/usr/lib/python2.7/dist-packages/chaco/image_data.py is in python-chaco 4.5.0-1.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 | """ Defines the ImageData class.
"""
# Standard library imports
from numpy import nanmax, nanmin, swapaxes
# Enthought library imports
from traits.api import Bool, Int, Property, ReadOnly, Tuple
# Local relative imports
from base import DimensionTrait, ImageTrait
from abstract_data_source import AbstractDataSource
class ImageData(AbstractDataSource):
"""
Represents a grid of data to be plotted using a Numpy 2-D grid.
The data array has dimensions NxM, but it may have more than just 2
dimensions. The appropriate dimensionality of the value array depends
on the context in which the ImageData instance will be used.
"""
# The dimensionality of the data.
dimension = ReadOnly(DimensionTrait('image'))
# Depth of the values at each i,j. Values that are used include:
#
# * 3: color images, without alpha channel
# * 4: color images, with alpha channel
value_depth = Int(1) # TODO: Modify ImageData to explicitly support scalar
# value arrays, as needed by CMapImagePlot
# Holds the grid data that forms the image. The shape of the array is
# (N, M, D) where:
#
# * D is 1, 3, or 4.
# * N is the length of the y-axis.
# * M is the length of the x-axis.
#
# Thus, data[0,:,:] must be the first row of data. If D is 1, then
# the array must be of type float; if D is 3 or 4, then the array
# must be of type uint8.
#
# NOTE: If this ImageData was constructed with a transposed data array,
# then internally it is still transposed (i.e., the x-axis is the first axis
# and the y-axis is the second), and the **data** array property might not be
# contiguous. If contiguousness is required and calling copy() is too
# expensive, use the **raw_value** attribute. Also note that setting this
# trait does not change the value of **transposed**,
# so be sure to set it to its proper value when using the same ImageData
# instance interchangeably to store transposed and non-transposed data.
data = Property(ImageTrait)
# Is **raw_value**, the actual underlying image data
# array, transposed from **data**? (I.e., does the first axis correspond to
# the x-direction and the second axis correspond to the y-direction?)
#
# Rather than transposing or swapping axes on the data and destroying
# continuity, this class exposes the data as both **data** and **raw_value**.
transposed = Bool(False)
# A read-only attribute that exposes the underlying array.
raw_value = Property(ImageTrait)
#------------------------------------------------------------------------
# Private traits
#------------------------------------------------------------------------
# The actual image data array. Can be MxN or NxM, depending on the value
# of **transposed**.
_data = ImageTrait
# Is the bounds cache valid? If False, it needs to be computed.
_bounds_cache_valid = Bool(False)
# Cached value of min and max as long as **data** doesn't change.
_bounds_cache = Tuple
#------------------------------------------------------------------------
# Public methods
#------------------------------------------------------------------------
@classmethod
def fromfile(cls, filename):
""" Alternate constructor to create an ImageData from an image file
on disk. 'filename' may be a file path or a file object.
"""
from kiva.image import Image
img = Image(filename)
imgdata = cls(data=img.bmp_array, transposed=False)
fmt = img.format()
if fmt == "rgb24":
imgdata.value_depth = 3
elif fmt == "rgba32":
imgdata.value_depth = 4
else:
raise ValueError("Unknown image format in file %s: %s" %
(filename, fmt))
return imgdata
def get_width(self):
""" Returns the shape of the x-axis.
"""
if self.transposed:
return self._data.shape[0]
else:
return self._data.shape[1]
def get_height(self):
""" Returns the shape of the y-axis.
"""
if self.transposed:
return self._data.shape[1]
else:
return self._data.shape[0]
def get_array_bounds(self):
""" Always returns ((0, width), (0, height)) for x-bounds and y-bounds.
"""
if self.transposed:
b = ((0,self._data.shape[0]), (0,self._data.shape[1]))
else:
b = ((0,self._data.shape[1]), (0,self._data.shape[0]))
return b
#------------------------------------------------------------------------
# Datasource interface
#------------------------------------------------------------------------
def get_data(self):
""" Returns the data for this data source.
Implements AbstractDataSource.
"""
return self.data
def is_masked(self):
"""is_masked() -> False
Implements AbstractDataSource.
"""
return False
def get_bounds(self):
""" Returns the minimum and maximum values of the data source's data.
Implements AbstractDataSource.
"""
if not self._bounds_cache_valid:
if self.raw_value.size == 0:
self._cached_bounds = (0,0)
else:
self._cached_bounds = (nanmin(self.raw_value),
nanmax(self.raw_value))
self._bounds_cache_valid = True
return self._cached_bounds
def get_size(self):
"""get_size() -> int
Implements AbstractDataSource.
"""
if self._data is not None and self._data.shape[0] != 0:
return self._data.shape[0] * self._data.shape[1]
else:
return 0
def set_data(self, data):
""" Sets the data for this data source.
Parameters
----------
data : array
The data to use.
"""
self._set_data(data)
#------------------------------------------------------------------------
# Private methods
#------------------------------------------------------------------------
def _get_data(self):
if self.transposed:
return swapaxes(self._data, 0, 1)
else:
return self._data
def _set_data(self, newdata):
self._data = newdata
self._bounds_cache_valid = False
self.data_changed = True
def _get_raw_value(self):
return self._data
#------------------------------------------------------------------------
# Event handlers
#------------------------------------------------------------------------
def _metadata_changed(self, event):
self.metadata_changed = True
def _metadata_items_changed(self, event):
self.metadata_changed = True
# EOF
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