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326 lines
10 KiB
Python
326 lines
10 KiB
Python
from __future__ import division, print_function, absolute_import
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from CDTools.tools import cmath
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import torch as t
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import numpy as np
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import matplotlib.pyplot as plt
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from matplotlib.colors import hsv_to_rgb
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__all__ = ['colorize','plot_1D','plot_amplitude','plot_phase',
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'plot_colorized', 'plot_translations','get_units_factor',
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'plot_nanomap']
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def colorize(z):
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""" Returns RGB values for a complex color plot given a complex array
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This function returns a set of RGB values that can be used directly
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in a call to imshow based on an input complex numpy array (not a
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torch tensor representing a complex field)
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Args:
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z (array_like) : A complex-valued array
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Returns:
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list : A list of arrays for R,G, and B channels of an image.
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"""
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amp = np.abs(z)
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rmin = 0
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rmax = np.max(amp)
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amp = np.where(amp < rmin, rmin, amp)
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amp = np.where(amp > rmax, rmax, amp)
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ph = np.angle(z, deg=1) + 90
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# HSV are values in range [0,1]
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h = (ph % 360) / 360
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s = 0.85 * np.ones_like(h)
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v = (amp - rmin) / (rmax - rmin)
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return hsv_to_rgb(np.dstack((h,s,v)))
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def get_units_factor(units):
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"""Gets the multiplicative factor associated with a length unit
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Args:
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units (str) : The abbreviation for the unit type
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Returns:
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(float) : The factor meters / (unit)
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"""
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u = units.lower()
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if u=='m':
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factor=1
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if u=='cm':
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factor=1e2
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if u=='mm':
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factor=1e3
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if u=='um':
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factor=1e6
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if u=='nm':
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factor=1e9
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if u=='a':
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factor=1e10
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if u=='pm':
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factor=1e12
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return factor
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def plot_1D(arr, fig = None, **kwargs):
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"""Simple 1D plotter
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Args:
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im (numpy array) : A 1D array with dimensions (,N)
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fig (matplotlib.figure.Figure) : A matplotlib figure to use to plot. If None,
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a new figure is created with an Axes subplot at 111.
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**kwargs: Can be used to set any keyword arguments for the matplotlib.axes.Axes class
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(see https://matplotlib.org/api/axes_api.html#the-axes-class)
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"""
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if fig is None:
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fig = plt.figure()
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ax = fig.add_subplot(111, **kwargs)
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else:
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plt.figure(fig.number)
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plt.gcf().clear()
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plt.scatter(np.arange(arr.shape[-1]), arr)
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def plot_amplitude(im, fig = None, basis=None, units='um', cmap='viridis', **kwargs):
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""" Plots the amplitude of a complex Tensor or numpy array with dimensions NxMx2.
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Args:
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im (t.Tensor) : An image with dimensions NxMx2.
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fig (matplotlib.figure.Figure) : A matplotlib figure to use to plot. If None,
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a new figure is created with an Axes subplot at 111.
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basis (numpy array) : Optional, the 3x2 probe basis, used to put the axis labels in real space units.
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units (str) : The units to convert the basis to
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cmap (str) : Default is 'viridis', the colormap to plot with
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**kwargs: Can be used to set any keyword arguments for the matplotlib.axes.Axes class
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(see https://matplotlib.org/api/axes_api.html#the-axes-class)
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"""
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if fig is None:
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fig = plt.figure()
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ax = fig.add_subplot(111, **kwargs)
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else:
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plt.figure(fig.number)
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plt.gcf().clear()
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if isinstance(im, t.Tensor):
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absolute = cmath.cabs(im).detach().cpu().numpy()
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else:
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absolute = np.absolute(im)
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#Plot in a basis if it exists, otherwise dont
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if basis is not None:
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if isinstance(basis,t.Tensor):
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basis = basis.detach().cpu().numpy()
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basis_norm = np.linalg.norm(basis, axis = 0)
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basis_norm = basis_norm * get_units_factor(units)
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extent = [0, absolute.shape[-1]*basis_norm[1], 0, absolute.shape[-2]*basis_norm[0]]
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else:
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extent=None
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plt.imshow(absolute, cmap = cmap, extent = extent)
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cbar = plt.colorbar()
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cbar.set_label('Amplitude (a.u.)')
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if basis is not None:
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plt.xlabel('X (' + units + ')')
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plt.ylabel('Y (' + units + ')')
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else:
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plt.xlabel('j (pixels)')
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plt.ylabel('i (pixels)')
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return fig
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def plot_phase(im, fig=None, basis=None, units='um', cmap='auto', **kwargs):
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""" Plots the phase of a complex Tensor or numpy array with dimensions NxMx2.
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Args:
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im (t.Tensor) : An image with dimensions NxMx2.
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fig (matplotlib.figure.Figure) : A matplotlib figure to use to plot. If None,
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a new figure is created with an Axes subplot at 111.
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basis (numpy array) : Optional, the 3x2 probe basis, used to put the axis labels in real space units.
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cmap (str) : Default is 'auto', which chooses between twilight and hsv based on availability.
