def set_image(self, image, row, col, cmap=None, vmin=None, vmax=None):
"""
Sets the data for a single window.
Parameters
----------
image : ndarray, ndim=2
The shape should be the same as the `shape` specified when
constructing the image grid.
row, col : int
The zero-index of the row and column to set.
cmap : cmap (from matplotlib.pylab.cm)
The color palette to use. Default is grayscale.
vmin, vmax : numerical or None
Defines the range of the color palette. None, which is default,
takes the range of the data.
"""
import matplotlib as mpl
import matplotlib.pylab as plt
from amitgroup.plot.resample import resample_and_arrange_image
if cmap is None:
cmap = plt.cm.gray
if vmin is None:
vmin = image.min()
if vmax is None:
vmax = image.max()
if vmin == vmax:
diff = 1
else:
diff = vmax - vmin
image_indices = np.clip((image - vmin) / diff, 0, 1) * 255
image_indices = image_indices.astype(np.uint8)
lut = mpl.colors.makeMappingArray(256, cmap)
rgb = resample_and_arrange_image(image_indices, self._shape, lut)
x0 = row * (self._shape[0] + self._border)
x1 = (row + 1) * (self._shape[0] + self._border) + self._border
y0 = col * (self._shape[1] + self._border)
y1 = (col + 1) * (self._shape[1] + self._border) + self._border
self._data[x0:x1, y0:y1] = self._border_color
anchor = (self._border + row * (self._shape[0] + self._border),
self._border + col * (self._shape[1] + self._border))
self._data[anchor[0]:anchor[0] + rgb.shape[0],
anchor[1]:anchor[1] + rgb.shape[1]] = rgb
def set_image(self, image, row, col, cmap=None, vmin=None, vmax=None):
"""
Sets the data for a single window.
Parameters
----------
image : ndarray, ndim=2
The shape should be the same as the `shape` specified when
constructing the image grid.
row, col : int
The zero-index of the row and column to set.
cmap : cmap (from matplotlib.pylab.cm)
The color palette to use. Default is grayscale.
vmin, vmax : numerical or None
Defines the range of the color palette. None, which is default,
takes the range of the data.
"""
import matplotlib as mpl
import matplotlib.pylab as plt
from amitgroup.plot.resample import resample_and_arrange_image
if cmap is None:
cmap = plt.cm.gray
if vmin is None:
vmin = image.min()
if vmax is None:
vmax = image.max()
if vmin == vmax:
diff = 1
else:
diff = vmax - vmin
image_indices = np.clip((image - vmin) / diff, 0, 1) * 255
image_indices = image_indices.astype(np.uint8)
lut = mpl.colors.makeMappingArray(256, cmap)
rgb = resample_and_arrange_image(image_indices, self._shape, lut)
x0 = row * (self._shape[0] + self._border)
x1 = (row + 1) * (self._shape[0] + self._border) + self._border
y0 = col * (self._shape[1] + self._border)
y1 = (col + 1) * (self._shape[1] + self._border) + self._border
self._data[x0:x1, y0:y1] = self._border_color
anchor = (self._border + row * (self._shape[0] + self._border),
self._border + col * (self._shape[1] + self._border))
self._data[anchor[0]:anchor[0] + rgb.shape[0],
anchor[1]:anchor[1] + rgb.shape[1]] = rgb
def set_image(self, image, row, col, cmap=None, vmin=None, vmax=None,
vsym=False):
"""
Sets the data for a single window.
Parameters
----------
image : ndarray, ndim=2
The shape should be the same as the `shape` specified when
constructing the image grid.
row, col : int
The zero-index of the row and column to set.
cmap : cmap (from matplotlib.pylab.cm)
The color palette to use. Default is grayscale.
vmin, vmax : numerical or None
Defines the range of the color palette. None, which is default,
takes the range of the data.
vsym : bool
If True, this means that the color palette will always be centered
around 0. Even if you have specified both `vmin` and `vmax`, this
will override that and extend the shorter one. Good practice is to
specify neither `vmin` or `vmax` or only `vmax` together with this
option.
"""
import matplotlib as mpl
from matplotlib.pylab import cm
from amitgroup.plot.resample import resample_and_arrange_image
if cmap is None:
cmap = cm.gray
if vmin is None:
vmin = np.nanmin(image)
if vmax is None:
vmax = np.nanmax(image)
if vsym and -vmin != vmax:
mx = max(abs(vmin), abs(vmax))
vmin = -mx
vmax = mx
if vmin == vmax:
diff = 1
else:
diff = vmax - vmin
image_indices = np.clip((image - vmin) / diff, 0, 1) * 255
image_indices = image_indices.astype(np.uint8)
nan_mask = np.isnan(image).astype(np.uint8)
lut = mpl.colors.makeMappingArray(256, cmap)
rgb = resample_and_arrange_image(image_indices, nan_mask, self._shape,
lut)
x0 = row * (self._shape[0] + self._border)
x1 = (row + 1) * (self._shape[0] + self._border) + self._border
y0 = col * (self._shape[1] + self._border)
y1 = (col + 1) * (self._shape[1] + self._border) + self._border
self._data[x0:x1, y0:y1] = self._border_color
anchor = (self._border + row * (self._shape[0] + self._border),
self._border + col * (self._shape[1] + self._border))
selection = [slice(anchor[0], anchor[0] + rgb.shape[0]),
slice(anchor[1], anchor[1] + rgb.shape[1])]
nan_data = np.isnan(rgb)
rgb[nan_data] = 0.0
self._data[selection] = (rgb * ~nan_data +
self._border_color * nan_data)
