def plot_pixel_mask(h, w, pixels, output_path=None):
"""
Display a binary image that shows the given pixel mask.
A black image with `w` x `h` pixels is created and the `pixels` are marked
with white.
Parameters
----------
h : int
The height of the image in pixels.
w : int
The width of the image in pixels.
pixels : ndarray
The 2D array of pixels that make up the mask. Each row is a coordinate
pair (x, y), such that `coords` has size len(`pixels`) x 2.
output_path : str, optional
Path (including file type extension) under which the plot is saved (the
default value is None which implies, that the plot is not saved).
Notes
-----
The resulting image is displayed in a figure using
`magni.imaging.visualisation.imshow`.
Examples
--------
For example,
>>> from magni.imaging.measurements import plot_pixel_mask
>>> h = 3
>>> w = 3
>>> pixels = np.array([[0, 0], [1, 1], [2, 1]])
>>> plot_pixel_mask(h, w, pixels)
"""
_validate_plot_pixel_mask(h, w, pixels, output_path)
mask = np.zeros((h, w))
mask[pixels[:, 1], pixels[:, 0]] = 1
fs = (np.abs([h, w])).min() / 10
_plotting.setup_matplotlib({'figure': {'figsize': (h / fs, w / fs)}},
cmap='gray')
fig, axes = plt.subplots(1, 1)
_imshow(mask, ax=axes, show_axis='top')
axes.set_xlabel('width [pixels]')
axes.set_ylabel('height [pixels]')
plt.tight_layout()
if output_path is not None:
plt.savefig(output_path)
def plot_pattern(l, w, coords, mode, output_path=None):
"""
Display a plot that shows the pattern given by a set of coordinates.
The pattern given by the `coords` is displayed on an `w` x `l` area. If
`mode` is 'surface', `l` and `w` are regarded as measured in meters. If
`mode` is 'image', `l` and `w` are regarded as measured in pixels. The
`coords` are marked by filled circles and connected by straight dashed
lines.
Parameters
----------
l : float or int
The length/height of the area. If `mode` is 'surface', it must be a
float. If `mode` is 'image', it must be an integer.
w : float or int
The width of the area. If `mode` is 'surface', it must be a
float. If `mode` is 'image', it must be an integer.
coords : ndarray
The 2D array of pixels that make up the mask. Each row is a coordinate
pair (x, y).
mode : {'surface', 'image'}
The display mode that dertermines the axis labeling and the type of `l`
and `w`.
output_path : str, optional
Path (including file type extension) under which the plot is saved (the
default value is None which implies, that the plot is not saved).
Notes
-----
The resulting plot is displayed in a figure using `matplotlib`'s
`pyplot.plot`.
Examples
--------
For example,
>>> import numpy as np
>>> from magni.imaging.measurements import plot_pattern
>>> l = 3
>>> w = 3
>>> coords = np.array([[0, 0], [1, 1], [2, 1]])
>>> mode = 'image'
>>> plot_pattern(l, w, coords, mode)
"""
@_decorate_validation
def validate_input():
_generic('mode', 'string', value_in=('surface', 'image'))
if mode == 'surface':
_numeric('l', 'floating', range_='[{};inf)'.format(_min_l))
_numeric('w', 'floating', range_='[{};inf)'.format(_min_w))
_numeric('coords', 'floating', shape=(-1, 2))
_numeric('coords[:, 0]', 'floating', range_='[0;{}]'.format(w),
shape=(-1,), var=coords[:, 0])
_numeric('coords[:, 1]', 'floating', range_='[0;{}]'.format(l),
shape=(-1,), var=coords[:, 1])
elif mode == 'image':
_numeric('l', 'integer', range_='[2;inf)')
_numeric('w', 'integer', range_='[2;inf)')
_numeric('coords', ('integer', 'floating'), shape=(-1, 2))
_numeric('coords[:, 0]', ('integer', 'floating'),
range_='[0;{})'.format(w), shape=(-1,),
var=coords[:, 0])
_numeric('coords[:, 1]', ('integer', 'floating'),
range_='[0;{})'.format(l), shape=(-1,),
var=coords[:, 1])
_generic('output_path', 'string', ignore_none=True)
