    if varargs is not None:
+        specs.append(formatvarargs(varargs))
+    if varkw is not None:
+        specs.append(formatvarkw(varkw))
+    return '(' + ', '.join(specs) + ')'
+
+def formatargvalues(args, varargs, varkw, locals,
+                    formatarg=str,
+                    formatvarargs=lambda name: '*' + name,
+                    formatvarkw=lambda name: '**' + name,
+                    formatvalue=lambda value: '=' + repr(value),
+                    join=joinseq):
+    """Format an argument spec from the 4 values returned by getargvalues.
+
+    The first four arguments are (args, varargs, varkw, locals).  The
+    next four arguments are the corresponding optional formatting functions
+    that are called to turn names and values into strings.  The ninth
+    argument is an optional function to format the sequence of arguments.
+
+    """
+    def convert(name, locals=locals,
+                formatarg=formatarg, formatvalue=formatvalue):
+        return formatarg(name) + formatvalue(locals[name])
+    specs = [strseq(arg, convert, join) for arg in args]
+
+    if varargs:
+        specs.append(formatvarargs(varargs) + formatvalue(locals[varargs]))
+    if varkw:
+        specs.append(formatvarkw(varkw) + formatvalue(locals[varkw]))
+    return '(' + ', '.join(specs) + ')'

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/compat/py3k.py

@@ -0,0 +1,141 @@
+"""
+Python 3.X compatibility tools.
+
+While this file was originally intended for Python 2 -> 3 transition,
+it is now used to create a compatibility layer between different
+minor versions of Python 3.
+
+While the active version of numpy may not support a given version of python, we
+allow downstream libraries to continue to use these shims for forward
+compatibility with numpy while they transition their code to newer versions of
+Python.
+"""
+__all__ = ['bytes', 'asbytes', 'isfileobj', 'getexception', 'strchar',
+           'unicode', 'asunicode', 'asbytes_nested', 'asunicode_nested',
+           'asstr', 'open_latin1', 'long', 'basestring', 'sixu',
+           'integer_types', 'is_pathlib_path', 'npy_load_module', 'Path',
+           'pickle', 'contextlib_nullcontext', 'os_fspath', 'os_PathLike']
+
+import sys
+import os
+from pathlib import Path
+import io
+
+import abc
+from abc import ABC as abc_ABC
+
+try:
+    import pickle5 as pickle
+except ImportError:
+    import pickle
+
+long = int
+integer_types = (int,)
+basestring = str
+unicode = str
+bytes = bytes
+
+def asunicode(s):
+    if isinstance(s, bytes):
+        return s.decode('latin1')
+    return str(s)
+
+def asbytes(s):
+    if isinstance(s, bytes):
+        return s
+    return str(s).encode('latin1')
+
+def asstr(s):
+    if isinstance(s, bytes):
+        return s.decode('latin1')
+    return str(s)
+
+def isfileobj(f):
+    return isinstance(f, (io.FileIO, io.BufferedReader, io.BufferedWriter))
+
+def open_latin1(filename, mode='r'):
+    return open(filename, mode=mode, encoding='iso-8859-1')
+
+def sixu(s):
+    return s
+
+strchar = 'U'
+
+def getexception():
+    return sys.exc_info()[1]
+
+def asbytes_nested(x):
+    if hasattr(x, '__iter__') and not isinstance(x, (bytes, unicode)):
+        return [asbytes_nested(y) for y in x]
+    else:
+        return asbytes(x)
+
+def asunicode_nested(x):
+    if hasattr(x, '__iter__') and not isinstance(x, (bytes, unicode)):
+        return [asunicode_nested(y) for y in x]
+    else:
+        return asunicode(x)
+
+def is_pathlib_path(obj):
+    """
+    Check whether obj is a `pathlib.Path` object.
+
+    Prefer using ``isinstance(obj, os.PathLike)`` instead of this function.
+    """
+    return isinstance(obj, Path)
+
+# from Python 3.7
+class contextlib_nullcontext:
+    """Context manager that does no additional processing.
+
+    Used as a stand-in for a normal context manager, when a particular
+    block of code is only sometimes used with a normal context manager:
+
+    cm = optional_cm if condition else nullcontext()
+    with cm:
+        # Perform operation, using optional_cm if condition is True
+
+    .. note::
+        Prefer using `contextlib.nullcontext` instead of this context manager.
+    """
+
+    def __init__(self, enter_result=None):
+        self.enter_result = enter_result
+
+    def __enter__(self):
+        return self.enter_result
+
+    def __exit__(self, *excinfo):
+        pass
+
+
+def npy_load_module(name, fn, info=None):
+    """
+    Load a module. Uses ``load_module`` which will be deprecated in python
+    3.12. An alternative that uses ``exec_module`` is in
+    numpy.distutils.misc_util.exec_mod_from_location
+
+    .. versionadded:: 1.11.2
+
+    Parameters
+    ----------
+    name : str
+        Full module name.
+    fn : str
+        Path to module file.
+    info : tuple, optional
+        Only here for backward compatibility with Python 2.*.
+
+    Returns
+    -------
+    mod : module
+
+    """
+    # Explicitly lazy import this to avoid paying the cost
+    # of importing importlib at startup
+    from importlib.machinery import SourceFileLoader
+    return SourceFileLoader(name, fn).load_module()
+
+
+os_fspath = os.fspath
+os_PathLike = os.PathLike

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/compat/setup.py

@@ -0,0 +1,10 @@
+def configuration(parent_package='',top_path=None):
+    from numpy.distutils.misc_util import Configuration
+
+    config = Configuration('compat', parent_package, top_path)
+    config.add_subpackage('tests')
+    return config
+
+if __name__ == '__main__':
+    from numpy.distutils.core import setup
+    setup(configuration=configuration)

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/compat/tests/test_compat.py

@@ -0,0 +1,19 @@
+from os.path import join
+
+from numpy.compat import isfileobj
+from numpy.testing import assert_
+from numpy.testing import tempdir
+
+
+def test_isfileobj():
+    with tempdir(prefix="numpy_test_compat_") as folder:
+        filename = join(folder, 'a.bin')
+
+        with open(filename, 'wb') as f:
+            assert_(isfileobj(f))
+
+        with open(filename, 'ab') as f:
+            assert_(isfileobj(f))
+
+        with open(filename, 'rb') as f:
+            assert_(isfileobj(f))

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/conftest.py

@@ -0,0 +1,119 @@
+"""
+Pytest configuration and fixtures for the Numpy test suite.
+"""
+import os
+import tempfile
+
+import hypothesis
+import pytest
+import numpy
+
+from numpy.core._multiarray_tests import get_fpu_mode
+
+
+_old_fpu_mode = None
+_collect_results = {}
+
+# Use a known and persistent tmpdir for hypothesis' caches, which
+# can be automatically cleared by the OS or user.
+hypothesis.configuration.set_hypothesis_home_dir(
+    os.path.join(tempfile.gettempdir(), ".hypothesis")
+)
+
+# We register two custom profiles for Numpy - for details see
+# https://hypothesis.readthedocs.io/en/latest/settings.html
+# The first is designed for our own CI runs; the latter also 
+# forces determinism and is designed for use via np.test()
+hypothesis.settings.register_profile(
+    name="numpy-profile", deadline=None, print_blob=True,
+)
+hypothesis.settings.register_profile(
+    name="np.test() profile",
+    deadline=None, print_blob=True, database=None, derandomize=True,
+    suppress_health_check=hypothesis.HealthCheck.all(),
+)
+# Note that the default profile is chosen based on the presence 
+# of pytest.ini, but can be overriden by passing the 
+# --hypothesis-profile=NAME argument to pytest.
+_pytest_ini = os.path.join(os.path.dirname(__file__), "..", "pytest.ini")
+hypothesis.settings.load_profile(
+    "numpy-profile" if os.path.isfile(_pytest_ini) else "np.test() profile"
+)
+
+
+def pytest_configure(config):
+    config.addinivalue_line("markers",
+        "valgrind_error: Tests that are known to error under valgrind.")
+    config.addinivalue_line("markers",
+        "leaks_references: Tests that are known to leak references.")
+    config.addinivalue_line("markers",
+        "slow: Tests that are very slow.")
+    config.addinivalue_line("markers",
+        "slow_pypy: Tests that are very slow on pypy.")
+
+
+def pytest_addoption(parser):
+    parser.addoption("--available-memory", action="store", default=None,
+                     help=("Set amount of memory available for running the "
+                           "test suite. This can result to tests requiring "
+                           "especially large amounts of memory to be skipped. "
+                           "Equivalent to setting environment variable "
+                           "NPY_AVAILABLE_MEM. Default: determined"
+                           "automatically."))
+
+
+def pytest_sessionstart(session):
+    available_mem = session.config.getoption('available_memory')
+    if available_mem is not None:
+        os.environ['NPY_AVAILABLE_MEM'] = available_mem
+
+
+#FIXME when yield tests are gone.
+@pytest.hookimpl()
+def pytest_itemcollected(item):
+    """
+    Check FPU precision mode was not changed during test collection.
+
+    The clumsy way we do it here is mainly necessary because numpy
+    still uses yield tests, which can execute code at test collection
+    time.
+    """
+    global _old_fpu_mode
+
+    mode = get_fpu_mode()
+
+    if _old_fpu_mode is None:
+        _old_fpu_mode = mode
+    elif mode != _old_fpu_mode:
+        _collect_results[item] = (_old_fpu_mode, mode)
+        _old_fpu_mode = mode
+
+
+@pytest.fixture(scope="function", autouse=True)
+def check_fpu_mode(request):
+    """
+    Check FPU precision mode was not changed during the test.
+    """
+    old_mode = get_fpu_mode()
+    yield
+    new_mode = get_fpu_mode()
+
+    if old_mode != new_mode:
+        raise AssertionError("FPU precision mode changed from {0:#x} to {1:#x}"
+                             " during the test".format(old_mode, new_mode))
+
+    collect_result = _collect_results.get(request.node)
+    if collect_result is not None:
+        old_mode, new_mode = collect_result
+        raise AssertionError("FPU precision mode changed from {0:#x} to {1:#x}"
+                             " when collecting the test".format(old_mode,
+                                                                new_mode))
+
+
+@pytest.fixture(autouse=True)
+def add_np(doctest_namespace):
+    doctest_namespace['np'] = numpy
+
+@pytest.fixture(autouse=True)
+def env_setup(monkeypatch):
+    monkeypatch.setenv('PYTHONHASHSEED', '0')

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/core/__init__.py

@@ -0,0 +1,166 @@
+"""
+Contains the core of NumPy: ndarray, ufuncs, dtypes, etc.
+
+Please note that this module is private.  All functions and objects
+are available in the main ``numpy`` namespace - use that instead.
+
+"""
+
+from numpy.version import version as __version__
+
+import os
+
+# disables OpenBLAS affinity setting of the main thread that limits
+# python threads or processes to one core
+env_added = []
+for envkey in ['OPENBLAS_MAIN_FREE', 'GOTOBLAS_MAIN_FREE']:
+    if envkey not in os.environ:
+        os.environ[envkey] = '1'
+        env_added.append(envkey)
+
+try:
+    from . import multiarray
+except ImportError as exc:
+    import sys
+    msg = """
+
+IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE!
+
+Importing the numpy C-extensions failed. This error can happen for
+many reasons, often due to issues with your setup or how NumPy was
+installed.
+
+We have compiled some common reasons and troubleshooting tips at:
+
+    https://numpy.org/devdocs/user/troubleshooting-importerror.html
+
+Please note and check the following:
+
+  * The Python version is: Python%d.%d from "%s"
+  * The NumPy version is: "%s"
+
+and make sure that they are the versions you expect.
+Please carefully study the documentation linked above for further help.
+
+Original error was: %s
+""" % (sys.version_info[0], sys.version_info[1], sys.executable,
+        __version__, exc)
+    raise ImportError(msg)
+finally:
+    for envkey in env_added:
+        del os.environ[envkey]
+del envkey
+del env_added
+del os
+
+from . import umath
+
+# Check that multiarray,umath are pure python modules wrapping
+# _multiarray_umath and not either of the old c-extension modules
+if not (hasattr(multiarray, '_multiarray_umath') and
+        hasattr(umath, '_multiarray_umath')):
+    import sys
+    path = sys.modules['numpy'].__path__
+    msg = ("Something is wrong with the numpy installation. "
+        "While importing we detected an older version of "
+        "numpy in {}. One method of fixing this is to repeatedly uninstall "
+        "numpy until none is found, then reinstall this version.")
+    raise ImportError(msg.format(path))
+
+from . import numerictypes as nt
+multiarray.set_typeDict(nt.sctypeDict)
+from . import numeric
+from .numeric import *
+from . import fromnumeric
+from .fromnumeric import *
+from . import defchararray as char
+from . import records as rec
+from .records import record, recarray, format_parser
+from .memmap import *
+from .defchararray import chararray
+from . import function_base
+from .function_base import *
+from . import machar
+from .machar import *
+from . import getlimits
+from .getlimits import *
+from . import shape_base
+from .shape_base import *
+from . import einsumfunc
+from .einsumfunc import *
+del nt
+
+from .fromnumeric import amax as max, amin as min, round_ as round
+from .numeric import absolute as abs
+
+# do this after everything else, to minimize the chance of this misleadingly
+# appearing in an import-time traceback
+from . import _add_newdocs
+from . import _add_newdocs_scalars
+# add these for module-freeze analysis (like PyInstaller)
+from . import _dtype_ctypes
+from . import _internal
+from . import _dtype
+from . import _methods
+
+__all__ = ['char', 'rec', 'memmap']
+__all__ += numeric.__all__
+__all__ += fromnumeric.__all__
+__all__ += ['record', 'recarray', 'format_parser']
+__all__ += ['chararray']
+__all__ += function_base.__all__
+__all__ += machar.__all__
+__all__ += getlimits.__all__
+__all__ += shape_base.__all__
+__all__ += einsumfunc.__all__
+
+# We used to use `np.core._ufunc_reconstruct` to unpickle. This is unnecessary,
+# but old pickles saved before 1.20 will be using it, and there is no reason
+# to break loading them.
+def _ufunc_reconstruct(module, name):
+    # The `fromlist` kwarg is required to ensure that `mod` points to the
+    # inner-most module rather than the parent package when module name is
+    # nested. This makes it possible to pickle non-toplevel ufuncs such as
+    # scipy.special.expit for instance.
+    mod = __import__(module, fromlist=[name])
+    return getattr(mod, name)
+
+
+def _ufunc_reduce(func):
+    # Report the `__name__`. pickle will try to find the module. Note that
+    # pickle supports for this `__name__` to be a `__qualname__`. It may
+    # make sense to add a `__qualname__` to ufuncs, to allow this more
+    # explicitly (Numba has ufuncs as attributes).
+    # See also: https://github.com/dask/distributed/issues/3450
+    return func.__name__
+
+
+def _DType_reconstruct(scalar_type):
+    # This is a work-around to pickle type(np.dtype(np.float64)), etc.
+    # and it should eventually be replaced with a better solution, e.g. when
+    # DTypes become HeapTypes.
+    return type(dtype(scalar_type))
+
+
+def _DType_reduce(DType):
+    # To pickle a DType without having to add top-level names, pickle the
+    # scalar type for now (and assume that reconstruction will be possible).
+    if DType is dtype:
+        return "dtype"  # must pickle `np.dtype` as a singleton.
+    scalar_type = DType.type  # pickle the scalar type for reconstruction
+    return _DType_reconstruct, (scalar_type,)
+
+
+import copyreg
+
+copyreg.pickle(ufunc, _ufunc_reduce)
+copyreg.pickle(type(dtype), _DType_reduce, _DType_reconstruct)
+
+# Unclutter namespace (must keep _*_reconstruct for unpickling)
+del copyreg
+del _ufunc_reduce
+del _DType_reduce
+
+from numpy._pytesttester import PytestTester
+test = PytestTester(__name__)
+del PytestTester

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/core/__init__.pyi

@@ -0,0 +1,2 @@
+# NOTE: The `np.core` namespace is deliberately kept empty due to it
+# being private (despite the lack of leading underscore)

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/core/_add_newdocs_scalars.py

@@ -0,0 +1,259 @@
+"""
+This file is separate from ``_add_newdocs.py`` so that it can be mocked out by
+our sphinx ``conf.py`` during doc builds, where we want to avoid showing
+platform-dependent information.
+"""
+from numpy.core import dtype
+from numpy.core import numerictypes as _numerictypes
+from numpy.core.function_base import add_newdoc
+import platform
+
+##############################################################################
+#
+# Documentation for concrete scalar classes
+#
+##############################################################################
+
+def numeric_type_aliases(aliases):
+    def type_aliases_gen():
+        for alias, doc in aliases:
+            try:
+                alias_type = getattr(_numerictypes, alias)
+            except AttributeError:
+                # The set of aliases that actually exist varies between platforms
+                pass
+            else:
+                yield (alias_type, alias, doc)
+    return list(type_aliases_gen())
+
+
+possible_aliases = numeric_type_aliases([
+    ('int8', '8-bit signed integer (``-128`` to ``127``)'),
+    ('int16', '16-bit signed integer (``-32_768`` to ``32_767``)'),
+    ('int32', '32-bit signed integer (``-2_147_483_648`` to ``2_147_483_647``)'),
+    ('int64', '64-bit signed integer (``-9_223_372_036_854_775_808`` to ``9_223_372_036_854_775_807``)'),
+    ('intp', 'Signed integer large enough to fit pointer, compatible with C ``intptr_t``'),
+    ('uint8', '8-bit unsigned integer (``0`` to ``255``)'),
+    ('uint16', '16-bit unsigned integer (``0`` to ``65_535``)'),
+    ('uint32', '32-bit unsigned integer (``0`` to ``4_294_967_295``)'),
+    ('uint64', '64-bit unsigned integer (``0`` to ``18_446_744_073_709_551_615``)'),
+    ('uintp', 'Unsigned integer large enough to fit pointer, compatible with C ``uintptr_t``'),
+    ('float16', '16-bit-precision floating-point number type: sign bit, 5 bits exponent, 10 bits mantissa'),
+    ('float32', '32-bit-precision floating-point number type: sign bit, 8 bits exponent, 23 bits mantissa'),
+    ('float64', '64-bit precision floating-point number type: sign bit, 11 bits exponent, 52 bits mantissa'),
+    ('float96', '96-bit extended-precision floating-point number type'),
+    ('float128', '128-bit extended-precision floating-point number type'),
+    ('complex64', 'Complex number type composed of 2 32-bit-precision floating-point numbers'),
+    ('complex128', 'Complex number type composed of 2 64-bit-precision floating-point numbers'),
+    ('complex192', 'Complex number type composed of 2 96-bit extended-precision floating-point numbers'),
+    ('complex256', 'Complex number type composed of 2 128-bit extended-precision floating-point numbers'),
+    ])
+
+
+
+
+def add_newdoc_for_scalar_type(obj, fixed_aliases, doc):
+    # note: `:field: value` is rST syntax which renders as field lists.
+    o = getattr(_numerictypes, obj)
+
+    character_code = dtype(o).char
+    canonical_name_doc = "" if obj == o.__name__ else ":Canonical name: `numpy.{}`\n    ".format(obj)
+    alias_doc = ''.join(":Alias: `numpy.{}`\n    ".format(alias) for alias in fixed_aliases)
+    alias_doc += ''.join(":Alias on this platform ({} {}): `numpy.{}`: {}.\n    ".format(platform.system(), platform.machine(), alias, doc)
+                         for (alias_type, alias, doc) in possible_aliases if alias_type is o)
+    docstring = """
+    {doc}
+
+    :Character code: ``'{character_code}'``
+    {canonical_name_doc}{alias_doc}
+    """.format(doc=doc.strip(), character_code=character_code,
+               canonical_name_doc=canonical_name_doc, alias_doc=alias_doc)
+
+    add_newdoc('numpy.core.numerictypes', obj, docstring)
+
+
+add_newdoc_for_scalar_type('bool_', ['bool8'],
+    """
+    Boolean type (True or False), stored as a byte.
+
+    .. warning::
+
+       The :class:`bool_` type is not a subclass of the :class:`int_` type
+       (the :class:`bool_` is not even a number type). This is different
+       than Python's default implementation of :class:`bool` as a
+       sub-class of :class:`int`.
+    """)
+
+add_newdoc_for_scalar_type('byte', [],
+    """
+    Signed integer type, compatible with C ``char``.
+    """)
+
+add_newdoc_for_scalar_type('short', [],
+    """
+    Signed integer type, compatible with C ``short``.
+    """)
+
+add_newdoc_for_scalar_type('intc', [],
+    """
+    Signed integer type, compatible with C ``int``.
+    """)
+
+add_newdoc_for_scalar_type('int_', [],
+    """
+    Signed integer type, compatible with Python `int` and C ``long``.
+    """)
+
+add_newdoc_for_scalar_type('longlong', [],
+    """
+    Signed integer type, compatible with C ``long long``.
+    """)
+
+add_newdoc_for_scalar_type('ubyte', [],
+    """
+    Unsigned integer type, compatible with C ``unsigned char``.
+    """)
+
+add_newdoc_for_scalar_type('ushort', [],
+    """
+    Unsigned integer type, compatible with C ``unsigned short``.
+    """)
+
+add_newdoc_for_scalar_type('uintc', [],
+    """
+    Unsigned integer type, compatible with C ``unsigned int``.
+    """)
+
+add_newdoc_for_scalar_type('uint', [],
+    """
+    Unsigned integer type, compatible with C ``unsigned long``.
+    """)
+
+add_newdoc_for_scalar_type('ulonglong', [],
+    """
+    Signed integer type, compatible with C ``unsigned long long``.
+    """)
+
+add_newdoc_for_scalar_type('half', [],
+    """
+    Half-precision floating-point number type.
+    """)
+
+add_newdoc_for_scalar_type('single', [],
+    """
+    Single-precision floating-point number type, compatible with C ``float``.
+    """)
+
+add_newdoc_for_scalar_type('double', ['float_'],
+    """
+    Double-precision floating-point number type, compatible with Python `float`
+    and C ``double``.
+    """)
+
+add_newdoc_for_scalar_type('longdouble', ['longfloat'],
+    """
+    Extended-precision floating-point number type, compatible with C
+    ``long double`` but not necessarily with IEEE 754 quadruple-precision.
+    """)
+
+add_newdoc_for_scalar_type('csingle', ['singlecomplex'],
+    """
+    Complex number type composed of two single-precision floating-point
+    numbers.
+    """)
+
+add_newdoc_for_scalar_type('cdouble', ['cfloat', 'complex_'],
+    """
+    Complex number type composed of two double-precision floating-point
+    numbers, compatible with Python `complex`.
+    """)
+
+add_newdoc_for_scalar_type('clongdouble', ['clongfloat', 'longcomplex'],
+    """
+    Complex number type composed of two extended-precision floating-point
+    numbers.
+    """)
+
+add_newdoc_for_scalar_type('object_', [],
+    """
+    Any Python object.
+    """)
+
+add_newdoc_for_scalar_type('str_', ['unicode_'],
+    r"""
+    A unicode string.
+
+    When used in arrays, this type strips trailing null codepoints.
+
+    Unlike the builtin `str`, this supports the :ref:`python:bufferobjects`, exposing its
+    contents as UCS4:
+
+    >>> m = memoryview(np.str_("abc"))
+    >>> m.format
+    '3w'
+    >>> m.tobytes()
+    b'a\x00\x00\x00b\x00\x00\x00c\x00\x00\x00'
+    """)
+
+add_newdoc_for_scalar_type('bytes_', ['string_'],
+    r"""
+    A byte string.
+
+    When used in arrays, this type strips trailing null bytes.
+    """)
+
+add_newdoc_for_scalar_type('void', [],
+    r"""
+    Either an opaque sequence of bytes, or a structure.
+    
+    >>> np.void(b'abcd')
+    void(b'\x61\x62\x63\x64')
+    
+    Structured `void` scalars can only be constructed via extraction from :ref:`structured_arrays`:
+    
+    >>> arr = np.array((1, 2), dtype=[('x', np.int8), ('y', np.int8)])
+    >>> arr[()]
+    (1, 2)  # looks like a tuple, but is `np.void`
+    """)
+
+add_newdoc_for_scalar_type('datetime64', [],
+    """
+    If created from a 64-bit integer, it represents an offset from
+    ``1970-01-01T00:00:00``.
+    If created from string, the string can be in ISO 8601 date
+    or datetime format.
+
+    >>> np.datetime64(10, 'Y')
+    numpy.datetime64('1980')
+    >>> np.datetime64('1980', 'Y')
+    numpy.datetime64('1980')   
+    >>> np.datetime64(10, 'D')
+    numpy.datetime64('1970-01-11')
+    
+    See :ref:`arrays.datetime` for more information.
+    """)
+
+add_newdoc_for_scalar_type('timedelta64', [],
+    """
+    A timedelta stored as a 64-bit integer.
+    
+    See :ref:`arrays.datetime` for more information.
+    """)
+
+# TODO: work out how to put this on the base class, np.floating
+for float_name in ('half', 'single', 'double', 'longdouble'):
+    add_newdoc('numpy.core.numerictypes', float_name, ('as_integer_ratio',
+        """
+        {ftype}.as_integer_ratio() -> (int, int)
+
+        Return a pair of integers, whose ratio is exactly equal to the original
+        floating point number, and with a positive denominator.
+        Raise `OverflowError` on infinities and a `ValueError` on NaNs.
+
+        >>> np.{ftype}(10.0).as_integer_ratio()
+        (10, 1)
+        >>> np.{ftype}(0.0).as_integer_ratio()
+        (0, 1)
+        >>> np.{ftype}(-.25).as_integer_ratio()
+        (-1, 4)
+        """.format(ftype=float_name)))

