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glorizen/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/transforms.py | 69 | 75638 | """
matplotlib includes a framework for arbitrary geometric
transformations that is used determine the final position of all
elements drawn on the canvas.
Transforms are composed into trees of :class:`TransformNode` objects
whose actual value depends on their children. When the contents of
children change, their parents are automatically invalidated. The
next time an invalidated transform is accessed, it is recomputed to
reflect those changes. This invalidation/caching approach prevents
unnecessary recomputations of transforms, and contributes to better
interactive performance.
For example, here is a graph of the transform tree used to plot data
to the graph:
.. image:: ../_static/transforms.png
The framework can be used for both affine and non-affine
transformations. However, for speed, we want use the backend
renderers to perform affine transformations whenever possible.
Therefore, it is possible to perform just the affine or non-affine
part of a transformation on a set of data. The affine is always
assumed to occur after the non-affine. For any transform::
full transform == non-affine part + affine part
The backends are not expected to handle non-affine transformations
themselves.
"""
import numpy as np
from numpy import ma
from matplotlib._path import affine_transform
from numpy.linalg import inv
from weakref import WeakKeyDictionary
import warnings
try:
set
except NameError:
from sets import Set as set
import cbook
from path import Path
from _path import count_bboxes_overlapping_bbox, update_path_extents
DEBUG = False
if DEBUG:
import warnings
MaskedArray = ma.MaskedArray
class TransformNode(object):
"""
:class:`TransformNode` is the base class for anything that
participates in the transform tree and needs to invalidate its
parents or be invalidated. This includes classes that are not
really transforms, such as bounding boxes, since some transforms
depend on bounding boxes to compute their values.
"""
_gid = 0
# Invalidation may affect only the affine part. If the
# invalidation was "affine-only", the _invalid member is set to
# INVALID_AFFINE_ONLY
INVALID_NON_AFFINE = 1
INVALID_AFFINE = 2
INVALID = INVALID_NON_AFFINE | INVALID_AFFINE
# Some metadata about the transform, used to determine whether an
# invalidation is affine-only
is_affine = False
is_bbox = False
# If pass_through is True, all ancestors will always be
# invalidated, even if 'self' is already invalid.
pass_through = False
def __init__(self):
"""
Creates a new :class:`TransformNode`.
"""
# Parents are stored in a WeakKeyDictionary, so that if the
# parents are deleted, references from the children won't keep
# them alive.
self._parents = WeakKeyDictionary()
# TransformNodes start out as invalid until their values are
# computed for the first time.
self._invalid = 1
def __copy__(self, *args):
raise NotImplementedError(
"TransformNode instances can not be copied. " +
"Consider using frozen() instead.")
__deepcopy__ = __copy__
def invalidate(self):
"""
Invalidate this :class:`TransformNode` and all of its
ancestors. Should be called any time the transform changes.
"""
# If we are an affine transform being changed, we can set the
# flag to INVALID_AFFINE_ONLY
value = (self.is_affine) and self.INVALID_AFFINE or self.INVALID
# Shortcut: If self is already invalid, that means its parents
# are as well, so we don't need to do anything.
if self._invalid == value:
return
if not len(self._parents):
self._invalid = value
return
# Invalidate all ancestors of self using pseudo-recursion.
stack = [self]
while len(stack):
root = stack.pop()
# Stop at subtrees that have already been invalidated
if root._invalid != value or root.pass_through:
root._invalid = self.INVALID
stack.extend(root._parents.keys())
def set_children(self, *children):
"""
Set the children of the transform, to let the invalidation
system know which transforms can invalidate this transform.
Should be called from the constructor of any transforms that
depend on other transforms.
"""
for child in children:
child._parents[self] = None
if DEBUG:
_set_children = set_children
def set_children(self, *children):
self._set_children(*children)
self._children = children
set_children.__doc__ = _set_children.__doc__
def frozen(self):
"""
Returns a frozen copy of this transform node. The frozen copy
will not update when its children change. Useful for storing
a previously known state of a transform where
``copy.deepcopy()`` might normally be used.
"""
return self
if DEBUG:
def write_graphviz(self, fobj, highlight=[]):
"""
For debugging purposes.
Writes the transform tree rooted at 'self' to a graphviz "dot"
format file. This file can be run through the "dot" utility
to produce a graph of the transform tree.
Affine transforms are marked in blue. Bounding boxes are
marked in yellow.
*fobj*: A Python file-like object
"""
seen = set()
def recurse(root):
if root in seen:
return
seen.add(root)
props = {}
label = root.__class__.__name__
if root._invalid:
label = '[%s]' % label
if root in highlight:
props['style'] = 'bold'
props['shape'] = 'box'
props['label'] = '"%s"' % label
props = ' '.join(['%s=%s' % (key, val) for key, val in props.items()])
fobj.write('%s [%s];\n' %
(hash(root), props))
if hasattr(root, '_children'):
for child in root._children:
name = '?'
for key, val in root.__dict__.items():
if val is child:
name = key
break
fobj.write('%s -> %s [label="%s", fontsize=10];\n' % (
hash(root),
hash(child),
name))
recurse(child)
fobj.write("digraph G {\n")
recurse(self)
fobj.write("}\n")
else:
def write_graphviz(self, fobj, highlight=[]):
return
class BboxBase(TransformNode):
"""
This is the base class of all bounding boxes, and provides
read-only access to its data. A mutable bounding box is provided
by the :class:`Bbox` class.
The canonical representation is as two points, with no
restrictions on their ordering. Convenience properties are
provided to get the left, bottom, right and top edges and width
and height, but these are not stored explicity.
"""
is_bbox = True
is_affine = True
#* Redundant: Removed for performance
#
# def __init__(self):
# TransformNode.__init__(self)
if DEBUG:
def _check(points):
if ma.isMaskedArray(points):
warnings.warn("Bbox bounds are a masked array.")
points = np.asarray(points)
if (points[1,0] - points[0,0] == 0 or
points[1,1] - points[0,1] == 0):
warnings.warn("Singular Bbox.")
_check = staticmethod(_check)
def frozen(self):
return Bbox(self.get_points().copy())
frozen.__doc__ = TransformNode.__doc__
def __array__(self, *args, **kwargs):
return self.get_points()
def is_unit(self):
"""
Returns True if the :class:`Bbox` is the unit bounding box
from (0, 0) to (1, 1).
"""
return list(self.get_points().flatten()) == [0., 0., 1., 1.]
def _get_x0(self):
return self.get_points()[0, 0]
x0 = property(_get_x0, None, None, """
(property) :attr:`x0` is the first of the pair of *x* coordinates that
define the bounding box. :attr:`x0` is not guaranteed to be
less than :attr:`x1`. If you require that, use :attr:`xmin`.""")
def _get_y0(self):
return self.get_points()[0, 1]
y0 = property(_get_y0, None, None, """
(property) :attr:`y0` is the first of the pair of *y* coordinates that
define the bounding box. :attr:`y0` is not guaranteed to be
less than :attr:`y1`. If you require that, use :attr:`ymin`.""")
def _get_x1(self):
return self.get_points()[1, 0]
x1 = property(_get_x1, None, None, """
(property) :attr:`x1` is the second of the pair of *x* coordinates that
define the bounding box. :attr:`x1` is not guaranteed to be
greater than :attr:`x0`. If you require that, use :attr:`xmax`.""")
def _get_y1(self):
return self.get_points()[1, 1]
y1 = property(_get_y1, None, None, """
(property) :attr:`y1` is the second of the pair of *y* coordinates that
define the bounding box. :attr:`y1` is not guaranteed to be
greater than :attr:`y0`. If you require that, use :attr:`ymax`.""")
def _get_p0(self):
return self.get_points()[0]
p0 = property(_get_p0, None, None, """
(property) :attr:`p0` is the first pair of (*x*, *y*) coordinates that
define the bounding box. It is not guaranteed to be the bottom-left
corner. For that, use :attr:`min`.""")
def _get_p1(self):
return self.get_points()[1]
p1 = property(_get_p1, None, None, """
(property) :attr:`p1` is the second pair of (*x*, *y*) coordinates that
define the bounding box. It is not guaranteed to be the top-right
corner. For that, use :attr:`max`.""")
def _get_xmin(self):
return min(self.get_points()[:, 0])
xmin = property(_get_xmin, None, None, """
(property) :attr:`xmin` is the left edge of the bounding box.""")
def _get_ymin(self):
return min(self.get_points()[:, 1])
ymin = property(_get_ymin, None, None, """
(property) :attr:`ymin` is the bottom edge of the bounding box.""")
def _get_xmax(self):
return max(self.get_points()[:, 0])
xmax = property(_get_xmax, None, None, """
(property) :attr:`xmax` is the right edge of the bounding box.""")
def _get_ymax(self):
return max(self.get_points()[:, 1])
ymax = property(_get_ymax, None, None, """
(property) :attr:`ymax` is the top edge of the bounding box.""")
def _get_min(self):
return [min(self.get_points()[:, 0]),
min(self.get_points()[:, 1])]
min = property(_get_min, None, None, """
(property) :attr:`min` is the bottom-left corner of the bounding
box.""")
def _get_max(self):
return [max(self.get_points()[:, 0]),
max(self.get_points()[:, 1])]
max = property(_get_max, None, None, """
(property) :attr:`max` is the top-right corner of the bounding box.""")
def _get_intervalx(self):
return self.get_points()[:, 0]
intervalx = property(_get_intervalx, None, None, """
(property) :attr:`intervalx` is the pair of *x* coordinates that define
the bounding box. It is not guaranteed to be sorted from left to
right.""")
def _get_intervaly(self):
return self.get_points()[:, 1]
intervaly = property(_get_intervaly, None, None, """
(property) :attr:`intervaly` is the pair of *y* coordinates that define
the bounding box. It is not guaranteed to be sorted from bottom to
top.""")
def _get_width(self):
points = self.get_points()
return points[1, 0] - points[0, 0]
width = property(_get_width, None, None, """
(property) The width of the bounding box. It may be negative if
:attr:`x1` < :attr:`x0`.""")
def _get_height(self):
points = self.get_points()
return points[1, 1] - points[0, 1]
height = property(_get_height, None, None, """
(property) The height of the bounding box. It may be negative if
:attr:`y1` < :attr:`y0`.""")
def _get_size(self):
points = self.get_points()
return points[1] - points[0]
size = property(_get_size, None, None, """
(property) The width and height of the bounding box. May be negative,
in the same way as :attr:`width` and :attr:`height`.""")
def _get_bounds(self):
x0, y0, x1, y1 = self.get_points().flatten()
return (x0, y0, x1 - x0, y1 - y0)
bounds = property(_get_bounds, None, None, """
(property) Returns (:attr:`x0`, :attr:`y0`, :attr:`width`,
:attr:`height`).""")
def _get_extents(self):
return self.get_points().flatten().copy()
extents = property(_get_extents, None, None, """
(property) Returns (:attr:`x0`, :attr:`y0`, :attr:`x1`, :attr:`y1`).""")
def get_points(self):
return NotImplementedError()
def containsx(self, x):
"""
Returns True if *x* is between or equal to :attr:`x0` and
:attr:`x1`.
