prysm.degredations¶

Degredations in the image chain.

class prysm.degredations.Smear(width, angle=0)¶

Bases: prysm.convolution.Convolvable

Smear (motion blur).

analytic_ft(x, y)¶

Analytic FT of the smear.

Parameters

x (numpy.ndarray) – x Cartesian spatial frequency, cy/um
y (numpy.ndarray) – y Cartesian spatial frequency, cy/um

Returns

analytical FT of the smear.

Return type

numpy.ndarray

center_x¶: Center “pixel” in x.

center_y¶: Center “pixel” in y.

conv(other)¶

Convolves this convolvable with another.

Parameters: other (Convolvable) – A convolvable object
Returns: a convolvable object
Return type: Convolvable

Notes

If self and other both have analytic Fourier transforms, no math will be done and the aFTs are merged directly.

If only one of self or other has an analytic Fourier transform, the output grid will be defined by the object which does not have an analytic Fourier transform.

If neither has an analytic transform, the output grid will: - span max(self.support, other.support) - have sample spacing min(self.sample_spacing, other.sample_spacing)

This ensures the signal remains Nyquist sampled and (probably) doesn’t expand beyond the extent of the output window. The latter condition will be violated when two large convolvables are convolved.

copy()¶: Return a (deep) copy of this instance.

deconv(other, balance=1000, reg=None, is_real=True, clip=False, postnormalize=True)¶

Perform the deconvolution of this convolvable object by another.

Parameters

other (Convolvable) – another convolvable object, used as the PSF in a Wiener deconvolution
balance (float, optional) – regularization parameter; passed through to skimage
reg (numpy.ndarray, optional) – regularization operator, passed through to skimage
is_real (bool, optional) – True if self and other are both real
clip (bool, optional) – clips self and other into (0,1)
postnormalize (bool, optional) – normalize the result such that it falls in [0,1]

Returns

a new Convolable object

Return type

Convolvable

Notes

See skimage: http://scikit-image.org/docs/dev/api/skimage.restoration.html#skimage.restoration.wiener

static from_file(path, scale)¶

Read a monochrome 8 bit per pixel file into a new Image instance.

Parameters

path (string) – path to a file
scale (float) – pixel scale, in microns

Returns

a new image object

Return type

Convolvable

plot_slice_xy(axlim=20, lw=3, zorder=3, fig=None, ax=None)¶

Create a plot of slices through the X and Y axes of the PSF.

Parameters

axlim (float or int, optional) – axis limits, in microns
lw (float, optional) – line width
zorder (int, optional) – zorder
fig (matplotlib.figure.Figure, optional) – Figure to draw plot in
ax (matplotlib.axes.Axis) – Axis to draw plot in

Returns

fig (matplotlib.figure.Figure, optional) – Figure containing the plot
ax (matplotlib.axes.Axis, optional) – Axis containing the plot

renorm()¶: Renormalize so that the peak is at a value of unity.

sample_spacing¶: center-to-center sample spacing.

samples_x¶: Number of samples in the x dimension.

samples_y¶: Number of samples in the y dimension.

save(path, nbits=8)¶

Write the image to a png, jpg, tiff, etc.

Parameters

path (string) – path to write the image to
nbits (int) – number of bits in the output image

shape¶: Proxy to phase or data shape.

show(xlim=None, ylim=None, interp_method=None, power=1, show_colorbar=True, fig=None, ax=None)¶

Display the image.

Parameters

xlim (iterable, optional) – x axis limits
ylim (iterable,) – y axis limits
interp_method (string) – interpolation technique used in display
power (float) – inverse of power to stretch image by. E.g. power=2 will plot img ** (1/2)
show_colorbar (bool) – whether to show the colorbar or not.
fig (matplotlib.figure.Figure, optional:) – Figure containing the plot
ax (matplotlib.axes.Axis, optional:) – Axis containing the plot

Returns

fig (matplotlib.figure.Figure, optional:) – Figure containing the plot
ax (matplotlib.axes.Axis, optional:) – Axis containing the plot

show_fourier(freq_x=None, freq_y=None, interp_method='lanczos', fig=None, ax=None)¶

Display the fourier transform of the image.

Parameters

interp_method (string) – method used to interpolate the data for display.
freq_x (iterable) – x frequencies to use for convolvable with analytical FT and no data
freq_y (iterable) – y frequencies to use for convolvable with analytic FT and no data
fig (matplotlib.figure.Figure) – Figure containing the plot
ax (matplotlib.axes.Axis) – Axis containing the plot

Returns

fig (matplotlib.figure.Figure) – Figure containing the plot
ax (matplotlib.axes.Axis) – Axis containing the plot

Notes

freq_x and freq_y are unused when the convolvable has a .data field.

size¶: Proxy to phase or data size.

slice_x¶

Retrieve a slice through the X axis of the phase.

Returns

self.unit (numpy.ndarray) – ordinate axis
slice of self.phase or self.data (numpy.ndarray)

slice_y¶

Retrieve a slice through the Y axis of the phase.

Returns

self.unit (numpy.ndarray) – ordinate axis
slice of self.phase or self.data (numpy.ndarray)

support¶: Width of the domain.

support_x¶: Width of the domain in X.

support_y¶: Width of the domain in Y.

class prysm.degredations.Jitter(scale, sample_spacing=None, samples=None)¶

Bases: prysm.convolution.Convolvable

Jitter (high frequency motion).

analytic_ft(x, y)¶

Analytic FT of jitter.