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/core/_asarray.py

@@ -0,0 +1,140 @@
+"""
+Functions in the ``as*array`` family that promote array-likes into arrays.
+
+`require` fits this category despite its name not matching this pattern.
+"""
+from .overrides import (
+    array_function_dispatch,
+    set_array_function_like_doc,
+    set_module,
+)
+from .multiarray import array, asanyarray
+
+
+__all__ = ["require"]
+
+
+
+def _require_dispatcher(a, dtype=None, requirements=None, *, like=None):
+    return (like,)
+
+
+@set_array_function_like_doc
+@set_module('numpy')
+def require(a, dtype=None, requirements=None, *, like=None):
+    """
+    Return an ndarray of the provided type that satisfies requirements.
+
+    This function is useful to be sure that an array with the correct flags
+    is returned for passing to compiled code (perhaps through ctypes).
+
+    Parameters
+    ----------
+    a : array_like
+       The object to be converted to a type-and-requirement-satisfying array.
+    dtype : data-type
+       The required data-type. If None preserve the current dtype. If your
+       application requires the data to be in native byteorder, include
+       a byteorder specification as a part of the dtype specification.
+    requirements : str or list of str
+       The requirements list can be any of the following
+
+       * 'F_CONTIGUOUS' ('F') - ensure a Fortran-contiguous array
+       * 'C_CONTIGUOUS' ('C') - ensure a C-contiguous array
+       * 'ALIGNED' ('A')      - ensure a data-type aligned array
+       * 'WRITEABLE' ('W')    - ensure a writable array
+       * 'OWNDATA' ('O')      - ensure an array that owns its own data
+       * 'ENSUREARRAY', ('E') - ensure a base array, instead of a subclass
+    ${ARRAY_FUNCTION_LIKE}
+
+        .. versionadded:: 1.20.0
+
+    Returns
+    -------
+    out : ndarray
+        Array with specified requirements and type if given.
+
+    See Also
+    --------
+    asarray : Convert input to an ndarray.
+    asanyarray : Convert to an ndarray, but pass through ndarray subclasses.
+    ascontiguousarray : Convert input to a contiguous array.
+    asfortranarray : Convert input to an ndarray with column-major
+                     memory order.
+    ndarray.flags : Information about the memory layout of the array.
+
+    Notes
+    -----
+    The returned array will be guaranteed to have the listed requirements
+    by making a copy if needed.
+
+    Examples
+    --------
+    >>> x = np.arange(6).reshape(2,3)
+    >>> x.flags
+      C_CONTIGUOUS : True
+      F_CONTIGUOUS : False
+      OWNDATA : False
+      WRITEABLE : True
+      ALIGNED : True
+      WRITEBACKIFCOPY : False
+      UPDATEIFCOPY : False
+
+    >>> y = np.require(x, dtype=np.float32, requirements=['A', 'O', 'W', 'F'])
+    >>> y.flags
+      C_CONTIGUOUS : False
+      F_CONTIGUOUS : True
+      OWNDATA : True
+      WRITEABLE : True
+      ALIGNED : True
+      WRITEBACKIFCOPY : False
+      UPDATEIFCOPY : False
+
+    """
+    if like is not None:
+        return _require_with_like(
+            a,
+            dtype=dtype,
+            requirements=requirements,
+            like=like,
+        )
+
+    possible_flags = {'C': 'C', 'C_CONTIGUOUS': 'C', 'CONTIGUOUS': 'C',
+                      'F': 'F', 'F_CONTIGUOUS': 'F', 'FORTRAN': 'F',
+                      'A': 'A', 'ALIGNED': 'A',
+                      'W': 'W', 'WRITEABLE': 'W',
+                      'O': 'O', 'OWNDATA': 'O',
+                      'E': 'E', 'ENSUREARRAY': 'E'}
+    if not requirements:
+        return asanyarray(a, dtype=dtype)
+    else:
+        requirements = {possible_flags[x.upper()] for x in requirements}
+
+    if 'E' in requirements:
+        requirements.remove('E')
+        subok = False
+    else:
+        subok = True
+
+    order = 'A'
+    if requirements >= {'C', 'F'}:
+        raise ValueError('Cannot specify both "C" and "F" order')
+    elif 'F' in requirements:
+        order = 'F'
+        requirements.remove('F')
+    elif 'C' in requirements:
+        order = 'C'
+        requirements.remove('C')
+
+    arr = array(a, dtype=dtype, order=order, copy=False, subok=subok)
+
+    for prop in requirements:
+        if not arr.flags[prop]:
+            arr = arr.copy(order)
+            break
+    return arr
+
+
+_require_with_like = array_function_dispatch(
+    _require_dispatcher
+)(require)

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/core/_asarray.pyi

@@ -0,0 +1,81 @@
+import sys
+from typing import TypeVar, Union, Iterable, overload
+
+from numpy import ndarray, _OrderKACF
+from numpy.typing import ArrayLike, DTypeLike
+
+if sys.version_info >= (3, 8):
+    from typing import Literal
+else:
+    from typing_extensions import Literal
+
+_ArrayType = TypeVar("_ArrayType", bound=ndarray)
+
+# TODO: The following functions are now defined in C, so should be defined
+#       in a (not yet existing) `multiarray.pyi`.
+#       (with the exception of `require`)
+
+def asarray(
+    a: object,
+    dtype: DTypeLike = ...,
+    order: _OrderKACF = ...,
+    *,
+    like: ArrayLike = ...
+) -> ndarray: ...
+@overload
+def asanyarray(
+    a: _ArrayType,
+    dtype: None = ...,
+    order: _OrderKACF = ...,
+    *,
+    like: ArrayLike = ...
+) -> _ArrayType: ...
+@overload
+def asanyarray(
+    a: object,
+    dtype: DTypeLike = ...,
+    order: _OrderKACF = ...,
+    *,
+    like: ArrayLike = ...
+) -> ndarray: ...
+def ascontiguousarray(
+    a: object, dtype: DTypeLike = ..., *, like: ArrayLike = ...
+) -> ndarray: ...
+def asfortranarray(
+    a: object, dtype: DTypeLike = ..., *, like: ArrayLike = ...
+) -> ndarray: ...
+
+_Requirements = Literal[
+    "C", "C_CONTIGUOUS", "CONTIGUOUS",
+    "F", "F_CONTIGUOUS", "FORTRAN",
+    "A", "ALIGNED",
+    "W", "WRITEABLE",
+    "O", "OWNDATA"
+]
+_E = Literal["E", "ENSUREARRAY"]
+_RequirementsWithE = Union[_Requirements, _E]
+
+@overload
+def require(
+    a: _ArrayType,
+    dtype: None = ...,
+    requirements: Union[None, _Requirements, Iterable[_Requirements]] = ...,
+    *,
+    like: ArrayLike = ...
+) -> _ArrayType: ...
+@overload
+def require(
+    a: object,
+    dtype: DTypeLike = ...,
+    requirements: Union[_E, Iterable[_RequirementsWithE]] = ...,
+    *,
+    like: ArrayLike = ...
+) -> ndarray: ...
+@overload
+def require(
+    a: object,
+    dtype: DTypeLike = ...,
+    requirements: Union[None, _Requirements, Iterable[_Requirements]] = ...,
+    *,
+    like: ArrayLike = ...
+) -> ndarray: ...

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/core/_dtype.py

@@ -0,0 +1,342 @@
+"""
+A place for code to be called from the implementation of np.dtype
+
+String handling is much easier to do correctly in python.
+"""
+import numpy as np
+
+
+_kind_to_stem = {
+    'u': 'uint',
+    'i': 'int',
+    'c': 'complex',
+    'f': 'float',
+    'b': 'bool',
+    'V': 'void',
+    'O': 'object',
+    'M': 'datetime',
+    'm': 'timedelta',
+    'S': 'bytes',
+    'U': 'str',
+}
+
+
+def _kind_name(dtype):
+    try:
+        return _kind_to_stem[dtype.kind]
+    except KeyError as e:
+        raise RuntimeError(
+            "internal dtype error, unknown kind {!r}"
+            .format(dtype.kind)
+        ) from None
+
+
+def __str__(dtype):
+    if dtype.fields is not None:
+        return _struct_str(dtype, include_align=True)
+    elif dtype.subdtype:
+        return _subarray_str(dtype)
+    elif issubclass(dtype.type, np.flexible) or not dtype.isnative:
+        return dtype.str
+    else:
+        return dtype.name
+
+
+def __repr__(dtype):
+    arg_str = _construction_repr(dtype, include_align=False)
+    if dtype.isalignedstruct:
+        arg_str = arg_str + ", align=True"
+    return "dtype({})".format(arg_str)
+
+
+def _unpack_field(dtype, offset, title=None):
+    """
+    Helper function to normalize the items in dtype.fields.
+
+    Call as:
+
+    dtype, offset, title = _unpack_field(*dtype.fields[name])
+    """
+    return dtype, offset, title
+
+
+def _isunsized(dtype):
+    # PyDataType_ISUNSIZED
+    return dtype.itemsize == 0
+
+
+def _construction_repr(dtype, include_align=False, short=False):
+    """
+    Creates a string repr of the dtype, excluding the 'dtype()' part
+    surrounding the object. This object may be a string, a list, or
+    a dict depending on the nature of the dtype. This
+    is the object passed as the first parameter to the dtype
+    constructor, and if no additional constructor parameters are
+    given, will reproduce the exact memory layout.
+
+    Parameters
+    ----------
+    short : bool
+        If true, this creates a shorter repr using 'kind' and 'itemsize', instead
+        of the longer type name.
+
+    include_align : bool
+        If true, this includes the 'align=True' parameter
+        inside the struct dtype construction dict when needed. Use this flag
+        if you want a proper repr string without the 'dtype()' part around it.
+
+        If false, this does not preserve the
+        'align=True' parameter or sticky NPY_ALIGNED_STRUCT flag for
+        struct arrays like the regular repr does, because the 'align'
+        flag is not part of first dtype constructor parameter. This
+        mode is intended for a full 'repr', where the 'align=True' is
+        provided as the second parameter.
+    """
+    if dtype.fields is not None:
+        return _struct_str(dtype, include_align=include_align)
+    elif dtype.subdtype:
+        return _subarray_str(dtype)
+    else:
+        return _scalar_str(dtype, short=short)
+
+
+def _scalar_str(dtype, short):
+    byteorder = _byte_order_str(dtype)
+
+    if dtype.type == np.bool_:
+        if short:
+            return "'?'"
+        else:
+            return "'bool'"
+
+    elif dtype.type == np.object_:
+        # The object reference may be different sizes on different
+        # platforms, so it should never include the itemsize here.
+        return "'O'"
+
+    elif dtype.type == np.string_:
+        if _isunsized(dtype):
+            return "'S'"
+        else:
+            return "'S%d'" % dtype.itemsize
+
+    elif dtype.type == np.unicode_:
+        if _isunsized(dtype):
+            return "'%sU'" % byteorder
+        else:
+            return "'%sU%d'" % (byteorder, dtype.itemsize / 4)
+
+    # unlike the other types, subclasses of void are preserved - but
+    # historically the repr does not actually reveal the subclass
+    elif issubclass(dtype.type, np.void):
+        if _isunsized(dtype):
+            return "'V'"
+        else:
+            return "'V%d'" % dtype.itemsize
+
+    elif dtype.type == np.datetime64:
+        return "'%sM8%s'" % (byteorder, _datetime_metadata_str(dtype))
+
+    elif dtype.type == np.timedelta64:
+        return "'%sm8%s'" % (byteorder, _datetime_metadata_str(dtype))
+
+    elif np.issubdtype(dtype, np.number):
+        # Short repr with endianness, like '<f8'
+        if short or dtype.byteorder not in ('=', '|'):
+            return "'%s%c%d'" % (byteorder, dtype.kind, dtype.itemsize)
+
+        # Longer repr, like 'float64'
+        else:
+            return "'%s%d'" % (_kind_name(dtype), 8*dtype.itemsize)
+
+    elif dtype.isbuiltin == 2:
+        return dtype.type.__name__
+
+    else:
+        raise RuntimeError(
+            "Internal error: NumPy dtype unrecognized type number")
+
+
+def _byte_order_str(dtype):
+    """ Normalize byteorder to '<' or '>' """
+    # hack to obtain the native and swapped byte order characters
+    swapped = np.dtype(int).newbyteorder('S')
+    native = swapped.newbyteorder('S')
+
+    byteorder = dtype.byteorder
+    if byteorder == '=':
+        return native.byteorder
+    if byteorder == 'S':
+        # TODO: this path can never be reached
+        return swapped.byteorder
+    elif byteorder == '|':
+        return ''
+    else:
+        return byteorder
+
+
+def _datetime_metadata_str(dtype):
+    # TODO: this duplicates the C metastr_to_unicode functionality
+    unit, count = np.datetime_data(dtype)
+    if unit == 'generic':
+        return ''
+    elif count == 1:
+        return '[{}]'.format(unit)
+    else:
+        return '[{}{}]'.format(count, unit)
+
+
+def _struct_dict_str(dtype, includealignedflag):
+    # unpack the fields dictionary into ls
+    names = dtype.names
+    fld_dtypes = []
+    offsets = []
+    titles = []
+    for name in names:
+        fld_dtype, offset, title = _unpack_field(*dtype.fields[name])
+        fld_dtypes.append(fld_dtype)
+        offsets.append(offset)
+        titles.append(title)
+
+    # Build up a string to make the dictionary
+
+    # First, the names
+    ret = "{'names':["
+    ret += ",".join(repr(name) for name in names)
+
+    # Second, the formats
+    ret += "], 'formats':["
+    ret += ",".join(
+        _construction_repr(fld_dtype, short=True) for fld_dtype in fld_dtypes)
+
+    # Third, the offsets
+    ret += "], 'offsets':["
+    ret += ",".join("%d" % offset for offset in offsets)
+
+    # Fourth, the titles
+    if any(title is not None for title in titles):
+        ret += "], 'titles':["
+        ret += ",".join(repr(title) for title in titles)
+
+    # Fifth, the itemsize
+    ret += "], 'itemsize':%d" % dtype.itemsize
+
+    if (includealignedflag and dtype.isalignedstruct):
+        # Finally, the aligned flag
+        ret += ", 'aligned':True}"
+    else:
+        ret += "}"
+
+    return ret
+
+
+def _is_packed(dtype):
+    """
+    Checks whether the structured data type in 'dtype'
+    has a simple layout, where all the fields are in order,
+    and follow each other with no alignment padding.
+
+    When this returns true, the dtype can be reconstructed
+    from a list of the field names and dtypes with no additional
+    dtype parameters.
+
+    Duplicates the C `is_dtype_struct_simple_unaligned_layout` function.
+    """
+    total_offset = 0
+    for name in dtype.names:
+        fld_dtype, fld_offset, title = _unpack_field(*dtype.fields[name])
+        if fld_offset != total_offset:
+            return False
+        total_offset += fld_dtype.itemsize
+    if total_offset != dtype.itemsize:
+        return False
+    return True
+
+
+def _struct_list_str(dtype):
+    items = []
+    for name in dtype.names:
+        fld_dtype, fld_offset, title = _unpack_field(*dtype.fields[name])
+
+        item = "("
+        if title is not None:
+            item += "({!r}, {!r}), ".format(title, name)
+        else:
+            item += "{!r}, ".format(name)
+        # Special case subarray handling here
+        if fld_dtype.subdtype is not None:
+            base, shape = fld_dtype.subdtype
+            item += "{}, {}".format(
+                _construction_repr(base, short=True),
+                shape
+            )
+        else:
+            item += _construction_repr(fld_dtype, short=True)
+
+        item += ")"
+        items.append(item)
+
+    return "[" + ", ".join(items) + "]"
+
+
+def _struct_str(dtype, include_align):
+    # The list str representation can't include the 'align=' flag,
+    # so if it is requested and the struct has the aligned flag set,
+    # we must use the dict str instead.
+    if not (include_align and dtype.isalignedstruct) and _is_packed(dtype):
+        sub = _struct_list_str(dtype)
+
+    else:
+        sub = _struct_dict_str(dtype, include_align)
+
+    # If the data type isn't the default, void, show it
+    if dtype.type != np.void:
+        return "({t.__module__}.{t.__name__}, {f})".format(t=dtype.type, f=sub)
+    else:
+        return sub
+
+
+def _subarray_str(dtype):
+    base, shape = dtype.subdtype
+    return "({}, {})".format(
+        _construction_repr(base, short=True),
+        shape
+    )
+
+
+def _name_includes_bit_suffix(dtype):
+    if dtype.type == np.object_:
+        # pointer size varies by system, best to omit it
+        return False
+    elif dtype.type == np.bool_:
+        # implied
+        return False
+    elif np.issubdtype(dtype, np.flexible) and _isunsized(dtype):
+        # unspecified
+        return False
+    else:
+        return True
+
+
+def _name_get(dtype):
+    # provides dtype.name.__get__, documented as returning a "bit name"
+
+    if dtype.isbuiltin == 2:
+        # user dtypes don't promise to do anything special
+        return dtype.type.__name__
+
+    if issubclass(dtype.type, np.void):
+        # historically, void subclasses preserve their name, eg `record64`
+        name = dtype.type.__name__
+    else:
+        name = _kind_name(dtype)
+
+    # append bit counts
+    if _name_includes_bit_suffix(dtype):
+        name += "{}".format(dtype.itemsize * 8)
+
+    # append metadata to datetimes
+    if dtype.type in (np.datetime64, np.timedelta64):
+        name += _datetime_metadata_str(dtype)
+
+    return name

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/core/_dtype_ctypes.py

@@ -0,0 +1,117 @@
+"""
+Conversion from ctypes to dtype.
+
+In an ideal world, we could achieve this through the PEP3118 buffer protocol,
+something like::
+
+    def dtype_from_ctypes_type(t):
+        # needed to ensure that the shape of `t` is within memoryview.format
+        class DummyStruct(ctypes.Structure):
+            _fields_ = [('a', t)]
+
+        # empty to avoid memory allocation
+        ctype_0 = (DummyStruct * 0)()
+        mv = memoryview(ctype_0)
+
+        # convert the struct, and slice back out the field
+        return _dtype_from_pep3118(mv.format)['a']
+
+Unfortunately, this fails because:
+
+* ctypes cannot handle length-0 arrays with PEP3118 (bpo-32782)
+* PEP3118 cannot represent unions, but both numpy and ctypes can
+* ctypes cannot handle big-endian structs with PEP3118 (bpo-32780)
+"""
+
+# We delay-import ctypes for distributions that do not include it.
+# While this module is not used unless the user passes in ctypes
+# members, it is eagerly imported from numpy/core/__init__.py.
+import numpy as np
+
+
+def _from_ctypes_array(t):
+    return np.dtype((dtype_from_ctypes_type(t._type_), (t._length_,)))
+
+
+def _from_ctypes_structure(t):
+    for item in t._fields_:
+        if len(item) > 2:
+            raise TypeError(
+                "ctypes bitfields have no dtype equivalent")
+
+    if hasattr(t, "_pack_"):
+        import ctypes
+        formats = []
+        offsets = []
+        names = []
+        current_offset = 0
+        for fname, ftyp in t._fields_:
+            names.append(fname)
+            formats.append(dtype_from_ctypes_type(ftyp))
+            # Each type has a default offset, this is platform dependent for some types.
+            effective_pack = min(t._pack_, ctypes.alignment(ftyp))
+            current_offset = ((current_offset + effective_pack - 1) // effective_pack) * effective_pack
+            offsets.append(current_offset)
+            current_offset += ctypes.sizeof(ftyp)
+
+        return np.dtype(dict(
+            formats=formats,
+            offsets=offsets,
+            names=names,
+            itemsize=ctypes.sizeof(t)))
+    else:
+        fields = []
+        for fname, ftyp in t._fields_:
+            fields.append((fname, dtype_from_ctypes_type(ftyp)))
+
+        # by default, ctypes structs are aligned
+        return np.dtype(fields, align=True)
+
+
+def _from_ctypes_scalar(t):
+    """
+    Return the dtype type with endianness included if it's the case
+    """
+    if getattr(t, '__ctype_be__', None) is t:
+        return np.dtype('>' + t._type_)
+    elif getattr(t, '__ctype_le__', None) is t:
+        return np.dtype('<' + t._type_)
+    else:
+        return np.dtype(t._type_)
+
+
+def _from_ctypes_union(t):
+    import ctypes
+    formats = []
+    offsets = []
+    names = []
+    for fname, ftyp in t._fields_:
+        names.append(fname)
+        formats.append(dtype_from_ctypes_type(ftyp))
+        offsets.append(0)  # Union fields are offset to 0
+
+    return np.dtype(dict(
+        formats=formats,
+        offsets=offsets,
+        names=names,
+        itemsize=ctypes.sizeof(t)))
+
+
+def dtype_from_ctypes_type(t):
+    """
+    Construct a dtype object from a ctypes type
+    """
+    import _ctypes
+    if issubclass(t, _ctypes.Array):
+        return _from_ctypes_array(t)
+    elif issubclass(t, _ctypes._Pointer):
+        raise TypeError("ctypes pointers have no dtype equivalent")
+    elif issubclass(t, _ctypes.Structure):
+        return _from_ctypes_structure(t)
+    elif issubclass(t, _ctypes.Union):
+        return _from_ctypes_union(t)
+    elif isinstance(getattr(t, '_type_', None), str):
+        return _from_ctypes_scalar(t)
+    else:
+        raise NotImplementedError(
+            "Unknown ctypes type {}".format(t.__name__))

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/core/_exceptions.py

@@ -0,0 +1,197 @@
+"""
+Various richly-typed exceptions, that also help us deal with string formatting
+in python where it's easier.
+
+By putting the formatting in `__str__`, we also avoid paying the cost for
+users who silence the exceptions.
+"""
+from numpy.core.overrides import set_module
+
+def _unpack_tuple(tup):
+    if len(tup) == 1:
+        return tup[0]
+    else:
+        return tup
+
+
+def _display_as_base(cls):
+    """
+    A decorator that makes an exception class look like its base.
+
+    We use this to hide subclasses that are implementation details - the user
+    should catch the base type, which is what the traceback will show them.
+
+    Classes decorated with this decorator are subject to removal without a
+    deprecation warning.
+    """
+    assert issubclass(cls, Exception)
+    cls.__name__ = cls.__base__.__name__
+    return cls
+
+
+class UFuncTypeError(TypeError):
+    """ Base class for all ufunc exceptions """
+    def __init__(self, ufunc):
+        self.ufunc = ufunc
+
+
+@_display_as_base
+class _UFuncBinaryResolutionError(UFuncTypeError):
+    """ Thrown when a binary resolution fails """
+    def __init__(self, ufunc, dtypes):
+        super().__init__(ufunc)
+        self.dtypes = tuple(dtypes)
+        assert len(self.dtypes) == 2
+
+    def __str__(self):
+        return (
+            "ufunc {!r} cannot use operands with types {!r} and {!r}"
+        ).format(
+            self.ufunc.__name__, *self.dtypes
+        )
+
+
+@_display_as_base
+class _UFuncNoLoopError(UFuncTypeError):
+    """ Thrown when a ufunc loop cannot be found """
+    def __init__(self, ufunc, dtypes):
+        super().__init__(ufunc)
+        self.dtypes = tuple(dtypes)
+
+    def __str__(self):
+        return (
+            "ufunc {!r} did not contain a loop with signature matching types "
+            "{!r} -> {!r}"
+        ).format(
+            self.ufunc.__name__,
+            _unpack_tuple(self.dtypes[:self.ufunc.nin]),
+            _unpack_tuple(self.dtypes[self.ufunc.nin:])
+        )
+
+
+@_display_as_base
+class _UFuncCastingError(UFuncTypeError):
+    def __init__(self, ufunc, casting, from_, to):
+        super().__init__(ufunc)
+        self.casting = casting
+        self.from_ = from_
+        self.to = to
+
+
+@_display_as_base
+class _UFuncInputCastingError(_UFuncCastingError):
+    """ Thrown when a ufunc input cannot be casted """
+    def __init__(self, ufunc, casting, from_, to, i):
+        super().__init__(ufunc, casting, from_, to)
+        self.in_i = i
+
+    def __str__(self):
+        # only show the number if more than one input exists
+        i_str = "{} ".format(self.in_i) if self.ufunc.nin != 1 else ""
+        return (
+            "Cannot cast ufunc {!r} input {}from {!r} to {!r} with casting "
+            "rule {!r}"
+        ).format(
+            self.ufunc.__name__, i_str, self.from_, self.to, self.casting
+        )
+
+
+@_display_as_base
+class _UFuncOutputCastingError(_UFuncCastingError):
+    """ Thrown when a ufunc output cannot be casted """
+    def __init__(self, ufunc, casting, from_, to, i):
+        super().__init__(ufunc, casting, from_, to)
+        self.out_i = i
+
+    def __str__(self):
+        # only show the number if more than one output exists
+        i_str = "{} ".format(self.out_i) if self.ufunc.nout != 1 else ""
+        return (
+            "Cannot cast ufunc {!r} output {}from {!r} to {!r} with casting "
+            "rule {!r}"
+        ).format(
+            self.ufunc.__name__, i_str, self.from_, self.to, self.casting
+        )
+
+
+# Exception used in shares_memory()
+@set_module('numpy')
+class TooHardError(RuntimeError):
+    pass
+
+
+@set_module('numpy')
+class AxisError(ValueError, IndexError):
+    """ Axis supplied was invalid. """
+    def __init__(self, axis, ndim=None, msg_prefix=None):
+        # single-argument form just delegates to base class
+        if ndim is None and msg_prefix is None:
+            msg = axis
+
+        # do the string formatting here, to save work in the C code
+        else:
+            msg = ("axis {} is out of bounds for array of dimension {}"
+                   .format(axis, ndim))
+            if msg_prefix is not None:
+                msg = "{}: {}".format(msg_prefix, msg)
+
+        super().__init__(msg)
+
+
+@_display_as_base
+class _ArrayMemoryError(MemoryError):
+    """ Thrown when an array cannot be allocated"""
+    def __init__(self, shape, dtype):
+        self.shape = shape
+        self.dtype = dtype
+
+    @property
+    def _total_size(self):
+        num_bytes = self.dtype.itemsize
+        for dim in self.shape:
+            num_bytes *= dim
+        return num_bytes
+
+    @staticmethod
+    def _size_to_string(num_bytes):
+        """ Convert a number of bytes into a binary size string """
+
+        # https://en.wikipedia.org/wiki/Binary_prefix
+        LOG2_STEP = 10
+        STEP = 1024
+        units = ['bytes', 'KiB', 'MiB', 'GiB', 'TiB', 'PiB', 'EiB']
+
+        unit_i = max(num_bytes.bit_length() - 1, 1) // LOG2_STEP
+        unit_val = 1 << (unit_i * LOG2_STEP)
+        n_units = num_bytes / unit_val
+        del unit_val
+
+        # ensure we pick a unit that is correct after rounding
+        if round(n_units) == STEP:
+            unit_i += 1
+            n_units /= STEP
+
+        # deal with sizes so large that we don't have units for them
+        if unit_i >= len(units):
+            new_unit_i = len(units) - 1
+            n_units *= 1 << ((unit_i - new_unit_i) * LOG2_STEP)
+            unit_i = new_unit_i
+
+        unit_name = units[unit_i]
+        # format with a sensible number of digits
+        if unit_i == 0:
+            # no decimal point on bytes
+            return '{:.0f} {}'.format(n_units, unit_name)
+        elif round(n_units) < 1000:
+            # 3 significant figures, if none are dropped to the left of the .
+            return '{:#.3g} {}'.format(n_units, unit_name)
+        else:
+            # just give all the digits otherwise
+            return '{:#.0f} {}'.format(n_units, unit_name)
+
+    def __str__(self):
+        size_str = self._size_to_string(self._total_size)
+        return (
+            "Unable to allocate {} for an array with shape {} and data type {}"
+            .format(size_str, self.shape, self.dtype)
+        )