"""
x0, x1 = self.intervalx
return ((x0 < x1
and (x >= x0 and x <= x1))
or (x >= x1 and x <= x0))
def containsy(self, y):
"""
Returns True if *y* is between or equal to :attr:`y0` and
:attr:`y1`.
"""
y0, y1 = self.intervaly
return ((y0 < y1
and (y >= y0 and y <= y1))
or (y >= y1 and y <= y0))
def contains(self, x, y):
"""
Returns *True* if (*x*, *y*) is a coordinate inside the
bounding box or on its edge.
"""
return self.containsx(x) and self.containsy(y)
def overlaps(self, other):
"""
Returns True if this bounding box overlaps with the given
bounding box *other*.
"""
ax1, ay1, ax2, ay2 = self._get_extents()
bx1, by1, bx2, by2 = other._get_extents()
if ax2 < ax1:
ax2, ax1 = ax1, ax2
if ay2 < ay1:
ay2, ay1 = ay1, ay2
if bx2 < bx1:
bx2, bx1 = bx1, bx2
if by2 < by1:
by2, by1 = by1, by2
return not ((bx2 < ax1) or
(by2 < ay1) or
(bx1 > ax2) or
(by1 > ay2))
def fully_containsx(self, x):
"""
Returns True if *x* is between but not equal to :attr:`x0` and
:attr:`x1`.
"""
x0, x1 = self.intervalx
return ((x0 < x1
and (x > x0 and x < x1))
or (x > x1 and x < x0))
def fully_containsy(self, y):
"""
Returns True if *y* is between but not equal to :attr:`y0` and
:attr:`y1`.
"""
y0, y1 = self.intervaly
return ((y0 < y1
and (x > y0 and x < y1))
or (x > y1 and x < y0))
def fully_contains(self, x, y):
"""
Returns True if (*x*, *y*) is a coordinate inside the bounding
box, but not on its edge.
"""
return self.fully_containsx(x) \
and self.fully_containsy(y)
def fully_overlaps(self, other):
"""
Returns True if this bounding box overlaps with the given
bounding box *other*, but not on its edge alone.
"""
ax1, ay1, ax2, ay2 = self._get_extents()
bx1, by1, bx2, by2 = other._get_extents()
if ax2 < ax1:
ax2, ax1 = ax1, ax2
if ay2 < ay1:
ay2, ay1 = ay1, ay2
if bx2 < bx1:
bx2, bx1 = bx1, bx2
if by2 < by1:
by2, by1 = by1, by2
return not ((bx2 <= ax1) or
(by2 <= ay1) or
(bx1 >= ax2) or
(by1 >= ay2))
def transformed(self, transform):
"""
Return a new :class:`Bbox` object, statically transformed by
the given transform.
"""
return Bbox(transform.transform(self.get_points()))
def inverse_transformed(self, transform):
"""
Return a new :class:`Bbox` object, statically transformed by
the inverse of the given transform.
"""
return Bbox(transform.inverted().transform(self.get_points()))
coefs = {'C': (0.5, 0.5),
'SW': (0,0),
'S': (0.5, 0),
'SE': (1.0, 0),
'E': (1.0, 0.5),
'NE': (1.0, 1.0),
'N': (0.5, 1.0),
'NW': (0, 1.0),
'W': (0, 0.5)}
def anchored(self, c, container = None):
"""
Return a copy of the :class:`Bbox`, shifted to position *c*
within a container.
*c*: may be either:
* a sequence (*cx*, *cy*) where *cx* and *cy* range from 0
to 1, where 0 is left or bottom and 1 is right or top
* a string:
- 'C' for centered
- 'S' for bottom-center
- 'SE' for bottom-left
- 'E' for left
- etc.
Optional argument *container* is the box within which the
:class:`Bbox` is positioned; it defaults to the initial
:class:`Bbox`.
"""
if container is None:
container = self
l, b, w, h = container.bounds
if isinstance(c, str):
cx, cy = self.coefs[c]
else:
cx, cy = c
L, B, W, H = self.bounds
return Bbox(self._points +
[(l + cx * (w-W)) - L,
(b + cy * (h-H)) - B])
def shrunk(self, mx, my):
"""
Return a copy of the :class:`Bbox`, shrunk by the factor *mx*
in the *x* direction and the factor *my* in the *y* direction.
The lower left corner of the box remains unchanged. Normally
*mx* and *my* will be less than 1, but this is not enforced.
"""
w, h = self.size
return Bbox([self._points[0],
self._points[0] + [mx * w, my * h]])
def shrunk_to_aspect(self, box_aspect, container = None, fig_aspect = 1.0):
"""
Return a copy of the :class:`Bbox`, shrunk so that it is as
large as it can be while having the desired aspect ratio,
*box_aspect*. If the box coordinates are relative---that
is, fractions of a larger box such as a figure---then the
physical aspect ratio of that figure is specified with
*fig_aspect*, so that *box_aspect* can also be given as a
ratio of the absolute dimensions, not the relative dimensions.
"""
assert box_aspect > 0 and fig_aspect > 0
if container is None:
container = self
w, h = container.size
H = w * box_aspect/fig_aspect
if H <= h:
W = w
else:
W = h * fig_aspect/box_aspect
H = h
return Bbox([self._points[0],
self._points[0] + (W, H)])
def splitx(self, *args):
"""
e.g., ``bbox.splitx(f1, f2, ...)``
Returns a list of new :class:`Bbox` objects formed by
splitting the original one with vertical lines at fractional
positions *f1*, *f2*, ...
"""
boxes = []
xf = [0] + list(args) + [1]
x0, y0, x1, y1 = self._get_extents()
w = x1 - x0
for xf0, xf1 in zip(xf[:-1], xf[1:]):
boxes.append(Bbox([[x0 + xf0 * w, y0], [x0 + xf1 * w, y1]]))
return boxes
def splity(self, *args):
"""
e.g., ``bbox.splitx(f1, f2, ...)``
Returns a list of new :class:`Bbox` objects formed by
splitting the original one with horizontal lines at fractional
positions *f1*, *f2*, ...
"""
boxes = []
yf = [0] + list(args) + [1]
x0, y0, x1, y1 = self._get_extents()
h = y1 - y0
for yf0, yf1 in zip(yf[:-1], yf[1:]):
boxes.append(Bbox([[x0, y0 + yf0 * h], [x1, y0 + yf1 * h]]))
return boxes
def count_contains(self, vertices):
"""
Count the number of vertices contained in the :class:`Bbox`.
*vertices* is a Nx2 Numpy array.
"""
if len(vertices) == 0:
return 0
vertices = np.asarray(vertices)
x0, y0, x1, y1 = self._get_extents()
dx0 = np.sign(vertices[:, 0] - x0)
dy0 = np.sign(vertices[:, 1] - y0)
dx1 = np.sign(vertices[:, 0] - x1)
dy1 = np.sign(vertices[:, 1] - y1)
inside = (abs(dx0 + dx1) + abs(dy0 + dy1)) <= 2
return np.sum(inside)
def count_overlaps(self, bboxes):
"""
Count the number of bounding boxes that overlap this one.
bboxes is a sequence of :class:`BboxBase` objects
"""
return count_bboxes_overlapping_bbox(self, bboxes)
def expanded(self, sw, sh):
"""
Return a new :class:`Bbox` which is this :class:`Bbox`
expanded around its center by the given factors *sw* and
*sh*.
"""
width = self.width
height = self.height
deltaw = (sw * width - width) / 2.0
deltah = (sh * height - height) / 2.0
a = np.array([[-deltaw, -deltah], [deltaw, deltah]])
return Bbox(self._points + a)
def padded(self, p):
"""
Return a new :class:`Bbox` that is padded on all four sides by
the given value.
"""
points = self._points
return Bbox(points + [[-p, -p], [p, p]])
def translated(self, tx, ty):
"""
Return a copy of the :class:`Bbox`, statically translated by
*tx* and *ty*.
"""
return Bbox(self._points + (tx, ty))
def corners(self):
"""
Return an array of points which are the four corners of this
rectangle. For example, if this :class:`Bbox` is defined by
the points (*a*, *b*) and (*c*, *d*), :meth:`corners` returns
(*a*, *b*), (*a*, *d*), (*c*, *b*) and (*c*, *d*).
"""
l, b, r, t = self.get_points().flatten()
return np.array([[l, b], [l, t], [r, b], [r, t]])
def rotated(self, radians):
"""
Return a new bounding box that bounds a rotated version of
this bounding box by the given radians. The new bounding box
is still aligned with the axes, of course.
"""
corners = self.corners()
corners_rotated = Affine2D().rotate(radians).transform(corners)
bbox = Bbox.unit()
bbox.update_from_data_xy(corners_rotated, ignore=True)
return bbox
#@staticmethod
def union(bboxes):
"""
Return a :class:`Bbox` that contains all of the given bboxes.
"""
assert(len(bboxes))
if len(bboxes) == 1:
return bboxes[0]
x0 = np.inf
y0 = np.inf
x1 = -np.inf
y1 = -np.inf
for bbox in bboxes:
points = bbox.get_points()
xs = points[:, 0]
ys = points[:, 1]
x0 = min(x0, np.min(xs))
y0 = min(y0, np.min(ys))
x1 = max(x1, np.max(xs))
y1 = max(y1, np.max(ys))
return Bbox.from_extents(x0, y0, x1, y1)
union = staticmethod(union)
class Bbox(BboxBase):
"""
A mutable bounding box.
"""
def __init__(self, points):
"""
*points*: a 2x2 numpy array of the form [[x0, y0], [x1, y1]]
If you need to create a :class:`Bbox` object from another form
of data, consider the static methods :meth:`unit`,
:meth:`from_bounds` and :meth:`from_extents`.
"""
BboxBase.__init__(self)
self._points = np.asarray(points, np.float_)
self._minpos = np.array([0.0000001, 0.0000001])
self._ignore = True
if DEBUG:
___init__ = __init__
def __init__(self, points):
self._check(points)
self.___init__(points)
def invalidate(self):
self._check(self._points)
TransformNode.invalidate(self)
_unit_values = np.array([[0.0, 0.0], [1.0, 1.0]], np.float_)
#@staticmethod
def unit():
"""
(staticmethod) Create a new unit :class:`Bbox` from (0, 0) to
(1, 1).
