left, right = split_path_inout(path, insideA) except ValueError: right = path path = right if patchB: def insideB(xy_display): xy_event = ConnectionStyle._Base.SimpleEvent(xy_display) return patchB.contains(xy_event)[0] try: left, right = split_path_inout(path, insideB) except ValueError: left = path path = left return path def _shrink(self, path, shrinkA, shrinkB): """ Shrink the path by fixed size (in points) with shrinkA and shrinkB """ if shrinkA: x, y = path.vertices[0] insideA = inside_circle(x, y, shrinkA) try: left, right = split_path_inout(path, insideA) path = right except ValueError: pass if shrinkB: x, y = path.vertices[-1] insideB = inside_circle(x, y, shrinkB) try: left, right = split_path_inout(path, insideB) path = left except ValueError: pass return path def __call__(self, posA, posB, shrinkA=2., shrinkB=2., patchA=None, patchB=None): """ Calls the *connect* method to create a path between *posA* and *posB*. The path is clipped and shrunken. """ path = self.connect(posA, posB) clipped_path = self._clip(path, patchA, patchB) shrunk_path = self._shrink(clipped_path, shrinkA, shrinkB) return shrunk_path def __reduce__(self): # because we have decided to nest these classes, we need to # add some more information to allow instance pickling. import matplotlib.cbook as cbook return (cbook._NestedClassGetter(), (ConnectionStyle, self.__class__.__name__), self.__dict__ ) class Arc3(_Base): """ Creates a simple quadratic bezier curve between two points. The curve is created so that the middle contol points (C1) is located at the same distance from the start (C0) and end points(C2) and the distance of the C1 to the line connecting C0-C2 is *rad* times the distance of C0-C2. """ def __init__(self, rad=0.): """ *rad* curvature of the curve. """ self.rad = rad def connect(self, posA, posB): x1, y1 = posA x2, y2 = posB x12, y12 = (x1 + x2) / 2., (y1 + y2) / 2. dx, dy = x2 - x1, y2 - y1 f = self.rad cx, cy = x12 + f * dy, y12 - f * dx vertices = [(x1, y1), (cx, cy), (x2, y2)] codes = [Path.MOVETO, Path.CURVE3, Path.CURVE3] return Path(vertices, codes) _style_list["arc3"] = Arc3 class Angle3(_Base): """ Creates a simple quadratic bezier curve between two points. The middle control points is placed at the intersecting point of two lines which crosses the start (or end) point and has a angle of angleA (or angleB). """ def __init__(self, angleA=90, angleB=0): """ *angleA* starting angle of the path *angleB* ending angle of the path """ self.angleA = angleA self.angleB = angleB def connect(self, posA, posB): x1, y1 = posA x2, y2 = posB cosA, sinA = (math.cos(self.angleA / 180. * math.pi), math.sin(self.angleA / 180. * math.pi)) cosB, sinB = (math.cos(self.angleB / 180. * math.pi), math.sin(self.angleB / 180. * math.pi)) cx, cy = get_intersection(x1, y1, cosA, sinA, x2, y2, cosB, sinB) vertices = [(x1, y1), (cx, cy), (x2, y2)] codes = [Path.MOVETO, Path.CURVE3, Path.CURVE3] return Path(vertices, codes) _style_list["angle3"] = Angle3 class Angle(_Base): """ Creates a picewise continuous quadratic bezier path between two points. The path has a one passing-through point placed at the intersecting point of two lines which crosses the start (or end) point and has a angle of angleA (or angleB). The connecting edges are rounded with *rad*. """ def __init__(self, angleA=90, angleB=0, rad=0.): """ *angleA* starting angle of the path *angleB* ending angle of the path *rad* rounding radius of the edge """ self.angleA = angleA self.angleB = angleB self.rad = rad def connect(self, posA, posB): x1, y1 = posA x2, y2 = posB cosA, sinA = (math.cos(self.angleA / 180. * math.pi), math.sin(self.angleA / 180. * math.pi)) cosB, sinB = (math.cos(self.angleB / 180. * math.pi), math.sin(self.angleB / 180. * math.pi)) cx, cy = get_intersection(x1, y1, cosA, sinA, x2, y2, cosB, sinB) vertices = [(x1, y1)] codes = [Path.MOVETO] if self.rad == 0.: vertices.append((cx, cy)) codes.append(Path.LINETO) else: dx1, dy1 = x1 - cx, y1 - cy d1 = (dx1 ** 2 + dy1 ** 2) ** .5 f1 = self.rad / d1 dx2, dy2 = x2 - cx, y2 - cy d2 = (dx2 ** 2 + dy2 ** 2) ** .5 f2 = self.rad / d2 vertices.extend([(cx + dx1 * f1, cy + dy1 * f1), (cx, cy), (cx + dx2 * f2, cy + dy2 * f2)]) codes.extend([Path.LINETO, Path.CURVE3, Path.CURVE3]) vertices.append((x2, y2)) codes.append(Path.LINETO) return Path(vertices, codes) _style_list["angle"] = Angle class Arc(_Base): """ Creates a picewise continuous quadratic bezier path between two points. The path can have two passing-through points, a point placed at the distance of armA and angle of angleA from point A, another point with respect to point B. The edges are rounded with *rad*. """ def __init__(self, angleA=0, angleB=0, armA=None, armB=None, rad=0.): """ *angleA* : starting angle of the path *angleB* : ending angle of the path *armA* : length of the starting arm *armB* : length of the ending arm *rad* : rounding radius of the edges """ self.angleA = angleA self.angleB = angleB self.armA = armA self.armB = armB self.rad = rad def connect(self, posA, posB): x1, y1 = posA x2, y2 = posB vertices = [(x1, y1)] rounded = [] codes = [Path.MOVETO] if self.armA: cosA = math.cos(self.angleA / 180. * math.pi) sinA = math.sin(self.angleA / 180. * math.pi) # x_armA, y_armB d = self.armA - self.rad rounded.append((x1 + d * cosA, y1 + d * sinA)) d = self.armA rounded.append((x1 + d * cosA, y1 + d * sinA)) if self.armB: cosB = math.cos(self.angleB / 180. * math.pi) sinB = math.sin(self.angleB / 180. * math.pi) x_armB, y_armB = x2 + self.armB * cosB, y2 + self.armB * sinB if rounded: xp, yp = rounded[-1] dx, dy = x_armB - xp, y_armB - yp dd = (dx * dx + dy * dy) ** .5 rounded.append((xp + self.rad * dx / dd, yp + self.rad * dy / dd)) vertices.extend(rounded) codes.extend([Path.LINETO, Path.CURVE3, Path.CURVE3]) else: xp, yp = vertices[-1] dx, dy = x_armB - xp, y_armB - yp dd = (dx * dx + dy * dy) ** .5 d = dd - self.rad rounded = [(xp + d * dx / dd, yp + d * dy / dd), (x_armB, y_armB)] if rounded: xp, yp = rounded[-1] dx, dy = x2 - xp, y2 - yp dd = (dx * dx + dy * dy) ** .5 rounded.append((xp + self.rad * dx / dd, yp + self.rad * dy / dd)) vertices.extend(rounded) codes.extend([Path.LINETO, Path.CURVE3, Path.CURVE3]) vertices.append((x2, y2)) codes.append(Path.LINETO) return Path(vertices, codes) _style_list["arc"] = Arc class Bar(_Base): """ A line with *angle* between A and B with *armA* and *armB*. One of the arms is extended so that they are connected in a right angle. The length of armA is determined by (*armA* + *fraction* x AB distance). Same for armB. """ def __init__(self, armA=0., armB=0., fraction=0.3, angle=None): """ Parameters ---------- armA : float minimum length of armA armB : float minimum length of armB fraction : float a fraction of the distance between two points that will be added to armA and armB. angle : float or None angle of the connecting line (if None, parallel to A and B) """ self.armA = armA self.armB = armB self.fraction = fraction self.angle = angle def connect(self, posA, posB): x1, y1 = posA x20, y20 = x2, y2 = posB x12, y12 = (x1 + x2) / 2., (y1 + y2) / 2. theta1 = math.atan2(y2 - y1, x2 - x1) dx, dy = x2 - x1, y2 - y1 dd = (dx * dx + dy * dy) ** .5 ddx, ddy = dx / dd, dy / dd armA, armB = self.armA, self.armB if self.angle is not None: #angle = self.angle % 180. #if angle < 0. or angle > 180.: # angle #theta0 = (self.angle%180.)/180.*math.pi theta0 = self.angle / 180. * math.pi #theta0 = (((self.angle+90)%180.) - 90.)/180.*math.pi dtheta = theta1 - theta0 dl = dd * math.sin(dtheta) dL = dd * math.cos(dtheta) #x2, y2 = x2 + dl*ddy, y2 - dl*ddx x2, y2 = x1 + dL * math.cos(theta0), y1 + dL * math.sin(theta0) armB = armB - dl # update dx, dy = x2 - x1, y2 - y1 dd2 = (dx * dx + dy * dy) ** .5 ddx, ddy = dx / dd2, dy / dd2 else: dl = 0. #if armA > armB: # armB = armA + dl #else: # armA = armB - dl arm = max(armA, armB) f = self.fraction * dd + arm #fB = self.fraction*dd + armB cx1, cy1 = x1 + f * ddy, y1 - f * ddx cx2, cy2 = x2 + f * ddy, y2 - f * ddx vertices = [(x1, y1), (cx1, cy1), (cx2, cy2), (x20, y20)] codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO] return Path(vertices, codes) _style_list["bar"] = Bar if __doc__: __doc__ = cbook.dedent(__doc__) % \ {"AvailableConnectorstyles": _pprint_styles(_style_list)} def _point_along_a_line(x0, y0, x1, y1, d): """ find a point along a line connecting (x0, y0) -- (x1, y1) whose distance from (x0, y0) is d. """ dx, dy = x0 - x1, y0 - y1 ff = d / (dx * dx + dy * dy) ** .5 x2, y2 = x0 - ff * dx, y0 - ff * dy return x2, y2 class ArrowStyle(_Style): """ :class:`ArrowStyle` is a container class which defines several arrowstyle classes, which is used to create an arrow path along a given path. These are mainly used with :class:`FancyArrowPatch`. A arrowstyle object can be either created as:: ArrowStyle.Fancy(head_length=.4, head_width=.4, tail_width=.4) or:: ArrowStyle("Fancy", head_length=.4, head_width=.4, tail_width=.4) or:: ArrowStyle("Fancy, head_length=.4, head_width=.4, tail_width=.4") The following classes are defined %(AvailableArrowstyles)s An instance of any arrow style class is a callable object, whose call signature is:: __call__(self, path, mutation_size, linewidth, aspect_ratio=1.) and it returns a tuple of a :class:`Path` instance and a boolean value. *path* is a :class:`Path` instance along which the arrow will be drawn. *mutation_size* and *aspect_ratio* have the same meaning as in :class:`BoxStyle`. *linewidth* is a line width to be stroked. This is meant to be used to correct the location of the head so that it does not overshoot the destination point, but not all classes support it. .. plot:: mpl_examples/pylab_examples/fancyarrow_demo.py """ _style_list = {} class _Base(object): """ Arrow Transmuter Base class ArrowTransmuterBase and its derivatives are used to make a fancy arrow around a given path. The __call__ method returns a path (which will be used to create a PathPatch instance) and a boolean value indicating the path is open therefore is not fillable. This class is not an artist and actual drawing of the fancy arrow is done by the FancyArrowPatch class. """ # The derived classes are required to be able to be initialized # w/o arguments, i.e., all its argument (except self) must have # the default values. def __init__(self): super(ArrowStyle._Base, self).__init__() @staticmethod def ensure_quadratic_bezier(path): """ Some ArrowStyle class only wokrs with a simple quaratic bezier curve (created with Arc3Connetion or Angle3Connector). This static method is to check if the provided path is a simple quadratic bezier curve and returns its control points if true. """ segments = list(path.iter_segments()) if ((len(segments) != 2) or (segments[0][1] != Path.MOVETO) or (segments[1][1] != Path.CURVE3)): msg = "'path' it's not a valid quadratic bezier curve" raise ValueError(msg) return list(segments[0][0]) + list(segments[1][0]) def transmute(self, path, mutation_size, linewidth): """ The transmute method is the very core of the ArrowStyle class and must be overriden in the subclasses. It receives the path object along which the arrow will be drawn, and the mutation_size, with which the arrow head etc. will be scaled. The linewidth may be used to adjust the path so that it does not pass beyond the given points. It returns a tuple of a Path instance and a boolean. The boolean value indicate whether the path can be filled or not. The return value can also be a list of paths and list of booleans of a same length. """ raise NotImplementedError('Derived must override') def __call__(self, path, mutation_size, linewidth, aspect_ratio=1.): """ The __call__ method is a thin wrapper around the transmute method and take care of the aspect ratio. """ path = make_path_regular(path) if aspect_ratio is not None: # Squeeze the given height by the aspect_ratio vertices, codes = path.vertices[:], path.codes[:] # Squeeze the height vertices[:, 1] = vertices[:, 1] / aspect_ratio path_shrunk = Path(vertices, codes) # call transmute method with squeezed height. path_mutated, fillable = self.transmute(path_shrunk, linewidth, mutation_size) if cbook.iterable(fillable): path_list = [] for p in zip(path_mutated): v, c = p.vertices, p.codes # Restore the height v[:, 1] = v[:, 1] * aspect_ratio path_list.append(Path(v, c)) return path_list, fillable else: return path_mutated, fillable else: return self.transmute(path, mutation_size, linewidth) def __reduce__(self): # because we have decided to nest thes classes, we need to # add some more information to allow instance pickling. import matplotlib.cbook as cbook return (cbook._NestedClassGetter(), (ArrowStyle, self.__class__.__name__), self.__dict__ ) class _Curve(_Base): """ A simple arrow which will work with any path instance. The returned path is simply concatenation of the original path + at most two paths representing the arrow head at the begin point and the at the end point. The arrow heads can be either open or closed. """ def __init__(self, beginarrow=None, endarrow=None, fillbegin=False, fillend=False, head_length=.2, head_width=.1): """ The arrows are drawn if *beginarrow* and/or *endarrow* are true. *head_length* and *head_width* determines the size of the arrow relative to the *mutation scale*. The arrowhead at the begin (or end) is closed if fillbegin (or fillend) is True. """ self.beginarrow, self.endarrow = beginarrow, endarrow self.head_length, self.head_width = head_length, head_width self.fillbegin, self.fillend = fillbegin, fillend super(ArrowStyle._Curve, self).__init__() def _get_arrow_wedge(self, x0, y0, x1, y1, head_dist, cos_t, sin_t, linewidth ): """ Return the paths for arrow heads. Since arrow lines are drawn with capstyle=projected, The arrow goes beyond the desired point. This method also returns the amount of the path to be shrunken so that it does not overshoot. """ # arrow from x0, y0 to x1, y1 dx, dy = x0 - x1, y0 - y1 cp_distance = np.hypot(dx, dy) # pad_projected : amount of pad to account the # overshooting of the projection of the wedge pad_projected = (.5 * linewidth / sin_t) # Account for division by zero if cp_distance == 0: cp_distance = 1 # apply pad for projected edge ddx = pad_projected * dx / cp_distance ddy = pad_projected * dy / cp_distance # offset for arrow wedge dx = dx / cp_distance * head_dist dy = dy / cp_distance * head_dist dx1, dy1 = cos_t * dx + sin_t * dy, -sin_t * dx + cos_t * dy dx2, dy2 = cos_t * dx - sin_t * dy, sin_t * dx + cos_t * dy vertices_arrow = [(x1 + ddx + dx1, y1 + ddy + dy1), (x1 + ddx, y1 + ddy), (x1 + ddx + dx2, y1 + ddy + dy2)] codes_arrow = [Path.MOVETO, Path.LINETO, Path.LINETO] return vertices_arrow, codes_arrow, ddx, ddy def transmute(self, path, mutation_size, linewidth): head_length, head_width = self.head_length * mutation_size, \ self.head_width * mutation_size head_dist = math.sqrt(head_length ** 2 + head_width ** 2) cos_t, sin_t = head_length / head_dist, head_width / head_dist # begin arrow x0, y0 = path.vertices[0] x1, y1 = path.vertices[1] # If there is no room for an arrow and a line, then skip the arrow has_begin_arrow = (self.beginarrow and not ((x0 == x1) and (y0 == y1))) if has_begin_arrow: verticesA, codesA, ddxA, ddyA = \ self._get_arrow_wedge(x1, y1, x0, y0, head_dist, cos_t, sin_t, linewidth) else: verticesA, codesA = [], [] ddxA, ddyA = 0., 0. # end arrow x2, y2 = path.vertices[-2] x3, y3 = path.vertices[-1] # If there is no room for an arrow and a line, then skip the arrow has_end_arrow = (self.endarrow and not ((x2 == x3) and (y2 == y3))) if has_end_arrow: verticesB, codesB, ddxB, ddyB = \ self._get_arrow_wedge(x2, y2, x3, y3, head_dist, cos_t, sin_t, linewidth) else: verticesB, codesB = [], [] ddxB, ddyB = 0., 0. # this simple code will not work if ddx, ddy is greater than # separation bettern vertices. _path = [Path(np.concatenate([[(x0 + ddxA, y0 + ddyA)], path.vertices[1:-1], [(x3 + ddxB, y3 + ddyB)]]), path.codes)] _fillable = [False] if has_begin_arrow: if self.fillbegin: p = np.concatenate([verticesA, [verticesA[0], verticesA[0]], ]) c = np.concatenate([codesA, [Path.LINETO, Path.CLOSEPOLY]]) _path.append(Path(p, c)) _fillable.append(True) else: _path.append(Path(verticesA, codesA)) _fillable.append(False) if has_end_arrow: if self.fillend: _fillable.append(True) p = np.concatenate([verticesB, [verticesB[0], verticesB[0]], ]) c = np.concatenate([codesB, [Path.LINETO, Path.CLOSEPOLY]]) _path.append(Path(p, c)) else: _fillable.append(False) _path.append(Path(verticesB, codesB)) return _path, _fillable class Curve(_Curve): """ A simple curve without any arrow head. """ def __init__(self): super(ArrowStyle.Curve, self).__init__( beginarrow=False, endarrow=False) _style_list["-"] = Curve class CurveA(_Curve): """ An arrow with a head at its begin point. """ def __init__(self, head_length=.4, head_width=.2): """ *head_length* length of the arrow head *head_width* width of the arrow head """ super(ArrowStyle.CurveA, self).__init__( beginarrow=True, endarrow=False, head_length=head_length, head_width=head_width) _style_list["<-"] = CurveA class CurveB(_Curve): """ An arrow with a head at its end point. """ def __init__(self, head_length=.4, head_width=.2): """ *head_length* length of the arrow head *head_width* width of the arrow head """ super(ArrowStyle.CurveB, self).__init__( beginarrow=False, endarrow=True, head_length=head_length, head_width=head_width) _style_list["->"] = CurveB class CurveAB(_Curve): """ An arrow with heads both at the begin and the end point. """ def __init__(self, head_length=.4, head_width=.2): """ *head_length* length of the arrow head *head_width* width of the arrow head """ super(ArrowStyle.CurveAB, self).__init__( beginarrow=True, endarrow=True, head_length=head_length, head_width=head_width) _style_list["<->"] = CurveAB class CurveFilledA(_Curve): """ An arrow with filled triangle head at the begin. """ def __init__(self, head_length=.4, head_width=.2): """ *head_length* length of the arrow head *head_width* width of the arrow head """ super(ArrowStyle.CurveFilledA, self).__init__( beginarrow=True, endarrow=False, fillbegin=True, fillend=False, head_length=head_length, head_width=head_width) _style_list["<|-"] = CurveFilledA class CurveFilledB(_Curve): """ An arrow with filled triangle head at the end. """ def __init__(self, head_length=.4, head_width=.2): """ *head_length* length of the arrow head *head_width* width of the arrow head """ super(ArrowStyle.CurveFilledB, self).__init__( beginarrow=False, endarrow=True, fillbegin=False, fillend=True, head_length=head_length, head_width=head_width) _style_list["-|>"] = CurveFilledB class CurveFilledAB(_Curve): """ An arrow with filled triangle heads both at the begin and the end point. """ def __init__(self, head_length=.4, head_width=.2): """ *head_length* length of the arrow head *head_width* width of the arrow head """ super(ArrowStyle.CurveFilledAB, self).__init__( beginarrow=True, endarrow=True, fillbegin=True, fillend=True, head_length=head_length, head_width=head_width) _style_list["<|-|>"] = CurveFilledAB class _Bracket(_Base): def __init__(self, bracketA=None, bracketB=None, widthA=1., widthB=1., lengthA=0.2, lengthB=0.2, angleA=None, angleB=None, scaleA=None, scaleB=None): self.bracketA, self.bracketB = bracketA, bracketB self.widthA, self.widthB = widthA, widthB self.lengthA, self.lengthB = lengthA, lengthB self.angleA, self.angleB = angleA, angleB self.scaleA, self.scaleB = scaleA, scaleB def _get_bracket(self, x0, y0, cos_t, sin_t, width, length): # arrow from x0, y0 to x1, y1 from matplotlib.bezier import get_normal_points x1, y1, x2, y2 = get_normal_points(x0, y0, cos_t, sin_t, width) dx, dy = length * cos_t, length * sin_t vertices_arrow = [(x1 + dx, y1 + dy), (x1, y1), (x2, y2), (x2 + dx, y2 + dy)] codes_arrow = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO] return vertices_arrow, codes_arrow def transmute(self, path, mutation_size, linewidth): if self.scaleA is None: scaleA = mutation_size else: scaleA = self.scaleA if self.scaleB is None: scaleB = mutation_size else: scaleB = self.scaleB vertices_list, codes_list = [], [] if self.bracketA: x0, y0 = path.vertices[0] x1, y1 = path.vertices[1] cos_t, sin_t = get_cos_sin(x1, y1, x0, y0) verticesA, codesA = self._get_bracket(x0, y0, cos_t, sin_t, self.widthA * scaleA, self.lengthA * scaleA) vertices_list.append(verticesA) codes_list.append(codesA) vertices_list.append(path.vertices) codes_list.append(path.codes) if self.bracketB: x0, y0 = path.vertices[-1] x1, y1 = path.vertices[-2] cos_t, sin_t = get_cos_sin(x1, y1, x0, y0) verticesB, codesB = self._get_bracket(x0, y0, cos_t, sin_t, self.widthB * scaleB, self.lengthB * scaleB) vertices_list.append(verticesB) codes_list.append(codesB) vertices = np.concatenate(vertices_list) codes = np.concatenate(codes_list) p = Path(vertices, codes) return p, False class BracketAB(_Bracket): """ An arrow with a bracket(]) at both ends. """ def __init__(self, widthA=1., lengthA=0.2, angleA=None, widthB=1., lengthB=0.2, angleB=None): """ *widthA* width of the bracket *lengthA* length of the bracket *angleA* angle between the bracket and the line *widthB* width of the bracket *lengthB* length of the bracket *angleB* angle between the bracket and the line """ super(ArrowStyle.BracketAB, self).__init__( True, True, widthA=widthA, lengthA=lengthA, angleA=angleA, widthB=widthB, lengthB=lengthB, angleB=angleB) _style_list["]-["] = BracketAB class BracketA(_Bracket): """ An arrow with a bracket(]) at its end. """ def __init__(self, widthA=1., lengthA=0.2, angleA=None): """ *widthA* width of the bracket *lengthA* length of the bracket *angleA* angle between the bracket and the line """ super(ArrowStyle.BracketA, self).__init__(True, None, widthA=widthA, lengthA=lengthA, angleA=angleA) _style_list["]-"] = BracketA class BracketB(_Bracket): """ An arrow with a bracket([) at its end. """ def __init__(self, widthB=1., lengthB=0.2, angleB=None): """ *widthB* width of the bracket *lengthB* length of the bracket *angleB* angle between the bracket and the line """ super(ArrowStyle.BracketB, self).__init__(None, True, widthB=widthB, lengthB=lengthB, angleB=angleB) _style_list["-["] = BracketB class BarAB(_Bracket): """ An arrow with a bar(|) at both ends. """ def __init__(self, widthA=1., angleA=None, widthB=1., angleB=None): """ *widthA* width of the bracket *lengthA* length of the bracket *angleA* angle between the bracket and the line *widthB* width of the bracket *lengthB* length of the bracket *angleB* angle between the bracket and the line """ super(ArrowStyle.BarAB, self).__init__( True, True, widthA=widthA, lengthA=0, angleA=angleA, widthB=widthB, lengthB=0, angleB=angleB) _style_list["|-|"] = BarAB class Simple(_Base): """ A simple arrow. Only works with a quadratic bezier curve. """ def __init__(self, head_length=.5, head_width=.5, tail_width=.2): """ *head_length* length of the arrow head *head_with* width of the arrow head *tail_width* width of the arrow tail """ self.head_length, self.head_width, self.tail_width = \ head_length, head_width, tail_width super(ArrowStyle.Simple, self).__init__() def transmute(self, path, mutation_size, linewidth): x0, y0, x1, y1, x2, y2 = self.ensure_quadratic_bezier(path) # divide the path into a head and a tail head_length = self.head_length * mutation_size in_f = inside_circle(x2, y2, head_length) arrow_path = [(x0, y0), (x1, y1), (x2, y2)] from .bezier import NonIntersectingPathException try: arrow_out, arrow_in = \ split_bezier_intersecting_with_closedpath(arrow_path, in_f, tolerence=0.01) except NonIntersectingPathException: # if this happens, make a straight line of the head_length # long. x0, y0 = _point_along_a_line(x2, y2, x1, y1, head_length) x1n, y1n = 0.5 * (x0 + x2), 0.5 * (y0 + y2) arrow_in = [(x0, y0), (x1n, y1n), (x2, y2)] arrow_out = None # head head_width = self.head_width * mutation_size head_left, head_right = make_wedged_bezier2(arrow_in, head_width / 2., wm=.5) # tail if arrow_out is not None: tail_width = self.tail_width * mutation_size tail_left, tail_right = get_parallels(arrow_out, tail_width / 2.) patch_path = [(Path.MOVETO, tail_right[0]), (Path.CURVE3, tail_right[1]), (Path.CURVE3, tail_right[2]), (Path.LINETO, head_right[0]), (Path.CURVE3, head_right[1]), (Path.CURVE3, head_right[2]), (Path.CURVE3, head_left[1]), (Path.CURVE3, head_left[0]), (Path.LINETO, tail_left[2]), (Path.CURVE3, tail_left[1]), (Path.CURVE3, tail_left[0]), (Path.LINETO, tail_right[0]), (Path.CLOSEPOLY, tail_right[0]), ] else: patch_path = [(Path.MOVETO, head_right[0]), (Path.CURVE3, head_right[1]), (Path.CURVE3, head_right[2]), (Path.CURVE3, head_left[1]), (Path.CURVE3, head_left[0]), (Path.CLOSEPOLY, head_left[0]), ] path = Path([p for c, p in patch_path], [c for c, p in patch_path]) return path, True _style_list["simple"] = Simple class Fancy(_Base): """ A fancy arrow. Only works with a quadratic bezier curve. """ def __init__(self, head_length=.4, head_width=.4, tail_width=.4): """ *head_length* length of the arrow head *head_with* width of the arrow head *tail_width* width of the arrow tail """ self.head_length, self.head_width, self.tail_width = \ head_length, head_width, tail_width super(ArrowStyle.Fancy, self).__init__() def transmute(self, path, mutation_size, linewidth): x0, y0, x1, y1, x2, y2 = self.ensure_quadratic_bezier(path) # divide the path into a head and a tail head_length = self.head_length * mutation_size arrow_path = [(x0, y0), (x1, y1), (x2, y2)] from .bezier import NonIntersectingPathException # path for head in_f = inside_circle(x2, y2, head_length) try: path_out, path_in = \ split_bezier_intersecting_with_closedpath( arrow_path, in_f, tolerence=0.01) except NonIntersectingPathException: # if this happens, make a straight line of the head_length # long. x0, y0 = _point_along_a_line(x2, y2, x1, y1, head_length) x1n, y1n = 0.5 * (x0 + x2), 0.5 * (y0 + y2) arrow_path = [(x0, y0), (x1n, y1n), (x2, y2)] path_head = arrow_path else: path_head = path_in # path for head in_f = inside_circle(x2, y2, head_length * .8) path_out, path_in = split_bezier_intersecting_with_closedpath( arrow_path, in_f, tolerence=0.01 ) path_tail = path_out # head head_width = self.head_width * mutation_size head_l, head_r = make_wedged_bezier2(path_head, head_width / 2., wm=.6) # tail tail_width = self.tail_width * mutation_size tail_left, tail_right = make_wedged_bezier2(path_tail, tail_width * .5, w1=1., wm=0.6, w2=0.3) # path for head in_f = inside_circle(x0, y0, tail_width * .3) path_in, path_out = split_bezier_intersecting_with_closedpath( arrow_path, in_f, tolerence=0.01 ) tail_start = path_in[-1] head_right, head_left = head_r, head_l patch_path = [(Path.MOVETO, tail_start), (Path.LINETO, tail_right[0]), (Path.CURVE3, tail_right[1]), (Path.CURVE3, tail_right[2]), (Path.LINETO, head_right[0]), (Path.CURVE3, head_right[1]), (Path.CURVE3, head_right[2]), (Path.CURVE3, head_left[1]), (Path.CURVE3, head_left[0]), (Path.LINETO, tail_left[2]), (Path.CURVE3, tail_left[1]), (Path.CURVE3, tail_left[0]), (Path.LINETO, tail_start), (Path.CLOSEPOLY, tail_start), ] path = Path([p for c, p in patch_path], [c for c, p in patch_path]) return path, True _style_list["fancy"] = Fancy class Wedge(_Base): """ Wedge(?) shape. Only works with a quadratic bezier curve. The begin point has a width of the tail_width and the end point has a width of 0. At the middle, the width is shrink_factor*tail_width. """ def __init__(self, tail_width=.3, shrink_factor=0.5): """ *tail_width* width of the tail *shrink_factor* fraction of the arrow width at the middle point """ self.tail_width = tail_width self.shrink_factor = shrink_factor super(ArrowStyle.Wedge, self).__init__() def transmute(self, path, mutation_size, linewidth): x0, y0, x1, y1, x2, y2 = self.ensure_quadratic_bezier(path) arrow_path = [(x0, y0), (x1, y1), (x2, y2)] b_plus, b_minus = make_wedged_bezier2( arrow_path, self.tail_width * mutation_size / 2., wm=self.shrink_factor) patch_path = [(Path.MOVETO, b_plus[0]), (Path.CURVE3, b_plus[1]), (Path.CURVE3, b_plus[2]), (Path.LINETO, b_minus[2]), (Path.CURVE3, b_minus[1]), (Path.CURVE3, b_minus[0]), (Path.CLOSEPOLY, b_minus[0]), ] path = Path([p for c, p in patch_path], [c for c, p in patch_path]) return path, True _style_list["wedge"] = Wedge if __doc__: __doc__ = cbook.dedent(__doc__) % \ {"AvailableArrowstyles": _pprint_styles(_style_list)} docstring.interpd.update( AvailableArrowstyles=_pprint_styles(ArrowStyle._style_list), AvailableConnectorstyles=_pprint_styles(ConnectionStyle._style_list), ) class FancyArrowPatch(Patch): """ A fancy arrow patch. It draws an arrow using the :class:ArrowStyle. """ _edge_default = True def __str__(self): if self._posA_posB is not None: (x1, y1), (x2, y2) = self._posA_posB return self.__class__.__name__ \ + "(%g,%g->%g,%g)" % (x1, y1, x2, y2) else: return self.__class__.__name__ \ + "(%s)" % (str(self._path_original),) @docstring.dedent_interpd def __init__(self, posA=None, posB=None, path=None, arrowstyle="simple", arrow_transmuter=None, connectionstyle="arc3", connector=None, patchA=None, patchB=None, shrinkA=2., shrinkB=2., mutation_scale=1., mutation_aspect=None, dpi_cor=1., **kwargs): """ If *posA* and *posB* is given, a path connecting two point are created according to the connectionstyle. The path will be clipped with *patchA* and *patchB* and further shrunken by *shrinkA* and *shrinkB*. An arrow is drawn along this resulting path using the *arrowstyle* parameter. If *path* provided, an arrow is drawn along this path and *patchA*, *patchB*, *shrinkA*, and *shrinkB* are ignored. The *connectionstyle* describes how *posA* and *posB* are connected. It can be an instance of the ConnectionStyle class (matplotlib.patches.ConnectionStlye) or a string of the connectionstyle name, with optional comma-separated attributes. The following connection styles are available. %(AvailableConnectorstyles)s The *arrowstyle* describes how the fancy arrow will be drawn. It can be string of the available arrowstyle names, with optional comma-separated attributes, or one of the ArrowStyle instance. The optional attributes are meant to be scaled with the *mutation_scale*. The following arrow styles are available. %(AvailableArrowstyles)s *mutation_scale* : a value with which attributes of arrowstyle (e.g., head_length) will be scaled. default=1. *mutation_aspect* : The height of the rectangle will be squeezed by this value before the mutation and the mutated box will be stretched by the inverse of it. default=None. Valid kwargs are: %(Patch)s """ Patch.