# coding: utf-8
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"""This module provides the :class:`Scatter` item of the :class:`Plot`.
"""
from __future__ import division
__authors__ = ["T. Vincent", "P. Knobel"]
__license__ = "MIT"
__date__ = "29/03/2017"
from collections import namedtuple
import logging
import threading
import numpy
from collections import defaultdict
from concurrent.futures import ThreadPoolExecutor, CancelledError
from ....utils.proxy import docstring
from ....math.combo import min_max
from ....utils.weakref import WeakList
from .._utils.delaunay import delaunay
from .core import PointsBase, ColormapMixIn, ScatterVisualizationMixIn
from .axis import Axis
from ._pick import PickingResult
_logger = logging.getLogger(__name__)
class _GreedyThreadPoolExecutor(ThreadPoolExecutor):
""":class:`ThreadPoolExecutor` with an extra :meth:`submit_greedy` method.
"""
def __init__(self, *args, **kwargs):
super(_GreedyThreadPoolExecutor, self).__init__(*args, **kwargs)
self.__futures = defaultdict(WeakList)
self.__lock = threading.RLock()
def submit_greedy(self, queue, fn, *args, **kwargs):
"""Same as :meth:`submit` but cancel previous tasks in given queue.
This means that when a new task is submitted for a given queue,
all other pending tasks of that queue are cancelled.
:param queue: Identifier of the queue. This must be hashable.
:param callable fn: The callable to call with provided extra arguments
:return: Future corresponding to this task
:rtype: concurrent.futures.Future
"""
with self.__lock:
# Cancel previous tasks in given queue
for future in self.__futures.pop(queue, []):
if not future.done():
future.cancel()
future = super(_GreedyThreadPoolExecutor, self).submit(
fn, *args, **kwargs)
self.__futures[queue].append(future)
return future
# Functions to guess grid shape from coordinates
def _get_z_line_length(array):
"""Return length of line if array is a Z-like 2D regular grid.
:param numpy.ndarray array: The 1D array of coordinates to check
:return: 0 if no line length could be found,
else the number of element per line.
:rtype: int
"""
sign = numpy.sign(numpy.diff(array))
if len(sign) == 0 or sign[0] == 0: # We don't handle that
return 0
# Check this way to account for 0 sign (i.e., diff == 0)
beginnings = numpy.where(sign == - sign[0])[0] + 1
if len(beginnings) == 0:
return 0
length = beginnings[0]
if numpy.all(numpy.equal(numpy.diff(beginnings), length)):
return length
return 0
def _guess_z_grid_shape(x, y):
"""Guess the shape of a grid from (x, y) coordinates.
The grid might contain more elements than x and y,
as the last line might be partly filled.
:param numpy.ndarray x:
:paran numpy.ndarray y:
:returns: (order, (height, width)) of the regular grid,
or None if could not guess one.
'order' is 'row' if X (i.e., column) is the fast dimension, else 'column'.
:rtype: Union[List(str,int),None]
"""
width = _get_z_line_length(x)
if width != 0:
return 'row', (int(numpy.ceil(len(x) / width)), width)
else:
height = _get_z_line_length(y)
if height != 0:
return 'column', (height, int(numpy.ceil(len(y) / height)))
return None
def is_monotonic(array):
"""Returns whether array is monotonic (increasing or decreasing).
:param numpy.ndarray array: 1D array-like container.
:returns: 1 if array is monotonically increasing,
-1 if array is monotonically decreasing,
0 if array is not monotonic
:rtype: int
"""
diff = numpy.diff(numpy.ravel(array))
if numpy.all(diff >= 0):
return 1
elif numpy.all(diff <= 0):
return -1
else:
return 0
def _guess_grid(x, y):
"""Guess a regular grid from the points.