"""
return Bbox(Bbox._unit_values.copy())
unit = staticmethod(unit)
#@staticmethod
def from_bounds(x0, y0, width, height):
"""
(staticmethod) Create a new :class:`Bbox` from *x0*, *y0*,
*width* and *height*.
*width* and *height* may be negative.
"""
return Bbox.from_extents(x0, y0, x0 + width, y0 + height)
from_bounds = staticmethod(from_bounds)
#@staticmethod
def from_extents(*args):
"""
(staticmethod) Create a new Bbox from *left*, *bottom*,
*right* and *top*.
The *y*-axis increases upwards.
"""
points = np.array(args, dtype=np.float_).reshape(2, 2)
return Bbox(points)
from_extents = staticmethod(from_extents)
def __repr__(self):
return 'Bbox(%s)' % repr(self._points)
__str__ = __repr__
def ignore(self, value):
"""
Set whether the existing bounds of the box should be ignored
by subsequent calls to :meth:`update_from_data` or
:meth:`update_from_data_xy`.
*value*:
- When True, subsequent calls to :meth:`update_from_data`
will ignore the existing bounds of the :class:`Bbox`.
- When False, subsequent calls to :meth:`update_from_data`
will include the existing bounds of the :class:`Bbox`.
"""
self._ignore = value
def update_from_data(self, x, y, ignore=None):
"""
Update the bounds of the :class:`Bbox` based on the passed in
data. After updating, the bounds will have positive *width*
and *height*; *x0* and *y0* will be the minimal values.
*x*: a numpy array of *x*-values
*y*: a numpy array of *y*-values
*ignore*:
- when True, ignore the existing bounds of the :class:`Bbox`.
- when False, include the existing bounds of the :class:`Bbox`.
- when None, use the last value passed to :meth:`ignore`.
"""
warnings.warn(
"update_from_data requires a memory copy -- please replace with update_from_data_xy")
xy = np.hstack((x.reshape((len(x), 1)), y.reshape((len(y), 1))))
return self.update_from_data_xy(xy, ignore)
def update_from_path(self, path, ignore=None, updatex=True, updatey=True):
"""
Update the bounds of the :class:`Bbox` based on the passed in
data. After updating, the bounds will have positive *width*
and *height*; *x0* and *y0* will be the minimal values.
*path*: a :class:`~matplotlib.path.Path` instance
*ignore*:
- when True, ignore the existing bounds of the :class:`Bbox`.
- when False, include the existing bounds of the :class:`Bbox`.
- when None, use the last value passed to :meth:`ignore`.
*updatex*: when True, update the x values
*updatey*: when True, update the y values
"""
if ignore is None:
ignore = self._ignore
if path.vertices.size == 0:
return
points, minpos, changed = update_path_extents(
path, None, self._points, self._minpos, ignore)
if changed:
self.invalidate()
if updatex:
self._points[:,0] = points[:,0]
self._minpos[0] = minpos[0]
if updatey:
self._points[:,1] = points[:,1]
self._minpos[1] = minpos[1]
def update_from_data_xy(self, xy, ignore=None, updatex=True, updatey=True):
"""
Update the bounds of the :class:`Bbox` based on the passed in
data. After updating, the bounds will have positive *width*
and *height*; *x0* and *y0* will be the minimal values.
*xy*: a numpy array of 2D points
*ignore*:
- when True, ignore the existing bounds of the :class:`Bbox`.
- when False, include the existing bounds of the :class:`Bbox`.
- when None, use the last value passed to :meth:`ignore`.
*updatex*: when True, update the x values
*updatey*: when True, update the y values
"""
if len(xy) == 0:
return
path = Path(xy)
self.update_from_path(path, ignore=ignore,
updatex=updatex, updatey=updatey)
def _set_x0(self, val):
self._points[0, 0] = val
self.invalidate()
x0 = property(BboxBase._get_x0, _set_x0)
def _set_y0(self, val):
self._points[0, 1] = val
self.invalidate()
y0 = property(BboxBase._get_y0, _set_y0)
def _set_x1(self, val):
self._points[1, 0] = val
self.invalidate()
x1 = property(BboxBase._get_x1, _set_x1)
def _set_y1(self, val):
self._points[1, 1] = val
self.invalidate()
y1 = property(BboxBase._get_y1, _set_y1)
def _set_p0(self, val):
self._points[0] = val
self.invalidate()
p0 = property(BboxBase._get_p0, _set_p0)
def _set_p1(self, val):
self._points[1] = val
self.invalidate()
p1 = property(BboxBase._get_p1, _set_p1)
def _set_intervalx(self, interval):
self._points[:, 0] = interval
self.invalidate()
intervalx = property(BboxBase._get_intervalx, _set_intervalx)
def _set_intervaly(self, interval):
self._points[:, 1] = interval
self.invalidate()
intervaly = property(BboxBase._get_intervaly, _set_intervaly)
def _set_bounds(self, bounds):
l, b, w, h = bounds
points = np.array([[l, b], [l+w, b+h]], np.float_)
if np.any(self._points != points):
self._points = points
self.invalidate()
bounds = property(BboxBase._get_bounds, _set_bounds)
def _get_minpos(self):
return self._minpos
minpos = property(_get_minpos)
def _get_minposx(self):
return self._minpos[0]
minposx = property(_get_minposx)
def _get_minposy(self):
return self._minpos[1]
minposy = property(_get_minposy)
def get_points(self):
"""
Get the points of the bounding box directly as a numpy array
of the form: [[x0, y0], [x1, y1]].
"""
self._invalid = 0
return self._points
def set_points(self, points):
"""
Set the points of the bounding box directly from a numpy array
of the form: [[x0, y0], [x1, y1]]. No error checking is
performed, as this method is mainly for internal use.
"""
if np.any(self._points != points):
self._points = points
self.invalidate()
def set(self, other):
"""
Set this bounding box from the "frozen" bounds of another
:class:`Bbox`.
"""
if np.any(self._points != other.get_points()):
self._points = other.get_points()
self.invalidate()
class TransformedBbox(BboxBase):
"""
A :class:`Bbox` that is automatically transformed by a given
transform. When either the child bounding box or transform
changes, the bounds of this bbox will update accordingly.
"""
def __init__(self, bbox, transform):
"""
*bbox*: a child :class:`Bbox`
*transform*: a 2D :class:`Transform`
"""
assert bbox.is_bbox
assert isinstance(transform, Transform)
assert transform.input_dims == 2
assert transform.output_dims == 2
BboxBase.__init__(self)
self._bbox = bbox
self._transform = transform
self.set_children(bbox, transform)
self._points = None
def __repr__(self):
return "TransformedBbox(%s, %s)" % (self._bbox, self._transform)
__str__ = __repr__
def get_points(self):
if self._invalid:
points = self._transform.transform(self._bbox.get_points())
if ma.isMaskedArray(points):
points.putmask(0.0)
points = np.asarray(points)
self._points = points
self._invalid = 0
return self._points
get_points.__doc__ = Bbox.get_points.__doc__
if DEBUG:
_get_points = get_points
def get_points(self):
points = self._get_points()
self._check(points)
return points
class Transform(TransformNode):
"""
The base class of all :class:`TransformNode` instances that
actually perform a transformation.
All non-affine transformations should be subclasses of this class.
New affine transformations should be subclasses of
:class:`Affine2D`.
Subclasses of this class should override the following members (at
minimum):
- :attr:`input_dims`
- :attr:`output_dims`
- :meth:`transform`
- :attr:`is_separable`
- :attr:`has_inverse`
- :meth:`inverted` (if :meth:`has_inverse` can return True)
If the transform needs to do something non-standard with
:class:`mathplotlib.path.Path` objects, such as adding curves
where there were once line segments, it should override:
- :meth:`transform_path`
"""
# The number of input and output dimensions for this transform.
# These must be overridden (with integers) in the subclass.
input_dims = None
output_dims = None
# True if this transform as a corresponding inverse transform.
has_inverse = False
# True if this transform is separable in the x- and y- dimensions.
is_separable = False
#* Redundant: Removed for performance
#
# def __init__(self):
# TransformNode.__init__(self)
def __add__(self, other):
"""
Composes two transforms together such that *self* is followed
by *other*.
"""
if isinstance(other, Transform):
return composite_transform_factory(self, other)
raise TypeError(
"Can not add Transform to object of type '%s'" % type(other))
def __radd__(self, other):
"""
Composes two transforms together such that *self* is followed
by *other*.
"""
if isinstance(other, Transform):
return composite_transform_factory(other, self)
raise TypeError(
"Can not add Transform to object of type '%s'" % type(other))
def __array__(self, *args, **kwargs):
"""
Used by C/C++ -based backends to get at the array matrix data.
"""
return self.frozen().__array__()
def transform(self, values):
"""
Performs the transformation on the given array of values.
Accepts a numpy array of shape (N x :attr:`input_dims`) and
returns a numpy array of shape (N x :attr:`output_dims`).
"""
raise NotImplementedError()
def transform_affine(self, values):
"""
Performs only the affine part of this transformation on the
given array of values.
``transform(values)`` is always equivalent to
``transform_affine(transform_non_affine(values))``.
In non-affine transformations, this is generally a no-op. In
affine transformations, this is equivalent to
``transform(values)``.
Accepts a numpy array of shape (N x :attr:`input_dims`) and
returns a numpy array of shape (N x :attr:`output_dims`).
"""
return values
def transform_non_affine(self, values):
"""
Performs only the non-affine part of the transformation.
``transform(values)`` is always equivalent to
``transform_affine(transform_non_affine(values))``.
In non-affine transformations, this is generally equivalent to
``transform(values)``. In affine transformations, this is
always a no-op.
Accepts a numpy array of shape (N x :attr:`input_dims`) and
returns a numpy array of shape (N x :attr:`output_dims`).
"""
return self.transform(values)
def get_affine(self):
"""
Get the affine part of this transform.
"""
return IdentityTransform()
def transform_point(self, point):
"""
A convenience function that returns the transformed copy of a
single point.
The point is given as a sequence of length :attr:`input_dims`.
The transformed point is returned as a sequence of length
:attr:`output_dims`.
"""
assert len(point) == self.input_dims
return self.transform(np.asarray([point]))[0]
def transform_path(self, path):
"""
Returns a transformed copy of path.
*path*: a :class:`~matplotlib.path.Path` instance.
In some cases, this transform may insert curves into the path
that began as line segments.