__init__(self, **kwargs) if posA is not None and posB is not None and path is None: self._posA_posB = [posA, posB] if connectionstyle is None: connectionstyle = "arc3" self.set_connectionstyle(connectionstyle) elif posA is None and posB is None and path is not None: self._posA_posB = None self._connetors = None else: raise ValueError("either posA and posB, or path need to provided") self.patchA = patchA self.patchB = patchB self.shrinkA = shrinkA self.shrinkB = shrinkB self._path_original = path self.set_arrowstyle(arrowstyle) self._mutation_scale = mutation_scale self._mutation_aspect = mutation_aspect self.set_dpi_cor(dpi_cor) #self._draw_in_display_coordinate = True def set_dpi_cor(self, dpi_cor): """ dpi_cor is currently used for linewidth-related things and shrink factor. Mutation scale is affected by this. """ self._dpi_cor = dpi_cor self.stale = True def get_dpi_cor(self): """ dpi_cor is currently used for linewidth-related things and shrink factor. Mutation scale is affected by this. """ return self._dpi_cor def set_positions(self, posA, posB): """ set the begin and end positions of the connecting path. Use current value if None. """ if posA is not None: self._posA_posB[0] = posA if posB is not None: self._posA_posB[1] = posB self.stale = True def set_patchA(self, patchA): """ set the begin patch. """ self.patchA = patchA self.stale = True def set_patchB(self, patchB): """ set the begin patch """ self.patchB = patchB self.stale = True def set_connectionstyle(self, connectionstyle, **kw): """ Set the connection style. *connectionstyle* can be a string with connectionstyle name with optional comma-separated attributes. Alternatively, the attrs can be provided as keywords. set_connectionstyle("arc,angleA=0,armA=30,rad=10") set_connectionstyle("arc", angleA=0,armA=30,rad=10) Old attrs simply are forgotten. Without argument (or with connectionstyle=None), return available styles as a list of strings. """ if connectionstyle is None: return ConnectionStyle.pprint_styles() if isinstance(connectionstyle, ConnectionStyle._Base): self._connector = connectionstyle elif six.callable(connectionstyle): # we may need check the calling convention of the given function self._connector = connectionstyle else: self._connector = ConnectionStyle(connectionstyle, **kw) self.stale = True def get_connectionstyle(self): """ Return the ConnectionStyle instance """ return self._connector def set_arrowstyle(self, arrowstyle=None, **kw): """ Set the arrow style. *arrowstyle* can be a string with arrowstyle name with optional comma-separated attributes. Alternatively, the attrs can be provided as keywords. set_arrowstyle("Fancy,head_length=0.2") set_arrowstyle("fancy", head_length=0.2) Old attrs simply are forgotten. Without argument (or with arrowstyle=None), return available box styles as a list of strings. """ if arrowstyle is None: return ArrowStyle.pprint_styles() if isinstance(arrowstyle, ArrowStyle._Base): self._arrow_transmuter = arrowstyle else: self._arrow_transmuter = ArrowStyle(arrowstyle, **kw) self.stale = True def get_arrowstyle(self): """ Return the arrowstyle object """ return self._arrow_transmuter def set_mutation_scale(self, scale): """ Set the mutation scale. ACCEPTS: float """ self._mutation_scale = scale self.stale = True def get_mutation_scale(self): """ Return the mutation scale. """ return self._mutation_scale def set_mutation_aspect(self, aspect): """ Set the aspect ratio of the bbox mutation. ACCEPTS: float """ self._mutation_aspect = aspect self.stale = True def get_mutation_aspect(self): """ Return the aspect ratio of the bbox mutation. """ return self._mutation_aspect def get_path(self): """ return the path of the arrow in the data coordinate. Use get_path_in_displaycoord() method to retrieve the arrow path in the display coord. """ _path, fillable = self.get_path_in_displaycoord() if cbook.iterable(fillable): _path = concatenate_paths(_path) return self.get_transform().inverted().transform_path(_path) def get_path_in_displaycoord(self): """ Return the mutated path of the arrow in the display coord """ dpi_cor = self.get_dpi_cor() if self._posA_posB is not None: posA = self.get_transform().transform_point(self._posA_posB[0]) posB = self.get_transform().transform_point(self._posA_posB[1]) _path = self.get_connectionstyle()(posA, posB, patchA=self.patchA, patchB=self.patchB, shrinkA=self.shrinkA * dpi_cor, shrinkB=self.shrinkB * dpi_cor ) else: _path = self.get_transform().transform_path(self._path_original) _path, fillable = self.get_arrowstyle()( _path, self.get_mutation_scale() * dpi_cor, self.get_linewidth() * dpi_cor, self.get_mutation_aspect() ) #if not fillable: # self._fill = False return _path, fillable def draw(self, renderer): if not self.get_visible(): return renderer.open_group('patch', self.get_gid()) gc = renderer.new_gc() gc.set_foreground(self._edgecolor, isRGBA=True) lw = self._linewidth if self._edgecolor[3] == 0: lw = 0 gc.set_linewidth(lw) gc.set_dashes(self._dashoffset, self._dashes) gc.set_antialiased(self._antialiased) self._set_gc_clip(gc) gc.set_capstyle('round') gc.set_snap(self.get_snap()) rgbFace = self._facecolor if rgbFace[3] == 0: rgbFace = None # (some?) renderers expect this as no-fill signal gc.set_alpha(self._alpha) if self._hatch: gc.set_hatch(self._hatch) if self.get_sketch_params() is not None: gc.set_sketch_params(*self.get_sketch_params()) # FIXME : dpi_cor is for the dpi-dependecy of the # linewidth. There could be room for improvement. # #dpi_cor = renderer.points_to_pixels(1.) self.set_dpi_cor(renderer.points_to_pixels(1.)) path, fillable = self.get_path_in_displaycoord() if not cbook.iterable(fillable): path = [path] fillable = [fillable] affine = transforms.IdentityTransform() if self.get_path_effects(): from matplotlib.patheffects import PathEffectRenderer renderer = PathEffectRenderer(self.get_path_effects(), renderer) for p, f in zip(path, fillable): if f: renderer.draw_path(gc, p, affine, rgbFace) else: renderer.draw_path(gc, p, affine, None) gc.restore() renderer.close_group('patch') self.stale = False class ConnectionPatch(FancyArrowPatch): """ A :class:`~matplotlib.patches.ConnectionPatch` class is to make connecting lines between two points (possibly in different axes). """ def __str__(self): return "ConnectionPatch((%g,%g),(%g,%g))" % \ (self.xy1[0], self.xy1[1], self.xy2[0], self.xy2[1]) @docstring.dedent_interpd def __init__(self, xyA, xyB, coordsA, coordsB=None, axesA=None, axesB=None, arrowstyle="-", arrow_transmuter=None, connectionstyle="arc3", connector=None, patchA=None, patchB=None, shrinkA=0., shrinkB=0., mutation_scale=10., mutation_aspect=None, clip_on=False, dpi_cor=1., **kwargs): """ Connect point *xyA* in *coordsA* with point *xyB* in *coordsB* Valid keys are =============== ====================================================== Key Description =============== ====================================================== arrowstyle the arrow style connectionstyle the connection style relpos default is (0.5, 0.5) patchA default is bounding box of the text patchB default is None shrinkA default is 2 points shrinkB default is 2 points mutation_scale default is text size (in points) mutation_aspect default is 1. ? any key for :class:`matplotlib.patches.PathPatch` =============== ====================================================== *coordsA* and *coordsB* are strings that indicate the coordinates of *xyA* and *xyB*. ================= =================================================== Property Description ================= =================================================== 'figure points' points from the lower left corner of the figure 'figure pixels' pixels from the lower left corner of the figure 'figure fraction' 0,0 is lower left of figure and 1,1 is upper, right 'axes points' points from lower left corner of axes 'axes pixels' pixels from lower left corner of axes 'axes fraction' 0,1 is lower left of axes and 1,1 is upper right 'data' use the coordinate system of the object being annotated (default) 'offset points' Specify an offset (in points) from the *xy* value 'polar' you can specify *theta*, *r* for the annotation, even in cartesian plots. Note that if you are using a polar axes, you do not need to specify polar for the coordinate system since that is the native "data" coordinate system. ================= =================================================== """ if coordsB is None: coordsB = coordsA # we'll draw ourself after the artist we annotate by default self.xy1 = xyA self.xy2 = xyB self.coords1 = coordsA self.coords2 = coordsB self.axesA = axesA self.axesB = axesB FancyArrowPatch.__init__(self, posA=(0, 0), posB=(1, 1), arrowstyle=arrowstyle, arrow_transmuter=arrow_transmuter, connectionstyle=connectionstyle, connector=connector, patchA=patchA, patchB=patchB, shrinkA=shrinkA, shrinkB=shrinkB, mutation_scale=mutation_scale, mutation_aspect=mutation_aspect, clip_on=clip_on, dpi_cor=dpi_cor, **kwargs) # if True, draw annotation only if self.xy is inside the axes self._annotation_clip = None def _get_xy(self, x, y, s, axes=None): """ caculate the pixel position of given point """ if axes is None: axes = self.axes if s == 'data': trans = axes.transData x = float(self.convert_xunits(x)) y = float(self.convert_yunits(y)) return trans.transform_point((x, y)) elif s == 'offset points': # convert the data point dx, dy = self.xy # prevent recursion if self.xycoords == 'offset points': return self._get_xy(dx, dy, 'data') dx, dy = self._get_xy(dx, dy, self.xycoords) # convert the offset dpi = self.figure.get_dpi() x *= dpi / 72. y *= dpi / 72. # add the offset to the data point x += dx y += dy return x, y elif s == 'polar': theta, r = x, y x = r * np.cos(theta) y = r * np.sin(theta) trans = axes.transData return trans.transform_point((x, y)) elif s == 'figure points': # points from the lower left corner of the figure dpi = self.figure.dpi l, b, w, h = self.figure.bbox.bounds r = l + w t = b + h x *= dpi / 72. y *= dpi / 72. if x < 0: x = r + x if y < 0: y = t + y return x, y elif s == 'figure pixels': # pixels from the lower left corner of the figure l, b, w, h = self.figure.bbox.bounds r = l + w t = b + h if x < 0: x = r + x if y < 0: y = t + y return x, y elif s == 'figure fraction': # (0,0) is lower left, (1,1) is upper right of figure trans = self.figure.transFigure return trans.transform_point((x, y)) elif s == 'axes points': # points from the lower left corner of the axes dpi = self.figure.dpi l, b, w, h = axes.bbox.bounds r = l + w t = b + h if x < 0: x = r + x * dpi / 72. else: x = l + x * dpi / 72. if y < 0: y = t + y * dpi / 72. else: y = b + y * dpi / 72. return x, y elif s == 'axes pixels': #pixels from the lower left corner of the axes l, b, w, h = axes.bbox.bounds r = l + w t = b + h if x < 0: x = r + x else: x = l + x if y < 0: y = t + y else: y = b + y return x, y elif s == 'axes fraction': #(0,0) is lower left, (1,1) is upper right of axes trans = axes.transAxes return trans.transform_point((x, y)) def set_annotation_clip(self, b): """ set *annotation_clip* attribute. * True: the annotation will only be drawn when self.xy is inside the axes. * False: the annotation will always be drawn regardless of its position. * None: the self.xy will be checked only if *xycoords* is "data" """ self._annotation_clip = b self.stale = True def get_annotation_clip(self): """ Return *annotation_clip* attribute. See :meth:`set_annotation_clip` for the meaning of return values. """ return self._annotation_clip def get_path_in_displaycoord(self): """ Return the mutated path of the arrow in the display coord """ dpi_cor = self.get_dpi_cor() x, y = self.xy1 posA = self._get_xy(x, y, self.coords1, self.axesA) x, y = self.xy2 posB = self._get_xy(x, y, self.coords2, self.axesB) _path = self.get_connectionstyle()(posA, posB, patchA=self.patchA, patchB=self.patchB, shrinkA=self.shrinkA * dpi_cor, shrinkB=self.shrinkB * dpi_cor ) _path, fillable = self.get_arrowstyle()( _path, self.get_mutation_scale() * dpi_cor, self.get_linewidth() * dpi_cor, self.get_mutation_aspect() ) return _path, fillable def _check_xy(self, renderer): """ check if the annotation need to be drawn. """ b = self.get_annotation_clip() if b or (b is None and self.coords1 == "data"): x, y = self.xy1 xy_pixel = self._get_xy(x, y, self.coords1, self.axesA) if not self.axes.contains_point(xy_pixel): return False if b or (b is None and self.coords2 == "data"): x, y = self.xy2 xy_pixel = self._get_xy(x, y, self.coords2, self.axesB) if self.axesB is None: axes = self.axes else: axes = self.axesB if not axes.contains_point(xy_pixel): return False return True def draw(self, renderer): """ Draw. """ if renderer is not None: self._renderer = renderer if not self.get_visible(): return if not self._check_xy(renderer): return FancyArrowPatch.draw(self, renderer) # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or # implied. # See the License for the specific language governing permissions and # limitations under the License. import itertools import time import unittest import mock from swift.account import utils, backend from swift.common.storage_policy import POLICIES from swift.common.utils import Timestamp from swift.common.swob import HeaderKeyDict from test.unit import patch_policies class TestFakeAccountBroker(unittest.TestCase): def test_fake_broker_get_info(self): broker = utils.FakeAccountBroker() now = time.time() with mock.patch('time.time', new=lambda: now): info = broker.get_info() timestamp = Timestamp(now) expected = { 'container_count': 0, 'object_count': 0, 'bytes_used': 0, 'created_at': timestamp.internal, 'put_timestamp': timestamp.internal, } self.assertEqual(info, expected) def test_fake_broker_list_containers_iter(self): broker = utils.FakeAccountBroker() self.assertEqual(broker.list_containers_iter(), []) def test_fake_broker_metadata(self): broker = utils.FakeAccountBroker() self.assertEqual(broker.metadata, {}) def test_fake_broker_get_policy_stats(self): broker = utils.FakeAccountBroker() self.assertEqual(broker.get_policy_stats(), {}) class TestAccountUtils(unittest.TestCase): def test_get_response_headers_fake_broker(self): broker = utils.FakeAccountBroker() now = time.time() expected = { 'X-Account-Container-Count': 0, 'X-Account-Object-Count': 0, 'X-Account-Bytes-Used': 0, 'X-Timestamp': Timestamp(now).normal, 'X-PUT-Timestamp': Timestamp(now).normal, } with mock.patch('time.time', new=lambda: now): resp_headers = utils.get_response_headers(broker) self.assertEqual(resp_headers, expected) def test_get_response_headers_empty_memory_broker(self): broker = backend.AccountBroker(':memory:', account='a') now = time.time() with mock.patch('time.time', new=lambda: now): broker.initialize(Timestamp(now).internal) expected = { 'X-Account-Container-Count': 0, 'X-Account-Object-Count': 0, 'X-Account-Bytes-Used': 0, 'X-Timestamp': Timestamp(now).normal, 'X-PUT-Timestamp': Timestamp(now).normal, } resp_headers = utils.get_response_headers(broker) self.assertEqual(resp_headers, expected) @patch_policies def test_get_response_headers_with_data(self): broker = backend.AccountBroker(':memory:', account='a') now = time.time() with mock.patch('time.time', new=lambda: now): broker.initialize(Timestamp(now).internal) # add some container data ts = (Timestamp(t).internal for t in itertools.count(int(now))) total_containers = 0 total_objects = 0 total_bytes = 0 for policy in POLICIES: delete_timestamp = ts.next() put_timestamp = ts.next() object_count = int(policy) bytes_used = int(policy) * 10 broker.put_container('c-%s' % policy.name, put_timestamp, delete_timestamp, object_count, bytes_used, int(policy)) total_containers += 1 total_objects += object_count total_bytes += bytes_used expected = HeaderKeyDict({ 'X-Account-Container-Count': total_containers, 'X-Account-Object-Count': total_objects, 'X-Account-Bytes-Used': total_bytes, 'X-Timestamp': Timestamp(now).normal, 'X-PUT-Timestamp': Timestamp(now).normal, }) for policy in POLICIES: prefix = 'X-Account-Storage-Policy-%s-' % policy.name expected[prefix + 'Object-Count'] = int(policy) expected[prefix + 'Bytes-Used'] = int(policy) * 10 resp_headers = utils.get_response_headers(broker) for key, value in resp_headers.items(): expected_value = expected.pop(key) self.assertEqual(expected_value, str(value), 'value for %r was %r not %r' % ( key, value, expected_value)) self.assertFalse(expected) # -*- coding: utf-8 -*- # Generated by Django 1.11.16 on 2019-08-18 18:10 from __future__ import unicode_literals import import_string import chamber.models.fields from django.conf import settings from django.db import migrations, models import django.db.models.deletion import is_core.contrib.background_export.models from is_core.config import settings as is_core_settings class Migration(migrations.Migration): initial = True dependencies = [ ('contenttypes', '0002_remove_content_type_name'), migrations.swappable_dependency(settings.AUTH_USER_MODEL), ] operations = [ migrations.CreateModel( name='ExportedFile', fields=[ ('created_at', models.DateTimeField(auto_now_add=True, db_index=True, verbose_name='created at')), ('changed_at', models.DateTimeField(auto_now=True, db_index=True, verbose_name='changed at')), ('slug', models.SlugField(max_length=32, primary_key=True, serialize=False, verbose_name='slug')), ('file', chamber.models.fields.FileField( blank=True, null=True, upload_to=is_core.contrib.background_export.models.generate_filename, verbose_name='file', storage=import_string(is_core_settings.BACKGROUND_EXPORT_STORAGE_CLASS)() )), ('content_type', models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, to='contenttypes.ContentType')), ('created_by', models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, related_name='created_exported_files', to=settings.AUTH_USER_MODEL, verbose_name='created by')), ('downloaded_by', models.ManyToManyField(blank=True, related_name='downloaded_exported_files', to=settings.AUTH_USER_MODEL, verbose_name='downloaded by')), ], options={ 'verbose_name': 'exported file', 'verbose_name_plural': 'exported files', 'ordering': ('-created_at',), }, ), ] from __future__ import absolute_import from ..packages.six.moves import http_client as httplib from ..exceptions import HeaderParsingError def is_fp_closed(obj): """ Checks whether a given file-like object is closed. :param obj: The file-like object to check. """ try: # Check via the official file-like-object way. return obj.closed except AttributeError: pass try: # Check if the object is a container for another file-like object that # gets released on exhaustion (e.g. HTTPResponse). return obj.fp is None except AttributeError: pass raise ValueError("Unable to determine whether fp is closed.") def assert_header_parsing(headers): """ Asserts whether all headers have been successfully parsed. Extracts encountered errors from the result of parsing headers. Only works on Python 3. :param headers: Headers to verify. :type headers: `httplib.HTTPMessage`. :raises urllib3.exceptions.HeaderParsingError: If parsing errors are found. """ # This will fail silently if we pass in the wrong kind of parameter. # To make debugging easier add an explicit check. if not isinstance(headers, httplib.HTTPMessage): raise TypeError('expected httplib.Message, got {0}.'.format( type(headers))) defects = getattr(headers, 'defects', None) get_payload = getattr(headers, 'get_payload', None) unparsed_data = None if get_payload: # Platform-specific: Python 3. unparsed_data = get_payload() if defects or unparsed_data: raise HeaderParsingError(defects=defects, unparsed_data=unparsed_data) def is_response_to_head(response): """ Checks whether the request of a response has been a HEAD-request. Handles the quirks of AppEngine. :param conn: :type conn: :class:`httplib.HTTPResponse` """ # FIXME: Can we do this somehow without accessing private httplib _method? method = response._method if isinstance(method, int): # Platform-specific: Appengine return method == 3 return method.upper() == 'HEAD' from __future__ import absolute_import from __future__ import print_function from typing import Any from argparse import ArgumentParser from django.core.management.base import BaseCommand from django.db.models import Q from zerver.models import Realm, Stream, Message, Subscription, Recipient, get_realm class Command(BaseCommand): help = "Generate statistics on the streams for a realm." def add_arguments(self, parser): # type: (ArgumentParser) -> None parser.add_argument('realms', metavar='', type=str, nargs='*', help="realm to generate statistics for") def handle(self, *args, **options): # type: (*Any, **str) -> None if options['realms']: try: realms = [get_realm(string_id) for string_id in options['realms']] except Realm.DoesNotExist as e: print(e) exit(1) else: realms = Realm.objects.all() for realm in realms: print(realm.string_id) print("------------") print("%25s %15s %10s" % ("stream", "subscribers", "messages")) streams = Stream.objects.filter(realm=realm).exclude(Q(name__istartswith="tutorial-")) invite_only_count = 0 for stream in streams: if stream.invite_only: invite_only_count += 1 continue print("%25s" % (stream.name,), end=' ') recipient = Recipient.objects.filter(type=Recipient.STREAM, type_id=stream.id) print("%10d" % (len(Subscription.objects.filter(recipient=recipient, active=True)),), end=' ') num_messages = len(Message.objects.filter(recipient=recipient)) print("%12d" % (num_messages,)) print("%d invite-only streams" % (invite_only_count,)) print("") #!/usr/bin/python from __future__ import (absolute_import, division, print_function) # Copyright 2019 Fortinet, Inc. # # 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 . # # the lib use python logging can get it if the following is set in your # Ansible config. __metaclass__ = type ANSIBLE_METADATA = {'status': ['preview'], 'supported_by': 'community', 'metadata_version': '1.1'} DOCUMENTATION = ''' --- module: fortios_firewall_ssh_setting short_description: SSH proxy settings in Fortinet's FortiOS and FortiGate. description: - This module is able to configure a FortiGate or FortiOS by allowing the user to configure firewall_ssh feature and setting category. Examples includes all options and need to be adjusted to datasources before usage. Tested with FOS v6.0.2 version_added: "2.8" author: - Miguel Angel Munoz (@mamunozgonzalez) - Nicolas Thomas (@thomnico) notes: - Requires fortiosapi library developed by Fortinet - Run as a local_action in your playbook requirements: - fortiosapi>=0.9.8 options: host: description: - FortiOS or FortiGate ip adress. required: true username: description: - FortiOS or FortiGate username. required: true password: description: - FortiOS or FortiGate password. default: "" vdom: description: - Virtual domain, among those defined previously. A vdom is a virtual instance of the FortiGate that can be configured and used as a different unit. default: root https: description: - Indicates if the requests towards FortiGate must use HTTPS protocol type: bool default: true firewall_ssh_setting: description: - SSH proxy settings. default: null suboptions: caname: description: - CA certificate used by SSH Inspection. Source firewall.ssh.local-ca.name. host-trusted-checking: description: - Enable/disable host trusted checking. choices: - enable - disable hostkey-dsa1024: description: - DSA certificate used by SSH proxy. Source firewall.ssh.local-key.name. hostkey-ecdsa256: description: - ECDSA nid256 certificate used by SSH proxy. Source firewall.ssh.local-key.name. hostkey-ecdsa384: description: - ECDSA nid384 certificate used by SSH proxy. Source firewall.ssh.local-key.name. hostkey-ecdsa521: description: - ECDSA nid384 certificate used by SSH proxy. Source firewall.ssh.local-key.name. hostkey-ed25519: description: - ED25519 hostkey used by SSH proxy. Source firewall.ssh.local-key.name. hostkey-rsa2048: description: - RSA certificate used by SSH proxy. Source firewall.ssh.local-key.name. untrusted-caname: description: - Untrusted CA certificate used by SSH Inspection. Source firewall.ssh.local-ca.name. ''' EXAMPLES = ''' - hosts: localhost vars: host: "192.168.122.40" username: "admin" password: "" vdom: "root" tasks: - name: SSH proxy settings. fortios_firewall_ssh_setting: host: "{{ host }}" username: "{{ username }}" password: "{{ password }}" vdom: "{{ vdom }}" https: "False" firewall_ssh_setting: caname: " (source firewall.ssh.local-ca.name)" host-trusted-checking: "enable" hostkey-dsa1024: "myhostname (source firewall.ssh.local-key.name)" hostkey-ecdsa256: "myhostname (source firewall.ssh.local-key.name)" hostkey-ecdsa384: "myhostname (source firewall.ssh.local-key.name)" hostkey-ecdsa521: "myhostname (source firewall.ssh.local-key.name)" hostkey-ed25519: "myhostname (source firewall.ssh.local-key.name)" hostkey-rsa2048: "myhostname (source firewall.ssh.local-key.name)" untrusted-caname: " (source firewall.ssh.local-ca.name)" ''' RETURN = ''' build: description: Build number of the fortigate image returned: always type: str sample: '1547' http_method: description: Last method used to provision the content into FortiGate returned: always type: str sample: 'PUT' http_status: description: Last result given by FortiGate on last operation applied returned: always type: str sample: "200" mkey: description: Master key (id) used in the last call to FortiGate returned: success type: str sample: "id" name: description: Name of the table used to fulfill the request returned: always type: str sample: "urlfilter" path: description: Path of the table used to fulfill the request returned: always type: str sample: "webfilter" revision: description: Internal revision number returned: always type: str sample: "17.0.2.10658" serial: description: Serial number of the unit returned: always type: str sample: "FGVMEVYYQT3AB5352" status: description: Indication of the operation's result returned: always type: str sample: "success" vdom: description: Virtual domain used returned: always type: str sample: "root" version: description: Version of the FortiGate returned: always type: str sample: "v5.6.3" ''' from ansible.module_utils.basic import AnsibleModule fos = None def login(data): host = data['host'] username = data['username'] password = data['password'] fos.debug('on') if 'https' in data and not data['https']: fos.https('off') else: fos.https('on') fos.login(host, username, password) def filter_firewall_ssh_setting_data(json): option_list = ['caname', 'host-trusted-checking', 'hostkey-dsa1024', 'hostkey-ecdsa256', 'hostkey-ecdsa384', 'hostkey-ecdsa521', 'hostkey-ed25519', 'hostkey-rsa2048', 'untrusted-caname'] dictionary = {} for attribute in option_list: if attribute in json and json[attribute] is not None: dictionary[attribute] = json[attribute] return dictionary def firewall_ssh_setting(data, fos): vdom = data['vdom'] firewall_ssh_setting_data = data['firewall_ssh_setting'] filtered_data = filter_firewall_ssh_setting_data(firewall_ssh_setting_data) return fos.set('firewall.ssh', 'setting', data=filtered_data, vdom=vdom) def fortios_firewall_ssh(data, fos): login(data) methodlist = ['firewall_ssh_setting'] for method in methodlist: if data[method]: resp = eval(method)(data, fos) break fos.logout() return not resp['status'] == "success", resp['status'] == "success", resp def main(): fields = { "host": {"required": True, "type": "str"}, "username": {"required": True, "type": "str"}, "password": {"required": False, "type": "str", "no_log": True}, "vdom": {"required": False, "type": "str", "default": "root"}, "https": {"required": False, "type": "bool", "default": True}, "firewall_ssh_setting": { "required": False, "type": "dict", "options": { "caname": {"required": False, "type": "str"}, "host-trusted-checking": {"required": False, "type": "str", "choices": ["enable", "disable"]}, "hostkey-dsa1024": {"required": False, "type": "str"}, "hostkey-ecdsa256": {"required": False, "type": "str"}, "hostkey-ecdsa384": {"required": False, "type": "str"}, "hostkey-ecdsa521": {"required": False, "type": "str"}, "hostkey-ed25519": {"required": False, "type": "str"}, "hostkey-rsa2048": {"required": False, "type": "str"}, "untrusted-caname": {"required": False, "type": "str"} } } } module = AnsibleModule(argument_spec=fields, supports_check_mode=False) try: from fortiosapi import FortiOSAPI except ImportError: module.fail_json(msg="fortiosapi module is required") global fos fos = FortiOSAPI() is_error, has_changed, result = fortios_firewall_ssh(module.params, fos) if not is_error: module.exit_json(changed=has_changed, meta=result) else: module.fail_json(msg="Error in repo", meta=result) if __name__ == '__main__': main() # Copyright 2012 Nebula, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. """The Flavor Swap API extension.""" from nova.api.openstack import extensions from nova.api.openstack import wsgi authorize = extensions.soft_extension_authorizer('compute', 'flavor_swap') class FlavorSwapController(wsgi.Controller): def _extend_flavors(self, req, flavors): for flavor in flavors: db_flavor = req.get_db_flavor(flavor['id']) key = 'swap' flavor[key] = db_flavor['swap'] or "" def _show(self, req, resp_obj): if not authorize(req.environ['nova.context']): return if 'flavor' in resp_obj.obj: self._extend_flavors(req, [resp_obj.obj['flavor']]) @wsgi.extends def show(self, req, resp_obj, id): return self._show(req, resp_obj) @wsgi.extends(action='create') def create(self, req, resp_obj, body): return self._show(req, resp_obj) @wsgi.extends def detail(self, req, resp_obj): if not authorize(req.environ['nova.context']): return self._extend_flavors(req, list(resp_obj.obj['flavors'])) class Flavor_swap(extensions.ExtensionDescriptor): """Support to show the swap status of a flavor.""" name = "FlavorSwap" alias = "os-flavor-swap" namespace = ("http://docs.openstack.org/compute/ext/" "flavor_swap/api/v1.1") updated = "2012-08-29T00:00:00Z" def get_controller_extensions(self): controller = FlavorSwapController() extension = extensions.ControllerExtension(self, 'flavors', controller) return [extension] # # Copyright 2015 Google Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ ==== TTX/FontTools Compreffor ==== This module automatically subroutines the CFF table in a TTFont object, for the purposes of compressing the outputted font file. In addition to providing a Python interface, this tool can be used on the command line. Usage (python): >> from fontTools.ttLib import TTFont >> import compreffor >> font = TTFont(filename) >> options = { ... } >> compreffor.compress(font, **options) >> font.save(filename) Compression Backends: There are 2 different ways the compreffor can be run. - The default method is backed by a C++ extension module. The logic is in cxxCompressor.py, cffCompressor.h, cffCompressor.cc and _compreffor.pyx. - The second is a pure Python approach, and can be selected from `compress` by passing `method_python=True`. This is significantly slower than the the other backend (~10-20x). The logic can be found in pyCompressor.py. Options: When running `compreffor.compress`, options can be set using keyword arguments: - nrounds (integer) -- the number of market iterations to run (default: 4) - max_subrs (integer) -- limit to number of subrs per INDEX (default: 65533) With `method_python=True`, the following additional options are available: - chunk_ratio (float) -- set the percentage of charstrings to be run by each process. The value must be a float between 0 < n <= 1 (default: 0.1) - processes (integer) -- the number of simultaneous processes to run. Use value 1 to perform operation serially. Usage (command line): From the command line, you can either run the package as a module, $ python -m compreffor --help Or call the `compreffor` console script installed with the package. Use -h/--help to list all the available options. """ import logging from fontTools.misc.loggingTools import Timer log = logging.getLogger(__name__) timer = Timer(logger=logging.getLogger(log.name + ".timer")) from compreffor import cxxCompressor, pyCompressor def compress(ttFont, method_python=False, **options): """ Subroutinize TTFont instance in-place using the C++ Compreffor. If 'method_python' is True, use the slower, pure-Python Compreffor. If the font already contains subroutines, it is first decompressed. """ if has_subrs(ttFont): log.warning( "There are subroutines in font; must decompress it first") decompress(ttFont) if method_python: pyCompressor.compreff(ttFont, **options) else: cxxCompressor.compreff(ttFont, **options) def decompress(ttFont, **kwargs): """ Use the FontTools Subsetter to desubroutinize the font's CFF table. Any keyword arguments are passed on as options to the Subsetter. Skip if the font contains no subroutines. """ if not has_subrs(ttFont): log.debug('No subroutines found; skip decompress') return from fontTools import subset # The FontTools subsetter modifies many tables by default; here # we only want to desubroutinize, so we run the subsetter on a # temporary copy and extract the resulting CFF table from it make_temp = kwargs.pop('make_temp', True) if make_temp: from io import BytesIO from fontTools.ttLib import TTFont, newTable stream = BytesIO() ttFont.save(stream, reorderTables=None) stream.flush() stream.seek(0) tmpfont = TTFont(stream) else: tmpfont = ttFont # run subsetter on the original font options = subset.Options(**kwargs) options.desubroutinize = True subsetter = subset.Subsetter(options=options) subsetter.populate(glyphs=tmpfont.getGlyphOrder()) subsetter.subset(tmpfont) if make_temp: # copy modified CFF table to original font data = tmpfont['CFF '].compile(tmpfont) table = newTable('CFF ') table.decompile(data, ttFont) ttFont['CFF '] = table tmpfont.close() def has_subrs(ttFont): """ Return True if the font's CFF table contains any subroutines. """ if 'CFF ' not in ttFont: raise ValueError("Invalid font: no 'CFF ' table found") td = ttFont['CFF '].cff.topDictIndex[0] priv_subrs = (hasattr(td, 'FDArray') and any((hasattr(fd, 'Subrs') and len(fd.Subrs) > 0) for fd in td.FDArray)) return len(td.GlobalSubrs) > 0 or priv_subrs def check(original_file, compressed_file): """ Compare the original and compressed font files to confirm they are functionally equivalent. Also check that the Charstrings in the compressed font's CFFFontSet don't exceed the maximum subroutine nesting level. Return True if all checks pass, else return False. """ from compreffor.test.util import check_compression_integrity from compreffor.test.util import check_call_depth rv = check_compression_integrity(original_file, compressed_file) rv &= check_call_depth(compressed_file) return rv # The `Methods` and `Compreffor` classes are now deprecated, but we keep # them here for backward compatibility class Methods: Py, Cxx = range(2) class Compreffor(object): def __init__(self, font, method=Methods.Cxx, **options): import warnings warnings.warn("'Compreffor' class is deprecated; use 'compress' function " "instead", UserWarning) self.font = font self.method = method self.options = options def compress(self): if self.method == Methods.Py: compress(self.font, method_python=True, **self.options) elif self.method == Methods.Cxx: compress(self.font, method_python=False, **self.options) else: raise ValueError("Invalid method: %r" % self.method) ''' Hello student. Thank you for downloading a CORGIS library. However, you do not need to open this library. Instead you should use the following: import election If you opened the file because you are curious how this library works, then well done! We hope that you find it a useful learning experience. However, you should know that this code is meant to solve somewhat esoteric pedagogical problems, so it is often not best practices. ''' import sys as _sys import os as _os import json as _json import sqlite3 as _sql import difflib as _difflib class _Constants(object): ''' Global singleton object to hide some of the constants; some IDEs reveal internal module details very aggressively, and there's no other way to hide stuff. ''' _HEADER = {'User-Agent': 'CORGIS Election library for educational purposes'} _PYTHON_3 = _sys.version_info >= (3, 0) _TEST = False _HARDWARE = 1000 if _Constants._PYTHON_3: import urllib.request as _request from urllib.parse import quote_plus as _quote_plus from urllib.error import HTTPError as _HTTPError else: import urllib2 as _urllib2 from urllib import quote_plus as _quote_plus from urllib2 import HTTPError as _HTTPError class DatasetException(Exception): ''' Thrown when there is an error loading the dataset for some reason.''' pass _Constants._DATABASE_NAME = "election.db" if not _os.access(_Constants._DATABASE_NAME, _os.F_OK): raise DatasetException("Error! Could not find a \"{0}\" file. Make sure that there is a \"{0}\" in the same directory as \"{1}.py\"! Spelling is very important here.".format(_Constants._DATABASE_NAME, __name__)) elif not _os.access(_Constants._DATABASE_NAME, _os.R_OK): raise DatasetException("Error! Could not read the \"{0}\" file. Make sure that it readable by changing its permissions. You may need to get help from your instructor.".format(_Constants._DATABASE_NAME, __name__)) elif not _os.access(_Constants._DATABASE_NAME, _os.W_OK): _sys.stderr.write('The local cache (\" \") will not be updated. Make sure that it is writable by changing its permissions. You may need to get help from your instructor.\n'.format(_Constants._DATABASE_NAME)) _sys.stderr.flush() _Constants._DATABASE = _sql.connect(_Constants._DATABASE_NAME) class _Auxiliary(object): @staticmethod def _parse_type(value, type_func): """ Attempt to cast *value* into *type_func*, returning *default* if it fails. """ default = type_func(0) if value is None: return default try: return type_func(value) except ValueError: return default @staticmethod def _byteify(input): """ Force the given input to only use `str` instead of `bytes` or `unicode`. This works even if the input is a dict, list, """ if isinstance(input, dict): return {_Auxiliary._byteify(key): _Auxiliary._byteify(value) for key, value in input.items()} elif isinstance(input, list): return [_Auxiliary._byteify(element) for element in input] elif _Constants._PYTHON_3 and isinstance(input, str): return str(input.encode('ascii', 'replace').decode('ascii')) elif not _Constants._PYTHON_3 and isinstance(input, unicode): return str(input.encode('ascii', 'replace').decode('ascii')) else: return input @staticmethod def _guess_schema(input): if isinstance(input, dict): return {str(key.encode('ascii', 'replace').decode('ascii')): _Auxiliary._guess_schema(value) for key, value in input.items()} elif isinstance(input, list): return [_Auxiliary._guess_schema(input[0])] if input else [] else: return type(input) ################################################################################ # Domain Objects ################################################################################ ################################################################################ # Interfaces ################################################################################ def get_results(test=True): """ Returns the result of each primary for every county from the dataset. """ if _Constants._TEST or test: rows = _Constants._DATABASE.execute("SELECT data FROM election LIMIT {hardware}".format( hardware=_Constants._HARDWARE)) data = [r[0] for r in rows] data = [_Auxiliary._byteify(_json.loads(r)) for r in data] return _Auxiliary._byteify(data) else: rows = _Constants._DATABASE.execute("SELECT data FROM election".format( hardware=_Constants._HARDWARE)) data = [r[0] for r in rows] data = [_Auxiliary._byteify(_json.loads(r)) for r in data] return _Auxiliary._byteify(data) ################################################################################ # Internalized testing code ################################################################################ def _test_interfaces(): from pprint import pprint as _pprint from timeit import default_timer as _default_timer # Production test print("Production get_results") start_time = _default_timer() result = get_results(test=False) print("{} entries found.".format(len(result))) _pprint(_Auxiliary._guess_schema(result)) print("Time taken: {}".format(_default_timer() - start_time)) # Test test print("Test get_results") start_time = _default_timer() result = get_results() print("{} entries found.".format(len(result))) _pprint(_Auxiliary._guess_schema(result)) print("Time taken: {}".format(_default_timer() - start_time)) if __name__ == '__main__': from optparse import OptionParser as _OptionParser _parser = _OptionParser() _parser.add_option("-t", "--test", action="store_true", default=False, help="Execute the interfaces to test them.") _parser.add_option("-r", "--reset", action="store_true", default=False, help="Reset the cache") (_options, _args) = _parser.parse_args() if _options.test: _test_interfaces() if _options.reset: _modify_self() """ Custom manager for Objects. """ from itertools import chain from django.db.models import Q from django.conf import settings from django.db.models.fields import exceptions from src.typeclasses.managers import TypedObjectManager from src.typeclasses.managers import returns_typeclass, returns_typeclass_list from src.utils import utils from src.utils.utils import to_unicode, is_iter, make_iter, string_partial_matching __all__ = ("ObjectManager",) _GA = object.__getattribute__ # delayed import _ATTR = None # Try to use a custom way to parse id-tagged multimatches. _AT_MULTIMATCH_INPUT = utils.variable_from_module(*settings.SEARCH_AT_MULTIMATCH_INPUT.rsplit('.', 1)) class ObjectManager(TypedObjectManager): """ This ObjectManager implementes methods for searching and manipulating Objects directly from the database. Evennia-specific search methods (will return Typeclasses or lists of Typeclasses, whereas Django-general methods will return Querysets or database objects). dbref (converter) get_id (alias: dbref_search) get_dbref_range object_totals typeclass_search get_object_with_player get_objs_with_key_and_typeclass get_objs_with_attr get_objs_with_attr_match get_objs_with_db_property get_objs_with_db_property_match get_objs_with_key_or_alias get_contents object_search (interface to many of the above methods, equivalent to ev.search_object) copy_object """ # # ObjectManager Get methods # # player related @returns_typeclass def get_object_with_player(self, ostring, exact=True, candidates=None): """ Search for an object based on its player's name or dbref. This search is sometimes initiated by appending a * to the beginning of the search criterion (e.g. in local_and_global_search). search_string: (string) The name or dbref to search for. """ ostring = to_unicode(ostring).lstrip('*') # simplest case - search by dbref dbref = self.dbref(ostring) if dbref: return dbref # not a dbref. Search by name. cand_restriction = candidates != None and Q(pk__in=[_GA(obj, "id") for obj in make_iter(candidates) if obj]) or Q() if exact: return self.filter(cand_restriction & Q(db_player__username__iexact=ostring)) else: # fuzzy matching ply_cands = self.filter(cand_restriction & Q(playerdb__username__istartswith=ostring)).values_list("db_key", flat=True) if candidates: index_matches = string_partial_matching(ply_cands, ostring, ret_index=True) return [obj for ind, obj in enumerate(make_iter(candidates)) if ind in index_matches] else: return string_partial_matching(ply_cands, ostring, ret_index=False) @returns_typeclass_list def get_objs_with_key_and_typeclass(self, oname, otypeclass_path, candidates=None): """ Returns objects based on simultaneous key and typeclass match. """ cand_restriction = candidates != None and Q(pk__in=[_GA(obj, "id") for obj in make_iter(candidates) if obj]) or Q() return self.filter(cand_restriction & Q(db_key__iexact=oname, db_typeclass_path__exact=otypeclass_path)) # attr/property related @returns_typeclass_list def get_objs_with_attr(self, attribute_name, candidates=None): """ Returns all objects having the given attribute_name defined at all. Location should be a valid location object. """ cand_restriction = candidates != None and Q(db_attributes__db_obj__pk__in=[_GA(obj, "id") for obj in make_iter(candidates) if obj]) or Q() return list(self.filter(cand_restriction & Q(db_attributes__db_key=attribute_name))) @returns_typeclass_list def get_objs_with_attr_value(self, attribute_name, attribute_value, candidates=None, typeclasses=None): """ Returns all objects having the valid attrname set to the given value. candidates - list of candidate objects to search typeclasses - list of typeclass-path strings to restrict matches with This uses the Attribute's PickledField to transparently search the database by matching the internal representation. This is reasonably effective but since Attribute values cannot be indexed, searching by Attribute key is to be preferred whenever possible. """ cand_restriction = candidates != None and Q(pk__in=[_GA(obj, "id") for obj in make_iter(candidates) if obj]) or Q() type_restriction = typeclasses and Q(db_typeclass_path__in=make_iter(typeclasses)) or Q() ## This doesn't work if attribute_value is an object. Workaround below if isinstance(attribute_value, (basestring, int, float, bool, long)): return self.filter(cand_restriction & type_restriction & Q(db_attributes__db_key=attribute_name, db_attributes__db_value=attribute_value)) else: # We have to loop for safety since the referenced lookup gives deepcopy error if attribute value is an object. global _ATTR if not _ATTR: from src.typeclasses.models import Attribute as _ATTR cands = list(self.filter(cand_restriction & type_restriction & Q(db_attributes__db_key=attribute_name))) results = [attr.objectdb_set.all() for attr in _ATTR.objects.filter(objectdb__in=cands, db_value=attribute_value)] return chain(*results) @returns_typeclass_list def get_objs_with_db_property(self, property_name, candidates=None): """ Returns all objects having a given db field property. property_name = search string candidates - list of candidate objects to search """ property_name = "db_%s" % property_name.lstrip('db_') cand_restriction = candidates != None and Q(pk__in=[_GA(obj, "id") for obj in make_iter(candidates) if obj]) or Q() querykwargs = {property_name:None} try: return list(self.filter(cand_restriction).exclude(Q(**querykwargs))) except exceptions.FieldError: return [] @returns_typeclass_list def get_objs_with_db_property_value(self, property_name, property_value, candidates=None, typeclasses=None): """ Returns all objects having a given db field property. candidates - list of objects to search typeclasses - list of typeclass-path strings to restrict matches with """ if isinstance(property_value, basestring): property_value = to_unicode(property_value) if isinstance(property_name, basestring): if not property_name.startswith('db_'): property_name = "db_%s" % property_name if hasattr(property_value, 'dbobj'): property_value = property_value.dbobj querykwargs = {property_name:property_value} cand_restriction = candidates != None and Q(pk__in=[_GA(obj, "id") for obj in make_iter(candidates) if obj]) or Q() type_restriction = typeclasses and Q(db_typeclass_path__in=make_iter(typeclasses)) or Q() try: return list(self.filter(cand_restriction & type_restriction & Q(**querykwargs))) except exceptions.FieldError: return [] except ValueError: from src.utils import logger logger.log_errmsg("The property '%s' does not support search criteria of the type %s." % (property_name, type(property_value))) return [] @returns_typeclass_list def get_contents(self, location, excludeobj=None): """ Get all objects that has a location set to this one. excludeobj - one or more object keys to exclude from the match """ exclude_restriction = Q(pk__in=[_GA(obj, "id") for obj in make_iter(excludeobj)]) if excludeobj else Q() return self.filter(db_location=location).exclude(exclude_restriction) @returns_typeclass_list def get_objs_with_key_or_alias(self, ostring, exact=True, candidates=None, typeclasses=None): """ Returns objects based on key or alias match. Will also do fuzzy matching based on the utils.string_partial_matching function. candidates - list of candidate objects to restrict on typeclasses - list of typeclass path strings to restrict on """ if not isinstance(ostring, basestring): if hasattr(ostring, "key"): ostring = ostring.key else: return [] if is_iter(candidates) and not len(candidates): # if candidates is an empty iterable there can be no matches # Exit early. return [] # build query objects candidates_id = [_GA(obj, "id") for obj in make_iter(candidates) if obj] cand_restriction = candidates != None and Q(pk__in=make_iter(candidates_id)) or Q() type_restriction = typeclasses and Q(db_typeclass_path__in=make_iter(typeclasses)) or Q() if exact: # exact match - do direct search return self.filter(cand_restriction & type_restriction & (Q(db_key__iexact=ostring) | Q(db_tags__db_key__iexact=ostring) & Q(db_tags__db_tagtype__iexact="alias"))).distinct() elif candidates: # fuzzy with candidates key_candidates = self.filter(cand_restriction & type_restriction) else: # fuzzy without supplied candidates - we select our own candidates key_candidates = self.filter(type_restriction & (Q(db_key__istartswith=ostring) | Q(db_tags__db_key__istartswith=ostring))).distinct() candidates_id = [_GA(obj, "id") for obj in key_candidates] # fuzzy matching key_strings = key_candidates.values_list("db_key", flat=True).order_by("id") index_matches = string_partial_matching(key_strings, ostring, ret_index=True) if index_matches: return [obj for ind, obj in enumerate(key_candidates) if ind in index_matches] else: alias_candidates = self.filter(id__in=candidates_id, db_tags__db_tagtype__iexact="alias") alias_strings = alias_candidates.values_list("db_key", flat=True) index_matches = string_partial_matching(alias_strings, ostring, ret_index=True) if index_matches: return [alias.db_obj for ind, alias in enumerate(alias_candidates) if ind in index_matches] return [] # main search methods and helper functions @returns_typeclass_list def object_search(self, searchdata, attribute_name=None, typeclass=None, candidates=None, exact=True): """ Search as an object globally or in a list of candidates and return results. The result is always an Object. Always returns a list. Arguments: searchdata: (str or obj) The entity to match for. This is usually a key string but may also be an object itself. By default (if not attribute_name is set), this will search object.key and object.aliases in order. Can also be on the form #dbref, which will, if exact=True be matched against primary key. attribute_name: (str): Use this named ObjectAttribute to match searchdata against, instead of the defaults. If this is the name of a database field (with or without the db_ prefix), that will be matched too. typeclass (str or TypeClass): restrict matches to objects having this typeclass. This will help speed up global searches. candidates (list obj ObjectDBs): If supplied, search will only be performed among the candidates in this list. A common list of candidates is the contents of the current location searched. exact (bool): Match names/aliases exactly or partially. Partial matching matches the beginning of words in the names/aliases, using a matching routine to separate multiple matches in names with multiple components (so "bi sw" will match "Big sword"). Since this is more expensive than exact matching, it is recommended to be used together with the objlist keyword to limit the number of possibilities. This value has no meaning if searching for attributes/properties. Returns: A list of matching objects (or a list with one unique match) """ def _searcher(searchdata, candidates, typeclass, exact=False): """ Helper method for searching objects. typeclass is only used for global searching (no candidates) """ if attribute_name: # attribute/property search (always exact). matches = self.get_objs_with_db_property_value(attribute_name, searchdata, candidates=candidates, typeclasses=typeclass) if matches: return matches return self.get_objs_with_attr_value(attribute_name, searchdata, candidates=candidates, typeclasses=typeclass) else: # normal key/alias search return self.get_objs_with_key_or_alias(searchdata, exact=exact, candidates=candidates, typeclasses=typeclass) if not searchdata and searchdata != 0: return [] if typeclass: # typeclass may also be a list typeclasses = make_iter(typeclass) for i, typeclass in enumerate(make_iter(typeclasses)): if callable(typeclass): typeclasses[i] = u"%s.%s" % (typeclass.__module__, typeclass.__name__) else: typeclasses[i] = u"%s" % typeclass typeclass = typeclasses if candidates: # Convenience check to make sure candidates are really dbobjs candidates = [cand.dbobj for cand in make_iter(candidates) if cand] if typeclass: candidates = [cand for cand in candidates if _GA(cand, "db_typeclass_path") in typeclass] dbref = not attribute_name and exact and self.dbref(searchdata) if dbref is not None: # Easiest case - dbref matching (always exact) dbref_match = self.dbref_search(dbref) if dbref_match: if not candidates or dbref_match.dbobj in candidates: return [dbref_match] else: return [] # Search through all possibilities. match_number = None # always run first check exact - we don't want partial matches # if on the form of 1-keyword etc. matches = _searcher(searchdata, candidates, typeclass, exact=True) if not matches: # no matches found - check if we are dealing with N-keyword # query - if so, strip it. match_number, searchdata = _AT_MULTIMATCH_INPUT(searchdata) # run search again, with the exactness set by call if match_number is not None or not exact: matches = _searcher(searchdata, candidates, typeclass, exact=exact) # deal with result if len(matches) > 1 and match_number is not None: # multiple matches, but a number was given to separate them try: matches = [matches[match_number]] except IndexError: pass # return a list (possibly empty) return matches # # ObjectManager Copy method # def copy_object(self, original_object, new_key=None, new_location=None, new_home=None, new_permissions=None, new_locks=None, new_aliases=None, new_destination=None): """ Create and return a new object as a copy of the original object. All will be identical to the original except for the arguments given specifically to this method. original_object (obj) - the object to make a copy from new_key (str) - name the copy differently from the original. new_location (obj) - if not None, change the location new_home (obj) - if not None, change the Home new_aliases (list of strings) - if not None, change object aliases. new_destination (obj) - if not None, change destination """ # get all the object's stats typeclass_path = original_object.typeclass_path if not new_key: new_key = original_object.key if not new_location: new_location = original_object.location if not new_home: new_home = original_object.home if not new_aliases: new_aliases = original_object.aliases.all() if not new_locks: new_locks = original_object.db_lock_storage if not new_permissions: new_permissions = original_object.permissions.all() if not new_destination: new_destination = original_object.destination # create new object from src.utils import create from src.scripts.models import ScriptDB new_object = create.create_object(typeclass_path, key=new_key, location=new_location, home=new_home, permissions=new_permissions, locks=new_locks, aliases=new_aliases, destination=new_destination) if not new_object: return None # copy over all attributes from old to new. for attr in original_object.attributes.all(): new_object.attributes.add(attr.key, attr.value) # copy over all cmdsets, if any for icmdset, cmdset in enumerate(original_object.cmdset.all()): if icmdset == 0: new_object.cmdset.add_default(cmdset) else: new_object.cmdset.add(cmdset) # copy over all scripts, if any for script in original_object.scripts.all(): ScriptDB.objects.copy_script(script, new_obj=new_object.dbobj) return new_object def clear_all_sessids(self): """ Clear the db_sessid field of all objects having also the db_player field set. """ self.filter(db_sessid__isnull=False).update(db_sessid=None) #~ url = "http://cmsdoc.cern.ch/cms/test/aprom/phedex/dev/gowri/datasvc/tbedi/requestDetails" #~ params = {'format':'json'} #~ import urllib #~ eparams = urllib.urlencode(params) #~ import urllib2 #~ request = urllib2.Request(url,eparams) #~ response = urllib2.urlopen(request) #~ s = response.read() #~ response.close() #~ print s s = """ {"phedex":{"request":[{"last_update":"1188037561", "numofapproved":"1", "id":"7425"}, {"last_update":"1188751826", "numofapproved":"1", "id":"8041"}, {"last_update":"1190116795", "numofapproved":"1", "id":"9281"}, {"last_update":"1190248781", "numofapproved":"1", "id":"9521"}, {"last_update":"1192615612", "numofapproved":"1", "id":"12821"}, {"last_update":"1192729887", "numofapproved":"1", "id":"13121"}, {"last_update":"1193152971", "numofapproved":"1", "id":"13501"}, {"last_update":"1194022054", "numofapproved":"1", "id":"14782"}, {"last_update":"1194429365", "numofapproved":"1", "id":"15081"}, {"last_update":"1195069848", "numofapproved":"1", "id":"16661"}, {"last_update":"1178403225", "numofapproved":"1", "id":"1281"}, {"last_update":"1179239056", "numofapproved":"1", "id":"1387"}, {"last_update":"1179842205", "numofapproved":"1", "id":"1665"}, {"last_update":"1179842040", "numofapproved":"1", "id":"1661"}, {"last_update":"1179935333", "numofapproved":"1", "id":"1741"}, {"last_update":"1183151195", "numofapproved":"1", "id":"3841"}, {"last_update":"1187031531", "numofapproved":"1", "id":"6601"}, {"last_update":"1188820478", "numofapproved":"1", "id":"8121"}, {"last_update":"1190652719", "numofapproved":"1", "id":"9983"}, {"last_update":"1192628950", "numofapproved":"1", "id":"12841"}, {"last_update":"1193075426", "numofapproved":"1", "id":"13341"}, {"last_update":"1194214609", "numofapproved":"1", "id":"14882"}, {"last_update":"1194387864", "numofapproved":"1", "id":"15062"}, {"last_update":"1195134504", "numofapproved":"1", "id":"16741"}, {"last_update":"1182431453", "numofapproved":"1", "id":"3421"}, {"last_update":"1183448188", "numofapproved":"1", "id":"4061"}, {"last_update":"1184588081", "numofapproved":"1", "id":"4908"}, {"last_update":"1184681258", "numofapproved":"1", "id":"4913"}, {"last_update":"1188039048", "numofapproved":"1", "id":"7426"}, {"last_update":"1192699041", "numofapproved":"1", "id":"12982"}, {"last_update":"1193219685", "numofapproved":"1", "id":"13529"}, {"last_update":"1193401408", "numofapproved":"1", "id":"14081"}, {"last_update":"1194454724", "numofapproved":"1", "id":"15201"}, {"last_update":"1194937690", "numofapproved":"1", "id":"16044"}, {"last_update":"1194947125", "numofapproved":"1", "id":"16103"}, {"last_update":"1195134890", "numofapproved":"1", "id":"16761"}, {"last_update":"1195486898", "numofapproved":"1", "id":"17301"}, {"last_update":"1195497774", "numofapproved":"1", "id":"17341"}, {"last_update":"1184744080", "numofapproved":"1", "id":"4941"}, {"last_update":"1186558911", "numofapproved":"1", "id":"6321"}, {"last_update":"1189524520", "numofapproved":"1", "id":"8802"}, {"last_update":"1192683178", "numofapproved":"1", "id":"12921"}, {"last_update":"1193260655", "numofapproved":"1", "id":"13530"}, {"last_update":"1194280038", "numofapproved":"1", "id":"15002"}, {"last_update":"1182077478", "numofapproved":"1", "id":"3162"}, {"last_update":"1183386650", "numofapproved":"1", "id":"3961"}, {"last_update":"1192063369", "numofapproved":"1", "id":"12182"}, {"last_update":"1181931262", "numofapproved":"1", "id":"3101"}, {"last_update":"1178648271", "numofapproved":"1", "id":"1308"}, {"last_update":"1179239923", "numofapproved":"1", "id":"1405"}, {"last_update":"1184370745", "numofapproved":"1", "id":"4861"}, {"last_update":"1185280568", "numofapproved":"1", "id":"5302"}, {"last_update":"1187875115", "numofapproved":"1", "id":"7344"}, {"last_update":"1189140441", "numofapproved":"1", "id":"8541"}, {"last_update":"1189180903", "numofapproved":"1", "id":"8661"}, {"last_update":"1189767643", "numofapproved":"1", "id":"9001"}, {"last_update":"1190726167", "numofapproved":"1", "id":"10101"}, {"last_update":"1190972990", "numofapproved":"1", "id":"10661"}, {"last_update":"1190990720", "numofapproved":"1", "id":"10712"}, {"last_update":"1192004838", "numofapproved":"1", "id":"12021"}, {"last_update":"1192612211", "numofapproved":"1", "id":"12803"}, {"last_update":"1194441407", "numofapproved":"1", "id":"15103"}, {"last_update":"1194792356", "numofapproved":"1", "id":"15681"}, {"last_update":"1194860650", "numofapproved":"1", "id":"15801"}, {"last_update":"1194877395", "numofapproved":"1", "id":"15881"}, {"last_update":"1194950552", "numofapproved":"1", "id":"16124"}, {"last_update":"1194992714", "numofapproved":"1", "id":"16421"}, {"last_update":"1195054500", "numofapproved":"1", "id":"16581"}, {"last_update":"1195228524", "numofapproved":"1", "id":"17001"}, {"last_update":"1195469382", "numofapproved":"1", "id":"17161"}, {"last_update":"1178035947", "numofapproved":"1", "id":"1202"}, {"last_update":"1178869668", "numofapproved":"1", "id":"1356"}, {"last_update":"1183563268", "numofapproved":"1", "id":"4201"}, {"last_update":"1185314677", "numofapproved":"1", "id":"5361"}, {"last_update":"1188467567", "numofapproved":"1", "id":"7781"}, {"last_update":"1190011821", "numofapproved":"1", "id":"9202"}, {"last_update":"1190206214", "numofapproved":"1", "id":"9481"}, {"last_update":"1190973037", "numofapproved":"1", "id":"10663"}, {"last_update":"1190819127", "numofapproved":"1", "id":"10342"}, {"last_update":"1192154959", "numofapproved":"1", "id":"12381"}, {"last_update":"1192634509", "numofapproved":"1", "id":"12862"}, {"last_update":"1194004677", "numofapproved":"1", "id":"14722"}, {"last_update":"1195548191", "numofapproved":"1", "id":"17501"}, {"last_update":"1195548953", "numofapproved":"1", "id":"17502"}, {"last_update":"1195559809", "numofapproved":"1", "id":"17541"}, {"last_update":"1177589103", 