Result convention is (x, y)
:param numpy.ndarray x: X coordinates of the points
:param numpy.ndarray y: Y coordinates of the points
:returns: (order, (height, width)
order is 'row' or 'column'
:rtype: Union[List[str,List[int]],None]
"""
x, y = numpy.ravel(x), numpy.ravel(y)
guess = _guess_z_grid_shape(x, y)
if guess is not None:
return guess
else:
# Cannot guess a regular grid
# Let's assume it's a single line
order = 'row' # or 'column' doesn't matter for a single line
y_monotonic = is_monotonic(y)
if is_monotonic(x) or y_monotonic: # we can guess a line
x_min, x_max = min_max(x)
y_min, y_max = min_max(y)
if not y_monotonic or x_max - x_min >= y_max - y_min:
# x only is monotonic or both are and X varies more
# line along X
shape = 1, len(x)
else:
# y only is monotonic or both are and Y varies more
# line along Y
shape = len(y), 1
else: # Cannot guess a line from the points
return None
return order, shape
def _quadrilateral_grid_coords(points):
"""Compute an irregular grid of quadrilaterals from a set of points
The input points are expected to lie on a grid.
:param numpy.ndarray points:
3D data set of 2D input coordinates (height, width, 2)
height and width must be at least 2.
:return: 3D dataset of 2D coordinates of the grid (height+1, width+1, 2)
"""
assert points.ndim == 3
assert points.shape[0] >= 2
assert points.shape[1] >= 2
assert points.shape[2] == 2
dim0, dim1 = points.shape[:2]
grid_points = numpy.zeros((dim0 + 1, dim1 + 1, 2), dtype=numpy.float64)
# Compute inner points as mean of 4 neighbours
neighbour_view = numpy.lib.stride_tricks.as_strided(
points,
shape=(dim0 - 1, dim1 - 1, 2, 2, points.shape[2]),
strides=points.strides[:2] + points.strides[:2] + points.strides[-1:], writeable=False)
inner_points = numpy.mean(neighbour_view, axis=(2, 3))
grid_points[1:-1, 1:-1] = inner_points
# Compute 'vertical' sides
# Alternative: grid_points[1:-1, [0, -1]] = points[:-1, [0, -1]] + points[1:, [0, -1]] - inner_points[:, [0, -1]]
grid_points[1:-1, [0, -1], 0] = points[:-1, [0, -1], 0] + points[1:, [0, -1], 0] - inner_points[:, [0, -1], 0]
grid_points[1:-1, [0, -1], 1] = inner_points[:, [0, -1], 1]
# Compute 'horizontal' sides
grid_points[[0, -1], 1:-1, 0] = inner_points[[0, -1], :, 0]
grid_points[[0, -1], 1:-1, 1] = points[[0, -1], :-1, 1] + points[[0, -1], 1:, 1] - inner_points[[0, -1], :, 1]
# Compute corners
d0, d1 = [0, 0, -1, -1], [0, -1, -1, 0]
grid_points[d0, d1] = 2 * points[d0, d1] - inner_points[d0, d1]
return grid_points
def _quadrilateral_grid_as_triangles(points):
"""Returns the points and indices to make a grid of quadirlaterals
:param numpy.ndarray points:
3D array of points (height, width, 2)
:return: triangle corners (4 * N, 2), triangle indices (2 * N, 3)
With N = height * width, the number of input points
"""
nbpoints = numpy.prod(points.shape[:2])
grid = _quadrilateral_grid_coords(points)
coords = numpy.empty((4 * nbpoints, 2), dtype=grid.dtype)
coords[::4] = grid[:-1, :-1].reshape(-1, 2)
coords[1::4] = grid[1:, :-1].reshape(-1, 2)
coords[2::4] = grid[:-1, 1:].reshape(-1, 2)
coords[3::4] = grid[1:, 1:].reshape(-1, 2)
indices = numpy.empty((2 * nbpoints, 3), dtype=numpy.uint32)
indices[::2, 0] = numpy.arange(0, 4 * nbpoints, 4)
indices[::2, 1] = numpy.arange(1, 4 * nbpoints, 4)
indices[::2, 2] = numpy.arange(2, 4 * nbpoints, 4)