"""
return Path(self.transform(path.vertices), path.codes)
def transform_path_affine(self, path):
"""
Returns a copy of path, transformed only by the affine part of
this transform.
*path*: a :class:`~matplotlib.path.Path` instance.
``transform_path(path)`` is equivalent to
``transform_path_affine(transform_path_non_affine(values))``.
"""
return path
def transform_path_non_affine(self, path):
"""
Returns a copy of path, transformed only by the non-affine
part of this transform.
*path*: a :class:`~matplotlib.path.Path` instance.
``transform_path(path)`` is equivalent to
``transform_path_affine(transform_path_non_affine(values))``.
"""
return Path(self.transform_non_affine(path.vertices), path.codes)
def transform_angles(self, angles, pts, radians=False, pushoff=1e-5):
"""
Performs transformation on a set of angles anchored at
specific locations.
The *angles* must be a column vector (i.e., numpy array).
The *pts* must be a two-column numpy array of x,y positions
(angle transforms currently only work in 2D). This array must
have the same number of rows as *angles*.
*radians* indicates whether or not input angles are given in
radians (True) or degrees (False; the default).
*pushoff* is the distance to move away from *pts* for
determining transformed angles (see discussion of method
below).
The transformed angles are returned in an array with the same
size as *angles*.
The generic version of this method uses a very generic
algorithm that transforms *pts*, as well as locations very
close to *pts*, to find the angle in the transformed system.
"""
# Must be 2D
if self.input_dims <> 2 or self.output_dims <> 2:
raise NotImplementedError('Only defined in 2D')
# pts must be array with 2 columns for x,y
assert pts.shape[1] == 2
# angles must be a column vector and have same number of
# rows as pts
assert np.prod(angles.shape) == angles.shape[0] == pts.shape[0]
# Convert to radians if desired
if not radians:
angles = angles / 180.0 * np.pi
# Move a short distance away
pts2 = pts + pushoff * np.c_[ np.cos(angles), np.sin(angles) ]
# Transform both sets of points
tpts = self.transform( pts )
tpts2 = self.transform( pts2 )
# Calculate transformed angles
d = tpts2 - tpts
a = np.arctan2( d[:,1], d[:,0] )
# Convert back to degrees if desired
if not radians:
a = a * 180.0 / np.pi
return a
def inverted(self):
"""
Return the corresponding inverse transformation.
The return value of this method should be treated as
temporary. An update to *self* does not cause a corresponding
update to its inverted copy.
``x === self.inverted().transform(self.transform(x))``
"""
raise NotImplementedError()
class TransformWrapper(Transform):
"""
A helper class that holds a single child transform and acts
equivalently to it.
This is useful if a node of the transform tree must be replaced at
run time with a transform of a different type. This class allows
that replacement to correctly trigger invalidation.
Note that :class:`TransformWrapper` instances must have the same
input and output dimensions during their entire lifetime, so the
child transform may only be replaced with another child transform
of the same dimensions.
"""
pass_through = True
is_affine = False
def __init__(self, child):
"""
*child*: A class:`Transform` instance. This child may later
be replaced with :meth:`set`.
"""
assert isinstance(child, Transform)
Transform.__init__(self)
self.input_dims = child.input_dims
self.output_dims = child.output_dims
self._set(child)
self._invalid = 0
def __repr__(self):
return "TransformWrapper(%r)" % self._child
__str__ = __repr__
def frozen(self):
return self._child.frozen()
frozen.__doc__ = Transform.frozen.__doc__
def _set(self, child):
self._child = child
self.set_children(child)
self.transform = child.transform
self.transform_affine = child.transform_affine
self.transform_non_affine = child.transform_non_affine
self.transform_path = child.transform_path
self.transform_path_affine = child.transform_path_affine
self.transform_path_non_affine = child.transform_path_non_affine
self.get_affine = child.get_affine
self.inverted = child.inverted
def set(self, child):
"""
Replace the current child of this transform with another one.
The new child must have the same number of input and output
dimensions as the current child.
"""
assert child.input_dims == self.input_dims
assert child.output_dims == self.output_dims
self._set(child)
self._invalid = 0
self.invalidate()
self._invalid = 0
def _get_is_separable(self):
return self._child.is_separable
is_separable = property(_get_is_separable)
def _get_has_inverse(self):
return self._child.has_inverse
has_inverse = property(_get_has_inverse)
class AffineBase(Transform):
"""
The base class of all affine transformations of any number of
dimensions.
"""
is_affine = True
def __init__(self):
Transform.__init__(self)
self._inverted = None
def __array__(self, *args, **kwargs):
return self.get_matrix()
#@staticmethod
def _concat(a, b):
"""
Concatenates two transformation matrices (represented as numpy
arrays) together.
"""
return np.dot(b, a)
_concat = staticmethod(_concat)
def get_matrix(self):
"""
Get the underlying transformation matrix as a numpy array.
"""
raise NotImplementedError()
def transform_non_affine(self, points):
return points
transform_non_affine.__doc__ = Transform.transform_non_affine.__doc__
def transform_path_affine(self, path):
return self.transform_path(path)
transform_path_affine.__doc__ = Transform.transform_path_affine.__doc__
def transform_path_non_affine(self, path):
return path
transform_path_non_affine.__doc__ = Transform.transform_path_non_affine.__doc__
def get_affine(self):
return self
get_affine.__doc__ = Transform.get_affine.__doc__
class Affine2DBase(AffineBase):
"""
The base class of all 2D affine transformations.
2D affine transformations are performed using a 3x3 numpy array::
a c e
b d f
0 0 1
This class provides the read-only interface. For a mutable 2D
affine transformation, use :class:`Affine2D`.
Subclasses of this class will generally only need to override a
constructor and :meth:`get_matrix` that generates a custom 3x3 matrix.
"""
input_dims = 2
output_dims = 2
#* Redundant: Removed for performance
#
# def __init__(self):
# Affine2DBase.__init__(self)
def frozen(self):
return Affine2D(self.get_matrix().copy())
frozen.__doc__ = AffineBase.frozen.__doc__
def _get_is_separable(self):
mtx = self.get_matrix()
return mtx[0, 1] == 0.0 and mtx[1, 0] == 0.0
is_separable = property(_get_is_separable)
def __array__(self, *args, **kwargs):
return self.get_matrix()
def to_values(self):
"""
Return the values of the matrix as a sequence (a,b,c,d,e,f)
"""
mtx = self.get_matrix()
return tuple(mtx[:2].swapaxes(0, 1).flatten())
#@staticmethod
def matrix_from_values(a, b, c, d, e, f):
"""
(staticmethod) Create a new transformation matrix as a 3x3
numpy array of the form::
a c e
b d f
0 0 1
"""
return np.array([[a, c, e], [b, d, f], [0.0, 0.0, 1.0]], np.float_)
matrix_from_values = staticmethod(matrix_from_values)
def transform(self, points):
mtx = self.get_matrix()
if isinstance(points, MaskedArray):
tpoints = affine_transform(points.data, mtx)
return ma.MaskedArray(tpoints, mask=ma.getmask(points))
return affine_transform(points, mtx)
def transform_point(self, point):
mtx = self.get_matrix()
return affine_transform(point, mtx)
transform_point.__doc__ = AffineBase.transform_point.__doc__
if DEBUG:
_transform = transform
def transform(self, points):
# The major speed trap here is just converting to the
# points to an array in the first place. If we can use
# more arrays upstream, that should help here.
if (not ma.isMaskedArray(points) and
not isinstance(points, np.ndarray)):
warnings.warn(
('A non-numpy array of type %s was passed in for ' +
'transformation. Please correct this.')
% type(values))
return self._transform(points)
transform.__doc__ = AffineBase.transform.__doc__
transform_affine = transform
transform_affine.__doc__ = AffineBase.transform_affine.__doc__
def inverted(self):
if self._inverted is None or self._invalid:
mtx = self.get_matrix()
self._inverted = Affine2D(inv(mtx))
self._invalid = 0
return self._inverted
inverted.__doc__ = AffineBase.inverted.__doc__
class Affine2D(Affine2DBase):
"""
A mutable 2D affine transformation.
"""
def __init__(self, matrix = None):
"""
Initialize an Affine transform from a 3x3 numpy float array::
a c e
b d f
0 0 1
If *matrix* is None, initialize with the identity transform.
"""
Affine2DBase.__init__(self)
if matrix is None:
matrix = np.identity(3)
elif DEBUG:
matrix = np.asarray(matrix, np.float_)
assert matrix.shape == (3, 3)
self._mtx = matrix
self._invalid = 0
def __repr__(self):
return "Affine2D(%s)" % repr(self._mtx)
__str__ = __repr__
def __cmp__(self, other):
if (isinstance(other, Affine2D) and
(self.get_matrix() == other.get_matrix()).all()):
return 0
return -1
#@staticmethod
def from_values(a, b, c, d, e, f):
"""
(staticmethod) Create a new Affine2D instance from the given
values::
a c e
b d f
0 0 1
"""
return Affine2D(
np.array([a, c, e, b, d, f, 0.0, 0.0, 1.0], np.float_)
.reshape((3,3)))
from_values = staticmethod(from_values)
def get_matrix(self):
"""
Get the underlying transformation matrix as a 3x3 numpy array::
a c e
b d f
0 0 1
"""
self._invalid = 0
return self._mtx
def set_matrix(self, mtx):
"""
Set the underlying transformation matrix from a 3x3 numpy array::
a c e
b d f
0 0 1
"""
self._mtx = mtx
self.invalidate()
def set(self, other):
"""
Set this transformation from the frozen copy of another
:class:`Affine2DBase` object.
"""
assert isinstance(other, Affine2DBase)
self._mtx = other.get_matrix()
self.invalidate()
#@staticmethod
def identity():
"""
(staticmethod) Return a new :class:`Affine2D` object that is
the identity transform.
Unless this transform will be mutated later on, consider using
the faster :class:`IdentityTransform` class instead.
"""
return Affine2D(np.identity(3))
identity = staticmethod(identity)
def clear(self):
"""
Reset the underlying matrix to the identity transform.
"""
self._mtx = np.identity(3)
self.invalidate()
return self
def rotate(self, theta):
"""
Add a rotation (in radians) to this transform in place.
Returns *self*, so this method can easily be chained with more
calls to :meth:`rotate`, :meth:`rotate_deg`, :meth:`translate`
and :meth:`scale`.
"""
a = np.cos(theta)
b = np.sin(theta)
rotate_mtx = np.array(
[[a, -b, 0.0], [b, a, 0.0], [0.0, 0.0, 1.0]],
np.float_)
self._mtx = np.dot(rotate_mtx, self._mtx)
self.invalidate()
return self
def rotate_deg(self, degrees):
"""
Add a rotation (in degrees) to this transform in place.
