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243 lines (211 loc) · 8.89 KB
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# https://drive.google.com/open?id=0B5vxvuZBEEfTRGdXZ2NXUjNKUUk
import h5py
import matplotlib.gridspec as gridspec
import matplotlib.widgets as mwidgets
from matplotlib import path
import numpy as np
# uncomment this to set the backend
# import matplotlib
# matplotlib.use('Qt4Agg')
import matplotlib.pyplot as plt
class XRFInteract(object):
def __init__(self, counts, positions, fig=None, pos_order=None,
norm=None):
if pos_order is None:
pos_order = {'x': 0,
'y': 1}
# extract x/y data
self.x_pos = xpos = positions[pos_order['x']]
self.y_pos = ypos = positions[pos_order['y']]
self.points = np.transpose((xpos.ravel(), ypos.ravel()))
# sort ouf the normalization
if norm is None:
norm = np.ones_like(self.x_pos)
norm = np.atleast_3d(norm[:])
self.counts = counts[:] / norm
# compute values we will use for extents below
dx = np.diff(xpos.mean(axis=0)).mean()
dy = np.diff(ypos.mean(axis=1)).mean()
left = xpos[:, 0].mean() - dx/2
right = xpos[:, -1].mean() + dx/2
top = ypos[0].mean() - dy/2
bot = ypos[-1].mean() + dy/2
# create a figure if we must
if fig is None:
import matplotlib.pyplot as plt
fig = plt.figure(tight_layout=True)
# clear the figure
fig.clf()
# set the window title (look at the tool bar)
fig.canvas.set_window_title('XRF map')
self.fig = fig
# set up the figure layout
gs = gridspec.GridSpec(2, 1, height_ratios=[4, 1])
# set up the top panel (the map)
self.ax_im = fig.add_subplot(gs[0, 0], gid='imgmap')
self.ax_im.set_xlabel('x [?]')
self.ax_im.set_ylabel('y [?]')
self.ax_im.set_title(
'shift-click to select pixel, '
'alt-drag to draw region, '
'right-click to reset')
# set up the lower axes (the average spectrum of the ROI)
self.ax_spec = fig.add_subplot(gs[1, 0], gid='spectrum')
self.ax_spec.set_ylabel('counts [?]')
self.ax_spec.set_xlabel('bin number')
self.ax_spec.set_yscale('log')
self.ax_spec.set_title('click-and-drag to select energy region')
self._EROI_txt = self.ax_spec.annotate('ROI: all',
xy=(0, 1),
xytext=(0, 5),
xycoords='axes fraction',
textcoords='offset points')
self._pixel_txt = self.ax_spec.annotate('map average',
xy=(1, 1),
xytext=(0, 5),
xycoords='axes fraction',
textcoords='offset points',
ha='right')
# show the initial image
self.im = self.ax_im.imshow(self.counts[:, :, :].sum(axis=2),
cmap='viridis',
interpolation='nearest',
extent=[left, right, bot, top]
)
# and colorbar
self.cb = self.fig.colorbar(self.im, ax=self.ax_im)
# and the ROI mask (overlay in red)
self.mask = np.ones(self.x_pos.shape, dtype='bool')
self.mask_im = self.ax_im.imshow(self._overlay_image,
interpolation='nearest',
extent=[left, right, bot, top],
zorder=self.im.get_zorder())
self.mask_im.mouseover = False # do not consider for mouseover text
# set up the spectrum, to start average everything
self.spec, = self.ax_spec.plot(
self.counts.mean(axis=(0, 1)),
lw=2)
# set up the selector widget for the specturm
self.selector = mwidgets.SpanSelector(self.ax_spec,
self._on_span,
'horizontal',
useblit=True, minspan=2,
span_stays=True)
# placeholder for the lasso selector
self.lasso = None
# hook up the mouse events for the XRF map
self.cid = self.fig.canvas.mpl_connect('button_press_event',
self._on_click)
@property
def _overlay_image(self):
ret = np.zeros(self.mask.shape + (4,), dtype='uint8')
if np.all(self.mask):
return ret
ret[:, :, 0] = 255
ret[:, :, 3] = 100 * self.mask.astype('uint8')
return ret
def _on_click(self, event):
# not in the right axes, bail
ax = event.inaxes
if ax is None or ax.get_gid() != 'imgmap':
return
# if right click, clear ROI
if event.button == 3:
return self._reset_spectrum()
# if alt, start lasso
if event.key == 'alt':
return self._lasso_on_press(event)
# if shift, select a pixel
if event.key == 'shift':
return self._pixel_select(event)
def _reset_spectrum(self):
self.mask = np.ones(self.x_pos.shape, dtype='bool')
self.mask_im.set_data(self._overlay_image)
new_y_data = self.counts.mean(axis=(0, 1))
self.spec.set_ydata(new_y_data)
self._pixel_txt.set_text('map average')
self.ax_spec.relim()
self.ax_spec.autoscale(True, axis='y')
self.fig.canvas.draw_idle()
def _pixel_select(self, event):