indices[1::2, 0] = indices[::2, 1]
indices[1::2, 1] = indices[::2, 2]
indices[1::2, 2] = numpy.arange(3, 4 * nbpoints, 4)
return coords, indices
_RegularGridInfo = namedtuple(
'_RegularGridInfo', ['bounds', 'origin', 'scale', 'shape', 'order'])
[docs]class Scatter(PointsBase, ColormapMixIn, ScatterVisualizationMixIn):
"""Description of a scatter"""
_DEFAULT_SELECTABLE = True
"""Default selectable state for scatter plots"""
_SUPPORTED_SCATTER_VISUALIZATION = (
ScatterVisualizationMixIn.Visualization.POINTS,
ScatterVisualizationMixIn.Visualization.SOLID,
ScatterVisualizationMixIn.Visualization.REGULAR_GRID,
ScatterVisualizationMixIn.Visualization.IRREGULAR_GRID,
)
"""Overrides supported Visualizations"""
def __init__(self):
PointsBase.__init__(self)
ColormapMixIn.__init__(self)
ScatterVisualizationMixIn.__init__(self)
self._value = ()
self.__alpha = None
# Cache Delaunay triangulation future object
self.__delaunayFuture = None
# Cache interpolator future object
self.__interpolatorFuture = None
self.__executor = None
# Cache triangles: x, y, indices
self.__cacheTriangles = None, None, None
# Cache regular grid info
self.__cacheRegularGridInfo = None
@docstring(ScatterVisualizationMixIn)
def setVisualizationParameter(self, parameter, value):
changed = super(Scatter, self).setVisualizationParameter(parameter, value)
if changed and parameter in (self.VisualizationParameter.GRID_BOUNDS,
self.VisualizationParameter.GRID_MAJOR_ORDER,
self.VisualizationParameter.GRID_SHAPE):
self.__cacheRegularGridInfo = None
return changed
@docstring(ScatterVisualizationMixIn)
def getCurrentVisualizationParameter(self, parameter):
value = self.getVisualizationParameter(parameter)
if value is not None:
return value # Value has been set, return it
elif parameter is self.VisualizationParameter.GRID_BOUNDS:
grid = self.__getRegularGridInfo()
return None if grid is None else grid.bounds
elif parameter is self.VisualizationParameter.GRID_MAJOR_ORDER:
grid = self.__getRegularGridInfo()
return None if grid is None else grid.order
elif parameter is self.VisualizationParameter.GRID_SHAPE:
grid = self.__getRegularGridInfo()
return None if grid is None else grid.shape
else:
raise NotImplementedError()
def __getRegularGridInfo(self):
"""Get grid info"""
if self.__cacheRegularGridInfo is None:
shape = self.getVisualizationParameter(
self.VisualizationParameter.GRID_SHAPE)
order = self.getVisualizationParameter(
self.VisualizationParameter.GRID_MAJOR_ORDER)
if shape is None or order is None:
guess = _guess_grid(self.getXData(copy=False),
self.getYData(copy=False))
if guess is None:
_logger.warning(
'Cannot guess a grid: Cannot display as regular grid image')
return None
if shape is None:
shape = guess[1]
if order is None:
order = guess[0]
bounds = self.getVisualizationParameter(
self.VisualizationParameter.GRID_BOUNDS)
if bounds is None:
x, y = self.getXData(copy=False), self.getYData(copy=False)
min_, max_ = min_max(x)
xRange = (min_, max_) if (x[0] - min_) < (max_ - x[0]) else (max_, min_)
min_, max_ = min_max(y)
yRange = (min_, max_) if (y[0] - min_) < (max_ - y[0]) else (max_, min_)
bounds = (xRange[0], yRange[0]), (xRange[1], yRange[1])
begin, end = bounds
scale = ((end[0] - begin[0]) / max(1, shape[1] - 1),
(end[1] - begin[1]) / max(1, shape[0] - 1))
if scale[0] == 0 and scale[1] == 0:
scale = 1., 1.
