def __init__(self,image,tiling,imginfo={},fnext=None,verbosity=0):
"""
image ... 2D Array for background image
tiling ... Tiling instance
imginfo... (opt) dictionary with image parameters (see ImageBrowser)
self.bDual.on_clicked(self.Dual);
verbosity. (opt) quiet (0), verbose (3), debug (4)
"""
import matplotlib.collections as c;
# init ImageBrowser
super(TilingBrowser,self).__init__(image,imginfo,fnext,verbosity);
self.axis.set_title('TilingBrowser: %s' % self.imginfo['desc']);
# draw edges
self.tiling= tiling;
segments = [self.tiling.points[e] for e in self.tiling.edges];
self.LineCol = c.LineCollection(segments);
self.LineCol.set_color('blue');
self.axis.add_collection(self.LineCol);
# add buttons
axEdit = self.fig.add_axes([0.85, 0.85,0.1, 0.04]);
axSave = self.fig.add_axes([0.85, 0.8, 0.1, 0.04]);
axLoad = self.fig.add_axes([0.85, 0.75,0.1, 0.04]);
self.bEdit = Button(axEdit,'Edit');
self.bSave = Button(axSave,'Save');
self.bLoad = Button(axLoad,'Load');
self.bEdit.on_clicked(self.ToggleEdit);
self.bSave.on_clicked(self.Save);
self.bLoad.on_clicked(self.Load);
self._update();
def setup_controls(self):
""" Set up button axes"""
buttons = [[('Auto levels', self.do_autolevels),
('Levels1', self.do_levels_test),
('Oldie', self.do_levels_old),
('Clip', self.do_levels_clip)],
[('Blur uniform', self.do_blur),
('Blur gaussian', self.do_blur_gaussian),
('Sharpen', self.do_sharpen),
('Unsharp', self.do_unsharp),
('Edge detection', self.do_edges)],
[('Apply', self.apply),
('Reset', self.reset),
('Save', self.save),
('Quit', self.quit)]]
max_cols = max(len(row) for row in buttons)
padding = 0.01
start, end = 0.03, 0.97
hspace = (end - start) / max_cols
row_height = 0.03
vspace = row_height + 0.003
for j, row in enumerate(buttons):
for i, b in enumerate(row):
box = [start + i*hspace, 0.01 + j*vspace,
hspace - padding, row_height]
print box
axis = self.fig.add_axes(box)
button = Button(axis, b[0])
self.axes.append(axis)
button.on_clicked(b[1])
self.buttons.append(button)
def setup_plot():
global rate_slider, delta_slider, fig, ax, l, radio
fig, ax = plt.subplots()
ax.grid('on')
plt.subplots_adjust(left=0.25, bottom=0.25)
moneyFmt = FuncFormatter(money)
ax.yaxis.set_major_formatter(moneyFmt)
s = calc_compounding( rate, periods, principal, contribution, delta,inflation)
t = np.arange(2014, (2014+periods))
l, = plt.plot(t,s, lw=2, color='red')
plt.ylabel("Investment Loss (FV)")
plt.axis([2014, (2014+periods), 0, principal])
plt.axis('tight')
axfreq = plt.axes([0.25, 0.1, 0.65, 0.03], axisbg=axcolor)
axamp = plt.axes([0.25, 0.15, 0.65, 0.03], axisbg=axcolor)
rate_slider = Slider(axfreq, 'Avg.Returns', 4, 8, valinit=rate)
delta_slider = Slider(axamp, 'Delta', 0.1, 1, valinit=delta)
resetax = plt.axes([0.8, 0.025, 0.1, 0.04])
button = Button(resetax, 'Reset', color=axcolor, hovercolor='0.975')
rate_slider.on_changed(update)
delta_slider.on_changed(update)
button.on_clicked(reset)
rax = plt.axes([0.015, 0.15, 0.15, 0.15], axisbg=axcolor)
radio = RadioButtons(rax, ('0','3000', '6000', '9000'), active=0)
radio.on_clicked(contribution_update)
plt.show()
return rate_slider,delta_slider,fig,ax,l,radio
def __init__(self, image, low, high):
self.image_name = image
self.low = low
self.high = high
self.filter = None
self.output = None
self.filter_type = None
self.padRows = None
self.padCols = None
original_input = cv2.imread(image,0)
rows, cols = original_input.shape
if(rows < cols):
original_input = cv2.transpose(original_input)
self.transposed = True
else:
self.transposed = False
rows, cols = original_input.shape
optimalRows = cv2.getOptimalDFTSize(rows)
optimalCols = cv2.getOptimalDFTSize(cols)
self.padRows = optimalRows - rows
self.padCols = optimalCols - cols
self.input = np.zeros((optimalRows,optimalCols))
self.input[:rows,:cols] = original_input
self.dftshift = get_dft_shift(self.input)
plt.subplots_adjust(left=0, bottom=0.05, right=1, top=1, wspace=0, hspace=0)
plt.subplot(131)
plt.axis('off')
plt.title('original image')
plt.imshow(self.input, cmap = 'gray',interpolation='nearest')
self.axslow = plt.axes([0.05, 0.01, 0.5, 0.02])
self.slow = Slider(self.axslow, 'low', 0.01, 1, valinit=self.low)
self.slow.on_changed(self.updateLow)
self.axhigh = plt.axes([0.05, 0.03, 0.5, 0.02])
self.shigh = Slider(self.axhigh, 'high', 0.01, 1, valinit=self.high)
self.shigh.on_changed(self.updateHigh)
self.slow.slidermax=self.shigh
self.shigh.slidermin=self.slow
self.axfilter = plt.axes([0.62, 0.01, 0.15, 0.04])
self.rfilter = RadioButtons(self.axfilter, ('Gaussian Filter', 'Ideal Filter'))
self.rfilter.on_clicked(self.updateFilterType)
self.filter_type = self.rfilter.value_selected
self.axprocess = plt.axes([0.8, 0.01, 0.08, 0.04])
self.bprocess = Button(self.axprocess, 'Process')
self.bprocess.on_clicked(self.process)
self.axsave = plt.axes([0.88, 0.01, 0.08, 0.04])
self.bsave = Button(self.axsave, 'Save')
self.bsave.on_clicked(self.save)
def __init__(self, viewer, Npoints, data):
self.viewer = viewer
self.Npoints = Npoints
self.data = data
self.pointsh = []
plt.sca(self.viewer.ax)
axclick = plt.axes([0.09, 0.15, 0.1, 0.075])
self.bclick = Button(axclick, 'Click')
self.bclick.on_clicked(self.click)
axreset = plt.axes([0.20, 0.15, 0.1, 0.075])
self.breset = Button(axreset, 'Reset')
self.breset.on_clicked(self.reset)
axfileload = plt.axes([0.09, 0.05, 0.1, 0.075])
self.bfileload = Button(axfileload, 'Load...')
self.bfileload.on_clicked(self.fileLoad)
axfilesave = plt.axes([0.20, 0.05, 0.1, 0.075])
self.bfilesave = Button(axfilesave, 'Save...')
self.bfilesave.on_clicked(self.fileSave)
self.showPoints()
def main():
from matplotlib import pyplot as plt
import utils
fig = utils.createfig()
a = WSin(fig, [0.1, 0.5, 0.8, 0.3] , nps=5)
b = WSin(fig, [0.1, 0.1, 0.8, 0.3], nps=3, om=3)
args11 = (dict(k=0, am=1, ph=0, t='v', n='1'),
dict(k=-1.5, am=0.1, ph=0.5, t='v', n='3'),)
args12 = (dict(k=1,am=2,ph=1,t='i',n='2'),)
args13 = (dict(k=100,am=3,ph=2,t='i',n='no',v=False),)
args2 = (dict(k=0,am=1,ph=0,t='v',n='1'),dict(k=1,am=2,ph=1,t='i',n='2'),
dict(k=-1.5,am=0.1,ph=0,t='v',n='3'),\
dict(k=100,am=3,ph=2,t='i',n='no',v=False),)
a.new(100, args11, args12, ())
b.new(101, *args2)
#b.set_anim(True)
# bottone
from matplotlib.widgets import Button
box = fig.add_axes([0.05, 0.90, 0.05, 0.05])
but = Button(box, 'next')
def func(wtf):
#args2 = _r_args(4)
#print args2
#b.new(100, *args2)
b.set_anim(not b.get_anim())
but.on_clicked(func)
plt.show()
def initUI(self):
self.fig, self.ax = plt.subplots()
self.fig.canvas.set_window_title('Numerik DGL 2 - Schattenprojektion')
self.fig.suptitle('Vorwaertsproblem')
plt.subplots_adjust(bottom=0.3)
plt.axis([0, 200, -100, 100])
plt.axis('equal')
axColor = 'lightgoldenrodyellow'
# Slider - Phi
axPhi = plt.axes([0.18, 0.2, 0.65, 0.03], axisbg=axColor)
self.sliderPhi = Slider(axPhi, 'Phi', 0, 360, valinit=self.phi, valfmt='%1d')
self.sliderPhi.on_changed(self.onUpdate)
# Slider - Delta Phi
axDeltaPhi = plt.axes([0.18, 0.15, 0.65, 0.03], axisbg=axColor)
self.sliderDeltaPhi = Slider(axDeltaPhi, 'Delta Phi', 5, 360, valinit=self.delta_phi, valfmt='%1d')
self.sliderDeltaPhi.on_changed(self.onUpdate)
# Slider - Psi
axPsi = plt.axes([0.18, 0.1, 0.65, 0.03], axisbg=axColor)
self.sliderPsi = Slider(axPsi, 'Psi', 0, 180, valinit=self.psi, valfmt='%1d')
self.sliderPsi.on_changed(self.onUpdate)
# Button - Previous
axPrev = plt.axes([0.18, 0.03, 0.1, 0.05])
self.buttonPrev = Button(axPrev, 'Previous')
self.buttonPrev.on_clicked(self.onPrevious)
# Button - Next
axNext = plt.axes([0.29, 0.03, 0.1, 0.05])
self.buttonNext = Button(axNext, 'Next')
self.buttonNext.on_clicked(self.onNext)
# Button - Reset
axReset = plt.axes([0.73, 0.03, 0.1, 0.05])
self.buttonReset = Button(axReset, 'Reset')
self.buttonReset.on_clicked(self.onReset)
self.onDraw()
def __setup_plots(self):
# Some actual plots.
self.plot_axis_z = plt.subplot2grid((6, 20), (0, 0), colspan=18)
self.plot_axis_n = plt.subplot2grid((6, 20), (1, 0), colspan=18)
self.plot_axis_e = plt.subplot2grid((6, 20), (2, 0), colspan=18)
self.misfit_axis = plt.subplot2grid((6, 20), (3, 0), colspan=11,
rowspan=3)
self.colorbar_axis = plt.subplot2grid((6, 20), (3, 12), colspan=1,
rowspan=3)
#self.adjoint_source_axis = plt.subplot2grid((6, 8), (4, 0), colspan=4,
#rowspan=1)
self.map_axis = plt.subplot2grid((6, 20), (3, 13), colspan=8,
rowspan=3)
# Plot the map and the beachball.
bounds = self.project.domain["bounds"]
self.map_obj = visualization.plot_domain(bounds["minimum_latitude"],
bounds["maximum_latitude"], bounds["minimum_longitude"],
bounds["maximum_longitude"], bounds["boundary_width_in_degree"],
rotation_axis=self.project.domain["rotation_axis"],
rotation_angle_in_degree=self.project.domain["rotation_angle"],
plot_simulation_domain=False, show_plot=False, zoom=True)
visualization.plot_events([self.event], map_object=self.map_obj)
# All kinds of buttons [left, bottom, width, height]
self.axnext = plt.axes([0.90, 0.95, 0.08, 0.03])
self.axprev = plt.axes([0.90, 0.90, 0.08, 0.03])
self.axreset = plt.axes([0.90, 0.85, 0.08, 0.03])
self.bnext = Button(self.axnext, 'Next')
self.bprev = Button(self.axprev, 'Prev')
self.breset = Button(self.axreset, 'Reset Station')
def param_gui(letter_name, shapeModeler):
global sliders, mainPlot, fig, pca_params
fig, ax = plt.subplots()
whiteSpace = 0.15 + num_params*0.05
plt.subplots_adjust( bottom=whiteSpace)
plt.axis('equal')
#plot of initial shape
params = np.zeros((num_params,1))
shape = shapeModeler.makeShape(params)
shape = ShapeModeler.normaliseShape(shape)
numPointsInShape = len(shape)/2
x_shape = shape[0:numPointsInShape]
y_shape = shape[numPointsInShape:]
mainPlot, = plt.plot(x_shape, -y_shape)
plt.axis([-1, 1, -1, 1],autoscale_on=False, aspect='equal')
plt.title(letter_name)
#add sliders to modify parameter values
parameterVariances = shapeModeler.getParameterVariances()
sliders = [0]*num_params
for i in range(num_params):
slider = Slider(plt.axes([0.25, 0.1+0.05*(num_params-i-1), 0.65, 0.03], axisbg=axcolor),
'Parameter '+str(i+1), -5*parameterVariances[i], 5*parameterVariances[i], valinit=0)
slider.on_changed(partial(update, shapeModeler))
sliders[i] = slider
resetax = plt.axes([0.8, 0.025, 0.1, 0.04])
button = Button(resetax, 'Reset', color=axcolor, hovercolor='0.975')
button.on_clicked(reset)
plt.show()
return pca_params
def _setup_display(self):
"""
Sets up the Matplotlib figures and axes required for the visualization.
"""
self.network_fig = figure(figsize=(20, 20))
self.network_fig.canvas.set_window_title("Hopfield Network Visualization")
gs = gridspec.GridSpec(2, 4)
self.main_network = subplot(gs[:,:2])
self.main_network.set_title("Network Diagram")
self.main_network.get_xaxis().set_ticks([])
self.main_network.get_yaxis().set_ticks([])
self.energy_diagram = subplot(gs[0,2], projection='Fovea3D')
self.energy_diagram.set_title("Energy Function")
self.contour_diagram = subplot(gs[0,3])
self.contour_diagram.set_title("Energy Contours")
self.state_diagram = subplot(gs[1,2])
self.state_diagram.set_title("Current Network State")
self.state_diagram.get_xaxis().set_ticks([])
self.state_diagram.get_yaxis().set_ticks([])
self.weight_diagram = subplot(gs[1,3])
self.weight_diagram.set_title("Weight Matrix Diagram")
self.weight_diagram.get_xaxis().set_ticks([])
self.weight_diagram.get_yaxis().set_ticks([])
self.network_fig.suptitle("Hopfield Network Visualization", fontsize=14)
self.mode = self.network_fig.text(0.4, 0.95, "Current Mode: Initialization",
fontsize=14, horizontalalignment='center')
self.iteration = self.network_fig.text(0.6, 0.95, "Current Iteration: 0",
fontsize=14, horizontalalignment='center')
# Widget Functionality
view_wf = axes([.53, 0.91, 0.08, 0.025])
self.view_wfbutton = Button(view_wf, 'Wireframe')
view_attract = axes([.615, 0.91, 0.08, 0.025])
self.view_attractbutton = Button(view_attract, 'Attractors')
def debug_plot(a, b, nodes, fs, coeffs):
global _debug_fig, _debug_cancelled
if _debug_cancelled:
return
if 'show' not in locals():
from pylab import axes, subplot, subplots_adjust, figure, draw, plot, axvline, xlim, title, waitforbuttonpress, gcf
from matplotlib.widgets import Button
if _debug_fig is None:
#curfig = gcf()
#print dir(curfig)
_debug_fig = figure()
ax = _debug_fig.add_subplot(111)
#subplots_adjust(bottom=0.15)
butax = axes([0.8, 0.015, 0.1, 0.04])
button = Button(butax, 'Debug', hovercolor='0.975')
def debug(event):
import pdb; pdb.set_trace()
button.on_clicked(debug)
_debug_fig.sca(ax)
draw()
#figure(curfig)
_debug_fig.gca().clear()
plot(nodes, fs, linewidth=5, figure = _debug_fig)
axvline(a, color="r", figure = _debug_fig)
axvline(b, color="r", figure = _debug_fig)
d = 0.05 * (b-a)
_debug_fig.gca().set_xlim(a-d, b+d)
title("press key in figure for next debugplot or close window to continue")
try:
while not _debug_cancelled and not _debug_fig.waitforbuttonpress(-1):
pass
except:
_debug_cancelled = True
class NavField:
def __init__(self, pltinfo):
self.fld = -1
self.nflds = len(pltinfo.selindices['field'])
self.pltinfo = pltinfo
prevwidth = 0.13
nextwidth = 0.12
butheight = 0.05
butleft = 0.7
butbot = 0.025
butgap = 0.5 * butbot
self.nextloc = [butleft + prevwidth + butgap,
butbot, nextwidth, butheight]
self.prevloc = [butleft, butbot, prevwidth, butheight]
self.inactivecolor = '#99aaff'
self.activecolor = '#aaffcc'
def _draw_buts(self):
if self.fld < self.nflds - 1:
axnext = pl.axes(self.nextloc)
self.bnext = Button(axnext, 'Next fld >',
color=self.inactivecolor,
hovercolor=self.activecolor)
self.bnext.on_clicked(self.next)
if self.fld > 0:
axprev = pl.axes(self.prevloc)
self.bprev = Button(axprev, '< Prev fld',
color=self.inactivecolor,
hovercolor=self.activecolor)
self.bprev.on_clicked(self.prev)
#pl.show()
def _do_plot(self):
didplot = plotfield(self.pltinfo.selindices['field'][self.fld],
self.pltinfo)
if didplot:
self._draw_buts()
return didplot
def next(self, event):
didplot = False
startfld = self.fld
while self.fld < self.nflds - 1 and not didplot:
self.fld += 1
didplot = self._do_plot()
if not didplot:
print "You are on the last field with any selected baselines."
self.fld = startfld
#plotfield(self.pltinfo.selindices['field'][self.fld],
# self.pltinfo, self.mytb)
def prev(self, event):
didplot = False
startfld = self.fld
while self.fld > 0 and not didplot:
self.fld -= 1
didplot = self._do_plot()
if not didplot:
print "You are on the first field with any selected baselines."