Returns *self*, so this method can easily be chained with more
calls to :meth:`rotate`, :meth:`rotate_deg`, :meth:`translate`
and :meth:`scale`.
"""
return self.rotate(degrees*np.pi/180.)
def rotate_around(self, x, y, theta):
"""
Add a rotation (in radians) around the point (x, y) in place.
Returns *self*, so this method can easily be chained with more
calls to :meth:`rotate`, :meth:`rotate_deg`, :meth:`translate`
and :meth:`scale`.
"""
return self.translate(-x, -y).rotate(theta).translate(x, y)
def rotate_deg_around(self, x, y, degrees):
"""
Add a rotation (in degrees) around the point (x, y) in place.
Returns *self*, so this method can easily be chained with more
calls to :meth:`rotate`, :meth:`rotate_deg`, :meth:`translate`
and :meth:`scale`.
"""
return self.translate(-x, -y).rotate_deg(degrees).translate(x, y)
def translate(self, tx, ty):
"""
Adds a translation in place.
Returns *self*, so this method can easily be chained with more
calls to :meth:`rotate`, :meth:`rotate_deg`, :meth:`translate`
and :meth:`scale`.
"""
translate_mtx = np.array(
[[1.0, 0.0, tx], [0.0, 1.0, ty], [0.0, 0.0, 1.0]],
np.float_)
self._mtx = np.dot(translate_mtx, self._mtx)
self.invalidate()
return self
def scale(self, sx, sy=None):
"""
Adds a scale in place.
If *sy* is None, the same scale is applied in both the *x*- and
*y*-directions.
Returns *self*, so this method can easily be chained with more
calls to :meth:`rotate`, :meth:`rotate_deg`, :meth:`translate`
and :meth:`scale`.
"""
if sy is None:
sy = sx
scale_mtx = np.array(
[[sx, 0.0, 0.0], [0.0, sy, 0.0], [0.0, 0.0, 1.0]],
np.float_)
self._mtx = np.dot(scale_mtx, self._mtx)
self.invalidate()
return self
def _get_is_separable(self):
mtx = self.get_matrix()
return mtx[0, 1] == 0.0 and mtx[1, 0] == 0.0
is_separable = property(_get_is_separable)
class IdentityTransform(Affine2DBase):
"""
A special class that does on thing, the identity transform, in a
fast way.
"""
_mtx = np.identity(3)
def frozen(self):
return self
frozen.__doc__ = Affine2DBase.frozen.__doc__
def __repr__(self):
return "IdentityTransform()"
__str__ = __repr__
def get_matrix(self):
return self._mtx
get_matrix.__doc__ = Affine2DBase.get_matrix.__doc__
def transform(self, points):
return points
transform.__doc__ = Affine2DBase.transform.__doc__
transform_affine = transform
transform_affine.__doc__ = Affine2DBase.transform_affine.__doc__
transform_non_affine = transform
transform_non_affine.__doc__ = Affine2DBase.transform_non_affine.__doc__
def transform_path(self, path):
return path
transform_path.__doc__ = Affine2DBase.transform_path.__doc__
transform_path_affine = transform_path
transform_path_affine.__doc__ = Affine2DBase.transform_path_affine.__doc__
transform_path_non_affine = transform_path
transform_path_non_affine.__doc__ = Affine2DBase.transform_path_non_affine.__doc__
def get_affine(self):
return self
get_affine.__doc__ = Affine2DBase.get_affine.__doc__
inverted = get_affine
inverted.__doc__ = Affine2DBase.inverted.__doc__
class BlendedGenericTransform(Transform):
"""
A "blended" transform uses one transform for the *x*-direction, and
another transform for the *y*-direction.
This "generic" version can handle any given child transform in the
*x*- and *y*-directions.
"""
input_dims = 2
output_dims = 2
is_separable = True
pass_through = True
def __init__(self, x_transform, y_transform):
"""
Create a new "blended" transform using *x_transform* to
transform the *x*-axis and *y_transform* to transform the
*y*-axis.
You will generally not call this constructor directly but use
the :func:`blended_transform_factory` function instead, which
can determine automatically which kind of blended transform to
create.
"""
# Here we ask: "Does it blend?"
Transform.__init__(self)
self._x = x_transform
self._y = y_transform
self.set_children(x_transform, y_transform)
self._affine = None
def _get_is_affine(self):
return self._x.is_affine and self._y.is_affine
is_affine = property(_get_is_affine)
def frozen(self):
return blended_transform_factory(self._x.frozen(), self._y.frozen())
frozen.__doc__ = Transform.frozen.__doc__
def __repr__(self):
return "BlendedGenericTransform(%s,%s)" % (self._x, self._y)
__str__ = __repr__
def transform(self, points):
x = self._x
y = self._y
if x is y and x.input_dims == 2:
return x.transform(points)
if x.input_dims == 2:
x_points = x.transform(points)[:, 0:1]
else:
x_points = x.transform(points[:, 0])
x_points = x_points.reshape((len(x_points), 1))
if y.input_dims == 2:
y_points = y.transform(points)[:, 1:]
else:
y_points = y.transform(points[:, 1])
y_points = y_points.reshape((len(y_points), 1))
if isinstance(x_points, MaskedArray) or isinstance(y_points, MaskedArray):
return ma.concatenate((x_points, y_points), 1)
else:
return np.concatenate((x_points, y_points), 1)
transform.__doc__ = Transform.transform.__doc__
def transform_affine(self, points):
return self.get_affine().transform(points)
transform_affine.__doc__ = Transform.transform_affine.__doc__
def transform_non_affine(self, points):
if self._x.is_affine and self._y.is_affine:
return points
return self.transform(points)
transform_non_affine.__doc__ = Transform.transform_non_affine.__doc__
def inverted(self):
return BlendedGenericTransform(self._x.inverted(), self._y.inverted())
inverted.__doc__ = Transform.inverted.__doc__
def get_affine(self):
if self._invalid or self._affine is None:
if self._x.is_affine and self._y.is_affine:
if self._x == self._y:
self._affine = self._x.get_affine()
else:
x_mtx = self._x.get_affine().get_matrix()
y_mtx = self._y.get_affine().get_matrix()
# This works because we already know the transforms are
# separable, though normally one would want to set b and
# c to zero.
mtx = np.vstack((x_mtx[0], y_mtx[1], [0.0, 0.0, 1.0]))
self._affine = Affine2D(mtx)
else:
self._affine = IdentityTransform()
self._invalid = 0
return self._affine
get_affine.__doc__ = Transform.get_affine.__doc__
class BlendedAffine2D(Affine2DBase):
"""
A "blended" transform uses one transform for the *x*-direction, and
another transform for the *y*-direction.
This version is an optimization for the case where both child
transforms are of type :class:`Affine2DBase`.
"""
is_separable = True
def __init__(self, x_transform, y_transform):
"""
Create a new "blended" transform using *x_transform* to
transform the *x*-axis and *y_transform* to transform the
*y*-axis.
Both *x_transform* and *y_transform* must be 2D affine
transforms.
You will generally not call this constructor directly but use
the :func:`blended_transform_factory` function instead, which
can determine automatically which kind of blended transform to
create.
"""
assert x_transform.is_affine
assert y_transform.is_affine
assert x_transform.is_separable
assert y_transform.is_separable
Transform.__init__(self)
self._x = x_transform
self._y = y_transform
self.set_children(x_transform, y_transform)
Affine2DBase.__init__(self)
self._mtx = None
def __repr__(self):
return "BlendedAffine2D(%s,%s)" % (self._x, self._y)
__str__ = __repr__
def get_matrix(self):
if self._invalid:
if self._x == self._y:
self._mtx = self._x.get_matrix()
else:
x_mtx = self._x.get_matrix()
y_mtx = self._y.get_matrix()
# This works because we already know the transforms are
# separable, though normally one would want to set b and
# c to zero.
self._mtx = np.vstack((x_mtx[0], y_mtx[1], [0.0, 0.0, 1.0]))
self._inverted = None
self._invalid = 0
return self._mtx
get_matrix.__doc__ = Affine2DBase.get_matrix.__doc__
def blended_transform_factory(x_transform, y_transform):
"""
Create a new "blended" transform using *x_transform* to transform
the *x*-axis and *y_transform* to transform the *y*-axis.
A faster version of the blended transform is returned for the case
where both child transforms are affine.
"""
if (isinstance(x_transform, Affine2DBase)
and isinstance(y_transform, Affine2DBase)):
return BlendedAffine2D(x_transform, y_transform)
return BlendedGenericTransform(x_transform, y_transform)
class CompositeGenericTransform(Transform):
"""
A composite transform formed by applying transform *a* then
transform *b*.
This "generic" version can handle any two arbitrary
transformations.
"""
pass_through = True
def __init__(self, a, b):
"""
Create a new composite transform that is the result of
applying transform *a* then transform *b*.
You will generally not call this constructor directly but use
the :func:`composite_transform_factory` function instead,
which can automatically choose the best kind of composite
transform instance to create.
"""
assert a.output_dims == b.input_dims
self.input_dims = a.input_dims
self.output_dims = b.output_dims
Transform.__init__(self)
self._a = a
self._b = b
self.set_children(a, b)
def frozen(self):
self._invalid = 0
frozen = composite_transform_factory(self._a.frozen(), self._b.frozen())
if not isinstance(frozen, CompositeGenericTransform):
return frozen.frozen()
return frozen
frozen.__doc__ = Transform.frozen.__doc__
def _get_is_affine(self):
return self._a.is_affine and self._b.is_affine
is_affine = property(_get_is_affine)
def _get_is_separable(self):
return self._a.is_separable and self._b.is_separable
is_separable = property(_get_is_separable)
def __repr__(self):
return "CompositeGenericTransform(%s, %s)" % (self._a, self._b)
__str__ = __repr__
def transform(self, points):
return self._b.transform(
self._a.transform(points))
transform.__doc__ = Transform.transform.__doc__
def transform_affine(self, points):
return self.get_affine().transform(points)
transform_affine.__doc__ = Transform.transform_affine.__doc__
def transform_non_affine(self, points):
if self._a.is_affine and self._b.is_affine:
return points
return self._b.transform_non_affine(
self._a.transform(points))
transform_non_affine.__doc__ = Transform.transform_non_affine.__doc__
def transform_path(self, path):
return self._b.transform_path(
self._a.transform_path(path))
transform_path.__doc__ = Transform.transform_path.__doc__
def transform_path_affine(self, path):
return self._b.transform_path_affine(
self._a.transform_path(path))
transform_path_affine.__doc__ = Transform.transform_path_affine.__doc__
def transform_path_non_affine(self, path):
if self._a.is_affine and self._b.is_affine:
return path
return self._b.transform_path_non_affine(
self._a.transform_path(path))
transform_path_non_affine.__doc__ = Transform.transform_path_non_affine.__doc__
def get_affine(self):
if self._a.is_affine and self._b.is_affine:
return Affine2D(np.dot(self._b.get_affine().get_matrix(),
self._a.get_affine().get_matrix()))
else:
return self._b.get_affine()
get_affine.__doc__ = Transform.get_affine.__doc__
def inverted(self):
return CompositeGenericTransform(self._b.inverted(), self._a.inverted())
inverted.__doc__ = Transform.inverted.__doc__
class CompositeAffine2D(Affine2DBase):
"""
A composite transform formed by applying transform *a* then transform *b*.