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"numofapproved":"1", "id":"2542"}, {"last_update":"1182356477", "numofapproved":"1", "id":"3343"}, {"last_update":"1182418583", "numofapproved":"1", "id":"3381"}, {"last_update":"1184568502", "numofapproved":"1", "id":"4905"}, {"last_update":"1189151603", "numofapproved":"1", "id":"8581"}, {"last_update":"1191595695", "numofapproved":"1", "id":"11621"}, {"last_update":"1193105000", "numofapproved":"1", "id":"13421"}, {"last_update":"1195104657", "numofapproved":"1", "id":"16701"}], "request_timestamp":1206363392.08521, "request_call":"requestDetails", "instance":"tbedi", "call_time":"0.10059", "request_date":"2008-03-2412:56:32 UTC", "request_url":"http://cmsdoc.cern.ch/cms/test/aprom/phedex/dev/gowri/datasvc/tbedi/requestDetails?format=json"}} """ from jsonParser import jsonObject data = jsonObject.parseString(s) #~ from pprint import pprint #~ pprint( data[0].asList() ) #~ print #~ print data.dump() print(data.phedex.call_time) print(data.phedex.instance) print(data.phedex.request_call) print(len(data.phedex.request)) for req in data.phedex.request[:10]: #~ print req.dump() print("-", req.id, req.last_update) from io import StringIO TEMPLATES = {} TEMPLATES['long'] = """ {{- start.plan_type }} ['{{ start.uid[:6] }}'] (scan num: {{ start.scan_id }}) Scan Plan --------- {{ start.plan_type }} {%- for k, v in start.plan_args | dictsort %} {{ k }}: {{ v }} {%- endfor %} {% if 'signature' in start -%} Call: {{ start.signature }} {% endif %} Metaadata --------- {% for k, v in start.items() -%} {%- if k not in ['plan_type', 'plan_args'] -%}{{ k }} : {{ v }} {% endif -%} {%- endfor -%}""" TEMPLATES['desc'] = """ {{- start.plan_type }} ['{{ start.uid[:6] }}'] (scan num: {{ start.scan_id }})""" TEMPLATES['call'] = """RE({{ start.plan_type }}( {%- for k, v in start.plan_args.items() %}{%- if not loop.first %} {% endif %}{{ k }}={{ v }} {%- if not loop.last %}, {% endif %}{% endfor %})) """ def logbook_cb_factory(logbook_func, desc_template=None, long_template=None): """Create a logbook run_start callback The returned function is suitable for registering as a 'start' callback on the the BlueSky run engine. Parameters ---------- logbook_func : callable The required signature is :: def logbok_func(text=None, logbooks=None, tags=None, properties=None, attachments=None, verify=True, ensure=False): ''' Parameters ---------- text : string The body of the log entry. logbooks : string or list of strings The logbooks which to add the log entry to. tags : string or list of strings The tags to add to the log entry. properties : dict of property dicts The properties to add to the log entry attachments : list of file like objects The attachments to add to the log entry verify : bool Check that properties, tags and logbooks are in the Olog instance. ensure : bool If a property, tag or logbook is not in the Olog then create the property, tag or logbook before making the log s entry. Seting ensure to True will set verify to False. ''' pass This matches the API on `SimpleOlogClient.log` """ import jinja2 env = jinja2.Environment() if long_template is None: long_template = TEMPLATES['long'] if desc_template is None: desc_template = TEMPLATES['desc'] # It seems that the olog only has one text field, which it calls # `text` on the python side and 'description' on the olog side. # There are some CSS applications that try to shove the entire # thing into a single line. We work around this by doing two # strings, a long one which will get put in a as an attachment # and a short one to go in as the 'text' which will be used as the # description long_msg = env.from_string(long_template) desc_msg = env.from_string(desc_template) def lbcb(name, doc): # This only applies to 'start' Documents. if name != 'start': return atch = StringIO(long_msg.render(start=doc)) desc = desc_msg.render(start=doc) logbook_func(text=desc, properties={'start':doc}, attachments=[atch], ensure=True) return lbcb def call_str(start, call_template=None): """Given a start document generate an evalable call scring The default template assumes that `plan_args` and `plan_type` are at the top level of the document. Parameter --------- start : dict A document which follows the runstart schema call_template : str, optional A jinja2 template rendered with `cr.render(start=start)` If not provided defaults to `CALL_TEMPLATE` """ import jinja2 env = jinja2.Environment() if call_template is None: call_template = TEMPLATES['call'] call_renderer = env.from_string(call_template) return call_renderer.render(start=start) from __future__ import division, absolute_import, print_function from subprocess import call, PIPE, Popen import sys import re import numpy as np from numpy.linalg import lapack_lite from numpy.testing import TestCase, dec from numpy.compat import asbytes_nested class FindDependenciesLdd(object): def __init__(self): self.cmd = ['ldd'] try: p = Popen(self.cmd, stdout=PIPE, stderr=PIPE) stdout, stderr = p.communicate() except OSError: raise RuntimeError("command %s cannot be run" % self.cmd) def get_dependencies(self, lfile): p = Popen(self.cmd + [lfile], stdout=PIPE, stderr=PIPE) stdout, stderr = p.communicate() if not (p.returncode == 0): raise RuntimeError("failed dependencies check for %s" % lfile) return stdout def grep_dependencies(self, lfile, deps): stdout = self.get_dependencies(lfile) rdeps = dict([(dep, re.compile(dep)) for dep in deps]) founds = [] for l in stdout.splitlines(): for k, v in rdeps.items(): if v.search(l): founds.append(k) return founds class TestF77Mismatch(TestCase): @dec.skipif(not(sys.platform[:5] == 'linux'), "Skipping fortran compiler mismatch on non Linux platform") def test_lapack(self): f = FindDependenciesLdd() deps = f.grep_dependencies(lapack_lite.__file__, asbytes_nested(['libg2c', 'libgfortran'])) self.assertFalse(len(deps) > 1, """Both g77 and gfortran runtimes linked in lapack_lite ! This is likely to cause random crashes and wrong results. See numpy INSTALL.txt for more information.""") import logging from unittest import TestResult logger = logging.getLogger(__name__) class CustomTestReport(TestResult): def __init__(self, change_callback=None): super(CustomTestReport, self).__init__() logger.debug('__init__') self.running = False self.change_callback = change_callback self.success = 0 def startTest(self, test): super(CustomTestReport, self).startTest(test) logger.debug('startTest') self.running = True if self.change_callback: self.change_callback({ "errors": len(self.errors), "failures": len(self.failures), "skipped": len(self.skipped), "expectedFailures": len(self.expectedFailures), "unexpectedSuccesses": len(self.unexpectedSuccesses), "testsRun": self.testsRun, "success": self.success }) def stopTest(self, test): super(CustomTestReport, self).stopTest(test) logger.debug("stopTest %s", test) self.running = False def startTestRun(self): super(CustomTestReport, self).startTestRun() logger.debug("startTestRun") self.running = True def stopTestRun(self): super(CustomTestReport, self).stopTestRun() logger.debug("stopTestRun") self.running = False def addError(self, test, err): super(CustomTestReport, self).addError(test, err) logger.debug("[E] %s %s", test, err) def addFailure(self, test, err): super(CustomTestReport, self).addFailure(test, err) logger.debug("[F] %s %s", test, err) def addSuccess(self, test): super(CustomTestReport, self).addSuccess(test) logger.debug("[S] %s", test) self.success += 1 def addSkip(self, test, reason): super(CustomTestReport, self).addSkip(test, reason) logger.debug("[s] %s %s", test, reason) def addExpectedFailure(self, test, err): super(CustomTestReport, self).addExpectedFailure(test, err) logger.debug("[EF] %s %s", test, err) def addUnexpectedSuccess(self, test): super(CustomTestReport, self).addUnexpectedSuccess(test) logger.debug("[US] %s", test) def addSubTest(self, test, subtest, outcome): super(CustomTestReport, self).addSubTest(test, subtest, outcome) logger.debug("[ST] %s %s %s", test, subtest, outcome) # Copyright 2015 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== # pylint: disable=protected-access """Recurrent layers. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from tensorflow.python.framework import tensor_shape from tensorflow.python.keras._impl.keras import activations from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras import constraints from tensorflow.python.keras._impl.keras import initializers from tensorflow.python.keras._impl.keras import regularizers from tensorflow.python.keras._impl.keras.engine import InputSpec from tensorflow.python.keras._impl.keras.engine import Layer # pylint: disable=access-member-before-definition def _time_distributed_dense(x, w, b=None, dropout=None, input_dim=None, output_dim=None, timesteps=None, training=None): """Apply `y . w + b` for every temporal slice y of x. Arguments: x: input tensor. w: weight matrix. b: optional bias vector. dropout: whether to apply dropout (same dropout mask for every temporal slice of the input). input_dim: integer; optional dimensionality of the input. output_dim: integer; optional dimensionality of the output. timesteps: integer; optional number of timesteps. training: training phase tensor or boolean. Returns: Output tensor. """ if not input_dim: input_dim = K.shape(x)[2] if not timesteps: timesteps = K.shape(x)[1] if not output_dim: output_dim = K.shape(w)[1] if dropout is not None and 0. < dropout < 1.: # apply the same dropout pattern at every timestep ones = K.ones_like(K.reshape(x[:, 0, :], (-1, input_dim))) dropout_matrix = K.dropout(ones, dropout) expanded_dropout_matrix = K.repeat(dropout_matrix, timesteps) x = K.in_train_phase(x * expanded_dropout_matrix, x, training=training) # collapse time dimension and batch dimension together x = K.reshape(x, (-1, input_dim)) x = K.dot(x, w) if b is not None: x = K.bias_add(x, b) # reshape to 3D tensor if K.backend() == 'tensorflow': x = K.reshape(x, K.stack([-1, timesteps, output_dim])) x.set_shape([None, None, output_dim]) else: x = K.reshape(x, (-1, timesteps, output_dim)) return x class Recurrent(Layer): """Abstract base class for recurrent layers. Do not use in a model -- it's not a valid layer! Use its children classes `LSTM`, `GRU` and `SimpleRNN` instead. All recurrent layers (`LSTM`, `GRU`, `SimpleRNN`) also follow the specifications of this class and accept the keyword arguments listed below. Example: ```python # as the first layer in a Sequential model model = Sequential() model.add(LSTM(32, input_shape=(10, 64))) # now model.output_shape == (None, 32) # note: `None` is the batch dimension. # for subsequent layers, no need to specify the input size: model.add(LSTM(16)) # to stack recurrent layers, you must use return_sequences=True # on any recurrent layer that feeds into another recurrent layer. # note that you only need to specify the input size on the first layer. model = Sequential() model.add(LSTM(64, input_dim=64, input_length=10, return_sequences=True)) model.add(LSTM(32, return_sequences=True)) model.add(LSTM(10)) ``` Arguments: weights: list of Numpy arrays to set as initial weights. The list should have 3 elements, of shapes: `[(input_dim, output_dim), (output_dim, output_dim), (output_dim,)]`. return_sequences: Boolean. Whether to return the last output in the output sequence, or the full sequence. return_state: Boolean. Whether to return the last state in addition to the output. go_backwards: Boolean (default False). If True, process the input sequence backwards and return the reversed sequence. stateful: Boolean (default False). If True, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch. unroll: Boolean (default False). If True, the network will be unrolled, else a symbolic loop will be used. Unrolling can speed-up a RNN, although it tends to be more memory-intensive. Unrolling is only suitable for short sequences. implementation: one of {0, 1, or 2}. If set to 0, the RNN will use an implementation that uses fewer, larger matrix products, thus running faster on CPU but consuming more memory. If set to 1, the RNN will use more matrix products, but smaller ones, thus running slower (may actually be faster on GPU) while consuming less memory. If set to 2 (LSTM/GRU only), the RNN will combine the input gate, the forget gate and the output gate into a single matrix, enabling more time-efficient parallelization on the GPU. Note: RNN dropout must be shared for all gates, resulting in a slightly reduced regularization. input_dim: dimensionality of the input (integer). This argument (or alternatively, the keyword argument `input_shape`) is required when using this layer as the first layer in a model. input_length: Length of input sequences, to be specified when it is constant. This argument is required if you are going to connect `Flatten` then `Dense` layers upstream (without it, the shape of the dense outputs cannot be computed). Note that if the recurrent layer is not the first layer in your model, you would need to specify the input length at the level of the first layer (e.g. via the `input_shape` argument) Input shape:s 3D tensor with shape `(batch_size, timesteps, input_dim)`, (Optional) 2D tensors with shape `(batch_size, output_dim)`. Output shape: - if `return_state`: a list of tensors. The first tensor is the output. The remaining tensors are the last states, each with shape `(batch_size, units)`. - if `return_sequences`: 3D tensor with shape `(batch_size, timesteps, units)`. - else, 2D tensor with shape `(batch_size, units)`. # Masking This layer supports masking for input data with a variable number of timesteps. To introduce masks to your data, use an `Embedding` layer with the `mask_zero` parameter set to `True`. # Note on using statefulness in RNNs You can set RNN layers to be 'stateful', which means that the states computed for the samples in one batch will be reused as initial states for the samples in the next batch. This assumes a one-to-one mapping between samples in different successive batches. To enable statefulness: - specify `stateful=True` in the layer constructor. - specify a fixed batch size for your model, by passing if sequential model: `batch_input_shape=(...)` to the first layer in your model. else for functional model with 1 or more Input layers: `batch_shape=(...)` to all the first layers in your model. This is the expected shape of your inputs *including the batch size*. It should be a tuple of integers, e.g. `(32, 10, 100)`. - specify `shuffle=False` when calling fit(). To reset the states of your model, call `.reset_states()` on either a specific layer, or on your entire model. # Note on specifying the initial state of RNNs You can specify the initial state of RNN layers symbolically by calling them with the keyword argument `initial_state`. The value of `initial_state` should be a tensor or list of tensors representing the initial state of the RNN layer. You can specify the initial state of RNN layers numerically by calling `reset_states` with the keyword argument `states`. The value of `states` should be a numpy array or list of numpy arrays representing the initial state of the RNN layer. """ def __init__(self, return_sequences=False, return_state=False, go_backwards=False, stateful=False, unroll=False, implementation=0, **kwargs): super(Recurrent, self).__init__(**kwargs) self.return_sequences = return_sequences self.return_state = return_state self.go_backwards = go_backwards self.stateful = stateful self.unroll = unroll self.implementation = implementation self.supports_masking = True self.input_spec = [InputSpec(ndim=3)] self.state_spec = None self.dropout = 0 self.recurrent_dropout = 0 def _compute_output_shape(self, input_shape): if isinstance(input_shape, list): input_shape = input_shape[0] input_shape = tensor_shape.TensorShape(input_shape).as_list() if self.return_sequences: output_shape = (input_shape[0], input_shape[1], self.units) else: output_shape = (input_shape[0], self.units) if self.return_state: state_shape = [tensor_shape.TensorShape( (input_shape[0], self.units)) for _ in self.states] return [tensor_shape.TensorShape(output_shape)] + state_shape return tensor_shape.TensorShape(output_shape) def compute_mask(self, inputs, mask): if isinstance(mask, list): mask = mask[0] output_mask = mask if self.return_sequences else None if self.return_state: state_mask = [None for _ in self.states] return [output_mask] + state_mask return output_mask def step(self, inputs, states): raise NotImplementedError def get_constants(self, inputs, training=None): return [] def get_initial_state(self, inputs): # build an all-zero tensor of shape (samples, output_dim) initial_state = K.zeros_like(inputs) # (samples, timesteps, input_dim) initial_state = K.sum(initial_state, axis=(1, 2)) # (samples,) initial_state = K.expand_dims(initial_state) # (samples, 1) initial_state = K.tile(initial_state, [1, self.units]) # (samples, output_dim) initial_state = [initial_state for _ in range(len(self.states))] return initial_state def preprocess_input(self, inputs, training=None): return inputs def __call__(self, inputs, initial_state=None, **kwargs): if (isinstance(inputs, (list, tuple)) and len(inputs) > 1 and initial_state is None): initial_state = inputs[1:] inputs = inputs[0] # If `initial_state` is specified, # and if it a Keras tensor, # then add it to the inputs and temporarily # modify the input spec to include the state. if initial_state is None: return super(Recurrent, self).__call__(inputs, **kwargs) if not isinstance(initial_state, (list, tuple)): initial_state = [initial_state] is_keras_tensor = hasattr(initial_state[0], '_keras_history') for tensor in initial_state: if hasattr(tensor, '_keras_history') != is_keras_tensor: raise ValueError('The initial state of an RNN layer cannot be' ' specified with a mix of Keras tensors and' ' non-Keras tensors') if is_keras_tensor: # Compute the full input spec, including state input_spec = self.input_spec state_spec = self.state_spec if not isinstance(input_spec, list): input_spec = [input_spec] if not isinstance(state_spec, list): state_spec = [state_spec] self.input_spec = input_spec + state_spec # Compute the full inputs, including state inputs = [inputs] + list(initial_state) # Perform the call output = super(Recurrent, self).__call__(inputs, **kwargs) # Restore original input spec self.input_spec = input_spec return output else: kwargs['initial_state'] = initial_state return super(Recurrent, self).__call__(inputs, **kwargs) def call(self, inputs, mask=None, training=None, initial_state=None): # input shape: `(samples, time (padded with zeros), input_dim)` # note that the .build() method of subclasses MUST define # self.input_spec and self.state_spec with complete input shapes. if isinstance(inputs, list): initial_state = inputs[1:] inputs = inputs[0] elif initial_state is not None: pass elif self.stateful: initial_state = self.states else: initial_state = self.get_initial_state(inputs) if isinstance(mask, list): mask = mask[0] if len(initial_state) != len(self.states): raise ValueError('Layer has ' + str(len(self.states)) + ' states but was passed ' + str(len(initial_state)) + ' initial states.') input_shape = K.int_shape(inputs) if self.unroll and input_shape[1] is None: raise ValueError('Cannot unroll a RNN if the ' 'time dimension is undefined. \n' '- If using a Sequential model, ' 'specify the time dimension by passing ' 'an `input_shape` or `batch_input_shape` ' 'argument to your first layer. If your ' 'first layer is an Embedding, you can ' 'also use the `input_length` argument.\n' '- If using the functional API, specify ' 'the time dimension by passing a `shape` ' 'or `batch_shape` argument to your Input layer.') constants = self.get_constants(inputs, training=None) preprocessed_input = self.preprocess_input(inputs, training=None) last_output, outputs, states = K.rnn( self.step, preprocessed_input, initial_state, go_backwards=self.go_backwards, mask=mask, constants=constants, unroll=self.unroll) if self.stateful: updates = [] for i in range(len(states)): updates.append((self.states[i], states[i])) self.add_update(updates, inputs) # Properly set learning phase if 0 < self.dropout + self.recurrent_dropout: last_output._uses_learning_phase = True outputs._uses_learning_phase = True if not self.return_sequences: outputs = last_output if self.return_state: if not isinstance(states, (list, tuple)): states = [states] else: states = list(states) return [outputs] + states return outputs def reset_states(self, states=None): if not self.stateful: raise AttributeError('Layer must be stateful.') batch_size = self.input_spec[0].shape[0] if not batch_size: raise ValueError('If a RNN is stateful, it needs to know ' 'its batch size. Specify the batch size ' 'of your input tensors: \n' '- If using a Sequential model, ' 'specify the batch size by passing ' 'a `batch_input_shape` ' 'argument to your first layer.\n' '- If using the functional API, specify ' 'the time dimension by passing a ' '`batch_shape` argument to your Input layer.') # initialize state if None if self.states[0] is None: self.states = [K.zeros((batch_size, self.units)) for _ in self.states] elif states is None: for state in self.states: K.set_value(state, np.zeros((batch_size, self.units))) else: if not isinstance(states, (list, tuple)): states = [states] if len(states) != len(self.states): raise ValueError('Layer ' + self.name + ' expects ' + str(len(self.states)) + ' states, ' 'but it received ' + str(len(states)) + ' state values. Input received: ' + str(states)) for index, (value, state) in enumerate(zip(states, self.states)): if value.shape != (batch_size, self.units): raise ValueError('State ' + str(index) + ' is incompatible with layer ' + self.name + ': expected shape=' + str((batch_size, self.units)) + ', found shape=' + str(value.shape)) K.set_value(state, value) def get_config(self): config = { 'return_sequences': self.return_sequences, 'return_state': self.return_state, 'go_backwards': self.go_backwards, 'stateful': self.stateful, 'unroll': self.unroll, 'implementation': self.implementation } base_config = super(Recurrent, self).get_config() return dict(list(base_config.items()) + list(config.items())) class SimpleRNN(Recurrent): """Fully-connected RNN where the output is to be fed back to input. Arguments: units: Positive integer, dimensionality of the output space. activation: Activation function to use. If you don't specify anything, no activation is applied If you pass None, no activation is applied (ie. "linear" activation: `a(x) = x`). use_bias: Boolean, whether the layer uses a bias vector. kernel_initializer: Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs.. recurrent_initializer: Initializer for the `recurrent_kernel` weights matrix, used for the linear transformation of the recurrent state.. bias_initializer: Initializer for the bias vector. kernel_regularizer: Regularizer function applied to the `kernel` weights matrix. recurrent_regularizer: Regularizer function applied to the `recurrent_kernel` weights matrix. bias_regularizer: Regularizer function applied to the bias vector. activity_regularizer: Regularizer function applied to the output of the layer (its "activation").. kernel_constraint: Constraint function applied to the `kernel` weights matrix. recurrent_constraint: Constraint function applied to the `recurrent_kernel` weights matrix. bias_constraint: Constraint function applied to the bias vector. dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. recurrent_dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state. References: - [A Theoretically Grounded Application of Dropout in Recurrent Neural Networks](http://arxiv.org/abs/1512.05287) """ def __init__(self, units, activation='tanh', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0., recurrent_dropout=0., **kwargs): super(SimpleRNN, self).__init__(**kwargs) self.units = units self.activation = activations.get(activation) self.use_bias = use_bias self.kernel_initializer = initializers.get(kernel_initializer) self.recurrent_initializer = initializers.get(recurrent_initializer) self.bias_initializer = initializers.get(bias_initializer) self.kernel_regularizer = regularizers.get(kernel_regularizer) self.recurrent_regularizer = regularizers.get(recurrent_regularizer) self.bias_regularizer = regularizers.get(bias_regularizer) self.activity_regularizer = regularizers.get(activity_regularizer) self.kernel_constraint = constraints.get(kernel_constraint) self.recurrent_constraint = constraints.get(recurrent_constraint) self.bias_constraint = constraints.get(bias_constraint) self.dropout = min(1., max(0., dropout)) self.recurrent_dropout = min(1., max(0., recurrent_dropout)) self.state_spec = InputSpec(shape=(None, self.units)) def build(self, input_shape): if isinstance(input_shape, list): input_shape = input_shape[0] input_shape = tensor_shape.TensorShape(input_shape).as_list() batch_size = input_shape[0] if self.stateful else None self.input_dim = input_shape[2] self.input_spec[0] = InputSpec(shape=(batch_size, None, self.input_dim)) self.states = [None] if self.stateful: self.reset_states() self.kernel = self.add_weight( shape=(self.input_dim, self.units), name='kernel', initializer=self.kernel_initializer, regularizer=self.kernel_regularizer, constraint=self.kernel_constraint) self.recurrent_kernel = self.add_weight( shape=(self.units, self.units), name='recurrent_kernel', initializer=self.recurrent_initializer, regularizer=self.recurrent_regularizer, constraint=self.recurrent_constraint) if self.use_bias: self.bias = self.add_weight( shape=(self.units,), name='bias', initializer=self.bias_initializer, regularizer=self.bias_regularizer, constraint=self.bias_constraint) else: self.bias = None self.built = True def preprocess_input(self, inputs, training=None): if self.implementation > 0: return inputs else: input_shape = inputs.get_shape().as_list() input_dim = input_shape[2] timesteps = input_shape[1] return _time_distributed_dense( inputs, self.kernel, self.bias, self.dropout, input_dim, self.units, timesteps, training=training) def step(self, inputs, states): if self.implementation == 0: h = inputs else: if 0 < self.dropout < 1: h = K.dot(inputs * states[1], self.kernel) else: h = K.dot(inputs, self.kernel) if self.bias is not None: h = K.bias_add(h, self.bias) prev_output = states[0] if 0 < self.recurrent_dropout < 1: prev_output *= states[2] output = h + K.dot(prev_output, self.recurrent_kernel) if self.activation is not None: output = self.activation(output) # Properly set learning phase on output tensor. if 0 < self.dropout + self.recurrent_dropout: output._uses_learning_phase = True return output, [output] def get_constants(self, inputs, training=None): constants = [] if self.implementation != 0 and 0 < self.dropout < 1: input_shape = K.int_shape(inputs) input_dim = input_shape[-1] ones = K.ones_like(K.reshape(inputs[:, 0, 0], (-1, 1))) ones = K.tile(ones, (1, int(input_dim))) def dropped_inputs(): return K.dropout(ones, self.dropout) dp_mask = K.in_train_phase(dropped_inputs, ones, training=training) constants.append(dp_mask) else: constants.append(K.cast_to_floatx(1.)) if 0 < self.recurrent_dropout < 1: ones = K.ones_like(K.reshape(inputs[:, 0, 0], (-1, 1))) ones = K.tile(ones, (1, self.units)) def dropped_inputs(): # pylint: disable=function-redefined return K.dropout(ones, self.recurrent_dropout) rec_dp_mask = K.in_train_phase(dropped_inputs, ones, training=training) constants.append(rec_dp_mask) else: constants.append(K.cast_to_floatx(1.)) return constants def get_config(self): config = { 'units': self.units, 'activation': activations.serialize(self.activation), 'use_bias': self.use_bias, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'recurrent_initializer': initializers.serialize(self.recurrent_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'recurrent_constraint': constraints.serialize(self.recurrent_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'dropout': self.dropout, 'recurrent_dropout': self.recurrent_dropout } base_config = super(SimpleRNN, self).get_config() return dict(list(base_config.items()) + list(config.items())) class GRU(Recurrent): """Gated Recurrent Unit - Cho et al. 2014. Arguments: units: Positive integer, dimensionality of the output space. activation: Activation function to use. If you pass None, no activation is applied (ie. "linear" activation: `a(x) = x`). recurrent_activation: Activation function to use for the recurrent step. use_bias: Boolean, whether the layer uses a bias vector. kernel_initializer: Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs.. recurrent_initializer: Initializer for the `recurrent_kernel` weights matrix, used for the linear transformation of the recurrent state.. bias_initializer: Initializer for the bias vector. kernel_regularizer: Regularizer function applied to the `kernel` weights matrix. recurrent_regularizer: Regularizer function applied to the `recurrent_kernel` weights matrix. bias_regularizer: Regularizer function applied to the bias vector. activity_regularizer: Regularizer function applied to the output of the layer (its "activation").. kernel_constraint: Constraint function applied to the `kernel` weights matrix. recurrent_constraint: Constraint function applied to the `recurrent_kernel` weights matrix. bias_constraint: Constraint function applied to the bias vector. dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. recurrent_dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state. References: - [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://arxiv.org/abs/1409.1259) - [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](http://arxiv.org/abs/1412.3555v1) - [A Theoretically Grounded Application of Dropout in Recurrent Neural Networks](http://arxiv.org/abs/1512.05287) """ def __init__(self, units, activation='tanh', recurrent_activation='hard_sigmoid', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0., recurrent_dropout=0., **kwargs): super(GRU, self).