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/core/_internal.py

@@ -0,0 +1,910 @@
+"""
+A place for internal code
+
+Some things are more easily handled Python.
+
+"""
+import ast
+import re
+import sys
+import platform
+import warnings
+
+from .multiarray import dtype, array, ndarray
+try:
+    import ctypes
+except ImportError:
+    ctypes = None
+
+IS_PYPY = platform.python_implementation() == 'PyPy'
+
+if sys.byteorder == 'little':
+    _nbo = '<'
+else:
+    _nbo = '>'
+
+def _makenames_list(adict, align):
+    allfields = []
+
+    for fname, obj in adict.items():
+        n = len(obj)
+        if not isinstance(obj, tuple) or n not in (2, 3):
+            raise ValueError("entry not a 2- or 3- tuple")
+        if n > 2 and obj[2] == fname:
+            continue
+        num = int(obj[1])
+        if num < 0:
+            raise ValueError("invalid offset.")
+        format = dtype(obj[0], align=align)
+        if n > 2:
+            title = obj[2]
+        else:
+            title = None
+        allfields.append((fname, format, num, title))
+    # sort by offsets
+    allfields.sort(key=lambda x: x[2])
+    names = [x[0] for x in allfields]
+    formats = [x[1] for x in allfields]
+    offsets = [x[2] for x in allfields]
+    titles = [x[3] for x in allfields]
+
+    return names, formats, offsets, titles
+
+# Called in PyArray_DescrConverter function when
+#  a dictionary without "names" and "formats"
+#  fields is used as a data-type descriptor.
+def _usefields(adict, align):
+    try:
+        names = adict[-1]
+    except KeyError:
+        names = None
+    if names is None:
+        names, formats, offsets, titles = _makenames_list(adict, align)
+    else:
+        formats = []
+        offsets = []
+        titles = []
+        for name in names:
+            res = adict[name]
+            formats.append(res[0])
+            offsets.append(res[1])
+            if len(res) > 2:
+                titles.append(res[2])
+            else:
+                titles.append(None)
+
+    return dtype({"names": names,
+                  "formats": formats,
+                  "offsets": offsets,
+                  "titles": titles}, align)
+
+
+# construct an array_protocol descriptor list
+#  from the fields attribute of a descriptor
+# This calls itself recursively but should eventually hit
+#  a descriptor that has no fields and then return
+#  a simple typestring
+
+def _array_descr(descriptor):
+    fields = descriptor.fields
+    if fields is None:
+        subdtype = descriptor.subdtype
+        if subdtype is None:
+            if descriptor.metadata is None:
+                return descriptor.str
+            else:
+                new = descriptor.metadata.copy()
+                if new:
+                    return (descriptor.str, new)
+                else:
+                    return descriptor.str
+        else:
+            return (_array_descr(subdtype[0]), subdtype[1])
+
+    names = descriptor.names
+    ordered_fields = [fields[x] + (x,) for x in names]
+    result = []
+    offset = 0
+    for field in ordered_fields:
+        if field[1] > offset:
+            num = field[1] - offset
+            result.append(('', f'|V{num}'))
+            offset += num
+        elif field[1] < offset:
+            raise ValueError(
+                "dtype.descr is not defined for types with overlapping or "
+                "out-of-order fields")
+        if len(field) > 3:
+            name = (field[2], field[3])
+        else:
+            name = field[2]
+        if field[0].subdtype:
+            tup = (name, _array_descr(field[0].subdtype[0]),
+                   field[0].subdtype[1])
+        else:
+            tup = (name, _array_descr(field[0]))
+        offset += field[0].itemsize
+        result.append(tup)
+
+    if descriptor.itemsize > offset:
+        num = descriptor.itemsize - offset
+        result.append(('', f'|V{num}'))
+
+    return result
+
+# Build a new array from the information in a pickle.
+# Note that the name numpy.core._internal._reconstruct is embedded in
+# pickles of ndarrays made with NumPy before release 1.0
+# so don't remove the name here, or you'll
+# break backward compatibility.
+def _reconstruct(subtype, shape, dtype):
+    return ndarray.__new__(subtype, shape, dtype)
+
+
+# format_re was originally from numarray by J. Todd Miller
+
+format_re = re.compile(r'(?P<order1>[<>|=]?)'
+                       r'(?P<repeats> *[(]?[ ,0-9]*[)]? *)'
+                       r'(?P<order2>[<>|=]?)'
+                       r'(?P<dtype>[A-Za-z0-9.?]*(?:\[[a-zA-Z0-9,.]+\])?)')
+sep_re = re.compile(r'\s*,\s*')
+space_re = re.compile(r'\s+$')
+
+# astr is a string (perhaps comma separated)
+
+_convorder = {'=': _nbo}
+
+def _commastring(astr):
+    startindex = 0
+    result = []
+    while startindex < len(astr):
+        mo = format_re.match(astr, pos=startindex)
+        try:
+            (order1, repeats, order2, dtype) = mo.groups()
+        except (TypeError, AttributeError):
+            raise ValueError(
+                f'format number {len(result)+1} of "{astr}" is not recognized'
+                ) from None
+        startindex = mo.end()
+        # Separator or ending padding
+        if startindex < len(astr):
+            if space_re.match(astr, pos=startindex):
+                startindex = len(astr)
+            else:
+                mo = sep_re.match(astr, pos=startindex)
+                if not mo:
+                    raise ValueError(
+                        'format number %d of "%s" is not recognized' %
+                        (len(result)+1, astr))
+                startindex = mo.end()
+
+        if order2 == '':
+            order = order1
+        elif order1 == '':
+            order = order2
+        else:
+            order1 = _convorder.get(order1, order1)
+            order2 = _convorder.get(order2, order2)
+            if (order1 != order2):
+                raise ValueError(
+                    'inconsistent byte-order specification %s and %s' %
+                    (order1, order2))
+            order = order1
+
+        if order in ('|', '=', _nbo):
+            order = ''
+        dtype = order + dtype
+        if (repeats == ''):
+            newitem = dtype
+        else:
+            newitem = (dtype, ast.literal_eval(repeats))
+        result.append(newitem)
+
+    return result
+
+class dummy_ctype:
+    def __init__(self, cls):
+        self._cls = cls
+    def __mul__(self, other):
+        return self
+    def __call__(self, *other):
+        return self._cls(other)
+    def __eq__(self, other):
+        return self._cls == other._cls
+    def __ne__(self, other):
+        return self._cls != other._cls
+
+def _getintp_ctype():
+    val = _getintp_ctype.cache
+    if val is not None:
+        return val
+    if ctypes is None:
+        import numpy as np
+        val = dummy_ctype(np.intp)
+    else:
+        char = dtype('p').char
+        if char == 'i':
+            val = ctypes.c_int
+        elif char == 'l':
+            val = ctypes.c_long
+        elif char == 'q':
+            val = ctypes.c_longlong
+        else:
+            val = ctypes.c_long
+    _getintp_ctype.cache = val
+    return val
+_getintp_ctype.cache = None
+
+# Used for .ctypes attribute of ndarray
+
+class _missing_ctypes:
+    def cast(self, num, obj):
+        return num.value
+
+    class c_void_p:
+        def __init__(self, ptr):
+            self.value = ptr
+
+
+class _ctypes:
+    def __init__(self, array, ptr=None):
+        self._arr = array
+
+        if ctypes:
+            self._ctypes = ctypes
+            self._data = self._ctypes.c_void_p(ptr)
+        else:
+            # fake a pointer-like object that holds onto the reference
+            self._ctypes = _missing_ctypes()
+            self._data = self._ctypes.c_void_p(ptr)
+            self._data._objects = array
+
+        if self._arr.ndim == 0:
+            self._zerod = True
+        else:
+            self._zerod = False
+
+    def data_as(self, obj):
+        """
+        Return the data pointer cast to a particular c-types object.
+        For example, calling ``self._as_parameter_`` is equivalent to
+        ``self.data_as(ctypes.c_void_p)``. Perhaps you want to use the data as a
+        pointer to a ctypes array of floating-point data:
+        ``self.data_as(ctypes.POINTER(ctypes.c_double))``.
+
+        The returned pointer will keep a reference to the array.
+        """
+        # _ctypes.cast function causes a circular reference of self._data in
+        # self._data._objects. Attributes of self._data cannot be released
+        # until gc.collect is called. Make a copy of the pointer first then let
+        # it hold the array reference. This is a workaround to circumvent the
+        # CPython bug https://bugs.python.org/issue12836
+        ptr = self._ctypes.cast(self._data, obj)
+        ptr._arr = self._arr
+        return ptr
+
+    def shape_as(self, obj):
+        """
+        Return the shape tuple as an array of some other c-types
+        type. For example: ``self.shape_as(ctypes.c_short)``.
+        """
+        if self._zerod:
+            return None
+        return (obj*self._arr.ndim)(*self._arr.shape)
+
+    def strides_as(self, obj):
+        """
+        Return the strides tuple as an array of some other
+        c-types type. For example: ``self.strides_as(ctypes.c_longlong)``.
+        """
+        if self._zerod:
+            return None
+        return (obj*self._arr.ndim)(*self._arr.strides)
+
+    @property
+    def data(self):
+        """
+        A pointer to the memory area of the array as a Python integer.
+        This memory area may contain data that is not aligned, or not in correct
+        byte-order. The memory area may not even be writeable. The array
+        flags and data-type of this array should be respected when passing this
+        attribute to arbitrary C-code to avoid trouble that can include Python
+        crashing. User Beware! The value of this attribute is exactly the same
+        as ``self._array_interface_['data'][0]``.
+
+        Note that unlike ``data_as``, a reference will not be kept to the array:
+        code like ``ctypes.c_void_p((a + b).ctypes.data)`` will result in a
+        pointer to a deallocated array, and should be spelt
+        ``(a + b).ctypes.data_as(ctypes.c_void_p)``
+        """
+        return self._data.value
+
+    @property
+    def shape(self):
+        """
+        (c_intp*self.ndim): A ctypes array of length self.ndim where
+        the basetype is the C-integer corresponding to ``dtype('p')`` on this
+        platform. This base-type could be `ctypes.c_int`, `ctypes.c_long`, or
+        `ctypes.c_longlong` depending on the platform.
+        The c_intp type is defined accordingly in `numpy.ctypeslib`.
+        The ctypes array contains the shape of the underlying array.
+        """
+        return self.shape_as(_getintp_ctype())
+
+    @property
+    def strides(self):
+        """
+        (c_intp*self.ndim): A ctypes array of length self.ndim where
+        the basetype is the same as for the shape attribute. This ctypes array
+        contains the strides information from the underlying array. This strides
+        information is important for showing how many bytes must be jumped to
+        get to the next element in the array.
+        """
+        return self.strides_as(_getintp_ctype())
+
+    @property
+    def _as_parameter_(self):
+        """
+        Overrides the ctypes semi-magic method
+
+        Enables `c_func(some_array.ctypes)`
+        """
+        return self.data_as(ctypes.c_void_p)
+
+    # Numpy 1.21.0, 2021-05-18
+
+    def get_data(self):
+        """Deprecated getter for the `_ctypes.data` property.
+
+        .. deprecated:: 1.21
+        """
+        warnings.warn('"get_data" is deprecated. Use "data" instead',
+                      DeprecationWarning, stacklevel=2)
+        return self.data
+
+    def get_shape(self):
+        """Deprecated getter for the `_ctypes.shape` property.
+
+        .. deprecated:: 1.21
+        """
+        warnings.warn('"get_shape" is deprecated. Use "shape" instead',
+                      DeprecationWarning, stacklevel=2)
+        return self.shape
+
+    def get_strides(self):
+        """Deprecated getter for the `_ctypes.strides` property.
+
+        .. deprecated:: 1.21
+        """
+        warnings.warn('"get_strides" is deprecated. Use "strides" instead',
+                      DeprecationWarning, stacklevel=2)
+        return self.strides
+
+    def get_as_parameter(self):
+        """Deprecated getter for the `_ctypes._as_parameter_` property.
+
+        .. deprecated:: 1.21
+        """
+        warnings.warn(
+            '"get_as_parameter" is deprecated. Use "_as_parameter_" instead',
+            DeprecationWarning, stacklevel=2,
+        )
+        return self._as_parameter_
+
+
+def _newnames(datatype, order):
+    """
+    Given a datatype and an order object, return a new names tuple, with the
+    order indicated
+    """
+    oldnames = datatype.names
+    nameslist = list(oldnames)
+    if isinstance(order, str):
+        order = [order]
+    seen = set()
+    if isinstance(order, (list, tuple)):
+        for name in order:
+            try:
+                nameslist.remove(name)
+            except ValueError:
+                if name in seen:
+                    raise ValueError(f"duplicate field name: {name}") from None
+                else:
+                    raise ValueError(f"unknown field name: {name}") from None
+            seen.add(name)
+        return tuple(list(order) + nameslist)
+    raise ValueError(f"unsupported order value: {order}")
+
+def _copy_fields(ary):
+    """Return copy of structured array with padding between fields removed.
+
+    Parameters
+    ----------
+    ary : ndarray
+       Structured array from which to remove padding bytes
+
+    Returns
+    -------
+    ary_copy : ndarray
+       Copy of ary with padding bytes removed
+    """
+    dt = ary.dtype
+    copy_dtype = {'names': dt.names,
+                  'formats': [dt.fields[name][0] for name in dt.names]}
+    return array(ary, dtype=copy_dtype, copy=True)
+
+def _getfield_is_safe(oldtype, newtype, offset):
+    """ Checks safety of getfield for object arrays.
+
+    As in _view_is_safe, we need to check that memory containing objects is not
+    reinterpreted as a non-object datatype and vice versa.
+
+    Parameters
+    ----------
+    oldtype : data-type
+        Data type of the original ndarray.
+    newtype : data-type
+        Data type of the field being accessed by ndarray.getfield
+    offset : int
+        Offset of the field being accessed by ndarray.getfield
+
+    Raises
+    ------
+    TypeError
+        If the field access is invalid
+
+    """
+    if newtype.hasobject or oldtype.hasobject:
+        if offset == 0 and newtype == oldtype:
+            return
+        if oldtype.names is not None:
+            for name in oldtype.names:
+                if (oldtype.fields[name][1] == offset and
+                        oldtype.fields[name][0] == newtype):
+                    return
+        raise TypeError("Cannot get/set field of an object array")
+    return
+
+def _view_is_safe(oldtype, newtype):
+    """ Checks safety of a view involving object arrays, for example when
+    doing::
+
+        np.zeros(10, dtype=oldtype).view(newtype)
+
+    Parameters
+    ----------
+    oldtype : data-type
+        Data type of original ndarray
+    newtype : data-type
+        Data type of the view
+
+    Raises
+    ------
+    TypeError
+        If the new type is incompatible with the old type.
+
+    """
+
+    # if the types are equivalent, there is no problem.
+    # for example: dtype((np.record, 'i4,i4')) == dtype((np.void, 'i4,i4'))
+    if oldtype == newtype:
+        return
+
+    if newtype.hasobject or oldtype.hasobject:
+        raise TypeError("Cannot change data-type for object array.")
+    return
+
+# Given a string containing a PEP 3118 format specifier,
+# construct a NumPy dtype
+
+_pep3118_native_map = {
+    '?': '?',
+    'c': 'S1',
+    'b': 'b',
+    'B': 'B',
+    'h': 'h',
+    'H': 'H',
+    'i': 'i',
+    'I': 'I',
+    'l': 'l',
+    'L': 'L',
+    'q': 'q',
+    'Q': 'Q',
+    'e': 'e',
+    'f': 'f',
+    'd': 'd',
+    'g': 'g',
+    'Zf': 'F',
+    'Zd': 'D',
+    'Zg': 'G',
+    's': 'S',
+    'w': 'U',
+    'O': 'O',
+    'x': 'V',  # padding
+}
+_pep3118_native_typechars = ''.join(_pep3118_native_map.keys())
+
+_pep3118_standard_map = {
+    '?': '?',
+    'c': 'S1',
+    'b': 'b',
+    'B': 'B',
+    'h': 'i2',
+    'H': 'u2',
+    'i': 'i4',
+    'I': 'u4',
+    'l': 'i4',
+    'L': 'u4',
+    'q': 'i8',
+    'Q': 'u8',
+    'e': 'f2',
+    'f': 'f',
+    'd': 'd',
+    'Zf': 'F',
+    'Zd': 'D',
+    's': 'S',
+    'w': 'U',
+    'O': 'O',
+    'x': 'V',  # padding
+}
+_pep3118_standard_typechars = ''.join(_pep3118_standard_map.keys())
+
+_pep3118_unsupported_map = {
+    'u': 'UCS-2 strings',
+    '&': 'pointers',
+    't': 'bitfields',
+    'X': 'function pointers',
+}
+
+class _Stream:
+    def __init__(self, s):
+        self.s = s
+        self.byteorder = '@'
+
+    def advance(self, n):
+        res = self.s[:n]
+        self.s = self.s[n:]
+        return res
+
+    def consume(self, c):
+        if self.s[:len(c)] == c:
+            self.advance(len(c))
+            return True
+        return False
+
+    def consume_until(self, c):
+        if callable(c):
+            i = 0
+            while i < len(self.s) and not c(self.s[i]):
+                i = i + 1
+            return self.advance(i)
+        else:
+            i = self.s.index(c)
+            res = self.advance(i)
+            self.advance(len(c))
+            return res
+
+    @property
+    def next(self):
+        return self.s[0]
+
+    def __bool__(self):
+        return bool(self.s)
+
+
+def _dtype_from_pep3118(spec):
+    stream = _Stream(spec)
+    dtype, align = __dtype_from_pep3118(stream, is_subdtype=False)
+    return dtype
+
+def __dtype_from_pep3118(stream, is_subdtype):
+    field_spec = dict(
+        names=[],
+        formats=[],
+        offsets=[],
+        itemsize=0
+    )
+    offset = 0
+    common_alignment = 1
+    is_padding = False
+
+    # Parse spec
+    while stream:
+        value = None
+
+        # End of structure, bail out to upper level
+        if stream.consume('}'):
+            break
+
+        # Sub-arrays (1)
+        shape = None
+        if stream.consume('('):
+            shape = stream.consume_until(')')
+            shape = tuple(map(int, shape.split(',')))
+
+        # Byte order
+        if stream.next in ('@', '=', '<', '>', '^', '!'):
+            byteorder = stream.advance(1)
+            if byteorder == '!':
+                byteorder = '>'
+            stream.byteorder = byteorder
+
+        # Byte order characters also control native vs. standard type sizes
+        if stream.byteorder in ('@', '^'):
+            type_map = _pep3118_native_map
+            type_map_chars = _pep3118_native_typechars
+        else:
+            type_map = _pep3118_standard_map
+            type_map_chars = _pep3118_standard_typechars
+
+        # Item sizes
+        itemsize_str = stream.consume_until(lambda c: not c.isdigit())
+        if itemsize_str:
+            itemsize = int(itemsize_str)
+        else:
+            itemsize = 1
+
+        # Data types
+        is_padding = False
+
+        if stream.consume('T{'):
+            value, align = __dtype_from_pep3118(
+                stream, is_subdtype=True)
+        elif stream.next in type_map_chars:
+            if stream.next == 'Z':
+                typechar = stream.advance(2)
+            else:
+                typechar = stream.advance(1)
+
+            is_padding = (typechar == 'x')
+            dtypechar = type_map[typechar]
+            if dtypechar in 'USV':
+                dtypechar += '%d' % itemsize
+                itemsize = 1
+            numpy_byteorder = {'@': '=', '^': '='}.get(
+                stream.byteorder, stream.byteorder)
+            value = dtype(numpy_byteorder + dtypechar)
+            align = value.alignment
+        elif stream.next in _pep3118_unsupported_map:
+            desc = _pep3118_unsupported_map[stream.next]
+            raise NotImplementedError(
+                "Unrepresentable PEP 3118 data type {!r} ({})"
+                .format(stream.next, desc))
+        else:
+            raise ValueError("Unknown PEP 3118 data type specifier %r" % stream.s)
+
+        #
+        # Native alignment may require padding
+        #
+        # Here we assume that the presence of a '@' character implicitly implies
+        # that the start of the array is *already* aligned.
+        #
+        extra_offset = 0
+        if stream.byteorder == '@':
+            start_padding = (-offset) % align
+            intra_padding = (-value.itemsize) % align
+
+            offset += start_padding
+
+            if intra_padding != 0:
+                if itemsize > 1 or (shape is not None and _prod(shape) > 1):
+                    # Inject internal padding to the end of the sub-item
+                    value = _add_trailing_padding(value, intra_padding)
+                else:
+                    # We can postpone the injection of internal padding,
+                    # as the item appears at most once
+                    extra_offset += intra_padding
+
+            # Update common alignment
+            common_alignment = _lcm(align, common_alignment)
+
+        # Convert itemsize to sub-array
+        if itemsize != 1:
+            value = dtype((value, (itemsize,)))
+
+        # Sub-arrays (2)
+        if shape is not None:
+            value = dtype((value, shape))
+
+        # Field name
+        if stream.consume(':'):
+            name = stream.consume_until(':')
+        else:
+            name = None
+
+        if not (is_padding and name is None):
+            if name is not None and name in field_spec['names']:
+                raise RuntimeError(f"Duplicate field name '{name}' in PEP3118 format")
+            field_spec['names'].append(name)
+            field_spec['formats'].append(value)
+            field_spec['offsets'].append(offset)
+
+        offset += value.itemsize
+        offset += extra_offset
+
+        field_spec['itemsize'] = offset
+
+    # extra final padding for aligned types
+    if stream.byteorder == '@':
+        field_spec['itemsize'] += (-offset) % common_alignment
+
+    # Check if this was a simple 1-item type, and unwrap it
+    if (field_spec['names'] == [None]
+            and field_spec['offsets'][0] == 0
+            and field_spec['itemsize'] == field_spec['formats'][0].itemsize
+            and not is_subdtype):
+        ret = field_spec['formats'][0]
+    else:
+        _fix_names(field_spec)
+        ret = dtype(field_spec)
+
+    # Finished
+    return ret, common_alignment
+
+def _fix_names(field_spec):
+    """ Replace names which are None with the next unused f%d name """
+    names = field_spec['names']
+    for i, name in enumerate(names):
+        if name is not None:
+            continue
+
+        j = 0
+        while True:
+            name = f'f{j}'
+            if name not in names:
+                break
+            j = j + 1
+        names[i] = name
+
+def _add_trailing_padding(value, padding):
+    """Inject the specified number of padding bytes at the end of a dtype"""
+    if value.fields is None:
+        field_spec = dict(
+            names=['f0'],
+            formats=[value],
+            offsets=[0],
+            itemsize=value.itemsize
+        )
+    else:
+        fields = value.fields
+        names = value.names
+        field_spec = dict(
+            names=names,
+            formats=[fields[name][0] for name in names],
+            offsets=[fields[name][1] for name in names],
+            itemsize=value.itemsize
+        )
+
+    field_spec['itemsize'] += padding
+    return dtype(field_spec)
+
+def _prod(a):
+    p = 1
+    for x in a:
+        p *= x
+    return p
+
+def _gcd(a, b):
+    """Calculate the greatest common divisor of a and b"""
+    while b:
+        a, b = b, a % b
+    return a
+
+def _lcm(a, b):
+    return a // _gcd(a, b) * b
+
+def array_ufunc_errmsg_formatter(dummy, ufunc, method, *inputs, **kwargs):
+    """ Format the error message for when __array_ufunc__ gives up. """
+    args_string = ', '.join(['{!r}'.format(arg) for arg in inputs] +
+                            ['{}={!r}'.format(k, v)
+                             for k, v in kwargs.items()])
+    args = inputs + kwargs.get('out', ())
+    types_string = ', '.join(repr(type(arg).__name__) for arg in args)
+    return ('operand type(s) all returned NotImplemented from '
+            '__array_ufunc__({!r}, {!r}, {}): {}'
+            .format(ufunc, method, args_string, types_string))
+
+
+def array_function_errmsg_formatter(public_api, types):
+    """ Format the error message for when __array_ufunc__ gives up. """
+    func_name = '{}.{}'.format(public_api.__module__, public_api.__name__)
+    return ("no implementation found for '{}' on types that implement "
+            '__array_function__: {}'.format(func_name, list(types)))
+
+
+def _ufunc_doc_signature_formatter(ufunc):
+    """
+    Builds a signature string which resembles PEP 457
+
+    This is used to construct the first line of the docstring
+    """
+
+    # input arguments are simple
+    if ufunc.nin == 1:
+        in_args = 'x'
+    else:
+        in_args = ', '.join(f'x{i+1}' for i in range(ufunc.nin))
+
+    # output arguments are both keyword or positional
+    if ufunc.nout == 0:
+        out_args = ', /, out=()'
+    elif ufunc.nout == 1:
+        out_args = ', /, out=None'
+    else:
+        out_args = '[, {positional}], / [, out={default}]'.format(
+            positional=', '.join(
+                'out{}'.format(i+1) for i in range(ufunc.nout)),
+            default=repr((None,)*ufunc.nout)
+        )
+
+    # keyword only args depend on whether this is a gufunc
+    kwargs = (
+        ", casting='same_kind'"
+        ", order='K'"
+        ", dtype=None"
+        ", subok=True"
+    )
+
+    # NOTE: gufuncs may or may not support the `axis` parameter
+    if ufunc.signature is None:
+        kwargs = f", where=True{kwargs}[, signature, extobj]"
+    else:
+        kwargs += "[, signature, extobj, axes, axis]"
+
+    # join all the parts together
+    return '{name}({in_args}{out_args}, *{kwargs})'.format(
+        name=ufunc.__name__,
+        in_args=in_args,
+        out_args=out_args,
+        kwargs=kwargs
+    )
+
+
+def npy_ctypes_check(cls):
+    # determine if a class comes from ctypes, in order to work around
+    # a bug in the buffer protocol for those objects, bpo-10746
+    try:
+        # ctypes class are new-style, so have an __mro__. This probably fails
+        # for ctypes classes with multiple inheritance.
+        if IS_PYPY:
+            # (..., _ctypes.basics._CData, Bufferable, object)
+            ctype_base = cls.__mro__[-3]
+        else:
+            # # (..., _ctypes._CData, object)
+            ctype_base = cls.__mro__[-2]
+        # right now, they're part of the _ctypes module
+        return '_ctypes' in ctype_base.__module__
+    except Exception:
+        return False
+
+
+class recursive:
+    '''
+    A decorator class for recursive nested functions.
+    Naive recursive nested functions hold a reference to themselves:
+
+    def outer(*args):
+        def stringify_leaky(arg0, *arg1):
+            if len(arg1) > 0:
+                return stringify_leaky(*arg1)  # <- HERE
+            return str(arg0)
+        stringify_leaky(*args)
+
+    This design pattern creates a reference cycle that is difficult for a
+    garbage collector to resolve. The decorator class prevents the
+    cycle by passing the nested function in as an argument `self`:
+
+    def outer(*args):
+        @recursive
+        def stringify(self, arg0, *arg1):
+            if len(arg1) > 0:
+                return self(*arg1)
+            return str(arg0)
+        stringify(*args)
+
+    '''
+    def __init__(self, func):
+        self.func = func
+    def __call__(self, *args, **kwargs):
+        return self.func(self, *args, **kwargs)
+

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/core/_internal.pyi

@@ -0,0 +1,35 @@
+from typing import Any, TypeVar, Type, overload, Optional, Generic
+import ctypes as ct
+
+from numpy import ndarray
+
+_CastT = TypeVar("_CastT", bound=ct._CanCastTo)  # Copied from `ctypes.cast`
+_CT = TypeVar("_CT", bound=ct._CData)
+_PT = TypeVar("_PT", bound=Optional[int])
+
+# TODO: Let the likes of `shape_as` and `strides_as` return `None`
+# for 0D arrays once we've got shape-support
+
+class _ctypes(Generic[_PT]):
+    @overload
+    def __new__(cls, array: ndarray[Any, Any], ptr: None = ...) -> _ctypes[None]: ...
+    @overload
+    def __new__(cls, array: ndarray[Any, Any], ptr: _PT) -> _ctypes[_PT]: ...
+
+    # NOTE: In practice `shape` and `strides` return one of the concrete
+    # platform dependant array-types (`c_int`, `c_long` or `c_longlong`)
+    # corresponding to C's `int_ptr_t`, as determined by `_getintp_ctype`
+    # TODO: Hook this in to the mypy plugin so that a more appropiate
+    # `ctypes._SimpleCData[int]` sub-type can be returned
+    @property
+    def data(self) -> _PT: ...
+    @property
+    def shape(self) -> ct.Array[ct.c_int64]: ...
+    @property
+    def strides(self) -> ct.Array[ct.c_int64]: ...
+    @property
+    def _as_parameter_(self) -> ct.c_void_p: ...
+
+    def data_as(self, obj: Type[_CastT]) -> _CastT: ...
+    def shape_as(self, obj: Type[_CT]) -> ct.Array[_CT]: ...
+    def strides_as(self, obj: Type[_CT]) -> ct.Array[_CT]: ...