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**kwargs: Can be used to set any keyword arguments for the matplotlib.axes.Axes class
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(see https://matplotlib.org/api/axes_api.html#the-axes-class)
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"""
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if fig is None:
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fig = plt.figure()
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ax = fig.add_subplot(111, **kwargs)
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else:
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plt.figure(fig.number)
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plt.gcf().clear()
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if isinstance(im, t.Tensor):
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phase = cmath.cphase(im).detach().cpu().numpy()
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else:
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phase = np.angle(im)
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if basis is not None:
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if isinstance(basis,t.Tensor):
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basis = basis.detach().cpu().numpy()
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basis_norm = np.linalg.norm(basis, axis = 0)
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basis_norm = basis_norm * get_units_factor(units)
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extent = [0, phase.shape[-1]*basis_norm[1], 0, phase.shape[-2]*basis_norm[0]]
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else:
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extent=None
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# If the user has matplotlib >=3.0, use the preferred colormap
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if cmap == 'auto':
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try:
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plt.imshow(phase, cmap = 'twilight', extent=extent)
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except:
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plt.imshow(phase, cmap = 'hsv', extent=extent)
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else:
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plt.imshow(phase)#, cmap = cmap, extent=extent)
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cbar = plt.colorbar()
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cbar.set_label('Phase (rad)')
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if basis is not None:
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plt.xlabel('X (' + units + ')')
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plt.ylabel('Y (' + units + ')')
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else:
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plt.xlabel('j (pixels)')
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plt.ylabel('i (pixels)')
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return fig
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def plot_colorized(im, fig=None, basis=None, units='um', **kwargs):
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""" Plots the colorized version of a complex Tensor or numpy array with dimensions NxMx2.
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The darkness corresponds to the intensity of the image, and the color corresponds
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to the phase.
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Args:
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im (t.Tensor) : An image with dimensions NxMx2.
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fig (matplotlib.figure.Figure) : A matplotlib figure to use to plot. If None,
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a new figure is created with an Axes subplot at 111.
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basis (numpy array) : Optional, the 3x2 probe basis, used to put the axis labels in real space units.
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**kwargs: Can be used to set any keyword arguments for the matplotlib.axes.Axes class
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(see https://matplotlib.org/api/axes_api.html#the-axes-class)
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"""
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if fig is None:
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fig = plt.figure()
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ax = fig.add_subplot(111, **kwargs)
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else:
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plt.figure(fig.number)
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plt.gcf().clear()
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if isinstance(im, t.Tensor):
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im = cmath.torch_to_complex(im.detach().cpu())
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if basis is not None:
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if isinstance(basis,t.Tensor):
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basis = basis.detach().cpu().numpy()
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basis_norm = np.linalg.norm(basis, axis = 0)
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basis_norm = basis_norm * get_units_factor(units)
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extent = [0, im.shape[-1]*basis_norm[1], 0, im.shape[-2]*basis_norm[0]]
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else:
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extent=None
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colorized = colorize(im)
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plt.imshow(colorized, extent=extent)
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if basis is not None:
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plt.xlabel('X (' + units + ')')
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plt.ylabel('Y (' + units + ')')
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else:
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plt.xlabel('j (pixels)')
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plt.ylabel('i (pixels)')
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return fig
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def plot_translations(translations, fig=None, units='um', lines=True):
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"""Plots a set of probe translations in a nicely formatted way
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Args:
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translations: An Nx2 or Nx3 set of translations in real space
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fig : Optional, a figure to plot into
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units : Default is um, units to report in (assuming input in m)
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lines : Whether to plot the lines indicating the path
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Returns:
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None
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"""
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factor = get_units_factor(units)
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if fig is None:
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fig = plt.figure()
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ax = fig.add_subplot(111)
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else:
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plt.figure(fig.number)
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plt.gcf().clear()
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if isinstance(translations, t.Tensor):
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translations = translations.detach().cpu().numpy()
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translations = translations * factor
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plt.plot(translations[:,0], translations[:,1],'k.')
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if lines:
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plt.plot(translations[:,0], translations[:,1],'b-', linewidth=0.5)
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plt.xlabel('X (' + units + ')')
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plt.ylabel('Y (' + units + ')')
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def plot_nanomap(translations, values, fig=None, units='um', convention='probe'):
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"""Plots a set of nanomap data in a flexible way
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Args:
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translations : An Nx2 or Nx3 set of translations in real space
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values : a length-N object of values associated with the translations
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fig : Optional, a figure to plot into
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units : Default is um, units to report in (assuming input in m)
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lines : Whether to plot the lines indicating the path
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convention : 'probe' if the translations refer to probe translations, 'obj' if they refer to object translations
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Returns:
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None
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"""
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if fig is None:
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fig = plt.figure()
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else:
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plt.figure(fig.number)
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plt.gcf().clear()
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factor = get_units_factor(units)
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bbox = fig.get_window_extent().transformed(fig.dpi_scale_trans.inverted())
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if isinstance(translations, t.Tensor):
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trans = translations.detach().cpu().numpy()
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else:
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trans = np.array(translations)
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if isinstance(values, t.Tensor):
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values = values.detach().cpu().numpy()
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else:
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values = np.array(values)
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if convention.lower() != 'probe':
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trans = trans * -1
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s = bbox.width * bbox.height / trans.shape[0] * 72**2 #72 is points per inch
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s /= 4 # A rough value to make the size work out
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plt.scatter(factor * trans[:,0],factor * trans[:,1],s=s,c=values)
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plt.gca().invert_xaxis()
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plt.gca().set_facecolor('k')
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plt.xlabel('Translation x (' + units + ')')
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plt.ylabel('Translation y (' + units + ')')
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plt.colorbar()
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