__init__(**kwargs) self.units = units self.activation = activations.get(activation) self.recurrent_activation = activations.get(recurrent_activation) self.use_bias = use_bias self.kernel_initializer = initializers.get(kernel_initializer) self.recurrent_initializer = initializers.get(recurrent_initializer) self.bias_initializer = initializers.get(bias_initializer) self.kernel_regularizer = regularizers.get(kernel_regularizer) self.recurrent_regularizer = regularizers.get(recurrent_regularizer) self.bias_regularizer = regularizers.get(bias_regularizer) self.activity_regularizer = regularizers.get(activity_regularizer) self.kernel_constraint = constraints.get(kernel_constraint) self.recurrent_constraint = constraints.get(recurrent_constraint) self.bias_constraint = constraints.get(bias_constraint) self.dropout = min(1., max(0., dropout)) self.recurrent_dropout = min(1., max(0., recurrent_dropout)) self.state_spec = InputSpec(shape=(None, self.units)) def build(self, input_shape): if isinstance(input_shape, list): input_shape = input_shape[0] input_shape = tensor_shape.TensorShape(input_shape).as_list() batch_size = input_shape[0] if self.stateful else None self.input_dim = input_shape[2] self.input_spec[0] = InputSpec(shape=(batch_size, None, self.input_dim)) self.states = [None] if self.stateful: self.reset_states() self.kernel = self.add_weight( shape=(self.input_dim, self.units * 3), name='kernel', initializer=self.kernel_initializer, regularizer=self.kernel_regularizer, constraint=self.kernel_constraint) self.recurrent_kernel = self.add_weight( shape=(self.units, self.units * 3), name='recurrent_kernel', initializer=self.recurrent_initializer, regularizer=self.recurrent_regularizer, constraint=self.recurrent_constraint) if self.use_bias: self.bias = self.add_weight( shape=(self.units * 3,), name='bias', initializer=self.bias_initializer, regularizer=self.bias_regularizer, constraint=self.bias_constraint) else: self.bias = None self.kernel_z = self.kernel[:, :self.units] self.recurrent_kernel_z = self.recurrent_kernel[:, :self.units] self.kernel_r = self.kernel[:, self.units:self.units * 2] self.recurrent_kernel_r = self.recurrent_kernel[:, self.units: self.units * 2] self.kernel_h = self.kernel[:, self.units * 2:] self.recurrent_kernel_h = self.recurrent_kernel[:, self.units * 2:] if self.use_bias: self.bias_z = self.bias[:self.units] self.bias_r = self.bias[self.units:self.units * 2] self.bias_h = self.bias[self.units * 2:] else: self.bias_z = None self.bias_r = None self.bias_h = None self.built = True def preprocess_input(self, inputs, training=None): if self.implementation == 0: input_shape = inputs.get_shape().as_list() input_dim = input_shape[2] timesteps = input_shape[1] x_z = _time_distributed_dense( inputs, self.kernel_z, self.bias_z, self.dropout, input_dim, self.units, timesteps, training=training) x_r = _time_distributed_dense( inputs, self.kernel_r, self.bias_r, self.dropout, input_dim, self.units, timesteps, training=training) x_h = _time_distributed_dense( inputs, self.kernel_h, self.bias_h, self.dropout, input_dim, self.units, timesteps, training=training) return K.concatenate([x_z, x_r, x_h], axis=2) else: return inputs def get_constants(self, inputs, training=None): constants = [] if self.implementation != 0 and 0 < self.dropout < 1: input_shape = K.int_shape(inputs) input_dim = input_shape[-1] ones = K.ones_like(K.reshape(inputs[:, 0, 0], (-1, 1))) ones = K.tile(ones, (1, int(input_dim))) def dropped_inputs(): return K.dropout(ones, self.dropout) dp_mask = [ K.in_train_phase(dropped_inputs, ones, training=training) for _ in range(3) ] constants.append(dp_mask) else: constants.append([K.cast_to_floatx(1.) for _ in range(3)]) if 0 < self.recurrent_dropout < 1: ones = K.ones_like(K.reshape(inputs[:, 0, 0], (-1, 1))) ones = K.tile(ones, (1, self.units)) def dropped_inputs(): # pylint: disable=function-redefined return K.dropout(ones, self.recurrent_dropout) rec_dp_mask = [ K.in_train_phase(dropped_inputs, ones, training=training) for _ in range(3) ] constants.append(rec_dp_mask) else: constants.append([K.cast_to_floatx(1.) for _ in range(3)]) return constants def step(self, inputs, states): h_tm1 = states[0] # previous memory dp_mask = states[1] # dropout matrices for recurrent units rec_dp_mask = states[2] if self.implementation == 2: matrix_x = K.dot(inputs * dp_mask[0], self.kernel) if self.use_bias: matrix_x = K.bias_add(matrix_x, self.bias) matrix_inner = K.dot(h_tm1 * rec_dp_mask[0], self.recurrent_kernel[:, :2 * self.units]) x_z = matrix_x[:, :self.units] x_r = matrix_x[:, self.units:2 * self.units] recurrent_z = matrix_inner[:, :self.units] recurrent_r = matrix_inner[:, self.units:2 * self.units] z = self.recurrent_activation(x_z + recurrent_z) r = self.recurrent_activation(x_r + recurrent_r) x_h = matrix_x[:, 2 * self.units:] recurrent_h = K.dot(r * h_tm1 * rec_dp_mask[0], self.recurrent_kernel[:, 2 * self.units:]) hh = self.activation(x_h + recurrent_h) else: if self.implementation == 0: x_z = inputs[:, :self.units] x_r = inputs[:, self.units:2 * self.units] x_h = inputs[:, 2 * self.units:] elif self.implementation == 1: x_z = K.dot(inputs * dp_mask[0], self.kernel_z) x_r = K.dot(inputs * dp_mask[1], self.kernel_r) x_h = K.dot(inputs * dp_mask[2], self.kernel_h) if self.use_bias: x_z = K.bias_add(x_z, self.bias_z) x_r = K.bias_add(x_r, self.bias_r) x_h = K.bias_add(x_h, self.bias_h) else: raise ValueError('Unknown `implementation` mode.') z = self.recurrent_activation(x_z + K.dot(h_tm1 * rec_dp_mask[0], self.recurrent_kernel_z)) r = self.recurrent_activation(x_r + K.dot(h_tm1 * rec_dp_mask[1], self.recurrent_kernel_r)) hh = self.activation(x_h + K.dot(r * h_tm1 * rec_dp_mask[2], self.recurrent_kernel_h)) h = z * h_tm1 + (1 - z) * hh if 0 < self.dropout + self.recurrent_dropout: h._uses_learning_phase = True return h, [h] def get_config(self): config = { 'units': self.units, 'activation': activations.serialize(self.activation), 'recurrent_activation': activations.serialize(self.recurrent_activation), 'use_bias': self.use_bias, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'recurrent_initializer': initializers.serialize(self.recurrent_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'recurrent_constraint': constraints.serialize(self.recurrent_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'dropout': self.dropout, 'recurrent_dropout': self.recurrent_dropout } base_config = super(GRU, self).get_config() return dict(list(base_config.items()) + list(config.items())) class LSTM(Recurrent): """Long-Short Term Memory unit - Hochreiter 1997. For a step-by-step description of the algorithm, see [this tutorial](http://deeplearning.net/tutorial/lstm.html). Arguments: units: Positive integer, dimensionality of the output space. activation: Activation function to use. If you pass None, no activation is applied (ie. "linear" activation: `a(x) = x`). recurrent_activation: Activation function to use for the recurrent step. use_bias: Boolean, whether the layer uses a bias vector. kernel_initializer: Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs.. recurrent_initializer: Initializer for the `recurrent_kernel` weights matrix, used for the linear transformation of the recurrent state.. bias_initializer: Initializer for the bias vector. unit_forget_bias: Boolean. If True, add 1 to the bias of the forget gate at initialization. Setting it to true will also force `bias_initializer="zeros"`. This is recommended in [Jozefowicz et al.](http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf) kernel_regularizer: Regularizer function applied to the `kernel` weights matrix. recurrent_regularizer: Regularizer function applied to the `recurrent_kernel` weights matrix. bias_regularizer: Regularizer function applied to the bias vector. activity_regularizer: Regularizer function applied to the output of the layer (its "activation").. kernel_constraint: Constraint function applied to the `kernel` weights matrix. recurrent_constraint: Constraint function applied to the `recurrent_kernel` weights matrix. bias_constraint: Constraint function applied to the bias vector. dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. recurrent_dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state. References: - [Long short-term memory]((http://www.bioinf.jku.at/publications/older/2604.pdf) (original 1997 paper) - [Supervised sequence labeling with recurrent neural networks](http://www.cs.toronto.edu/~graves/preprint.pdf) - [A Theoretically Grounded Application of Dropout in Recurrent Neural Networks](http://arxiv.org/abs/1512.05287) """ def __init__(self, units, activation='tanh', recurrent_activation='hard_sigmoid', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', unit_forget_bias=True, kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0., recurrent_dropout=0., **kwargs): super(LSTM, self).__init__(**kwargs) self.units = units self.activation = activations.get(activation) self.recurrent_activation = activations.get(recurrent_activation) self.use_bias = use_bias self.kernel_initializer = initializers.get(kernel_initializer) self.recurrent_initializer = initializers.get(recurrent_initializer) self.bias_initializer = initializers.get(bias_initializer) self.unit_forget_bias = unit_forget_bias self.kernel_regularizer = regularizers.get(kernel_regularizer) self.recurrent_regularizer = regularizers.get(recurrent_regularizer) self.bias_regularizer = regularizers.get(bias_regularizer) self.activity_regularizer = regularizers.get(activity_regularizer) self.kernel_constraint = constraints.get(kernel_constraint) self.recurrent_constraint = constraints.get(recurrent_constraint) self.bias_constraint = constraints.get(bias_constraint) self.dropout = min(1., max(0., dropout)) self.recurrent_dropout = min(1., max(0., recurrent_dropout)) self.state_spec = [ InputSpec(shape=(None, self.units)), InputSpec(shape=(None, self.units)) ] def build(self, input_shape): if isinstance(input_shape, list): input_shape = input_shape[0] input_shape = tensor_shape.TensorShape(input_shape).as_list() batch_size = input_shape[0] if self.stateful else None self.input_dim = input_shape[2] self.input_spec[0] = InputSpec(shape=(batch_size, None, self.input_dim)) self.states = [None, None] if self.stateful: self.reset_states() self.kernel = self.add_weight( shape=(self.input_dim, self.units * 4), name='kernel', initializer=self.kernel_initializer, regularizer=self.kernel_regularizer, constraint=self.kernel_constraint) self.recurrent_kernel = self.add_weight( shape=(self.units, self.units * 4), name='recurrent_kernel', initializer=self.recurrent_initializer, regularizer=self.recurrent_regularizer, constraint=self.recurrent_constraint) if self.use_bias: if self.unit_forget_bias: def bias_initializer(_, *args, **kwargs): return K.concatenate([ self.bias_initializer((self.units,), *args, **kwargs), initializers.Ones()((self.units,), *args, **kwargs), self.bias_initializer((self.units * 2,), *args, **kwargs), ]) else: bias_initializer = self.bias_initializer self.bias = self.add_weight( shape=(self.units * 4,), name='bias', initializer=bias_initializer, regularizer=self.bias_regularizer, constraint=self.bias_constraint) else: self.bias = None self.kernel_i = self.kernel[:, :self.units] self.kernel_f = self.kernel[:, self.units:self.units * 2] self.kernel_c = self.kernel[:, self.units * 2:self.units * 3] self.kernel_o = self.kernel[:, self.units * 3:] self.recurrent_kernel_i = self.recurrent_kernel[:, :self.units] self.recurrent_kernel_f = self.recurrent_kernel[:, self.units: self.units * 2] self.recurrent_kernel_c = self.recurrent_kernel[:, self.units * 2: self.units * 3] self.recurrent_kernel_o = self.recurrent_kernel[:, self.units * 3:] if self.use_bias: self.bias_i = self.bias[:self.units] self.bias_f = self.bias[self.units:self.units * 2] self.bias_c = self.bias[self.units * 2:self.units * 3] self.bias_o = self.bias[self.units * 3:] else: self.bias_i = None self.bias_f = None self.bias_c = None self.bias_o = None self.built = True def preprocess_input(self, inputs, training=None): if self.implementation == 0: input_shape = inputs.get_shape().as_list() input_dim = input_shape[2] timesteps = input_shape[1] x_i = _time_distributed_dense( inputs, self.kernel_i, self.bias_i, self.dropout, input_dim, self.units, timesteps, training=training) x_f = _time_distributed_dense( inputs, self.kernel_f, self.bias_f, self.dropout, input_dim, self.units, timesteps, training=training) x_c = _time_distributed_dense( inputs, self.kernel_c, self.bias_c, self.dropout, input_dim, self.units, timesteps, training=training) x_o = _time_distributed_dense( inputs, self.kernel_o, self.bias_o, self.dropout, input_dim, self.units, timesteps, training=training) return K.concatenate([x_i, x_f, x_c, x_o], axis=2) else: return inputs def get_constants(self, inputs, training=None): constants = [] if self.implementation != 0 and 0 < self.dropout < 1: input_shape = K.int_shape(inputs) input_dim = input_shape[-1] ones = K.ones_like(K.reshape(inputs[:, 0, 0], (-1, 1))) ones = K.tile(ones, (1, int(input_dim))) def dropped_inputs(): return K.dropout(ones, self.dropout) dp_mask = [ K.in_train_phase(dropped_inputs, ones, training=training) for _ in range(4) ] constants.append(dp_mask) else: constants.append([K.cast_to_floatx(1.) for _ in range(4)]) if 0 < self.recurrent_dropout < 1: ones = K.ones_like(K.reshape(inputs[:, 0, 0], (-1, 1))) ones = K.tile(ones, (1, self.units)) def dropped_inputs(): # pylint: disable=function-redefined return K.dropout(ones, self.recurrent_dropout) rec_dp_mask = [ K.in_train_phase(dropped_inputs, ones, training=training) for _ in range(4) ] constants.append(rec_dp_mask) else: constants.append([K.cast_to_floatx(1.) for _ in range(4)]) return constants def step(self, inputs, states): h_tm1 = states[0] c_tm1 = states[1] dp_mask = states[2] rec_dp_mask = states[3] if self.implementation == 2: z = K.dot(inputs * dp_mask[0], self.kernel) z += K.dot(h_tm1 * rec_dp_mask[0], self.recurrent_kernel) if self.use_bias: z = K.bias_add(z, self.bias) z0 = z[:, :self.units] z1 = z[:, self.units:2 * self.units] z2 = z[:, 2 * self.units:3 * self.units] z3 = z[:, 3 * self.units:] i = self.recurrent_activation(z0) f = self.recurrent_activation(z1) c = f * c_tm1 + i * self.activation(z2) o = self.recurrent_activation(z3) else: if self.implementation == 0: x_i = inputs[:, :self.units] x_f = inputs[:, self.units:2 * self.units] x_c = inputs[:, 2 * self.units:3 * self.units] x_o = inputs[:, 3 * self.units:] elif self.implementation == 1: x_i = K.dot(inputs * dp_mask[0], self.kernel_i) + self.bias_i x_f = K.dot(inputs * dp_mask[1], self.kernel_f) + self.bias_f x_c = K.dot(inputs * dp_mask[2], self.kernel_c) + self.bias_c x_o = K.dot(inputs * dp_mask[3], self.kernel_o) + self.bias_o else: raise ValueError('Unknown `implementation` mode.') i = self.recurrent_activation(x_i + K.dot(h_tm1 * rec_dp_mask[0], self.recurrent_kernel_i)) f = self.recurrent_activation(x_f + K.dot(h_tm1 * rec_dp_mask[1], self.recurrent_kernel_f)) c = f * c_tm1 + i * self.activation( x_c + K.dot(h_tm1 * rec_dp_mask[2], self.recurrent_kernel_c)) o = self.recurrent_activation(x_o + K.dot(h_tm1 * rec_dp_mask[3], self.recurrent_kernel_o)) h = o * self.activation(c) if 0 < self.dropout + self.recurrent_dropout: h._uses_learning_phase = True return h, [h, c] def get_config(self): config = { 'units': self.units, 'activation': activations.serialize(self.activation), 'recurrent_activation': activations.serialize(self.recurrent_activation), 'use_bias': self.use_bias, 'kernel_initializer': initializers.serialize(self.kernel_initializer), 'recurrent_initializer': initializers.serialize(self.recurrent_initializer), 'bias_initializer': initializers.serialize(self.bias_initializer), 'unit_forget_bias': self.unit_forget_bias, 'kernel_regularizer': regularizers.serialize(self.kernel_regularizer), 'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer), 'bias_regularizer': regularizers.serialize(self.bias_regularizer), 'activity_regularizer': regularizers.serialize(self.activity_regularizer), 'kernel_constraint': constraints.serialize(self.kernel_constraint), 'recurrent_constraint': constraints.serialize(self.recurrent_constraint), 'bias_constraint': constraints.serialize(self.bias_constraint), 'dropout': self.dropout, 'recurrent_dropout': self.recurrent_dropout } base_config = super(LSTM, self).get_config() return dict(list(base_config.items()) + list(config.items())) #!/usr/bin/python """Delete file not in Solr ...""" import os import requests import json import sys import os.path import sys import argparse import shutil from common import splitString from DirectoryWalker import DirectoryWalker import itertools from OmeroPropertiesParser import OmeroPropertiesParser responseFailed=0 numberOfImageDownloadAttemps=0 totalNumberOfImagesWeHave=0 numFoundInSolr=0 uniquePathsFromSolr=set() uniqueFilesWithPathsFromNfs=set() def main(argv): parser = argparse.ArgumentParser( description='Delete files not in Solr' ) parser.add_argument('-d', '--rootDestinationDir', dest='rootDestinationDir', help='Directory for root of destination to store images' ) parser.add_argument('-s', '--rootSolrUrl', dest='rootSolrUrl', help='URL to root of solr index' ) parser.add_argument('-H', '--host', dest='komp2Host', help='Hostname for server hosting komp2 db' ) parser.add_argument('-p', '--port', dest='komp2Port', help='Port by which to connect to komp2 db' ) parser.add_argument('-u', '--user', dest='komp2User', help='Username for connecting to komp2 db' ) parser.add_argument('-db', '--database', dest='komp2Db', help='Database to connect to for komp2db' ) parser.add_argument('--pass', dest='komp2Pass', help='Password for komp2db' ) parser.add_argument('--profile', dest='profile', default='dev', help='profile from which to read config: dev, prod, live, ...') args = parser.parse_args() # Get values from property file and use as defaults that can be overridden # by command line parameters try: pp = OmeroPropertiesParser(args.profile) omeroProps = pp.getOmeroProps() except: omeroProps = {} rootSolrUrl = args.rootSolrUrl if args.rootSolrUrl <> None else omeroProps['solrurl'] komp2Host = args.komp2Host if args.komp2Host<>None else omeroProps['komp2host'] komp2Port = args.komp2Port if args.komp2Port<>None else omeroProps['komp2port'] komp2db = args.komp2Db if args.komp2Db<>None else omeroProps['komp2db'] komp2User = args.komp2User if args.komp2User<>None else omeroProps['komp2user'] komp2Pass = args.komp2Pass if args.komp2Pass<>None else omeroProps['komp2pass'] rootDestinationDir = args.rootDestinationDir if args.rootDestinationDir<>None else omeroProps['rootdestinationdir'] #note cant split this url over a few lines as puts in newlines into url which doesn't work #solrQuery="""experiment/select?q=observation_type:image_record&fq=download_file_path:(download_file_path:*bhjlk01.jax.org/images/IMPC_ALZ_001/*%20AND%20!download_file_path:*.mov)&fl=id,download_file_path,phenotyping_center,pipeline_stable_id,procedure_stable_id,datasource_name,parameter_stable_id&wt=json&indent=on&rows=10000000""" solrQuery="""experiment/select?q=observation_type:image_record&fq=(download_file_path:*mousephenotype.org*%20AND%20!download_file_path:*.mov)&fl=id,download_file_path,phenotyping_center,pipeline_stable_id,procedure_stable_id,datasource_name,parameter_stable_id&wt=json&indent=on&rows=1000000""" print("running python image copy script for impc images") print 'rootDestinationDir is "', rootDestinationDir solrUrl=rootSolrUrl+solrQuery; print 'solrUrl', solrUrl getPathsFromSolr(solrUrl, rootDestinationDir) #for solrPath in itertools.islice(uniquePathsFromSolr, 3): #print "solrPath="+solrPath dirWalker=DirectoryWalker() rel_directory_to_filenames_map=dirWalker.getFilesFromDir(rootDestinationDir) n=10 for key, value in rel_directory_to_filenames_map.items(): count=0 for name in value: filePath=rootDestinationDir+key+"/"+name uniqueFilesWithPathsFromNfs.add(filePath) count+=1 #if count<= n: print "filePath="+str(filePath) print "uniquePathsFromSolr length=",len(uniquePathsFromSolr) print "uniqueFilesWithPathsFromNfs=", len(uniqueFilesWithPathsFromNfs) filesNotInSolrAnymore=uniqueFilesWithPathsFromNfs.difference(uniquePathsFromSolr) print "files not in solr anymore size=",len(filesNotInSolrAnymore) for currentPath in filesNotInSolrAnymore: print "trying to move this file="+currentPath destinationPath=currentPath.replace("/nfs/komp2/web/images/clean/impc/", "/nfs/komp2/web/images/impc/") print "destPath="+destinationPath moveFile(currentPath, destinationPath) #os.remove(filePath) maybe move to another directory rather than delete #runWithSolrAsDataSource(solrUrl, cnx, rootDestinationDir) def getPathsFromSolr(solrUrl, rootDestinationDir): """ need to get these passed in as arguments - the host and db name etc for jenkins to run first get the list of download urls and the data source, experiment, procdure and parameter and observation id for the images """ v = json.loads(requests.get(solrUrl).text) docs=v['response']['docs'] numFoundInSolr=v['response']['numFound'] for doc in docs: download_file_path=doc['download_file_path'] datasource_id=doc['datasource_name'] phenotyping_center=doc['phenotyping_center'] #experiment=doc['experiment'] pipeline_stable_id=doc['pipeline_stable_id'] observation_id=doc['id'] procedure_stable_id=doc['procedure_stable_id'] parameter_stable_id=doc['parameter_stable_id'] global rootDesitinationdir getUniqueFilePathsFromSolr(observation_id, rootDestinationDir,phenotyping_center,pipeline_stable_id, procedure_stable_id, parameter_stable_id, download_file_path) print 'number found in solr='+str(numFoundInSolr)+' number of failed responses='+str(responseFailed)+' number of requests='+str(numberOfImageDownloadAttemps)+' total totalNumberOfImagesWeHave='+str(totalNumberOfImagesWeHave) def createDestinationFilePath(rootDestinationDir, phenotyping_center, pipeline_stable_id, procedure, parameter, download_file_path): directory="/".join([rootDestinationDir,phenotyping_center, pipeline_stable_id,procedure,parameter]) return directory def getUniqueFilePathsFromSolr(observation_id, rootDestinationDir, phenotyping_center,pipeline_stable_id, procedure, parameter, downloadFilePath): global totalNumberOfImagesWeHave global responseFailed global numberOfImageDownloadAttemps directory = createDestinationFilePath(rootDestinationDir, phenotyping_center, pipeline_stable_id, procedure,parameter, downloadFilePath) #print "directory "+str(directory) dstfilename=directory+"/"+str(downloadFilePath.split('/')[-1]) #print "dstfilename="+str(dstfilename) #/nfs/komp2/web/images/impc/MRC Harwell/HRWL_001/IMPC_XRY_001/IMPC_XRY_034_001/114182.dcm # new file paths are /nfs/public/ro/pheno-archive-images/images/impc if dstfilename in uniquePathsFromSolr: print '---------------------!!!!!!!!!!error the filePath is not unique and has been specified before:'+dstfilename uniquePathsFromSolr.add(dstfilename.replace("impc//", "impc/"))#hack to remove the extra slash after impc but don't want to effect other module used by other code #destDirectory=os.path.dirname(destPath) #print "destination directory for copy is "+destDirectory #if not os.path.exists(destDirectory): # os.makedirs(destDirectory) #print 'saving file to '+destPath #if not os.path.isfile(destPath): # try: # shutil.copyfile(dstfilename,destPath) # except IOError: # print "file does not exist "+str(dstfilename)+" continuing" #totalNumberOfImagesWeHave=totalNumberOfImagesWeHave+1 #if totalNumberOfImagesWeHave%1000==0 : # print "totalNumber of images we have="+str(totalNumberOfImagesWeHave) def moveFile(currentPath, destinationPath): filename=str(destinationPath.split('/')[-1]) destDirectory=destinationPath.replace(filename, "") print "making directory="+destDirectory if not os.path.exists(destDirectory): os.makedirs(destDirectory) print 'moving file to '+destinationPath if not os.path.isfile(destinationPath): try: shutil.move(currentPath,destinationPath) except IOError: print "file does not exist "+str(currentPath)+" continuing" if __name__ == "__main__": main(sys.argv[1:]) ######################## BEGIN LICENSE BLOCK ######################## # The Original Code is mozilla.org code. # # The Initial Developer of the Original Code is # Netscape Communications Corporation. # Portions created by the Initial Developer are Copyright (C) 1998 # the Initial Developer. All Rights Reserved. # # Contributor(s): # Mark Pilgrim - port to Python # # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Lesser General Public # License as published by the Free Software Foundation; either # version 2.1 of the License, or (at your option) any later version. # # This library 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 # Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with this library; if not, write to the Free Software # Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA # 02110-1301 USA ######################### END LICENSE BLOCK ######################### from .mbcharsetprober import MultiByteCharSetProber from .codingstatemachine import CodingStateMachine from .chardistribution import EUCTWDistributionAnalysis from .mbcssm import EUCTWSMModel class EUCTWProber(MultiByteCharSetProber): def __init__(self): MultiByteCharSetProber.