elif scale[0] == 0:
scale = scale[1], scale[1]
elif scale[1] == 0:
scale = scale[0], scale[0]
origin = begin[0] - 0.5 * scale[0], begin[1] - 0.5 * scale[1]
self.__cacheRegularGridInfo = _RegularGridInfo(
bounds=bounds, origin=origin, scale=scale, shape=shape, order=order)
return self.__cacheRegularGridInfo
def _addBackendRenderer(self, backend):
"""Update backend renderer"""
# Filter-out values <= 0
xFiltered, yFiltered, valueFiltered, xerror, yerror = self.getData(
copy=False, displayed=True)
# Remove not finite numbers (this includes filtered out x, y <= 0)
mask = numpy.logical_and(numpy.isfinite(xFiltered), numpy.isfinite(yFiltered))
xFiltered = xFiltered[mask]
yFiltered = yFiltered[mask]
if len(xFiltered) == 0:
return None # No data to display, do not add renderer to backend
# Compute colors
cmap = self.getColormap()
rgbacolors = cmap.applyToData(self._value)
if self.__alpha is not None:
rgbacolors[:, -1] = (rgbacolors[:, -1] * self.__alpha).astype(numpy.uint8)
# Apply mask to colors
rgbacolors = rgbacolors[mask]
visualization = self.getVisualization()
if visualization is self.Visualization.POINTS:
return backend.addCurve(xFiltered, yFiltered,
color=rgbacolors,
symbol=self.getSymbol(),
linewidth=0,
linestyle="",
yaxis='left',
xerror=xerror,
yerror=yerror,
z=self.getZValue(),
fill=False,
alpha=self.getAlpha(),
symbolsize=self.getSymbolSize(),
baseline=None)
else:
plot = self.getPlot()
if (plot is None or
plot.getXAxis().getScale() != Axis.LINEAR or
plot.getYAxis().getScale() != Axis.LINEAR):
# Those visualizations are not available with log scaled axes
return None
if visualization is self.Visualization.SOLID:
triangulation = self._getDelaunay().result()
if triangulation is None:
_logger.warning(
'Cannot get a triangulation: Cannot display as solid surface')
return None
else:
triangles = triangulation.simplices.astype(numpy.int32)
return backend.addTriangles(xFiltered,
yFiltered,
triangles,
color=rgbacolors,
z=self.getZValue(),
alpha=self.getAlpha())
elif visualization is self.Visualization.REGULAR_GRID:
gridInfo = self.__getRegularGridInfo()
if gridInfo is None:
return None
dim0, dim1 = gridInfo.shape
if gridInfo.order == 'column': # transposition needed
dim0, dim1 = dim1, dim0
if len(rgbacolors) == dim0 * dim1:
image = rgbacolors.reshape(dim0, dim1, -1)
else:
# The points do not fill the whole image
image = numpy.empty((dim0 * dim1, 4), dtype=rgbacolors.dtype)
image[:len(rgbacolors)] = rgbacolors
image[len(rgbacolors):] = 0, 0, 0, 0 # Transparent pixels
image.shape = dim0, dim1, -1
if gridInfo.order == 'column':
image = numpy.transpose(image, axes=(1, 0, 2))
return backend.addImage(
data=image,
origin=gridInfo.origin,
scale=gridInfo.scale,
z=self.getZValue(),
colormap=None,
alpha=self.getAlpha())
elif visualization is self.Visualization.IRREGULAR_GRID:
gridInfo = self.__getRegularGridInfo()
if gridInfo is None:
return None
shape = gridInfo.shape
if shape is None: # No shape, no display
return None
# clip shape to fully filled lines
if len(xFiltered) != numpy.prod(shape):
if gridInfo.order == 'row':
shape = len(xFiltered) // shape[1], shape[1]
else: # column-major order
shape = shape[0], len(xFiltered) // shape[0]
if shape[0] < 2 or shape[1] < 2: # Not enough points
return None
nbpoints = numpy.prod(shape)
if gridInfo.order == 'row':
points = numpy.transpose((xFiltered[:nbpoints], yFiltered[:nbpoints]))
points = points.reshape(shape[0], shape[1], 2)
else: # column-major order
points = numpy.transpose((yFiltered[:nbpoints], xFiltered[:nbpoints]))
points = points.reshape(shape[1], shape[0], 2)
coords, indices = _quadrilateral_grid_as_triangles(points)
if gridInfo.order == 'row':
x, y = coords[:, 0], coords[:, 1]
else: # column-major order
y, x = coords[:, 0], coords[:, 1]
gridcolors = numpy.empty(
(4 * nbpoints, rgbacolors.shape[-1]), dtype=rgbacolors.dtype)
for first in range(4):
gridcolors[first::4] = rgbacolors[:nbpoints]
return backend.addTriangles(x,
y,
indices,
color=gridcolors,
z=self.getZValue(),