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/core/_methods.py

@@ -0,0 +1,290 @@
+"""
+Array methods which are called by both the C-code for the method
+and the Python code for the NumPy-namespace function
+
+"""
+import warnings
+from contextlib import nullcontext
+
+from numpy.core import multiarray as mu
+from numpy.core import umath as um
+from numpy.core.multiarray import asanyarray
+from numpy.core import numerictypes as nt
+from numpy.core import _exceptions
+from numpy._globals import _NoValue
+from numpy.compat import pickle, os_fspath
+
+# save those O(100) nanoseconds!
+umr_maximum = um.maximum.reduce
+umr_minimum = um.minimum.reduce
+umr_sum = um.add.reduce
+umr_prod = um.multiply.reduce
+umr_any = um.logical_or.reduce
+umr_all = um.logical_and.reduce
+
+# Complex types to -> (2,)float view for fast-path computation in _var()
+_complex_to_float = {
+    nt.dtype(nt.csingle) : nt.dtype(nt.single),
+    nt.dtype(nt.cdouble) : nt.dtype(nt.double),
+}
+# Special case for windows: ensure double takes precedence
+if nt.dtype(nt.longdouble) != nt.dtype(nt.double):
+    _complex_to_float.update({
+        nt.dtype(nt.clongdouble) : nt.dtype(nt.longdouble),
+    })
+
+# avoid keyword arguments to speed up parsing, saves about 15%-20% for very
+# small reductions
+def _amax(a, axis=None, out=None, keepdims=False,
+          initial=_NoValue, where=True):
+    return umr_maximum(a, axis, None, out, keepdims, initial, where)
+
+def _amin(a, axis=None, out=None, keepdims=False,
+          initial=_NoValue, where=True):
+    return umr_minimum(a, axis, None, out, keepdims, initial, where)
+
+def _sum(a, axis=None, dtype=None, out=None, keepdims=False,
+         initial=_NoValue, where=True):
+    return umr_sum(a, axis, dtype, out, keepdims, initial, where)
+
+def _prod(a, axis=None, dtype=None, out=None, keepdims=False,
+          initial=_NoValue, where=True):
+    return umr_prod(a, axis, dtype, out, keepdims, initial, where)
+
+def _any(a, axis=None, dtype=None, out=None, keepdims=False, *, where=True):
+    # Parsing keyword arguments is currently fairly slow, so avoid it for now
+    if where is True:
+        return umr_any(a, axis, dtype, out, keepdims)
+    return umr_any(a, axis, dtype, out, keepdims, where=where)
+
+def _all(a, axis=None, dtype=None, out=None, keepdims=False, *, where=True):
+    # Parsing keyword arguments is currently fairly slow, so avoid it for now
+    if where is True:
+        return umr_all(a, axis, dtype, out, keepdims)
+    return umr_all(a, axis, dtype, out, keepdims, where=where)
+
+def _count_reduce_items(arr, axis, keepdims=False, where=True):
+    # fast-path for the default case
+    if where is True:
+        # no boolean mask given, calculate items according to axis
+        if axis is None:
+            axis = tuple(range(arr.ndim))
+        elif not isinstance(axis, tuple):
+            axis = (axis,)
+        items = nt.intp(1)
+        for ax in axis:
+            items *= arr.shape[mu.normalize_axis_index(ax, arr.ndim)]
+    else:
+        # TODO: Optimize case when `where` is broadcast along a non-reduction
+        # axis and full sum is more excessive than needed.
+
+        # guarded to protect circular imports
+        from numpy.lib.stride_tricks import broadcast_to
+        # count True values in (potentially broadcasted) boolean mask
+        items = umr_sum(broadcast_to(where, arr.shape), axis, nt.intp, None,
+                        keepdims)
+    return items
+
+# Numpy 1.17.0, 2019-02-24
+# Various clip behavior deprecations, marked with _clip_dep as a prefix.
+
+def _clip_dep_is_scalar_nan(a):
+    # guarded to protect circular imports
+    from numpy.core.fromnumeric import ndim
+    if ndim(a) != 0:
+        return False
+    try:
+        return um.isnan(a)
+    except TypeError:
+        return False
+
+def _clip_dep_is_byte_swapped(a):
+    if isinstance(a, mu.ndarray):
+        return not a.dtype.isnative
+    return False
+
+def _clip_dep_invoke_with_casting(ufunc, *args, out=None, casting=None, **kwargs):
+    # normal path
+    if casting is not None:
+        return ufunc(*args, out=out, casting=casting, **kwargs)
+
+    # try to deal with broken casting rules
+    try:
+        return ufunc(*args, out=out, **kwargs)
+    except _exceptions._UFuncOutputCastingError as e:
+        # Numpy 1.17.0, 2019-02-24
+        warnings.warn(
+            "Converting the output of clip from {!r} to {!r} is deprecated. "
+            "Pass `casting=\"unsafe\"` explicitly to silence this warning, or "
+            "correct the type of the variables.".format(e.from_, e.to),
+            DeprecationWarning,
+            stacklevel=2
+        )
+        return ufunc(*args, out=out, casting="unsafe", **kwargs)
+
+def _clip(a, min=None, max=None, out=None, *, casting=None, **kwargs):
+    if min is None and max is None:
+        raise ValueError("One of max or min must be given")
+
+    # Numpy 1.17.0, 2019-02-24
+    # This deprecation probably incurs a substantial slowdown for small arrays,
+    # it will be good to get rid of it.
+    if not _clip_dep_is_byte_swapped(a) and not _clip_dep_is_byte_swapped(out):
+        using_deprecated_nan = False
+        if _clip_dep_is_scalar_nan(min):
+            min = -float('inf')
+            using_deprecated_nan = True
+        if _clip_dep_is_scalar_nan(max):
+            max = float('inf')
+            using_deprecated_nan = True
+        if using_deprecated_nan:
+            warnings.warn(
+                "Passing `np.nan` to mean no clipping in np.clip has always "
+                "been unreliable, and is now deprecated. "
+                "In future, this will always return nan, like it already does "
+                "when min or max are arrays that contain nan. "
+                "To skip a bound, pass either None or an np.inf of an "
+                "appropriate sign.",
+                DeprecationWarning,
+                stacklevel=2
+            )
+
+    if min is None:
+        return _clip_dep_invoke_with_casting(
+            um.minimum, a, max, out=out, casting=casting, **kwargs)
+    elif max is None:
+        return _clip_dep_invoke_with_casting(
+            um.maximum, a, min, out=out, casting=casting, **kwargs)
+    else:
+        return _clip_dep_invoke_with_casting(
+            um.clip, a, min, max, out=out, casting=casting, **kwargs)
+
+def _mean(a, axis=None, dtype=None, out=None, keepdims=False, *, where=True):
+    arr = asanyarray(a)
+
+    is_float16_result = False
+
+    rcount = _count_reduce_items(arr, axis, keepdims=keepdims, where=where)
+    if rcount == 0 if where is True else umr_any(rcount == 0, axis=None):
+        warnings.warn("Mean of empty slice.", RuntimeWarning, stacklevel=2)
+
+    # Cast bool, unsigned int, and int to float64 by default
+    if dtype is None:
+        if issubclass(arr.dtype.type, (nt.integer, nt.bool_)):
+            dtype = mu.dtype('f8')
+        elif issubclass(arr.dtype.type, nt.float16):
+            dtype = mu.dtype('f4')
+            is_float16_result = True
+
+    ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)
+    if isinstance(ret, mu.ndarray):
+        ret = um.true_divide(
+                ret, rcount, out=ret, casting='unsafe', subok=False)
+        if is_float16_result and out is None:
+            ret = arr.dtype.type(ret)
+    elif hasattr(ret, 'dtype'):
+        if is_float16_result:
+            ret = arr.dtype.type(ret / rcount)
+        else:
+            ret = ret.dtype.type(ret / rcount)
+    else:
+        ret = ret / rcount
+
+    return ret
+
+def _var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False, *,
+         where=True):
+    arr = asanyarray(a)
+
+    rcount = _count_reduce_items(arr, axis, keepdims=keepdims, where=where)
+    # Make this warning show up on top.
+    if ddof >= rcount if where is True else umr_any(ddof >= rcount, axis=None):
+        warnings.warn("Degrees of freedom <= 0 for slice", RuntimeWarning,
+                      stacklevel=2)
+
+    # Cast bool, unsigned int, and int to float64 by default
+    if dtype is None and issubclass(arr.dtype.type, (nt.integer, nt.bool_)):
+        dtype = mu.dtype('f8')
+
+    # Compute the mean.
+    # Note that if dtype is not of inexact type then arraymean will
+    # not be either.
+    arrmean = umr_sum(arr, axis, dtype, keepdims=True, where=where)
+    # The shape of rcount has to match arrmean to not change the shape of out
+    # in broadcasting. Otherwise, it cannot be stored back to arrmean.
+    if rcount.ndim == 0:
+        # fast-path for default case when where is True
+        div = rcount
+    else:
+        # matching rcount to arrmean when where is specified as array
+        div = rcount.reshape(arrmean.shape)
+    if isinstance(arrmean, mu.ndarray):
+        arrmean = um.true_divide(arrmean, div, out=arrmean, casting='unsafe',
+                                 subok=False)
+    else:
+        arrmean = arrmean.dtype.type(arrmean / rcount)
+
+    # Compute sum of squared deviations from mean
+    # Note that x may not be inexact and that we need it to be an array,
+    # not a scalar.
+    x = asanyarray(arr - arrmean)
+
+    if issubclass(arr.dtype.type, (nt.floating, nt.integer)):
+        x = um.multiply(x, x, out=x)
+    # Fast-paths for built-in complex types
+    elif x.dtype in _complex_to_float:
+        xv = x.view(dtype=(_complex_to_float[x.dtype], (2,)))
+        um.multiply(xv, xv, out=xv)
+        x = um.add(xv[..., 0], xv[..., 1], out=x.real).real
+    # Most general case; includes handling object arrays containing imaginary
+    # numbers and complex types with non-native byteorder
+    else:
+        x = um.multiply(x, um.conjugate(x), out=x).real
+
+    ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where)
+
+    # Compute degrees of freedom and make sure it is not negative.
+    rcount = um.maximum(rcount - ddof, 0)
+
+    # divide by degrees of freedom
+    if isinstance(ret, mu.ndarray):
+        ret = um.true_divide(
+                ret, rcount, out=ret, casting='unsafe', subok=False)
+    elif hasattr(ret, 'dtype'):
+        ret = ret.dtype.type(ret / rcount)
+    else:
+        ret = ret / rcount
+
+    return ret
+
+def _std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False, *,
+         where=True):
+    ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
+               keepdims=keepdims, where=where)
+
+    if isinstance(ret, mu.ndarray):
+        ret = um.sqrt(ret, out=ret)
+    elif hasattr(ret, 'dtype'):
+        ret = ret.dtype.type(um.sqrt(ret))
+    else:
+        ret = um.sqrt(ret)
+
+    return ret
+
+def _ptp(a, axis=None, out=None, keepdims=False):
+    return um.subtract(
+        umr_maximum(a, axis, None, out, keepdims),
+        umr_minimum(a, axis, None, None, keepdims),
+        out
+    )
+
+def _dump(self, file, protocol=2):
+    if hasattr(file, 'write'):
+        ctx = nullcontext(file)
+    else:
+        ctx = open(os_fspath(file), "wb")
+    with ctx as f:
+        pickle.dump(self, f, protocol=protocol)
+
+def _dumps(self, protocol=2):
+    return pickle.dumps(self, protocol=protocol)

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/core/_string_helpers.py

@@ -0,0 +1,100 @@
+"""
+String-handling utilities to avoid locale-dependence.
+
+Used primarily to generate type name aliases.
+"""
+# "import string" is costly to import!
+# Construct the translation tables directly
+#   "A" = chr(65), "a" = chr(97)
+_all_chars = [chr(_m) for _m in range(256)]
+_ascii_upper = _all_chars[65:65+26]
+_ascii_lower = _all_chars[97:97+26]
+LOWER_TABLE = "".join(_all_chars[:65] + _ascii_lower + _all_chars[65+26:])
+UPPER_TABLE = "".join(_all_chars[:97] + _ascii_upper + _all_chars[97+26:])
+
+
+def english_lower(s):
+    """ Apply English case rules to convert ASCII strings to all lower case.
+
+    This is an internal utility function to replace calls to str.lower() such
+    that we can avoid changing behavior with changing locales. In particular,
+    Turkish has distinct dotted and dotless variants of the Latin letter "I" in
+    both lowercase and uppercase. Thus, "I".lower() != "i" in a "tr" locale.
+
+    Parameters
+    ----------
+    s : str
+
+    Returns
+    -------
+    lowered : str
+
+    Examples
+    --------
+    >>> from numpy.core.numerictypes import english_lower
+    >>> english_lower('ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789_')
+    'abcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyz0123456789_'
+    >>> english_lower('')
+    ''
+    """
+    lowered = s.translate(LOWER_TABLE)
+    return lowered
+
+
+def english_upper(s):
+    """ Apply English case rules to convert ASCII strings to all upper case.
+
+    This is an internal utility function to replace calls to str.upper() such
+    that we can avoid changing behavior with changing locales. In particular,
+    Turkish has distinct dotted and dotless variants of the Latin letter "I" in
+    both lowercase and uppercase. Thus, "i".upper() != "I" in a "tr" locale.
+
+    Parameters
+    ----------
+    s : str
+
+    Returns
+    -------
+    uppered : str
+
+    Examples
+    --------
+    >>> from numpy.core.numerictypes import english_upper
+    >>> english_upper('ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789_')
+    'ABCDEFGHIJKLMNOPQRSTUVWXYZABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789_'
+    >>> english_upper('')
+    ''
+    """
+    uppered = s.translate(UPPER_TABLE)
+    return uppered
+
+
+def english_capitalize(s):
+    """ Apply English case rules to convert the first character of an ASCII
+    string to upper case.
+
+    This is an internal utility function to replace calls to str.capitalize()
+    such that we can avoid changing behavior with changing locales.
+
+    Parameters
+    ----------
+    s : str
+
+    Returns
+    -------
+    capitalized : str
+
+    Examples
+    --------
+    >>> from numpy.core.numerictypes import english_capitalize
+    >>> english_capitalize('int8')
+    'Int8'
+    >>> english_capitalize('Int8')
+    'Int8'
+    >>> english_capitalize('')
+    ''
+    """
+    if s:
+        return english_upper(s[0]) + s[1:]
+    else:
+        return s

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/core/_type_aliases.py

@@ -0,0 +1,244 @@
+"""
+Due to compatibility, numpy has a very large number of different naming
+conventions for the scalar types (those subclassing from `numpy.generic`).
+This file produces a convoluted set of dictionaries mapping names to types,
+and sometimes other mappings too.
+
+.. data:: allTypes
+    A dictionary of names to types that will be exposed as attributes through
+    ``np.core.numerictypes.*``
+
+.. data:: sctypeDict
+    Similar to `allTypes`, but maps a broader set of aliases to their types.
+
+.. data:: sctypes
+    A dictionary keyed by a "type group" string, providing a list of types
+    under that group.
+
+"""
+
+from numpy.compat import unicode
+from numpy.core._string_helpers import english_lower
+from numpy.core.multiarray import typeinfo, dtype
+from numpy.core._dtype import _kind_name
+
+
+sctypeDict = {}      # Contains all leaf-node scalar types with aliases
+allTypes = {}            # Collect the types we will add to the module
+
+
+# separate the actual type info from the abstract base classes
+_abstract_types = {}
+_concrete_typeinfo = {}
+for k, v in typeinfo.items():
+    # make all the keys lowercase too
+    k = english_lower(k)
+    if isinstance(v, type):
+        _abstract_types[k] = v
+    else:
+        _concrete_typeinfo[k] = v
+
+_concrete_types = {v.type for k, v in _concrete_typeinfo.items()}
+
+
+def _bits_of(obj):
+    try:
+        info = next(v for v in _concrete_typeinfo.values() if v.type is obj)
+    except StopIteration:
+        if obj in _abstract_types.values():
+            msg = "Cannot count the bits of an abstract type"
+            raise ValueError(msg) from None
+
+        # some third-party type - make a best-guess
+        return dtype(obj).itemsize * 8
+    else:
+        return info.bits
+
+
+def bitname(obj):
+    """Return a bit-width name for a given type object"""
+    bits = _bits_of(obj)
+    dt = dtype(obj)
+    char = dt.kind
+    base = _kind_name(dt)
+
+    if base == 'object':
+        bits = 0
+
+    if bits != 0:
+        char = "%s%d" % (char, bits // 8)
+
+    return base, bits, char
+
+
+def _add_types():
+    for name, info in _concrete_typeinfo.items():
+        # define C-name and insert typenum and typechar references also
+        allTypes[name] = info.type
+        sctypeDict[name] = info.type
+        sctypeDict[info.char] = info.type
+        sctypeDict[info.num] = info.type
+
+    for name, cls in _abstract_types.items():
+        allTypes[name] = cls
+_add_types()
+
+# This is the priority order used to assign the bit-sized NPY_INTxx names, which
+# must match the order in npy_common.h in order for NPY_INTxx and np.intxx to be
+# consistent.
+# If two C types have the same size, then the earliest one in this list is used
+# as the sized name.
+_int_ctypes = ['long', 'longlong', 'int', 'short', 'byte']
+_uint_ctypes = list('u' + t for t in _int_ctypes)
+
+def _add_aliases():
+    for name, info in _concrete_typeinfo.items():
+        # these are handled by _add_integer_aliases
+        if name in _int_ctypes or name in _uint_ctypes:
+            continue
+
+        # insert bit-width version for this class (if relevant)
+        base, bit, char = bitname(info.type)
+
+        myname = "%s%d" % (base, bit)
+
+        # ensure that (c)longdouble does not overwrite the aliases assigned to
+        # (c)double
+        if name in ('longdouble', 'clongdouble') and myname in allTypes:
+            continue
+
+        allTypes[myname] = info.type
+
+        # add mapping for both the bit name and the numarray name
+        sctypeDict[myname] = info.type
+
+        # add forward, reverse, and string mapping to numarray
+        sctypeDict[char] = info.type
+
+    # Add deprecated numeric-style type aliases manually, at some point
+    # we may want to deprecate the lower case "bytes0" version as well.
+    for name in ["Bytes0", "Datetime64", "Str0", "Uint32", "Uint64"]:
+        if english_lower(name) not in allTypes:
+            # Only one of Uint32 or Uint64, aliases of `np.uintp`, was (and is) defined, note that this
+            # is not UInt32/UInt64 (capital i), which is removed.
+            continue
+        allTypes[name] = allTypes[english_lower(name)]
+        sctypeDict[name] = sctypeDict[english_lower(name)]
+
+_add_aliases()
+
+def _add_integer_aliases():
+    seen_bits = set()
+    for i_ctype, u_ctype in zip(_int_ctypes, _uint_ctypes):
+        i_info = _concrete_typeinfo[i_ctype]
+        u_info = _concrete_typeinfo[u_ctype]
+        bits = i_info.bits  # same for both
+
+        for info, charname, intname in [
+                (i_info,'i%d' % (bits//8,), 'int%d' % bits),
+                (u_info,'u%d' % (bits//8,), 'uint%d' % bits)]:
+            if bits not in seen_bits:
+                # sometimes two different types have the same number of bits
+                # if so, the one iterated over first takes precedence
+                allTypes[intname] = info.type
+                sctypeDict[intname] = info.type
+                sctypeDict[charname] = info.type
+
+        seen_bits.add(bits)
+
+_add_integer_aliases()
+
+# We use these later
+void = allTypes['void']
+
+#
+# Rework the Python names (so that float and complex and int are consistent
+#                            with Python usage)
+#
+def _set_up_aliases():
+    type_pairs = [('complex_', 'cdouble'),
+                  ('int0', 'intp'),
+                  ('uint0', 'uintp'),
+                  ('single', 'float'),
+                  ('csingle', 'cfloat'),
+                  ('singlecomplex', 'cfloat'),
+                  ('float_', 'double'),
+                  ('intc', 'int'),
+                  ('uintc', 'uint'),
+                  ('int_', 'long'),
+                  ('uint', 'ulong'),
+                  ('cfloat', 'cdouble'),
+                  ('longfloat', 'longdouble'),
+                  ('clongfloat', 'clongdouble'),
+                  ('longcomplex', 'clongdouble'),
+                  ('bool_', 'bool'),
+                  ('bytes_', 'string'),
+                  ('string_', 'string'),
+                  ('str_', 'unicode'),
+                  ('unicode_', 'unicode'),
+                  ('object_', 'object')]
+    for alias, t in type_pairs:
+        allTypes[alias] = allTypes[t]
+        sctypeDict[alias] = sctypeDict[t]
+    # Remove aliases overriding python types and modules
+    to_remove = ['ulong', 'object', 'int', 'float',
+                 'complex', 'bool', 'string', 'datetime', 'timedelta',
+                 'bytes', 'str']
+
+    for t in to_remove:
+        try:
+            del allTypes[t]
+            del sctypeDict[t]
+        except KeyError:
+            pass
+_set_up_aliases()
+
+
+sctypes = {'int': [],
+           'uint':[],
+           'float':[],
+           'complex':[],
+           'others':[bool, object, bytes, unicode, void]}
+
+def _add_array_type(typename, bits):
+    try:
+        t = allTypes['%s%d' % (typename, bits)]
+    except KeyError:
+        pass
+    else:
+        sctypes[typename].append(t)
+
+def _set_array_types():
+    ibytes = [1, 2, 4, 8, 16, 32, 64]
+    fbytes = [2, 4, 8, 10, 12, 16, 32, 64]
+    for bytes in ibytes:
+        bits = 8*bytes
+        _add_array_type('int', bits)
+        _add_array_type('uint', bits)
+    for bytes in fbytes:
+        bits = 8*bytes
+        _add_array_type('float', bits)
+        _add_array_type('complex', 2*bits)
+    _gi = dtype('p')
+    if _gi.type not in sctypes['int']:
+        indx = 0
+        sz = _gi.itemsize
+        _lst = sctypes['int']
+        while (indx < len(_lst) and sz >= _lst[indx](0).itemsize):
+            indx += 1
+        sctypes['int'].insert(indx, _gi.type)
+        sctypes['uint'].insert(indx, dtype('P').type)
+_set_array_types()
+
+
+# Add additional strings to the sctypeDict
+_toadd = ['int', 'float', 'complex', 'bool', 'object',
+          'str', 'bytes', ('a', 'bytes_')]
+
+for name in _toadd:
+    if isinstance(name, tuple):
+        sctypeDict[name[0]] = allTypes[name[1]]
+    else:
+        sctypeDict[name] = allTypes['%s_' % name]
+
+del _toadd, name

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/core/_type_aliases.pyi

@@ -0,0 +1,19 @@
+import sys
+from typing import Dict, Union, Type, List
+
+from numpy import generic, signedinteger, unsignedinteger, floating, complexfloating
+
+if sys.version_info >= (3, 8):
+    from typing import TypedDict
+else:
+    from typing_extensions import TypedDict
+
+class _SCTypes(TypedDict):
+    int: List[Type[signedinteger]]
+    uint: List[Type[unsignedinteger]]
+    float: List[Type[floating]]
+    complex: List[Type[complexfloating]]
+    others: List[type]
+
+sctypeDict: Dict[Union[int, str], Type[generic]]
+sctypes: _SCTypes