__init__(self) self._mCodingSM = CodingStateMachine(EUCTWSMModel) self._mDistributionAnalyzer = EUCTWDistributionAnalysis() self.reset() def get_charset_name(self): return "EUC-TW" # encoding: utf-8 import datetime from south.db import db from south.v2 import SchemaMigration from django.db import models class Migration(SchemaMigration): def forwards(self, orm): # Adding field 'Workflow.managed' db.add_column('oozie_workflow', 'managed', self.gf('django.db.models.fields.BooleanField')(default=True, blank=True), keep_default=False) def backwards(self, orm): # Deleting field 'Workflow.managed' db.delete_column('oozie_workflow', 'managed') models = { 'auth.group': { 'Meta': {'object_name': 'Group'}, 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '80'}), 'permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'}) }, 'auth.permission': { 'Meta': {'unique_together': "(('content_type', 'codename'),)", 'object_name': 'Permission'}, 'codename': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'content_type': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['contenttypes.ContentType']"}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '50'}) }, 'auth.user': { 'Meta': {'object_name': 'User'}, 'date_joined': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}), 'email': ('django.db.models.fields.EmailField', [], {'max_length': '75', 'blank': 'True'}), 'first_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}), 'groups': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.Group']", 'symmetrical': 'False', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'is_active': ('django.db.models.fields.BooleanField', [], {'default': 'True', 'blank': 'True'}), 'is_staff': ('django.db.models.fields.BooleanField', [], {'default': 'False', 'blank': 'True'}), 'is_superuser': ('django.db.models.fields.BooleanField', [], {'default': 'False', 'blank': 'True'}), 'last_login': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}), 'last_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}), 'password': ('django.db.models.fields.CharField', [], {'max_length': '128'}), 'user_permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'}), 'username': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '30'}) }, 'contenttypes.contenttype': { 'Meta': {'unique_together': "(('app_label', 'model'),)", 'object_name': 'ContentType', 'db_table': "'django_content_type'"}, 'app_label': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'model': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '100'}) }, 'oozie.bundle': { 'Meta': {'object_name': 'Bundle', '_ormbases': ['oozie.Job']}, 'coordinators': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['oozie.Coordinator']", 'through': "orm['oozie.BundledCoordinator']", 'symmetrical': 'False'}), 'job_ptr': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['oozie.Job']", 'unique': 'True', 'primary_key': 'True'}), 'kick_off_time': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime(2013, 2, 28, 13, 56, 0, 347089)'}) }, 'oozie.bundledcoordinator': { 'Meta': {'object_name': 'BundledCoordinator'}, 'bundle': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['oozie.Bundle']"}), 'coordinator': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['oozie.Coordinator']"}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'parameters': ('django.db.models.fields.TextField', [], {'default': '\'[{"name":"oozie.use.system.libpath","value":"true"}]\''}) }, 'oozie.coordinator': { 'Meta': {'object_name': 'Coordinator', '_ormbases': ['oozie.Job']}, 'concurrency': ('django.db.models.fields.PositiveSmallIntegerField', [], {'null': 'True', 'blank': 'True'}), 'end': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime(2013, 3, 3, 13, 56, 0, 344924)'}), 'execution': ('django.db.models.fields.CharField', [], {'max_length': '10', 'null': 'True', 'blank': 'True'}), 'frequency_number': ('django.db.models.fields.SmallIntegerField', [], {'default': '1'}), 'frequency_unit': ('django.db.models.fields.CharField', [], {'default': "'days'", 'max_length': '20'}), 'job_properties': ('django.db.models.fields.TextField', [], {'default': "'[]'"}), 'job_ptr': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['oozie.Job']", 'unique': 'True', 'primary_key': 'True'}), 'start': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime(2013, 2, 28, 13, 56, 0, 344893)'}), 'throttle': ('django.db.models.fields.PositiveSmallIntegerField', [], {'null': 'True', 'blank': 'True'}), 'timeout': ('django.db.models.fields.SmallIntegerField', [], {'null': 'True', 'blank': 'True'}), 'timezone': ('django.db.models.fields.CharField', [], {'default': "'America/Los_Angeles'", 'max_length': '24'}), 'workflow': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['oozie.Workflow']", 'null': 'True'}) }, 'oozie.datainput': { 'Meta': {'object_name': 'DataInput'}, 'coordinator': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['oozie.Coordinator']"}), 'dataset': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['oozie.Dataset']", 'unique': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '40'}) }, 'oozie.dataoutput': { 'Meta': {'object_name': 'DataOutput'}, 'coordinator': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['oozie.Coordinator']"}), 'dataset': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['oozie.Dataset']", 'unique': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '40'}) }, 'oozie.dataset': { 'Meta': {'object_name': 'Dataset'}, 'advanced_end_instance': ('django.db.models.fields.CharField', [], {'default': "'0'", 'max_length': '128', 'blank': 'True'}), 'advanced_start_instance': ('django.db.models.fields.CharField', [], {'default': "'0'", 'max_length': '128'}), 'coordinator': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['oozie.Coordinator']"}), 'description': ('django.db.models.fields.CharField', [], {'default': "''", 'max_length': '1024', 'blank': 'True'}), 'done_flag': ('django.db.models.fields.CharField', [], {'default': "''", 'max_length': '64', 'blank': 'True'}), 'frequency_number': ('django.db.models.fields.SmallIntegerField', [], {'default': '1'}), 'frequency_unit': ('django.db.models.fields.CharField', [], {'default': "'days'", 'max_length': '20'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'instance_choice': ('django.db.models.fields.CharField', [], {'default': "'default'", 'max_length': '10'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '40'}), 'start': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime(2013, 2, 28, 13, 56, 0, 345574)'}), 'timezone': ('django.db.models.fields.CharField', [], {'default': "'America/Los_Angeles'", 'max_length': '24'}), 'uri': ('django.db.models.fields.CharField', [], {'default': "'/data/${YEAR}${MONTH}${DAY}'", 'max_length': '1024'}) }, 'oozie.decision': { 'Meta': {'object_name': 'Decision'}, 'node_ptr': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['oozie.Node']", 'unique': 'True', 'primary_key': 'True'}) }, 'oozie.decisionend': { 'Meta': {'object_name': 'DecisionEnd'}, 'node_ptr': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['oozie.Node']", 'unique': 'True', 'primary_key': 'True'}) }, 'oozie.distcp': { 'Meta': {'object_name': 'DistCp'}, 'job_properties': ('django.db.models.fields.TextField', [], {'default': "'[]'"}), 'job_xml': ('django.db.models.fields.CharField', [], {'default': "''", 'max_length': '512', 'blank': 'True'}), 'node_ptr': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['oozie.Node']", 'unique': 'True', 'primary_key': 'True'}), 'params': ('django.db.models.fields.TextField', [], {'default': "'[]'"}), 'prepares': ('django.db.models.fields.TextField', [], {'default': "'[]'"}) }, 'oozie.email': { 'Meta': {'object_name': 'Email'}, 'body': ('django.db.models.fields.TextField', [], {'default': "''"}), 'cc': ('django.db.models.fields.TextField', [], {'default': "''", 'blank': 'True'}), 'node_ptr': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['oozie.Node']", 'unique': 'True', 'primary_key': 'True'}), 'subject': ('django.db.models.fields.TextField', [], {'default': "''"}), 'to': ('django.db.models.fields.TextField', [], {'default': "''"}) }, 'oozie.end': { 'Meta': {'object_name': 'End'}, 'node_ptr': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['oozie.Node']", 'unique': 'True', 'primary_key': 'True'}) }, 'oozie.fork': { 'Meta': {'object_name': 'Fork'}, 'node_ptr': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['oozie.Node']", 'unique': 'True', 'primary_key': 'True'}) }, 'oozie.fs': { 'Meta': {'object_name': 'Fs'}, 'chmods': ('django.db.models.fields.TextField', [], {'default': "'[]'", 'blank': 'True'}), 'deletes': ('django.db.models.fields.TextField', [], {'default': "'[]'", 'blank': 'True'}), 'mkdirs': ('django.db.models.fields.TextField', [], {'default': "'[]'", 'blank': 'True'}), 'moves': ('django.db.models.fields.TextField', [], {'default': "'[]'", 'blank': 'True'}), 'node_ptr': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['oozie.Node']", 'unique': 'True', 'primary_key': 'True'}), 'touchzs': ('django.db.models.fields.TextField', [], {'default': "'[]'", 'blank': 'True'}) }, 'oozie.generic': { 'Meta': {'object_name': 'Generic'}, 'node_ptr': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['oozie.Node']", 'unique': 'True', 'primary_key': 'True'}), 'xml': ('django.db.models.fields.TextField', [], {'default': "''"}) }, 'oozie.history': { 'Meta': {'object_name': 'History'}, 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'job': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['oozie.Job']"}), 'oozie_job_id': ('django.db.models.fields.CharField', [], {'max_length': '128'}), 'properties': ('django.db.models.fields.TextField', [], {}), 'submission_date': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'db_index': 'True', 'blank': 'True'}), 'submitter': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['auth.User']"}) }, 'oozie.hive': { 'Meta': {'object_name': 'Hive'}, 'archives': ('django.db.models.fields.TextField', [], {'default': "'[]'"}), 'files': ('django.db.models.fields.TextField', [], {'default': "'[]'"}), 'job_properties': ('django.db.models.fields.TextField', [], {'default': '\'[{"name":"oozie.hive.defaults","value":"hive-site.xml"}]\''}), 'job_xml': ('django.db.models.fields.CharField', [], {'default': "''", 'max_length': '512', 'blank': 'True'}), 'node_ptr': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['oozie.Node']", 'unique': 'True', 'primary_key': 'True'}), 'params': ('django.db.models.fields.TextField', [], {'default': "'[]'"}), 'prepares': ('django.db.models.fields.TextField', [], {'default': "'[]'"}), 'script_path': ('django.db.models.fields.CharField', [], {'max_length': '256'}) }, 'oozie.java': { 'Meta': {'object_name': 'Java'}, 'archives': ('django.db.models.fields.TextField', [], {'default': "'[]'"}), 'args': ('django.db.models.fields.CharField', [], {'max_length': '4096', 'blank': 'True'}), 'files': ('django.db.models.fields.TextField', [], {'default': "'[]'"}), 'jar_path': ('django.db.models.fields.CharField', [], {'max_length': '512'}), 'java_opts': ('django.db.models.fields.CharField', [], {'max_length': '256', 'blank': 'True'}), 'job_properties': ('django.db.models.fields.TextField', [], {'default': "'[]'"}), 'job_xml': ('django.db.models.fields.CharField', [], {'default': "''", 'max_length': '512', 'blank': 'True'}), 'main_class': ('django.db.models.fields.CharField', [], {'max_length': '256'}), 'node_ptr': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['oozie.Node']", 'unique': 'True', 'primary_key': 'True'}), 'prepares': ('django.db.models.fields.TextField', [], {'default': "'[]'"}) }, 'oozie.job': { 'Meta': {'object_name': 'Job'}, 'deployment_dir': ('django.db.models.fields.CharField', [], {'max_length': '1024', 'blank': 'True'}), 'description': ('django.db.models.fields.CharField', [], {'max_length': '1024', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'is_shared': ('django.db.models.fields.BooleanField', [], {'default': 'False', 'db_index': 'True', 'blank': 'True'}), 'last_modified': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'db_index': 'True', 'blank': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '40'}), 'owner': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['auth.User']"}), 'parameters': ('django.db.models.fields.TextField', [], {'default': '\'[{"name":"oozie.use.system.libpath","value":"true"}]\''}), 'schema_version': ('django.db.models.fields.CharField', [], {'max_length': '128'}) }, 'oozie.join': { 'Meta': {'object_name': 'Join'}, 'node_ptr': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['oozie.Node']", 'unique': 'True', 'primary_key': 'True'}) }, 'oozie.kill': { 'Meta': {'object_name': 'Kill'}, 'message': ('django.db.models.fields.CharField', [], {'default': "'Action failed, error message[${wf:errorMessage(wf:lastErrorNode())}]'", 'max_length': '256'}), 'node_ptr': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['oozie.Node']", 'unique': 'True', 'primary_key': 'True'}) }, 'oozie.link': { 'Meta': {'object_name': 'Link'}, 'child': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'parent_node'", 'to': "orm['oozie.Node']"}), 'comment': ('django.db.models.fields.CharField', [], {'default': "''", 'max_length': '1024', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '40'}), 'parent': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'child_node'", 'to': "orm['oozie.Node']"}) }, 'oozie.mapreduce': { 'Meta': {'object_name': 'Mapreduce'}, 'archives': ('django.db.models.fields.TextField', [], {'default': "'[]'"}), 'files': ('django.db.models.fields.TextField', [], {'default': "'[]'"}), 'jar_path': ('django.db.models.fields.CharField', [], {'max_length': '512'}), 'job_properties': ('django.db.models.fields.TextField', [], {'default': "'[]'"}), 'job_xml': ('django.db.models.fields.CharField', [], {'default': "''", 'max_length': '512', 'blank': 'True'}), 'node_ptr': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['oozie.Node']", 'unique': 'True'}), 'prepares': ('django.db.models.fields.TextField', [], {'default': "'[]'"}) }, 'oozie.node': { 'Meta': {'object_name': 'Node'}, 'children': ('django.db.models.fields.related.ManyToManyField', [], {'related_name': "'parents'", 'symmetrical': 'False', 'through': "orm['oozie.Link']", 'to': "orm['oozie.Node']"}), 'description': ('django.db.models.fields.CharField', [], {'default': "''", 'max_length': '1024', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '40'}), 'node_type': ('django.db.models.fields.CharField', [], {'max_length': '64'}), 'workflow': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['oozie.Workflow']"}) }, 'oozie.pig': { 'Meta': {'object_name': 'Pig'}, 'archives': ('django.db.models.fields.TextField', [], {'default': "'[]'"}), 'files': ('django.db.models.fields.TextField', [], {'default': "'[]'"}), 'job_properties': ('django.db.models.fields.TextField', [], {'default': "'[]'"}), 'job_xml': ('django.db.models.fields.CharField', [], {'default': "''", 'max_length': '512', 'blank': 'True'}), 'node_ptr': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['oozie.Node']", 'unique': 'True', 'primary_key': 'True'}), 'params': ('django.db.models.fields.TextField', [], {'default': "'[]'"}), 'prepares': ('django.db.models.fields.TextField', [], {'default': "'[]'"}), 'script_path': ('django.db.models.fields.CharField', [], {'max_length': '256'}) }, 'oozie.shell': { 'Meta': {'object_name': 'Shell'}, 'archives': ('django.db.models.fields.TextField', [], {'default': "'[]'"}), 'capture_output': ('django.db.models.fields.BooleanField', [], {'default': 'False', 'blank': 'True'}), 'command': ('django.db.models.fields.CharField', [], {'max_length': '256'}), 'files': ('django.db.models.fields.TextField', [], {'default': "'[]'"}), 'job_properties': ('django.db.models.fields.TextField', [], {'default': "'[]'"}), 'job_xml': ('django.db.models.fields.CharField', [], {'default': "''", 'max_length': '512', 'blank': 'True'}), 'node_ptr': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['oozie.Node']", 'unique': 'True', 'primary_key': 'True'}), 'params': ('django.db.models.fields.TextField', [], {'default': "'[]'"}), 'prepares': ('django.db.models.fields.TextField', [], {'default': "'[]'"}) }, 'oozie.sqoop': { 'Meta': {'object_name': 'Sqoop'}, 'archives': ('django.db.models.fields.TextField', [], {'default': "'[]'"}), 'files': ('django.db.models.fields.TextField', [], {'default': "'[]'"}), 'job_properties': ('django.db.models.fields.TextField', [], {'default': "'[]'"}), 'job_xml': ('django.db.models.fields.CharField', [], {'default': "''", 'max_length': '512', 'blank': 'True'}), 'node_ptr': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['oozie.Node']", 'unique': 'True', 'primary_key': 'True'}), 'params': ('django.db.models.fields.TextField', [], {'default': "'[]'"}), 'prepares': ('django.db.models.fields.TextField', [], {'default': "'[]'"}), 'script_path': ('django.db.models.fields.TextField', [], {'default': "''", 'blank': 'True'}) }, 'oozie.ssh': { 'Meta': {'object_name': 'Ssh'}, 'capture_output': ('django.db.models.fields.BooleanField', [], {'default': 'False', 'blank': 'True'}), 'command': ('django.db.models.fields.CharField', [], {'max_length': '256'}), 'host': ('django.db.models.fields.CharField', [], {'max_length': '256'}), 'node_ptr': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['oozie.Node']", 'unique': 'True', 'primary_key': 'True'}), 'params': ('django.db.models.fields.TextField', [], {'default': "'[]'"}), 'user': ('django.db.models.fields.CharField', [], {'max_length': '64'}) }, 'oozie.start': { 'Meta': {'object_name': 'Start'}, 'node_ptr': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['oozie.Node']", 'unique': 'True'}) }, 'oozie.streaming': { 'Meta': {'object_name': 'Streaming'}, 'archives': ('django.db.models.fields.TextField', [], {'default': "'[]'"}), 'files': ('django.db.models.fields.TextField', [], {'default': "'[]'"}), 'job_properties': ('django.db.models.fields.TextField', [], {'default': "'[]'"}), 'mapper': ('django.db.models.fields.CharField', [], {'max_length': '512'}), 'node_ptr': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['oozie.Node']", 'unique': 'True', 'primary_key': 'True'}), 'reducer': ('django.db.models.fields.CharField', [], {'max_length': '512'}) }, 'oozie.subworkflow': { 'Meta': {'object_name': 'SubWorkflow'}, 'job_properties': ('django.db.models.fields.TextField', [], {'default': "'[]'"}), 'node_ptr': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['oozie.Node']", 'unique': 'True', 'primary_key': 'True'}), 'propagate_configuration': ('django.db.models.fields.BooleanField', [], {'default': 'True', 'blank': 'True'}), 'sub_workflow': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['oozie.Workflow']"}) }, 'oozie.workflow': { 'Meta': {'object_name': 'Workflow', '_ormbases': ['oozie.Job']}, 'end': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'end_workflow'", 'null': 'True', 'to': "orm['oozie.End']"}), 'is_single': ('django.db.models.fields.BooleanField', [], {'default': 'False', 'blank': 'True'}), 'job_properties': ('django.db.models.fields.TextField', [], {'default': "'[]'"}), 'job_ptr': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['oozie.Job']", 'unique': 'True', 'primary_key': 'True'}), 'job_xml': ('django.db.models.fields.CharField', [], {'default': "''", 'max_length': '512', 'blank': 'True'}), 'managed': ('django.db.models.fields.BooleanField', [], {'default': 'True', 'blank': 'True'}), 'start': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'start_workflow'", 'null': 'True', 'to': "orm['oozie.Start']"}) } } complete_apps = ['oozie'] #!/usr/bin/python # -*- coding: utf-8 -*- # NOTE: the shebang and encoding lines are for ScriptHeaderTests; do not remove from unittest import TestCase, makeSuite; from pkg_resources import * from setuptools.command.easy_install import get_script_header, is_sh import os, pkg_resources, sys, StringIO, tempfile, shutil try: frozenset except NameError: from sets import ImmutableSet as frozenset def safe_repr(obj, short=False): """ copied from Python2.7""" try: result = repr(obj) except Exception: result = object.__repr__(obj) if not short or len(result) < _MAX_LENGTH: return result return result[:_MAX_LENGTH] + ' [truncated]...' class Metadata(EmptyProvider): """Mock object to return metadata as if from an on-disk distribution""" def __init__(self,*pairs): self.metadata = dict(pairs) def has_metadata(self,name): return name in self.metadata def get_metadata(self,name): return self.metadata[name] def get_metadata_lines(self,name): return yield_lines(self.get_metadata(name)) class DistroTests(TestCase): def testCollection(self): # empty path should produce no distributions ad = Environment([], platform=None, python=None) self.assertEqual(list(ad), []) self.assertEqual(ad['FooPkg'],[]) ad.add(Distribution.from_filename("FooPkg-1.3_1.egg")) ad.add(Distribution.from_filename("FooPkg-1.4-py2.4-win32.egg")) ad.add(Distribution.from_filename("FooPkg-1.2-py2.4.egg")) # Name is in there now self.assert_(ad['FooPkg']) # But only 1 package self.assertEqual(list(ad), ['foopkg']) # Distributions sort by version self.assertEqual( [dist.version for dist in ad['FooPkg']], ['1.4','1.3-1','1.2'] ) # Removing a distribution leaves sequence alone ad.remove(ad['FooPkg'][1]) self.assertEqual( [dist.version for dist in ad['FooPkg']], ['1.4','1.2'] ) # And inserting adds them in order ad.add(Distribution.from_filename("FooPkg-1.9.egg")) self.assertEqual( [dist.version for dist in ad['FooPkg']], ['1.9','1.4','1.2'] ) ws = WorkingSet([]) foo12 = Distribution.from_filename("FooPkg-1.2-py2.4.egg") foo14 = Distribution.from_filename("FooPkg-1.4-py2.4-win32.egg") req, = parse_requirements("FooPkg>=1.3") # Nominal case: no distros on path, should yield all applicable self.assertEqual(ad.best_match(req,ws).version, '1.9') # If a matching distro is already installed, should return only that ws.add(foo14); self.assertEqual(ad.best_match(req,ws).version, '1.4') # If the first matching distro is unsuitable, it's a version conflict ws = WorkingSet([]); ws.add(foo12); ws.add(foo14) self.assertRaises(VersionConflict, ad.best_match, req, ws) # If more than one match on the path, the first one takes precedence ws = WorkingSet([]); ws.add(foo14); ws.add(foo12); ws.add(foo14); self.assertEqual(ad.best_match(req,ws).version, '1.4') def checkFooPkg(self,d): self.assertEqual(d.project_name, "FooPkg") self.assertEqual(d.key, "foopkg") self.assertEqual(d.version, "1.3-1") self.assertEqual(d.py_version, "2.4") self.assertEqual(d.platform, "win32") self.assertEqual(d.parsed_version, parse_version("1.3-1")) def testDistroBasics(self): d = Distribution( "/some/path", project_name="FooPkg",version="1.3-1",py_version="2.4",platform="win32" ) self.checkFooPkg(d) d = Distribution("/some/path") self.assertEqual(d.py_version, sys.version[:3]) self.assertEqual(d.platform, None) def testDistroParse(self): d = Distribution.from_filename("FooPkg-1.3_1-py2.4-win32.egg") self.checkFooPkg(d) d = Distribution.from_filename("FooPkg-1.3_1-py2.4-win32.egg-info") self.checkFooPkg(d) def testDistroMetadata(self): d = Distribution( "/some/path", project_name="FooPkg", py_version="2.4", platform="win32", metadata = Metadata( ('PKG-INFO',"Metadata-Version: 1.0\nVersion: 1.3-1\n") ) ) self.checkFooPkg(d) def distRequires(self, txt): return Distribution("/foo", metadata=Metadata(('depends.txt', txt))) def checkRequires(self, dist, txt, extras=()): self.assertEqual( list(dist.requires(extras)), list(parse_requirements(txt)) ) def testDistroDependsSimple(self): for v in "Twisted>=1.5", "Twisted>=1.5\nZConfig>=2.0": self.checkRequires(self.distRequires(v), v) def testResolve(self): ad = Environment([]); ws = WorkingSet([]) # Resolving no requirements -> nothing to install self.assertEqual( list(ws.resolve([],ad)), [] ) # Request something not in the collection -> DistributionNotFound self.assertRaises( DistributionNotFound, ws.resolve, parse_requirements("Foo"), ad ) Foo = Distribution.from_filename( "/foo_dir/Foo-1.2.egg", metadata=Metadata(('depends.txt', "[bar]\nBaz>=2.0")) ) ad.add(Foo); ad.add(Distribution.from_filename("Foo-0.9.egg")) # Request thing(s) that are available -> list to activate for i in range(3): targets = list(ws.resolve(parse_requirements("Foo"), ad)) self.assertEqual(targets, [Foo]) map(ws.add,targets) self.assertRaises(VersionConflict, ws.resolve, parse_requirements("Foo==0.9"), ad) ws = WorkingSet([]) # reset # Request an extra that causes an unresolved dependency for "Baz" self.assertRaises( DistributionNotFound, ws.resolve,parse_requirements("Foo[bar]"), ad ) Baz = Distribution.from_filename( "/foo_dir/Baz-2.1.egg", metadata=Metadata(('depends.txt', "Foo")) ) ad.add(Baz) # Activation list now includes resolved dependency self.assertEqual( list(ws.resolve(parse_requirements("Foo[bar]"), ad)), [Foo,Baz] ) # Requests for conflicting versions produce VersionConflict self.assertRaises( VersionConflict, ws.resolve, parse_requirements("Foo==1.2\nFoo!=1.2"), ad ) def testDistroDependsOptions(self): d = self.distRequires(""" Twisted>=1.5 [docgen] ZConfig>=2.0 docutils>=0.3 [fastcgi] fcgiapp>=0.1""") self.checkRequires(d,"Twisted>=1.5") self.checkRequires( d,"Twisted>=1.5 ZConfig>=2.0 docutils>=0.3".split(), ["docgen"] ) self.checkRequires( d,"Twisted>=1.5 fcgiapp>=0.1".split(), ["fastcgi"] ) self.checkRequires( d,"Twisted>=1.5 ZConfig>=2.0 docutils>=0.3 fcgiapp>=0.1".split(), ["docgen","fastcgi"] ) self.checkRequires( d,"Twisted>=1.5 fcgiapp>=0.1 ZConfig>=2.0 docutils>=0.3".split(), ["fastcgi", "docgen"] ) self.assertRaises(UnknownExtra, d.requires, ["foo"]) def testSetuptoolsDistributeCombination(self): # Ensure that installing a 0.7-series setuptools fails. PJE says that # it will not co-exist. ws = WorkingSet([]) d = Distribution( "/some/path", project_name="setuptools", version="0.7a1") self.assertRaises(ValueError, ws.add, d) # A 0.6-series is no problem d2 = Distribution( "/some/path", project_name="setuptools", version="0.6c9") ws.add(d2) # a unexisting version needs to work ws = WorkingSet([]) d3 = Distribution( "/some/path", project_name="setuptools") ws.add(d3) class EntryPointTests(TestCase): def assertfields(self, ep): self.assertEqual(ep.name,"foo") self.assertEqual(ep.module_name,"setuptools.tests.test_resources") self.assertEqual(ep.attrs, ("EntryPointTests",)) self.assertEqual(ep.extras, ("x",)) self.assert_(ep.load() is EntryPointTests) self.assertEqual( str(ep), "foo = setuptools.tests.test_resources:EntryPointTests [x]" ) def setUp(self): self.dist = Distribution.from_filename( "FooPkg-1.2-py2.4.egg", metadata=Metadata(('requires.txt','[x]'))) def testBasics(self): ep = EntryPoint( "foo", "setuptools.tests.test_resources", ["EntryPointTests"], ["x"], self.dist ) self.assertfields(ep) def testParse(self): s = "foo = setuptools.tests.test_resources:EntryPointTests [x]" ep = EntryPoint.parse(s, self.dist) self.assertfields(ep) ep = EntryPoint.parse("bar baz= spammity[PING]") self.assertEqual(ep.name,"bar baz") self.assertEqual(ep.module_name,"spammity") self.assertEqual(ep.attrs, ()) self.assertEqual(ep.extras, ("ping",)) ep = EntryPoint.parse(" fizzly = wocka:foo") self.assertEqual(ep.name,"fizzly") self.assertEqual(ep.module_name,"wocka") self.assertEqual(ep.attrs, ("foo",)) self.assertEqual(ep.extras, ()) def testRejects(self): for ep in [ "foo", "x=1=2", "x=a:b:c", "q=x/na", "fez=pish:tush-z", "x=f[a]>2", ]: try: EntryPoint.parse(ep) except ValueError: pass else: raise AssertionError("Should've been bad", ep) def checkSubMap(self, m): self.assertEqual(len(m), len(self.submap_expect)) for key, ep in self.submap_expect.iteritems(): self.assertEqual(repr(m.get(key)), repr(ep)) submap_expect = dict( feature1=EntryPoint('feature1', 'somemodule', ['somefunction']), feature2=EntryPoint('feature2', 'another.module', ['SomeClass'], ['extra1','extra2']), feature3=EntryPoint('feature3', 'this.module', extras=['something']) ) submap_str = """ # define features for blah blah feature1 = somemodule:somefunction feature2 = another.module:SomeClass [extra1,extra2] feature3 = this.module [something] """ def testParseList(self): self.checkSubMap(EntryPoint.parse_group("xyz", self.submap_str)) self.assertRaises(ValueError, EntryPoint.parse_group, "x a", "foo=bar") self.assertRaises(ValueError, EntryPoint.parse_group, "x", ["foo=baz", "foo=bar"]) def testParseMap(self): m = EntryPoint.parse_map({'xyz':self.submap_str}) self.checkSubMap(m['xyz']) self.assertEqual(m.keys(),['xyz']) m = EntryPoint.parse_map("[xyz]\n"+self.submap_str) self.checkSubMap(m['xyz']) self.assertEqual(m.keys(),['xyz']) self.assertRaises(ValueError, EntryPoint.parse_map, ["[xyz]", "[xyz]"]) self.assertRaises(ValueError, EntryPoint.parse_map, self.submap_str) class RequirementsTests(TestCase): def testBasics(self): r = Requirement.parse("Twisted>=1.2") self.assertEqual(str(r),"Twisted>=1.2") self.assertEqual(repr(r),"Requirement.parse('Twisted>=1.2')") self.assertEqual(r, Requirement("Twisted", [('>=','1.2')], ())) self.assertEqual(r, Requirement("twisTed", [('>=','1.2')], ())) self.assertNotEqual(r, Requirement("Twisted", [('>=','2.0')], ())) self.assertNotEqual(r, Requirement("Zope", [('>=','1.2')], ())) self.assertNotEqual(r, Requirement("Zope", [('>=','3.0')], ())) self.assertNotEqual(r, Requirement.parse("Twisted[extras]>=1.2")) def testOrdering(self): r1 = Requirement("Twisted", [('==','1.2c1'),('>=','1.2')], ()) r2 = Requirement("Twisted", [('>=','1.2'),('==','1.2c1')], ()) self.assertEqual(r1,r2) self.assertEqual(str(r1),str(r2)) self.assertEqual(str(r2),"Twisted==1.2c1,>=1.2") def testBasicContains(self): r = Requirement("Twisted", [('>=','1.2')], ()) foo_dist = Distribution.from_filename("FooPkg-1.3_1.egg") twist11 = Distribution.from_filename("Twisted-1.1.egg") twist12 = Distribution.from_filename("Twisted-1.2.egg") self.assert_(parse_version('1.2') in r) self.assert_(parse_version('1.1') not in r) self.assert_('1.2' in r) self.assert_('1.1' not in r) self.assert_(foo_dist not in r) self.assert_(twist11 not in r) self.assert_(twist12 in r) def testAdvancedContains(self): r, = parse_requirements("Foo>=1.2,<=1.3,==1.9,>2.0,!=2.5,<3.0,==4.5") for v in ('1.2','1.2.2','1.3','1.9','2.0.1','2.3','2.6','3.0c1','4.5'): self.assert_(v in r, (v,r)) for v in ('1.2c1','1.3.1','1.5','1.9.1','2.0','2.5','3.0','4.0'): self.assert_(v not in r, (v,r)) def testOptionsAndHashing(self): r1 = Requirement.parse("Twisted[foo,bar]>=1.2") r2 = Requirement.parse("Twisted[bar,FOO]>=1.2") r3 = Requirement.parse("Twisted[BAR,FOO]>=1.2.0") self.assertEqual(r1,r2) self.assertEqual(r1,r3) self.assertEqual(r1.extras, ("foo","bar")) self.assertEqual(r2.extras, ("bar","foo")) # extras are normalized self.assertEqual(hash(r1), hash(r2)) self.assertEqual( hash(r1), hash(("twisted", ((">=",parse_version("1.2")),), frozenset(["foo","bar"]))) ) def testVersionEquality(self): r1 = Requirement.parse("foo==0.3a2") r2 = Requirement.parse("foo!=0.3a4") d = Distribution.from_filename self.assert_(d("foo-0.3a4.egg") not in r1) self.assert_(d("foo-0.3a1.egg") not in r1) self.assert_(d("foo-0.3a4.egg") not in r2) self.assert_(d("foo-0.3a2.egg") in r1) self.assert_(d("foo-0.3a2.egg") in r2) self.assert_(d("foo-0.3a3.egg") in r2) self.assert_(d("foo-0.3a5.egg") in r2) def testDistributeSetuptoolsOverride(self): # Plain setuptools or distribute mean we return distribute. self.assertEqual( Requirement.parse('setuptools').project_name, 'distribute') self.assertEqual( Requirement.parse('distribute').project_name, 'distribute') # setuptools lower than 0.7 means distribute self.assertEqual( Requirement.parse('setuptools==0.6c9').project_name, 'distribute') self.assertEqual( Requirement.parse('setuptools==0.6c10').project_name, 'distribute') self.assertEqual( Requirement.parse('setuptools>=0.6').project_name, 'distribute') self.assertEqual( Requirement.parse('setuptools < 0.7').project_name, 'distribute') # setuptools 0.7 and higher means setuptools. self.assertEqual( Requirement.parse('setuptools == 0.7').project_name, 'setuptools') self.assertEqual( Requirement.parse('setuptools == 0.7a1').project_name, 'setuptools') self.assertEqual( Requirement.parse('setuptools >= 0.7').project_name, 'setuptools') class ParseTests(TestCase): def testEmptyParse(self): self.assertEqual(list(parse_requirements('')), []) def testYielding(self): for inp,out in [ ([], []), ('x',['x']), ([[]],[]), (' x\n y', ['x','y']), (['x\n\n','y'], ['x','y']), ]: self.assertEqual(list(pkg_resources.yield_lines(inp)),out) def testSplitting(self): self.assertEqual( list( pkg_resources.split_sections(""" x [Y] z a [b ] # foo c [ d] [q] v """ ) ), [(None,["x"]), ("Y",["z","a"]), ("b",["c"]), ("d",[]), ("q",["v"])] ) self.assertRaises(ValueError,list,pkg_resources.split_sections("[foo")) def testSafeName(self): self.assertEqual(safe_name("adns-python"), "adns-python") self.assertEqual(safe_name("WSGI Utils"), "WSGI-Utils") self.assertEqual(safe_name("WSGI Utils"), "WSGI-Utils") self.assertEqual(safe_name("Money$$$Maker"), "Money-Maker") self.assertNotEqual(safe_name("peak.web"), "peak-web") def testSafeVersion(self): self.assertEqual(safe_version("1.2-1"), "1.2-1") self.assertEqual(safe_version("1.2 alpha"), "1.2.alpha") self.assertEqual(safe_version("2.3.4 20050521"), "2.3.4.20050521") self.assertEqual(safe_version("Money$$$Maker"), "Money-Maker") self.assertEqual(safe_version("peak.web"), "peak.web") def testSimpleRequirements(self): self.assertEqual( list(parse_requirements('Twis-Ted>=1.2-1')), [Requirement('Twis-Ted',[('>=','1.2-1')], ())] ) self.assertEqual( list(parse_requirements('Twisted >=1.2, \ # more\n<2.0')), [Requirement('Twisted',[('>=','1.2'),('<','2.0')], ())] ) self.assertEqual( Requirement.parse("FooBar==1.99a3"), Requirement("FooBar", [('==','1.99a3')], ()) ) self.assertRaises(ValueError,Requirement.parse,">=2.3") self.assertRaises(ValueError,Requirement.parse,"x\\") self.assertRaises(ValueError,Requirement.parse,"x==2 q") self.assertRaises(ValueError,Requirement.parse,"X==1\nY==2") self.assertRaises(ValueError,Requirement.parse,"#") def testVersionEquality(self): def c(s1,s2): p1, p2 = parse_version(s1),parse_version(s2) self.assertEqual(p1,p2, (s1,s2,p1,p2)) c('0.4', '0.4.0') c('0.4.0.0', '0.4.0') c('0.4.0-0', '0.4-0') c('0pl1', '0.0pl1') c('0pre1', '0.0c1') c('0.0.0preview1', '0c1') c('0.0c1', '0rc1') c('1.2a1', '1.2.a.1'); c('1.2...a', '1.2a') def testVersionOrdering(self): def c(s1,s2): p1, p2 = parse_version(s1),parse_version(s2) self.assert_(p1= (3,) and os.environ.get("LC_CTYPE") in (None, "C", "POSIX")): return platform = sys.platform sys.platform = 'java1.5.0_13' stdout = sys.stdout try: # A mock sys.executable that uses a shebang line (this file) exe = os.path.normpath(os.path.splitext(__file__)[0] + '.py') self.assertEqual( get_script_header('#!