validate_input()
figsize = plt.rcParams['figure.figsize']
if w / l > figsize[0] / figsize[1]:
figsize_local = [figsize[0], figsize[0] * l / w]
else:
figsize_local = [figsize[1] * w / l, figsize[1]]
_plotting.setup_matplotlib({'figure': {'figsize': figsize_local}})
fig, axes = plt.subplots(1, 1)
axes.plot(coords[:, 0], coords[:, 1],
marker=_plotting.markers[0],
ls=_plotting.linestyles[1],
ms=6)
axes.set_xlim([0, w])
axes.xaxis.tick_top()
axes.invert_yaxis()
axes.set_ylim([l, 0])
axes.xaxis.set_label_position('top')
axes.set_aspect('equal')
if mode == 'surface':
axes.set_xlabel('width [m]')
axes.set_ylabel('length [m]')
axes.ticklabel_format(style='sci', scilimits=(0, 0))
# Fix position of exponential multipliers in scientific notation
exp_texts = [axes.xaxis.get_children()[1],
axes.yaxis.get_children()[1]]
vert_aligns = ['bottom', 'center']
hori_aligns = ['center', 'right']
xy_texts = [(0, 25), (-35, 0)]
xys = [(1, 1), (0, 0)]
annos = [None, None]
for k, text in enumerate(exp_texts):
text.set_visible(False)
annos[k] = axes.annotate('', xys[k], xytext=xy_texts[k],
xycoords='axes fraction',
textcoords='offset points',
va=vert_aligns[k], ha=hori_aligns[k])
fig.canvas.mpl_connect(
'draw_event', lambda event: annos[0].set_text(
exp_texts[0].get_text()))
fig.canvas.mpl_connect(
'draw_event', lambda event: annos[1].set_text(
exp_texts[1].get_text()))
elif mode == 'image':
axes.set_xlabel('width [pixels]')
axes.set_ylabel('height [pixels]')
plt.tight_layout()
if output_path is not None:
plt.savefig(output_path)
_plotting.setup_matplotlib({'figure': {'figsize': figsize}})
def plot_pixel_mask(h, w, pixels, output_path=None):
"""
Display a binary image that shows the given pixel mask.
A black image with `w` x `h` pixels is created and the `pixels` are marked
with white.
Parameters
----------
h : int
The height of the image in pixels.
w : int
The width of the image in pixels.
pixels : ndarray
The 2D array of pixels that make up the mask. Each row is a coordinate
pair (x, y), such that `coords` has size len(`pixels`) x 2.
output_path : str, optional
Path (including file type extension) under which the plot is saved (the
default value is None which implies, that the plot is not saved).
Notes
-----
The resulting image is displayed in a figure using
`magni.imaging.visualisation.imshow`.
Examples
--------
For example,
>>> import numpy as np
>>> from magni.imaging.measurements import plot_pixel_mask
>>> h = 3
>>> w = 3
>>> pixels = np.array([[0, 0], [1, 1], [2, 1]])
>>> plot_pixel_mask(h, w, pixels)
"""
@_decorate_validation
def validate_input():
_numeric('h', 'integer', range_='[2;inf)')
_numeric('w', 'integer', range_='[2;inf)')
_numeric('pixels', 'integer', shape=(-1, 2))
_numeric('pixels[:, 0]', 'integer', range_='[0;{}]'.format(w - 1),
shape=(-1,), var=pixels[:, 0])
_numeric('pixels[:, 1]', 'integer', range_='[0;{}]'.format(h - 1),
shape=(-1,), var=pixels[:, 1])
_generic('output_path', 'string', ignore_none=True)
validate_input()
mask = _util.construct_pixel_mask(h, w, pixels)
figsize = plt.rcParams['figure.figsize']
if w / h > figsize[0] / figsize[1]:
figsize_local = [figsize[0], figsize[0] * h / w]
else:
figsize_local = [figsize[1] * w / h, figsize[1]]
_plotting.setup_matplotlib({'figure': {'figsize': figsize_local}})
fig, axes = plt.subplots(1, 1)
_imshow(mask, ax=axes, show_axis='top')
axes.set_xlabel('width [pixels]')
axes.set_ylabel('height [pixels]')
plt.tight_layout()
if output_path is not None:
plt.savefig(output_path)
_plotting.setup_matplotlib({'figure': {'figsize': figsize}})
def imshow(X, ax=None, intensity_func=None, intensity_args=(),
show_axis='frame', **kwargs):
"""
Display an image.