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/core/_ufunc_config.py

@@ -0,0 +1,446 @@
+"""
+Functions for changing global ufunc configuration
+
+This provides helpers which wrap `umath.geterrobj` and `umath.seterrobj`
+"""
+import collections.abc
+import contextlib
+
+from .overrides import set_module
+from .umath import (
+    UFUNC_BUFSIZE_DEFAULT,
+    ERR_IGNORE, ERR_WARN, ERR_RAISE, ERR_CALL, ERR_PRINT, ERR_LOG, ERR_DEFAULT,
+    SHIFT_DIVIDEBYZERO, SHIFT_OVERFLOW, SHIFT_UNDERFLOW, SHIFT_INVALID,
+)
+from . import umath
+
+__all__ = [
+    "seterr", "geterr", "setbufsize", "getbufsize", "seterrcall", "geterrcall",
+    "errstate",
+]
+
+_errdict = {"ignore": ERR_IGNORE,
+            "warn": ERR_WARN,
+            "raise": ERR_RAISE,
+            "call": ERR_CALL,
+            "print": ERR_PRINT,
+            "log": ERR_LOG}
+
+_errdict_rev = {value: key for key, value in _errdict.items()}
+
+
+@set_module('numpy')
+def seterr(all=None, divide=None, over=None, under=None, invalid=None):
+    """
+    Set how floating-point errors are handled.
+
+    Note that operations on integer scalar types (such as `int16`) are
+    handled like floating point, and are affected by these settings.
+
+    Parameters
+    ----------
+    all : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
+        Set treatment for all types of floating-point errors at once:
+
+        - ignore: Take no action when the exception occurs.
+        - warn: Print a `RuntimeWarning` (via the Python `warnings` module).
+        - raise: Raise a `FloatingPointError`.
+        - call: Call a function specified using the `seterrcall` function.
+        - print: Print a warning directly to ``stdout``.
+        - log: Record error in a Log object specified by `seterrcall`.
+
+        The default is not to change the current behavior.
+    divide : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
+        Treatment for division by zero.
+    over : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
+        Treatment for floating-point overflow.
+    under : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
+        Treatment for floating-point underflow.
+    invalid : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
+        Treatment for invalid floating-point operation.
+
+    Returns
+    -------
+    old_settings : dict
+        Dictionary containing the old settings.
+
+    See also
+    --------
+    seterrcall : Set a callback function for the 'call' mode.
+    geterr, geterrcall, errstate
+
+    Notes
+    -----
+    The floating-point exceptions are defined in the IEEE 754 standard [1]_:
+
+    - Division by zero: infinite result obtained from finite numbers.
+    - Overflow: result too large to be expressed.
+    - Underflow: result so close to zero that some precision
+      was lost.
+    - Invalid operation: result is not an expressible number, typically
+      indicates that a NaN was produced.
+
+    .. [1] https://en.wikipedia.org/wiki/IEEE_754
+
+    Examples
+    --------
+    >>> old_settings = np.seterr(all='ignore')  #seterr to known value
+    >>> np.seterr(over='raise')
+    {'divide': 'ignore', 'over': 'ignore', 'under': 'ignore', 'invalid': 'ignore'}
+    >>> np.seterr(**old_settings)  # reset to default
+    {'divide': 'ignore', 'over': 'raise', 'under': 'ignore', 'invalid': 'ignore'}
+
+    >>> np.int16(32000) * np.int16(3)
+    30464
+    >>> old_settings = np.seterr(all='warn', over='raise')
+    >>> np.int16(32000) * np.int16(3)
+    Traceback (most recent call last):
+      File "<stdin>", line 1, in <module>
+    FloatingPointError: overflow encountered in short_scalars
+
+    >>> old_settings = np.seterr(all='print')
+    >>> np.geterr()
+    {'divide': 'print', 'over': 'print', 'under': 'print', 'invalid': 'print'}
+    >>> np.int16(32000) * np.int16(3)
+    30464
+
+    """
+
+    pyvals = umath.geterrobj()
+    old = geterr()
+
+    if divide is None:
+        divide = all or old['divide']
+    if over is None:
+        over = all or old['over']
+    if under is None:
+        under = all or old['under']
+    if invalid is None:
+        invalid = all or old['invalid']
+
+    maskvalue = ((_errdict[divide] << SHIFT_DIVIDEBYZERO) +
+                 (_errdict[over] << SHIFT_OVERFLOW) +
+                 (_errdict[under] << SHIFT_UNDERFLOW) +
+                 (_errdict[invalid] << SHIFT_INVALID))
+
+    pyvals[1] = maskvalue
+    umath.seterrobj(pyvals)
+    return old
+
+
+@set_module('numpy')
+def geterr():
+    """
+    Get the current way of handling floating-point errors.
+
+    Returns
+    -------
+    res : dict
+        A dictionary with keys "divide", "over", "under", and "invalid",
+        whose values are from the strings "ignore", "print", "log", "warn",
+        "raise", and "call". The keys represent possible floating-point
+        exceptions, and the values define how these exceptions are handled.
+
+    See Also
+    --------
+    geterrcall, seterr, seterrcall
+
+    Notes
+    -----
+    For complete documentation of the types of floating-point exceptions and
+    treatment options, see `seterr`.
+
+    Examples
+    --------
+    >>> np.geterr()
+    {'divide': 'warn', 'over': 'warn', 'under': 'ignore', 'invalid': 'warn'}
+    >>> np.arange(3.) / np.arange(3.)
+    array([nan,  1.,  1.])
+
+    >>> oldsettings = np.seterr(all='warn', over='raise')
+    >>> np.geterr()
+    {'divide': 'warn', 'over': 'raise', 'under': 'warn', 'invalid': 'warn'}
+    >>> np.arange(3.) / np.arange(3.)
+    array([nan,  1.,  1.])
+
+    """
+    maskvalue = umath.geterrobj()[1]
+    mask = 7
+    res = {}
+    val = (maskvalue >> SHIFT_DIVIDEBYZERO) & mask
+    res['divide'] = _errdict_rev[val]
+    val = (maskvalue >> SHIFT_OVERFLOW) & mask
+    res['over'] = _errdict_rev[val]
+    val = (maskvalue >> SHIFT_UNDERFLOW) & mask
+    res['under'] = _errdict_rev[val]
+    val = (maskvalue >> SHIFT_INVALID) & mask
+    res['invalid'] = _errdict_rev[val]
+    return res
+
+
+@set_module('numpy')
+def setbufsize(size):
+    """
+    Set the size of the buffer used in ufuncs.
+
+    Parameters
+    ----------
+    size : int
+        Size of buffer.
+
+    """
+    if size > 10e6:
+        raise ValueError("Buffer size, %s, is too big." % size)
+    if size < 5:
+        raise ValueError("Buffer size, %s, is too small." % size)
+    if size % 16 != 0:
+        raise ValueError("Buffer size, %s, is not a multiple of 16." % size)
+
+    pyvals = umath.geterrobj()
+    old = getbufsize()
+    pyvals[0] = size
+    umath.seterrobj(pyvals)
+    return old
+
+
+@set_module('numpy')
+def getbufsize():
+    """
+    Return the size of the buffer used in ufuncs.
+
+    Returns
+    -------
+    getbufsize : int
+        Size of ufunc buffer in bytes.
+
+    """
+    return umath.geterrobj()[0]
+
+
+@set_module('numpy')
+def seterrcall(func):
+    """
+    Set the floating-point error callback function or log object.
+
+    There are two ways to capture floating-point error messages.  The first
+    is to set the error-handler to 'call', using `seterr`.  Then, set
+    the function to call using this function.
+
+    The second is to set the error-handler to 'log', using `seterr`.
+    Floating-point errors then trigger a call to the 'write' method of
+    the provided object.
+
+    Parameters
+    ----------
+    func : callable f(err, flag) or object with write method
+        Function to call upon floating-point errors ('call'-mode) or
+        object whose 'write' method is used to log such message ('log'-mode).
+
+        The call function takes two arguments. The first is a string describing
+        the type of error (such as "divide by zero", "overflow", "underflow",
+        or "invalid value"), and the second is the status flag.  The flag is a
+        byte, whose four least-significant bits indicate the type of error, one
+        of "divide", "over", "under", "invalid"::
+
+          [0 0 0 0 divide over under invalid]
+
+        In other words, ``flags = divide + 2*over + 4*under + 8*invalid``.
+
+        If an object is provided, its write method should take one argument,
+        a string.
+
+    Returns
+    -------
+    h : callable, log instance or None
+        The old error handler.
+
+    See Also
+    --------
+    seterr, geterr, geterrcall
+
+    Examples
+    --------
+    Callback upon error:
+
+    >>> def err_handler(type, flag):
+    ...     print("Floating point error (%s), with flag %s" % (type, flag))
+    ...
+
+    >>> saved_handler = np.seterrcall(err_handler)
+    >>> save_err = np.seterr(all='call')
+
+    >>> np.array([1, 2, 3]) / 0.0
+    Floating point error (divide by zero), with flag 1
+    array([inf, inf, inf])
+
+    >>> np.seterrcall(saved_handler)
+    <function err_handler at 0x...>
+    >>> np.seterr(**save_err)
+    {'divide': 'call', 'over': 'call', 'under': 'call', 'invalid': 'call'}
+
+    Log error message:
+
+    >>> class Log:
+    ...     def write(self, msg):
+    ...         print("LOG: %s" % msg)
+    ...
+
+    >>> log = Log()
+    >>> saved_handler = np.seterrcall(log)
+    >>> save_err = np.seterr(all='log')
+
+    >>> np.array([1, 2, 3]) / 0.0
+    LOG: Warning: divide by zero encountered in true_divide
+    array([inf, inf, inf])
+
+    >>> np.seterrcall(saved_handler)
+    <numpy.core.numeric.Log object at 0x...>
+    >>> np.seterr(**save_err)
+    {'divide': 'log', 'over': 'log', 'under': 'log', 'invalid': 'log'}
+
+    """
+    if func is not None and not isinstance(func, collections.abc.Callable):
+        if (not hasattr(func, 'write') or
+                not isinstance(func.write, collections.abc.Callable)):
+            raise ValueError("Only callable can be used as callback")
+    pyvals = umath.geterrobj()
+    old = geterrcall()
+    pyvals[2] = func
+    umath.seterrobj(pyvals)
+    return old
+
+
+@set_module('numpy')
+def geterrcall():
+    """
+    Return the current callback function used on floating-point errors.
+
+    When the error handling for a floating-point error (one of "divide",
+    "over", "under", or "invalid") is set to 'call' or 'log', the function
+    that is called or the log instance that is written to is returned by
+    `geterrcall`. This function or log instance has been set with
+    `seterrcall`.
+
+    Returns
+    -------
+    errobj : callable, log instance or None
+        The current error handler. If no handler was set through `seterrcall`,
+        ``None`` is returned.
+
+    See Also
+    --------
+    seterrcall, seterr, geterr
+
+    Notes
+    -----
+    For complete documentation of the types of floating-point exceptions and
+    treatment options, see `seterr`.
+
+    Examples
+    --------
+    >>> np.geterrcall()  # we did not yet set a handler, returns None
+
+    >>> oldsettings = np.seterr(all='call')
+    >>> def err_handler(type, flag):
+    ...     print("Floating point error (%s), with flag %s" % (type, flag))
+    >>> oldhandler = np.seterrcall(err_handler)
+    >>> np.array([1, 2, 3]) / 0.0
+    Floating point error (divide by zero), with flag 1
+    array([inf, inf, inf])
+
+    >>> cur_handler = np.geterrcall()
+    >>> cur_handler is err_handler
+    True
+
+    """
+    return umath.geterrobj()[2]
+
+
+class _unspecified:
+    pass
+
+
+_Unspecified = _unspecified()
+
+
+@set_module('numpy')
+class errstate(contextlib.ContextDecorator):
+    """
+    errstate(**kwargs)
+
+    Context manager for floating-point error handling.
+
+    Using an instance of `errstate` as a context manager allows statements in
+    that context to execute with a known error handling behavior. Upon entering
+    the context the error handling is set with `seterr` and `seterrcall`, and
+    upon exiting it is reset to what it was before.
+
+    ..  versionchanged:: 1.17.0
+        `errstate` is also usable as a function decorator, saving
+        a level of indentation if an entire function is wrapped.
+        See :py:class:`contextlib.ContextDecorator` for more information.
+
+    Parameters
+    ----------
+    kwargs : {divide, over, under, invalid}
+        Keyword arguments. The valid keywords are the possible floating-point
+        exceptions. Each keyword should have a string value that defines the
+        treatment for the particular error. Possible values are
+        {'ignore', 'warn', 'raise', 'call', 'print', 'log'}.
+
+    See Also
+    --------
+    seterr, geterr, seterrcall, geterrcall
+
+    Notes
+    -----
+    For complete documentation of the types of floating-point exceptions and
+    treatment options, see `seterr`.
+
+    Examples
+    --------
+    >>> olderr = np.seterr(all='ignore')  # Set error handling to known state.
+
+    >>> np.arange(3) / 0.
+    array([nan, inf, inf])
+    >>> with np.errstate(divide='warn'):
+    ...     np.arange(3) / 0.
+    array([nan, inf, inf])
+
+    >>> np.sqrt(-1)
+    nan
+    >>> with np.errstate(invalid='raise'):
+    ...     np.sqrt(-1)
+    Traceback (most recent call last):
+      File "<stdin>", line 2, in <module>
+    FloatingPointError: invalid value encountered in sqrt
+
+    Outside the context the error handling behavior has not changed:
+
+    >>> np.geterr()
+    {'divide': 'ignore', 'over': 'ignore', 'under': 'ignore', 'invalid': 'ignore'}
+
+    """
+
+    def __init__(self, *, call=_Unspecified, **kwargs):
+        self.call = call
+        self.kwargs = kwargs
+
+    def __enter__(self):
+        self.oldstate = seterr(**self.kwargs)
+        if self.call is not _Unspecified:
+            self.oldcall = seterrcall(self.call)
+
+    def __exit__(self, *exc_info):
+        seterr(**self.oldstate)
+        if self.call is not _Unspecified:
+            seterrcall(self.oldcall)
+
+
+def _setdef():
+    defval = [UFUNC_BUFSIZE_DEFAULT, ERR_DEFAULT, None]
+    umath.seterrobj(defval)
+
+
+# set the default values
+_setdef()

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/core/_ufunc_config.pyi

@@ -0,0 +1,43 @@
+import sys
+from typing import Optional, Union, Callable, Any
+
+if sys.version_info >= (3, 8):
+    from typing import Literal, Protocol, TypedDict
+else:
+    from typing_extensions import Literal, Protocol, TypedDict
+
+_ErrKind = Literal["ignore", "warn", "raise", "call", "print", "log"]
+_ErrFunc = Callable[[str, int], Any]
+
+class _SupportsWrite(Protocol):
+    def write(self, __msg: str) -> Any: ...
+
+class _ErrDict(TypedDict):
+    divide: _ErrKind
+    over: _ErrKind
+    under: _ErrKind
+    invalid: _ErrKind
+
+class _ErrDictOptional(TypedDict, total=False):
+    all: Optional[_ErrKind]
+    divide: Optional[_ErrKind]
+    over: Optional[_ErrKind]
+    under: Optional[_ErrKind]
+    invalid: Optional[_ErrKind]
+
+def seterr(
+    all: Optional[_ErrKind] = ...,
+    divide: Optional[_ErrKind] = ...,
+    over: Optional[_ErrKind] = ...,
+    under: Optional[_ErrKind] = ...,
+    invalid: Optional[_ErrKind] = ...,
+) -> _ErrDict: ...
+def geterr() -> _ErrDict: ...
+def setbufsize(size: int) -> int: ...
+def getbufsize() -> int: ...
+def seterrcall(
+    func: Union[None, _ErrFunc, _SupportsWrite]
+) -> Union[None, _ErrFunc, _SupportsWrite]: ...
+def geterrcall() -> Union[None, _ErrFunc, _SupportsWrite]: ...
+
+# See `numpy/__init__.pyi` for the `errstate` class

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/core/arrayprint.pyi

@@ -0,0 +1,147 @@
+import sys
+from types import TracebackType
+from typing import Any, Optional, Callable, Union, Type
+
+# Using a private class is by no means ideal, but it is simply a consquence
+# of a `contextlib.context` returning an instance of aformentioned class
+from contextlib import _GeneratorContextManager
+
+from numpy import (
+    ndarray,
+    generic,
+    bool_,
+    integer,
+    timedelta64,
+    datetime64,
+    floating,
+    complexfloating,
+    void,
+    str_,
+    bytes_,
+    longdouble,
+    clongdouble,
+)
+from numpy.typing import ArrayLike, _CharLike_co, _FloatLike_co
+
+if sys.version_info > (3, 8):
+    from typing import Literal, TypedDict, SupportsIndex
+else:
+    from typing_extensions import Literal, TypedDict, SupportsIndex
+
+_FloatMode = Literal["fixed", "unique", "maxprec", "maxprec_equal"]
+
+class _FormatDict(TypedDict, total=False):
+    bool: Callable[[bool_], str]
+    int: Callable[[integer[Any]], str]
+    timedelta: Callable[[timedelta64], str]
+    datetime: Callable[[datetime64], str]
+    float: Callable[[floating[Any]], str]
+    longfloat: Callable[[longdouble], str]
+    complexfloat: Callable[[complexfloating[Any, Any]], str]
+    longcomplexfloat: Callable[[clongdouble], str]
+    void: Callable[[void], str]
+    numpystr: Callable[[_CharLike_co], str]
+    object: Callable[[object], str]
+    all: Callable[[object], str]
+    int_kind: Callable[[integer[Any]], str]
+    float_kind: Callable[[floating[Any]], str]
+    complex_kind: Callable[[complexfloating[Any, Any]], str]
+    str_kind: Callable[[_CharLike_co], str]
+
+class _FormatOptions(TypedDict):
+    precision: int
+    threshold: int
+    edgeitems: int
+    linewidth: int
+    suppress: bool
+    nanstr: str
+    infstr: str
+    formatter: Optional[_FormatDict]
+    sign: Literal["-", "+", " "]
+    floatmode: _FloatMode
+    legacy: Literal[False, "1.13"]
+
+def set_printoptions(
+    precision: Optional[SupportsIndex] = ...,
+    threshold: Optional[int] = ...,
+    edgeitems: Optional[int] = ...,
+    linewidth: Optional[int] = ...,
+    suppress: Optional[bool] = ...,
+    nanstr: Optional[str] = ...,
+    infstr: Optional[str] = ...,
+    formatter: Optional[_FormatDict] = ...,
+    sign: Optional[Literal["-", "+", " "]] = ...,
+    floatmode: Optional[_FloatMode] = ...,
+    *,
+    legacy: Optional[Literal[False, "1.13"]] = ...
+) -> None: ...
+def get_printoptions() -> _FormatOptions: ...
+def array2string(
+    a: ndarray[Any, Any],
+    max_line_width: Optional[int] = ...,
+    precision: Optional[SupportsIndex] = ...,
+    suppress_small: Optional[bool] = ...,
+    separator: str = ...,
+    prefix: str = ...,
+    # NOTE: With the `style` argument being deprecated,
+    # all arguments between `formatter` and `suffix` are de facto
+    # keyworld-only arguments
+    *,
+    formatter: Optional[_FormatDict] = ...,
+    threshold: Optional[int] = ...,
+    edgeitems: Optional[int] = ...,
+    sign: Optional[Literal["-", "+", " "]] = ...,
+    floatmode: Optional[_FloatMode] = ...,
+    suffix: str = ...,
+    legacy: Optional[Literal[False, "1.13"]] = ...,
+) -> str: ...
+def format_float_scientific(
+    x: _FloatLike_co,
+    precision: Optional[int] = ...,
+    unique: bool = ...,
+    trim: Literal["k", ".", "0", "-"] = ...,
+    sign: bool = ...,
+    pad_left: Optional[int] = ...,
+    exp_digits: Optional[int] = ...,
+    min_digits: Optional[int] = ...,
+) -> str: ...
+def format_float_positional(
+    x: _FloatLike_co,
+    precision: Optional[int] = ...,
+    unique: bool = ...,
+    fractional: bool = ...,
+    trim: Literal["k", ".", "0", "-"] = ...,
+    sign: bool = ...,
+    pad_left: Optional[int] = ...,
+    pad_right: Optional[int] = ...,
+    min_digits: Optional[int] = ...,
+) -> str: ...
+def array_repr(
+    arr: ndarray[Any, Any],
+    max_line_width: Optional[int] = ...,
+    precision: Optional[SupportsIndex] = ...,
+    suppress_small: Optional[bool] = ...,
+) -> str: ...
+def array_str(
+    a: ndarray[Any, Any],
+    max_line_width: Optional[int] = ...,
+    precision: Optional[SupportsIndex] = ...,
+    suppress_small: Optional[bool] = ...,
+) -> str: ...
+def set_string_function(
+    f: Optional[Callable[[ndarray[Any, Any]], str]], repr: bool = ...
+) -> None: ...
+def printoptions(
+    precision: Optional[SupportsIndex] = ...,
+    threshold: Optional[int] = ...,
+    edgeitems: Optional[int] = ...,
+    linewidth: Optional[int] = ...,
+    suppress: Optional[bool] = ...,
+    nanstr: Optional[str] = ...,
+    infstr: Optional[str] = ...,
+    formatter: Optional[_FormatDict] = ...,
+    sign: Optional[Literal["-", "+", " "]] = ...,
+    floatmode: Optional[_FloatMode] = ...,
+    *,
+    legacy: Optional[Literal[False, "1.13"]] = ...
+) -> _GeneratorContextManager[_FormatOptions]: ...

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/core/cversions.py

@@ -0,0 +1,13 @@
+"""Simple script to compute the api hash of the current API.
+
+The API has is defined by numpy_api_order and ufunc_api_order.
+
+"""
+from os.path import dirname
+
+from code_generators.genapi import fullapi_hash
+from code_generators.numpy_api import full_api
+
+if __name__ == '__main__':
+    curdir = dirname(__file__)
+    print(fullapi_hash(full_api))

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/core/einsumfunc.pyi

@@ -0,0 +1,142 @@
+import sys
+from typing import List, TypeVar, Optional, Any, overload, Union, Tuple, Sequence
+
+from numpy import (
+    ndarray,
+    dtype,
+    bool_,
+    unsignedinteger,
+    signedinteger,
+    floating,
+    complexfloating,
+    number,
+    _OrderKACF,
+)
+from numpy.typing import (
+    _ArrayLikeBool_co,
+    _ArrayLikeUInt_co,
+    _ArrayLikeInt_co,
+    _ArrayLikeFloat_co,
+    _ArrayLikeComplex_co,
+    _DTypeLikeBool,
+    _DTypeLikeUInt,
+    _DTypeLikeInt,
+    _DTypeLikeFloat,
+    _DTypeLikeComplex,
+    _DTypeLikeComplex_co,
+)
+
+if sys.version_info >= (3, 8):
+    from typing import Literal
+else:
+    from typing_extensions import Literal
+
+_ArrayType = TypeVar(
+    "_ArrayType",
+    bound=ndarray[Any, dtype[Union[bool_, number[Any]]]],
+)
+
+_OptimizeKind = Union[
+    None, bool, Literal["greedy", "optimal"], Sequence[Any]
+]
+_CastingSafe = Literal["no", "equiv", "safe", "same_kind"]
+_CastingUnsafe = Literal["unsafe"]
+
+__all__: List[str]
+
+# TODO: Properly handle the `casting`-based combinatorics
+# TODO: We need to evaluate the content `__subscripts` in order
+# to identify whether or an array or scalar is returned. At a cursory
+# glance this seems like something that can quite easilly be done with
+# a mypy plugin.
+# Something like `is_scalar = bool(__subscripts.partition("->")[-1])`
+@overload
+def einsum(
+    __subscripts: str,
+    *operands: _ArrayLikeBool_co,
+    out: None = ...,
+    dtype: Optional[_DTypeLikeBool] = ...,
+    order: _OrderKACF = ...,
+    casting: _CastingSafe = ...,
+    optimize: _OptimizeKind = ...,
+) -> Any: ...
+@overload
+def einsum(
+    __subscripts: str,
+    *operands: _ArrayLikeUInt_co,
+    out: None = ...,
+    dtype: Optional[_DTypeLikeUInt] = ...,
+    order: _OrderKACF = ...,
+    casting: _CastingSafe = ...,
+    optimize: _OptimizeKind = ...,
+) -> Any: ...
+@overload
+def einsum(
+    __subscripts: str,
+    *operands: _ArrayLikeInt_co,
+    out: None = ...,
+    dtype: Optional[_DTypeLikeInt] = ...,
+    order: _OrderKACF = ...,
+    casting: _CastingSafe = ...,
+    optimize: _OptimizeKind = ...,
+) -> Any: ...
+@overload
+def einsum(
+    __subscripts: str,
+    *operands: _ArrayLikeFloat_co,
+    out: None = ...,
+    dtype: Optional[_DTypeLikeFloat] = ...,
+    order: _OrderKACF = ...,
+    casting: _CastingSafe = ...,
+    optimize: _OptimizeKind = ...,
+) -> Any: ...
+@overload
+def einsum(
+    __subscripts: str,
+    *operands: _ArrayLikeComplex_co,
+    out: None = ...,
+    dtype: Optional[_DTypeLikeComplex] = ...,
+    order: _OrderKACF = ...,
+    casting: _CastingSafe = ...,
+    optimize: _OptimizeKind = ...,
+) -> Any: ...
+@overload
+def einsum(
+    __subscripts: str,
+    *operands: Any,
+    casting: _CastingUnsafe,
+    dtype: Optional[_DTypeLikeComplex_co] = ...,
+    out: None = ...,
+    order: _OrderKACF = ...,
+    optimize: _OptimizeKind = ...,
+) -> Any: ...
+@overload
+def einsum(
+    __subscripts: str,
+    *operands: _ArrayLikeComplex_co,
+    out: _ArrayType,
+    dtype: Optional[_DTypeLikeComplex_co] = ...,
+    order: _OrderKACF = ...,
+    casting: _CastingSafe = ...,
+    optimize: _OptimizeKind = ...,
+) -> _ArrayType: ...
+@overload
+def einsum(
+    __subscripts: str,
+    *operands: Any,
+    out: _ArrayType,
+    casting: _CastingUnsafe,
+    dtype: Optional[_DTypeLikeComplex_co] = ...,
+    order: _OrderKACF = ...,
+    optimize: _OptimizeKind = ...,
+) -> _ArrayType: ...
+
+# NOTE: `einsum_call` is a hidden kwarg unavailable for public use.
+# It is therefore excluded from the signatures below.
+# NOTE: In practice the list consists of a `str` (first element)
+# and a variable number of integer tuples.
+def einsum_path(
+    __subscripts: str,
+    *operands: _ArrayLikeComplex_co,
+    optimize: _OptimizeKind = ...,
+) -> Tuple[List[Any], str]: ...