alpha=self.getAlpha())
else:
_logger.error("Unhandled visualization %s", visualization)
return None
@docstring(PointsBase)
def pick(self, x, y):
result = super(Scatter, self).pick(x, y)
if result is not None:
visualization = self.getVisualization()
if visualization is self.Visualization.IRREGULAR_GRID:
# Specific handling of picking for the irregular grid mode
index = result.getIndices(copy=False)[0] // 4
result = PickingResult(self, (index,))
elif visualization is self.Visualization.REGULAR_GRID:
# Specific handling of picking for the regular grid mode
plot = self.getPlot()
if plot is None:
return None
dataPos = plot.pixelToData(x, y)
if dataPos is None:
return None
gridInfo = self.__getRegularGridInfo()
if gridInfo is None:
return None
origin = gridInfo.origin
scale = gridInfo.scale
column = int((dataPos[0] - origin[0]) / scale[0])
row = int((dataPos[1] - origin[1]) / scale[1])
if gridInfo.order == 'row':
index = row * gridInfo.shape[1] + column
else:
index = row + column * gridInfo.shape[0]
if index >= len(self.getXData(copy=False)): # OK as long as not log scale
return None # Image can be larger than scatter
result = PickingResult(self, (index,))
return result
def __getExecutor(self):
"""Returns async greedy executor
:rtype: _GreedyThreadPoolExecutor
"""
if self.__executor is None:
self.__executor = _GreedyThreadPoolExecutor(max_workers=2)
return self.__executor
def _getDelaunay(self):
"""Returns a :class:`Future` which result is the Delaunay object.
:rtype: concurrent.futures.Future
"""
if self.__delaunayFuture is None or self.__delaunayFuture.cancelled():
# Need to init a new delaunay
x, y = self.getData(copy=False)[:2]
# Remove not finite points
mask = numpy.logical_and(numpy.isfinite(x), numpy.isfinite(y))
self.__delaunayFuture = self.__getExecutor().submit_greedy(
'delaunay', delaunay, x[mask], y[mask])
return self.__delaunayFuture
@staticmethod
def __initInterpolator(delaunayFuture, values):
"""Returns an interpolator for the given data points
:param concurrent.futures.Future delaunayFuture:
Future object which result is a Delaunay object
:param numpy.ndarray values: The data value of valid points.
:rtype: Union[callable,None]
"""
# Wait for Delaunay to complete
try:
triangulation = delaunayFuture.result()
except CancelledError:
triangulation = None
if triangulation is None:
interpolator = None # Error case
else:
# Lazy-loading of interpolator
try:
from scipy.interpolate import LinearNDInterpolator
except ImportError:
LinearNDInterpolator = None
if LinearNDInterpolator is not None:
interpolator = LinearNDInterpolator(triangulation, values)
# First call takes a while, do it here
interpolator([(0., 0.)])
else:
# Fallback using matplotlib interpolator
import matplotlib.tri
x, y = triangulation.points.T
tri = matplotlib.tri.Triangulation(
x, y, triangles=triangulation.simplices)
mplInterpolator = matplotlib.tri.LinearTriInterpolator(
tri, values)
# Wrap interpolator to have same API as scipy's one
def interpolator(points):
return mplInterpolator(*points.T)
return interpolator
def _getInterpolator(self):
"""Returns a :class:`Future` which result is the interpolator.
The interpolator is a callable taking an array Nx2 of points
as a single argument.
The :class:`Future` result is None in case the interpolator cannot
be initialized.
:rtype: concurrent.futures.Future
"""
if (self.__interpolatorFuture is None or
self.__interpolatorFuture.cancelled()):
# Need to init a new interpolator
x, y, values = self.getData(copy=False)[:3]
# Remove not finite points
mask = numpy.logical_and(numpy.isfinite(x), numpy.isfinite(y))
x, y, values = x[mask], y[mask], values[mask]
self.__interpolatorFuture = self.__getExecutor().submit_greedy(
'interpolator',
self.__initInterpolator, self._getDelaunay(), values)
return self.__interpolatorFuture
def _logFilterData(self, xPositive, yPositive):
"""Filter out values with x or y <= 0 on log axes
:param bool xPositive: True to filter arrays according to X coords.