This version is an optimization that handles the case where both *a*
and *b* are 2D affines.
"""
def __init__(self, a, b):
"""
Create a new composite transform that is the result of
applying transform *a* then transform *b*.
Both *a* and *b* must be instances of :class:`Affine2DBase`.
You will generally not call this constructor directly but use
the :func:`composite_transform_factory` function instead,
which can automatically choose the best kind of composite
transform instance to create.
"""
assert a.output_dims == b.input_dims
self.input_dims = a.input_dims
self.output_dims = b.output_dims
assert a.is_affine
assert b.is_affine
Affine2DBase.__init__(self)
self._a = a
self._b = b
self.set_children(a, b)
self._mtx = None
def __repr__(self):
return "CompositeAffine2D(%s, %s)" % (self._a, self._b)
__str__ = __repr__
def get_matrix(self):
if self._invalid:
self._mtx = np.dot(
self._b.get_matrix(),
self._a.get_matrix())
self._inverted = None
self._invalid = 0
return self._mtx
get_matrix.__doc__ = Affine2DBase.get_matrix.__doc__
def composite_transform_factory(a, b):
"""
Create a new composite transform that is the result of applying
transform a then transform b.
Shortcut versions of the blended transform are provided for the
case where both child transforms are affine, or one or the other
is the identity transform.
Composite transforms may also be created using the '+' operator,
e.g.::
c = a + b
"""
if isinstance(a, IdentityTransform):
return b
elif isinstance(b, IdentityTransform):
return a
elif isinstance(a, AffineBase) and isinstance(b, AffineBase):
return CompositeAffine2D(a, b)
return CompositeGenericTransform(a, b)
class BboxTransform(Affine2DBase):
"""
:class:`BboxTransform` linearly transforms points from one
:class:`Bbox` to another :class:`Bbox`.
"""
is_separable = True
def __init__(self, boxin, boxout):
"""
Create a new :class:`BboxTransform` that linearly transforms
points from *boxin* to *boxout*.
"""
assert boxin.is_bbox
assert boxout.is_bbox
Affine2DBase.__init__(self)
self._boxin = boxin
self._boxout = boxout
self.set_children(boxin, boxout)
self._mtx = None
self._inverted = None
def __repr__(self):
return "BboxTransform(%s, %s)" % (self._boxin, self._boxout)
__str__ = __repr__
def get_matrix(self):
if self._invalid:
inl, inb, inw, inh = self._boxin.bounds
outl, outb, outw, outh = self._boxout.bounds
x_scale = outw / inw
y_scale = outh / inh
if DEBUG and (x_scale == 0 or y_scale == 0):
raise ValueError("Transforming from or to a singular bounding box.")
self._mtx = np.array([[x_scale, 0.0 , (-inl*x_scale+outl)],
[0.0 , y_scale, (-inb*y_scale+outb)],
[0.0 , 0.0 , 1.0 ]],
np.float_)
self._inverted = None
self._invalid = 0
return self._mtx
get_matrix.__doc__ = Affine2DBase.get_matrix.__doc__
class BboxTransformTo(Affine2DBase):
"""
:class:`BboxTransformTo` is a transformation that linearly
transforms points from the unit bounding box to a given
:class:`Bbox`.
"""
is_separable = True
def __init__(self, boxout):
"""
Create a new :class:`BboxTransformTo` that linearly transforms
points from the unit bounding box to *boxout*.
"""
assert boxout.is_bbox
Affine2DBase.__init__(self)
self._boxout = boxout
self.set_children(boxout)
self._mtx = None
self._inverted = None
def __repr__(self):
return "BboxTransformTo(%s)" % (self._boxout)
__str__ = __repr__
def get_matrix(self):
if self._invalid:
outl, outb, outw, outh = self._boxout.bounds
if DEBUG and (outw == 0 or outh == 0):
raise ValueError("Transforming to a singular bounding box.")
self._mtx = np.array([[outw, 0.0, outl],
[ 0.0, outh, outb],
[ 0.0, 0.0, 1.0]],
np.float_)
self._inverted = None
self._invalid = 0
return self._mtx
get_matrix.__doc__ = Affine2DBase.get_matrix.__doc__
class BboxTransformFrom(Affine2DBase):
"""
:class:`BboxTransformFrom` linearly transforms points from a given
:class:`Bbox` to the unit bounding box.
"""
is_separable = True
def __init__(self, boxin):
assert boxin.is_bbox
Affine2DBase.__init__(self)
self._boxin = boxin
self.set_children(boxin)
self._mtx = None
self._inverted = None
def __repr__(self):
return "BboxTransformFrom(%s)" % (self._boxin)
__str__ = __repr__
def get_matrix(self):
if self._invalid:
inl, inb, inw, inh = self._boxin.bounds
if DEBUG and (inw == 0 or inh == 0):
raise ValueError("Transforming from a singular bounding box.")
x_scale = 1.0 / inw
y_scale = 1.0 / inh
self._mtx = np.array([[x_scale, 0.0 , (-inl*x_scale)],
[0.0 , y_scale, (-inb*y_scale)],
[0.0 , 0.0 , 1.0 ]],
np.float_)
self._inverted = None
self._invalid = 0
return self._mtx
get_matrix.__doc__ = Affine2DBase.get_matrix.__doc__
class ScaledTranslation(Affine2DBase):
"""
A transformation that translates by *xt* and *yt*, after *xt* and *yt*
have been transformad by the given transform *scale_trans*.
"""
def __init__(self, xt, yt, scale_trans):
Affine2DBase.__init__(self)
self._t = (xt, yt)
self._scale_trans = scale_trans
self.set_children(scale_trans)
self._mtx = None
self._inverted = None
def __repr__(self):
return "ScaledTranslation(%s)" % (self._t,)
__str__ = __repr__
def get_matrix(self):
if self._invalid:
xt, yt = self._scale_trans.transform_point(self._t)
self._mtx = np.array([[1.0, 0.0, xt],
[0.0, 1.0, yt],
[0.0, 0.0, 1.0]],
np.float_)
self._invalid = 0
self._inverted = None
return self._mtx
get_matrix.__doc__ = Affine2DBase.get_matrix.__doc__
class TransformedPath(TransformNode):
"""
A :class:`TransformedPath` caches a non-affine transformed copy of
the :class:`~matplotlib.path.Path`. This cached copy is
automatically updated when the non-affine part of the transform
changes.
"""
def __init__(self, path, transform):
"""
Create a new :class:`TransformedPath` from the given
:class:`~matplotlib.path.Path` and :class:`Transform`.
"""
assert isinstance(transform, Transform)
TransformNode.__init__(self)
self._path = path
self._transform = transform
self.set_children(transform)
self._transformed_path = None
self._transformed_points = None
def _revalidate(self):
if ((self._invalid & self.INVALID_NON_AFFINE == self.INVALID_NON_AFFINE)
or self._transformed_path is None):
self._transformed_path = \
self._transform.transform_path_non_affine(self._path)
self._transformed_points = \
Path(self._transform.transform_non_affine(self._path.vertices))
self._invalid = 0
def get_transformed_points_and_affine(self):
"""
Return a copy of the child path, with the non-affine part of
the transform already applied, along with the affine part of
the path necessary to complete the transformation. Unlike
:meth:`get_transformed_path_and_affine`, no interpolation will
be performed.
"""
self._revalidate()
return self._transformed_points, self.get_affine()
def get_transformed_path_and_affine(self):
"""
Return a copy of the child path, with the non-affine part of
the transform already applied, along with the affine part of
the path necessary to complete the transformation.
"""
self._revalidate()
return self._transformed_path, self.get_affine()
def get_fully_transformed_path(self):
"""
Return a fully-transformed copy of the child path.
"""
if ((self._invalid & self.INVALID_NON_AFFINE == self.INVALID_NON_AFFINE)
or self._transformed_path is None):
self._transformed_path = \
self._transform.transform_path_non_affine(self._path)
self._invalid = 0
return self._transform.transform_path_affine(self._transformed_path)
def get_affine(self):
return self._transform.get_affine()
def nonsingular(vmin, vmax, expander=0.001, tiny=1e-15, increasing=True):
'''
Ensure the endpoints of a range are finite and not too close together.
"too close" means the interval is smaller than 'tiny' times
the maximum absolute value.
If they are too close, each will be moved by the 'expander'.
If 'increasing' is True and vmin > vmax, they will be swapped,
regardless of whether they are too close.
If either is inf or -inf or nan, return - expander, expander.