self.fld = startfld
def preparePlot(shapeModeler):
global sliders, mainPlot, fig
fig, ax = plt.subplots();
whiteSpace = 0.15 + numParams*0.05;
plt.subplots_adjust( bottom=whiteSpace);
plt.axis('equal');
#plot of initial shape
params = np.zeros((numParams,1));
shape = shapeModeler.makeShape(params);
shape = ShapeModeler.normaliseShape(shape);
numPointsInShape = len(shape)/2;
x_shape = shape[0:numPointsInShape];
y_shape = shape[numPointsInShape:];
mainPlot, = plt.plot(x_shape, -y_shape);
plt.axis([-1, 1, -1, 1],autoscale_on=False, aspect='equal');
#add sliders to modify parameter values
parameterVariances = shapeModeler.getParameterVariances();
sliders = [0]*numParams;
for i in range(numParams):
slider = Slider(plt.axes([0.25, 0.1+0.05*(numParams-i-1), 0.65, 0.03], axisbg=axcolor),
'Parameter '+str(i+1), -5*parameterVariances[i], 5*parameterVariances[i], valinit=0);
slider.on_changed(update);
sliders[i] = slider;
resetax = plt.axes([0.8, 0.025, 0.1, 0.04])
button = Button(resetax, 'Reset', color=axcolor, hovercolor='0.975')
button.on_clicked(reset)
plt.show()
def __init__(self, title, instances, quadrants, clusters):
self.title = title
self.instances = instances
self.quadrants = quadrants
self.clusters = clusters
self.overlay = False
self.trend = False
self.fig = plt.figure()
# Button axes
self.axtrends = self.fig.add_axes([0.75, 0.4, 0.2, 0.05])
self.btrends = Button(self.axtrends, 'Trends')
self.btrends.on_clicked(self.trends)
self.axalerts = self.fig.add_axes([0.75, 0.3, 0.2, 0.05])
self.balerts = Button(self.axalerts, 'Alerts')
self.balerts.on_clicked(self.alerts)
self.axoverlays = self.fig.add_axes([0.75, 0.2, 0.2, 0.05])
self.boverlays = Button(self.axoverlays, 'Overlays')
self.boverlays.on_clicked(self.overlays)
# Axes are defined [left, bottom, width, height]
self.canvas = self.fig.add_axes([0.1, 0.1, 0.6, 0.8])
self.draw_canvas_instances()
self.draw_canvas_quadrants_vanilla()
self.info = self.fig.add_axes([0.75, 0.5, 0.2, 0.4])
plt.setp(self.info, xticks=[], yticks=[])
cid = self.fig.canvas.mpl_connect('button_press_event', self.onclick)
def __init__(self,image,points,imginfo={},fnext=None,verbosity=0):
"""
image ... 2D Array for background image
points ... initial list of peak positions shape(N,2)
imginfo... (opt) dictionary with image parameters (see ImageBrowser)
self.bDual.on_clicked(self.Dual);
verbosity. (opt) quiet (0), verbose (3), debug (4)
"""
# init ImageBrowser
super(PointBrowser,self).__init__(image,imginfo,fnext,verbosity);
self.axis.set_title('PointBrowser: %s' % self.imginfo['desc']);
# draw point list
self.points = np.asarray(points);
assert( self.points.ndim==2 and self.points.shape[1]==2 );
self.Line2D, = self.axis.plot(self.points[:,0], self.points[:,1],\
'ro',picker=5);
#self.axis.set_xlim(*self.imginfo['extent'][0:2]);
#self.axis.set_ylim(*self.imginfo['extent'][2:4]);
# add buttons
axEdit = self.fig.add_axes([0.85, 0.85,0.1, 0.04]);
axSave = self.fig.add_axes([0.85, 0.8, 0.1, 0.04]);
axLoad = self.fig.add_axes([0.85, 0.75,0.1, 0.04]);
self.bEdit = Button(axEdit,'Edit');
self.bSave = Button(axSave,'Save');
self.bLoad = Button(axLoad,'Load');
self.bEdit.on_clicked(self.ToggleEdit);
self.bSave.on_clicked(self.Save);
self.bLoad.on_clicked(self.Load);
self._update();
def show(self):
plot_idx = 0
self.current_plot_idx = plot_idx
self.plots[plot_idx].set_visible(True)
self.set_data_cursor()
if(len(self.plots) > 1 and self.use_matplotlib_widget is True):
button_width = .05
index_width = .03*len(str(len(self.plots)-1))
button_height = .05
button_bottom = .025
center = .5
margin = .005
axes_prev_rect = [center - index_width/2 - margin - button_width,
button_bottom, button_width, button_height]
axes_prev = self.figure.add_axes(axes_prev_rect)
self.button_prev = Button(axes_prev, '<')
self.button_prev.on_clicked(self.show_prev_plot)
self.index_text = self.figure.text(
center - index_width/2, (button_bottom+button_height)/2,
"{}/{}".format(self.current_plot_idx, len(self.plots)-1)
)
axes_next_rect = [center + index_width/2 + margin,
button_bottom, button_width, button_height]
axes_next = self.figure.add_axes(axes_next_rect)
self.button_next = Button(axes_next, '>')
self.button_next.on_clicked(self.show_next_plot)
self.figure.show()
def __init__(self, px, mx, kx):
self.buttonP = Button(px, '+')
self.buttonM = Button(mx, '-')
self.stVal = kx
self.buttonP.on_clicked(self.button_handler_p)
self.buttonM.on_clicked(self.button_handler_m)
class slider():
def __init__(self,res):
self.res=res
def slider_bar(self,res):
#setupthe slider
axcolor = 'lightgoldenrodyellow'
self.axtime = plt.axes([0.1, 0.05, 0.8, 0.03], axisbg=axcolor)
self.stime = Slider(self.axtime, 'Timestep', 0, len(res.resobj.fils.fid.dimensions['Time']), valinit=0)
def update(val,res):
t = self.stime.val
res.refresh_plot(t)
self.stime.on_changed(update(self,res))
def foward_button(self,res):
self.fwdax = plt.axes([0.1, 0.1, 0.1, 0.04])
self.fwdb = Button(self.fwdax, 'forward', color='lightgoldenrodyellow', hovercolor='0.975')
self.fwdb.on_clicked(slider.update(res.resobj.timestep-1,res))
def _init_ui(self):
self.click_ev = self.fig.canvas.mpl_connect('button_press_event',
self._onclick)
self.motion_ev = self.fig.canvas.mpl_connect('motion_notify_event',
self._onmotion)
self.release_ev = self.fig.canvas.mpl_connect('button_release_event',
self._onrelease)
ax_load_img = self.fig.add_subplot(self.gs[1,:])
ax_load = self.fig.add_subplot(self.gs[2,0])
ax_save = self.fig.add_subplot(self.gs[2,1])
ax_reset = self.fig.add_subplot(self.gs[2,2])
self.fig.add_subplot(ax_load_img)
self.fig.add_subplot(ax_load)
self.fig.add_subplot(ax_save)
self.fig.add_subplot(ax_reset)
self._load_img_button = Button(ax_load_img, 'Load image')
self._load_img_button.on_clicked(self._on_load_image)
self._load_button = Button(ax_load, 'Load LMs')
self._load_button.on_clicked(self._on_load_landmarks)
self._save_button = Button(ax_save, 'Save LMs')
self._save_button.on_clicked(self._on_save_landmarks)
self._reset_button = Button(ax_reset, 'Reset LMs')
self._reset_button.on_clicked(self._on_reset_landmarks)
def __init__(self, image_sequence):
# Fix the size of subplot
self.fig, self.axes = plt.subplots(2, sharey=True, figsize=(10, 5))
# Set background to be gray
self.fig.patch.set_facecolor('#333333')
# Store image sequence in self.seq and display the sequence
self.seq = image_sequence
self.image = self.seq.init_image()
self.image2 = self.seq.init_image_original()
self.image_figure = plt.subplot2grid((3, 3), (0, 0), colspan=3, rowspan=2)
self.image_figure.axis('off')
self.image_plot = self.image_figure.imshow(self.image)
self.image_figure.set_title('Dinic', color='white')
self.init_figure = plt.subplot2grid((3, 3), (2, 1))
self.init_figure.axis('off')
self.init_plot = plt.imshow(self.image2)
self.init_figure.set_title('Flow Graph', color = 'white' )
self.text_figure = plt.subplot2grid((3, 3), (2, 2))
self.text_figure.axis('off')
self.text_figure.set_title('',color = 'white')
plt.subplots_adjust(bottom=0.2)
axnext = plt.axes([0.81, 0.05, 0.1, 0.075])
bnext = Button(axnext, 'Next')
bnext.on_clicked(self.next)
plt.show()
def __init__(self):
'''variable definitions'''
SEGMENTS = int(3) #number of segments
#could probably make these lists instead of arrays
self.l = np.array([0, 100, 100, 80])# actual measurements of segment length in mm
self.w = np.array([0]*SEGMENTS,dtype=float) #horizontal coordinate
self.z = np.array([0]*SEGMENTS,dtype=float) #vertical coordinate
self.x = np.array([0]*SEGMENTS,dtype=float) #x axis components
self.y = np.array([0]*SEGMENTS,dtype=float) #y axis components
self.a = np.array([np.pi]*SEGMENTS,dtype=float) #angle for the link, reference is previous link
self.gripper_angle = np.array([0, -45, -90, 45, 90])#preselected gripper angles
self.current_gripper = 2 #gripper angle selection
self.tw = 30.0 # w axis position depth
self.tz = 20.0 # z axis starting position height
self.l12 = 0.0 # hypotenuse belween a1 & a2
self.a12 = 0.0 #inscribed angle between hypotenuse, w
self.fig = plt.figure("CS 4TE3 Robot Simulator") #create the frame
self.ax = plt.axes([0.05, 0.2, 0.90, .75], projection='3d') #3d ax panel
self.axe = plt.axes([0.25, 0.85, 0.001, .001])#panel for error message
self.count = 0
self.coords = ((20,30),(50,60),(30,40),(70,100),(70,150))
self.display_error()
self.draw_robot()
butval = plt.axes([0.35, 0.1, 0.1, 0.075])
but = Button(butval, 'move')
but.on_clicked(self.move_click)
#error = dist(1,2);
#print (error)
plt.show()#end of constructor
class ButtonClickProcessor(object):
def __init__(self, axes, label, color, viewer):
self.groups = viewer.groups
self.fig_text = viewer.fig_text
self.button = Button(axes, label=label, color=color)
self.button.on_clicked(self.print_group)
def print_group(self, event):
print(self.button.label.get_text())
group = self.groups[int(self.button.label.get_text())]
self.fig_text.clf()
self.ax_text = self.fig_text.add_subplot(111)
# plt.rcParams['text.usetex'] = True
y_write = np.linspace(0, 0.9, len(group.keys()))
for ii, key in enumerate(group.keys()):
self.ax_text.text(0, y_write[ii], str(key) + ' : ' + str(group[key]), fontsize=15, fontstyle='oblique')
#Show it
self.fig_text.canvas.draw()
def __init__(self, camera_matrix, dist_coeffs, homography, rvec, tvec, angspeed, center, axis):
self.cm = camera_matrix
self.dc = dist_coeffs
self.homography = homography
self.rvec = rvec
self.tvec = tvec
self.angspeed = angspeed
self.center = center
self.axis = axis
# Socket
self.HOST = "localhost"
self.PORT = 8888
self.sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
# Struct object for the linescan
self.linescan_struct = struct.Struct(2048*'H')
# Matplotlib plot setup
self.fig = pylab.figure(1)
self.ax = self.fig.add_subplot(111)
self.ax.grid(True)
self.ax.set_title('Linescan plotter')
self.ax.axis([0, 2048, 1088, 0])
self.range = pylab.arange(0, 2048, 1)
self.line1, = self.ax.plot(2048, 1088)
self.manager = pylab.get_current_fig_manager()
# Mouse input
self.cid = self.fig.canvas.mpl_connect('button_press_event', self.on_click)
# Buttons
# Save button
self.save_button_ax = pylab.axes([0.8, 0.91, 0.1, 0.075])
self.save_button = Button(self.save_button_ax, 'Save')
self.save_button.on_clicked(self.save_scanlines)
# Stop button
self.stop_button_ax = pylab.axes([0.23, 0.91, 0.1, 0.075])
self.stop_button = Button(self.stop_button_ax, 'Stop')
self.stop_button.on_clicked(self.stop_scan)
# Start button
self.start_button_ax = pylab.axes([0.125, 0.91, 0.1, 0.075])
self.start_button = Button(self.start_button_ax, 'Start')
self.start_button.on_clicked(self.start_scan)
# Timer thread
self.timer = self.fig.canvas.new_timer(interval=20)
self.timer.add_callback(self.callback, ())
self.timer.start()
# Scan variables
self.scan_range = []
self.scanlines = []
self.scanline_times = []
self.start_scan_time = None
self.stop_scan_time = None
self.scan_time = None
self.scanning = False
# Get transforms
self.get_laser_to_turntable_transform()
# Start
pylab.show()
def __init__(self,*arg,**kwargs):
"""Optional parameters:
- fig: the matplotlib figure to use ("current" if left blank)
z-slice selector slider; otherwise use array index.
"""
fig = kwargs.get('fig', None )
if fig==None: fig = plt.gcf()
self.fig=fig
fig.clf()
self.text = fig.text(0,.99,"",va='top')
self.num = len(arg)
self.row,self.col = [1,1]
fig.subplots_adjust(left=0.05, bottom=0.25,top=0.95,right=0.95,wspace=0.1,hspace=0.15)
args = []
self.zs = []
self.image_index = 0
# initialize using an array of images
for a in arg:
args.append(a)
self.zs.append(np.arange(len(a)))
maxz = max([max(z) for z in self.zs])
minz = min([min(z) for z in self.zs])
self.data = args
self.im = []
self.ax = []
for i in range(len(self.data)):
self.ax.append(fig.add_subplot(self.row,self.col,i+1))
self.im.append(self.ax[-1].imshow(self.data[i][0],origin='upper',cmap=cmap,aspect='equal'))
self.cmap=cmap
axcolor = 'lightgoldenrodyellow'
self.sliceax = fig.add_axes([0.25, 0.15, 0.65, 0.03], axisbg=axcolor)
self.minax = fig.add_axes([0.25, 0.1, 0.65, 0.03], axisbg=axcolor)
self.maxax = fig.add_axes([0.25, 0.05, 0.65, 0.03], axisbg=axcolor)
self.sli = 0
self.sli_wid = Slider(self.sliceax, 'Slice', minz, maxz, valinit=minz,valfmt="%i")
self.vmin = Slider(self.minax, 'min', 0, 100, valinit=0,valfmt="%i")
self.vmax = Slider(self.maxax, 'max', 0, 100, valinit=100,valfmt="%i")
self.sli_wid.on_changed(self.update)
self.vmin.on_changed(self.update)
self.vmax.on_changed(self.update)
self.multi = XYMultiCursor(self.fig.canvas, self.ax, color='b', lw=1, ls=':')
self.fig.canvas.mpl_connect('button_press_event',self.key)
self.fig.canvas.draw()
self.points = {}
# defines the positions of the previous/next slice buttons
axprev = plt.axes([0.81, 0.9, 0.1, 0.075])
axnext = plt.axes([0.81, 0.8, 0.1, 0.075])
self.bprev = Button(axprev, 'Previous')
self.bnext = Button(axnext, 'Next')
# previous/next slice select buttons
self.bprev.on_clicked(self.next_image)
self.bnext.on_clicked(self.prev_image)
pprint(self.data[0][0])
class MButton(object):
"""Custom button."""
def __init__(self, fig, rect, label, func, fargs=None,
c='#0DE51B', hc='#5CFF67'):
"""init function
Parameters:
fig: matplotlib.figure.Figure instance
rect: [left, bottom, width, height]
each of which in 0-to-1-fraction i.e. 0<=x<=1
label: string
button label
func: function
function called when button is clicked
fargs: array, optional, default: None
(optional) arguments for func
c: a matplotlib color i.e. string
color i.e. background color
hc: a matplotlib color i.e. string
hovercolor i.e. background color when cursor is on button
"""
self.fig = fig
self._rect = rect
self._label = label
self._func = func
self._fargs = fargs
self.axes = self.fig.add_axes(rect)
self.button = Button(self.axes, label, color=c, hovercolor=hc)
self.button.label.set_fontsize('x-small')
self.button.label.set_family('monospace')
self.button.on_clicked(self._onclick)
def _onclick(self, wtf):
"""Actual function called when button is clicked."""
if self.get_visible():
if self._func is not None: # not necessary
if self._fargs is not None:
self._func(*self._fargs)
else:
self._func()
self.fig.canvas.draw()
def set_visible(self, b):
"""Set its visibility.
Parameters:
b: boolean
"""
self.axes.set_visible(b)
def get_visible(self):
"""Get its visibility.
Returns: b
b: boolean
"""
return self.axes.get_visible()
def __init__(self, interval=1):
self.interval = interval
self.fig, self.ax = plt.subplots()
self.ax.set_xlim(-2.6, 2.6)
self.ax.set_ylim(-2, 2)
plt.subplots_adjust(left=0.25, bottom=0.25)
self.line, = self.ax.plot([], [], lw=.5);
self.c1line, = self.ax.plot([], [], lw=.5);
self.c2line, = self.ax.plot([], [], lw=.5);
self.xdata, self.ydata = [], []
self.frame = 0
# [left, bottom, width, height] in normalized (0, 1) units
w = 0.1
h = 0.03
l0 = 0.05
dl = 0.2
b0 = 0.05
db = 0.05
self.slider = dict()
# r1, r2
# xr1 xw1 xp1
# xr2 xw2 xp2
# yr1 yw1 yp1
# yr2 yw2 yp2
p2 = np.pi * 2
sliders = ((0, 0, 0, 2, 1.5, 'r1'),
(0, 1, 0, 2, 1.5, 'r2'),
(1, 0, 0, 2, 1, 'xr1'),
(1, 1, 0, .1, .01, 'xw1'),
(1, 2, 0, p2, 0, 'xp1'),
(2, 0, 0, 2, 0.3, 'xr2'),
(2, 1, 0, .1, 0.05, 'xw2'),
(2, 2, 0, p2, 0, 'xp2'),
(3, 0, 0, 2, 1, 'yr1'),
(3, 1, 0, .1, 0.01, 'yw1'),
(3, 2, 0, p2, 1.5, 'yp1'),
(4, 0, 0, 2, 0.3, 'yr2'),
(4, 1, 0, .1, 0.05, 'yw2'),
(4, 2, 0, p2, 0, 'yp2'))
for x, y, v0, v1, vi, n in sliders:
l = x * dl + l0
b = y * db + b0
ax = plt.axes([l, b, w, h])
self.slider[n] = Slider(ax, n, v0, v1, valinit=vi)
ax = plt.axes([0, 0.5, 0.1, 0.04])
self.rndbut = Button(ax, 'Rand')
self.rndbut.on_clicked(self.rand)
ax = plt.axes([0, 0.6, 0.1, 0.04])
self.resetbut = Button(ax, 'Reset')
self.resetbut.on_clicked(self.init)
ax = plt.axes([0, 0.7, 0.1, 0.04])
self.savebut = Button(ax, 'Save')
self.savebut.on_clicked(self.save)
def __init__(self, scope):
self.num_events = scope.num_events
self.event_ind = 0
self.toggle_volts = True
self.toggle_offset = False
self.fig = mp.subplot(2, 2, 1)
xlabel('Time (s)')
ylabel('Volts (V)')
self.num_waves = scope.events[self.event_ind].num_waves
self.lines = []
for wave_ind in range(self.num_waves):
y = scope.events[self.event_ind].waves[wave_ind].volts
t = scope.events[self.event_ind].waves[wave_ind].times
line, = plot(t, y, label = 'Channel ' + str(wave_ind+1) )
legend()
self.lines.append(line)
self.fig2 = mp.subplot(2, 2, 2)
xlabel('Frequency (Hz)')
ylabel('Power (dB)')
self.lines2 = []
for wave_ind in range(self.num_waves):
p = scope.events[self.event_ind].waves[wave_ind].pow_spec_dB
f = scope.events[self.event_ind].waves[wave_ind].freqs
line, = plot(f, p, label = 'Channel ' + str(wave_ind+1) )
self.lines2.append(line)
legend()
self.fig3 = mp.subplot(2, 2, 3)
self.fig3.set_title(scope_name + ' Event Header Information')
axis('off')
self.info = []
self.update()
self.axprev = mp.axes([0.7, 0.05, 0.1, 0.075])
self.axnext = mp.axes([0.81, 0.05, 0.1, 0.075])
self.axvolt = mp.axes([0.1, 0.05, 0.1, 0.075])
self.axoff = mp.axes([0.19, 0.05, 0.1, 0.075])
self.bnext = Button(self.axnext, 'Next')
self.bnext.on_clicked(self.next)
self.bprev = Button(self.axprev, 'Previous')
self.bprev.on_clicked(self.prev)
self.bvolts = Button(self.axvolt, 'Volts/ADC')
self.bvolts.on_clicked(self.volts_toggle)
self.bvoff = Button(self.axoff, 'Offset')
self.bvoff.on_clicked(self.offset_toggle)
mp.show()
def main(argv):
"""Test maximum density."""
