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https://github.com/cdtools-developers/cdtools.git
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Make a program to visualize the shot to shot fluctuations when using the unified mode model
This commit is contained in:
Abe Levitan
@@ -13,11 +13,14 @@ 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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from matplotlib.widgets import Slider
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from matplotlib import ticker, patheffects
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__all__ = ['colorize', 'plot_amplitude', 'plot_phase',
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'plot_colorized', 'plot_translations', 'get_units_factor',
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'plot_nanomap', 'plot_real', 'plot_imag']
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'plot_nanomap', 'plot_real', 'plot_imag',
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'plot_nanomap_with_images']
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def colorize(z):
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@@ -516,3 +519,254 @@ def plot_nanomap(translations, values, fig=None, units='$\\mu$m', convention='pr
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plt.colorbar()
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return fig
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def plot_nanomap_with_images(translations, get_image_func, values=None, mask=None, basis=None, fig=None, nanomap_units='$\\mu$m', image_units='$\\mu$m', convention='probe', image_title='Image', image_colorbar_title='Image Amplitude', nanomap_colorbar_title='Integrated Intensity', cmap='viridis', **kwargs):
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"""Plots a nanomap, with an image or stack of images for each point
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In many situations, ptychography data or the output of ptychography
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reconstructions is formatted as a set of images associated with various
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points in real space. This function is designed to allow for browsing
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through this kind of data, by making it possible to visualize a
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"""
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# This should pull heavily from the dataset.inspect function
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# In fact, I should be able to replace most of that function with a
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# call to this function once it's built
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# We start by making the figure and axes
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# The key will be writing this so it works okay when called in "update"
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# mode, i.e. on a figure that already has this thing showing.
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if fig is None:
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fig = plt.figure(figsize=(8,5.3))
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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 hasattr(fig, 'nanomap_cids'):
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for cid in fig.nanomap_cids:
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fig.canvas.mpl_disconnect(cid)
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# Does figsize work with the fig.subplots, or just for plt.subplots?
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axes = fig.subplots(1,2)
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fig.tight_layout(rect=[0.04, 0.09, 0.98, 0.96])
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plt.subplots_adjust(wspace=0.25) #avoids overlap of labels with plots
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axslider = plt.axes([0.15,0.06,0.75,0.03])
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# This gets the set of sizes for the points in the nanomap
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def calculate_sizes(idx):
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bbox = axes[0].get_window_extent().transformed(fig.dpi_scale_trans.inverted())
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s0 = bbox.width * bbox.height / translations.shape[0] * 72**2 #72 is points per inch
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s0 /= 4 # A rough value to make the size work out
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s = np.ones(translations.shape[0]) * s0
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s[idx] *= 4
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return s
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def update_colorbar(im):
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#
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# This solves the problem of the colorbar being changed
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# when the forward and back buttons are used!!!
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#
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if hasattr(im, 'norecurse') and im.norecurse:
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im.norecurse=False
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return
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im.norecurse=True
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# This is needed to update the colorbar
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# only change limits if array contains multiple values
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if np.min(im.get_array()) != np.max(im.get_array()):
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im.set_clim(vmin=np.min(im.get_array()),
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vmax=np.max(im.get_array()))
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#
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# The meatiest part of this program, here we just go through and
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# set up the plot how we want it
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#
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# First we set up the left-hand plot, which shows an overview map
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axes[0].set_title('Relative Displacement Map')
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translations = translations.detach().cpu().numpy()
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if convention.lower() != 'probe':
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translations = translations * -1
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s = calculate_sizes(0)
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nanomap_units_factor = get_units_factor(nanomap_units)
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nanomap = axes[0].scatter(nanomap_units_factor * translations[:,0],
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nanomap_units_factor * translations[:,1],
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s=s,c=values, picker=True)
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axes[0].invert_xaxis()
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axes[0].set_facecolor('k')
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axes[0].set_xlabel('Translation x ('+nanomap_units+')', labelpad=1)
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axes[0].set_ylabel('Translation y ('+nanomap_units+')', labelpad=1)
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cb1 = plt.colorbar(nanomap, ax=axes[0], orientation='horizontal',
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format='%.2e',
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ticks=ticker.LinearLocator(numticks=5),
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pad=0.17,fraction=0.1)
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cb1.ax.set_title(nanomap_colorbar_title, size="medium", pad=5)
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cb1.ax.tick_params(labelrotation=20)
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if values is None:
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# This seems to do a good job of leaving the appropriate space
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# where the colorbar should have been to avoid stretching the
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# nanomap plot, while still not showing the (now useless) colorbar.