/usr/local/bin/python', executable=exe), '#!/usr/bin/env %s\n' % exe) # Ensure we generate what is basically a broken shebang line # when there's options, with a warning emitted sys.stdout = sys.stderr = StringIO.StringIO() self.assertEqual(get_script_header('#!/usr/bin/python -x', executable=exe), '#!%s -x\n' % exe) self.assert_('Unable to adapt shebang line' in sys.stdout.getvalue()) sys.stdout = sys.stderr = StringIO.StringIO() self.assertEqual(get_script_header('#!/usr/bin/python', executable=self.non_ascii_exe), '#!%s -x\n' % self.non_ascii_exe) self.assert_('Unable to adapt shebang line' in sys.stdout.getvalue()) finally: sys.platform = platform sys.stdout = stdout class NamespaceTests(TestCase): def setUp(self): self._ns_pkgs = pkg_resources._namespace_packages.copy() self._tmpdir = tempfile.mkdtemp(prefix="tests-distribute-") os.makedirs(os.path.join(self._tmpdir, "site-pkgs")) self._prev_sys_path = sys.path[:] sys.path.append(os.path.join(self._tmpdir, "site-pkgs")) def tearDown(self): shutil.rmtree(self._tmpdir) pkg_resources._namespace_packages = self._ns_pkgs.copy() sys.path = self._prev_sys_path[:] def _assertIn(self, member, container): """ assertIn and assertTrue does not exist in Python2.3""" if member not in container: standardMsg = '%s not found in %s' % (safe_repr(member), safe_repr(container)) self.fail(self._formatMessage(msg, standardMsg)) def test_two_levels_deep(self): """ Test nested namespace packages Create namespace packages in the following tree : site-packages-1/pkg1/pkg2 site-packages-2/pkg1/pkg2 Check both are in the _namespace_packages dict and that their __path__ is correct """ sys.path.append(os.path.join(self._tmpdir, "site-pkgs2")) os.makedirs(os.path.join(self._tmpdir, "site-pkgs", "pkg1", "pkg2")) os.makedirs(os.path.join(self._tmpdir, "site-pkgs2", "pkg1", "pkg2")) ns_str = "__import__('pkg_resources').declare_namespace(__name__)\n" for site in ["site-pkgs", "site-pkgs2"]: pkg1_init = open(os.path.join(self._tmpdir, site, "pkg1", "__init__.py"), "w") pkg1_init.write(ns_str) pkg1_init.close() pkg2_init = open(os.path.join(self._tmpdir, site, "pkg1", "pkg2", "__init__.py"), "w") pkg2_init.write(ns_str) pkg2_init.close() import pkg1 self._assertIn("pkg1", pkg_resources._namespace_packages.keys()) try: import pkg1.pkg2 except ImportError, e: self.fail("Distribute tried to import the parent namespace package") # check the _namespace_packages dict self._assertIn("pkg1.pkg2", pkg_resources._namespace_packages.keys()) self.assertEqual(pkg_resources._namespace_packages["pkg1"], ["pkg1.pkg2"]) # check the __path__ attribute contains both paths self.assertEqual(pkg1.pkg2.__path__, [ os.path.join(self._tmpdir, "site-pkgs", "pkg1", "pkg2"), os.path.join(self._tmpdir, "site-pkgs2", "pkg1", "pkg2") ]) # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. from openstack_dashboard.test.integration_tests import decorators from openstack_dashboard.test.integration_tests import helpers from openstack_dashboard.test.integration_tests.regions import messages @decorators.services_required("neutron") class TestNetworks(helpers.TestCase): NETWORK_NAME = helpers.gen_random_resource_name("network") SUBNET_NAME = helpers.gen_random_resource_name("subnet") def test_private_network_create(self): """tests the network creation and deletion functionalities: * creates a new private network and a new subnet associated with it * verifies the network appears in the networks table as active * deletes the newly created network * verifies the network does not appear in the table after deletion """ networks_page = self.home_pg.go_to_network_networkspage() networks_page.create_network(self.NETWORK_NAME, self.SUBNET_NAME) self.assertTrue( networks_page.find_message_and_dismiss(messages.SUCCESS)) self.assertFalse( networks_page.find_message_and_dismiss(messages.ERROR)) self.assertTrue(networks_page.is_network_present(self.NETWORK_NAME)) self.assertTrue(networks_page.is_network_active(self.NETWORK_NAME)) networks_page.delete_network(self.NETWORK_NAME) self.assertTrue( networks_page.find_message_and_dismiss(messages.SUCCESS)) self.assertFalse( networks_page.find_message_and_dismiss(messages.ERROR)) self.assertFalse(networks_page.is_network_present(self.NETWORK_NAME)) # Copyright 2013 OpenStack Foundation # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import datetime import sys from keystoneclient.common import cms from oslo.utils import timeutils import six from keystone.common import controller from keystone.common import dependency from keystone.common import wsgi from keystone import config from keystone import exception from keystone.i18n import _ from keystone.models import token_model from keystone.openstack.common import jsonutils from keystone.openstack.common import log from keystone.token import provider CONF = config.CONF LOG = log.getLogger(__name__) class ExternalAuthNotApplicable(Exception): """External authentication is not applicable.""" pass @dependency.requires('assignment_api', 'catalog_api', 'identity_api', 'token_provider_api', 'trust_api') class Auth(controller.V2Controller): @controller.v2_deprecated def ca_cert(self, context, auth=None): ca_file = open(CONF.signing.ca_certs, 'r') data = ca_file.read() ca_file.close() return data @controller.v2_deprecated def signing_cert(self, context, auth=None): cert_file = open(CONF.signing.certfile, 'r') data = cert_file.read() cert_file.close() return data @controller.v2_deprecated def authenticate(self, context, auth=None): """Authenticate credentials and return a token. Accept auth as a dict that looks like:: { "auth":{ "passwordCredentials":{ "username":"test_user", "password":"mypass" }, "tenantName":"customer-x" } } In this case, tenant is optional, if not provided the token will be considered "unscoped" and can later be used to get a scoped token. Alternatively, this call accepts auth with only a token and tenant that will return a token that is scoped to that tenant. """ if auth is None: raise exception.ValidationError(attribute='auth', target='request body') if "token" in auth: # Try to authenticate using a token auth_info = self._authenticate_token( context, auth) else: # Try external authentication try: auth_info = self._authenticate_external( context, auth) except ExternalAuthNotApplicable: # Try local authentication auth_info = self._authenticate_local( context, auth) user_ref, tenant_ref, metadata_ref, expiry, bind, audit_id = auth_info # Validate that the auth info is valid and nothing is disabled try: self.identity_api.assert_user_enabled( user_id=user_ref['id'], user=user_ref) self.assignment_api.assert_domain_enabled( domain_id=user_ref['domain_id']) if tenant_ref: self.assignment_api.assert_project_enabled( project_id=tenant_ref['id'], project=tenant_ref) except AssertionError as e: six.reraise(exception.Unauthorized, exception.Unauthorized(e), sys.exc_info()[2]) # NOTE(morganfainberg): Make sure the data is in correct form since it # might be consumed external to Keystone and this is a v2.0 controller. # The user_ref is encoded into the auth_token_data which is returned as # part of the token data. The token provider doesn't care about the # format. user_ref = self.v3_to_v2_user(user_ref) if tenant_ref: tenant_ref = self.filter_domain_id(tenant_ref) auth_token_data = self._get_auth_token_data(user_ref, tenant_ref, metadata_ref, expiry, audit_id) if tenant_ref: catalog_ref = self.catalog_api.get_catalog( user_ref['id'], tenant_ref['id'], metadata_ref) else: catalog_ref = {} auth_token_data['id'] = 'placeholder' if bind: auth_token_data['bind'] = bind roles_ref = [] for role_id in metadata_ref.get('roles', []): role_ref = self.assignment_api.get_role(role_id) roles_ref.append(dict(name=role_ref['name'])) (token_id, token_data) = self.token_provider_api.issue_v2_token( auth_token_data, roles_ref=roles_ref, catalog_ref=catalog_ref) # NOTE(wanghong): We consume a trust use only when we are using trusts # and have successfully issued a token. if CONF.trust.enabled and 'trust_id' in auth: self.trust_api.consume_use(auth['trust_id']) return token_data def _authenticate_token(self, context, auth): """Try to authenticate using an already existing token. Returns auth_token_data, (user_ref, tenant_ref, metadata_ref) """ if 'token' not in auth: raise exception.ValidationError( attribute='token', target='auth') if "id" not in auth['token']: raise exception.ValidationError( attribute="id", target="token") old_token = auth['token']['id'] if len(old_token) > CONF.max_token_size: raise exception.ValidationSizeError(attribute='token', size=CONF.max_token_size) try: token_model_ref = token_model.KeystoneToken( token_id=old_token, token_data=self.token_provider_api.validate_token(old_token)) except exception.NotFound as e: raise exception.Unauthorized(e) wsgi.validate_token_bind(context, token_model_ref) # A trust token cannot be used to get another token if token_model_ref.trust_scoped: raise exception.Forbidden() user_id = token_model_ref.user_id tenant_id = self._get_project_id_from_auth(auth) if not CONF.trust.enabled and 'trust_id' in auth: raise exception.Forbidden('Trusts are disabled.') elif CONF.trust.enabled and 'trust_id' in auth: trust_ref = self.trust_api.get_trust(auth['trust_id']) if trust_ref is None: raise exception.Forbidden() if user_id != trust_ref['trustee_user_id']: raise exception.Forbidden() if (trust_ref['project_id'] and tenant_id != trust_ref['project_id']): raise exception.Forbidden() if ('expires' in trust_ref) and (trust_ref['expires']): expiry = trust_ref['expires'] if expiry < timeutils.parse_isotime(timeutils.isotime()): raise exception.Forbidden()() user_id = trust_ref['trustor_user_id'] trustor_user_ref = self.identity_api.get_user( trust_ref['trustor_user_id']) if not trustor_user_ref['enabled']: raise exception.Forbidden()() trustee_user_ref = self.identity_api.get_user( trust_ref['trustee_user_id']) if not trustee_user_ref['enabled']: raise exception.Forbidden()() if trust_ref['impersonation'] is True: current_user_ref = trustor_user_ref else: current_user_ref = trustee_user_ref else: current_user_ref = self.identity_api.get_user(user_id) metadata_ref = {} tenant_ref, metadata_ref['roles'] = self._get_project_roles_and_ref( user_id, tenant_id) expiry = token_model_ref.expires if CONF.trust.enabled and 'trust_id' in auth: trust_id = auth['trust_id'] trust_roles = [] for role in trust_ref['roles']: if 'roles' not in metadata_ref: raise exception.Forbidden()() if role['id'] in metadata_ref['roles']: trust_roles.append(role['id']) else: raise exception.Forbidden() if 'expiry' in trust_ref and trust_ref['expiry']: trust_expiry = timeutils.parse_isotime(trust_ref['expiry']) if trust_expiry < expiry: expiry = trust_expiry metadata_ref['roles'] = trust_roles metadata_ref['trustee_user_id'] = trust_ref['trustee_user_id'] metadata_ref['trust_id'] = trust_id bind = token_model_ref.bind audit_id = token_model_ref.audit_chain_id return (current_user_ref, tenant_ref, metadata_ref, expiry, bind, audit_id) def _authenticate_local(self, context, auth): """Try to authenticate against the identity backend. Returns auth_token_data, (user_ref, tenant_ref, metadata_ref) """ if 'passwordCredentials' not in auth: raise exception.ValidationError( attribute='passwordCredentials', target='auth') if "password" not in auth['passwordCredentials']: raise exception.ValidationError( attribute='password', target='passwordCredentials') password = auth['passwordCredentials']['password'] if password and len(password) > CONF.identity.max_password_length: raise exception.ValidationSizeError( attribute='password', size=CONF.identity.max_password_length) if ("userId" not in auth['passwordCredentials'] and "username" not in auth['passwordCredentials']): raise exception.ValidationError( attribute='username or userId', target='passwordCredentials') user_id = auth['passwordCredentials'].get('userId') if user_id and len(user_id) > CONF.max_param_size: raise exception.ValidationSizeError(attribute='userId', size=CONF.max_param_size) username = auth['passwordCredentials'].get('username', '') if username: if len(username) > CONF.max_param_size: raise exception.ValidationSizeError(attribute='username', size=CONF.max_param_size) try: user_ref = self.identity_api.get_user_by_name( username, CONF.identity.default_domain_id) user_id = user_ref['id'] except exception.UserNotFound as e: raise exception.Unauthorized(e) try: user_ref = self.identity_api.authenticate( context, user_id=user_id, password=password) except AssertionError as e: raise exception.Unauthorized(e.args[0]) metadata_ref = {} tenant_id = self._get_project_id_from_auth(auth) tenant_ref, metadata_ref['roles'] = self._get_project_roles_and_ref( user_id, tenant_id) expiry = provider.default_expire_time() bind = None audit_id = None return (user_ref, tenant_ref, metadata_ref, expiry, bind, audit_id) def _authenticate_external(self, context, auth): """Try to authenticate an external user via REMOTE_USER variable. Returns auth_token_data, (user_ref, tenant_ref, metadata_ref) """ environment = context.get('environment', {}) if not environment.get('REMOTE_USER'): raise ExternalAuthNotApplicable() # NOTE(jamielennox): xml and json differ and get confused about what # empty auth should look like so just reset it. if not auth: auth = {} username = environment['REMOTE_USER'] try: user_ref = self.identity_api.get_user_by_name( username, CONF.identity.default_domain_id) user_id = user_ref['id'] except exception.UserNotFound as e: raise exception.Unauthorized(e) metadata_ref = {} tenant_id = self._get_project_id_from_auth(auth) tenant_ref, metadata_ref['roles'] = self._get_project_roles_and_ref( user_id, tenant_id) expiry = provider.default_expire_time() bind = None if ('kerberos' in CONF.token.bind and environment.get('AUTH_TYPE', '').lower() == 'negotiate'): bind = {'kerberos': username} audit_id = None return (user_ref, tenant_ref, metadata_ref, expiry, bind, audit_id) def _get_auth_token_data(self, user, tenant, metadata, expiry, audit_id): return dict(user=user, tenant=tenant, metadata=metadata, expires=expiry, parent_audit_id=audit_id) def _get_project_id_from_auth(self, auth): """Extract tenant information from auth dict. Returns a valid tenant_id if it exists, or None if not specified. """ tenant_id = auth.get('tenantId') if tenant_id and len(tenant_id) > CONF.max_param_size: raise exception.ValidationSizeError(attribute='tenantId', size=CONF.max_param_size) tenant_name = auth.get('tenantName') if tenant_name and len(tenant_name) > CONF.max_param_size: raise exception.ValidationSizeError(attribute='tenantName', size=CONF.max_param_size) if tenant_name: try: tenant_ref = self.assignment_api.get_project_by_name( tenant_name, CONF.identity.default_domain_id) tenant_id = tenant_ref['id'] except exception.ProjectNotFound as e: raise exception.Unauthorized(e) return tenant_id def _get_project_roles_and_ref(self, user_id, tenant_id): """Returns the project roles for this user, and the project ref.""" tenant_ref = None role_list = [] if tenant_id: try: tenant_ref = self.assignment_api.get_project(tenant_id) role_list = self.assignment_api.get_roles_for_user_and_project( user_id, tenant_id) except exception.ProjectNotFound: pass if not role_list: msg = _('User %(u_id)s is unauthorized for tenant %(t_id)s') msg = msg % {'u_id': user_id, 't_id': tenant_id} LOG.warning(msg) raise exception.Unauthorized(msg) return (tenant_ref, role_list) def _get_token_ref(self, token_id, belongs_to=None): """Returns a token if a valid one exists. Optionally, limited to a token owned by a specific tenant. """ token_ref = token_model.KeystoneToken( token_id=token_id, token_data=self.token_provider_api.validate_token(token_id)) if belongs_to: if not token_ref.project_scoped: raise exception.Unauthorized( _('Token does not belong to specified tenant.')) if token_ref.project_id != belongs_to: raise exception.Unauthorized( _('Token does not belong to specified tenant.')) return token_ref @controller.v2_deprecated @controller.protected() def validate_token_head(self, context, token_id): """Check that a token is valid. Optionally, also ensure that it is owned by a specific tenant. Identical to ``validate_token``, except does not return a response. The code in ``keystone.common.wsgi.render_response`` will remove the content body. """ # TODO(ayoung) validate against revocation API belongs_to = context['query_string'].get('belongsTo') return self.token_provider_api.validate_v2_token(token_id, belongs_to) @controller.v2_deprecated @controller.protected() def validate_token(self, context, token_id): """Check that a token is valid. Optionally, also ensure that it is owned by a specific tenant. Returns metadata about the token along any associated roles. """ belongs_to = context['query_string'].get('belongsTo') # TODO(ayoung) validate against revocation API return self.token_provider_api.validate_v2_token(token_id, belongs_to) @controller.v2_deprecated def delete_token(self, context, token_id): """Delete a token, effectively invalidating it for authz.""" # TODO(termie): this stuff should probably be moved to middleware self.assert_admin(context) self.token_provider_api.revoke_token(token_id) @controller.v2_deprecated @controller.protected() def revocation_list(self, context, auth=None): if not CONF.token.revoke_by_id: raise exception.Gone() tokens = self.token_provider_api.list_revoked_tokens() for t in tokens: expires = t['expires'] if expires and isinstance(expires, datetime.datetime): t['expires'] = timeutils.isotime(expires) data = {'revoked': tokens} json_data = jsonutils.dumps(data) signed_text = cms.cms_sign_text(json_data, CONF.signing.certfile, CONF.signing.keyfile) return {'signed': signed_text} @controller.v2_deprecated def endpoints(self, context, token_id): """Return a list of endpoints available to the token.""" self.assert_admin(context) token_ref = self._get_token_ref(token_id) catalog_ref = None if token_ref.project_id: catalog_ref = self.catalog_api.get_catalog( token_ref.user_id, token_ref.project_id, token_ref.metadata) return Auth.format_endpoint_list(catalog_ref) @classmethod def format_endpoint_list(cls, catalog_ref): """Formats a list of endpoints according to Identity API v2. The v2.0 API wants an endpoint list to look like:: { 'endpoints': [ { 'id': $endpoint_id, 'name': $SERVICE[name], 'type': $SERVICE, 'tenantId': $tenant_id, 'region': $REGION, } ], 'endpoints_links': [], } """ if not catalog_ref: return {} endpoints = [] for region_name, region_ref in six.iteritems(catalog_ref): for service_type, service_ref in six.iteritems(region_ref): endpoints.append({ 'id': service_ref.get('id'), 'name': service_ref.get('name'), 'type': service_type, 'region': region_name, 'publicURL': service_ref.get('publicURL'), 'internalURL': service_ref.get('internalURL'), 'adminURL': service_ref.get('adminURL'), }) return {'endpoints': endpoints, 'endpoints_links': []} import distutils.command.bdist_rpm as orig class bdist_rpm(orig.bdist_rpm): """ Override the default bdist_rpm behavior to do the following: 1. Run egg_info to ensure the name and version are properly calculated. 2. Always run 'install' using --single-version-externally-managed to disable eggs in RPM distributions. 3. Replace dash with underscore in the version numbers for better RPM compatibility. """ def run(self): # ensure distro name is up-to-date self.run_command('egg_info') orig.bdist_rpm.run(self) def _make_spec_file(self): version = self.distribution.get_version() rpmversion = version.replace('-', '_') spec = orig.bdist_rpm._make_spec_file(self) line23 = '%define version ' + version line24 = '%define version ' + rpmversion spec = [ line.replace( "Source0: %{name}-%{version}.tar", "Source0: %{name}-%{unmangled_version}.tar" ).replace( "setup.py install ", "setup.py install --single-version-externally-managed " ).replace( "%setup", "%setup -n %{name}-%{unmangled_version}" ).replace(line23, line24) for line in spec ] insert_loc = spec.index(line24) + 1 unmangled_version = "%define unmangled_version " + version spec.insert(insert_loc, unmangled_version) return spec # This file implements a shared lock that lets us ensure that the test cases in # this directory run serially. Each test case obtains this lock as its first # step, and releases it as its last. (The nel_test helper function in # nel.sub.js automates this process.) Because the lock needs to be shared # across all of the test cases, we use a hard-coded stash key. This hard-coded # key is a random UUID, which should not conflict with any other auto-generated # stash keys. import time _LOCK_KEY = "67966d2e-a847-41d8-b7c3-5f6aee3375ba" _TIMEOUT = 5 # seconds def wait_for_lock(request): t0 = time.time() while time.time() - t0 < _TIMEOUT: time.sleep(0.5) value = request.server.stash.take(key=_LOCK_KEY) if value is None: return True return False def lock(request, report_id): with request.server.stash.lock: # Loop until the lock is free if not wait_for_lock(request): return (503, [], "Cannot obtain lock") request.server.stash.put(key=_LOCK_KEY, value=report_id) return "Obtained lock for %s" % report_id def unlock(request, report_id): with request.server.stash.lock: lock_holder = request.server.stash.take(key=_LOCK_KEY) if lock_holder != report_id: # Return the lock holder to the stash request.server.stash.put(key=_LOCK_KEY, value=lock_holder) return (503, [], "Cannot release lock held by %s" % lock_holder) return "Released lock for %s" % report_id def main(request, response): op = request.GET.first("op") report_id = request.GET.first("reportID") if op == "lock": return lock(request, report_id) elif op == "unlock": return unlock(request, report_id) else: return (400, [], "Invalid op") # ---------------------------------------------------------------------------- # Copyright 2014 Nervana Systems Inc. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ---------------------------------------------------------------------------- """ Contains various loss related metrics ex. log loss """ import numpy from neon.metrics.metric import Metric from neon.util.param import opt_param class LogLossSum(Metric): """ Logistic loss (aka cross-entropy loss) for a multi-class classification task. Defined to be the negative log of the likelihood summed across all data points received. Arguments: eps (float, optional): Amount to clip values by to prevent potential numeric difficulties (taking log of 0). See Also: LogLossMean References: Bishop2006 (p. 209) """ def __init__(self, **kwargs): super(LogLossSum, self).__init__(**kwargs) opt_param(self, ['eps'], 1e-15) def add(self, reference, outputs): """ Add the the expected reference and predicted outputs passed to the set of values used to calculate this metric. Arguments: reference (neon.backend.Tensor): Ground truth, expected outcomes. If each outcome is a vector, we expect it to be a column vector, with each case in a separate (one-hot encoded) column. outputs (neon.backend.Tensor): Predicted outputs. Must have the same dimensions as reference. To prevent numeric difficulties, output probabilities will be scaled to lie within [self.eps, 1 - self.eps] """ ismixed = ((reference.shape[0] == 1) and (outputs.shape[0] > 1) and (reference.shape[1] == outputs.shape[1])) if (reference.shape != outputs.shape) and (not ismixed): raise ValueError("reference dimensions: %s, incompatible with " "outputs dimensions: %s" % (str(reference.shape), str(outputs.shape))) # clip and normalize predictions preds = outputs.asnumpyarray().clip(self.eps, (1.0 - self.eps)) preds = numpy.log(preds / preds.sum(axis=0)) if ismixed: ref = reference.asnumpyarray().ravel().astype(int) reference = numpy.eye(outputs.shape[0], dtype=int)[ref].T self.logloss += (reference * preds).sum() else: self.logloss += (reference.asnumpyarray() * preds).sum() def report(self): """ Report the log loss value Returns: float: log loss value """ return - self.logloss def clear(self): """ Reset this metric's calculated value """ self.logloss = 0.0 class LogLossMean(LogLossSum): """ Logistic loss (aka cross-entropy loss) for a multi-class classification task. Defined to be the negative log of the likelihood averaged across all data points received. Arguments: eps (float, optional): Amount to clip values by to prevent potential numeric difficulties (taking log of 0). See Also: LogLossSum """ def add(self, reference, outputs): """ Add the the expected reference and predicted outputs passed to the set of values used to calculate this metric. Arguments: reference (neon.backend.Tensor): Ground truth, expected outcomes. If each outcome is a vector, we expect it to be a column vector, with each case in a separate (one-hot encoded) column. outputs (neon.backend.Tensor): Predicted outputs. Must have the same dimensions as reference. To prevent numeric difficulties, output probabilities will be scaled to lie within [self.eps, 1 - self.eps] """ super(LogLossMean, self).add(reference, outputs) self.rec_count += reference.shape[-1] def report(self): """ Report the mean log loss value Returns: float: log loss mean value """ return super(LogLossMean, self).report() / self.rec_count def clear(self): """ Reset this metric's calculated value """ super(LogLossMean, self).clear() self.rec_count = 0.0 # -*- coding: utf-8 -*- ############################################################################## # # OpenERP, Open Source Management Solution # Copyright (C) 2004-2009 Tiny SPRL (). # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero 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 Affero General Public License for more details. # # You should have received a copy of the GNU Affero General Public License # along with this program. If not, see . # ############################################################################## import time from openerp.report import report_sxw class ir_module_reference_print(report_sxw.rml_parse): def __init__(self, cr, uid, name, context): super(ir_module_reference_print, self).__init__(cr, uid, name, context=context) self.localcontext.update({ 'time': time, 'findobj': self._object_find, 'objdoc': self._object_doc, 'objdoc2': self._object_doc2, 'findflds': self._fields_find, }) def _object_doc(self, obj): modobj = self.pool[obj] strdocs= modobj.__doc__ if not strdocs: return None else: strdocs=strdocs.strip().splitlines(True) res = '' for stre in strdocs: if not stre or stre.isspace(): break res += stre return res def _object_doc2(self, obj): modobj = self.pool[obj] strdocs= modobj.__doc__ if not strdocs: return None else: strdocs=strdocs.strip().splitlines(True) res = [] fou = False for stre in strdocs: if fou: res.append(stre.strip()) elif not stre or stre.isspace(): fou = True return res def _object_find(self, module): ids2 = self.pool['ir.model.data'].search(self.cr, self.uid, [('module','=',module), ('model','=','ir.model')]) ids = [] for mod in self.pool['ir.model.data'].browse(self.cr, self.uid, ids2): ids.append(mod.res_id) modobj = self.pool['ir.model'] return modobj.browse(self.cr, self.uid, ids) def _fields_find(self, obj, module): res = [] data_obj = self.pool['ir.model.data'] modobj = self.pool[obj] fname_wildcard = 'field_' + modobj._name.replace('.', '_') + '_%' module_fields_ids = data_obj.search(self.cr, self.uid, [('model', '=', 'ir.model.fields'), ('module', '=', module), ('name', 'like', fname_wildcard)]) if module_fields_ids: module_fields_res_ids = [x['res_id'] for x in data_obj.read(self.cr, self.uid, module_fields_ids, ['res_id'])] module_fields_names = [x['name'] for x in self.pool['ir.model.fields'].read(self.cr, self.uid, module_fields_res_ids, ['name'])] res = modobj.fields_get(self.cr, self.uid, allfields=module_fields_names).items() res.sort() return res report_sxw.report_sxw('report.ir.module.reference', 'ir.module.module', 'addons/base/module/report/ir_module_reference.rml', parser=ir_module_reference_print, header=False) # vim:expandtab:smartindent:tabstop=4:softtabstop=4:shiftwidth=4: """Gaussian Process models for Keras 2.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from keras.models import Model as KerasModel from keras.engine.topology import _to_list from keras.engine.training import _standardize_input_data from .callbacks import UpdateGP class Model(KerasModel): """Model that supports arbitrary structure with GP output layers. This class extends `keras.models.Model` and allows using Gaussian Processes as output layers. The model completely inherits the function interface of Keras and is constructed in a standard way. On training, GPs are optimized using empirical Bayes (log marginal likelihood maximization) using semi-stochastic alternating scheme with delayed kernel matrix updates [1]. Non-GP output layers can use one the standard Keras objectives, e.g., the mean squared error. """ def __init__(self, inputs, outputs, name=None): super(Model, self).