Wrap `matplotlib.pyplot.imshow` to display a possibly intensity manipulated
version of the image `X`.
Parameters
----------
X : ndarray
The image to be displayed.
ax : matplotlib.axes.Axes, optional
The axes on which the image is displayed (the default is None, which
implies that the current axes is used).
intensity_func : FunctionType, optional
The handle to the function used to manipulate the image intensity
before the image is displayed (the default is None, which implies that
no intensity manipulation is used).
intensity_args : list or tuple, optional
The arguments that are passed to the `intensity_func` (the default is
(), which implies that no arguments are passed).
show_axis : {'none', 'top', 'inherit', 'frame'}
How the x- and y-axis are display. If 'none', no axis are displayed. If
'top', the x-axis is displayed at the top of the image. If 'inherit',
the axis display is inherited from `matplotlib.pyplot.imshow`. If
'frame' only the frame is shown and not the ticks.
Returns
-------
im_out : matplotlib.image.AxesImage
The AxesImage returned by matplotlibs imshow.
See Also
--------
matplotlib.pyplot.imshow : Matplotlib's imshow function.
Examples
--------
For example,
>>> import numpy as np
>>> from magni.imaging.visualisation import imshow
>>> X = np.arange(4).reshape(2, 2)
>>> add_k = lambda X, k: X + k
>>> im_out = imshow(X, intensity_func=add_k, intensity_args=(2,))
"""
@_decorate_validation
def validate_input():
_numeric('X', ('boolean', 'integer', 'floating'), shape=(-1, -1))
_generic('ax', mpl.axes.Axes, ignore_none=True)
_generic('intensity_func', 'function', ignore_none=True)
_generic('intensity_args', 'explicit collection')
_generic('show_axis', 'string', value_in=('none', 'top', 'inherit',
'frame'))
validate_input()
_plotting.setup_matplotlib()
# Handle ax keyword argument
ca = plt.gca()
if ax is not None:
plt.sca(ax)
# Intensity manipulation
if intensity_func is not None:
im_out = plt.imshow(intensity_func(X, *intensity_args), **kwargs)
else:
im_out = plt.imshow(X, **kwargs)
# Display of axis
axes = plt.gca()
if show_axis == 'none':
axes.axis('off')
elif show_axis == 'top':
axes.xaxis.tick_top()
axes.xaxis.set_label_position('top')
elif show_axis == 'frame':
xlabels = axes.get_xticklabels()
ylabels = axes.get_yticklabels()
empty_xlabels = ['']*len(xlabels)
empty_ylabels = ['']*len(ylabels)
axes.set_xticklabels(empty_xlabels)
axes.set_yticklabels(empty_ylabels)
axes.tick_params(length=0)
plt.sca(ca)
return im_out
def plot_phase_transitions(curves, plot_l1=True, output_path=None):
r"""
Plot of a set of phase transition boundary curves.
The set of phase transition boundary curves are plotted an saved under the
`output_path`, if specified. The `curves` must be a list of dictionaries
each having keys *delta*, *rho*, and *label*. *delta* must be an `ndarray`
of :math:`\delta` values in the phase space. *rho* must be an `ndarray` of
the corresponding :math:`\rho` values in the phase space. *label* must be a
`str` describing the curve.