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cb1.remove()
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# Now we set up the second plot, which shows the individual
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# diffraction patterns
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axes[1].set_title(image_title)
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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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axes[1].set_xlabel('X (' + image_units + ')')
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axes[1].set_ylabel('Y (' + image_units + ')')
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example_im = get_image_func(0)
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if isinstance(example_im, t.Tensor):
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example_im = example_im.cpu().numpy()
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if isinstance(basis,t.Tensor):
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np_basis = basis.detach().cpu().numpy()
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else:
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np_basis = basis
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# This fails if the basis is not rectangular
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basis_norm = np.linalg.norm(np_basis, axis = 0)
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basis_norm = basis_norm * get_units_factor(image_units)
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extent = [0, example_im.shape[-1]*basis_norm[1], 0,
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example_im.shape[-2]*basis_norm[0]]
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else:
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axes[1].set_xlabel('j (pixels)')
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axes[1].set_ylabel('i (pixels)')
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extent=None
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im=get_image_func(0)
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if len(im.shape) >= 3:
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im_idx=0
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axes[1].image_idx = im_idx
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im = im.reshape(-1,im.shape[-2],im.shape[-1])[im_idx]
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axes[1].text_box = axes[1].text(0.98, 0.98, str(im_idx), color='w',
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fontsize=14,
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horizontalalignment='right',
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verticalalignment='top',
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transform=axes[1].transAxes)
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axes[1].text_box.set_path_effects(
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[patheffects.Stroke(linewidth=2, foreground='black'),
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patheffects.Normal()])
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meas = axes[1].imshow(im, extent=extent, cmap=cmap)
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cb2 = plt.colorbar(meas, ax=axes[1], orientation='horizontal',
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format='%.2e',
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ticks=ticker.LinearLocator(numticks=5),
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pad=0.17,fraction=0.1)
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cb2.ax.tick_params(labelrotation=20)
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cb2.ax.set_title(image_colorbar_title, size="medium", pad=5)
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cb2.ax.callbacks.connect('xlim_changed', lambda ax: update_colorbar(meas))
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# This function handles all the updating, except for moving the
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# slider value. This is done because the slider widget is
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# ultimately responsible for triggering an update, so all other
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# updates are done by changing the slider widget value which
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# then triggers this
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def update(idx, im_idx=None):
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# We have to explicitly make it an integer because the slider will
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# output floats (even if they are still integer-valued)
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idx = int(idx)
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# Get the new data for this index
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im = get_image_func(idx)
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if len(im.shape) >= 3:
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if im_idx == None and hasattr(axes[1],'image_idx'):
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im_idx = axes[1].image_idx
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elif im_idx == None:
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im_idx=0
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axes[1].image_idx = im_idx
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axes[1].text_box.set_text(str(im_idx))
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im = im.reshape(-1,im.shape[-2],im.shape[-1])[im_idx]
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# Now we resize the nanomap to show the new selection
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axes[0].collections[0].set_sizes(calculate_sizes(idx))
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# And we update the data in the image as well
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ax_im = axes[1].images[-1]
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ax_im.set_data(im)
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update_colorbar(ax_im)
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#
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# Now we define the functions to handle various kinds of events
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# that can be thrown our way
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#
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# We start by creating the slider here, so it can be used
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# by the update hooks.
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slider = Slider(axslider, 'Image #', 0, translations.shape[0]-1, valstep=1, valfmt="%d")
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# This handles scroll wheel and keypress events
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def on_action(event):
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im = get_image_func(0)
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if event.inaxes is axes[1] and len(im.shape) >=3:
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# This is triggered if the data to display has more than 2
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# dimensions (i.e. is an image stack) and the event originates
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# while the mouse is within the image display
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im = im.reshape(-1,im.shape[-2],im.shape[-1])
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im_idx = axes[1].image_idx
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if event.key == 'up' or event.button == 'up' \
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or event.key == 'left':
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im_idx = (im_idx - 1) % im.shape[0]
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if event.key == 'down' or event.button == 'down' \
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or event.key == 'right':
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im_idx = (im_idx + 1) % im.shape[0]
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axes[1].image_idx=im_idx
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slider.set_val(slider.val)#update(slider.val,im_idx=im_idx)
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return # This prevents the rest from also happening
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# Otherwise the if statements can throw errors when the
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# event type isn't right, this way they just don't trigger
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if not hasattr(event, 'button'):
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event.button = None
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if not hasattr(event, 'key'):
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event.key = None
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if event.key == 'up' or event.button == 'up' or event.key == 'left':
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idx = slider.val - 1
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elif event.key == 'down' or event.button == 'down' or event.key == 'right':
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idx = slider.val + 1
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else:
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# This prevents errors from being thrown on irrelevant key
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# or mouse input
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return
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# Handle the wraparound and trigger the update
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idx = int(idx) % translations.shape[0]
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slider.set_val(idx)
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# This handles "pick" events in the nanomap
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def on_pick(event):
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# If we don't filter on type of event, this will also capture,
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# for example, scroll events that happen over the nanomap
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if event.mouseevent.button == 1:
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slider.set_val(event.ind[0])
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# Here we connect the various update functions
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cid1 = fig.canvas.mpl_connect('pick_event',on_pick)
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cid2 = fig.canvas.mpl_connect('key_press_event',on_action)
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cid3 = fig.canvas.mpl_connect('scroll_event',on_action)
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# It's so dumb that matplotlib doesn't automatically track this for you
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fig.nanomap_cids = [cid1,cid2,cid3]
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slider.on_changed(update)
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# Throw an extra update into the mix just to get rid of any things
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# (like the nanomap dot sizes) that otherwise would change on the
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# first update
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update(0)
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