:param bool yPositive: True to filter arrays according to Y coords.
:return: The filtered arrays or unchanged object if not filtering needed
:rtype: (x, y, value, xerror, yerror)
"""
# overloaded from PointsBase to filter also value.
value = self.getValueData(copy=False)
if xPositive or yPositive:
clipped = self._getClippingBoolArray(xPositive, yPositive)
if numpy.any(clipped):
# copy to keep original array and convert to float
value = numpy.array(value, copy=True, dtype=numpy.float)
value[clipped] = numpy.nan
x, y, xerror, yerror = PointsBase._logFilterData(self, xPositive, yPositive)
return x, y, value, xerror, yerror
[docs] def getValueData(self, copy=True):
"""Returns the value assigned to the scatter data points.
:param copy: True (Default) to get a copy,
False to use internal representation (do not modify!)
:rtype: numpy.ndarray
"""
return numpy.array(self._value, copy=copy)
def getAlphaData(self, copy=True):
"""Returns the alpha (transparency) assigned to the scatter data points.
:param copy: True (Default) to get a copy,
False to use internal representation (do not modify!)
:rtype: numpy.ndarray
"""
return numpy.array(self.__alpha, copy=copy)
[docs] def getData(self, copy=True, displayed=False):
"""Returns the x, y coordinates and the value of the data points
:param copy: True (Default) to get a copy,
False to use internal representation (do not modify!)
:param bool displayed: True to only get curve points that are displayed
in the plot. Default: False.
Note: If plot has log scale, negative points
are not displayed.
:returns: (x, y, value, xerror, yerror)
:rtype: 5-tuple of numpy.ndarray
"""
if displayed:
data = self._getCachedData()
if data is not None:
assert len(data) == 5
return data
return (self.getXData(copy),
self.getYData(copy),
self.getValueData(copy),
self.getXErrorData(copy),
self.getYErrorData(copy))
# reimplemented from PointsBase to handle `value`
[docs] def setData(self, x, y, value, xerror=None, yerror=None, alpha=None, copy=True):
"""Set the data of the scatter.
:param numpy.ndarray x: The data corresponding to the x coordinates.
:param numpy.ndarray y: The data corresponding to the y coordinates.
:param numpy.ndarray value: The data corresponding to the value of
the data points.
:param xerror: Values with the uncertainties on the x values
:type xerror: A float, or a numpy.ndarray of float32.
If it is an array, it can either be a 1D array of
same length as the data or a 2D array with 2 rows
of same length as the data: row 0 for positive errors,
row 1 for negative errors.
:param yerror: Values with the uncertainties on the y values
:type yerror: A float, or a numpy.ndarray of float32. See xerror.
:param alpha: Values with the transparency (between 0 and 1)
:type alpha: A float, or a numpy.ndarray of float32
:param bool copy: True make a copy of the data (default),
False to use provided arrays.
"""
value = numpy.array(value, copy=copy)
assert value.ndim == 1
assert len(x) == len(value)
# Reset triangulation and interpolator
if self.__delaunayFuture is not None:
self.__delaunayFuture.cancel()
self.__delaunayFuture = None
if self.__interpolatorFuture is not None:
self.__interpolatorFuture.cancel()
self.__interpolatorFuture = None
# Data changed, this needs update
self.__cacheRegularGridInfo = None
self._value = value
if alpha is not None:
# Make sure alpha is an array of float in [0, 1]
alpha = numpy.array(alpha, copy=copy)
assert alpha.ndim == 1
assert len(x) == len(alpha)
if alpha.dtype.kind != 'f':
alpha = alpha.astype(numpy.float32)
if numpy.any(numpy.logical_or(alpha < 0., alpha > 1.)):
alpha = numpy.clip(alpha, 0., 1.)
self.__alpha = alpha
# set x, y, xerror, yerror
# call self._updated + plot._invalidateDataRange()
PointsBase.setData(self, x, y, xerror, yerror, copy)