Parameters
----------
curves : list
A list of dicts describing the curves to plot.
plot_l1 : bool, optional
Whether or not to plot the theoretical :math:`\ell_1` curve (the
default is True).
output_path : str, optional
Path (including file type extension) under which the plot is saved (the
default value is None which implies, that the plot is not saved).
Notes
-----
The plotting is done using `matplotlib`, which implies that an open figure
containing the plot will result from using this function.
Tabulated values of the theoretical :math:`\ell_1` phase transition
boundary is available at
http://people.maths.ox.ac.uk/tanner/polytopes.shtml
Examples
--------
For example,
>>> from magni.cs.phase_transition.plotting import plot_phase_transitions
>>> delta = np.array([0.1, 0.2, 0.9])
>>> rho = np.array([0.1, 0.3, 0.8])
>>> curves = [{'delta': delta, 'rho': rho, 'label': 'data1'}]
>>> output_path = 'phase_transitions.pdf'
>>> plot_phase_transitions(curves, output_path=output_path)
"""
_validate_plot_phase_transitions(curves, plot_l1, output_path)
_plotting.setup_matplotlib()
fig, axes = plt.subplots(1, 1)
for curve in curves:
axes.plot(curve['delta'], curve['rho'], label=curve['label'])
if plot_l1:
_plot_theoretical_l1(axes)
axes.legend(loc='upper left')
axes.set_xlabel(r'$\delta = m/n$')
axes.set_ylabel(r'$\rho = k/m$')
plt.tight_layout()
if output_path is not None:
plt.savefig(output_path)
def plot_phase_transition_colormap(dist, delta, rho, plot_l1=True,
output_path=None):
r"""
Create a colormap of the phase space reconstruction probabilities.
The `delta` and `rho` values span a 2D grid in the phase space.
Reconstruction probabilities are then calculated from the `dist` 3D array
of reconstruction error distances. The resulting 2D grid of reconstruction
probabilites is visualised over the square centers in this 2D grid using a
colormap. Values in this grid at lower indicies correspond to lower values
of :math:`\delta` and :math:`\rho`. If `plot_l1` is True, then the
theoretical l1 curve is overlayed the colormap. The colormap is saved under
the `output_path`, if specified.
Parameters
----------
dist : ndarray
A 3D array of reconstruction error distances.
delta : ndarray
:math:`\delta` values used in the 2D grid.
rho : ndarray
:math:`\rho` values used in the 2D grid.
plot_l1 : bool
Whether or not to plot the theoretical :math:`\ell_1` curve. (the
default is True)
output_path : str, optional
Path (including file type extension) under which the plot is saved (the
default value is None which implies, that the plot is not saved).
See Also
--------
magni.cs.phase_transition.io.load_phase_transition : Loading phase
transitions from an HDF database.
Notes
-----
The plotting is done using `matplotlib`, which implies that an open figure
containing the plot will result from using this function.
The values in `delta` and `rho` are assumed to be equally spaced.
Due to the *centering* of the color coded rectangles, they are not
necessarily square towards the ends of the intervals defined by `delta` and
`rho`.