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/core/fromnumeric.pyi

@@ -0,0 +1,361 @@
+import sys
+import datetime as dt
+from typing import Optional, Union, Sequence, Tuple, Any, overload, TypeVar
+
+from numpy import (
+    ndarray,
+    number,
+    integer,
+    intp,
+    bool_,
+    generic,
+    _OrderKACF,
+    _OrderACF,
+    _ModeKind,
+    _PartitionKind,
+    _SortKind,
+    _SortSide,
+)
+from numpy.typing import (
+    DTypeLike,
+    ArrayLike,
+    _ShapeLike,
+    _Shape,
+    _ArrayLikeBool_co,
+    _ArrayLikeInt_co,
+    _NumberLike_co,
+)
+
+if sys.version_info >= (3, 8):
+    from typing import Literal
+else:
+    from typing_extensions import Literal
+
+# Various annotations for scalars
+
+# While dt.datetime and dt.timedelta are not technically part of NumPy,
+# they are one of the rare few builtin scalars which serve as valid return types.
+# See https://github.com/numpy/numpy-stubs/pull/67#discussion_r412604113.
+_ScalarNumpy = Union[generic, dt.datetime, dt.timedelta]
+_ScalarBuiltin = Union[str, bytes, dt.date, dt.timedelta, bool, int, float, complex]
+_Scalar = Union[_ScalarBuiltin, _ScalarNumpy]
+
+# Integers and booleans can generally be used interchangeably
+_ScalarGeneric = TypeVar("_ScalarGeneric", bound=generic)
+
+_Number = TypeVar("_Number", bound=number)
+
+# The signature of take() follows a common theme with its overloads:
+# 1. A generic comes in; the same generic comes out
+# 2. A scalar comes in; a generic comes out
+# 3. An array-like object comes in; some keyword ensures that a generic comes out
+# 4. An array-like object comes in; an ndarray or generic comes out
+def take(
+    a: ArrayLike,
+    indices: _ArrayLikeInt_co,
+    axis: Optional[int] = ...,
+    out: Optional[ndarray] = ...,
+    mode: _ModeKind = ...,
+) -> Any: ...
+
+def reshape(
+    a: ArrayLike,
+    newshape: _ShapeLike,
+    order: _OrderACF = ...,
+) -> ndarray: ...
+
+def choose(
+    a: _ArrayLikeInt_co,
+    choices: ArrayLike,
+    out: Optional[ndarray] = ...,
+    mode: _ModeKind = ...,
+) -> Any: ...
+
+def repeat(
+    a: ArrayLike,
+    repeats: _ArrayLikeInt_co,
+    axis: Optional[int] = ...,
+) -> ndarray: ...
+
+def put(
+    a: ndarray,
+    ind: _ArrayLikeInt_co,
+    v: ArrayLike,
+    mode: _ModeKind = ...,
+) -> None: ...
+
+def swapaxes(
+    a: ArrayLike,
+    axis1: int,
+    axis2: int,
+) -> ndarray: ...
+
+def transpose(
+    a: ArrayLike,
+    axes: Union[None, Sequence[int], ndarray] = ...
+) -> ndarray: ...
+
+def partition(
+    a: ArrayLike,
+    kth: _ArrayLikeInt_co,
+    axis: Optional[int] = ...,
+    kind: _PartitionKind = ...,
+    order: Union[None, str, Sequence[str]] = ...,
+) -> ndarray: ...
+
+def argpartition(
+    a: ArrayLike,
+    kth: _ArrayLikeInt_co,
+    axis: Optional[int] = ...,
+    kind: _PartitionKind = ...,
+    order: Union[None, str, Sequence[str]] = ...,
+) -> Any: ...
+
+def sort(
+    a: ArrayLike,
+    axis: Optional[int] = ...,
+    kind: Optional[_SortKind] = ...,
+    order: Union[None, str, Sequence[str]] = ...,
+) -> ndarray: ...
+
+def argsort(
+    a: ArrayLike,
+    axis: Optional[int] = ...,
+    kind: Optional[_SortKind] = ...,
+    order: Union[None, str, Sequence[str]] = ...,
+) -> ndarray: ...
+
+@overload
+def argmax(
+    a: ArrayLike,
+    axis: None = ...,
+    out: Optional[ndarray] = ...,
+) -> intp: ...
+@overload
+def argmax(
+    a: ArrayLike,
+    axis: Optional[int] = ...,
+    out: Optional[ndarray] = ...,
+) -> Any: ...
+
+@overload
+def argmin(
+    a: ArrayLike,
+    axis: None = ...,
+    out: Optional[ndarray] = ...,
+) -> intp: ...
+@overload
+def argmin(
+    a: ArrayLike,
+    axis: Optional[int] = ...,
+    out: Optional[ndarray] = ...,
+) -> Any: ...
+
+@overload
+def searchsorted(
+    a: ArrayLike,
+    v: _Scalar,
+    side: _SortSide = ...,
+    sorter: Optional[_ArrayLikeInt_co] = ...,  # 1D int array
+) -> intp: ...
+@overload
+def searchsorted(
+    a: ArrayLike,
+    v: ArrayLike,
+    side: _SortSide = ...,
+    sorter: Optional[_ArrayLikeInt_co] = ...,  # 1D int array
+) -> ndarray: ...
+
+def resize(
+    a: ArrayLike,
+    new_shape: _ShapeLike,
+) -> ndarray: ...
+
+@overload
+def squeeze(
+    a: _ScalarGeneric,
+    axis: Optional[_ShapeLike] = ...,
+) -> _ScalarGeneric: ...
+@overload
+def squeeze(
+    a: ArrayLike,
+    axis: Optional[_ShapeLike] = ...,
+) -> ndarray: ...
+
+def diagonal(
+    a: ArrayLike,
+    offset: int = ...,
+    axis1: int = ...,
+    axis2: int = ...,  # >= 2D array
+) -> ndarray: ...
+
+def trace(
+    a: ArrayLike,  # >= 2D array
+    offset: int = ...,
+    axis1: int = ...,
+    axis2: int = ...,
+    dtype: DTypeLike = ...,
+    out: Optional[ndarray] = ...,
+) -> Any: ...
+
+def ravel(a: ArrayLike, order: _OrderKACF = ...) -> ndarray: ...
+
+def nonzero(a: ArrayLike) -> Tuple[ndarray, ...]: ...
+
+def shape(a: ArrayLike) -> _Shape: ...
+
+def compress(
+    condition: ArrayLike,  # 1D bool array
+    a: ArrayLike,
+    axis: Optional[int] = ...,
+    out: Optional[ndarray] = ...,
+) -> ndarray: ...
+
+@overload
+def clip(
+    a: ArrayLike,
+    a_min: ArrayLike,
+    a_max: Optional[ArrayLike],
+    out: Optional[ndarray] = ...,
+    **kwargs: Any,
+) -> Any: ...
+@overload
+def clip(
+    a: ArrayLike,
+    a_min: None,
+    a_max: ArrayLike,
+    out: Optional[ndarray] = ...,
+    **kwargs: Any,
+) -> Any: ...
+
+def sum(
+    a: ArrayLike,
+    axis: _ShapeLike = ...,
+    dtype: DTypeLike = ...,
+    out: Optional[ndarray] = ...,
+    keepdims: bool = ...,
+    initial: _NumberLike_co = ...,
+    where: _ArrayLikeBool_co = ...,
+) -> Any: ...
+
+@overload
+def all(
+    a: ArrayLike,
+    axis: None = ...,
+    out: None = ...,
+    keepdims: Literal[False] = ...,
+) -> bool_: ...
+@overload
+def all(
+    a: ArrayLike,
+    axis: Optional[_ShapeLike] = ...,
+    out: Optional[ndarray] = ...,
+    keepdims: bool = ...,
+) -> Any: ...
+
+@overload
+def any(
+    a: ArrayLike,
+    axis: None = ...,
+    out: None = ...,
+    keepdims: Literal[False] = ...,
+) -> bool_: ...
+@overload
+def any(
+    a: ArrayLike,
+    axis: Optional[_ShapeLike] = ...,
+    out: Optional[ndarray] = ...,
+    keepdims: bool = ...,
+) -> Any: ...
+
+def cumsum(
+    a: ArrayLike,
+    axis: Optional[int] = ...,
+    dtype: DTypeLike = ...,
+    out: Optional[ndarray] = ...,
+) -> ndarray: ...
+
+def ptp(
+    a: ArrayLike,
+    axis: Optional[_ShapeLike] = ...,
+    out: Optional[ndarray] = ...,
+    keepdims: bool = ...,
+) -> Any: ...
+
+def amax(
+    a: ArrayLike,
+    axis: Optional[_ShapeLike] = ...,
+    out: Optional[ndarray] = ...,
+    keepdims: bool = ...,
+    initial: _NumberLike_co = ...,
+    where: _ArrayLikeBool_co = ...,
+) -> Any: ...
+
+def amin(
+    a: ArrayLike,
+    axis: Optional[_ShapeLike] = ...,
+    out: Optional[ndarray] = ...,
+    keepdims: bool = ...,
+    initial: _NumberLike_co = ...,
+    where: _ArrayLikeBool_co = ...,
+) -> Any: ...
+
+# TODO: `np.prod()``: For object arrays `initial` does not necessarily
+# have to be a numerical scalar.
+# The only requirement is that it is compatible
+# with the `.__mul__()` method(s) of the passed array's elements.
+
+# Note that the same situation holds for all wrappers around
+# `np.ufunc.reduce`, e.g. `np.sum()` (`.__add__()`).
+def prod(
+    a: ArrayLike,
+    axis: Optional[_ShapeLike] = ...,
+    dtype: DTypeLike = ...,
+    out: Optional[ndarray] = ...,
+    keepdims: bool = ...,
+    initial: _NumberLike_co = ...,
+    where: _ArrayLikeBool_co = ...,
+) -> Any: ...
+
+def cumprod(
+    a: ArrayLike,
+    axis: Optional[int] = ...,
+    dtype: DTypeLike = ...,
+    out: Optional[ndarray] = ...,
+) -> ndarray: ...
+
+def ndim(a: ArrayLike) -> int: ...
+
+def size(a: ArrayLike, axis: Optional[int] = ...) -> int: ...
+
+def around(
+    a: ArrayLike,
+    decimals: int = ...,
+    out: Optional[ndarray] = ...,
+) -> Any: ...
+
+def mean(
+    a: ArrayLike,
+    axis: Optional[_ShapeLike] = ...,
+    dtype: DTypeLike = ...,
+    out: Optional[ndarray] = ...,
+    keepdims: bool = ...,
+) -> Any: ...
+
+def std(
+    a: ArrayLike,
+    axis: Optional[_ShapeLike] = ...,
+    dtype: DTypeLike = ...,
+    out: Optional[ndarray] = ...,
+    ddof: int = ...,
+    keepdims: bool = ...,
+) -> Any: ...
+
+def var(
+    a: ArrayLike,
+    axis: Optional[_ShapeLike] = ...,
+    dtype: DTypeLike = ...,
+    out: Optional[ndarray] = ...,
+    ddof: int = ...,
+    keepdims: bool = ...,
+) -> Any: ...

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/core/function_base.py

@@ -0,0 +1,529 @@
+import functools
+import warnings
+import operator
+import types
+
+from . import numeric as _nx
+from .numeric import result_type, NaN, asanyarray, ndim
+from numpy.core.multiarray import add_docstring
+from numpy.core import overrides
+
+__all__ = ['logspace', 'linspace', 'geomspace']
+
+
+array_function_dispatch = functools.partial(
+    overrides.array_function_dispatch, module='numpy')
+
+
+def _linspace_dispatcher(start, stop, num=None, endpoint=None, retstep=None,
+                         dtype=None, axis=None):
+    return (start, stop)
+
+
+@array_function_dispatch(_linspace_dispatcher)
+def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None,
+             axis=0):
+    """
+    Return evenly spaced numbers over a specified interval.
+
+    Returns `num` evenly spaced samples, calculated over the
+    interval [`start`, `stop`].
+
+    The endpoint of the interval can optionally be excluded.
+
+    .. versionchanged:: 1.16.0
+        Non-scalar `start` and `stop` are now supported.
+
+    .. versionchanged:: 1.20.0
+        Values are rounded towards ``-inf`` instead of ``0`` when an
+        integer ``dtype`` is specified. The old behavior can
+        still be obtained with ``np.linspace(start, stop, num).astype(int)``
+
+    Parameters
+    ----------
+    start : array_like
+        The starting value of the sequence.
+    stop : array_like
+        The end value of the sequence, unless `endpoint` is set to False.
+        In that case, the sequence consists of all but the last of ``num + 1``
+        evenly spaced samples, so that `stop` is excluded.  Note that the step
+        size changes when `endpoint` is False.
+    num : int, optional
+        Number of samples to generate. Default is 50. Must be non-negative.
+    endpoint : bool, optional
+        If True, `stop` is the last sample. Otherwise, it is not included.
+        Default is True.
+    retstep : bool, optional
+        If True, return (`samples`, `step`), where `step` is the spacing
+        between samples.
+    dtype : dtype, optional
+        The type of the output array.  If `dtype` is not given, the data type
+        is inferred from `start` and `stop`. The inferred dtype will never be
+        an integer; `float` is chosen even if the arguments would produce an
+        array of integers.
+
+        .. versionadded:: 1.9.0
+
+    axis : int, optional
+        The axis in the result to store the samples.  Relevant only if start
+        or stop are array-like.  By default (0), the samples will be along a
+        new axis inserted at the beginning. Use -1 to get an axis at the end.
+
+        .. versionadded:: 1.16.0
+
+    Returns
+    -------
+    samples : ndarray
+        There are `num` equally spaced samples in the closed interval
+        ``[start, stop]`` or the half-open interval ``[start, stop)``
+        (depending on whether `endpoint` is True or False).
+    step : float, optional
+        Only returned if `retstep` is True
+
+        Size of spacing between samples.
+
+
+    See Also
+    --------
+    arange : Similar to `linspace`, but uses a step size (instead of the
+             number of samples).
+    geomspace : Similar to `linspace`, but with numbers spaced evenly on a log
+                scale (a geometric progression).
+    logspace : Similar to `geomspace`, but with the end points specified as
+               logarithms.
+
+    Examples
+    --------
+    >>> np.linspace(2.0, 3.0, num=5)
+    array([2.  , 2.25, 2.5 , 2.75, 3.  ])
+    >>> np.linspace(2.0, 3.0, num=5, endpoint=False)
+    array([2. ,  2.2,  2.4,  2.6,  2.8])
+    >>> np.linspace(2.0, 3.0, num=5, retstep=True)
+    (array([2.  ,  2.25,  2.5 ,  2.75,  3.  ]), 0.25)
+
+    Graphical illustration:
+
+    >>> import matplotlib.pyplot as plt
+    >>> N = 8
+    >>> y = np.zeros(N)
+    >>> x1 = np.linspace(0, 10, N, endpoint=True)
+    >>> x2 = np.linspace(0, 10, N, endpoint=False)
+    >>> plt.plot(x1, y, 'o')
+    [<matplotlib.lines.Line2D object at 0x...>]
+    >>> plt.plot(x2, y + 0.5, 'o')
+    [<matplotlib.lines.Line2D object at 0x...>]
+    >>> plt.ylim([-0.5, 1])
+    (-0.5, 1)
+    >>> plt.show()
+
+    """
+    num = operator.index(num)
+    if num < 0:
+        raise ValueError("Number of samples, %s, must be non-negative." % num)
+    div = (num - 1) if endpoint else num
+
+    # Convert float/complex array scalars to float, gh-3504
+    # and make sure one can use variables that have an __array_interface__, gh-6634
+    start = asanyarray(start) * 1.0
+    stop  = asanyarray(stop)  * 1.0
+
+    dt = result_type(start, stop, float(num))
+    if dtype is None:
+        dtype = dt
+
+    delta = stop - start
+    y = _nx.arange(0, num, dtype=dt).reshape((-1,) + (1,) * ndim(delta))
+    # In-place multiplication y *= delta/div is faster, but prevents the multiplicant
+    # from overriding what class is produced, and thus prevents, e.g. use of Quantities,
+    # see gh-7142. Hence, we multiply in place only for standard scalar types.
+    _mult_inplace = _nx.isscalar(delta)
+    if div > 0:
+        step = delta / div
+        if _nx.any(step == 0):
+            # Special handling for denormal numbers, gh-5437
+            y /= div
+            if _mult_inplace:
+                y *= delta
+            else:
+                y = y * delta
+        else:
+            if _mult_inplace:
+                y *= step
+            else:
+                y = y * step
+    else:
+        # sequences with 0 items or 1 item with endpoint=True (i.e. div <= 0)
+        # have an undefined step
+        step = NaN
+        # Multiply with delta to allow possible override of output class.
+        y = y * delta
+
+    y += start
+
+    if endpoint and num > 1:
+        y[-1] = stop
+
+    if axis != 0:
+        y = _nx.moveaxis(y, 0, axis)
+
+    if _nx.issubdtype(dtype, _nx.integer):
+        _nx.floor(y, out=y)
+
+    if retstep:
+        return y.astype(dtype, copy=False), step
+    else:
+        return y.astype(dtype, copy=False)
+
+
+def _logspace_dispatcher(start, stop, num=None, endpoint=None, base=None,
+                         dtype=None, axis=None):
+    return (start, stop)
+
+
+@array_function_dispatch(_logspace_dispatcher)
+def logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None,
+             axis=0):
+    """
+    Return numbers spaced evenly on a log scale.
+
+    In linear space, the sequence starts at ``base ** start``
+    (`base` to the power of `start`) and ends with ``base ** stop``
+    (see `endpoint` below).
+
+    .. versionchanged:: 1.16.0
+        Non-scalar `start` and `stop` are now supported.
+
+    Parameters
+    ----------
+    start : array_like
+        ``base ** start`` is the starting value of the sequence.
+    stop : array_like
+        ``base ** stop`` is the final value of the sequence, unless `endpoint`
+        is False.  In that case, ``num + 1`` values are spaced over the
+        interval in log-space, of which all but the last (a sequence of
+        length `num`) are returned.
+    num : integer, optional
+        Number of samples to generate.  Default is 50.
+    endpoint : boolean, optional
+        If true, `stop` is the last sample. Otherwise, it is not included.
+        Default is True.
+    base : array_like, optional
+        The base of the log space. The step size between the elements in
+        ``ln(samples) / ln(base)`` (or ``log_base(samples)``) is uniform.
+        Default is 10.0.
+    dtype : dtype
+        The type of the output array.  If `dtype` is not given, the data type
+        is inferred from `start` and `stop`. The inferred type will never be
+        an integer; `float` is chosen even if the arguments would produce an
+        array of integers.
+    axis : int, optional
+        The axis in the result to store the samples.  Relevant only if start
+        or stop are array-like.  By default (0), the samples will be along a
+        new axis inserted at the beginning. Use -1 to get an axis at the end.
+
+        .. versionadded:: 1.16.0
+
+
+    Returns
+    -------
+    samples : ndarray
+        `num` samples, equally spaced on a log scale.
+
+    See Also
+    --------
+    arange : Similar to linspace, with the step size specified instead of the
+             number of samples. Note that, when used with a float endpoint, the
+             endpoint may or may not be included.
+    linspace : Similar to logspace, but with the samples uniformly distributed
+               in linear space, instead of log space.
+    geomspace : Similar to logspace, but with endpoints specified directly.
+
+    Notes
+    -----
+    Logspace is equivalent to the code
+
+    >>> y = np.linspace(start, stop, num=num, endpoint=endpoint)
+    ... # doctest: +SKIP
+    >>> power(base, y).astype(dtype)
+    ... # doctest: +SKIP
+
+    Examples
+    --------
+    >>> np.logspace(2.0, 3.0, num=4)
+    array([ 100.        ,  215.443469  ,  464.15888336, 1000.        ])
+    >>> np.logspace(2.0, 3.0, num=4, endpoint=False)
+    array([100.        ,  177.827941  ,  316.22776602,  562.34132519])
+    >>> np.logspace(2.0, 3.0, num=4, base=2.0)
+    array([4.        ,  5.0396842 ,  6.34960421,  8.        ])
+
+    Graphical illustration:
+
+    >>> import matplotlib.pyplot as plt
+    >>> N = 10
+    >>> x1 = np.logspace(0.1, 1, N, endpoint=True)
+    >>> x2 = np.logspace(0.1, 1, N, endpoint=False)
+    >>> y = np.zeros(N)
+    >>> plt.plot(x1, y, 'o')
+    [<matplotlib.lines.Line2D object at 0x...>]
+    >>> plt.plot(x2, y + 0.5, 'o')
+    [<matplotlib.lines.Line2D object at 0x...>]
+    >>> plt.ylim([-0.5, 1])
+    (-0.5, 1)
+    >>> plt.show()
+
+    """
+    y = linspace(start, stop, num=num, endpoint=endpoint, axis=axis)
+    if dtype is None:
+        return _nx.power(base, y)
+    return _nx.power(base, y).astype(dtype, copy=False)
+
+
+def _geomspace_dispatcher(start, stop, num=None, endpoint=None, dtype=None,
+                          axis=None):
+    return (start, stop)
+
+
+@array_function_dispatch(_geomspace_dispatcher)
+def geomspace(start, stop, num=50, endpoint=True, dtype=None, axis=0):
+    """
+    Return numbers spaced evenly on a log scale (a geometric progression).
+
+    This is similar to `logspace`, but with endpoints specified directly.
+    Each output sample is a constant multiple of the previous.
+
+    .. versionchanged:: 1.16.0
+        Non-scalar `start` and `stop` are now supported.
+
+    Parameters
+    ----------
+    start : array_like
+        The starting value of the sequence.
+    stop : array_like
+        The final value of the sequence, unless `endpoint` is False.
+        In that case, ``num + 1`` values are spaced over the
+        interval in log-space, of which all but the last (a sequence of
+        length `num`) are returned.
+    num : integer, optional
+        Number of samples to generate.  Default is 50.
+    endpoint : boolean, optional
+        If true, `stop` is the last sample. Otherwise, it is not included.
+        Default is True.
+    dtype : dtype
+        The type of the output array.  If `dtype` is not given, the data type
+        is inferred from `start` and `stop`. The inferred dtype will never be
+        an integer; `float` is chosen even if the arguments would produce an
+        array of integers.
+    axis : int, optional
+        The axis in the result to store the samples.  Relevant only if start
+        or stop are array-like.  By default (0), the samples will be along a
+        new axis inserted at the beginning. Use -1 to get an axis at the end.
+
+        .. versionadded:: 1.16.0
+
+    Returns
+    -------
+    samples : ndarray
+        `num` samples, equally spaced on a log scale.
+
+    See Also
+    --------
+    logspace : Similar to geomspace, but with endpoints specified using log
+               and base.
+    linspace : Similar to geomspace, but with arithmetic instead of geometric
+               progression.
+    arange : Similar to linspace, with the step size specified instead of the
+             number of samples.
+
+    Notes
+    -----
+    If the inputs or dtype are complex, the output will follow a logarithmic
+    spiral in the complex plane.  (There are an infinite number of spirals
+    passing through two points; the output will follow the shortest such path.)
+
+    Examples
+    --------
+    >>> np.geomspace(1, 1000, num=4)
+    array([    1.,    10.,   100.,  1000.])
+    >>> np.geomspace(1, 1000, num=3, endpoint=False)
+    array([   1.,   10.,  100.])
+    >>> np.geomspace(1, 1000, num=4, endpoint=False)
+    array([   1.        ,    5.62341325,   31.6227766 ,  177.827941  ])
+    >>> np.geomspace(1, 256, num=9)
+    array([   1.,    2.,    4.,    8.,   16.,   32.,   64.,  128.,  256.])
+
+    Note that the above may not produce exact integers:
+
+    >>> np.geomspace(1, 256, num=9, dtype=int)
+    array([  1,   2,   4,   7,  16,  32,  63, 127, 256])
+    >>> np.around(np.geomspace(1, 256, num=9)).astype(int)
+    array([  1,   2,   4,   8,  16,  32,  64, 128, 256])
+
+    Negative, decreasing, and complex inputs are allowed:
+
+    >>> np.geomspace(1000, 1, num=4)
+    array([1000.,  100.,   10.,    1.])
+    >>> np.geomspace(-1000, -1, num=4)
+    array([-1000.,  -100.,   -10.,    -1.])
+    >>> np.geomspace(1j, 1000j, num=4)  # Straight line
+    array([0.   +1.j, 0.  +10.j, 0. +100.j, 0.+1000.j])
+    >>> np.geomspace(-1+0j, 1+0j, num=5)  # Circle
+    array([-1.00000000e+00+1.22464680e-16j, -7.07106781e-01+7.07106781e-01j,
+            6.12323400e-17+1.00000000e+00j,  7.07106781e-01+7.07106781e-01j,
+            1.00000000e+00+0.00000000e+00j])
+
+    Graphical illustration of `endpoint` parameter:
+
+    >>> import matplotlib.pyplot as plt
+    >>> N = 10
+    >>> y = np.zeros(N)
+    >>> plt.semilogx(np.geomspace(1, 1000, N, endpoint=True), y + 1, 'o')
+    [<matplotlib.lines.Line2D object at 0x...>]
+    >>> plt.semilogx(np.geomspace(1, 1000, N, endpoint=False), y + 2, 'o')
+    [<matplotlib.lines.Line2D object at 0x...>]
+    >>> plt.axis([0.5, 2000, 0, 3])
+    [0.5, 2000, 0, 3]
+    >>> plt.grid(True, color='0.7', linestyle='-', which='both', axis='both')
+    >>> plt.show()
+
+    """
+    start = asanyarray(start)
+    stop = asanyarray(stop)
+    if _nx.any(start == 0) or _nx.any(stop == 0):
+        raise ValueError('Geometric sequence cannot include zero')
+
+    dt = result_type(start, stop, float(num), _nx.zeros((), dtype))
+    if dtype is None:
+        dtype = dt
+    else:
+        # complex to dtype('complex128'), for instance
+        dtype = _nx.dtype(dtype)
+
+    # Promote both arguments to the same dtype in case, for instance, one is
+    # complex and another is negative and log would produce NaN otherwise.
+    # Copy since we may change things in-place further down.
+    start = start.astype(dt, copy=True)
+    stop = stop.astype(dt, copy=True)
+
+    out_sign = _nx.ones(_nx.broadcast(start, stop).shape, dt)
+    # Avoid negligible real or imaginary parts in output by rotating to
+    # positive real, calculating, then undoing rotation
+    if _nx.issubdtype(dt, _nx.complexfloating):
+        all_imag = (start.real == 0.) & (stop.real == 0.)
+        if _nx.any(all_imag):
+            start[all_imag] = start[all_imag].imag
+            stop[all_imag] = stop[all_imag].imag
+            out_sign[all_imag] = 1j
+
+    both_negative = (_nx.sign(start) == -1) & (_nx.sign(stop) == -1)
+    if _nx.any(both_negative):
+        _nx.negative(start, out=start, where=both_negative)
+        _nx.negative(stop, out=stop, where=both_negative)
+        _nx.negative(out_sign, out=out_sign, where=both_negative)
+
+    log_start = _nx.log10(start)
+    log_stop = _nx.log10(stop)
+    result = logspace(log_start, log_stop, num=num,
+                      endpoint=endpoint, base=10.0, dtype=dtype)
+
+    # Make sure the endpoints match the start and stop arguments. This is
+    # necessary because np.exp(np.log(x)) is not necessarily equal to x.
+    if num > 0:
+        result[0] = start
+        if num > 1 and endpoint:
+            result[-1] = stop
+
+    result = out_sign * result
+
+    if axis != 0:
+        result = _nx.moveaxis(result, 0, axis)
+
+    return result.astype(dtype, copy=False)
+
+
+def _needs_add_docstring(obj):
+    """
+    Returns true if the only way to set the docstring of `obj` from python is
+    via add_docstring.
+
+    This function errs on the side of being overly conservative.
+    """
+    Py_TPFLAGS_HEAPTYPE = 1 << 9
+
+    if isinstance(obj, (types.FunctionType, types.MethodType, property)):
+        return False
+
+    if isinstance(obj, type) and obj.__flags__ & Py_TPFLAGS_HEAPTYPE:
+        return False
+
+    return True
+
+
+def _add_docstring(obj, doc, warn_on_python):
+    if warn_on_python and not _needs_add_docstring(obj):
+        warnings.warn(
+            "add_newdoc was used on a pure-python object {}. "
+            "Prefer to attach it directly to the source."
+            .format(obj),
+            UserWarning,
+            stacklevel=3)
+    try:
+        add_docstring(obj, doc)
+    except Exception:
+        pass
+
+
+def add_newdoc(place, obj, doc, warn_on_python=True):
+    """
+    Add documentation to an existing object, typically one defined in C
+
+    The purpose is to allow easier editing of the docstrings without requiring
+    a re-compile. This exists primarily for internal use within numpy itself.
+
+    Parameters
+    ----------
+    place : str
+        The absolute name of the module to import from
+    obj : str
+        The name of the object to add documentation to, typically a class or
+        function name
+    doc : {str, Tuple[str, str], List[Tuple[str, str]]}
+        If a string, the documentation to apply to `obj`
+
+        If a tuple, then the first element is interpreted as an attribute of
+        `obj` and the second as the docstring to apply - ``(method, docstring)``
+
+        If a list, then each element of the list should be a tuple of length
+        two - ``[(method1, docstring1), (method2, docstring2), ...]``
+    warn_on_python : bool
+        If True, the default, emit `UserWarning` if this is used to attach
+        documentation to a pure-python object.
+
+    Notes
+    -----
+    This routine never raises an error if the docstring can't be written, but
+    will raise an error if the object being documented does not exist.
+
+    This routine cannot modify read-only docstrings, as appear
+    in new-style classes or built-in functions. Because this
+    routine never raises an error the caller must check manually
+    that the docstrings were changed.
+
+    Since this function grabs the ``char *`` from a c-level str object and puts
+    it into the ``tp_doc`` slot of the type of `obj`, it violates a number of
+    C-API best-practices, by:
+
+    - modifying a `PyTypeObject` after calling `PyType_Ready`
+    - calling `Py_INCREF` on the str and losing the reference, so the str
+      will never be released
+
+    If possible it should be avoided.
+    """
+    new = getattr(__import__(place, globals(), {}, [obj]), obj)
+    if isinstance(doc, str):
+        _add_docstring(new, doc.strip(), warn_on_python)
+    elif isinstance(doc, tuple):
+        attr, docstring = doc
+        _add_docstring(getattr(new, attr), docstring.strip(), warn_on_python)
+    elif isinstance(doc, list):
+        for attr, docstring in doc:
+            _add_docstring(getattr(new, attr), docstring.strip(), warn_on_python)