print(len(argv))
if len(argv) < 1:
shot_number=int(open('shot_number.txt','r').read())
else:
if (len(argv) == 1) & ("py" in argv[0]):
shot_number=int(open('shot_number.txt','r').read())
else:
shot_number = int(argv[1])
fig, ax = plt.subplots()
plt.subplots_adjust(bottom=0.25)
shot = ppb.ProcProfile(shot_number)
shot.reference_gd(all_shot=1)
cluster = 20
shot.plasma_gd(1, cluster, 1)
ax.pcolormesh(shot.X['K'], shot.Y['K'], shot.matrix_k_mean)
ax.pcolormesh(shot.X['Ka'], shot.Y['Ka'], shot.matrix_ka_mean)
plt.plot(shot.X['K'], shot.Y['K'][shot.matrix_k_mean.argmax(axis=0)] - 3.36, color='b', linewidth=2.0)
plt.plot(shot.X['Ka'], shot.Y['Ka'][shot.matrix_ka_mean.argmax(axis=0)] - 3.36, color='b', linewidth=2.0)
plt.plot(shot.X['K'], shot.Y['K'][shot.matrix_k_mean.argmax(axis=0)], color='b', linewidth=2.0)
plt.plot(shot.X['Ka'], shot.Y['Ka'][shot.matrix_ka_mean.argmax(axis=0)], color='b', linewidth=2.0)
l, = plt.plot(shot.X['K'], shot.Y['K'][shot.matrix_k_mean.argmax(axis=0)], color='r', linewidth=2.0)
m, = plt.plot(shot.X['Ka'], shot.Y['Ka'][shot.matrix_ka_mean.argmax(axis=0)], color='r', linewidth=2.0)
plt.xlabel("freq (GHz)")
plt.ylabel("group delay (ns)")
plt.title("# %s - time: %s ms" % (shot.shot, shot.sweep2time(shot.sweep_cur)))
plt.ylim(0, 12)
plt.xlim(shot.X['K'].min(), shot.X['Ka'].max())
axcolor = 'lightgoldenrodyellow'
axfreq = plt.axes([0.25, 0.1, 0.65, 0.03], axisbg=axcolor)
sweep = Slider(axfreq, 'Sweep', 1, len(shot.points) - 1 - cluster, valinit=1, valfmt='%1.f')
def update(val):
shot.plasma_gd(int(sweep.val), cluster, 1)
ax.pcolormesh(shot.X['K'], shot.Y['K'], shot.matrix_k_mean)
ax.pcolormesh(shot.X['Ka'], shot.Y['Ka'], shot.matrix_ka_mean)
l.set_ydata(shot.Y['K'][shot.matrix_k_mean.argmax(axis=0)])
m.set_ydata(shot.Y['Ka'][shot.matrix_ka_mean.argmax(axis=0)])
ax.set_title("# %s - time: %.3f ms" % (shot.shot, shot.sweep2time(shot.sweep_cur)))
fig.canvas.draw_idle()
sweep.on_changed(update)
resetax = plt.axes([0.8, 0.025, 0.1, 0.04])
button = Button(resetax, 'Reset', color=axcolor, hovercolor='0.975')
def reset(event):
sweep.reset()
button.on_clicked(reset)
plt.show()
def connect(self):
self.axfron = self.axs['Fron']
self.axprev = self.axs['Prev']
self.axnext = self.axs['Next']
self.axlast = self.axs['Last']
self.axzoba = self.axs['Zoba']
self.axshdo = self.axs['Shdo']
self.axshfp = self.axs['Shfp']
self.axshod = self.axs['Shod']
self.axquit = self.axs['Quit']
self.bnfron = Button(self.axfron, 'Front')
self.bnprev = Button(self.axprev, 'Prev')
self.bnnext = Button(self.axnext, 'Next')
self.bnlast = Button(self.axlast, 'Last')
self.bnzoba = Button(self.axzoba, 'Zoom \n Back')
self.bnshdo = Button(self.axshdo, 'Save')
self.bnshfp = Button(self.axshfp, 'Save \n Params')
self.bnshod = Button(self.axshod, 'Save \n Override')
self.bnquit = Button(self.axquit, 'Quit')
self.cidfron = self.bnfron.on_clicked(self.fron)
self.cidprev = self.bnprev.on_clicked(self.prev)
self.cidnext = self.bnnext.on_clicked(self.next)
self.cidlast = self.bnlast.on_clicked(self.last)
self.cidzoba = self.bnzoba.on_clicked(self.zoba)
self.cidshdo = self.bnshdo.on_clicked(self.shdo)
self.cidshfp = self.bnshfp.on_clicked(self.shfp)
self.cidshod = self.bnshod.on_clicked(self.shod)
self.cidquit = self.bnquit.on_clicked(self.quit)
self.cidpress = self.axpp.figure.canvas.mpl_connect('key_press_event', self.on_zoom)
def slide_ddg(args):
global new_df, radio, color_by, picked
global scat, ax, sliders, sc_df, fig, cm, cbar
sc_df = Rf.score_file2df(args['sc'], args['names'])
args['logger'].log('score file has %i entries' % len(sc_df))
if args['pc'] is not None:
pc_df = get_rmsds_from_table(args['pc'])
args['logger'].log('pc file had %i entries' % len(pc_df))
a = sc_df.merge(pc_df, on='description')
args['logger'].log('combined there are %i entries' % len(a))
sc_df = a.copy()
if args['percent'] != 100:
threshold = np.percentile(sc_df[args['y']], args['percent'])
sc_df = sc_df[ sc_df[args['y']] < threshold ]
color_by = args['y']
picked = False
new_df = sc_df.copy()
fig, ax = plt.subplots()
plt.subplots_adjust(left=0.25, bottom=0.25)
cm = plt.cm.get_cmap('RdYlBu')
scat = ax.scatter(sc_df[args['x']].values, sc_df[args['y']].values, s=40, cmap=cm, c=sc_df[color_by], picker=True)
cbar = plt.colorbar(scat)
sliders = {}
for i, term in enumerate(args['terms']):
slider_ax = plt.axes([0.25, 0.01+i*0.035, 0.65, 0.03])
sliders[term] = Slider(slider_ax, term, np.min(sc_df[term].values), np.max(sc_df[term].values), 0)
sliders[term].on_changed(update)
ax.set_xlim(np.min(new_df[args['x']].values)-1, np.max(new_df[args['x']].values)+1)
ax.set_ylim(np.min(new_df[args['y']].values)-1, np.max(new_df[args['y']].values)+1)
ax.set_xlabel(args['x'])
ax.set_ylabel(args['y'])
resetax = plt.axes([0.025, 0.7, 0.15, 0.15]) #[0.8, 0.025, 0.1, 0.04])
button = Button(resetax, 'Reset', color='lightgoldenrodyellow', hovercolor='0.975')
button.on_clicked(reset)
printax = plt.axes([0.025, 0.3, 0.15, 0.15])
printbutton = Button(printax, 'Print', color='green', hovercolor='red')
printbutton.on_clicked(print_table)
logax = plt.axes([0.025, 0.1, 0.15, 0.15])
logbutton = Button(logax, 'log table', color='blue', hovercolor='red')
logbutton.on_clicked(log_table)
rax = plt.axes([0.025, 0.5, 0.15, 0.15], axisbg='white')
radio = RadioButtons(rax, args['terms'], active=0)
radio.on_clicked(colorfunc)
# cbar = plt.colorbar(scat)
pl = PointLabel(new_df, ax, fig, args['x'], args['y'], ['description', 'a_sasa', 'a_res_solv', 'a_pack', 'a_span_topo', 'a_ddg', 'fa_elec'], args['logger'])
fig.canvas.mpl_connect('pick_event', pl.onpick)
plt.show()
else:
l2.set_ydata(
np.sqrt(a1**2 + a2**2 + 2 * a1 * a2 * s(t, f2 - 1, 1, phi)))
# elif(a1>a2):
# l2.set_ydata(np.abs(a1-a2)+np.abs(s(t,(f2-1)/2.0,2*a2,phi/2.0)))
# else:
# l2.set_ydata(np.abs(a2-a1)+np.abs(s(t,(f2-1)/2.0,2*a1,phi/2.0)))
ax.axes.axis([0, 100, 1.1 * min(y), 1.1 * max(y)])
py.draw()
sld_amplitude1 = py.axes([0.2, 0.1, 0.7, 0.03], axisbg='grey')
sld_amplitude2 = py.axes([0.2, 0.15, 0.7, 0.03], axisbg='grey')
sld_frequence2 = py.axes([0.2, 0.2, 0.7, 0.03], axisbg='grey')
sld_phase2 = py.axes([0.2, 0.25, 0.7, 0.03], axisbg='grey')
amplitude1 = Slider(sld_amplitude1, 'amplitude 1', 0.0, 2.0, valinit=1)
amplitude2 = Slider(sld_amplitude2, 'amplitude 2', 0, 2.0, valinit=1)
frequence2 = Slider(sld_frequence2, r'$f_2/f_1$', 0.9, 1.1, valinit=1)
phase = Slider(sld_phase2, r'$\varphi_2-\varphi_1$', 1, 5.0, valinit=0)
button_demarre = py.axes([0.7, 0.02, 0.2, 0.05])
button = Button(button_demarre, 'animation', color='grey', hovercolor='white')
button.on_clicked(rebond)
py.show()
os.system("pause")
def __init__(self,
print_manager,
combo_prints,
hulls_df,
print_numb,
vid_panel,
invert_axes=True):
self.root = tk.Tk()
self.root.wm_title("Split the Selected Print")
self.fig = Figure(figsize=(10, 8), dpi=100)
self.canvas = FigureCanvasTkAgg(self.fig,
master=self.root) # A tk.DrawingArea.
self.canvas.draw()
self.canvas.get_tk_widget().pack(side=tk.TOP, fill=tk.BOTH, expand=1)
self.toolbar = NavigationToolbar2TkAgg(self.canvas, self.root)
self.toolbar.update()
self.canvas._tkcanvas.pack(side=tk.TOP, fill=tk.BOTH, expand=1)
self.print_manager = print_manager
self.combo_prints = combo_prints
self.print_numb = print_numb
self.first_frame = self.combo_prints.first_frame[print_numb]
n_frames = (self.combo_prints.last_frame[print_numb] -
self.first_frame) + 1
grid_size = int(np.ceil(np.sqrt(n_frames)))
self.axes = []
self.fig.set_size_inches(16, 9)
button_axes = self.fig.add_axes([.01, .01, .05, .05])
self.button = Button(button_axes, 'Split')
self.button.on_clicked(self.create_new_hulls)
these_hulls = hulls_df[hulls_df.print_numb == print_numb]
self.collections = {}
self.xyes = {}
for i in range(0, n_frames):
ax = self.fig.add_subplot(grid_size, grid_size, i + 1)
self.axes.append(ax)
ax.set_axis_off()
frame = vid_panel.get_frame(self.first_frame + i)
X = self.combo_prints.X[print_numb]
Y = self.combo_prints.Y[print_numb]
ax.imshow(frame)
xes = []
yes = []
for c in (these_hulls.contours[these_hulls.frame ==
self.first_frame + i].values[0]):
#add all to a single list to make one collection
xes = xes + list(c[:, 0, 0])
yes = yes + list(c[:, 0, 1])
self.collections[ax] = ax.scatter(xes, yes)
self.xyes[ax] = self.collections[ax].get_offsets()
#set it to have list of facecolors so that selection works
facecolors = self.collections[ax].get_facecolors()
npts = len(self.xyes[ax])
facecolors = np.tile(facecolors, npts).reshape(npts, -1)
self.collections[ax].set_facecolor(facecolors)
if invert_axes:
ax.set_xlim(X + 50, X - 50)
else:
ax.set_xlim(X - 50, X + 50)
ax.set_ylim(Y - 50, Y + 50)
ax.set_title('Frame ' + str(self.first_frame + i))
self.axes = np.asarray(self.axes)
self.cid = self.canvas.mpl_connect('button_press_event', self.onpress)
self.canvas.draw()
tk.mainloop()
# Add a time slider
if len(all_times) > 1:
axfreq = plt.axes([0.1, 0.0, 0.5, 0.03])
time_slider = Slider(axfreq, 'ix', 0., len(all_times) - 1, 0.)
time_slider.on_changed(update)
# Add buttons to go to next/previous variable and snapshot, and one to switch
# between primitive
axprev_iw = plt.axes([0.8, 0.0, 0.075, 0.05])
axnext_iw = plt.axes([0.875, 0.0, 0.075, 0.05])
axprim = plt.axes([0.95, 0.0, 0.05, 0.05])
axprev_file = plt.axes([0.0, 0.0, 0.05, 0.05])
axnext_file = plt.axes([0.7, 0.0, 0.05, 0.05])
bnext_iw = Button(axnext_iw, '+iw')
bnext_iw.on_clicked(next_var)
bprev_iw = Button(axprev_iw, '-iw')
bprev_iw.on_clicked(prev_var)
bnext_file = Button(axnext_file, '+')
bnext_file.on_clicked(next_file)
bprev_file = Button(axprev_file, '-')
bprev_file.on_clicked(prev_file)
bprim = Button(axprim, 'P/C')
bprim.on_clicked(switch_cons_prim)
# Initial state
i_var = 1
i_file = 0
is_conservative = True
def held_karp(dists):
"""
Implementation of Held-Karp, an algorithm that solves the Traveling
Salesman Problem using dynamic programming with memoization.
Parameters:
dists: distance matrix
Returns:
A tuple, (cost, path).
"""
n = len(dists)
C = {}
for k in range(1, n):
C[(1 << k, k)] = (dists[0][k], 0)
for subset_size in range(2, n):
for subset in itertools.combinations(range(1, n), subset_size):
# Set bits for all nodes in this subset
bits = 0
for bit in subset:
bits |= 1 << bit
# Find the lowest cost to get to this subset
for k in subset:
prev = bits & ~(1 << k)
res = []
for m in subset:
if m == 0 or m == k:
continue
res.append((C[(prev, m)][0] + dists[m][k], m))
C[(bits, k)] = min(res)
bits = (2**n - 1) - 1
res = []
for k in range(1, n):
res.append((C[(bits, k)][0] + dists[k][0], k))
opt, parent = min(res)
path = []
for i in range(n - 1):
path.append(parent)
new_bits = bits & ~(1 << parent)
_, parent = C[(bits, parent)]
bits = new_bits
path.append(0)
return opt, list(reversed(path))
def solve(event):
global n, trucks, solver, li, ax, warehouses, loc_x, loc_y, lines_x, lines_y, obj_value, exec_value
global distances, distances2
print("solving...")
ax.clear()
ax.set_xlim(0,1000)
ax.set_ylim(0,1000)
plot_data(ax, loc_x, loc_y, warehouses)
ax.text(400, 1075, "solving...", family="serif", horizontalalignment='left', verticalalignment='top')
plt.draw()
fig.canvas.draw()
start_time = time.time()
# Calculate constraint for Li
total_l = 0
total_c = 0.0
paths = 0
slack = False
while total_c < n or paths < trucks:
c1 = min(c,n)
total_c += c1
total_l += c1*(c1+1)/2
paths += 1
if total_c > n:
slack = True
if solver=="cbc" or solver=="glpk" or solver=="gurobi":
# Objective function
z = pulp.LpProblem('Test', pulp.LpMinimize)
# Generate decision variables
x = {}
y = {}
variables = []
l = {}
s = {}
for i in range(n+warehouses):
for j in range(n+warehouses):
if i==j:
continue
x[i,j] = pulp.LpVariable('x_' + str(i) + '_' + str(j), 0, 1, pulp.LpInteger)
if i >= warehouses:
l[i] = pulp.LpVariable('l_' + str(i), 1, min(n,c), pulp.LpInteger)
# Objective function
z += pulp.lpSum([distances[i][j] * x[i,j] for i in range(n+warehouses) for j in
list(range(i)) + list(range(i+1,n+warehouses))])
# Constraints
constraintSeq = []
constraintTrucks = []
for i in range(n+warehouses):
if i>=warehouses:
constraintSeq.append(l[i])
constraintFrom = []
constraintTo = []
for j in range(n+warehouses):
if i==j:
continue
if i>=warehouses and j>=warehouses:
z += pulp.lpSum([l[i], -1*l[j], n*x[i,j], -n+1]) <= 0
if i>=warehouses:
constraintFrom.append(x[i,j])
constraintTo.append(x[j,i])
if i<warehouses:
constraintTrucks.append(x[j,i])
if i>=warehouses:
z += pulp.lpSum(constraintFrom) == 1 # paths from location
z += pulp.lpSum(constraintTo) == 1 # paths to location
if i==warehouses and (paths > 1 or warehouses>1):
z += pulp.lpSum(constraintTrucks) == paths # paths to warehouse
if not slack:
z += pulp.lpSum(constraintSeq) == total_l
else:
z += pulp.lpSum(constraintSeq) <= total_l
# Solve
if solver=="cbc":
status = z.solve()
if solver=="glpk":
status = z.solve(pulp.GLPK())
if solver=="gurobi":
status = z.solve(pulp.GUROBI_CMD())
# should be 'Optimal'
if pulp.LpStatus[status]!="Optimal":
print("RESULT: ".pulp.LpStatus[status])
print("Objective function value: "+str(z.objective.value()))
# Print variables & save path
lines_x = []
lines_y = []
li = [0] * n
for i in range(n+warehouses):
if i>=warehouses:
li[i-warehouses] = pulp.value(l[i])
for j in range(n+warehouses):
if i==j:
continue
if pulp.value(x[i,j]) == 1:
lines_x.append(loc_x[i])
lines_x.append(loc_x[j])
lines_y.append(loc_y[i])
lines_y.append(loc_y[j])
lines_x.append(np.nan)
lines_y.append(np.nan)
obj_value = "c=" + str(round(z.objective.value(),2))
elif solver=="cut plane":
model = Model("tsp")
model.hideOutput()
x = {}
l = {}
for i in range(n+warehouses):
for j in range(n+warehouses):
if i != j:
x[i,j] = model.addVar(ub=1, name="x(%s,%s)"%(i,j))
if (paths > 1 or warehouses > 1) and i >= warehouses:
l[i] = model.addVar(ub=min(c,n),lb=1, name="l(%s)"%(i))
if paths == 1 and warehouses == 1:
# SYMMETRIC DISTANCE MATRIX ONLY
#for i in range(n+warehouses):
#model.addCons(quicksum(x[j,i] for j in range(n+warehouses) if j != i) + \
# quicksum(x[i,j] for j in range(n+warehouses) if j != i) == 2,"Degree(%s)"%i)
# ASYMMETRIC DISTANCE MATRIX
for i in range(n+warehouses):
model.addCons(quicksum(x[j,i] for j in range(n+warehouses) if j != i) == 1,"In(%s)"%i)
model.addCons(quicksum(x[i,j] for j in range(n+warehouses) if j != i) == 1,"Out(%s)"%i)
else:
for i in range(warehouses, n+warehouses):
model.addCons(quicksum(x[j,i] for j in range(n+warehouses) if j != i) == 1,"In(%s)"%i)
model.addCons(quicksum(x[i,j] for j in range(n+warehouses) if j != i) == 1,"Out(%s)"%i)
for i in range(warehouses, n+warehouses):
for j in range(warehouses, n+warehouses):
if i!=j:
model.addCons(l[i] -l[j] +n*x[i,j] <= n-1, "Li(%s,%s)"%(i,j))
model.addCons(quicksum(x[j,i] for i in range(warehouses) for j in range(n+warehouses) if i!=j) == paths, "Paths(%s)"%paths)
if not slack:
model.addCons(quicksum(l[i] for i in range(warehouses,n+warehouses)) == total_l,"TotalL")
else:
model.addCons(quicksum(l[i] for i in range(warehouses,n+warehouses)) <= total_l, "TotalL")
model.setObjective(quicksum(distances2[i,j]*x[i,j] for (i,j) in x), "minimize")
EPS = 1.e-6
isMIP = False
model.setPresolve(SCIP_PARAMSETTING.OFF)
while True:
model.optimize()
#edges = []
lines_x = []
lines_y = []
edges = []
li = [0] * n
for (i,j) in x:
# i=j already skipped
if model.getVal(x[i,j]) > EPS:
#edges.append( (i,j) )
lines_x.append(loc_x[i])
lines_x.append(loc_x[j])
lines_y.append(loc_y[i])
lines_y.append(loc_y[j])
lines_x.append(np.nan)
lines_y.append(np.nan)
edges.append( (i,j) )
if paths>1 or warehouses>1:
for i in range(warehouses, n+warehouses):
li[i-warehouses] = int(model.getVal(l[i]))
obj_value = "c=" + str(round(model.getObjVal(),2))
ax.clear()
ax.set_xlim(0,1000)
ax.set_ylim(0,1000)
plot_data_lines(lines_x,lines_y)
plot_data(ax, loc_x, loc_y, warehouses)
ax.text(400, 1075, "solving...", family="serif", horizontalalignment='left', verticalalignment='top')
fig.canvas.draw()
if addcut(edges,model,x,warehouses) == False:
if isMIP: # integer variables, components connected: solution found
break
model.freeTransform()
for (i,j) in x: # all components connected, switch to integer model
model.chgVarType(x[i,j], "B")
if paths > 1 or warehouses > 1:
for i in range(warehouses,n+warehouses):
model.chgVarType(l[i], "I")
isMIP = True
sol_li = [0] * (n+warehouses)
sol_xij = {}
print('solved.')