Tabulated values of the theoretical :math:`\ell_1` phase transition
boundary is available at
http://people.maths.ox.ac.uk/tanner/polytopes.shtml
Examples
--------
For example,
>>> from magni.cs.phase_transition.plotting import (
... plot_phase_transition_colormap)
>>> delta = np.array([0.2, 0.5, 0.8])
>>> rho = np.array([0.3, 0.6])
>>> dist = np.array([[[1.35e-08, 1.80e-08], [1.08, 1.11]],
... [[1.40e-12, 8.32e-12], [8.57e-01, 7.28e-01]], [[1.92e-13, 1.17e-13],
... [2.10e-10, 1.12e-10]]])
>>> out_path = 'phase_transition_cmap.pdf'
>>> plot_phase_transition_colormap(dist, delta, rho, output_path=out_path)
"""
_validate_plot_phase_transition_colormap(dist, delta, rho, plot_l1,
output_path)
probs = np.sum(dist < 1e-4, axis=-1, dtype=float) / np.size(dist, -1)
d_delta = (delta[1] - delta[0]) / 2
d_rho = (rho[1] - rho[0]) / 2
x = np.hstack((np.array([0]), delta[1:] - d_delta, np.array([1])))
y = np.hstack((np.array([0]), rho[1:] - d_rho, np.array([1])))
X, Y = np.meshgrid(x, y)
_plotting.setup_matplotlib()
fig, axes = plt.subplots(1, 1)
p_mesh = axes.pcolormesh(X, Y, probs.T, vmin=0, vmax=1, edgecolor='face')
c_bar = plt.colorbar(p_mesh)
c_bar.set_label('Estimated probability of reconstruction')
# Apply vector graphics render workaround to avoid white gaps in colorbar
# See: http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.colorbar
c_bar.solids.set_edgecolor("face")
if plot_l1:
_plot_theoretical_l1(axes)
axes.legend(loc='upper left')
axes.set_xticks(np.arange(11) / 10)
axes.set_yticks(np.arange(11) / 10)
axes.set_xlabel(r'$\delta = m/n$')
axes.set_ylabel(r'$\rho = k/m$')
plt.tight_layout()
if output_path is not None:
plt.savefig(output_path)
def plot_pattern(l, w, coords, mode, output_path=None):
"""
Display a plot that shows the pattern given by a set of coordinates.
The pattern given by the `coords` is displayed on an `w` x `l` area. If
`mode` is 'surface', `l` and `w` are regarded as measured in meters. If
`mode` is 'image', `l` and `w` are regarded as measured in pixels. The
`coords` are marked by filled circles and connected by straight dashed
lines.
Parameters
----------
l : float or int
The length/height of the area. If `mode` is 'surface', it must be a
float. If `mode` is 'image', it must be an integer.
w : float or int
The width of the area. If `mode` is 'surface', it must be a
float. If `mode` is 'image', it must be an integer.
coords : ndarray
The 2D array of pixels that make up the mask. Each row is a coordinate
pair (x, y).
mode : {'surface', 'image'}
The display mode that dertermines the axis labeling and the type of `l`
and `w`.
output_path : str, optional
Path (including file type extension) under which the plot is saved (the
default value is None which implies, that the plot is not saved).
Notes
-----
The resulting plot is displayed in a figure using `matplotlib`'s
`pyplot.plot`.
Examples
--------
For example,
>>> from magni.imaging.measurements import plot_pattern
>>> l = 3
>>> w = 3
>>> coords = np.array([[0, 0], [1, 1], [2, 1]], dtype=np.float)
>>> mode = 'image'
>>> plot_pattern(l, w, coords, mode)
"""
_validate_plot_pattern(l, w, coords, mode, output_path)
fs = (np.abs([l, w])).min() / 10
_plotting.setup_matplotlib({'figure': {'figsize': (l / fs, w / fs)}})
fig, axes = plt.subplots(1, 1)
axes.plot(coords[:, 0], coords[:, 1],
marker=_plotting.markers[0],
ls=_plotting.linestyles[1],
ms=6)
axes.set_xlim([0, w])
axes.xaxis.tick_top()
axes.invert_yaxis()
axes.set_ylim([l, 0])
axes.xaxis.set_label_position('top')
axes.set_aspect('equal')
if mode == 'surface':
axes.set_xlabel('width [m]')
axes.set_ylabel('length [m]')
axes.ticklabel_format(style='sci', scilimits=(0, 0))
# Fix position of exponential multipliers in scientific notation
exp_texts = [axes.xaxis.get_children()[1],
axes.yaxis.get_children()[1]]
vert_aligns = ['bottom', 'center']
hori_aligns = ['center', 'right']
xy_texts = [(0, 25), (-35, 0)]
xys = [(1, 1), (0, 0)]
annos = [None, None]
for k, text in enumerate(exp_texts):
text.set_visible(False)
annos[k] = axes.annotate('', xys[k], xytext=xy_texts[k],
xycoords='axes fraction',
textcoords='offset points',
va=vert_aligns[k], ha=hori_aligns[k])
fig.canvas.mpl_connect(
'draw_event', lambda event: annos[0].set_text(
exp_texts[0].get_text()))
fig.canvas.mpl_connect(
'draw_event', lambda event: annos[1].set_text(
exp_texts[1].get_text()))
elif mode == 'image':
axes.set_xlabel('width [pixels]')
axes.set_ylabel('height [pixels]')
plt.tight_layout()
if output_path is not None:
plt.savefig(output_path)
def imshow(X, ax=None, intensity_func=None, intensity_args=(),
show_axis='none', **kwargs):
"""
Display an image.