'''
if (not np.isfinite(vmin)) or (not np.isfinite(vmax)):
return -expander, expander
swapped = False
if vmax < vmin:
vmin, vmax = vmax, vmin
swapped = True
if vmax - vmin <= max(abs(vmin), abs(vmax)) * tiny:
if vmin == 0.0:
vmin = -expander
vmax = expander
else:
vmin -= expander*abs(vmin)
vmax += expander*abs(vmax)
if swapped and not increasing:
vmin, vmax = vmax, vmin
return vmin, vmax
def interval_contains(interval, val):
a, b = interval
return (
((a < b) and (a <= val and b >= val))
or (b <= val and a >= val))
def interval_contains_open(interval, val):
a, b = interval
return (
((a < b) and (a < val and b > val))
or (b < val and a > val))
def offset_copy(trans, fig, x=0.0, y=0.0, units='inches'):
'''
Return a new transform with an added offset.
args:
trans is any transform
kwargs:
fig is the current figure; it can be None if units are 'dots'
x, y give the offset
units is 'inches', 'points' or 'dots'
'''
if units == 'dots':
return trans + Affine2D().translate(x, y)
if fig is None:
raise ValueError('For units of inches or points a fig kwarg is needed')
if units == 'points':
x /= 72.0
y /= 72.0
elif not units == 'inches':
raise ValueError('units must be dots, points, or inches')
return trans + ScaledTranslation(x, y, fig.dpi_scale_trans)
| agpl-3.0 |
agartland/utils | ics/.ipynb_checkpoints/plotting-checkpoint.py | 1 | 12289 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import palettable
import itertools
from hclusterplot import plotHCluster
import re
from myboxplot import myboxplot
import networkx as nx
import seaborn as sns
sns.set(style='darkgrid', palette='muted', font_scale=1.5)
__all__ = ['icsTicks',
'icsTickLabels',
'swarmBox']
from .loading import *
from .analyzing import *
icsTicks = np.log10([0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 1])
icsTickLabels = ['0.01', '0.025', '0.05', '0.1', '0.25', '0.5', '1']
# icsTicks = np.log10([0.01, 0.025, 0.05, 0.1, 0.2, 0.4, 0.6, 0.8, 1])
#icsTickLabels = ['0.01','0.025', '0.05', '0.1','0.2','0.4','0.6','0.8', '1']
def prepPlotDf(jDf, antigen, rxIDs, visitno, tcellsubset='CD4+', column='pvalue', cutoff='pvalue', pAdjust=True, allSubsets=False):
cytokineSubsets = jDf.cytokine.unique()
subset = cytokineSubsets[0].replace('-', '+').split('+')[:-1]
cyCols = [c for c in cytokineSubsets if not c == '-'.join(subset)+'-']
ind = (jDf.tcellsub == tcellsubset) & (jDf.visitno == visitno) & (jDf.TreatmentGroupID.isin(rxIDs))
agInd = (jDf.antigen == antigen) & ind
pvalueDf = pivotPvalues(jDf.loc[agInd], adjust=pAdjust)
"""Use cutoff from HVTN ICS SAP, p < 0.00001"""
responseAlpha = 1e-5
callDf = (pvalueDf < responseAlpha).astype(float)
magDf = jDf.loc[agInd].pivot(index='sample', columns='cytokine', values='mag')
magAdjDf = jDf.loc[agInd].pivot(index='sample', columns='cytokine', values='mag_adj')
bgDf = jDf.loc[agInd].pivot(index='sample', columns='cytokine', values='bg')
"""Positive subsets (to-be plotted) includes all columns unless a cutoff is specified"""
if cutoff == 'mag':
posSubsets = pvalueDf[cyCols].columns[(magDf[cyCols] > 0.00025).any(axis=0)]
elif cutoff == 'mag_adj':
posSubsets = pvalueDf[cyCols].columns[(magAdjDf[cyCols] > 0.00025).any(axis=0)]
elif cutoff == 'bg':
posSubsets = pvalueDf[cyCols].columns[(bgDf[cyCols] > 0).any(axis=0)]
elif cutoff == 'pvalue':
posSubsets = pvalueDf[cyCols].columns[(callDf[cyCols] > 0).any(axis=0)]
else:
posSubsets = pvalueDf[cyCols].columns
if allSubsets:
posSubsets = sorted(cytokineSubsets, key=lambda s: s.count('+'), reverse=True)
else:
posSubsets = sorted(posSubsets, key=lambda s: s.count('+'), reverse=True)
if column == 'pvalue':
plotDf = callDf
elif column == 'mag':
plotDf = magDf.applymap(np.log)
elif column == 'mag_adj':
plotDf = magAdjDf.applymap(np.log)
elif column == 'bg':
plotDf = bgDf.applymap(np.log)
"""Give labels a more readable look"""
plotDf = plotDf.rename_axis(cytokineSubsetLabel, axis=1)
posSubsets = list(map(cytokineSubsetLabel, posSubsets))
return plotDf[posSubsets]
def plotPolyBP(jDf,
antigen,
rxIDs,
visitno,
tcellsubset='CD4+',
column='pvalue', cutoff='pvalue',
pAdjust=True,
allSubsets=False, plotSubsets=None, returnPlotSubsets=False):
if plotSubsets is None:
plotDf = prepPlotDf(jDf, antigen, rxIDs, visitno, tcellsubset=tcellsubset, column=column, cutoff=cutoff, pAdjust=pAdjust, allSubsets=allSubsets)
posSubsets = plotDf.columns
else:
plotDf = prepPlotDf(jDf, antigen, rxIDs, visitno, tcellsubset=tcellsubset, column=column, cutoff=cutoff, pAdjust=pAdjust, allSubsets=True)
posSubsets = plotSubsets
cbt = np.log([0.0001, 0.00025, 0.0005, 0.001, 0.002, 0.004, 0.006, 0.008, 0.01])
cbtl = ['0.01', '0.025', '0.05', '0.1', '0.2', '0.4', '0.6', '0.8', '1']
plt.clf()
plotDf = pd.DataFrame(plotDf.stack().reset_index())
plotDf = plotDf.set_index('sample')
plotDf = plotDf.join(ptidDf[['TreatmentGroupID', 'TreatmentGroupName']], how='left').sort_values(by='TreatmentGroupID')
if column == 'mag' or column == 'mag_adj':
plotDf[0].loc[(plotDf[0] < np.log(0.00025)) | plotDf[0].isnull()] = np.log(0.00025)
yl = np.log([0.0002, 0.01])
elif column == 'bg':
plotDf[0].loc[(plotDf[0] < np.log(0.00001)) | plotDf[0].isnull()] = np.log(0.00001)
yl = np.log([0.00001, 0.01])
else:
print('Must specify mag, mag_adj or bg (not %s)' % column)
axh = plt.subplot(111)
sns.boxplot(x='cytokine', y=0, data=plotDf, hue='TreatmentGroupName', fliersize=0, ax=axh, order=posSubsets)
sns.stripplot(x='cytokine', y=0, data=plotDf, hue='TreatmentGroupName', jitter=True, ax=axh, order=posSubsets)
plt.yticks(cbt, cbtl)
plt.ylim(yl)
plt.xticks(list(range(len(posSubsets))), posSubsets, fontsize='large', fontname='Consolas')
plt.ylabel('% cytokine expressing cells')
handles, labels = axh.get_legend_handles_labels()
l = plt.legend(handles[len(rxIDs):], labels[len(rxIDs):], loc='upper right')
if returnPlotSubsets:
return axh, posSubsets
else:
return axh
def plotPolyHeat(jDf, antigen, rxIDs, visitno, tcellsubset='CD4+', cluster=False, column='pvalue', cutoff='pvalue', pAdjust=True, allSubsets=False):
plotDf = prepPlotDf(jDf, antigen, rxIDs, visitno, tcellsubset=tcellsubset, column=column, cutoff=cutoff, pAdjust=pAdjust, allSubsets=allSubsets)
posSubsets = plotDf.columns
plotDf = plotDf.join(ptidDf[['TreatmentGroupID', 'TreatmentGroupName']], how='left').sort_values(by='TreatmentGroupID')
cbt = np.log([0.0001, 0.00025, 0.0005, 0.001, 0.002, 0.004, 0.006, 0.008, 0.01])
cbtl = ['0.01', '0.025', '0.05', '0.1', '0.2', '0.4', '0.6', '0.8', '1']
if cluster:
clusterBool = [True, True]
else:
clusterBool = [False, False]
if column == 'pvalue':
vRange = [0, 2]
elif column == 'mag':
vRange = np.log([0.0001, 0.01])
elif column == 'mag_adj':
vRange = np.log([0.0001, 0.01])
elif column == 'bg':
vRange = np.log([0.0001, 0.01])
#valVec = tmp[posSubsets].values.flatten()
#vRange = [log(valVec[valVec>0].min()),log(valVec.max())]
ptidInd, cyColInd, handles = plotHCluster(plotDf[posSubsets],
row_labels=plotDf.TreatmentGroupID,
cmap=palettable.colorbrewer.sequential.YlOrRd_9.mpl_colormap,
yTickSz=None,
xTickSz='large',
clusterBool=clusterBool,
vRange=vRange)
if column == 'pvalue':
handles['cb'].remove()
else:
handles['cb'].set_ticks(cbt)
handles['cb'].set_ticklabels(cbtl)
handles['cb'].set_label('% cells')
for xh in handles['xlabelsL']:
xh.set_rotation(0)
handles['heatmapAX'].grid(b=None)
#handles['heatmapGS'].tight_layout(handles['fig'], h_pad=0.1, w_pad=0.5)
return handles
def plotResponsePattern(jDf, antigen, rxIDs, visitno, tcellsubset='CD4+', column='pvalue', cluster=False, cutoff='pvalue', pAdjust=True, boxplot=False, allSubsets=False):
if column == 'pvalue' and boxplot:
boxplot = False
print('Forced heatmap for p-value plotting.')
if boxplot:
axh = plotPolyBP(jDf, antigen, rxIDs, visitno, tcellsubset=tcellsubset, column=column, cutoff=cutoff, pAdjust=pAdjust, allSubsets=allSubsets)
else:
axh = plotPolyHeat(jDf, antigen, rxIDs, visitno, tcellsubset=tcellsubset, cluster=cluster, column=column, cutoff=cutoff, pAdjust=pAdjust, allSubsets=allSubsets)
return axh
def _szscale(vec, mx=np.inf, mn=1):
"""Normalize values of vec to [mn, mx] interval
such that sz ratios remain representative."""