__init__(inputs, outputs, name) # List all output GP layers self.output_gp_layers = [layer for layer in self.output_layers if layer.name.startswith('gp')] def compile(self, optimizer, loss, metrics=None, loss_weights=None, sample_weight_mode=None, **kwargs): super(Model, self).compile(optimizer, loss, metrics, loss_weights, sample_weight_mode, **kwargs) # Remove the metrics meaningless for GP output layers self.metrics_tensors = [ mt for mt, mn in zip(self.metrics_tensors, self.metrics_names[1:]) if not (mn.startswith('gp') and mn.endswith('loss')) ] self.metrics_names = [ mn for mn in self.metrics_names if not (mn.startswith('gp') and mn.endswith('loss')) ] # Add MSE and NLML metrics for each output GP for gp in self.output_gp_layers: self.metrics_tensors.extend([gp.mse, gp.nlml]) self.metrics_names.extend([gp.name + '_mse', gp.name + '_nlml']) # Add cumulative MSE & NLML metrics self.mse = sum([gp.mse for gp in self.output_gp_layers]) self.nlml = sum([gp.nlml for gp in self.output_gp_layers]) self.metrics_tensors.extend([self.mse, self.nlml]) self.metrics_names.extend(['mse', 'nlml']) def transform(self, x, batch_size=32, learning_phase=0., verbose=0): h = super(Model, self).predict(x, batch_size, learning_phase, verbose) return _to_list(h) def fit(self, X, Y, batch_size=32, epochs=1, gp_n_iter=1, verbose=1, callbacks=None, validation_split=0., validation_data=None, shuffle=True, class_weight=None, sample_weight=None, initial_epoch=0, **kwargs): """Trains the model for a fixed number of epochs (iterations on a dataset). For argument details, refer to `keras.engine.training.Model.fit`. Notes: The following arguments are currently unsupported by models with GP output layers: - validation_split - class_weight - sample_weight """ # Validate user data X, Y, _ = self._standardize_user_data( X, Y, sample_weight=None, class_weight=None, check_batch_axis=False, batch_size=batch_size) if validation_data is not None: X_val, Y_val, _ = self._standardize_user_data( *validation_data, sample_weight=None, class_weight=None, check_batch_axis=False, batch_size=batch_size) validation_data = (X_val, Y_val) # Setup GP updates update_gp = UpdateGP(ins=(X, Y), val_ins=validation_data, batch_size=batch_size, gp_n_iter=gp_n_iter, verbose=verbose) callbacks = [update_gp] + (callbacks or []) return super(Model, self).fit( X, Y, batch_size=batch_size, epochs=epochs, verbose=verbose, callbacks=callbacks, shuffle=shuffle, initial_epoch=initial_epoch, **kwargs) def finetune(self, X, Y, batch_size=32, gp_n_iter=1, verbose=1): """Finetune the output GP layers assuming the network is pre-trained. Arguments: ---------- X : np.ndarray or list of np.ndarrays Y : np.ndarray or list of np.ndarrays batch_size : uint (default: 128) Batch size used for data streaming through the network. gp_n_iter : uint (default: 100) Number of iterations for GP training. verbose : uint (default: 1) Verbosity mode, 0 or 1. """ # Validate user data X = _standardize_input_data( X, self._feed_input_names, self._feed_input_shapes, check_batch_axis=False, exception_prefix='input') H = self.transform(X, batch_size=batch_size) if verbose: print("Finetuning output GPs...") for gp, h, y in zip(self.output_gp_layers, H, Y): # Update GP data (and grid if necessary) gp.backend.update_data('tr', h, y) if gp.update_grid: gp.backend.update_grid('tr') # Train GP gp.hyp = gp.backend.train(gp_n_iter, verbose=verbose) if verbose: print("Done.") def evaluate(self, X, Y, batch_size=32, verbose=0): """Compute NLML on the given data. Arguments: ---------- X : np.ndarray or list of np.ndarrays Y : np.ndarray or list of np.ndarrays batch_size : uint (default: 128) verbose : uint (default: 0) Verbosity mode, 0 or 1. Returns: -------- nlml : float """ # Validate user data X, Y, _ = self._standardize_user_data( X, Y, sample_weight=None, class_weight=None, check_batch_axis=False, batch_size=batch_size) H = self.transform(X, batch_size=batch_size) nlml = 0. for gp, h, y in zip(self.output_gp_layers, H, Y): nlml += gp.backend.evaluate('tmp', h, y) return nlml def predict(self, X, X_tr=None, Y_tr=None, batch_size=32, return_var=False, verbose=0): """Generate output predictions for the input samples batch by batch. Arguments: ---------- X : np.ndarray or list of np.ndarrays batch_size : uint (default: 128) return_var : bool (default: False) Whether predictive variance is returned. verbose : uint (default: 0) Verbosity mode, 0 or 1. Returns: -------- preds : a list or a tuple of lists Lists of output predictions and variance estimates. """ # Update GP data if provided (and grid if necessary) if X_tr is not None and Y_tr is not None: X_tr, Y_tr, _ = self._standardize_user_data( X_tr, Y_tr, sample_weight=None, class_weight=None, check_batch_axis=False, batch_size=batch_size) H_tr = self.transform(X_tr, batch_size=batch_size) for gp, h, y in zip(self.output_gp_layers, H_tr, Y_tr): gp.backend.update_data('tr', h, y) if gp.update_grid: gp.backend.update_grid('tr') # Validate user data X = _standardize_input_data( X, self._feed_input_names, self._feed_input_shapes, check_batch_axis=False, exception_prefix='input') H = self.transform(X, batch_size=batch_size) preds = [] for gp, h in zip(self.output_gp_layers, H): preds.append(gp.backend.predict(h, return_var=return_var)) if return_var: preds = map(list, zip(*preds)) return preds # Apply tweaks from . import tweaks """ Glyph renderer models for displaying simple scatter-type markers on Bokeh plots. """ from __future__ import absolute_import from .glyphs import Glyph from ..enums import enumeration from ..mixins import FillProps, LineProps from ..properties import abstract from ..properties import DistanceSpec, Enum, Include, NumberSpec, ScreenDistanceSpec @abstract class Marker(Glyph): """ Base class for glyphs that are simple markers with line and fill properties, located at an (x, y) location with a specified size. .. note:: For simplicity, all markers have both line and fill properties declared, however some markers (`Asterisk`, `Cross`, `X`) only draw lines. For these markers, the fill values are simply ignored. """ x = NumberSpec("x", help=""" The x-axis coordinates for the center of the markers. """) y = NumberSpec("y", help=""" The y-axis coordinates for the center of the markers. """) size = ScreenDistanceSpec(default=4, help=""" The size (diameter) values for the markers. Interpreted as "screen space" units by default. """) angle = NumberSpec("angle", help=""" The angles to rotate the markers. """) line_props = Include(LineProps, use_prefix=False, help=""" The %s values for the markers. """) fill_props = Include(FillProps, use_prefix=False, help=""" The %s values for the markers. """) class Asterisk(Marker): """ Render asterisk '*' markers. Example ------- .. bokeh-plot:: ../tests/glyphs/Asterisk.py :source-position: none *source:* ``tests/glyphs/Asterisk.py`` """ class Circle(Marker): """ Render circle markers. Example ------- .. bokeh-plot:: ../tests/glyphs/Circle.py :source-position: none *source:* ``tests/glyphs/Circle.py`` """ radius = DistanceSpec("radius", help=""" The radius values for circle markers. Interpreted in "data space" units by default. .. note:: Circle markers are slightly unusual in that they support specifying a radius in addition to a size. Only one of ``radius`` or ``size`` should be given. .. warning:: Note that ``Circle`` glyphs are always drawn as circles on the screen, even in cases where the data space aspect ratio is not 1-1. In all cases where radius or size units are specified as "data", the "distance" for the radius is measured along the horizontal axis. If the aspect ratio is very large or small, the drawn circles may appear much larger or smaller than expected. See :bokeh-issue:`626` for more information. """) radius_dimension = Enum(enumeration('x', 'y'), help=""" What dimension to measure circle radii along. When the data space aspect ratio is not 1-1, then the size of the drawn circles depends on what direction is used to measure the "distance" of the radius. This property allows that direction to be controlled. """) class CircleCross(Marker): """ Render circle markers with a '+' cross through the center. Example ------- .. bokeh-plot:: ../tests/glyphs/CircleCross.py :source-position: none *source:* ``tests/glyphs/CircleCross.py`` """ class CircleX(Marker): """ Render circle markers with an 'X' cross through the center. Example ------- .. bokeh-plot:: ../tests/glyphs/CircleX.py :source-position: none *source:* ``tests/glyphs/CircleX.py`` """ class Cross(Marker): """ Render '+' cross markers. Example ------- .. bokeh-plot:: ../tests/glyphs/Cross.py :source-position: none *source:* ``tests/glyphs/Cross.py`` """ class Diamond(Marker): """ Render diamond markers. Example ------- .. bokeh-plot:: ../tests/glyphs/Diamond.py :source-position: none *source:* ``tests/glyphs/Diamond.py`` """ class DiamondCross(Marker): """ Render diamond markers with a '+' cross through the center. Example ------- .. bokeh-plot:: ../tests/glyphs/DiamondCross.py :source-position: none *source:* ``tests/glyphs/DiamondCross.py`` """ class InvertedTriangle(Marker): """ Render upside-down triangle markers. Example ------- .. bokeh-plot:: ../tests/glyphs/InvertedTriangle.py :source-position: none *source:* ``tests/glyphs/InvertedTriangle.py`` """ class Square(Marker): """ Render a square marker, optionally rotated. Example ------- .. bokeh-plot:: ../tests/glyphs/Square.py :source-position: none *source:* ``tests/glyphs/Square.py`` """ class SquareCross(Marker): """ Render square markers with a '+' cross through the center. Example ------- .. bokeh-plot:: ../tests/glyphs/SquareCross.py :source-position: none *source:* ``tests/glyphs/SquareCross.py`` """ class SquareX(Marker): """ Render square markers with an 'X' cross through the center. Example ------- .. bokeh-plot:: ../tests/glyphs/SquareX.py :source-position: none *source:* ``tests/glyphs/SquareX.py`` """ class Triangle(Marker): """ Render triangle markers. Example ------- .. bokeh-plot:: ../tests/glyphs/Triangle.py :source-position: none *source:* ``tests/glyphs/Triangle.py`` """ class X(Marker): """ Render a 'X' cross markers. Example ------- .. bokeh-plot:: ../tests/glyphs/X.py :source-position: none *source:* ``tests/glyphs/X.py`` """ # (c) 2015, Ansible Inc, # # This file is part of Ansible # # Ansible 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. # # Ansible 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 Ansible. If not, see . from __future__ import (absolute_import, division, print_function) __metaclass__ = type from ansible.plugins.action import ActionBase class ActionModule(ActionBase): TRANSFERS_FILES = False UNUSED_PARAMS = { 'systemd': ['pattern', 'runlevel', 'sleep', 'arguments', 'args'], } def run(self, tmp=None, task_vars=None): ''' handler for package operations ''' self._supports_check_mode = True self._supports_async = True result = super(ActionModule, self).run(tmp, task_vars) module = self._task.args.get('use', 'auto').lower() if module == 'auto': try: if self._task.delegate_to: # if we delegate, we should use delegated host's facts module = self._templar.template("{{hostvars['%s']['ansible_facts']['ansible_service_mgr']}}" % self._task.delegate_to) else: module = self._templar.template('{{ansible_facts["ansible_service_mgr"]}}') except: pass # could not get it from template! if module == 'auto': facts = self._execute_module(module_name='setup', module_args=dict(gather_subset='!all', filter='ansible_service_mgr'), task_vars=task_vars) self._display.debug("Facts %s" % facts) if 'ansible_facts' in facts and 'ansible_service_mgr' in facts['ansible_facts']: module = facts['ansible_facts']['ansible_service_mgr'] if not module or module == 'auto' or module not in self._shared_loader_obj.module_loader: module = 'service' if module != 'auto': # run the 'service' module new_module_args = self._task.args.copy() if 'use' in new_module_args: del new_module_args['use'] # for backwards compatibility if 'state' in new_module_args and new_module_args['state'] == 'running': self._display.deprecated(msg="state=running is deprecated. Please use state=started", version="2.7") new_module_args['state'] = 'started' if module in self.UNUSED_PARAMS: for unused in self.UNUSED_PARAMS[module]: if unused in new_module_args: del new_module_args[unused] self._display.warning('Ignoring "%s" as it is not used in "%s"' % (unused, module)) self._display.vvvv("Running %s" % module) result.update(self._execute_module(module_name=module, module_args=new_module_args, task_vars=task_vars, wrap_async=self._task.async)) else: result['failed'] = True result['msg'] = 'Could not detect which service manager to use. Try gathering facts or setting the "use" option.' return result # # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import sys import time from pyspark.sql import SparkSession if __name__ == "__main__": """ Usage: decommissioning """ print("Starting decom test") spark = SparkSession \ .builder \ .appName("DecomTest") \ .getOrCreate() sc = spark._sc acc = sc.accumulator(0) def addToAcc(x): acc.add(1) return x initialRdd = sc.parallelize(range(100), 5) accRdd = initialRdd.map(addToAcc) # Trigger a shuffle so there are shuffle blocks to migrate rdd = accRdd.map(lambda x: (x, x)).groupByKey() # Make enough shuffle files to increase the chance of the race condition. for i in range(1, 2): shuffleRdd = sc.parallelize(range(1, 10), 5).map(lambda x: (x, x)).groupByKey() shuffleRdd.collect() rdd.collect() print("1st accumulator value is: " + str(acc.value)) print("Waiting to give nodes time to finish migration, decom exec 1.") print("...") time.sleep(30) rdd.count() rdd.collect() print("Final accumulator value is: " + str(acc.value)) print("Finished waiting, stopping Spark.") spark.stop() print("Done, exiting Python") sys.exit(0) #!/usr/bin/env python # Copyright (c) 2012 Google Inc. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. """Utility functions for Windows builds. These functions are executed via gyp-win-tool when using the ninja generator. """ import os import re import shutil import subprocess import string import sys BASE_DIR = os.path.dirname(os.path.abspath(__file__)) # A regex matching an argument corresponding to the output filename passed to # link.exe. _LINK_EXE_OUT_ARG = re.compile('/OUT:(?P.+)$', re.IGNORECASE) def main(args): executor = WinTool() exit_code = executor.Dispatch(args) if exit_code is not None: sys.exit(exit_code) class WinTool(object): """This class performs all the Windows tooling steps. The methods can either be executed directly, or dispatched from an argument list.""" def _UseSeparateMspdbsrv(self, env, args): """Allows to use a unique instance of mspdbsrv.exe per linker instead of a shared one.""" if len(args) < 1: raise Exception("Not enough arguments") if args[0] != 'link.exe': return # Use the output filename passed to the linker to generate an endpoint name # for mspdbsrv.exe. endpoint_name = None for arg in args: m = _LINK_EXE_OUT_ARG.match(arg) if m: endpoint_name = '%s_%d' % (m.group('out'), os.getpid()) break if endpoint_name is None: return # Adds the appropriate environment variable. This will be read by link.exe # to know which instance of mspdbsrv.exe it should connect to (if it's # not set then the default endpoint is used). env['_MSPDBSRV_ENDPOINT_'] = endpoint_name def Dispatch(self, args): """Dispatches a string command to a method.""" if len(args) < 1: raise Exception("Not enough arguments") method = "Exec%s" % self._CommandifyName(args[0]) return getattr(self, method)(*args[1:]) def _CommandifyName(self, name_string): """Transforms a tool name like recursive-mirror to RecursiveMirror.""" return name_string.title().replace('-', '') def _GetEnv(self, arch): """Gets the saved environment from a file for a given architecture.""" # The environment is saved as an "environment block" (see CreateProcess # and msvs_emulation for details). We convert to a dict here. # Drop last 2 NULs, one for list terminator, one for trailing vs. separator. pairs = open(arch).read()[:-2].split('\0') kvs = [item.split('=', 1) for item in pairs] return dict(kvs) def ExecStamp(self, path): """Simple stamp command.""" open(path, 'w').close() def ExecRecursiveMirror(self, source, dest): """Emulation of rm -rf out && cp -af in out.""" if os.path.exists(dest): if os.path.isdir(dest): shutil.rmtree(dest) else: os.unlink(dest) if os.path.isdir(source): shutil.copytree(source, dest) else: shutil.copy2(source, dest) def ExecLinkWrapper(self, arch, use_separate_mspdbsrv, *args): """Filter diagnostic output from link that looks like: ' Creating library ui.dll.lib and object ui.dll.exp' This happens when there are exports from the dll or exe. """ env = self._GetEnv(arch) if use_separate_mspdbsrv == 'True': self._UseSeparateMspdbsrv(env, args) link = subprocess.Popen(args, shell=True, env=env, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) out, _ = link.communicate() for line in out.splitlines(): if not line.startswith(' Creating library '): print line return link.returncode def ExecLinkWithManifests(self, arch, embed_manifest, out, ldcmd, resname, mt, rc, intermediate_manifest, *manifests): """A wrapper for handling creating a manifest resource and then executing a link command.""" # The 'normal' way to do manifests is to have link generate a manifest # based on gathering dependencies from the object files, then merge that # manifest with other manifests supplied as sources, convert the merged # manifest to a resource, and then *relink*, including the compiled # version of the manifest resource. This breaks incremental linking, and # is generally overly complicated. Instead, we merge all the manifests # provided (along with one that includes what would normally be in the # linker-generated one, see msvs_emulation.py), and include that into the # first and only link. We still tell link to generate a manifest, but we # only use that to assert that our simpler process did not miss anything. variables = { 'python': sys.executable, 'arch': arch, 'out': out, 'ldcmd': ldcmd, 'resname': resname, 'mt': mt, 'rc': rc, 'intermediate_manifest': intermediate_manifest, 'manifests': ' '.join(manifests), } add_to_ld = '' if manifests: subprocess.check_call( '%(python)s gyp-win-tool manifest-wrapper %(arch)s %(mt)s -nologo ' '-manifest %(manifests)s -out:%(out)s.manifest' % variables) if embed_manifest == 'True': subprocess.check_call( '%(python)s gyp-win-tool manifest-to-rc %(arch)s %(out)s.manifest' ' %(out)s.manifest.rc %(resname)s' % variables) subprocess.check_call( '%(python)s gyp-win-tool rc-wrapper %(arch)s %(rc)s ' '%(out)s.manifest.rc' % variables) add_to_ld = ' %(out)s.manifest.res' % variables subprocess.check_call(ldcmd + add_to_ld) # Run mt.exe on the theoretically complete manifest we generated, merging # it with the one the linker generated to confirm that the linker # generated one does not add anything. This is strictly unnecessary for # correctness, it's only to verify that e.g. /MANIFESTDEPENDENCY was not # used in a #pragma comment. if manifests: # Merge the intermediate one with ours to .assert.manifest, then check # that .assert.manifest is identical to ours. subprocess.check_call( '%(python)s gyp-win-tool manifest-wrapper %(arch)s %(mt)s -nologo ' '-manifest %(out)s.manifest %(intermediate_manifest)s ' '-out:%(out)s.assert.manifest' % variables) assert_manifest = '%(out)s.assert.manifest' % variables our_manifest = '%(out)s.manifest' % variables # Load and normalize the manifests. mt.exe sometimes removes whitespace, # and sometimes doesn't unfortunately. with open(our_manifest, 'rb') as our_f: with open(assert_manifest, 'rb') as assert_f: our_data = our_f.read().translate(None, string.whitespace) assert_data = assert_f.read().translate(None, string.whitespace) if our_data != assert_data: os.unlink(out) def dump(filename): sys.stderr.write('%s\n-----\n' % filename) with open(filename, 'rb') as f: sys.stderr.write(f.read() + '\n-----\n') dump(intermediate_manifest) dump(our_manifest) dump(assert_manifest) sys.stderr.write( 'Linker generated manifest "%s" added to final manifest "%s" ' '(result in "%s"). ' 'Were /MANIFEST switches used in #pragma statements? ' % ( intermediate_manifest, our_manifest, assert_manifest)) return 1 def ExecManifestWrapper(self, arch, *args): """Run manifest tool with environment set. Strip out undesirable warning (some XML blocks are recognized by the OS loader, but not the manifest tool).""" env = self._GetEnv(arch) popen = subprocess.Popen(args, shell=True, env=env, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) out, _ = popen.communicate() for line in out.splitlines(): if line and 'manifest authoring warning 81010002' not in line: print line return popen.returncode def ExecManifestToRc(self, arch, *args): """Creates a resource file pointing a SxS assembly manifest. |args| is tuple containing path to resource file, path to manifest file and resource name which can be "1" (for executables) or "2" (for DLLs).""" manifest_path, resource_path, resource_name = args with open(resource_path, 'wb') as output: output.write('#include \n%s RT_MANIFEST "%s"' % ( resource_name, os.path.abspath(manifest_path).replace('\\', '/'))) def ExecMidlWrapper(self, arch, outdir, tlb, h, dlldata, iid, proxy, idl, *flags): """Filter noisy filenames output from MIDL compile step that isn't quietable via command line flags. """ args = ['midl', '/nologo'] + list(flags) + [ '/out', outdir, '/tlb', tlb, '/h', h, '/dlldata', dlldata, '/iid', iid, '/proxy', proxy, idl] env = self._GetEnv(arch) popen = subprocess.Popen(args, shell=True, env=env, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) out, _ = popen.communicate() # Filter junk out of stdout, and write filtered versions. Output we want # to filter is pairs of lines that look like this: # Processing C:\Program Files (x86)\Microsoft SDKs\...\include\objidl.idl # objidl.idl lines = out.splitlines() prefix = 'Processing ' processing = set(os.path.basename(x) for x in lines if x.startswith(prefix)) for line in lines: if not line.startswith(prefix) and line not in processing: print line return popen.returncode def ExecAsmWrapper(self, arch, *args): """Filter logo banner from invocations of asm.exe.""" env = self._GetEnv(arch) # MSVS doesn't assemble x64 asm files. if arch == 'environment.x64': return 0 popen = subprocess.Popen(args, shell=True, env=env, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) out, _ = popen.communicate() for line in out.splitlines(): if (not line.startswith('Copyright (C) Microsoft Corporation') and not line.startswith('Microsoft (R) Macro Assembler') and not line.startswith(' Assembling: ') and line): print line return popen.returncode def ExecRcWrapper(self, arch, *args): """Filter logo banner from invocations of rc.exe. Older versions of RC don't support the /nologo flag.""" env = self._GetEnv(arch) popen = subprocess.Popen(args, shell=True, env=env, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) out, _ = popen.communicate() for line in out.splitlines(): if (not line.startswith('Microsoft (R) Windows (R) Resource Compiler') and not line.startswith('Copyright (C) Microsoft Corporation') and line): print line return popen.returncode def ExecActionWrapper(self, arch, rspfile, *dir): """Runs an action command line from a response file using the environment for |arch|. If |dir| is supplied, use that as the working directory.""" env = self._GetEnv(arch) # TODO(scottmg): This is a temporary hack to get some specific variables # through to actions that are set after gyp-time. http://crbug.com/333738. for k, v in os.environ.iteritems(): if k not in env: env[k] = v args = open(rspfile).read() dir = dir[0] if dir else None return subprocess.call(args, shell=True, env=env, cwd=dir) if __name__ == '__main__': sys.exit(main(sys.argv[1:])) # -*- coding: utf-8 -*- ############################################################################## # # OpenERP, Open Source Management Solution # Copyright (C) 2004-today OpenERP SA () # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero 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 Affero General Public License for more details. # # You should have received a copy of the GNU Affero General Public License # along with this program. If not, see . # ############################################################################## from openerp.osv import fields, osv import logging _logger = logging.getLogger(__name__) class calendar_event(osv.Model): """ Model for Calendar Event """ _inherit = 'calendar.event' _columns = { 'phonecall_id': fields.many2one('crm.phonecall', 'Phonecall'), 'opportunity_id': fields.many2one('crm.lead', 'Opportunity', domain="[('type', '=', 'opportunity')]"), } def create(self, cr, uid, vals, context=None): res = super(calendar_event, self).create(cr, uid, vals, context=context) obj = self.browse(cr, uid, res, context=context) if obj.opportunity_id: self.pool.get('crm.lead').log_meeting(cr, uid, [obj.opportunity_id.id], obj.name, obj.start, obj.duration, context=context) return res # vim:expandtab:smartindent:tabstop=4:softtabstop=4:shiftwidth=4: # Copyright 2016 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Tests for tf.contrib.training.evaluation.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import glob import os import time import numpy as np from tensorflow.contrib.framework.python.ops import variables from tensorflow.contrib.layers.python.layers import layers from tensorflow.contrib.losses.python.losses import loss_ops from tensorflow.contrib.training.python.training import evaluation from tensorflow.contrib.training.python.training import training from tensorflow.core.protobuf import config_pb2 from tensorflow.python.client import session as session_lib from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import random_seed from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import metrics from tensorflow.python.ops import state_ops from tensorflow.python.ops import variables as variables_lib from tensorflow.python.platform import gfile from tensorflow.python.platform import test from tensorflow.python.summary import summary as summary_lib from tensorflow.python.summary import summary_iterator from tensorflow.python.training import basic_session_run_hooks from tensorflow.python.training import gradient_descent from tensorflow.python.training import saver as saver_lib class CheckpointIteratorTest(test.TestCase): def testReturnsEmptyIfNoCheckpointsFound(self): checkpoint_dir = os.path.join(self.get_temp_dir(), 'no_checkpoints_found') num_found = 0 for _ in evaluation.checkpoints_iterator(checkpoint_dir, timeout=0): num_found += 1 self.assertEqual(num_found, 0) def testReturnsSingleCheckpointIfOneCheckpointFound(self): checkpoint_dir = os.path.join(self.get_temp_dir(), 'one_checkpoint_found') if not gfile.Exists(checkpoint_dir): gfile.MakeDirs(checkpoint_dir) global_step = variables.get_or_create_global_step() saver = saver_lib.Saver() # Saves the global step. with self.cached_session() as session: session.run(variables_lib.global_variables_initializer()) save_path = os.path.join(checkpoint_dir, 'model.ckpt') saver.save(session, save_path, global_step=global_step) num_found = 0 for _ in evaluation.checkpoints_iterator(checkpoint_dir, timeout=0): num_found += 1 self.assertEqual(num_found, 1) def testReturnsSingleCheckpointIfOneShardedCheckpoint(self): checkpoint_dir = os.path.join(self.get_temp_dir(), 'one_checkpoint_found_sharded') if not gfile.Exists(checkpoint_dir): gfile.MakeDirs(checkpoint_dir) global_step = variables.get_or_create_global_step() # This will result in 3 different checkpoint shard files. with ops.device('/cpu:0'): variables_lib.Variable(10, name='v0') with ops.device('/cpu:1'): variables_lib.Variable(20, name='v1') saver = saver_lib.Saver(sharded=True) with session_lib.Session( target='', config=config_pb2.ConfigProto(device_count={'CPU': 2})) as session: session.run(variables_lib.global_variables_initializer()) save_path = os.path.join(checkpoint_dir, 'model.ckpt') saver.save(session, save_path, global_step=global_step) num_found = 0 for _ in evaluation.checkpoints_iterator(checkpoint_dir, timeout=0): num_found += 1 self.assertEqual(num_found, 1) def testTimeoutFn(self): timeout_fn_calls = [0] def timeout_fn(): timeout_fn_calls[0] += 1 return timeout_fn_calls[0] > 3 results = list( evaluation.checkpoints_iterator( '/non-existent-dir', timeout=0.1, timeout_fn=timeout_fn)) self.assertEqual([], results) self.assertEqual(4, timeout_fn_calls[0]) class WaitForNewCheckpointTest(test.TestCase): def testReturnsNoneAfterTimeout(self): start = time.time() ret = evaluation.wait_for_new_checkpoint( '/non-existent-dir', 'foo', timeout=1.0, seconds_to_sleep=0.5) end = time.time() self.assertIsNone(ret) # We've waited one second. self.assertGreater(end, start + 0.5) # The timeout kicked in. self.assertLess(end, start + 1.1) def logistic_classifier(inputs): return layers.fully_connected(inputs, 1, activation_fn=math_ops.sigmoid) class EvaluateOnceTest(test.TestCase): def setUp(self): super(EvaluateOnceTest, self).setUp() # Create an easy training set: np.random.seed(0) self._inputs = np.zeros((16, 4)) self._labels = np.random.randint(0, 2, size=(16, 1)).astype(np.float32) for i in range(16): j = int(2 * self._labels[i] + np.random.randint(0, 2)) self._inputs[i, j] = 1 def _train_model(self, checkpoint_dir, num_steps): """Trains a simple classification model. Note that the data has been configured such that after around 300 steps, the model has memorized the dataset (e.g. we can expect %100 accuracy). Args: checkpoint_dir: The directory where the checkpoint is written to. num_steps: The number of steps to train for. """ with ops.Graph().as_default(): random_seed.set_random_seed(0) tf_inputs = constant_op.constant(self._inputs, dtype=dtypes.float32) tf_labels = constant_op.constant(self._labels, dtype=dtypes.float32) tf_predictions = logistic_classifier(tf_inputs) loss = loss_ops.log_loss(tf_predictions, tf_labels) optimizer = gradient_descent.GradientDescentOptimizer(learning_rate=1.0) train_op = training.create_train_op(loss, optimizer) loss = training.train( train_op, checkpoint_dir, hooks=[basic_session_run_hooks.StopAtStepHook(num_steps)]) if num_steps >= 300: assert loss < .015 def testEvaluatePerfectModel(self): checkpoint_dir = os.path.join(self.get_temp_dir(),