elif solver == 'held-karp':
li = [0] * n
opt, path = held_karp(distances)
print(path)
obj_value = "c=" + str(round(opt,2))
x = [[0 for x in range(n+warehouses)] for y in range(n+warehouses)]
for idx, val in enumerate(path):
if idx < (len(path)-1):
x[val][path[idx+1]] = 1;
elif idx == (len(path)-1):
x[val][path[0]] = 1;
for i in range(n+warehouses):
for j in range(n+warehouses):
if x[i][j] == 1:
#edges.append( (i,j) )
lines_x.append(loc_x[i])
lines_x.append(loc_x[j])
lines_y.append(loc_y[i])
lines_y.append(loc_y[j])
lines_x.append(np.nan)
lines_y.append(np.nan)
#edges.append( (i,j) )
# Print computation time
time2 = time.time() - start_time
exec_value = time2
units = 'secs'
if time2 > 60:
time2 /= 60
units = 'mins'
if time2 > 60:
time2 /= 60
units = 'hours'
time2 = round(time2,2)
exec_value = "exec=" + str(time2) + " " + units
print("--- " + str(time2) + " " + units + " ---")
# Redraw points
ax.clear()
ax.set_xlim(0,1000)
ax.set_ylim(0,1000)
plot_data_lines(lines_x,lines_y)
plot_data(ax, loc_x, loc_y, warehouses)
ax.text(350, 1075, obj_value, family="serif", horizontalalignment='right', verticalalignment='top')
ax.text(450, 1075, exec_value, family="serif", horizontalalignment='left', verticalalignment='top')
fig.canvas.draw()
axsolve = plt.axes([0.87, 0.905, 0.1, 0.075])
bsolve = Button(axsolve, 'Solve')
bsolve.on_clicked(solve)
plt.show()
def init_graphes(self):
''' Inititialise les graphiques
'''
### IMAGE REMUTRACE
image_capteur = plt.imread('capteur.png')
self.image_capteur_plot = self.fig.add_subplot(233)
self.image_capteur_plot.set_axis_off()
self.image_capteur_plot.imshow(image_capteur)
### 3D (non implementé)
#self.3D = figure3D().add_subplot(233)
#self.3D = self.fig.add_subplot(233)
### GRAPHE XYZ
#self.xyz=self.fig.add_subplot(231)
#self.xyz.set_xlabel('Temps')
#self.xyz.set_ylabel('x,y,z')
#self.xyz.plot(self.dates, self.acc_Xs, label='acc_X')
#self.xyz.plot(self.dates, self.acc_Ys, label='acc_Y')
#self.xyz.plot(self.dates, self.acc_Zs, label='acc_Z')
#self.xyz.legend()
#self.xyz_line = False
### GRAPHE GxGyGz
self.GxGyGz = self.fig.add_subplot(231) #, sharex = self.xyz)
self.GxGyGz.set_title(u'Vitesse angulaire')
self.GxGyGz.set_xlabel(u'Temps')
self.GxGyGz.set_ylabel(u'Deg/s')
self.GxGyGz.plot(self.dates, self.gyro_Xs, label='gyro_X')
self.GxGyGz.plot(self.dates, self.gyro_Ys, label='gyro_Y')
self.GxGyGz.plot(self.dates, self.gyro_Zs, label='gyro_Z')
#self.GxGyGz.set_xticks(self.dates)
self.GxGyGz.xaxis.set_major_locator(mdates.DayLocator())
#self.GxGyGz.xaxis.set_minor_locator(mdates.HourLocator())
self.GxGyGz.xaxis.set_major_formatter(
ticker.FormatStrFormatter("%d %b"))
self.GxGyGz.legend()
self.GxGyGz_line = False
#self.fig.autofmt_xdate()
#plt.xticks(rotation=90)
logging.debug("GRAPHE GxGyGz ok")
### GRAPHE INCLINAISON
self.Inclinaison = self.fig.add_subplot(234, sharex=self.GxGyGz)
self.Inclinaison.plot(self.dates, self.angle_Ys, label='Inclinaison')
#self.Inclinaison.legend()
self.Inclinaison.set_title(u'Inclinaison')
self.Inclinaison.set_xlabel(u'Temps')
self.Inclinaison.set_ylabel(u'Deg')
self.Inclinaison_line = False
### GRAPHE ROTATION
self.Rotation = self.fig.add_subplot(235, sharex=self.GxGyGz)
self.Rotation.plot(self.dates, self.angle_Xs, label='Rotation')
#self.Rotation.legend()
self.Rotation.set_title(u'Rotation')
self.Rotation.set_xlabel(u'Temps')
self.Rotation.set_ylabel(u'Deg')
self.Rotation_line = False
### PHOTO
self.image_plot = self.fig.add_subplot(232)
self.image_plot.set_axis_off()
self.image_plot.set_title(u'Caméra')
### BOUTONS
self.fig.canvas.mpl_connect('button_press_event', self.on_click)
self.bt_lecture = Button(plt.axes([0.65, 0.3, 0.05, 0.03]), 'lecture')
self.bt_lecture.on_clicked(self.on_bt_lecture_click)
self.bt_lecture10 = Button(plt.axes([0.65, 0.2, 0.05, 0.03]),
'lecturex10')
self.bt_lecture10.on_clicked(self.on_bt_lecture_click10)
#self.tb_vitesse = TextBox(plt.axes([0.9, 0.8, 0.05, 0.03]),'Vitesse', initial = '1')
#self.tb_vitesse.on_submit(self.on_tb_vitesse_submit)
### Détail étape
axe_etape = self.fig.add_axes([0.65, 0.4, 0.03, 0.03])
axe_etape.set_axis_off()
self.etape = plt.text(0, 3, u'Etape n° ?')
self.etape_debut_mvt = plt.text(0, 2, u'Début : --')
self.etape_fin_mvt = plt.text(0, 1, u'Fin : --')
self.etape_acceleration_max = plt.text(0, 0, u'Accélération max : --')
### LEGENDE : FILEUROPE-CMP
legende = self.fig.add_axes([0.75, 0.07, 0.2, 0.3])
legende.set_axis_off()
#rect = Rectangle((0,0),1,1,fill=True, color= 'blue')
#legende.add_patch(rect)
logo_img = plt.imread('logo.png')
legende.imshow(logo_img)
text_legende = plt.text(0.05, 0.05, u"Remutrace - Brevet déposé.")
self.fig.canvas.mpl_connect('key_press_event', self.press)
ax[i].set_xlabel('Freq (MHz)')
ax[i].set_yticklabels([])
ax[i].yaxis_date()
ax[0].set_ylabel('Local Time')
ax[0].yaxis_date()
ax[0].yaxis.set_major_formatter(date_format)
cbar_ax = f.add_axes([0.85, 0.15, 0.05, 0.7])
c = f.colorbar(p[0], cax=cbar_ax)
c.set_label('Power (dB, arbitrary)')
from matplotlib.widgets import Slider, Button
rax = plt.axes([0.82, 0.03, 0.15, 0.04])
check = Button(rax, 'Med Subtract')
def func(event):
global medsub, check, colorscale
medsub = not medsub
if medsub:
check.label.set_text("Med Subtracted")
colorscale = [-0.5, 0.5]
else:
check.label.set_text("Raw Power")
colorscale = [-10, 10]
check.on_clicked(func)
def submit(self):
if (self.type == game.SheetType.MATCH):
#If the match is empty, reset the data and display fields
if self.matchData['Team'] == 0:
print("Found an empty match, skipping")
self.matchData = dict(game.SCOUT_FIELDS)
self.display = cv2.cvtColor(self.sheet, cv2.COLOR_GRAY2BGR)
return
#Open the database and check if the match has already been processed
datapath = 'data_' + CURRENT_EVENT + '.db'
conn = sql.connect(datapath)
conn.row_factory = sql.Row
cursor = conn.cursor()
history = cursor.execute(
'SELECT * FROM scout WHERE Team=? AND Match=?',
(str(self.matchData['Team']), str(
self.matchData['Match']))).fetchall()
if history and not self.matchData['Replay']:
print("Already processed this match, skipping")
self.data = dict(game.SCOUT_FIELDS)
self.display = cv2.cvtColor(self.sheet, cv2.COLOR_GRAY2BGR)
return
elif (self.type == game.SheetType.PIT):
if self.pitData['Team'] == 0:
print("Found an empty pit sheet, skipping")
self.pitData = dict(game.PIT_SCOUT_FIELDS)
self.display = cv2.cvtColor(self.sheet, cv2.COLOR_GRAY2BGR)
return
datapath = 'data_' + CURRENT_EVENT + '.db'
conn = sql.connect(datapath)
conn.row_factory = sql.Row
cursor = conn.cursor()
history = cursor.execute('SELECT * FROM pitScout WHERE Team=?',
(str(self.pitData['Team']), )).fetchall()
if history:
print("Already processed this team, skipping")
self.pitData = dict(game.PIT_SCOUT_FIELDS)
self.display = cv2.cvtColor(self.sheet, cv2.COLOR_GRAY2BGR)
return
#Create and open the GUI to verify data
print("Found new data, opening")
output = ''
if self.type == game.SheetType.MATCH:
for key, value in self.matchData.items():
output += key + "=" + str(value) + '\n'
elif self.type == game.SheetType.PIT:
for key, value in self.pitData.items():
output += key + "=" + str(value) + '\n'
fig = plt.figure('PiScout')
fig.subplots_adjust(left=0, right=0.6)
plt.subplot(111)
plt.imshow(self.display)
plt.title('Scanned Sheet')
plt.text(600, 784, output, fontsize=12)
upload = Button(plt.axes([0.68, 0.31, 0.15, 0.07]), 'Upload Data')
upload.on_clicked(self.upload)
save = Button(plt.axes([0.68, 0.24, 0.15, 0.07]), 'Save Data Offline')
save.on_clicked(self.save)
edit = Button(plt.axes([0.68, 0.17, 0.15, 0.07]), 'Edit Data')
edit.on_clicked(self.edit)
cancel = Button(plt.axes([0.68, 0.1, 0.15, 0.07]), 'Cancel')
cancel.on_clicked(self.cancel)
mng = plt.get_current_fig_manager()
try:
mng.window.state('zoomed')
except AttributeError:
print("Window resizing exploded, oh well.")
plt.show()
self.matchData = dict(game.SCOUT_FIELDS)
self.pitData = dict(game.PIT_SCOUT_FIELDS)
self.display = cv2.cvtColor(self.sheet, cv2.COLOR_GRAY2BGR)
class simulate():
"""
Class that simulates a dvhb object based on diferent inputs in sliders
obs: funtions for this class are only used by the class itself and
will not work outside of animation context
args:
dvhb: dvhb object already configured with mtrix and joints
returns:
perform the simulation of the robot with sliders to represent the
inputs of the joint
"""
def __init__(self, dvhb):
from IPython import get_ipython
ipy = get_ipython()
if ipy != None:
ipy.run_line_magic('matplotlib', 'auto')
self.dvhb = deepcopy(dvhb)
self.limits = self.get_limits()
self.scale = self.limits[1] * 0.1
self.fig = plt.figure()
self.ax = self.fig.add_subplot(111, projection='3d')
plt.subplots_adjust(left=0.25, bottom=0.40, top=1)
self.variables = []
for n, variable in enumerate(self.dvhb.joints):
joint_slider_loc = plt.axes([0.25, 0.30 - 0.05 * n, 0.65, 0.03],
facecolor=axcolor)
if variable[1] == "r":
joint = Slider(joint_slider_loc,
f'Joint {variable[0]}',
0,
360,
valinit=self.dvhb.get_curr_theta(variable[0]))
elif variable[1] == "t":
joint = Slider(joint_slider_loc,
f'Joint {variable[0]}',
0,
5,
valinit=self.dvhb.get_curr_d(variable[0]))
joint.on_changed(self.update)
self.variables.append(joint)
resetax = plt.axes([0.1, 0.4, 0.1, 0.04])
self.button = Button(resetax,
'Reset',
color=axcolor,
hovercolor='0.975')
self.button.on_clicked(self.reset)
self.update(None, init=True)
plt.show()
def get_limits(self):
# Verifica quais são todos os pontos de dados
all_points = []
for cordsys in trace(self.dvhb.to_cordsys()):
all_points.append(cordsys[:3, 3])
all_points = np.array(all_points)
maxs, mins = all_points.max(axis=0), all_points.min(axis=0)
abs_max = max(max(abs(maxs)), min(abs(mins)))
vec_scale = 0.1 * abs_max
space = 2 * vec_scale
return [-abs_max - space, abs_max + space]
def get_points(self):
# Verifica quais são todos os pontos de dados
all_points = []
for cordsys in trace(self.dvhb.to_cordsys()):
all_points.append(cordsys[:3, 3])
all_points = np.array(all_points)
# Liga os pontos com uma linha
return [all_points[:, 0], all_points[:, 1], all_points[:, 2]]
def update(self, val, init=False):
if not init:
new_pos = [variable.val for variable in self.variables]
self.dvhb.recalculate_joints(new_pos)
x, y, z = self.get_points()
self.ax.cla()
# For the body of the robot
self.ax.plot(x, y, z, lw=2)
self.ax.plot(x[1:], y[1:], z[1:], "bo")
# For the origin marker
self.ax.plot([0], [0], [0], "ro")
# For the coodnates in the tip of the robot
matrix = self.dvhb.final_position()
cord_x = matrix[:3, 0]
cord_y = matrix[:3, 1]
cord_z = matrix[:3, 2]
x, y, z = matrix[:3, 3]
self.ax.quiver(x, y, z, *cord_x * self.scale, color="red")
self.ax.quiver(x, y, z, *cord_y * self.scale, color="green")
self.ax.quiver(x, y, z, *cord_z * self.scale, color="blue")
# For the ax limits
self.ax.set_xlim(*self.limits)
self.ax.set_ylim(*self.limits)
self.ax.set_zlim(*self.limits)
# For the ax labels
self.ax.set_xlabel("X")
self.ax.set_ylabel("Y")
self.ax.set_zlabel("Z")
self.fig.canvas.draw_idle()
plt.pause(0.0001)
def reset(self, event):
for variable in self.variables:
variable.reset()
# False negative
else:
errors.append(fn)
# Correct prediction
else:
errors.append(0)
total_error = sum(errors)
results.append((total_error, threshold))
optimal_p = sorted(results)[0][1]
optimal_costs = sorted(results)[0][0]
print('Optimal probability threshold = {} \twith costs = {}'.format(round(optimal_p, 4), round(optimal_costs, 1)))
y, x = zip(*results)
l.set_ydata(y)
fig.canvas.draw_idle()
c_fp.on_changed(update)
c_fn.on_changed(update)
resetax = plt.axes([0.8, 0.025, 0.1, 0.04])
button = Button(resetax, 'Reset', color=axcolor, hovercolor='0.975')
def reset(event):
c_fp.reset()
c_fn.reset()
button.on_clicked(reset)
plt.show()
if state == 3:
if adjacent[1]:
prex, prey = random.choice(adjacent[1])
states[prex][prey] = 3
ages[prex][prey] = age + 1
ages[i][j] = age + 1
lock.release()
age = age + 1
sleep(0.01)
rabbit_slider = Slider(plt.axes([0.25, 0.15, 0.65, 0.03]), 'Rabbits starting population', 0.0, 1.0, valinit=0.2)
wolf_slider = Slider(plt.axes([0.25, 0.10, 0.65, 0.03]), 'Wolves starting population', 0.0, 1.0, valinit=0.05)
start_button = Button(plt.axes([0.10, 0.05, 0.1, 0.03]), 'Start')
setup(0.2, 0.05)
t1 = threading.Thread(target=show)
t2 = threading.Thread(target=draw)
t1.start()
t2.start()
def update(val):
rabbits_population = rabbit_slider.val
wolves_population = wolf_slider.val
ax.clear()
ax.axis('off')
lock.acquire()
setup(rabbits_population, wolves_population)
lock.release()
min_y = data.min()
max_y = data.max()
ax2.set_ylim(min_y, max_y)
from matplotlib.widgets import Button
fig, ax = plt.subplots()
plt.subplots_adjust(bottom=0.2)
axcenter = plt.axes([0.59, 0.05, 0.1, 0.075])
axpause = plt.axes([0.7, 0.05, 0.1, 0.075])
axclear = plt.axes([0.81, 0.05, 0.1, 0.075])
timer = fig.canvas.new_timer(interval=50)
timer.add_callback(update_graph, ax)
timer.start()
bnpause = Button(axpause, 'Pause')
bnpause.on_clicked(pause)
bnclear = Button(axclear, 'Clear')
bnclear.on_clicked(clear)
bncenter = Button(axcenter, 'Center')
bncenter.on_clicked(center)
if args.type == 'pid':
axis_hdls = plot_pid(args.topic)
if args.type == 'vector':
axis_hdls = plot_vec(args.topic)
plt.show()
#ax_prd.plot(cell_nums,new_hds[1,:],lw=1.5,color='0.5',alpha=0.5)
ax_prd.scatter([cell_nums[7]], [new_hds[1, 7]],
marker='s',
color='0.05',
alpha=0.5,
s=30)
prd.set_ydata(new_hds[1, :])
ax_prd.set_xlim(0, 100)
fig.canvas.draw_idle()
s_K.on_changed(update)
resetax = plt.axes([0.15, 0.4, 0.25, 0.04])
button = Button(resetax,
'Start Uncertainty Analysis',
color='0.75',
hovercolor='0.5')
def reset(event):
plt.sca(ax_prd)
plt.cla()
tracked = []
new_hds = np.loadtxt(HDS_PATH)
ax_prd.plot(cell_nums, initial_hds[1, :], lw=1.5, color='0.5', ls="--")
ax_prd.text(50, 7.5, "Forecast", fontsize=15, ha="center")
prd, = ax_prd.plot(cell_nums,
new_hds[1, :],
lw=1.5,
color='b',
marker='.',
sGain = Slider(axGain, 'Gain', -1, 6, valinit=gain)
def update(val): # this function updates the exposure and gain values
cap.set(cv2.CAP_PROP_EXPOSURE, sExp.val)