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/core/function_base.pyi

@@ -0,0 +1,55 @@
+import sys
+from typing import overload, Tuple, Union, Sequence, Any
+
+from numpy import ndarray
+from numpy.typing import ArrayLike, DTypeLike, _SupportsArray, _NumberLike_co
+
+if sys.version_info >= (3, 8):
+    from typing import SupportsIndex, Literal
+else:
+    from typing_extensions import SupportsIndex, Literal
+
+# TODO: wait for support for recursive types
+_ArrayLikeNested = Sequence[Sequence[Any]]
+_ArrayLikeNumber = Union[
+    _NumberLike_co, Sequence[_NumberLike_co], ndarray, _SupportsArray, _ArrayLikeNested
+]
+@overload
+def linspace(
+    start: _ArrayLikeNumber,
+    stop: _ArrayLikeNumber,
+    num: SupportsIndex = ...,
+    endpoint: bool = ...,
+    retstep: Literal[False] = ...,
+    dtype: DTypeLike = ...,
+    axis: SupportsIndex = ...,
+) -> ndarray: ...
+@overload
+def linspace(
+    start: _ArrayLikeNumber,
+    stop: _ArrayLikeNumber,
+    num: SupportsIndex = ...,
+    endpoint: bool = ...,
+    retstep: Literal[True] = ...,
+    dtype: DTypeLike = ...,
+    axis: SupportsIndex = ...,
+) -> Tuple[ndarray, Any]: ...
+
+def logspace(
+    start: _ArrayLikeNumber,
+    stop: _ArrayLikeNumber,
+    num: SupportsIndex = ...,
+    endpoint: bool = ...,
+    base: _ArrayLikeNumber = ...,
+    dtype: DTypeLike = ...,
+    axis: SupportsIndex = ...,
+) -> ndarray: ...
+
+def geomspace(
+    start: _ArrayLikeNumber,
+    stop: _ArrayLikeNumber,
+    num: SupportsIndex = ...,
+    endpoint: bool = ...,
+    dtype: DTypeLike = ...,
+    axis: SupportsIndex = ...,
+) -> ndarray: ...

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/core/generate_numpy_api.py

@@ -0,0 +1,239 @@
+import os
+import genapi
+
+from genapi import \
+        TypeApi, GlobalVarApi, FunctionApi, BoolValuesApi
+
+import numpy_api
+
+# use annotated api when running under cpychecker
+h_template = r"""
+#if defined(_MULTIARRAYMODULE) || defined(WITH_CPYCHECKER_STEALS_REFERENCE_TO_ARG_ATTRIBUTE)
+
+typedef struct {
+        PyObject_HEAD
+        npy_bool obval;
+} PyBoolScalarObject;
+
+extern NPY_NO_EXPORT PyTypeObject PyArrayMapIter_Type;
+extern NPY_NO_EXPORT PyTypeObject PyArrayNeighborhoodIter_Type;
+extern NPY_NO_EXPORT PyBoolScalarObject _PyArrayScalar_BoolValues[2];
+
+%s
+
+#else
+
+#if defined(PY_ARRAY_UNIQUE_SYMBOL)
+#define PyArray_API PY_ARRAY_UNIQUE_SYMBOL
+#endif
+
+#if defined(NO_IMPORT) || defined(NO_IMPORT_ARRAY)
+extern void **PyArray_API;
+#else
+#if defined(PY_ARRAY_UNIQUE_SYMBOL)
+void **PyArray_API;
+#else
+static void **PyArray_API=NULL;
+#endif
+#endif
+
+%s
+
+#if !defined(NO_IMPORT_ARRAY) && !defined(NO_IMPORT)
+static int
+_import_array(void)
+{
+  int st;
+  PyObject *numpy = PyImport_ImportModule("numpy.core._multiarray_umath");
+  PyObject *c_api = NULL;
+
+  if (numpy == NULL) {
+      return -1;
+  }
+  c_api = PyObject_GetAttrString(numpy, "_ARRAY_API");
+  Py_DECREF(numpy);
+  if (c_api == NULL) {
+      PyErr_SetString(PyExc_AttributeError, "_ARRAY_API not found");
+      return -1;
+  }
+
+  if (!PyCapsule_CheckExact(c_api)) {
+      PyErr_SetString(PyExc_RuntimeError, "_ARRAY_API is not PyCapsule object");
+      Py_DECREF(c_api);
+      return -1;
+  }
+  PyArray_API = (void **)PyCapsule_GetPointer(c_api, NULL);
+  Py_DECREF(c_api);
+  if (PyArray_API == NULL) {
+      PyErr_SetString(PyExc_RuntimeError, "_ARRAY_API is NULL pointer");
+      return -1;
+  }
+
+  /* Perform runtime check of C API version */
+  if (NPY_VERSION != PyArray_GetNDArrayCVersion()) {
+      PyErr_Format(PyExc_RuntimeError, "module compiled against "\
+             "ABI version 0x%%x but this version of numpy is 0x%%x", \
+             (int) NPY_VERSION, (int) PyArray_GetNDArrayCVersion());
+      return -1;
+  }
+  if (NPY_FEATURE_VERSION > PyArray_GetNDArrayCFeatureVersion()) {
+      PyErr_Format(PyExc_RuntimeError, "module compiled against "\
+             "API version 0x%%x but this version of numpy is 0x%%x", \
+             (int) NPY_FEATURE_VERSION, (int) PyArray_GetNDArrayCFeatureVersion());
+      return -1;
+  }
+
+  /*
+   * Perform runtime check of endianness and check it matches the one set by
+   * the headers (npy_endian.h) as a safeguard
+   */
+  st = PyArray_GetEndianness();
+  if (st == NPY_CPU_UNKNOWN_ENDIAN) {
+      PyErr_Format(PyExc_RuntimeError, "FATAL: module compiled as unknown endian");
+      return -1;
+  }
+#if NPY_BYTE_ORDER == NPY_BIG_ENDIAN
+  if (st != NPY_CPU_BIG) {
+      PyErr_Format(PyExc_RuntimeError, "FATAL: module compiled as "\
+             "big endian, but detected different endianness at runtime");
+      return -1;
+  }
+#elif NPY_BYTE_ORDER == NPY_LITTLE_ENDIAN
+  if (st != NPY_CPU_LITTLE) {
+      PyErr_Format(PyExc_RuntimeError, "FATAL: module compiled as "\
+             "little endian, but detected different endianness at runtime");
+      return -1;
+  }
+#endif
+
+  return 0;
+}
+
+#define import_array() {if (_import_array() < 0) {PyErr_Print(); PyErr_SetString(PyExc_ImportError, "numpy.core.multiarray failed to import"); return NULL; } }
+
+#define import_array1(ret) {if (_import_array() < 0) {PyErr_Print(); PyErr_SetString(PyExc_ImportError, "numpy.core.multiarray failed to import"); return ret; } }
+
+#define import_array2(msg, ret) {if (_import_array() < 0) {PyErr_Print(); PyErr_SetString(PyExc_ImportError, msg); return ret; } }
+
+#endif
+
+#endif
+"""
+
+
+c_template = r"""
+/* These pointers will be stored in the C-object for use in other
+    extension modules
+*/
+
+void *PyArray_API[] = {
+%s
+};
+"""
+
+c_api_header = """
+===========
+NumPy C-API
+===========
+"""
+
+def generate_api(output_dir, force=False):
+    basename = 'multiarray_api'
+
+    h_file = os.path.join(output_dir, '__%s.h' % basename)
+    c_file = os.path.join(output_dir, '__%s.c' % basename)
+    d_file = os.path.join(output_dir, '%s.txt' % basename)
+    targets = (h_file, c_file, d_file)
+
+    sources = numpy_api.multiarray_api
+
+    if (not force and not genapi.should_rebuild(targets, [numpy_api.__file__, __file__])):
+        return targets
+    else:
+        do_generate_api(targets, sources)
+
+    return targets
+
+def do_generate_api(targets, sources):
+    header_file = targets[0]
+    c_file = targets[1]
+    doc_file = targets[2]
+
+    global_vars = sources[0]
+    scalar_bool_values = sources[1]
+    types_api = sources[2]
+    multiarray_funcs = sources[3]
+
+    multiarray_api = sources[:]
+
+    module_list = []
+    extension_list = []
+    init_list = []
+
+    # Check multiarray api indexes
+    multiarray_api_index = genapi.merge_api_dicts(multiarray_api)
+    genapi.check_api_dict(multiarray_api_index)
+
+    numpyapi_list = genapi.get_api_functions('NUMPY_API',
+                                             multiarray_funcs)
+
+    # FIXME: ordered_funcs_api is unused
+    ordered_funcs_api = genapi.order_dict(multiarray_funcs)
+
+    # Create dict name -> *Api instance
+    api_name = 'PyArray_API'
+    multiarray_api_dict = {}
+    for f in numpyapi_list:
+        name = f.name
+        index = multiarray_funcs[name][0]
+        annotations = multiarray_funcs[name][1:]
+        multiarray_api_dict[f.name] = FunctionApi(f.name, index, annotations,
+                                                  f.return_type,
+                                                  f.args, api_name)
+
+    for name, val in global_vars.items():
+        index, type = val
+        multiarray_api_dict[name] = GlobalVarApi(name, index, type, api_name)
+
+    for name, val in scalar_bool_values.items():
+        index = val[0]
+        multiarray_api_dict[name] = BoolValuesApi(name, index, api_name)
+
+    for name, val in types_api.items():
+        index = val[0]
+        internal_type =  None if len(val) == 1 else val[1]
+        multiarray_api_dict[name] = TypeApi(
+            name, index, 'PyTypeObject', api_name, internal_type)
+
+    if len(multiarray_api_dict) != len(multiarray_api_index):
+        keys_dict = set(multiarray_api_dict.keys())
+        keys_index = set(multiarray_api_index.keys())
+        raise AssertionError(
+            "Multiarray API size mismatch - "
+            "index has extra keys {}, dict has extra keys {}"
+            .format(keys_index - keys_dict, keys_dict - keys_index)
+        )
+
+    extension_list = []
+    for name, index in genapi.order_dict(multiarray_api_index):
+        api_item = multiarray_api_dict[name]
+        extension_list.append(api_item.define_from_array_api_string())
+        init_list.append(api_item.array_api_define())
+        module_list.append(api_item.internal_define())
+
+    # Write to header
+    s = h_template % ('\n'.join(module_list), '\n'.join(extension_list))
+    genapi.write_file(header_file, s)
+
+    # Write to c-code
+    s = c_template % ',\n'.join(init_list)
+    genapi.write_file(c_file, s)
+
+    # write to documentation
+    s = c_api_header
+    for func in numpyapi_list:
+        s += func.to_ReST()
+        s += '\n\n'
+    genapi.write_file(doc_file, s)
+
+    return targets

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/core/getlimits.py

@@ -0,0 +1,564 @@
+"""Machine limits for Float32 and Float64 and (long double) if available...
+
+"""
+__all__ = ['finfo', 'iinfo']
+
+import warnings
+
+from .machar import MachAr
+from .overrides import set_module
+from . import numeric
+from . import numerictypes as ntypes
+from .numeric import array, inf
+from .umath import log10, exp2
+from . import umath
+
+
+def _fr0(a):
+    """fix rank-0 --> rank-1"""
+    if a.ndim == 0:
+        a = a.copy()
+        a.shape = (1,)
+    return a
+
+
+def _fr1(a):
+    """fix rank > 0 --> rank-0"""
+    if a.size == 1:
+        a = a.copy()
+        a.shape = ()
+    return a
+
+class MachArLike:
+    """ Object to simulate MachAr instance """
+
+    def __init__(self,
+                 ftype,
+                 *, eps, epsneg, huge, tiny, ibeta, **kwargs):
+        params = _MACHAR_PARAMS[ftype]
+        float_conv = lambda v: array([v], ftype)
+        float_to_float = lambda v : _fr1(float_conv(v))
+        float_to_str = lambda v: (params['fmt'] % array(_fr0(v)[0], ftype))
+
+        self.title = params['title']
+        # Parameter types same as for discovered MachAr object.
+        self.epsilon = self.eps = float_to_float(eps)
+        self.epsneg = float_to_float(epsneg)
+        self.xmax = self.huge = float_to_float(huge)
+        self.xmin = self.tiny = float_to_float(tiny)
+        self.ibeta = params['itype'](ibeta)
+        self.__dict__.update(kwargs)
+        self.precision = int(-log10(self.eps))
+        self.resolution = float_to_float(float_conv(10) ** (-self.precision))
+        self._str_eps = float_to_str(self.eps)
+        self._str_epsneg = float_to_str(self.epsneg)
+        self._str_xmin = float_to_str(self.xmin)
+        self._str_xmax = float_to_str(self.xmax)
+        self._str_resolution = float_to_str(self.resolution)
+
+_convert_to_float = {
+    ntypes.csingle: ntypes.single,
+    ntypes.complex_: ntypes.float_,
+    ntypes.clongfloat: ntypes.longfloat
+    }
+
+# Parameters for creating MachAr / MachAr-like objects
+_title_fmt = 'numpy {} precision floating point number'
+_MACHAR_PARAMS = {
+    ntypes.double: dict(
+        itype = ntypes.int64,
+        fmt = '%24.16e',
+        title = _title_fmt.format('double')),
+    ntypes.single: dict(
+        itype = ntypes.int32,
+        fmt = '%15.7e',
+        title = _title_fmt.format('single')),
+    ntypes.longdouble: dict(
+        itype = ntypes.longlong,
+        fmt = '%s',
+        title = _title_fmt.format('long double')),
+    ntypes.half: dict(
+        itype = ntypes.int16,
+        fmt = '%12.5e',
+        title = _title_fmt.format('half'))}
+
+# Key to identify the floating point type.  Key is result of
+# ftype('-0.1').newbyteorder('<').tobytes()
+# See:
+# https://perl5.git.perl.org/perl.git/blob/3118d7d684b56cbeb702af874f4326683c45f045:/Configure
+_KNOWN_TYPES = {}
+def _register_type(machar, bytepat):
+    _KNOWN_TYPES[bytepat] = machar
+_float_ma = {}
+
+def _register_known_types():
+    # Known parameters for float16
+    # See docstring of MachAr class for description of parameters.
+    f16 = ntypes.float16
+    float16_ma = MachArLike(f16,
+                            machep=-10,
+                            negep=-11,
+                            minexp=-14,
+                            maxexp=16,
+                            it=10,
+                            iexp=5,
+                            ibeta=2,
+                            irnd=5,
+                            ngrd=0,
+                            eps=exp2(f16(-10)),
+                            epsneg=exp2(f16(-11)),
+                            huge=f16(65504),
+                            tiny=f16(2 ** -14))
+    _register_type(float16_ma, b'f\xae')
+    _float_ma[16] = float16_ma
+
+    # Known parameters for float32
+    f32 = ntypes.float32
+    float32_ma = MachArLike(f32,
+                            machep=-23,
+                            negep=-24,
+                            minexp=-126,
+                            maxexp=128,
+                            it=23,
+                            iexp=8,
+                            ibeta=2,
+                            irnd=5,
+                            ngrd=0,
+                            eps=exp2(f32(-23)),
+                            epsneg=exp2(f32(-24)),
+                            huge=f32((1 - 2 ** -24) * 2**128),
+                            tiny=exp2(f32(-126)))
+    _register_type(float32_ma, b'\xcd\xcc\xcc\xbd')
+    _float_ma[32] = float32_ma
+
+    # Known parameters for float64
+    f64 = ntypes.float64
+    epsneg_f64 = 2.0 ** -53.0
+    tiny_f64 = 2.0 ** -1022.0
+    float64_ma = MachArLike(f64,
+                            machep=-52,
+                            negep=-53,
+                            minexp=-1022,
+                            maxexp=1024,
+                            it=52,
+                            iexp=11,
+                            ibeta=2,
+                            irnd=5,
+                            ngrd=0,
+                            eps=2.0 ** -52.0,
+                            epsneg=epsneg_f64,
+                            huge=(1.0 - epsneg_f64) / tiny_f64 * f64(4),
+                            tiny=tiny_f64)
+    _register_type(float64_ma, b'\x9a\x99\x99\x99\x99\x99\xb9\xbf')
+    _float_ma[64] = float64_ma
+
+    # Known parameters for IEEE 754 128-bit binary float
+    ld = ntypes.longdouble
+    epsneg_f128 = exp2(ld(-113))
+    tiny_f128 = exp2(ld(-16382))
+    # Ignore runtime error when this is not f128
+    with numeric.errstate(all='ignore'):
+        huge_f128 = (ld(1) - epsneg_f128) / tiny_f128 * ld(4)
+    float128_ma = MachArLike(ld,
+                             machep=-112,
+                             negep=-113,
+                             minexp=-16382,
+                             maxexp=16384,
+                             it=112,
+                             iexp=15,
+                             ibeta=2,
+                             irnd=5,
+                             ngrd=0,
+                             eps=exp2(ld(-112)),
+                             epsneg=epsneg_f128,
+                             huge=huge_f128,
+                             tiny=tiny_f128)
+    # IEEE 754 128-bit binary float
+    _register_type(float128_ma,
+        b'\x9a\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\xfb\xbf')
+    _register_type(float128_ma,
+        b'\x9a\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\xfb\xbf')
+    _float_ma[128] = float128_ma
+
+    # Known parameters for float80 (Intel 80-bit extended precision)
+    epsneg_f80 = exp2(ld(-64))
+    tiny_f80 = exp2(ld(-16382))
+    # Ignore runtime error when this is not f80
+    with numeric.errstate(all='ignore'):
+        huge_f80 = (ld(1) - epsneg_f80) / tiny_f80 * ld(4)
+    float80_ma = MachArLike(ld,
+                            machep=-63,
+                            negep=-64,
+                            minexp=-16382,
+                            maxexp=16384,
+                            it=63,
+                            iexp=15,
+                            ibeta=2,
+                            irnd=5,
+                            ngrd=0,
+                            eps=exp2(ld(-63)),
+                            epsneg=epsneg_f80,
+                            huge=huge_f80,
+                            tiny=tiny_f80)
+    # float80, first 10 bytes containing actual storage
+    _register_type(float80_ma, b'\xcd\xcc\xcc\xcc\xcc\xcc\xcc\xcc\xfb\xbf')
+    _float_ma[80] = float80_ma
+
+    # Guessed / known parameters for double double; see:
+    # https://en.wikipedia.org/wiki/Quadruple-precision_floating-point_format#Double-double_arithmetic
+    # These numbers have the same exponent range as float64, but extended number of
+    # digits in the significand.
+    huge_dd = (umath.nextafter(ld(inf), ld(0))
+                if hasattr(umath, 'nextafter')  # Missing on some platforms?
+                else float64_ma.huge)
+    float_dd_ma = MachArLike(ld,
+                              machep=-105,
+                              negep=-106,
+                              minexp=-1022,
+                              maxexp=1024,
+                              it=105,
+                              iexp=11,
+                              ibeta=2,
+                              irnd=5,
+                              ngrd=0,
+                              eps=exp2(ld(-105)),
+                              epsneg= exp2(ld(-106)),
+                              huge=huge_dd,
+                              tiny=exp2(ld(-1022)))
+    # double double; low, high order (e.g. PPC 64)
+    _register_type(float_dd_ma,
+        b'\x9a\x99\x99\x99\x99\x99Y<\x9a\x99\x99\x99\x99\x99\xb9\xbf')
+    # double double; high, low order (e.g. PPC 64 le)
+    _register_type(float_dd_ma,
+        b'\x9a\x99\x99\x99\x99\x99\xb9\xbf\x9a\x99\x99\x99\x99\x99Y<')
+    _float_ma['dd'] = float_dd_ma
+
+
+def _get_machar(ftype):
+    """ Get MachAr instance or MachAr-like instance
+
+    Get parameters for floating point type, by first trying signatures of
+    various known floating point types, then, if none match, attempting to
+    identify parameters by analysis.
+
+    Parameters
+    ----------
+    ftype : class
+        Numpy floating point type class (e.g. ``np.float64``)
+
+    Returns
+    -------
+    ma_like : instance of :class:`MachAr` or :class:`MachArLike`
+        Object giving floating point parameters for `ftype`.
+
+    Warns
+    -----
+    UserWarning
+        If the binary signature of the float type is not in the dictionary of
+        known float types.
+    """
+    params = _MACHAR_PARAMS.get(ftype)
+    if params is None:
+        raise ValueError(repr(ftype))
+    # Detect known / suspected types
+    key = ftype('-0.1').newbyteorder('<').tobytes()
+    ma_like = None
+    if ftype == ntypes.longdouble:
+        # Could be 80 bit == 10 byte extended precision, where last bytes can
+        # be random garbage.
+        # Comparing first 10 bytes to pattern first to avoid branching on the
+        # random garbage.
+        ma_like = _KNOWN_TYPES.get(key[:10])
+    if ma_like is None:
+        ma_like = _KNOWN_TYPES.get(key)
+    if ma_like is not None:
+        return ma_like
+    # Fall back to parameter discovery
+    warnings.warn(
+        'Signature {} for {} does not match any known type: '
+        'falling back to type probe function'.format(key, ftype),
+        UserWarning, stacklevel=2)
+    return _discovered_machar(ftype)
+
+
+def _discovered_machar(ftype):
+    """ Create MachAr instance with found information on float types
+    """
+    params = _MACHAR_PARAMS[ftype]
+    return MachAr(lambda v: array([v], ftype),
+                  lambda v:_fr0(v.astype(params['itype']))[0],
+                  lambda v:array(_fr0(v)[0], ftype),
+                  lambda v: params['fmt'] % array(_fr0(v)[0], ftype),
+                  params['title'])
+
+
+@set_module('numpy')
+class finfo:
+    """
+    finfo(dtype)
+
+    Machine limits for floating point types.
+
+    Attributes
+    ----------
+    bits : int
+        The number of bits occupied by the type.
+    eps : float
+        The difference between 1.0 and the next smallest representable float
+        larger than 1.0. For example, for 64-bit binary floats in the IEEE-754
+        standard, ``eps = 2**-52``, approximately 2.22e-16.
+    epsneg : float
+        The difference between 1.0 and the next smallest representable float
+        less than 1.0. For example, for 64-bit binary floats in the IEEE-754
+        standard, ``epsneg = 2**-53``, approximately 1.11e-16.
+    iexp : int
+        The number of bits in the exponent portion of the floating point
+        representation.
+    machar : MachAr
+        The object which calculated these parameters and holds more
+        detailed information.
+    machep : int
+        The exponent that yields `eps`.
+    max : floating point number of the appropriate type
+        The largest representable number.
+    maxexp : int
+        The smallest positive power of the base (2) that causes overflow.
+    min : floating point number of the appropriate type
+        The smallest representable number, typically ``-max``.
+    minexp : int
+        The most negative power of the base (2) consistent with there
+        being no leading 0's in the mantissa.
+    negep : int
+        The exponent that yields `epsneg`.
+    nexp : int
+        The number of bits in the exponent including its sign and bias.
+    nmant : int
+        The number of bits in the mantissa.
+    precision : int
+        The approximate number of decimal digits to which this kind of
+        float is precise.
+    resolution : floating point number of the appropriate type
+        The approximate decimal resolution of this type, i.e.,
+        ``10**-precision``.
+    tiny : float
+        The smallest positive floating point number with full precision
+        (see Notes).
+
+    Parameters
+    ----------
+    dtype : float, dtype, or instance
+        Kind of floating point data-type about which to get information.
+
+    See Also
+    --------
+    MachAr : The implementation of the tests that produce this information.
+    iinfo : The equivalent for integer data types.
+    spacing : The distance between a value and the nearest adjacent number
+    nextafter : The next floating point value after x1 towards x2
+
+    Notes
+    -----
+    For developers of NumPy: do not instantiate this at the module level.
+    The initial calculation of these parameters is expensive and negatively
+    impacts import times.  These objects are cached, so calling ``finfo()``
+    repeatedly inside your functions is not a problem.
+
+    Note that ``tiny`` is not actually the smallest positive representable
+    value in a NumPy floating point type. As in the IEEE-754 standard [1]_,
+    NumPy floating point types make use of subnormal numbers to fill the
+    gap between 0 and ``tiny``. However, subnormal numbers may have
+    significantly reduced precision [2]_.
+    
+    References
+    ----------
+    .. [1] IEEE Standard for Floating-Point Arithmetic, IEEE Std 754-2008,
+           pp.1-70, 2008, http://www.doi.org/10.1109/IEEESTD.2008.4610935
+    .. [2] Wikipedia, "Denormal Numbers",
+           https://en.wikipedia.org/wiki/Denormal_number
+    """
+
+    _finfo_cache = {}
+
+    def __new__(cls, dtype):
+        try:
+            dtype = numeric.dtype(dtype)
+        except TypeError:
+            # In case a float instance was given
+            dtype = numeric.dtype(type(dtype))
+
+        obj = cls._finfo_cache.get(dtype, None)
+        if obj is not None:
+            return obj
+        dtypes = [dtype]
+        newdtype = numeric.obj2sctype(dtype)
+        if newdtype is not dtype:
+            dtypes.append(newdtype)
+            dtype = newdtype
+        if not issubclass(dtype, numeric.inexact):
+            raise ValueError("data type %r not inexact" % (dtype))
+        obj = cls._finfo_cache.get(dtype, None)
+        if obj is not None:
+            return obj
+        if not issubclass(dtype, numeric.floating):
+            newdtype = _convert_to_float[dtype]
+            if newdtype is not dtype:
+                dtypes.append(newdtype)
+                dtype = newdtype
+        obj = cls._finfo_cache.get(dtype, None)
+        if obj is not None:
+            return obj
+        obj = object.__new__(cls)._init(dtype)
+        for dt in dtypes:
+            cls._finfo_cache[dt] = obj
+        return obj
+
+    def _init(self, dtype):
+        self.dtype = numeric.dtype(dtype)
+        machar = _get_machar(dtype)
+
+        for word in ['precision', 'iexp',
+                     'maxexp', 'minexp', 'negep',
+                     'machep']:
+            setattr(self, word, getattr(machar, word))
+        for word in ['tiny', 'resolution', 'epsneg']:
+            setattr(self, word, getattr(machar, word).flat[0])
+        self.bits = self.dtype.itemsize * 8
+        self.max = machar.huge.flat[0]
+        self.min = -self.max
+        self.eps = machar.eps.flat[0]
+        self.nexp = machar.iexp
+        self.nmant = machar.it
+        self.machar = machar
+        self._str_tiny = machar._str_xmin.strip()
+        self._str_max = machar._str_xmax.strip()
+        self._str_epsneg = machar._str_epsneg.strip()
+        self._str_eps = machar._str_eps.strip()
+        self._str_resolution = machar._str_resolution.strip()
+        return self
+
+    def __str__(self):
+        fmt = (
+            'Machine parameters for %(dtype)s\n'
+            '---------------------------------------------------------------\n'
+            'precision = %(precision)3s   resolution = %(_str_resolution)s\n'
+            'machep = %(machep)6s   eps =        %(_str_eps)s\n'
+            'negep =  %(negep)6s   epsneg =     %(_str_epsneg)s\n'
+            'minexp = %(minexp)6s   tiny =       %(_str_tiny)s\n'
+            'maxexp = %(maxexp)6s   max =        %(_str_max)s\n'
+            'nexp =   %(nexp)6s   min =        -max\n'
+            '---------------------------------------------------------------\n'
+            )
+        return fmt % self.__dict__
+
+    def __repr__(self):
+        c = self.__class__.__name__
+        d = self.__dict__.copy()
+        d['klass'] = c
+        return (("%(klass)s(resolution=%(resolution)s, min=-%(_str_max)s,"
+                 " max=%(_str_max)s, dtype=%(dtype)s)") % d)
+
+
+@set_module('numpy')
+class iinfo:
+    """
+    iinfo(type)
+
+    Machine limits for integer types.
+
+    Attributes
+    ----------
+    bits : int
+        The number of bits occupied by the type.
+    min : int
+        The smallest integer expressible by the type.
+    max : int
+        The largest integer expressible by the type.
+
+    Parameters
+    ----------
+    int_type : integer type, dtype, or instance
+        The kind of integer data type to get information about.
+
+    See Also
+    --------
+    finfo : The equivalent for floating point data types.
+
+    Examples
+    --------
+    With types:
+
+    >>> ii16 = np.iinfo(np.int16)
+    >>> ii16.min
+    -32768
+    >>> ii16.max
+    32767
+    >>> ii32 = np.iinfo(np.int32)
+    >>> ii32.min
+    -2147483648
+    >>> ii32.max
+    2147483647
+
+    With instances:
+
+    >>> ii32 = np.iinfo(np.int32(10))
+    >>> ii32.min
+    -2147483648
+    >>> ii32.max
+    2147483647
+
+    """
+
+    _min_vals = {}
+    _max_vals = {}
+
+    def __init__(self, int_type):
+        try:
+            self.dtype = numeric.dtype(int_type)
+        except TypeError:
+            self.dtype = numeric.dtype(type(int_type))
+        self.kind = self.dtype.kind
+        self.bits = self.dtype.itemsize * 8
+        self.key = "%s%d" % (self.kind, self.bits)
+        if self.kind not in 'iu':
+            raise ValueError("Invalid integer data type %r." % (self.kind,))
+
+    @property
+    def min(self):
+        """Minimum value of given dtype."""
+        if self.kind == 'u':
+            return 0
+        else:
+            try:
+                val = iinfo._min_vals[self.key]
+            except KeyError:
+                val = int(-(1 << (self.bits-1)))
+                iinfo._min_vals[self.key] = val
+            return val
+
+    @property
+    def max(self):
+        """Maximum value of given dtype."""
+        try:
+            val = iinfo._max_vals[self.key]
+        except KeyError:
+            if self.kind == 'u':
+                val = int((1 << self.bits) - 1)
+            else:
+                val = int((1 << (self.bits-1)) - 1)
+            iinfo._max_vals[self.key] = val
+        return val
+
+    def __str__(self):
+        """String representation."""
+        fmt = (
+            'Machine parameters for %(dtype)s\n'
+            '---------------------------------------------------------------\n'
+            'min = %(min)s\n'
+            'max = %(max)s\n'
+            '---------------------------------------------------------------\n'
+            )
+        return fmt % {'dtype': self.dtype, 'min': self.min, 'max': self.max}
+
+    def __repr__(self):
+        return "%s(min=%s, max=%s, dtype=%s)" % (self.__class__.__name__,
+                                    self.min, self.max, self.dtype)