Wrap `matplotlib.pyplot.imshow` to display a possibly intensity manipulated
version of the image `X`.
Parameters
----------
X : ndarray
The image to be displayed.
ax : matplotlib.axes.Axes, optional
The axes on which the image is displayed (the default is None, which
implies that the current axes is used).
intensity_func : FunctionType, optional
The handle to the function used to manipulate the image intensity
before the image is displayed (the default is None, which implies that
no intensity manipulation is used).
intensity_args : list or tuple, optional
The arguments that are passed to the `intensity_func` (the default is
(), which implies that no arguments are passed).
show_axis : {'none', 'top', 'inherit'}
How the x- and y-axis are display. If 'none', no axis are displayed. If
'top', the x-axis is displayed at the top of the image. If 'inherit',
the axis display is inherited from `matplotlib.pyplot.imshow`.
Returns
-------
im_out : matplotlib.image.AxesImage
The AxesImage returned by matplotlibs imshow.
See Also
--------
matplotlib.pyplot.imshow : Matplotlib's imshow function.
Examples
--------
For example,
>>> from magni.imaging.visualisation import imshow
>>> X = np.arange(4).reshape(2, 2)
>>> add_k = lambda X, k: X + k
>>> im_out = imshow(X, intensity_func=add_k, intensity_args=(2,))
"""
_validate_imshow(X, ax, intensity_func, intensity_args, show_axis)
_plotting.setup_matplotlib()
# Handle ax keyword argument
ca = plt.gca()
if ax is not None:
plt.sca(ax)
# Intensity manipulation
if intensity_func is not None:
im_out = plt.imshow(intensity_func(X, *intensity_args), **kwargs)
else:
im_out = plt.imshow(X, **kwargs)
# Display of axis
axes = plt.gca()
if show_axis == 'none':
xlabels = axes.get_xticklabels()
ylabels = axes.get_yticklabels()
empty_xlabels = ['']*len(xlabels)
empty_ylabels = ['']*len(ylabels)
axes.set_xticklabels(empty_xlabels)
axes.set_yticklabels(empty_ylabels)
axes.tick_params(length=0)
elif show_axis == 'top':
axes.xaxis.tick_top()
axes.xaxis.set_label_position('top')
plt.sca(ca)
return im_out
def imshow(X,
ax=None,
intensity_func=None,
intensity_args=(),
show_axis='frame',
**kwargs):
"""
Display an image.
Wrap `matplotlib.pyplot.imshow` to display a possibly intensity manipulated
version of the image `X`.
Parameters
----------
X : ndarray
The image to be displayed.
ax : matplotlib.axes.Axes, optional
The axes on which the image is displayed (the default is None, which
implies that the current axes is used).
intensity_func : FunctionType, optional
The handle to the function used to manipulate the image intensity
before the image is displayed (the default is None, which implies that
no intensity manipulation is used).
intensity_args : list or tuple, optional
The arguments that are passed to the `intensity_func` (the default is
(), which implies that no arguments are passed).
show_axis : {'none', 'top', 'inherit', 'frame'}
How the x- and y-axis are display. If 'none', no axis are displayed. If
'top', the x-axis is displayed at the top of the image. If 'inherit',
the axis display is inherited from `matplotlib.pyplot.imshow`. If
'frame' only the frame is shown and not the ticks.
Returns
-------
im_out : matplotlib.image.AxesImage
The AxesImage returned by matplotlibs imshow.