cap.set(cv2.CAP_PROP_GAIN, sGain.val)
fig.canvas.draw_idle()
sExp.on_changed(update) # call for update everytime the cursors change
sGain.on_changed(update)
### define buttons here
RECax = plt.axes([0.01, (0.15 + rat) / 2, 0.05,
0.05]) # define size and position
button = Button(RECax, 'REC', color='red', hovercolor='0.975') # define button
def REC(
event
): # when called, read "nbr_images" and save them as .tiff in save_directory
t0 = time.time()
last_t = 0
i = 0
while (i < nbr_images):
if set_FPS == True and last_t != 0: #This loop is used to set the FPS
while (time.time() - last_t) < 1. / FPS:
indent = True
last_t = time.time()
ret, frame = cap.read()
image = sitk.GetImageFromArray(frame)
def FindLidarPoints():
fig = plt.figure()
ax = fig.add_subplot(111, projection="3d")
ax.set_xlabel('X', fontsize=10)
ax.set_ylabel('Y', fontsize=10)
ax.set_zlabel('Z', fontsize=10)
plt.subplots_adjust(left=0.05, bottom=0.2)
marker_size, grid_length = (6, 8), 0.075
Rx, Ry, Rz = 0, 0, 0
global chess, corn, t, points, corners3D, axis_on, col
plotInit, plotSmoothed, plotFinal = False, False, True
points, Cloud3D = getPointCoud(ax=ax,
plotInit=plotInit,
plotSmoothed=plotSmoothed,
plotFinal=plotFinal)
axis_on, col = True, False
R, t = [0, 0, 0], [0, 0, 0]
t = np.mean(Cloud3D, axis=0) / grid_length
chess, corn, corners3D = chessBoard(ax=ax, org=t, scale=grid_length)
Rx_Slider = Slider(plt.axes([0.25, 0.15, 0.65, 0.03]),
'Rx',
-180,
180.0,
valinit=Rx)
Ry_Slider = Slider(plt.axes([0.25, 0.1, 0.65, 0.03]),
'Ry',
-180,
180.0,
valinit=Ry)
Rz_Slider = Slider(plt.axes([0.25, 0.05, 0.65, 0.03]),
'Rz',
-180,
180.0,
valinit=Rz)
def update(val):
Rx, Ry, Rz = Rx_Slider.val, Ry_Slider.val, Rz_Slider.val
#print('Rx:{}, Ry:{}, Rz:{}'.format(Rx, Ry, Rz))
global chess, corn, corners3D
chess.remove()
corn.remove()
R = [np.deg2rad(Rx), np.deg2rad(Ry), np.deg2rad(Rz)]
chess, corn, corners3D = chessBoard(ax=ax,
org=t,
R=R,
scale=grid_length)
#yellow = ax.scatter(corners3D[:, 0], corners3D[:, 1], corners3D[:, 2], c='yellow', marker='o', s=5)
fig.canvas.draw_idle()
Rx_Slider.on_changed(update)
Ry_Slider.on_changed(update)
Rz_Slider.on_changed(update)
check = CheckButtons(plt.axes([0.03, 0.3, 0.15, 0.08]), ('Axes', 'Black'),
(True, False))
def func_CheckButtons(label):
global col, axis_on
if label == 'Axes':
if axis_on:
ax.set_axis_off()
axis_on = False
else:
ax.set_axis_on()
axis_on = True
elif label == 'Black':
if col:
col = False
ax.set_facecolor((1, 1, 1))
else:
col = True
ax.set_facecolor((0, 0, 0))
fig.canvas.draw_idle()
check.on_clicked(func_CheckButtons)
class Index(object):
step = 1
def Tx(self, event):
t[0] += self.step
update(0)
def Tx_(self, event):
t[0] -= self.step
update(0)
def Ty(self, event):
t[1] += self.step
update(0)
def Ty_(self, event):
t[1] -= self.step
update(0)
def Tz(self, event):
t[2] += self.step
update(0)
def Tz_(self, event):
t[2] -= self.step
update(0)
callback = Index()
Tx = Button(plt.axes([0.05, 0.15, 0.04, 0.045]), '+Tx', color='white')
Tx.on_clicked(callback.Tx)
Tx_ = Button(plt.axes([0.12, 0.15, 0.04, 0.045]), '-Tx', color='white')
Tx_.on_clicked(callback.Tx_)
Ty = Button(plt.axes([0.05, 0.1, 0.04, 0.045]), '+Ty', color='white')
Ty.on_clicked(callback.Ty)
Ty_ = Button(plt.axes([0.12, 0.1, 0.04, 0.045]), '-Ty', color='white')
Ty_.on_clicked(callback.Ty_)
Tz = Button(plt.axes([0.05, 0.05, 0.04, 0.045]), '+Tz', color='white')
Tz.on_clicked(callback.Tz)
Tz_ = Button(plt.axes([0.12, 0.05, 0.04, 0.045]), '-Tz', color='white')
Tz_.on_clicked(callback.Tz_)
def getClosestPoints(arg):
global chess, corn, points, corners3D
print('Cloud3D:{}, corners3D:{}'.format(np.shape(Cloud3D),
np.shape(corners3D)))
dist_mat, k = distance_matrix(corners3D, Cloud3D), 1
neighbours = np.argsort(dist_mat, axis=1)[:, 0]
finaPoints = np.asarray(Cloud3D[neighbours, :]).squeeze()
points[1].remove()
chess.remove()
corn.remove()
ax.scatter(finaPoints[:, 0],
finaPoints[:, 1],
finaPoints[:, 2],
c='g',
marker='o',
s=7)
#ax.plot_wireframe(finaPoints[:, 0], finaPoints[:, 1], finaPoints[:, 2], markersize=2)
corn = ax.scatter(corners3D[:, 0],
corners3D[:, 1],
corners3D[:, 2],
c='tab:blue',
marker='x',
s=6)
fig.canvas.draw_idle()
savePoints = Button(plt.axes([0.03, 0.45, 0.15, 0.04], ),
'save points',
color='white')
savePoints.on_clicked(getClosestPoints)
def reset(args):
ax.cla()
global chess, corn, corners3D, points
points, Cloud3D = getPointCoud(ax=ax,
plotInit=plotInit,
plotSmoothed=plotSmoothed,
plotFinal=plotFinal)
t = np.mean(Cloud3D, axis=0) / grid_length
chess, corn, corners3D = chessBoard(ax=ax, org=t, scale=grid_length)
fig.canvas.draw_idle()
resetBtn = Button(plt.axes([0.03, 0.25, 0.15, 0.04], ),
'reset',
color='white')
resetBtn.on_clicked(reset)
def auto_fitChessboard(args):
# estimate 3D-R and 3D-t between chess and PointCloud
global chess, corn, points, corners3D
# Inital guess of the transformation
x0 = np.array([0, 0, 0, 0, 0, 0])
def f_min(x):
global corners3D
R = [np.deg2rad(x[0]), np.deg2rad(x[1]), np.deg2rad(x[2])]
t = [x[3], x[4], x[5]]
_, _, corners3D = chessBoard(ax=ax,
org=t,
R=R,
scale=grid_length,
plot=False)
dist_mat = distance_matrix(corners3D, Cloud3D)
err_func = dist_mat.sum(axis=1) # 63 x 1
#print('x:{}, err_func:{}, sum of errors = {}, dist_mat:{} corners3D:{},Cloud3D:{}'.format([], np.shape(err_func),round(np.sum(err_func),2),np.shape(dist_mat), np.shape(corners3D), np.shape(Cloud3D)))
return err_func
sol, status = leastsq(f_min, x0, ftol=1.49012e-06, xtol=1.49012e-06)
print('sol:{}, status:{}'.format(sol, status))
R = [np.deg2rad(sol[0]), np.deg2rad(sol[1]), np.deg2rad(sol[2])]
t = [sol[3], sol[4], sol[5]]
chess.remove()
corn.remove()
chess, corn, corners3D = chessBoard(ax=ax,
org=t,
R=R,
scale=grid_length)
fig.canvas.draw_idle()
#37073.64
fitChessboard = Button(plt.axes([0.03, 0.66, 0.15, 0.04], ),
'auto fit',
color='white')
fitChessboard.on_clicked(auto_fitChessboard)
radio = RadioButtons(plt.axes([0.03, 0.5, 0.15, 0.15], ),
('Final', 'Smoothed', 'Init'),
active=0)
def colorfunc(label):
plotInit, plotSmoothed, plotFinal = False, False, False
if label == 'Init':
plotInit = True
elif label == 'Smoothed':
plotSmoothed = True
else:
plotFinal = True
global points
[p.remove() for p in points]
points, Cloud3D = getPointCoud(ax=ax,
plotInit=plotInit,
plotSmoothed=plotSmoothed,
plotFinal=plotFinal)
fig.canvas.draw_idle()
radio.on_clicked(colorfunc)
plt.show()
ax_time = plt.axes([0.25, 0.1, 0.5, 0.03], axisbg='lightgoldenrodyellow')
slider_time = Slider(ax_time, 'timestep', 0.0, N_t, valinit=0, valfmt='%i')
ax._widgets = [slider_time] # Avoids garbage collection
def format_coord(theta, r):
value = intpol(theta, r, sigma[:, :].cgs)
return r'theta=%1.4f rad, R=%1.4f AU, Sigma=%1.4f g/cm2' % (theta, r,
value)
ax.format_coord = format_coord
# Create movie button
ax_button = plt.axes([0.25, 0.04, 0.17, 0.04])
button_movie = Button(ax_button, 'Create movie', hovercolor='0.975')
ax._widgets += [button_movie] # Avoids garbage collection
plt.show() # Show plot
##### FUNCTIONS ####################################################################################################################
def update(val):
i = int(np.floor(slider_time.val)) # Slider position
try:
sigma[:, :] = np.fromfile(data_dir + '/gasvrad' + repr(i) +
'.dat').reshape(N_R, N_theta) * np.sqrt(
aconst.G * aconst.M_sun / u.AU)
except:
print 'Could not load gasvrad' + repr(i) + '.dat'
class MisfitGUI:
def __init__(self,
event,
seismogram_generator,
project,
window_manager,
adjoint_source_directory=None):
self.event = event
self.event_latitude = event.origins[0].latitude
self.event_longitude = event.origins[0].longitude
self.project = project
self.adjoint_source_dir = adjoint_source_directory
self.seismogram_generator = seismogram_generator
self.window_manager = window_manager
self.__setup_plots()
self.__connect_signals()
self.next()
plt.tight_layout()
plt.show()
def __setup_plots(self):
# Some actual plots.
self.plot_axis_z = plt.subplot2grid((6, 20), (0, 0), colspan=18)
self.plot_axis_n = plt.subplot2grid((6, 20), (1, 0), colspan=18)
self.plot_axis_e = plt.subplot2grid((6, 20), (2, 0), colspan=18)
self.misfit_axis = plt.subplot2grid((6, 20), (3, 0),
colspan=11,
rowspan=3)
self.colorbar_axis = plt.subplot2grid((6, 20), (3, 12),
colspan=1,
rowspan=3)
#self.adjoint_source_axis = plt.subplot2grid((6, 8), (4, 0), colspan=4,
#rowspan=1)
self.map_axis = plt.subplot2grid((6, 20), (3, 13),
colspan=8,
rowspan=3)
# Plot the map and the beachball.
bounds = self.project.domain["bounds"]
self.map_obj = visualization.plot_domain(
bounds["minimum_latitude"],
bounds["maximum_latitude"],
bounds["minimum_longitude"],
bounds["maximum_longitude"],
bounds["boundary_width_in_degree"],
rotation_axis=self.project.domain["rotation_axis"],
rotation_angle_in_degree=self.project.domain["rotation_angle"],
plot_simulation_domain=False,
show_plot=False,
zoom=True)
visualization.plot_events([self.event], map_object=self.map_obj)
# All kinds of buttons [left, bottom, width, height]
self.axnext = plt.axes([0.90, 0.95, 0.08, 0.03])
self.axprev = plt.axes([0.90, 0.90, 0.08, 0.03])
self.axreset = plt.axes([0.90, 0.85, 0.08, 0.03])
self.bnext = Button(self.axnext, 'Next')
self.bprev = Button(self.axprev, 'Prev')
self.breset = Button(self.axreset, 'Reset Station')
def __connect_signals(self):
self.bnext.on_clicked(self.next)
self.bprev.on_clicked(self.prev)
self.breset.on_clicked(self.reset)
self.plot_axis_z.figure.canvas.mpl_connect('button_press_event',
self._onButtonPress)
self.plot_axis_n.figure.canvas.mpl_connect('button_press_event',
self._onButtonPress)
self.plot_axis_e.figure.canvas.mpl_connect('button_press_event',
self._onButtonPress)
self.plot_axis_z.figure.canvas.mpl_connect('resize_event',
self._on_resize)
def _on_resize(self, *args):
plt.tight_layout()
def next(self, *args):
while True:
try:
data = self.seismogram_generator.next()
except StopIteration:
return
if not data:
continue
break
self.data = data
self.update()
def prev(self, *args):
while True:
try:
data = self.seismogram_generator.prev()
except StopIteration:
return
if not data:
continue
break
self.data = data
self.update()
def update(self):
self.selected_windows = {"Z": [], "N": [], "E": []}
self.plot()
for trace in self.data["data"]:
windows = self.window_manager.get_windows(trace.id)
if not windows or "windows" not in windows or \
not windows["windows"]:
continue
for window in windows["windows"]:
self.plot_window(component=windows["channel_id"][-1],
starttime=window["starttime"],
endtime=window["endtime"])
plt.draw()
def plot_window(self, component, starttime, endtime):
if component == "Z":
axis = self.plot_axis_z
elif component == "N":
axis = self.plot_axis_n
elif component == "E":
axis = self.plot_axis_e
else:
raise NotImplementedError
trace = self.data["synthetics"][0]
ymin, ymax = axis.get_ylim()
rect = Rectangle((starttime - trace.stats.starttime, ymin),
endtime - starttime,
ymax - ymin,
color="0.6",
alpha=0.5,
edgecolor="0.5")
axis.add_patch(rect)
def reset(self, event):
for trace in self.data["data"]:
self.window_manager.delete_windows(trace.id)
self.update()
def plot(self):
self.misfit_axis.cla()
self.misfit_axis.set_xticks([])
self.misfit_axis.set_yticks([])
self.colorbar_axis.cla()
self.colorbar_axis.set_xticks([])
self.colorbar_axis.set_yticks([])
try:
self.misfit_axis.twin_axis.cla()
self.misfit_axis.twin_axis.set_xticks([])
self.misfit_axis.twin_axis.set_yticks([])
del self.misfit_axis.twin_axis
except:
pass
try:
del self.rect
except:
pass
self.rect = None
# Clear all three plot axes.
self.plot_axis_z.cla()
self.plot_axis_n.cla()
self.plot_axis_e.cla()
s_stats = self.data["synthetics"][0].stats
time_axis = np.linspace(0, s_stats.npts * s_stats.delta, s_stats.npts)
def plot_trace(axis, component):
real_trace = self.data["data"].select(component=component)
synth_trace = self.data["synthetics"].select(component=component)
if real_trace:
axis.plot(time_axis, real_trace[0].data, color="black")
if synth_trace:
axis.plot(time_axis, synth_trace[0].data, color="red")
if real_trace:
text = real_trace[0].id
axis.text(x=0.01,
y=0.95,
s=text,
transform=axis.transAxes,
bbox=dict(facecolor='white', alpha=0.5),
verticalalignment="top")
else:
text = "No data, component %s" % component
axis.text(x=0.01,
y=0.95,
s=text,
transform=axis.transAxes,
bbox=dict(facecolor='red', alpha=0.5),
verticalalignment="top")
axis.set_xlim(time_axis[0], time_axis[-1])
axis.set_ylabel("m/s")
axis.grid()
plot_trace(self.plot_axis_z, "Z")
plot_trace(self.plot_axis_n, "N")
plot_trace(self.plot_axis_e, "E")
self.plot_axis_e.set_xlabel("Seconds since Event")
try:
self.greatcircle[0].remove()
self.greatcircle = None
except:
pass
try:
self.station_icon.remove()
self.station_icon = None
except:
pass
self.greatcircle = self.map_obj.drawgreatcircle(
self.data["coordinates"]["longitude"],
self.data["coordinates"]["latitude"],
self.event_longitude,
self.event_latitude,
linewidth=2,
color='green',
ax=self.map_axis)
lng, lats = self.map_obj([self.data["coordinates"]["longitude"]],
[self.data["coordinates"]["latitude"]])
self.station_icon = self.map_axis.scatter(lng,
lats,
color="blue",
edgecolor="black",
zorder=10000,
marker="^",
s=40)
plt.draw()
def _onButtonPress(self, event):
if event.button != 1: # or event.inaxes != self.plot_axis_z:
return
# Store the axis.
if event.name == "button_press_event":
if event.inaxes == self.plot_axis_z:
data = self.data["data"].select(component="Z")
if not data:
return
self.rect = WindowSelectionRectangle(event, self.plot_axis_z,
self._onWindowSelected)
if event.inaxes == self.plot_axis_n:
data = self.data["data"].select(component="N")
if not data:
return
self.rect = WindowSelectionRectangle(event, self.plot_axis_n,
self._onWindowSelected)
if event.inaxes == self.plot_axis_e:
data = self.data["data"].select(component="E")
if not data:
return
self.rect = WindowSelectionRectangle(event, self.plot_axis_e,
self._onWindowSelected)
def _onWindowSelected(self, window_start, window_width, axis):
"""
Function called upon window selection.