Parameters

x (numpy.ndarray) – x Cartesian spatial frequency, units of cy/um
y (numpy.ndarray) – y Cartesian spatial frequency, units of cy/um

Returns

value of analytic FT

Return type

numpy.ndarray

center_x¶: Center “pixel” in x.

center_y¶: Center “pixel” in y.

conv(other)¶

Convolves this convolvable with another.

Parameters: other (Convolvable) – A convolvable object
Returns: a convolvable object
Return type: Convolvable

Notes

If self and other both have analytic Fourier transforms, no math will be done and the aFTs are merged directly.

If only one of self or other has an analytic Fourier transform, the output grid will be defined by the object which does not have an analytic Fourier transform.

If neither has an analytic transform, the output grid will: - span max(self.support, other.support) - have sample spacing min(self.sample_spacing, other.sample_spacing)

This ensures the signal remains Nyquist sampled and (probably) doesn’t expand beyond the extent of the output window. The latter condition will be violated when two large convolvables are convolved.

copy()¶: Return a (deep) copy of this instance.

deconv(other, balance=1000, reg=None, is_real=True, clip=False, postnormalize=True)¶

Perform the deconvolution of this convolvable object by another.

Parameters

other (Convolvable) – another convolvable object, used as the PSF in a Wiener deconvolution
balance (float, optional) – regularization parameter; passed through to skimage
reg (numpy.ndarray, optional) – regularization operator, passed through to skimage
is_real (bool, optional) – True if self and other are both real
clip (bool, optional) – clips self and other into (0,1)
postnormalize (bool, optional) – normalize the result such that it falls in [0,1]

Returns

a new Convolable object

Return type

Convolvable

Notes

See skimage: http://scikit-image.org/docs/dev/api/skimage.restoration.html#skimage.restoration.wiener

static from_file(path, scale)¶

Read a monochrome 8 bit per pixel file into a new Image instance.

Parameters

path (string) – path to a file
scale (float) – pixel scale, in microns

Returns

a new image object

Return type

Convolvable

plot_slice_xy(axlim=20, lw=3, zorder=3, fig=None, ax=None)¶

Create a plot of slices through the X and Y axes of the PSF.

Parameters

axlim (float or int, optional) – axis limits, in microns
lw (float, optional) – line width
zorder (int, optional) – zorder
fig (matplotlib.figure.Figure, optional) – Figure to draw plot in
ax (matplotlib.axes.Axis) – Axis to draw plot in

Returns

fig (matplotlib.figure.Figure, optional) – Figure containing the plot
ax (matplotlib.axes.Axis, optional) – Axis containing the plot

renorm()¶: Renormalize so that the peak is at a value of unity.

sample_spacing¶: center-to-center sample spacing.

samples_x¶: Number of samples in the x dimension.

samples_y¶: Number of samples in the y dimension.

save(path, nbits=8)¶

Write the image to a png, jpg, tiff, etc.

Parameters

path (string) – path to write the image to
nbits (int) – number of bits in the output image

shape¶: Proxy to phase or data shape.

show(xlim=None, ylim=None, interp_method=None, power=1, show_colorbar=True, fig=None, ax=None)¶

Display the image.

Parameters

xlim (iterable, optional) – x axis limits
ylim (iterable,) – y axis limits
interp_method (string) – interpolation technique used in display
power (float) – inverse of power to stretch image by. E.g. power=2 will plot img ** (1/2)
show_colorbar (bool) – whether to show the colorbar or not.
fig (matplotlib.figure.Figure, optional:) – Figure containing the plot
ax (matplotlib.axes.Axis, optional:) – Axis containing the plot

Returns

fig (matplotlib.figure.Figure, optional:) – Figure containing the plot
ax (matplotlib.axes.Axis, optional:) – Axis containing the plot

show_fourier(freq_x=None, freq_y=None, interp_method='lanczos', fig=None, ax=None)¶

Display the fourier transform of the image.

Parameters

interp_method (string) – method used to interpolate the data for display.
freq_x (iterable) – x frequencies to use for convolvable with analytical FT and no data
freq_y (iterable) – y frequencies to use for convolvable with analytic FT and no data
fig (matplotlib.figure.Figure) – Figure containing the plot
ax (matplotlib.axes.Axis) – Axis containing the plot

Returns

fig (matplotlib.figure.Figure) – Figure containing the plot
ax (matplotlib.axes.Axis) – Axis containing the plot

Notes

freq_x and freq_y are unused when the convolvable has a .data field.

size¶: Proxy to phase or data size.

slice_x¶

Retrieve a slice through the X axis of the phase.

Returns

self.unit (numpy.ndarray) – ordinate axis
slice of self.phase or self.data (numpy.ndarray)

slice_y¶

Retrieve a slice through the Y axis of the phase.

Returns

self.unit (numpy.ndarray) – ordinate axis
slice of self.phase or self.data (numpy.ndarray)

support¶: Width of the domain.

support_x¶: Width of the domain in X.

support_y¶: Width of the domain in Y.

prysm.degredations¶

prysm

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