x, y = event.xdata, event.ydata
# get index by assuming even spacing
# TODO use kdtree?
diff = np.hypot((self.x_pos - x), (self.y_pos - y))
y_ind, x_ind = np.unravel_index(np.argmin(diff), diff.shape)
# get the spectrum for this point
new_y_data = self.counts[y_ind, x_ind, :]
self.mask = np.zeros(self.x_pos.shape, dtype='bool')
self.mask[y_ind, x_ind] = True
self.mask_im.set_data(self._overlay_image)
self._pixel_txt.set_text(
'pixel: [{:d}, {:d}] ({:.3g}, {:.3g})'.format(
y_ind, x_ind,
self.x_pos[y_ind, x_ind],
self.y_pos[y_ind, x_ind]))
self.spec.set_ydata(new_y_data)
self.ax_spec.relim()
self.ax_spec.autoscale(True, axis='y')
self.fig.canvas.draw_idle()
def _on_span(self, vmin, vmax):
vmin, vmax = map(int, (vmin, vmax))
new_image = self.counts[:, :, vmin:vmax].sum(axis=2)
new_max = new_image.max()
self._EROI_txt.set_text('ROI: {}:{}'.format(vmin, vmax))
self.im.set_data(new_image)
self.im.set_clim(0, new_max)
self.fig.canvas.draw_idle()
def _lasso_on_press(self, event):
self.lasso = mwidgets.Lasso(event.inaxes, (event.xdata, event.ydata),
self._lasso_call_back)
def _lasso_call_back(self, verts):
p = path.Path(verts)
new_mask = p.contains_points(self.points).reshape(*self.x_pos.shape)
self.mask = new_mask
self.mask_im.set_data(self._overlay_image)
new_y_data = self.counts[new_mask].mean(axis=0)
self._pixel_txt.set_text('lasso mask')
self.spec.set_ydata(new_y_data)
self.ax_spec.relim()
self.ax_spec.autoscale(True, axis='y')
self.fig.canvas.draw_idle()
# def make_text_demo(inp='BNL', n_chan=1000):
# '''Make some synthetic data
# '''
# from matplotlib.figure import Figure
# from matplotlib.backends.backend_agg import FigureCanvas
# fig = Figure()
# canvas = FigureCanvas(fig)
# canvas.draw()
# im_shape = fig.canvas.get_width_height()[::-1] + (3,)
# t = fig.text(.5, .5, '', fontsize=350, ha='center', va='center')
# counts = np.random.rand(*(im_shape[:2] + (n_chan,)))
# x = np.linspace(0, 1, n_chan)
# for j, l in enumerate(inp):
# t.set_text(l)
# fig.canvas.draw()
# im = np.fromstring(fig.canvas.tostring_rgb(),
# dtype=np.uint8).reshape(im_shape)
# im = 255 - np.mean(im, axis=2, keepdims=True)
# counts += (150 * im * np.exp(-500 * ((1+j)/(len(inp) + 1) - x)**2)
# .reshape(1, 1, -1))
# del im
#
# return counts
#
#
# counts = make_text_demo()
# N, M = counts.shape[:2]
# X, Y = np.meshgrid(range(M), range(N))
# pos = np.stack([.01*X + 100, .01*Y + 50])
#
# xrf = XRFInteract(counts, pos)
# to look at a data file
fn = 'scan_3624.h5'
F = h5py.File(fn, 'r')
g = F['xrfmap']
xrf = XRFInteract(g['detsum']['counts'][:], g['positions']['pos'][:],
norm=g['scalers']['val'][:, :, 0])
# un comment out this line to use 'interacitve' mode
# plt.ion()
plt.show()
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