"""
if window_width <= 0:
return
if axis is self.plot_axis_z:
data = self.data["data"].select(component="Z")[0]
synth = self.data["synthetics"].select(component="Z")[0]
elif axis is self.plot_axis_n:
data = self.data["data"].select(component="N")[0]
synth = self.data["synthetics"].select(component="N")[0]
elif axis is self.plot_axis_e:
data = self.data["data"].select(component="E")[0]
synth = self.data["synthetics"].select(component="E")[0]
else:
return
if not data:
return
trace = data
time_range = trace.stats.endtime - trace.stats.starttime
plot_range = axis.get_xlim()[1] - axis.get_xlim()[0]
starttime = trace.stats.starttime + (window_start / plot_range) * \
time_range
endtime = starttime + window_width / plot_range * time_range
self.window_manager.write_window(
trace.id, starttime, endtime, 1.0, "cosine",
"TimeFrequencyPhaseMisfitFichtner2008")
# Window the data.
data_trimmed = data.copy()
data_trimmed.trim(starttime, endtime)
data_trimmed.taper()
data_trimmed.trim(synth.stats.starttime,
synth.stats.endtime,
pad=True,
fill_value=0.0)
synth_trimmed = synth.copy()
synth_trimmed.trim(starttime, endtime)
synth_trimmed.taper()
synth_trimmed.trim(synth.stats.starttime,
synth.stats.endtime,
pad=True,
fill_value=0.0)
t = np.linspace(0, synth.stats.npts * synth.stats.delta,
synth.stats.npts)
self.misfit_axis.cla()
self.colorbar_axis.cla()
try:
self.misfit_axis.twin_axis.cla()
self.misfit_axis.twin_axis.set_xticks([])
self.misfit_axis.twin_axis.set_yticks([])
except:
pass
t = np.require(t, dtype="float64", requirements="C")
data_d = np.require(data_trimmed.data,
dtype="float64",
requirements="C")
synth_d = np.require(synth_trimmed.data,
dtype="float64",
requirements="C")
adsrc = adsrc_tf_phase_misfit(t,
data_d,
synth_d,
5.0,
50.0,
0.00000001,
axis=self.misfit_axis,
colorbar_axis=self.colorbar_axis)
plt.tight_layout()
plt.draw()
# Also save the adjoint source function.
#self.write_adj_src(adsrc["adjoint_source"], synth.id, starttime,
#endtime)
def _write_adj_src(self, adj_src, channel_id, starttime, endtime):
filename = "adjoint_source_%s_%s_%s.npy" % (channel_id, str(starttime),
str(endtime))
filename = os.path.join(self.adjoint_source_dir, filename)
class RotatableAxes:
def __init__(self, fig: mpl.figure.Figure, axes: mpl.axes.Axes,
rect_angle: list, rect_reset: list):
self.fig = fig
# Suppose that there exists an image in the axes
self.axes = axes
self.renderer = self.axes.figure.canvas.get_renderer()
self.axes_img_instance = self.axes.get_images()[0]
self.original_axes_img = self.axes_img_instance.make_image(
self.renderer, unsampled=True)[0]
self.axes_for_angle_slider = self.fig.add_axes(rect_angle)
self.axes_for_reset_button = self.fig.add_axes(rect_reset)
self.angle_slider = Slider(self.axes_for_angle_slider,
'Angle(Degree)',
0.0,
359.0,
valinit=0.0,
valstep=0.1)
self.angle_slider.on_changed(self.update_img)
self.reset_button = Button(self.axes_for_reset_button, 'Reset')
self.reset_button.on_clicked(self.reset)
def connect(self) -> None:
# connect to all the events we need
self.fig.canvas.mpl_connect('button_press_event', self.onclick)
def disconnect(self) -> None:
# disconnect all the stored connection ids
self.fig.canvas.mpl_disconnect(self.onclick)
def update_la_img(self):
self.axes_img_instance = self.axes.get_images()[0]
self.original_axes_img = self.axes_img_instance.make_image(
self.renderer, unsampled=True)[0]
self.angle_slider.reset()
def onclick(self, event: mpl.backend_bases.Event) -> None:
if self.axes == event.inaxes:
cur_img = self.axes_img_instance.make_image(self.renderer,
unsampled=True)[0]
if event.button == mpl.backend_bases.MouseButton.LEFT and event.inaxes is not None:
rotated_image = ndimage.rotate(cur_img, 90.0, reshape=False)
self.axes_img_instance.set_data(rotated_image)
elif event.button == mpl.backend_bases.MouseButton.RIGHT and event.inaxes is not None:
flipped_img = cur_img[:, ::-1]
self.axes_img_instance.set_data(flipped_img)
self.axes.figure.canvas.draw()
self.axes.figure.canvas.flush_events()
def update_img(self, new_angle: float) -> None:
axes_images_list = self.axes.get_images()
rotated_img = ndimage.rotate(self.original_axes_img,
new_angle,
reshape=False)
axes_images_list[0].set_data(rotated_img)
self.axes.figure.canvas.update()
self.axes.figure.canvas.flush_events()
def reset(self, event: mpl.backend_bases.Event):
self.angle_slider.reset()
plotMin = np.min(adatok.iloc[:, i])
plotMax = np.max(adatok.iloc[:, i])
uniques = pd.unique(adatok.iloc[:, i])
if plotMax == plotMin:
output.write(str(i) + "\t" + adatok.columns[i] + "\t" + str(plotMin))
output.write('\n')
else:
output.write(
str(i) + "\t" + adatok.columns[i] + "\t\t" + str(plotMin) + "\t" +
str(plotMax) + "\t" + str(uniques))
output.write('\n')
# Close the output file
output.close()
#Endregion
# Create the scatter plot and add labels
plt.scatter(adatok.loc[:, 'Relativzeit'], adatok.iloc[:, index])
plt.xlabel(adatok.columns[index])
plt.ylabel('Relativzeit')
# Create the 'next' nextButton
subax = plt.axes([0.8, 0.025, 0.1, 0.04])
nextButton = Button(subax, 'Next', color='red', hovercolor='0.975')
# Enter the update method when clicked
nextButton.on_clicked(update)
# Show plot
plt.show()
class gyro_ui(object):
'''Classe pour interface graphique de visualisation des données de remuage
'''
format_date = "%d %b %Y %H:%M:%S"
date_formatter = mdates.DateFormatter("%d %b") # For axis
#date_formatter = ticker.FormatStrFormatter('%d %b') # for axis
ratio_gyro = 131.0 #Gyro : 1/131 degrés/secondes
ratio_acceleration = 16384.0 #Acceleration : 1/16384 g (g=9.81 N.s-2)
seuil_gyro_mvt = 200.0 / 131.0 #Seuil au delà duquel une vitesse angulaire est considéré comme un mvt
accel_detect = 1000.0 / 131.0
trig_detect = datetime.timedelta(minutes=10)
def __init__(self, bdd, images_folder=None):
'''Initialisation
- bdd : base de données gyro_db
'''
self.bdd = bdd
self.dates = []
self.acc_Xs = []
self.acc_Ys = []
self.acc_Zs = []
self.gyro_Xs = []
self.gyro_Ys = []
self.gyro_Zs = []
self.angle_Xs = []
self.angle_Ys = []
#self.angle_Zs = []
self.angle_X = 0
self.angle_Y = 0
self.angle_Z = 0
self.lecture_donnees()
self.scan_images(images_folder)
self.fig = plt.figure()
self.fig.canvas.set_window_title('Fileurope - CMP - FGYRO')
self.init_graphes()
self.lecture = False
self.th_lecture_image = None
self.vitesse_lecture = 1
def run(self):
'''Run the ui
'''
plt.show()
def lecture_donnees(self):
'''lecture de la base de données et correction des données
'''
logging.info('Lecture des donnees')
for data in self.bdd.mesures():
try:
self.dates.append(gyro_db.utc_to_local(data['date']))
self.acc_Xs.append(data['acc_X'] / gyro_ui.ratio_acceleration)
self.acc_Ys.append(data['acc_Y'] / gyro_ui.ratio_acceleration)
self.acc_Zs.append(data['acc_Z'] / gyro_ui.ratio_acceleration)
self.gyro_Xs.append(data['gyro_X'] / gyro_ui.ratio_gyro)
self.gyro_Ys.append(data['gyro_Y'] / gyro_ui.ratio_gyro)
self.gyro_Zs.append(data['gyro_Z'] / gyro_ui.ratio_gyro)
###Calculs angulaires
# Accélération totale : c'est la pesanteur
acc = sqrt(data['acc_X']**2 + data['acc_Y']**2 +
data['acc_Z']**2)
#Rotation autour de l'axe de la bouteille (c'est le roulis)
self.angle_Xs.append(asin(data['acc_Z'] / acc) * 180 / pi)
#Inclinaison de la bouteille vers le bas (c'est le tanguage)
self.angle_Ys.append(acos(data['acc_X'] / acc) * 180 / pi)
#self.angle_Zs.append(0)
except Exception as e:
print(e)
logging.info("%s mesures trouvees." % (len(self.dates)))
#Moyenne des vitesses angulaires pour auto-calibration
try:
corr_gyro_Xs = sum(self.gyro_Xs) / len(self.gyro_Xs)
except:
corr_gyro_Xs = 0
try:
corr_gyro_Ys = sum(self.gyro_Ys) / len(self.gyro_Ys)
except:
corr_gyro_Ys = 0
try:
corr_gyro_Zs = sum(self.gyro_Zs) / len(self.gyro_Zs)
except:
corr_gyro_Zs = 0
logging.info(
"Correction des moyennes des vitesses angulaires : x:%s, y:%s, z:%s"
% (corr_gyro_Xs, corr_gyro_Ys, corr_gyro_Zs))
nb_supp_mesures_angle = 0
for i in range(len(self.dates)):
#Correction des vitesses angulaires
self.gyro_Xs[i] -= corr_gyro_Xs
self.gyro_Ys[i] -= corr_gyro_Ys
self.gyro_Zs[i] -= corr_gyro_Zs
# "Suppression" des mesures d'angle quand il y a une vitesses angulaire
if i > 0 and (abs(self.gyro_Xs[i]) > gyro_ui.seuil_gyro_mvt
or abs(self.gyro_Ys[i]) > gyro_ui.seuil_gyro_mvt
or abs(self.gyro_Zs[i]) > gyro_ui.seuil_gyro_mvt):
self.angle_Xs[i] = self.angle_Xs[i - 1]
self.angle_Ys[i] = self.angle_Ys[i - 1]
#self.angle_Zs[i] = self.angle_Zs[i-1]
nb_supp_mesures_angle += 1
logging.info("%s mesures d'angle supprimees" % nb_supp_mesures_angle)
# Recherche des phases
self.phases = [] # Un enregistrement par phase
self.etapes = [] # A chaque point, la phase correspondante
i = 0
while i < len(self.dates):
acceleration = sqrt(self.gyro_Xs[i]**2 + self.gyro_Ys[i]**2 +
self.gyro_Zs[i]**2)
if acceleration > gyro_ui.accel_detect:
acceleration_max = acceleration
date_debut_mouvement = self.dates[i]
date_fin_mouvement = date_debut_mouvement
date_fin_trig = date_debut_mouvement + gyro_ui.trig_detect
while self.dates[i] < date_fin_trig:
acceleration = sqrt(self.gyro_Xs[i]**2 +
self.gyro_Ys[i]**2 +
self.gyro_Zs[i]**2)
acceleration_max = max(acceleration_max, acceleration)
if acceleration > gyro_ui.accel_detect:
date_fin_phase = self.dates[i]
i += 1
self.phases.append({
'debut_mvt': date_debut_mouvement,
'fin_mvt': date_fin_mouvement,
'acceleration_max': acceleration_max
})
logging.info("Phase detectee : no %s : debut = %s, fin = %s" %
(len(self.phases), date_debut_mouvement,
date_fin_mouvement))
else:
i += 1
self.etapes.append(len(self.phases))
self.export_xls()
def export_xls(self, filename=None):
'''Exporte les données vers un fichier excel
'''
#TODO mettre menu dans interface
if filename is None:
filename = "gyro.xlsx"
import xlsxwriter
workbook = xlsxwriter.Workbook(filename)
worksheet = workbook.add_worksheet("phases")
format_date = workbook.add_format({'num_format': 'dd/mm/yy hh:mm:ss'})
col = 0
for key in self.phases[0]:
row = 0
worksheet.write(row, col, key)
row += 1
for item in self.phases:
if "date" in key:
worksheet.write_datetime(row, col,
self.phases[row - 1][key],
format_date)
else:
worksheet.write(row, col, self.phases[row - 1][key])
row += 1
col += 1
workbook.close()
def init_graphes(self):
''' Inititialise les graphiques
'''
### IMAGE REMUTRACE
image_capteur = plt.imread('capteur.png')
self.image_capteur_plot = self.fig.add_subplot(233)
self.image_capteur_plot.set_axis_off()
self.image_capteur_plot.imshow(image_capteur)
### 3D (non implementé)
#self.3D = figure3D().add_subplot(233)
#self.3D = self.fig.add_subplot(233)
### GRAPHE XYZ
#self.xyz=self.fig.add_subplot(231)
#self.xyz.set_xlabel('Temps')
#self.xyz.set_ylabel('x,y,z')
#self.xyz.plot(self.dates, self.acc_Xs, label='acc_X')
#self.xyz.plot(self.dates, self.acc_Ys, label='acc_Y')
#self.xyz.plot(self.dates, self.acc_Zs, label='acc_Z')
#self.xyz.legend()
#self.xyz_line = False
### GRAPHE GxGyGz
self.GxGyGz = self.fig.add_subplot(231) #, sharex = self.xyz)
self.GxGyGz.set_title(u'Vitesse angulaire')
self.GxGyGz.set_xlabel(u'Temps')
self.GxGyGz.set_ylabel(u'Deg/s')
self.GxGyGz.plot(self.dates, self.gyro_Xs, label='gyro_X')
self.GxGyGz.plot(self.dates, self.gyro_Ys, label='gyro_Y')
self.GxGyGz.plot(self.dates, self.gyro_Zs, label='gyro_Z')
#self.GxGyGz.set_xticks(self.dates)
self.GxGyGz.xaxis.set_major_locator(mdates.DayLocator())
#self.GxGyGz.xaxis.set_minor_locator(mdates.HourLocator())
self.GxGyGz.xaxis.set_major_formatter(
ticker.FormatStrFormatter("%d %b"))
self.GxGyGz.legend()
self.GxGyGz_line = False
#self.fig.autofmt_xdate()
#plt.xticks(rotation=90)
logging.debug("GRAPHE GxGyGz ok")
### GRAPHE INCLINAISON
self.Inclinaison = self.fig.add_subplot(234, sharex=self.GxGyGz)
self.Inclinaison.plot(self.dates, self.angle_Ys, label='Inclinaison')
#self.Inclinaison.legend()
self.Inclinaison.set_title(u'Inclinaison')
self.Inclinaison.set_xlabel(u'Temps')
self.Inclinaison.set_ylabel(u'Deg')
self.Inclinaison_line = False
### GRAPHE ROTATION
self.Rotation = self.fig.add_subplot(235, sharex=self.GxGyGz)
self.Rotation.plot(self.dates, self.angle_Xs, label='Rotation')
#self.Rotation.legend()
self.Rotation.set_title(u'Rotation')
self.Rotation.set_xlabel(u'Temps')
self.Rotation.set_ylabel(u'Deg')
self.Rotation_line = False
### PHOTO
self.image_plot = self.fig.add_subplot(232)
self.image_plot.set_axis_off()
self.image_plot.set_title(u'Caméra')
### BOUTONS
self.fig.canvas.mpl_connect('button_press_event', self.on_click)
self.bt_lecture = Button(plt.axes([0.65, 0.3, 0.05, 0.03]), 'lecture')
self.bt_lecture.on_clicked(self.on_bt_lecture_click)
self.bt_lecture10 = Button(plt.axes([0.65, 0.2, 0.05, 0.03]),
'lecturex10')
self.bt_lecture10.on_clicked(self.on_bt_lecture_click10)
#self.tb_vitesse = TextBox(plt.axes([0.9, 0.8, 0.05, 0.03]),'Vitesse', initial = '1')
#self.tb_vitesse.on_submit(self.on_tb_vitesse_submit)
### Détail étape
axe_etape = self.fig.add_axes([0.65, 0.4, 0.03, 0.03])
axe_etape.set_axis_off()
self.etape = plt.text(0, 3, u'Etape n° ?')
self.etape_debut_mvt = plt.text(0, 2, u'Début : --')
self.etape_fin_mvt = plt.text(0, 1, u'Fin : --')
self.etape_acceleration_max = plt.text(0, 0, u'Accélération max : --')
### LEGENDE : FILEUROPE-CMP
legende = self.fig.add_axes([0.75, 0.07, 0.2, 0.3])
legende.set_axis_off()
#rect = Rectangle((0,0),1,1,fill=True, color= 'blue')
#legende.add_patch(rect)
logo_img = plt.imread('logo.png')
legende.imshow(logo_img)
text_legende = plt.text(0.05, 0.05, u"Remutrace - Brevet déposé.")
self.fig.canvas.mpl_connect('key_press_event', self.press)
def scan_images(self, rep):
'''Scan le repertoire des images
et peuble le dict self.images : {date:nom_fichier _image}
'''
#logging.info("Scan du repertoir images : %s ..."%(rep))
self.images = {}
self.images_dates = {}
if rep:
for fichier in os.listdir(rep):
if os.path.isfile(os.path.join(rep, fichier)):
try:
self.images[datetime.datetime.strptime(
fichier[3:-4],
"%Y-%m-%d %H-%M-%S.%f")] = os.path.join(
rep, fichier)
except:
pass
self.images_dates = self.images.keys()
self.images_dates.sort()
logging.info("%s images trouvees." % (len(self.images)))
self.xdate_index = 0
def show_image(self):
'''Update the image and the cursors and the etape
'''
if len(self.images) > 0:
date = self.images_dates[self.xdate_index]
image_path = self.images[date]
#logging.debug("Image %s"%(image_path))
image_file = cbook.get_sample_data(image_path)
image = plt.imread(image_file)
self.image_plot.clear()
self.image_plot.set_axis_off()
self.image_plot.imshow(image)
self.image_plot.set_title(u'Caméra')
self.image_plot.text(
0, 800, "Date : " +
self.dates[self.xdate_index].strftime(gyro_ui.format_date))
if self.GxGyGz_line:
self.GxGyGz_line.remove()
self.GxGyGz_line = self.GxGyGz.vlines(
date,
self.GxGyGz.get_ylim()[0] * 0.75,
self.GxGyGz.get_ylim()[1] * 0.75)
if self.Inclinaison_line:
self.Inclinaison_line.remove()
self.Inclinaison_line = self.Inclinaison.vlines(
date,
self.Inclinaison.get_ylim()[0] * 0.75,
self.Inclinaison.get_ylim()[1] * 0.75)
if self.Rotation_line:
self.Rotation_line.remove()
self.Rotation_line = self.Rotation.vlines(
date,
self.Rotation.get_ylim()[0] * 0.75,
self.Rotation.get_ylim()[1] * 0.75)
etape = self.etapes[self.xdate_index]
self.etape.set_text(u"Etape n° %s" % etape)
self.etape_debut_mvt.set_text(
u"Début : " +
self.phases[etape]["debut_mvt"].strftime(gyro_ui.format_date))
self.etape_fin_mvt.set_text(
u"Fin : " +
self.phases[etape]["fin_mvt"].strftime(gyro_ui.format_date))
self.etape_acceleration_max.set_text(
u"Accél. max : %s" % self.phases[etape]["acceleration_max"])
# TODO et plus d'info
self.fig.canvas.draw() # C'est ici que c'est long (0.3 s)
def on_click(self, event):
'''callback function when click on graphe
'''
#logging.debug('button=%d, inaxes = %s, x=%d, y=%d, xdata=%f, ydata=%f' %(event.button, event.inaxes, event.x, event.y, event.xdata, event.ydata))
if len(self.images) > 0:
if event.inaxes in [self.GxGyGz, self.Inclinaison, self.Rotation]:
date = mdates.num2date(event.xdata).replace(tzinfo=None)
#try:
self.xdate_index = self.images_dates.index(
min(self.images_dates, key=lambda d: abs(d - date)))
logging.debug(self.xdate_index)
self.show_image()
# except:
# pass
# Attention : bricolage+++
def on_bt_lecture_click10(self, event):
self.vitesse_lecture = 10
self.on_bt_lecture_click_all(event)
def on_bt_lecture_click(self, event):
'''
'''
self.vitesse_lecture = 1
self.on_bt_lecture_click_all(event)
def on_bt_lecture_click_all(self, event):
if self.lecture:
self.lecture = False
self.bt_lecture.Label = matplotlib_text.Text(text='lecture')
logging.info("Pause")
self.th_lecture_image.stop()
self.fig.canvas.draw()
else:
self.lecture = True
self.bt_lecture.Label = matplotlib_text.Text(text='pause')
logging.info("Lecture")
self.fig.canvas.draw()
self.th_lecture_image = f_thread(self.lecture_images)
self.th_lecture_image.start()
# Fin du bricolage
def lecture_images(self, sens=1):
''' affichage de l'image suivante
Utilisation : en threading : f_thread(self.lecture_images)
'''
self.xdate_index += sens * self.vitesse_lecture
if self.xdate_index > len(self.images_dates):
self.xdate_index = 0
self.lecture = False
self.show_image()
time.sleep(0.05) #Pour donner la main à l'affichage
def press(self, event):
''' Quand une fleche droite ou gauche est appuyée : image suivante ou précédente
'''
print("Key pressed : " + event.key)
sys.stdout.flush()
if event.key == "down":
self.lecture_images(1)
elif event.key == "up":
self.lecture_images(-1)
elif event.key == "pagedown":
self.lecture_images(10)
elif event.key == "pageup":
self.lecture_images(-10)
def on_tb_vitesse_submit(self, text):
''' quand changement vitesse de lecture
'''
try:
self.vitesse_lecture = int(text)
except:
pass
def __init__(self, load_path, save_path=None):
# init manipulator
self.savepath = save_path
self.filepaths = self._load_paths(
load_path) # load all scans filepaths in path
self.fileindex = 0
self.manipulator = scan_manipulator()
self.manipulator.load_target_scan(self.filepaths[self.fileindex])
self.hist_state = True
self.inject_coords = []
self.remove_coords = []
# init plot
self.eq = histEq(self.manipulator.scan)
self.slices, self.cols, self.rows = self.manipulator.scan.shape
self.ind = self.slices // 2
self.pause_start = 0
self.fig, self.ax = plt.subplots(1, 1, dpi=100)
self.fig.suptitle(
'CT-GAN: Malicious Tampering of 3D Medical Imagery using Deep Learning\nTool by Yisroel Mirsky',
fontsize=14,
fontweight='bold')
plt.subplots_adjust(bottom=0.2)
self.ani_direction = 'down'
self.animation = None
self.animation_state = True
self.plot()
self.ax.set_title(os.path.split(
self.filepaths[self.fileindex])[-1]) #filename
# register click/scroll events
self.action_state = 'inject' #default state
self.fig.canvas.mpl_connect('button_press_event', self.onclick)
self.fig.canvas.mpl_connect('scroll_event', self.onscroll)
# register buttons
axanim = plt.axes([0.1, 0.21, 0.2, 0.075])
self.banim = Button(axanim, 'Toggle Animation')
self.banim.on_clicked(self.toggle_animation)
axinj = plt.axes([0.1, 0.05, 0.1, 0.075])
axrem = plt.axes([0.21, 0.05, 0.1, 0.075])
self.binj = Button(axinj, 'Inject')
self.binj.on_clicked(self.inj_on)
self.brem = Button(axrem, 'Remove')
self.brem.on_clicked(self.rem_on)
axhist = plt.axes([0.35, 0.05, 0.2, 0.075])
self.bhist = Button(axhist, 'Toggle HistEQ')
self.bhist.on_clicked(self.hist)
axprev = plt.axes([0.59, 0.05, 0.1, 0.075])
axsave = plt.axes([0.7, 0.05, 0.1, 0.075])
axnext = plt.axes([0.81, 0.05, 0.1, 0.075])
self.bnext = Button(axnext, 'Next')
self.bnext.on_clicked(self.next)
self.bprev = Button(axprev, 'Previous')
self.bprev.on_clicked(self.prev)
self.bsave = Button(axsave, 'Save')
self.bsave.on_clicked(self.save)
self.maximize_window()
self.update()
plt.show()
T = Tvals[Tind]
point.set_data(Tvals[Tind], states[Tind])
val_slider.set_val(states[Tind])
ax.set_ylim([
np.min(W.States[0:N_slider.val]) - 1,
np.max(W.States[0:N_slider.val]) + 1
])
fig.canvas.draw_idle()
N_slider.on_changed(sliders_on_changed)
t_slider.on_changed(sliders_on_changed)
# Add a button for resetting the parameters
reset_button_ax = fig.add_axes([0.8, 0.02, 0.1, 0.01])
reset_button = Button(reset_button_ax,
'Reset',
color=axis_color,
hovercolor='0.975')
def reset_button_on_clicked(mouse_event):
N_slider.reset()
reset_button.on_clicked(reset_button_on_clicked)
plt.show()
class GUI(object):