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/core/include/numpy/__ufunc_api.h

@@ -0,0 +1,311 @@
+
+#ifdef _UMATHMODULE
+
+extern NPY_NO_EXPORT PyTypeObject PyUFunc_Type;
+
+extern NPY_NO_EXPORT PyTypeObject PyUFunc_Type;
+
+NPY_NO_EXPORT  PyObject * PyUFunc_FromFuncAndData \
+       (PyUFuncGenericFunction *, void **, char *, int, int, int, int, const char *, const char *, int);
+NPY_NO_EXPORT  int PyUFunc_RegisterLoopForType \
+       (PyUFuncObject *, int, PyUFuncGenericFunction, const int *, void *);
+NPY_NO_EXPORT  int PyUFunc_GenericFunction \
+       (PyUFuncObject *NPY_UNUSED(ufunc), PyObject *NPY_UNUSED(args), PyObject *NPY_UNUSED(kwds), PyArrayObject **NPY_UNUSED(op));
+NPY_NO_EXPORT  void PyUFunc_f_f_As_d_d \
+       (char **, npy_intp const *, npy_intp const *, void *);
+NPY_NO_EXPORT  void PyUFunc_d_d \
+       (char **, npy_intp const *, npy_intp const *, void *);
+NPY_NO_EXPORT  void PyUFunc_f_f \
+       (char **, npy_intp const *, npy_intp const *, void *);
+NPY_NO_EXPORT  void PyUFunc_g_g \
+       (char **, npy_intp const *, npy_intp const *, void *);
+NPY_NO_EXPORT  void PyUFunc_F_F_As_D_D \
+       (char **, npy_intp const *, npy_intp const *, void *);
+NPY_NO_EXPORT  void PyUFunc_F_F \
+       (char **, npy_intp const *, npy_intp const *, void *);
+NPY_NO_EXPORT  void PyUFunc_D_D \
+       (char **, npy_intp const *, npy_intp const *, void *);
+NPY_NO_EXPORT  void PyUFunc_G_G \
+       (char **, npy_intp const *, npy_intp const *, void *);
+NPY_NO_EXPORT  void PyUFunc_O_O \
+       (char **, npy_intp const *, npy_intp const *, void *);
+NPY_NO_EXPORT  void PyUFunc_ff_f_As_dd_d \
+       (char **, npy_intp const *, npy_intp const *, void *);
+NPY_NO_EXPORT  void PyUFunc_ff_f \
+       (char **, npy_intp const *, npy_intp const *, void *);
+NPY_NO_EXPORT  void PyUFunc_dd_d \
+       (char **, npy_intp const *, npy_intp const *, void *);
+NPY_NO_EXPORT  void PyUFunc_gg_g \
+       (char **, npy_intp const *, npy_intp const *, void *);
+NPY_NO_EXPORT  void PyUFunc_FF_F_As_DD_D \
+       (char **, npy_intp const *, npy_intp const *, void *);
+NPY_NO_EXPORT  void PyUFunc_DD_D \
+       (char **, npy_intp const *, npy_intp const *, void *);
+NPY_NO_EXPORT  void PyUFunc_FF_F \
+       (char **, npy_intp const *, npy_intp const *, void *);
+NPY_NO_EXPORT  void PyUFunc_GG_G \
+       (char **, npy_intp const *, npy_intp const *, void *);
+NPY_NO_EXPORT  void PyUFunc_OO_O \
+       (char **, npy_intp const *, npy_intp const *, void *);
+NPY_NO_EXPORT  void PyUFunc_O_O_method \
+       (char **, npy_intp const *, npy_intp const *, void *);
+NPY_NO_EXPORT  void PyUFunc_OO_O_method \
+       (char **, npy_intp const *, npy_intp const *, void *);
+NPY_NO_EXPORT  void PyUFunc_On_Om \
+       (char **, npy_intp const *, npy_intp const *, void *);
+NPY_NO_EXPORT  int PyUFunc_GetPyValues \
+       (char *, int *, int *, PyObject **);
+NPY_NO_EXPORT  int PyUFunc_checkfperr \
+       (int, PyObject *, int *);
+NPY_NO_EXPORT  void PyUFunc_clearfperr \
+       (void);
+NPY_NO_EXPORT  int PyUFunc_getfperr \
+       (void);
+NPY_NO_EXPORT  int PyUFunc_handlefperr \
+       (int, PyObject *, int, int *);
+NPY_NO_EXPORT  int PyUFunc_ReplaceLoopBySignature \
+       (PyUFuncObject *, PyUFuncGenericFunction, const int *, PyUFuncGenericFunction *);
+NPY_NO_EXPORT  PyObject * PyUFunc_FromFuncAndDataAndSignature \
+       (PyUFuncGenericFunction *, void **, char *, int, int, int, int, const char *, const char *, int, const char *);
+NPY_NO_EXPORT  int PyUFunc_SetUsesArraysAsData \
+       (void **NPY_UNUSED(data), size_t NPY_UNUSED(i));
+NPY_NO_EXPORT  void PyUFunc_e_e \
+       (char **, npy_intp const *, npy_intp const *, void *);
+NPY_NO_EXPORT  void PyUFunc_e_e_As_f_f \
+       (char **, npy_intp const *, npy_intp const *, void *);
+NPY_NO_EXPORT  void PyUFunc_e_e_As_d_d \
+       (char **, npy_intp const *, npy_intp const *, void *);
+NPY_NO_EXPORT  void PyUFunc_ee_e \
+       (char **, npy_intp const *, npy_intp const *, void *);
+NPY_NO_EXPORT  void PyUFunc_ee_e_As_ff_f \
+       (char **, npy_intp const *, npy_intp const *, void *);
+NPY_NO_EXPORT  void PyUFunc_ee_e_As_dd_d \
+       (char **, npy_intp const *, npy_intp const *, void *);
+NPY_NO_EXPORT  int PyUFunc_DefaultTypeResolver \
+       (PyUFuncObject *, NPY_CASTING, PyArrayObject **, PyObject *, PyArray_Descr **);
+NPY_NO_EXPORT  int PyUFunc_ValidateCasting \
+       (PyUFuncObject *, NPY_CASTING, PyArrayObject **, PyArray_Descr **);
+NPY_NO_EXPORT  int PyUFunc_RegisterLoopForDescr \
+       (PyUFuncObject *, PyArray_Descr *, PyUFuncGenericFunction, PyArray_Descr **, void *);
+NPY_NO_EXPORT  PyObject * PyUFunc_FromFuncAndDataAndSignatureAndIdentity \
+       (PyUFuncGenericFunction *, void **, char *, int, int, int, int, const char *, const char *, const int, const char *, PyObject *);
+
+#else
+
+#if defined(PY_UFUNC_UNIQUE_SYMBOL)
+#define PyUFunc_API PY_UFUNC_UNIQUE_SYMBOL
+#endif
+
+#if defined(NO_IMPORT) || defined(NO_IMPORT_UFUNC)
+extern void **PyUFunc_API;
+#else
+#if defined(PY_UFUNC_UNIQUE_SYMBOL)
+void **PyUFunc_API;
+#else
+static void **PyUFunc_API=NULL;
+#endif
+#endif
+
+#define PyUFunc_Type (*(PyTypeObject *)PyUFunc_API[0])
+#define PyUFunc_FromFuncAndData \
+        (*(PyObject * (*)(PyUFuncGenericFunction *, void **, char *, int, int, int, int, const char *, const char *, int)) \
+         PyUFunc_API[1])
+#define PyUFunc_RegisterLoopForType \
+        (*(int (*)(PyUFuncObject *, int, PyUFuncGenericFunction, const int *, void *)) \
+         PyUFunc_API[2])
+#define PyUFunc_GenericFunction \
+        (*(int (*)(PyUFuncObject *NPY_UNUSED(ufunc), PyObject *NPY_UNUSED(args), PyObject *NPY_UNUSED(kwds), PyArrayObject **NPY_UNUSED(op))) \
+         PyUFunc_API[3])
+#define PyUFunc_f_f_As_d_d \
+        (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
+         PyUFunc_API[4])
+#define PyUFunc_d_d \
+        (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
+         PyUFunc_API[5])
+#define PyUFunc_f_f \
+        (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
+         PyUFunc_API[6])
+#define PyUFunc_g_g \
+        (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
+         PyUFunc_API[7])
+#define PyUFunc_F_F_As_D_D \
+        (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
+         PyUFunc_API[8])
+#define PyUFunc_F_F \
+        (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
+         PyUFunc_API[9])
+#define PyUFunc_D_D \
+        (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
+         PyUFunc_API[10])
+#define PyUFunc_G_G \
+        (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
+         PyUFunc_API[11])
+#define PyUFunc_O_O \
+        (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
+         PyUFunc_API[12])
+#define PyUFunc_ff_f_As_dd_d \
+        (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
+         PyUFunc_API[13])
+#define PyUFunc_ff_f \
+        (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
+         PyUFunc_API[14])
+#define PyUFunc_dd_d \
+        (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
+         PyUFunc_API[15])
+#define PyUFunc_gg_g \
+        (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
+         PyUFunc_API[16])
+#define PyUFunc_FF_F_As_DD_D \
+        (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
+         PyUFunc_API[17])
+#define PyUFunc_DD_D \
+        (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
+         PyUFunc_API[18])
+#define PyUFunc_FF_F \
+        (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
+         PyUFunc_API[19])
+#define PyUFunc_GG_G \
+        (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
+         PyUFunc_API[20])
+#define PyUFunc_OO_O \
+        (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
+         PyUFunc_API[21])
+#define PyUFunc_O_O_method \
+        (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
+         PyUFunc_API[22])
+#define PyUFunc_OO_O_method \
+        (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
+         PyUFunc_API[23])
+#define PyUFunc_On_Om \
+        (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
+         PyUFunc_API[24])
+#define PyUFunc_GetPyValues \
+        (*(int (*)(char *, int *, int *, PyObject **)) \
+         PyUFunc_API[25])
+#define PyUFunc_checkfperr \
+        (*(int (*)(int, PyObject *, int *)) \
+         PyUFunc_API[26])
+#define PyUFunc_clearfperr \
+        (*(void (*)(void)) \
+         PyUFunc_API[27])
+#define PyUFunc_getfperr \
+        (*(int (*)(void)) \
+         PyUFunc_API[28])
+#define PyUFunc_handlefperr \
+        (*(int (*)(int, PyObject *, int, int *)) \
+         PyUFunc_API[29])
+#define PyUFunc_ReplaceLoopBySignature \
+        (*(int (*)(PyUFuncObject *, PyUFuncGenericFunction, const int *, PyUFuncGenericFunction *)) \
+         PyUFunc_API[30])
+#define PyUFunc_FromFuncAndDataAndSignature \
+        (*(PyObject * (*)(PyUFuncGenericFunction *, void **, char *, int, int, int, int, const char *, const char *, int, const char *)) \
+         PyUFunc_API[31])
+#define PyUFunc_SetUsesArraysAsData \
+        (*(int (*)(void **NPY_UNUSED(data), size_t NPY_UNUSED(i))) \
+         PyUFunc_API[32])
+#define PyUFunc_e_e \
+        (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
+         PyUFunc_API[33])
+#define PyUFunc_e_e_As_f_f \
+        (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
+         PyUFunc_API[34])
+#define PyUFunc_e_e_As_d_d \
+        (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
+         PyUFunc_API[35])
+#define PyUFunc_ee_e \
+        (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
+         PyUFunc_API[36])
+#define PyUFunc_ee_e_As_ff_f \
+        (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
+         PyUFunc_API[37])
+#define PyUFunc_ee_e_As_dd_d \
+        (*(void (*)(char **, npy_intp const *, npy_intp const *, void *)) \
+         PyUFunc_API[38])
+#define PyUFunc_DefaultTypeResolver \
+        (*(int (*)(PyUFuncObject *, NPY_CASTING, PyArrayObject **, PyObject *, PyArray_Descr **)) \
+         PyUFunc_API[39])
+#define PyUFunc_ValidateCasting \
+        (*(int (*)(PyUFuncObject *, NPY_CASTING, PyArrayObject **, PyArray_Descr **)) \
+         PyUFunc_API[40])
+#define PyUFunc_RegisterLoopForDescr \
+        (*(int (*)(PyUFuncObject *, PyArray_Descr *, PyUFuncGenericFunction, PyArray_Descr **, void *)) \
+         PyUFunc_API[41])
+#define PyUFunc_FromFuncAndDataAndSignatureAndIdentity \
+        (*(PyObject * (*)(PyUFuncGenericFunction *, void **, char *, int, int, int, int, const char *, const char *, const int, const char *, PyObject *)) \
+         PyUFunc_API[42])
+
+static NPY_INLINE int
+_import_umath(void)
+{
+  PyObject *numpy = PyImport_ImportModule("numpy.core._multiarray_umath");
+  PyObject *c_api = NULL;
+
+  if (numpy == NULL) {
+      PyErr_SetString(PyExc_ImportError,
+                      "numpy.core._multiarray_umath failed to import");
+      return -1;
+  }
+  c_api = PyObject_GetAttrString(numpy, "_UFUNC_API");
+  Py_DECREF(numpy);
+  if (c_api == NULL) {
+      PyErr_SetString(PyExc_AttributeError, "_UFUNC_API not found");
+      return -1;
+  }
+
+  if (!PyCapsule_CheckExact(c_api)) {
+      PyErr_SetString(PyExc_RuntimeError, "_UFUNC_API is not PyCapsule object");
+      Py_DECREF(c_api);
+      return -1;
+  }
+  PyUFunc_API = (void **)PyCapsule_GetPointer(c_api, NULL);
+  Py_DECREF(c_api);
+  if (PyUFunc_API == NULL) {
+      PyErr_SetString(PyExc_RuntimeError, "_UFUNC_API is NULL pointer");
+      return -1;
+  }
+  return 0;
+}
+
+#define import_umath() \
+    do {\
+        UFUNC_NOFPE\
+        if (_import_umath() < 0) {\
+            PyErr_Print();\
+            PyErr_SetString(PyExc_ImportError,\
+                    "numpy.core.umath failed to import");\
+            return NULL;\
+        }\
+    } while(0)
+
+#define import_umath1(ret) \
+    do {\
+        UFUNC_NOFPE\
+        if (_import_umath() < 0) {\
+            PyErr_Print();\
+            PyErr_SetString(PyExc_ImportError,\
+                    "numpy.core.umath failed to import");\
+            return ret;\
+        }\
+    } while(0)
+
+#define import_umath2(ret, msg) \
+    do {\
+        UFUNC_NOFPE\
+        if (_import_umath() < 0) {\
+            PyErr_Print();\
+            PyErr_SetString(PyExc_ImportError, msg);\
+            return ret;\
+        }\
+    } while(0)
+
+#define import_ufunc() \
+    do {\
+        UFUNC_NOFPE\
+        if (_import_umath() < 0) {\
+            PyErr_Print();\
+            PyErr_SetString(PyExc_ImportError,\
+                    "numpy.core.umath failed to import");\
+        }\
+    } while(0)
+
+#endif

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/core/include/numpy/_neighborhood_iterator_imp.h

@@ -0,0 +1,90 @@
+#ifndef _NPY_INCLUDE_NEIGHBORHOOD_IMP
+#error You should not include this header directly
+#endif
+/*
+ * Private API (here for inline)
+ */
+static NPY_INLINE int
+_PyArrayNeighborhoodIter_IncrCoord(PyArrayNeighborhoodIterObject* iter);
+
+/*
+ * Update to next item of the iterator
+ *
+ * Note: this simply increment the coordinates vector, last dimension
+ * incremented first , i.e, for dimension 3
+ * ...
+ * -1, -1, -1
+ * -1, -1,  0
+ * -1, -1,  1
+ *  ....
+ * -1,  0, -1
+ * -1,  0,  0
+ *  ....
+ * 0,  -1, -1
+ * 0,  -1,  0
+ *  ....
+ */
+#define _UPDATE_COORD_ITER(c) \
+    wb = iter->coordinates[c] < iter->bounds[c][1]; \
+    if (wb) { \
+        iter->coordinates[c] += 1; \
+        return 0; \
+    } \
+    else { \
+        iter->coordinates[c] = iter->bounds[c][0]; \
+    }
+
+static NPY_INLINE int
+_PyArrayNeighborhoodIter_IncrCoord(PyArrayNeighborhoodIterObject* iter)
+{
+    npy_intp i, wb;
+
+    for (i = iter->nd - 1; i >= 0; --i) {
+        _UPDATE_COORD_ITER(i)
+    }
+
+    return 0;
+}
+
+/*
+ * Version optimized for 2d arrays, manual loop unrolling
+ */
+static NPY_INLINE int
+_PyArrayNeighborhoodIter_IncrCoord2D(PyArrayNeighborhoodIterObject* iter)
+{
+    npy_intp wb;
+
+    _UPDATE_COORD_ITER(1)
+    _UPDATE_COORD_ITER(0)
+
+    return 0;
+}
+#undef _UPDATE_COORD_ITER
+
+/*
+ * Advance to the next neighbour
+ */
+static NPY_INLINE int
+PyArrayNeighborhoodIter_Next(PyArrayNeighborhoodIterObject* iter)
+{
+    _PyArrayNeighborhoodIter_IncrCoord (iter);
+    iter->dataptr = iter->translate((PyArrayIterObject*)iter, iter->coordinates);
+
+    return 0;
+}
+
+/*
+ * Reset functions
+ */
+static NPY_INLINE int
+PyArrayNeighborhoodIter_Reset(PyArrayNeighborhoodIterObject* iter)
+{
+    npy_intp i;
+
+    for (i = 0; i < iter->nd; ++i) {
+        iter->coordinates[i] = iter->bounds[i][0];
+    }
+    iter->dataptr = iter->translate((PyArrayIterObject*)iter, iter->coordinates);
+
+    return 0;
+}

Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/numpy/core/include/numpy/_numpyconfig.h

@@ -0,0 +1,29 @@
+#define NPY_SIZEOF_SHORT SIZEOF_SHORT
+#define NPY_SIZEOF_INT SIZEOF_INT
+#define NPY_SIZEOF_LONG SIZEOF_LONG
+#define NPY_SIZEOF_FLOAT 4
+#define NPY_SIZEOF_COMPLEX_FLOAT 8
+#define NPY_SIZEOF_DOUBLE 8
+#define NPY_SIZEOF_COMPLEX_DOUBLE 16
+#define NPY_SIZEOF_LONGDOUBLE 8
+#define NPY_SIZEOF_COMPLEX_LONGDOUBLE 16
+#define NPY_SIZEOF_PY_INTPTR_T 8
+#define NPY_SIZEOF_OFF_T 4
+#define NPY_SIZEOF_PY_LONG_LONG 8
+#define NPY_SIZEOF_LONGLONG 8
+#define NPY_NO_SIGNAL 1
+#define NPY_NO_SMP 0
+#define NPY_HAVE_DECL_ISNAN
+#define NPY_HAVE_DECL_ISINF
+#define NPY_HAVE_DECL_SIGNBIT
+#define NPY_HAVE_DECL_ISFINITE
+#define NPY_USE_C99_COMPLEX 1
+#define NPY_RELAXED_STRIDES_CHECKING 1
+#define NPY_USE_C99_FORMATS 1
+#define NPY_VISIBILITY_HIDDEN 
+#define NPY_ABI_VERSION 0x01000009
+#define NPY_API_VERSION 0x0000000E
+
+#ifndef __STDC_FORMAT_MACROS
+#define __STDC_FORMAT_MACROS 1
+#endif