See Also
--------
matplotlib.pyplot.imshow : Matplotlib's imshow function.
Examples
--------
For example,
>>> import numpy as np
>>> from magni.imaging.visualisation import imshow
>>> X = np.arange(4).reshape(2, 2)
>>> add_k = lambda X, k: X + k
>>> im_out = imshow(X, intensity_func=add_k, intensity_args=(2,))
"""
@_decorate_validation
def validate_input():
_numeric('X', ('boolean', 'integer', 'floating'), shape=(-1, -1))
_generic('ax', mpl.axes.Axes, ignore_none=True)
_generic('intensity_func', 'function', ignore_none=True)
_generic('intensity_args', 'explicit collection')
_generic('show_axis',
'string',
value_in=('none', 'top', 'inherit', 'frame'))
validate_input()
_plotting.setup_matplotlib()
# Handle ax keyword argument
ca = plt.gca()
if ax is not None:
plt.sca(ax)
# Intensity manipulation
if intensity_func is not None:
im_out = plt.imshow(intensity_func(X, *intensity_args), **kwargs)
else:
im_out = plt.imshow(X, **kwargs)
# Display of axis
axes = plt.gca()
if show_axis == 'none':
axes.axis('off')
elif show_axis == 'top':
axes.xaxis.tick_top()
axes.xaxis.set_label_position('top')
elif show_axis == 'frame':
xlabels = axes.get_xticklabels()
ylabels = axes.get_yticklabels()
empty_xlabels = [''] * len(xlabels)
empty_ylabels = [''] * len(ylabels)
axes.set_xticklabels(empty_xlabels)
axes.set_yticklabels(empty_ylabels)
axes.tick_params(length=0)
plt.sca(ca)
return im_out
def plot_pixel_mask(h, w, pixels, output_path=None):
"""
Display a binary image that shows the given pixel mask.
A black image with `w` x `h` pixels is created and the `pixels` are marked
with white.
Parameters
----------
h : int
The height of the image in pixels.
w : int
The width of the image in pixels.
pixels : ndarray
The 2D array of pixels that make up the mask. Each row is a coordinate
pair (x, y), such that `coords` has size len(`pixels`) x 2.
output_path : str, optional
Path (including file type extension) under which the plot is saved (the
default value is None which implies, that the plot is not saved).
Notes
-----
The resulting image is displayed in a figure using
`magni.imaging.visualisation.imshow`.
Examples
--------
For example,
>>> import numpy as np
>>> from magni.imaging.measurements import plot_pixel_mask
>>> h = 3
>>> w = 3
>>> pixels = np.array([[0, 0], [1, 1], [2, 1]])
>>> plot_pixel_mask(h, w, pixels)
"""
@_decorate_validation
def validate_input():
_numeric('h', 'integer', range_='[2;inf)')
_numeric('w', 'integer', range_='[2;inf)')
_numeric('pixels', 'integer', shape=(-1, 2))
_numeric('pixels[:, 0]', 'integer', range_='[0;{}]'.format(w - 1),
shape=(-1,), var=pixels[:, 0])
_numeric('pixels[:, 1]', 'integer', range_='[0;{}]'.format(h - 1),
shape=(-1,), var=pixels[:, 1])
_generic('output_path', 'string', ignore_none=True)
validate_input()
mask = np.zeros((h, w))
mask[pixels[:, 1], pixels[:, 0]] = 1
figsize = plt.rcParams['figure.figsize']
if w / h > figsize[0] / figsize[1]:
figsize_local = [figsize[0], figsize[0] * h / w]
else:
figsize_local = [figsize[1] * w / h, figsize[1]]
_plotting.setup_matplotlib({'figure': {'figsize': figsize_local}})
fig, axes = plt.subplots(1, 1)
_imshow(mask, ax=axes, show_axis='top')
axes.set_xlabel('width [pixels]')
axes.set_ylabel('height [pixels]')
plt.tight_layout()
if output_path is not None:
plt.savefig(output_path)
_plotting.setup_matplotlib({'figure': {'figsize': figsize}})