# If load_path is to a *.dcm or *.mhd file then only this scan is loaded
# If load_path is to a directory, then all scans are loaded. It is assumed that each scan is in its own subdirectory.
# save_path is the directory to save the tampered scans (as dicom)
def __init__(self, load_path, save_path=None):
# init manipulator
self.savepath = save_path
self.filepaths = self._load_paths(
load_path) # load all scans filepaths in path
self.fileindex = 0
self.manipulator = scan_manipulator()
self.manipulator.load_target_scan(self.filepaths[self.fileindex])
self.hist_state = True
self.inject_coords = []
self.remove_coords = []
# init plot
self.eq = histEq(self.manipulator.scan)
self.slices, self.cols, self.rows = self.manipulator.scan.shape
self.ind = self.slices // 2
self.pause_start = 0
self.fig, self.ax = plt.subplots(1, 1, dpi=100)
self.fig.suptitle(
'CT-GAN: Malicious Tampering of 3D Medical Imagery using Deep Learning\nTool by Yisroel Mirsky',
fontsize=14,
fontweight='bold')
plt.subplots_adjust(bottom=0.2)
self.ani_direction = 'down'
self.animation = None
self.animation_state = True
self.plot()
self.ax.set_title(os.path.split(
self.filepaths[self.fileindex])[-1]) #filename
# register click/scroll events
self.action_state = 'inject' #default state
self.fig.canvas.mpl_connect('button_press_event', self.onclick)
self.fig.canvas.mpl_connect('scroll_event', self.onscroll)
# register buttons
axanim = plt.axes([0.1, 0.21, 0.2, 0.075])
self.banim = Button(axanim, 'Toggle Animation')
self.banim.on_clicked(self.toggle_animation)
axinj = plt.axes([0.1, 0.05, 0.1, 0.075])
axrem = plt.axes([0.21, 0.05, 0.1, 0.075])
self.binj = Button(axinj, 'Inject')
self.binj.on_clicked(self.inj_on)
self.brem = Button(axrem, 'Remove')
self.brem.on_clicked(self.rem_on)
axhist = plt.axes([0.35, 0.05, 0.2, 0.075])
self.bhist = Button(axhist, 'Toggle HistEQ')
self.bhist.on_clicked(self.hist)
axprev = plt.axes([0.59, 0.05, 0.1, 0.075])
axsave = plt.axes([0.7, 0.05, 0.1, 0.075])
axnext = plt.axes([0.81, 0.05, 0.1, 0.075])
self.bnext = Button(axnext, 'Next')
self.bnext.on_clicked(self.next)
self.bprev = Button(axprev, 'Previous')
self.bprev.on_clicked(self.prev)
self.bsave = Button(axsave, 'Save')
self.bsave.on_clicked(self.save)
self.maximize_window()
self.update()
plt.show()
def _load_paths(self, path):
filepaths = []
# load single scan?
if (path.split('.')[-1] == "dcm") or (path.split('.')[-1] == "mhd"):
filepaths.append(path)
return filepaths
# try load directory of scans...
files = os.listdir(path)
for file in files:
if os.path.isdir(file):
subdir = os.path.join(path, file)
subdir_files = os.listdir(subdir)
if subdir_files[0].split(
'.')[-1] == "dcm": #folder contains dicom
filepaths.append(os.path.join(path, subdir))
elif (subdir_files[0].split('.')[-1]
== "mhd") or (subdir_files[0].split('.')[-1]
== "raw"): # MHD
filepaths.append(
os.path.join(path, subdir, subdir_files[0]))
elif file.split('.')[-1] == "mhd":
filepaths.append(os.path.join(path, file))
return filepaths
def onclick(self, event):
# print('%s click: button=%d, x=%d, y=%d, xdata=%f, ydata=%f' %
# ('double' if event.dblclick else 'single', event.button,
# event.x, event.y, event.xdata, event.ydata))
if event.xdata is not None:
coord = np.array([self.ind, event.ydata, event.xdata], dtype=int)
if coord[1] > 0 and coord[2] > 0:
self.pause_start = np.Inf #pause while working
if self.action_state == 'inject':
self.ax.set_title("Injecting...")
self.im.axes.figure.canvas.draw()
self.manipulator.tamper(coord, action='inject', isVox=True)
self.inject_coords.append(coord)
else:
self.ax.set_title("Removing...")
self.im.axes.figure.canvas.draw()
self.manipulator.tamper(coord, action='remove', isVox=True)
self.remove_coords.append(coord)
self.pause_start = time.time(
) #pause few secs to see result before continue
self.ax.set_title(
os.path.split(
self.filepaths[self.fileindex])[-1]) # filename
self.update()
def onscroll(self, event):
if event.button == 'up':
self.ind = (self.ind + 1) % self.slices
else:
self.ind = (self.ind - 1) % self.slices
self.update()
def toggle_animation(self, event):
self.animation_state = not self.animation_state
if self.animation_state:
self.pause_start = 0
else:
self.pause_start = np.Inf
def inj_on(self, event):
self.action_state = 'inject'
def rem_on(self, event):
self.action_state = 'remove'
def hist(self, event):
self.hist_state = not self.hist_state
self.plot()
self.update()
def next(self, event):
self.fileindex = (self.fileindex + 1) % len(self.filepaths)
self.loadscan(self.fileindex)
def prev(self, event):
self.fileindex = (self.fileindex - 1) % len(self.filepaths)
self.loadscan(self.fileindex)
def save(self, event):
if self.savepath is not None:
self.ax.set_title("Saving...")
self.im.axes.figure.canvas.draw()
uuid = os.path.split(self.filepaths[self.fileindex])[-1][:-4]
#save scan
self.manipulator.save_tampered_scan(os.path.join(
self.savepath, uuid),
output_type='dicom')
#save coords
file_exists = False
if os.path.exists(
os.path.join(self.savepath, "tamper_coordinates.csv")):
file_exists = True
f = open(os.path.join(self.savepath, "tamper_coordinates.csv"),
"a+")
load_filename = os.path.split(self.filepaths[self.fileindex])[-1]
if not file_exists:
f.write("filename, x, y, z, tamper_type\n") #header
for coord in self.inject_coords:
f.write(load_filename + ", " + str(coord[2]) + ", " +
str(coord[1]) + ", " + str(coord[0]) + ", " +
"inject\n")
for coord in self.remove_coords:
f.write(load_filename + ", " + str(coord[2]) + ", " +
str(coord[1]) + ", " + str(coord[0]) + ", " +
"remove\n")
f.close()
self.ax.set_title(load_filename) # filename
self.im.axes.figure.canvas.draw()
def update(self):
if self.hist_state:
self.im.set_data(
self.eq.equalize(self.manipulator.scan[self.ind, :, :]))
else:
self.im.set_data(self.manipulator.scan[self.ind, :, :])
self.ax.set_ylabel('slice %s' % self.ind)
self.im.axes.figure.canvas.draw()
def loadscan(self, fileindex):
#load screen
self.im.set_data(np.ones((self.cols, self.rows)) * -1000)
self.ax.set_title("Loading...")
self.im.axes.figure.canvas.draw()
self.remove_coords.clear()
self.inject_coords.clear()
#load scan
self.manipulator.load_target_scan(self.filepaths[fileindex])
self.slices, self.cols, self.rows = self.manipulator.scan.shape
self.ind = self.slices // 2
self.ax.clear()
self.eq = histEq(self.manipulator.scan)
self.plot()
self.ax.set_title(os.path.split(
self.filepaths[fileindex])[-1]) #filename
self.ax.set_ylabel('slice %s' % self.ind)
self.im.axes.figure.canvas.draw()
def plot(self):
self.ax.clear()
if self.hist_state:
self.im = self.ax.imshow(
self.eq.equalize(self.manipulator.scan[self.ind, :, :]),
cmap="bone") #, cmap="bone", vmin=-1000, vmax=1750)
else:
self.im = self.ax.imshow(self.manipulator.scan[self.ind, :, :],
cmap="bone",
vmin=-1000,
vmax=1750)
self.animation = animation.FuncAnimation(self.fig,
self.animate,
interval=100)
def animate(self, i):
if self.animation_state:
if time.time() - self.pause_start > 1:
if self.ind == self.slices - 1:
self.ani_direction = 'up'
elif self.ind == 0:
self.ani_direction = 'down'
if self.ani_direction == 'up':
self.ind -= 1
else:
self.ind += 1
self.update()
def maximize_window(self):
try: #'QT4Agg'
figManager = plt.get_current_fig_manager()
figManager.window.showMaximized()
except:
try: #'TkAgg'
mng = plt.get_current_fig_manager()
mng.window.state('zoomed')
except:
try: #'wxAgg'
mng = plt.get_current_fig_manager()
mng.frame.Maximize(True)
except:
print("Could not maximize window")
def calibrate(self):
plt.clf()
self.img = cv2.cvtColor(self.img, cv2.COLOR_BGR2RGB)
plt.imshow(self.img)
self.anounce(
'You will define a rectangle in the image \n by selecting its corners, click to begin'
)
plt.waitforbuttonpress()
while True:
pts = []
while len(pts) < 3:
self.anounce(
'Select 4 corners of the rectangle in the following order:\n top left, top right, bottom left, bottom right'
)
pts = np.asarray(plt.ginput(4, timeout=-1))
if len(pts) < 4:
self.anounce('Too few points, starting over')
self.anounce('Happy? Key click for yes, mouse click for no')
fill_pts = np.copy(pts)
fill_pts[[2, 3]] = fill_pts[[3, 2]]
print(pts)
print(fill_pts)
ph = plt.fill(fill_pts[:, 0], fill_pts[:, 1], 'r', lw=2)
if plt.waitforbuttonpress():
break
for p in ph:
p.remove()
pts = pts.tolist()
for pt in pts:
pt[0] = round(pt[0])
pt[1] = round(pt[1])
self.corners = pts
def submitWidth(text):
self.width = eval(text)
def submitHeight(text):
self.height = eval(text)
def submit(event):
print("Corners: {}".format(self.corners))
print("Width is {}m".format(self.width))
print("Height is {}m".format(self.height))
print("Calibration is complete")
plt.close('all')
self.anounce(
"Enter the width and height (in metres) in the fields below \n Width: top left corner to top right corner \n Height: top left corner to bottom left corner"
)
width_box = TextBox(plt.axes([0.2, 0.02, 0.2, 0.05]),
'Width:',
initial='')
width_box.on_text_change(submitWidth)
height_box = TextBox(plt.axes([0.6, 0.02, 0.2, 0.05]),
'Height:',
initial='')
height_box.on_text_change(submitHeight)
submit_button = Button(plt.axes([0.85, 0.02, 0.1, 0.05]), 'Submit')
submit_button.on_clicked(submit)
plt.show()
def __init__(self,
mov,
roi,
traces,
images={},
cmap=pl.cm.viridis,
**kwargs):
"""
Args:
mov : 3d np array, 0'th axis is time/frames
roi : 3d np array, one roi per item in 0'th axis, each of which is a True/False mask indicating roi (True=inside roi)
traces : 2d np array, 0'th axis is time, 1st axis is sources
images: dictionary of still images
Attributes:
roi_kept : boolean array of length of supplied roi, indicating whether or not roi should be kept based on user input
"""
self.mov = mov
self.roi_idxs = np.array([np.argwhere(r.flat).squeeze() for r in roi])
self.roi_centers = np.array(
[np.mean(np.argwhere(r), axis=0) for r in roi])
self.roi_orig = roi.copy()
self.roi = pretty_roi(roi)
self.roi_kept = np.ones(len(self.roi_idxs)).astype(bool)
self.traces = traces
self.images = images
# figure setup
self.cmap = cmap
self.fig = pl.figure()
NR, NC = 128, 32
gs = gridspec.GridSpec(nrows=NR, ncols=NC)
gs.update(wspace=0.1,
hspace=0.1,
left=.04,
right=.96,
top=.98,
bottom=.02)
# movie axes
self.ax_contrast0 = self.fig.add_subplot(gs[0:5, 0:NC // 3])
self.ax_contrast1 = self.fig.add_subplot(gs[5:10, 0:NC // 3])
self.ax_mov = self.fig.add_subplot(gs[10:55, 0:NC // 3])
self.ax_mov.axis('off')
self.ax_img = self.fig.add_subplot(gs[55:100, 0:NC // 3])
self.ax_img.axis('off')
self.axs_imbuts = [
self.fig.add_subplot(gs[110:128, idx * 2:idx * 2 + 2])
for idx, i in enumerate(self.images)
]
# trace axes
self.ax_trcs = self.fig.add_subplot(gs[0:64, NC // 2:])
self.ax_trc = self.fig.add_subplot(gs[65:85, NC // 2:])
self.ax_trc.set_xlim([0, len(self.traces)])
self.ax_nav = self.fig.add_subplot(gs[85:90, NC // 2:])
self.ax_nav.set_xlim([0, len(self.traces)])
self.ax_nav.axis('off')
self.ax_rm = self.fig.add_subplot(gs[95:110, NC // 2:])
# interactivity
self.c0, self.c1 = 0, 100
self.sl_contrast0 = Slider(self.ax_contrast0,
'Low',
0.,
255.0,
valinit=self.c0,
valfmt='%d')
self.sl_contrast1 = Slider(self.ax_contrast1,
'Hi',
0.,
255.0,
valinit=self.c1,
valfmt='%d')
self.sl_contrast0.on_changed(self.evt_contrast)
self.sl_contrast1.on_changed(self.evt_contrast)
self.img_buttons = [
Button(ax, k)
for k, ax in zip(list(self.images.keys()), self.axs_imbuts)
]
self.but_rm = Button(self.ax_rm, 'Remove All ROIs Currently in FOV')
# display initial things
self.movdata = self.ax_mov.imshow(self.mov[0])
self.movdata.set_animated(True)
self.roidata = self.ax_mov.imshow(self.roi,
cmap=self.cmap,
alpha=0.5,
vmin=np.nanmin(self.roi),
vmax=np.nanmax(self.roi))
self.trdata, = self.ax_trc.plot(np.zeros(len(self.traces)))
self.navdata, = self.ax_nav.plot([-2, -2],
[-1, np.max(self.traces)], 'r-')
if len(self.images):
lab, im = list(self.images.items())[0]
self.imgdata = self.ax_img.imshow(im)
self.ax_img.set_ylabel(lab)
self.plot_current_traces()
# callbacks
for ib, lab in zip(self.img_buttons, list(self.images.keys())):
ib.on_clicked(lambda evt, lab=lab: self.evt_imbut(evt, lab))
self.but_rm.on_clicked(self.remove_roi)
self.fig.canvas.mpl_connect('button_press_event', self.evt_click)
self.ax_mov.callbacks.connect('xlim_changed', self.evt_zoom)
self.ax_mov.callbacks.connect('ylim_changed', self.evt_zoom)
# runtime
self._idx = -1
self.t0 = time.clock()
self.always_draw = [self.movdata, self.roidata, self.navdata]
self.blit_clear_axes = [self.ax_mov, self.ax_nav]
# parent init
animation.TimedAnimation.__init__(self,
self.fig,
interval=40,
blit=True,
**kwargs)
def draw(self):
while True:
try:
_ = self.xMl
break
except:
time.sleep(5)
self.fig = plt.figure()
self.ax = self.fig.add_subplot(1, 1, 1)
plt.subplots_adjust(bottom=0.25)
axnext = plt.axes([0.6, 0.05, 0.1, 0.075])
bnext = Button(axnext, 'Refresh')
bnext.on_clicked(self.refresh_func)
while True:
if self.refresh:
self.ax.lines = list()
self.ax.texts = list()
self.ax.plot(self.xM, self.yM, color="black", linewidth=1)
for n, xpos, ypos, value in zip(self.Nl, self.xMl, self.yMl,
self.slope_list):
self.ax.text(xpos - 1,
ypos + 0.0005,
str(abs(value)),
fontsize=6,
color="gray",
fontweight="bold")
for x_slice, y_slice, x_point, y_point, sig_type in self.signal_list:
self.ax.plot(x_slice,
y_slice,
color="steelblue",
linewidth=1)
self.ax.text(x_point,
self.y_min - 0.002,
sig_type,
fontsize=5,
color="steelblue",
fontweight="bold")
for xpos, ypos, close_type, x, start_price in self.close_list:
self.ax.plot([
xpos,
], [
ypos,
], "*k", markersize=5)
if ypos != start_price:
if close_type == "long":
color = "red" if ypos > start_price else "green"
y_2_points = [start_price, ypos]
else:
color = "red" if ypos < start_price else "green"
y_2_points = [start_price, 2 * start_price - ypos]
self.ax.plot([x, x + 0.001],
y_2_points,
color=color,
linewidth=2)
if self.long_trigger_price is not None:
self.ax.plot(self.xM, [
self.long_trigger_price,
] * len(self.xM),
"--r",
linewidth=0.5)
if self.short_trigger_price is not None:
self.ax.plot(self.xM, [
self.short_trigger_price,
] * len(self.xM),
"--g",
linewidth=0.5)
self.ax.set_title(str(round(self.pnl, 3)))
plt.pause(30)
else:
plt.pause(120)
class GUI(animation.TimedAnimation):
"""
interface for viewing a movie and its associated roi, traces, other things
implementation is only through matplotlib, and is non-blocking
backend affects performance somewhat dramatically. have achieved decent performance with qt5agg and tkagg
Methods:
-------
Attributes:
----------
roi_kept : boolean array of length of supplied roi, indicating whether or not roi should be kept based on user input
"""
#todo: todocument *14
def __init__(self,
mov,
roi,
traces,
images={},
cmap=pl.cm.viridis,
**kwargs):
"""
Args:
mov : 3d np array, 0'th axis is time/frames
roi : 3d np array, one roi per item in 0'th axis, each of which is a True/False mask indicating roi (True=inside roi)