factor = mn/np.nanmin(vec)
vec = vec*factor
vec[vec > mx] = mx
vec[np.isnan(vec)] = mn
return vec
def plotPolyFunNetwork(cdf):
"""This visualization isn't promising, but its also the start to how
I'd think about defining a pairwise sample distance matrix. Instead
of considering each subset as independent they could be related by their
distance on this graph (just the sum of the binayr vector representation),
then the distance would be somekind of earth over's distance between the two graphs"""
binSubsets = np.concatenate([m[None, :] for m in map(_subset2vec, cdf.cytokine.unique())], axis=0)
nColors = (np.unique(binSubsets.sum(axis=1)) > 0).sum()
cmap = sns.light_palette('red', as_cmap=True, n_colors=nColors)
freqDf = cdf.groupby('cytokine')['mag'].agg(np.mean)
freqDf = freqDf.drop(vec2subset([0]*len(binSubsets)), axis=0)
g = nx.Graph()
for ss,f in freqDf.iteritems():
g.add_node(ss, freq=f, fscore=subset2vec(ss).sum())
for ss1, ss2 in itertools.product(freqDf.index, freqDf.index):
if np.abs(subset2vec(ss1) - subset2vec(ss2)).sum() <= 1:
g.add_edge(ss1, ss2)
nodesize = np.array([d['freq'] for n, d in g.nodes(data=True)])
nodecolor = np.array([d['fscore'] for n, d in g.nodes(data=True)])
nodecolor = (nodecolor - nodecolor.min() + 1) / (nodecolor.max() - nodecolor.min() + 1)
freq = {n:d['freq'] for n, d in g.nodes(data=True)}
pos = nx.nx_pydot.graphviz_layout(g, prog=layout, root=max(list(freq.keys()), key=freq.get))
#pos = spring_layout(g)
#pos = spectral_layout(g)
#layouts = ['twopi', 'fdp', 'circo', 'neato', 'dot', 'spring', 'spectral']
#pos = nx.graphviz_layout(g, prog=layout)
plt.clf()
figh = plt.gcf()
axh = figh.add_axes([0.04, 0.04, 0.92, 0.92])
axh.axis('off')
figh.set_facecolor('white')
#nx.draw_networkx_edges(g,pos,alpha=0.5,width=sznorm(edgewidth,mn=0.5,mx=10), edge_color='k')
#nx.draw_networkx_nodes(g,pos,node_size=sznorm(nodesize,mn=500,mx=5000),node_color=nodecolors,alpha=1)
for e in g.edges_iter():
x1, y1=pos[e[0]]
x2, y2=pos[e[1]]
props = dict(color='black', alpha=0.4, zorder=1)
plt.plot([x1, x2], [y1, y2], '-', lw=2, **props)
plt.scatter(x=[pos[s][0] for s in g.nodes()],
y=[pos[s][1] for s in g.nodes()],
s=_szscale(nodesize, mn=20, mx=200), #Units for scatter is (size in points)**2
c=nodecolor,
alpha=1, zorder=2, cmap=cmap)
for n, d in g.nodes(data=True):
if d['freq'] >= 0:
plt.annotate(n,
xy=pos[n],
fontname='Arial',
size=10,
weight='bold',
color='black',
va='center',
ha='center')
def swarmBox(data, x, y, hue, palette=None, order=None, hue_order=None, connect=False):
"""Depends on plot order of the swarm plot which does not seem dependable at the moment.
Better idea would be to adopt code from the actual swarm function for this, adding boxplots separately"""
if palette is None:
palette = sns.color_palette('Set2', n_colors=data[hue].unique().shape[0])
if hue_order is None:
hue_order = sorted(data[hue].unique())
if order is None:
order = sorted(data[x].unqiue())
params = dict(data=data, x=x, y=y, hue=hue, palette=palette, order=order, hue_order=hue_order)
sns.boxplot(**params, fliersize=0, linewidth=0.5)
swarm = sns.swarmplot(**params, linewidth=0.5, edgecolor='black', dodge=True)
if connect:
zipper = [order] + [swarm.collections[i::len(hue_order)] for i in range(len(hue_order))]
for z in zip(*zipper):
curx = z[0]
collections = z[1:]
offsets = []
for c,h in zip(collections, hue_order):
ind = (data[x] == curx) & (data[hue] == h)
sortii = np.argsort(np.argsort(data.loc[ind, y]))
offsets.append(c.get_offsets()[sortii,:])
for zoffsets in zip(*offsets):
xvec = [o[0] for o in zoffsets]
yvec = [o[1] for o in zoffsets]
plt.plot(xvec, yvec, '-', color='gray', linewidth=0.5)
plt.legend([plt.Circle(1, color=c) for c in palette], hue_order, title=hue)
| mit |
HolgerPeters/scikit-learn | examples/ensemble/plot_forest_iris.py | 335 | 6271 | """
====================================================================
Plot the decision surfaces of ensembles of trees on the iris dataset
====================================================================
Plot the decision surfaces of forests of randomized trees trained on pairs of
features of the iris dataset.
This plot compares the decision surfaces learned by a decision tree classifier
(first column), by a random forest classifier (second column), by an extra-
trees classifier (third column) and by an AdaBoost classifier (fourth column).
In the first row, the classifiers are built using the sepal width and the sepal
length features only, on the second row using the petal length and sepal length
only, and on the third row using the petal width and the petal length only.
In descending order of quality, when trained (outside of this example) on all
4 features using 30 estimators and scored using 10 fold cross validation, we see::
ExtraTreesClassifier() # 0.95 score
RandomForestClassifier() # 0.94 score
AdaBoost(DecisionTree(max_depth=3)) # 0.94 score
DecisionTree(max_depth=None) # 0.94 score
Increasing `max_depth` for AdaBoost lowers the standard deviation of the scores (but
the average score does not improve).
See the console's output for further details about each model.
In this example you might try to:
1) vary the ``max_depth`` for the ``DecisionTreeClassifier`` and
``AdaBoostClassifier``, perhaps try ``max_depth=3`` for the
``DecisionTreeClassifier`` or ``max_depth=None`` for ``AdaBoostClassifier``
2) vary ``n_estimators``
It is worth noting that RandomForests and ExtraTrees can be fitted in parallel
on many cores as each tree is built independently of the others. AdaBoost's
samples are built sequentially and so do not use multiple cores.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import clone
from sklearn.datasets import load_iris
from sklearn.ensemble import (RandomForestClassifier, ExtraTreesClassifier,
AdaBoostClassifier)
from sklearn.externals.six.moves import xrange
from sklearn.tree import DecisionTreeClassifier
# Parameters
n_classes = 3
n_estimators = 30
plot_colors = "ryb"
cmap = plt.cm.RdYlBu
plot_step = 0.02 # fine step width for decision surface contours
plot_step_coarser = 0.5 # step widths for coarse classifier guesses
RANDOM_SEED = 13 # fix the seed on each iteration
# Load data
iris = load_iris()
plot_idx = 1
models = [DecisionTreeClassifier(max_depth=None),
RandomForestClassifier(n_estimators=n_estimators),
ExtraTreesClassifier(n_estimators=n_estimators),
AdaBoostClassifier(DecisionTreeClassifier(max_depth=3),
n_estimators=n_estimators)]
for pair in ([0, 1], [0, 2], [2, 3]):
for model in models:
# We only take the two corresponding features
X = iris.data[:, pair]
y = iris.target
# Shuffle
idx = np.arange(X.shape[0])
np.random.seed(RANDOM_SEED)
np.random.shuffle(idx)
X = X[idx]
y = y[idx]
# Standardize
mean = X.mean(axis=0)
std = X.std(axis=0)
X = (X - mean) / std
# Train
clf = clone(model)
clf = model.fit(X, y)
scores = clf.score(X, y)
# Create a title for each column and the console by using str() and
# slicing away useless parts of the string
model_title = str(type(model)).split(".")[-1][:-2][:-len("Classifier")]
model_details = model_title
if hasattr(model, "estimators_"):
model_details += " with {} estimators".format(len(model.estimators_))
print( model_details + " with features", pair, "has a score of", scores )
plt.subplot(3, 4, plot_idx)
if plot_idx <= len(models):
# Add a title at the top of each column
plt.title(model_title)
# Now plot the decision boundary using a fine mesh as input to a
# filled contour plot
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),
np.arange(y_min, y_max, plot_step))
# Plot either a single DecisionTreeClassifier or alpha blend the
# decision surfaces of the ensemble of classifiers
if isinstance(model, DecisionTreeClassifier):
Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
cs = plt.contourf(xx, yy, Z, cmap=cmap)
else:
# Choose alpha blend level with respect to the number of estimators
# that are in use (noting that AdaBoost can use fewer estimators
# than its maximum if it achieves a good enough fit early on)
estimator_alpha = 1.0 / len(model.estimators_)
for tree in model.estimators_:
Z = tree.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
cs = plt.contourf(xx, yy, Z, alpha=estimator_alpha, cmap=cmap)
# Build a coarser grid to plot a set of ensemble classifications
# to show how these are different to what we see in the decision
# surfaces. These points are regularly space and do not have a black outline
xx_coarser, yy_coarser = np.meshgrid(np.arange(x_min, x_max, plot_step_coarser),
np.arange(y_min, y_max, plot_step_coarser))
Z_points_coarser = model.predict(np.c_[xx_coarser.ravel(), yy_coarser.ravel()]).reshape(xx_coarser.shape)
cs_points = plt.scatter(xx_coarser, yy_coarser, s=15, c=Z_points_coarser, cmap=cmap, edgecolors="none")
# Plot the training points, these are clustered together and have a
# black outline
for i, c in zip(xrange(n_classes), plot_colors):
idx = np.where(y == i)
plt.scatter(X[idx, 0], X[idx, 1], c=c, label=iris.target_names[i],
cmap=cmap)
plot_idx += 1 # move on to the next plot in sequence
plt.suptitle("Classifiers on feature subsets of the Iris dataset")
plt.axis("tight")
plt.show()
| bsd-3-clause |
valexandersaulys/prudential_insurance_kaggle | venv/lib/python2.7/site-packages/pandas/core/series.py | 9 | 95521 | "\"\"\"\nData structure for 1-dimensional cross-sectional and time series data\n\"\"\"\nfrom __futur(...TRUNCATED) | gpl-2.0 |
Limags/MissionPlanner | Lib/site-packages/numpy/lib/twodim_base.py | 70 | 23431 | "\"\"\" Basic functions for manipulating 2d arrays\n\n\"\"\"\n\n__all__ = ['diag','diagflat','eye','(...TRUNCATED) | gpl-3.0 |
dsm054/pandas | pandas/tests/arrays/categorical/test_repr.py | 2 | 25897 | "# -*- coding: utf-8 -*-\n\nimport numpy as np\n\nfrom pandas.compat import PY3, u\n\nfrom pandas im(...TRUNCATED) | bsd-3-clause |
dopplershift/MetPy | examples/gridding/Point_Interpolation.py | 6 | 5187 | "# Copyright (c) 2016 MetPy Developers.\n# Distributed under the terms of the BSD 3-Clause License.\(...TRUNCATED) | bsd-3-clause |
lenovor/scikit-learn | sklearn/calibration.py | 137 | 18876 | "\"\"\"Calibration of predicted probabilities.\"\"\"\n\n# Author: Alexandre Gramfort <alexandre.gram(...TRUNCATED) | bsd-3-clause |
MJJoyce/climate | mccsearch/code/mainProgTemplate.py | 5 | 4713 | "#\n# Licensed to the Apache Software Foundation (ASF) under one or more\n# contributor license ag(...TRUNCATED) | apache-2.0 |
samehuman/fast-rcnn | lib/roi_data_layer/minibatch.py | 44 | 7337 | "# --------------------------------------------------------\n# Fast R-CNN\n# Copyright (c) 2015 Micr(...TRUNCATED) | mit |
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