traces : 2d np array, 0'th axis is time, 1st axis is sources
images: dictionary of still images
Attributes:
roi_kept : boolean array of length of supplied roi, indicating whether or not roi should be kept based on user input
"""
self.mov = mov
self.roi_idxs = np.array([np.argwhere(r.flat).squeeze() for r in roi])
self.roi_centers = np.array(
[np.mean(np.argwhere(r), axis=0) for r in roi])
self.roi_orig = roi.copy()
self.roi = pretty_roi(roi)
self.roi_kept = np.ones(len(self.roi_idxs)).astype(bool)
self.traces = traces
self.images = images
# figure setup
self.cmap = cmap
self.fig = pl.figure()
NR, NC = 128, 32
gs = gridspec.GridSpec(nrows=NR, ncols=NC)
gs.update(wspace=0.1,
hspace=0.1,
left=.04,
right=.96,
top=.98,
bottom=.02)
# movie axes
self.ax_contrast0 = self.fig.add_subplot(gs[0:5, 0:NC // 3])
self.ax_contrast1 = self.fig.add_subplot(gs[5:10, 0:NC // 3])
self.ax_mov = self.fig.add_subplot(gs[10:55, 0:NC // 3])
self.ax_mov.axis('off')
self.ax_img = self.fig.add_subplot(gs[55:100, 0:NC // 3])
self.ax_img.axis('off')
self.axs_imbuts = [
self.fig.add_subplot(gs[110:128, idx * 2:idx * 2 + 2])
for idx, i in enumerate(self.images)
]
# trace axes
self.ax_trcs = self.fig.add_subplot(gs[0:64, NC // 2:])
self.ax_trc = self.fig.add_subplot(gs[65:85, NC // 2:])
self.ax_trc.set_xlim([0, len(self.traces)])
self.ax_nav = self.fig.add_subplot(gs[85:90, NC // 2:])
self.ax_nav.set_xlim([0, len(self.traces)])
self.ax_nav.axis('off')
self.ax_rm = self.fig.add_subplot(gs[95:110, NC // 2:])
# interactivity
self.c0, self.c1 = 0, 100
self.sl_contrast0 = Slider(self.ax_contrast0,
'Low',
0.,
255.0,
valinit=self.c0,
valfmt='%d')
self.sl_contrast1 = Slider(self.ax_contrast1,
'Hi',
0.,
255.0,
valinit=self.c1,
valfmt='%d')
self.sl_contrast0.on_changed(self.evt_contrast)
self.sl_contrast1.on_changed(self.evt_contrast)
self.img_buttons = [
Button(ax, k)
for k, ax in zip(list(self.images.keys()), self.axs_imbuts)
]
self.but_rm = Button(self.ax_rm, 'Remove All ROIs Currently in FOV')
# display initial things
self.movdata = self.ax_mov.imshow(self.mov[0])
self.movdata.set_animated(True)
self.roidata = self.ax_mov.imshow(self.roi,
cmap=self.cmap,
alpha=0.5,
vmin=np.nanmin(self.roi),
vmax=np.nanmax(self.roi))
self.trdata, = self.ax_trc.plot(np.zeros(len(self.traces)))
self.navdata, = self.ax_nav.plot([-2, -2],
[-1, np.max(self.traces)], 'r-')
if len(self.images):
lab, im = list(self.images.items())[0]
self.imgdata = self.ax_img.imshow(im)
self.ax_img.set_ylabel(lab)
self.plot_current_traces()
# callbacks
for ib, lab in zip(self.img_buttons, list(self.images.keys())):
ib.on_clicked(lambda evt, lab=lab: self.evt_imbut(evt, lab))
self.but_rm.on_clicked(self.remove_roi)
self.fig.canvas.mpl_connect('button_press_event', self.evt_click)
self.ax_mov.callbacks.connect('xlim_changed', self.evt_zoom)
self.ax_mov.callbacks.connect('ylim_changed', self.evt_zoom)
# runtime
self._idx = -1
self.t0 = time.clock()
self.always_draw = [self.movdata, self.roidata, self.navdata]
self.blit_clear_axes = [self.ax_mov, self.ax_nav]
# parent init
animation.TimedAnimation.__init__(self,
self.fig,
interval=40,
blit=True,
**kwargs)
@property
def frame_seq(self):
self._idx += 1
if self._idx == len(self.mov):
self._idx = 0
self.navdata.set_xdata([self._idx, self._idx])
yield self.mov[self._idx]
@frame_seq.setter
def frame_seq(self, val):
pass
def new_frame_seq(self):
return self.mov
def _init_draw(self):
self._draw_frame(self.mov[0])
self._drawn_artists = self.always_draw
def _draw_frame(self, d):
self.t0 = time.clock()
self.movdata.set_data(d)
# blit
self._drawn_artists = self.always_draw
for da in self._drawn_artists:
da.set_animated(True)
def _blit_clear(self, artists, bg_cache):
for ax in self.blit_clear_axes:
if ax in bg_cache:
self.fig.canvas.restore_region(bg_cache[ax])
def evt_contrast(self, val):
self.c0 = self.sl_contrast0.val
self.c1 = self.sl_contrast1.val
if self.c0 > self.c1:
self.c0 = self.c1 - 1
self.sl_contrast0.set_val(self.c0)
if self.c1 < self.c0:
self.c1 = self.c0 + 1
self.sl_contrast1.set_val(self.c1)
self.movdata.set_clim(vmin=self.c0, vmax=self.c1)
self.imgdata.set_clim(vmin=self.c0, vmax=self.c1)
def evt_imbut(self, evt, lab):
self.imgdata.set_data(self.images[lab])
self.ax_img.set_title(lab)
def evt_click(self, evt):
if not evt.inaxes:
return
elif evt.inaxes == self.ax_mov:
# select roi
x, y = int(np.round(evt.xdata)), int(np.round(evt.ydata))
idx = np.ravel_multi_index((y, x), self.roi.shape)
inside = np.argwhere([idx in ri for ri in self.roi_idxs])
if len(inside) == 0:
return
i = inside[0]
self.set_current_trace(i)
elif evt.inaxes in [self.ax_nav]:
x = int(np.round(evt.xdata))
self._idx = x
def evt_zoom(self, *args):
self.plot_current_traces()
def set_current_trace(self, idx):
col = self.cmap(np.linspace(0, 1,
np.sum(self.roi_kept)))[np.squeeze(idx)]
t = self.traces[:, idx]
self.trdata.set_ydata(t)
self.trdata.set_color(col)
self.ax_trc.set_ylim([t.min(), t.max()])
self.ax_trc.set_title('ROI {}'.format(idx))
self.ax_trc.figure.canvas.draw()
def get_current_roi(self):
croi = np.array([isin(rc, self.ax_mov) for rc in self.roi_centers])
croi[self.roi_kept is False] = False
return croi
def remove_roi(self, evt):
self.current_roi = self.get_current_roi()
self.roi_kept[self.current_roi] = False
# update
if np.sum(self.roi_kept):
proi = pretty_roi(self.roi_orig[self.roi_kept])
self.roidata.set_data(proi)
self.roidata.set_clim(vmin=np.nanmin(proi), vmax=np.nanmax(proi))
else:
self.roidata.remove()
def plot_current_traces(self):
self.current_roi = self.get_current_roi()
if np.sum(self.current_roi) == 0:
return
for line in self.ax_trcs.get_lines():
line.remove()
cols = self.cmap(np.linspace(0, 1,
len(self.roi_idxs)))[self.current_roi]
lastmax = 0
for t, c in zip(self.traces.T[self.current_roi], cols):
self.ax_trcs.plot((t - t.min()) + lastmax, color=c)
lastmax = np.max(t)
self.ax_trcs.set_ylim([0, lastmax])
class SplitPrintWindow():
def __init__(self,
print_manager,
combo_prints,
hulls_df,
print_numb,
vid_panel,
invert_axes=True):
self.root = tk.Tk()
self.root.wm_title("Split the Selected Print")
self.fig = Figure(figsize=(10, 8), dpi=100)
self.canvas = FigureCanvasTkAgg(self.fig,
master=self.root) # A tk.DrawingArea.
self.canvas.draw()
self.canvas.get_tk_widget().pack(side=tk.TOP, fill=tk.BOTH, expand=1)
self.toolbar = NavigationToolbar2TkAgg(self.canvas, self.root)
self.toolbar.update()
self.canvas._tkcanvas.pack(side=tk.TOP, fill=tk.BOTH, expand=1)
self.print_manager = print_manager
self.combo_prints = combo_prints
self.print_numb = print_numb
self.first_frame = self.combo_prints.first_frame[print_numb]
n_frames = (self.combo_prints.last_frame[print_numb] -
self.first_frame) + 1
grid_size = int(np.ceil(np.sqrt(n_frames)))
self.axes = []
self.fig.set_size_inches(16, 9)
button_axes = self.fig.add_axes([.01, .01, .05, .05])
self.button = Button(button_axes, 'Split')
self.button.on_clicked(self.create_new_hulls)
these_hulls = hulls_df[hulls_df.print_numb == print_numb]
self.collections = {}
self.xyes = {}
for i in range(0, n_frames):
ax = self.fig.add_subplot(grid_size, grid_size, i + 1)
self.axes.append(ax)
ax.set_axis_off()
frame = vid_panel.get_frame(self.first_frame + i)
X = self.combo_prints.X[print_numb]
Y = self.combo_prints.Y[print_numb]
ax.imshow(frame)
xes = []
yes = []
for c in (these_hulls.contours[these_hulls.frame ==
self.first_frame + i].values[0]):
#add all to a single list to make one collection
xes = xes + list(c[:, 0, 0])
yes = yes + list(c[:, 0, 1])
self.collections[ax] = ax.scatter(xes, yes)
self.xyes[ax] = self.collections[ax].get_offsets()
#set it to have list of facecolors so that selection works
facecolors = self.collections[ax].get_facecolors()
npts = len(self.xyes[ax])
facecolors = np.tile(facecolors, npts).reshape(npts, -1)
self.collections[ax].set_facecolor(facecolors)
if invert_axes:
ax.set_xlim(X + 50, X - 50)
else:
ax.set_xlim(X - 50, X + 50)
ax.set_ylim(Y - 50, Y + 50)
ax.set_title('Frame ' + str(self.first_frame + i))
self.axes = np.asarray(self.axes)
self.cid = self.canvas.mpl_connect('button_press_event', self.onpress)
self.canvas.draw()
tk.mainloop()
def create_new_hulls(self, event):
new_hull_points = []
old_hull_points = []
frame = []
if (sys.version_info > (3, 0)):
iterab = self.collections.items()
else:
iterab = self.collections.iteritems()
for ax, collection in iterab:
#make np array of bools with whether color matches the selection color for each point
inds = np.asarray([
True if np.array_equal(i, [.4, .4, .9, 1.0]) else False
for i in collection.get_facecolors()
])
#use np bool indexing to get x,y values for selected and unselected
new_hull_points.append(self.xyes[ax][inds])
old_hull_points.append(self.xyes[ax][~inds])
frame.append(self.first_frame + np.where(self.axes == ax)[0])
self.print_manager.split_print(new_hull_points, old_hull_points, frame,
self.print_numb)
self.root.destroy()
def callback(self, verts):
facecolors = self.collections[self.current_axes].get_facecolors()
p = path.Path(verts)
ind = p.contains_points(self.xyes[self.current_axes])
for i in range(len(self.xyes[self.current_axes])):
if ind[i]:
facecolors[i] = [.4, .4, .9, 1.0]
else:
facecolors[i] = [.4, .4, .2, .5]
self.collections[self.current_axes].set_facecolor(facecolors)
self.fig.canvas.draw_idle()
self.fig.canvas.widgetlock.release(self.lasso)
del self.lasso
#TODO: troubleshoot widget locks
def onpress(self, event):
if self.fig.canvas.widgetlock.locked(): return
if event.inaxes is None: return
self.current_axes = event.inaxes
self.lasso = Lasso(event.inaxes, (event.xdata, event.ydata),
self.callback)
# acquire a lock on the widget drawing
self.fig.canvas.widgetlock(self.lasso)
class Animation:
"""The foundation of all animations.
Parameters
----------
blocks : list of animatplot.animations.Block
A list of blocks to be animated
timeline : Timeline or 1D array, optional
If an array is passed in, it will be converted to a Timeline.
If not given, a timeline will be created using the length of the
first block.
fig : matplotlib figure, optional
The figure that the animation is to occur on
Attributes
----------
animation
a matplotlib animation returned from FuncAnimation
"""
def __init__(self, blocks, timeline=None, fig=None):
if timeline is None:
self.timeline = Timeline(range(len(blocks[0])))
elif not isinstance(timeline, Timeline):
self.timeline = Timeline(timeline)
else:
self.timeline = timeline
_len_time = len(self.timeline)
for block in blocks:
if len(block) != _len_time:
raise ValueError(
"All blocks must animate for the same amount of time")
self.blocks = blocks
self.fig = plt.gcf() if fig is None else fig
self._has_slider = False
self._pause = False
def animate(i):
updates = []
for block in self.blocks:
updates.append(block._update(self.timeline.index))
if self._has_slider:
self.slider.set_val(self.timeline.index)
self.timeline._update()
return updates
self.animation = FuncAnimation(self.fig,
animate,
frames=self.timeline._len,
interval=1000 / self.timeline.fps)
def toggle(self, axis=None):
"""Creates a play/pause button to start/stop the animation
Parameters
----------
axis : optional
A matplotlib axis to attach the button to.
"""
if axis is None:
adjust_plot = {'bottom': .2}
rect = [.78, .03, .1, .07]
plt.subplots_adjust(**adjust_plot)
self.button_ax = plt.axes(rect)
else:
self.button_ax = axis
self.button = Button(self.button_ax, "Pause")
self.button.label2 = self.button_ax.text(
0.5,
0.5,
'Play',
verticalalignment='center',
horizontalalignment='center',
transform=self.button_ax.transAxes)
self.button.label2.set_visible(False)
def pause(event):
if self._pause:
self.animation.event_source.start()
self.button.label.set_visible(True)
self.button.label2.set_visible(False)
else:
self.animation.event_source.stop()
self.button.label.set_visible(False)
self.button.label2.set_visible(True)
self.fig.canvas.draw()
self._pause ^= True
self.button.on_clicked(pause)
def timeline_slider(self,
text='Time',
axis=None,
valfmt='%1.2f',
color=None):
"""Creates a timeline slider.
Parameters
----------
text : str, optional
The text to display for the slider. Defaults to 'Time'
axis : optional
A matplotlib axis to attach the slider to
valfmt : str, optional
a format specifier used to print the time
Defaults to '%1.2f'
color :
The color of the slider.
"""
if axis is None:
adjust_plot = {'bottom': .2}
rect = [.18, .05, .5, .03]
plt.subplots_adjust(**adjust_plot)
self.slider_ax = plt.axes(rect)
else:
self.slider_ax = axis
if self.timeline.log:
valfmt = '$10^{%s}$' % valfmt
self.slider = Slider(self.slider_ax,
text,
0,
self.timeline._len - 1,
valinit=0,
valfmt=(valfmt + self.timeline.units),
valstep=1,
color=color)
self._has_slider = True
def set_time(t):
self.timeline.index = int(self.slider.val)
self.slider.valtext.set_text(self.slider.valfmt %
(self.timeline[self.timeline.index]))
if self._pause:
for block in self.blocks:
block._update(self.timeline.index)
self.fig.canvas.draw()
self.slider.on_changed(set_time)
def controls(self, timeline_slider_args={}, toggle_args={}):
"""Creates interactive controls for the animation
Creates both a play/pause button, and a time slider at once
Parameters
----------
timeline_slider_args : Dict, optional
A dictionary of arguments to be passed to timeline_slider()
toggle_args : Dict, optional
A dictionary of argyments to be passed to toggle()
"""
self.timeline_slider(**timeline_slider_args)
self.toggle(**toggle_args)
def save_gif(self, filename):
"""Saves the animation to a gif
A convience function. Provided to let the user avoid dealing
with writers.
Parameters
----------
filename : str
the name of the file to be created without the file extension
"""
self.timeline.index -= 1 # required for proper starting point for save
self.animation.save(filename + '.gif',
writer=PillowWriter(fps=self.timeline.fps))
def save(self, *args, **kwargs):
"""Saves an animation
A wrapper around :meth:`matplotlib.animation.Animation.save`
"""
self.timeline.index -= 1 # required for proper starting point for save
self.animation.save(*args, **kwargs)
def add_button_to(plt, fn):
#print(fn)
from matplotlib.widgets import Button
ax = plt.axes([0.85, 0.95, 0.15, 0.03])
btn = Button(ax, 'New')
btn.on_clicked(fn)
R = 6
T = 0.15
CW = 0.65 / 2.0 + 0.65 / 4.0
L = 0.15 + 0.65 / 2.0
H = 0.03
ButtonQueue.reverse()
for Row in range(R):
TRow = []
for Col in range(C):
A = plt.axes(
[L + (CW / float(C)) * Col, T - H * Row, CW / float(C), H])
if len(ButtonQueue) > 0:
B = ButtonQueue.pop()
if B != "":
TRow.append(Button(A, B[0]))
TRow[-1].on_clicked(B[1])
TRow[-1].drawon = False
else:
TRow.append(Button(A, ""))
TRow[-1].on_clicked(ButtonClick)
TRow[-1].drawon = False
else:
TRow.append(Button(A, ""))
TRow[-1].on_clicked(ButtonClick)
TRow[-1].drawon = False
ButtonGrid.append(TRow)
#PlotType = "FracMaxAnyA0"
PlotType = "T99"