def _do_plot(self):
stats = self._statistics.deserialize()
metrics_to_plot = self._statistics.get_metrics_to_plot()
subplots_count = len(stats)
if not subplots_count:
return
fig, axarr = plt.subplots(subplots_count)
fig.canvas.set_window_title(self._plot_title)
time = range(len(stats[stats.keys()[0]]))
axes_by_names = {}
for i, key in enumerate(stats.keys()):
axarr[i].plot(time,
stats[key],
label=metrics_to_plot[key].name,
lw=1,
color=COLORS[i])
axarr[i].set_xlabel('time (sec)')
axarr[i].set_ylabel(metrics_to_plot[key].unit.name)
axarr[i].legend()
axes_by_names[key] = i
rax = plt.axes([0.01, 0.8, 0.1, 0.1])
check_btns = CheckButtons(rax, stats.keys(), [True] * subplots_count)
check_btns.on_clicked(self._get_show_hide_fn(fig, axarr,
axes_by_names))
plt.subplots_adjust(left=0.2)
plt.show()
def build_plot(datas: List[PlotData], checkbox=True):
fig, ax = plt.subplots()
lines = []
for data in datas:
line, = ax.plot(data.xdata,
data.ydata,
'xb-',
lw=2,
color=data.color,
label=data.label)
lines.append(line)
plt.subplots_adjust(left=0.2)
if checkbox:
def change_state(label):
index = labels.index(label)
lines[index].set_visible(not lines[index].get_visible())
plt.draw()
rax = plt.axes([0.05, 0.4, 0.1, 0.15])
labels = [str(line.get_label()) for line in lines]
visibility = [line.get_visible() for line in lines]
check = CheckButtons(rax, labels, visibility)
check.on_clicked(change_state)
plt.show()
def plotAllFlags():
fig, ax = subplots(figsize=(16, 8))
offsets = getOffsets()
start = 22
numFlags = [(flags[allChannels][:, -1] & (1 << start)) >> start]
cmax = np.max(numFlags[0])
cmin = np.min(numFlags[0])
cmap = get_cmap('jet', cmax - cmin + 1)
collection = RegularPolyCollection(numsides=4,
rotation=np.pi / 4,
sizes=(1000, ),
linewidths=(1, ),
offsets=offsets,
transOffset=ax.transData,
alpha=0.5,
cmap=cmap)
collection.set_clim(vmin=cmin - 0.5, vmax=cmax + 0.5)
for i, w in enumerate(range(91) + range(91)):
ax.annotate(str(w + 1), offsets[i], ha='center', va='center')
subplots_adjust(left=0.2)
ax.add_collection(collection)
axis('equal')
collection.set_array(numFlags[0])
cb = colorbar(collection, ticks=np.arange(cmin, cmax + 1))
rax = axes([0.05, 0.1, 0.1, 0.8])
status = [True, False, False]
check = CheckButtons(rax, ('22', '23', '24'), tuple(status))
def func(label):
bit = int(label)
i = bit - start
status[i] = not status[i]
if status[i]:
numFlags[0] += (flags[allChannels][:, -1] & (1 << bit)) >> bit
else:
numFlags[0] -= (flags[allChannels][:, -1] & (1 << bit)) >> bit
cmax = np.max(numFlags[0])
cmin = np.min(numFlags[0])
cmap = get_cmap('jet', cmax - cmin + 1)
ax.collections[0].set_array(numFlags[0])
ax.collections[0].set_clim(vmin=cmin - 0.5, vmax=cmax + 0.5)
ax.collections[0].set_cmap(cmap)
cb.set_ticks(np.arange(cmin, cmax + 1))
fig.canvas.draw()
check.on_clicked(func)
show()
def _recreate_show_lines_check_button(self):
axcolor = 'lightgoldenrodyellow'
if hasattr(self, 'rax_showlines'):
self.rax_showlines.cla()
self.rax_showlines = plt.axes([self.x_start__ + self.slider_length__ + self.text_length__,
self.y_start__,
self.button_length__,
self.button_height__], facecolor=axcolor)
visible = self.prop_ifs.get_visible()
show_ann = self.prop_ifs.get_annotation_visible()
labels = ('showIFS',
#'marks',
#'edges',
'labels')
actives = (visible,
# linestyle not in ['None', None],
show_ann)
self.holder_actives = dict(zip(labels, actives))
self.w_check_components = CheckButtons(self.rax_showlines,
labels,
actives)
self.w_check_components.on_clicked(self.update_show_components)
return self.w_check_components
def draw_plots(signals_descriptions):
def on_click(label):
plot = plots[label]
plot.set_visible(not plot.get_visible())
signals_figure.canvas.draw()
signals_figure = plt.figure('Signals')
plots = {}
plt.subplots_adjust(bottom=0.25)
plt.subplots_adjust(left=0.2)
for signal_values, signal_label, visibility, spectrum in signals_descriptions:
x_points, y_points = plot_values(signal_values)
line, = plt.plot(x_points, y_points)
plots[signal_label] = line
plt.figure(signal_label)
plt.stem(*plot_values(spectrum), linefmt='b', markerfmt=' ', basefmt=' ')
plt.figure('Signals')
checkboxes_axes = plt.axes([0.05, 0.4, 0.12, 0.15])
checkboxes = CheckButtons(
checkboxes_axes,
[x.label for x in signals_descriptions],
[x.visibility for x in signals_descriptions]
)
checkboxes.on_clicked(on_click)
plt.figlegend(list(plots.values()), [x.label for x in signals_descriptions], loc='lower center')
# need to hide after figlegend to use real colors for legend
for _, label, visibility, _ in signals_descriptions:
plots[label].set_visible(visibility)
plt.show()
def _do_plot(self):
stats = self._statistics.deserialize()
metrics_to_plot = self._statistics.get_metrics_to_plot()
subplots_count = len(stats)
if not subplots_count:
return
fig, axarr = plt.subplots(subplots_count)
fig.canvas.set_window_title(self._plot_title)
time = range(len(stats[stats.keys()[0]]))
axes_by_names = {}
for i, key in enumerate(stats.keys()):
axarr[i].plot(time, stats[key], label=metrics_to_plot[key].name, lw=1, color=COLORS[i])
axarr[i].set_xlabel('time (sec)')
axarr[i].set_ylabel(metrics_to_plot[key].unit.name)
axarr[i].legend()
axes_by_names[key] = i
rax = plt.axes([0.01, 0.8, 0.1, 0.1])
check_btns = CheckButtons(rax, stats.keys(), [True] * subplots_count)
check_btns.on_clicked(self._get_show_hide_fn(fig, axarr, axes_by_names))
plt.subplots_adjust(left=0.2)
plt.show()
def add_checkboxes(self):
"""
Checkboxes offer the ability to select multiple tags such as Motion, Ghosting Aliasing etc,
instead of one from a list of mutual exclusive rating options (such as Good, Bad, Error etc).
"""
ax_checkbox = plt.axes(cfg.position_checkbox,
facecolor=cfg.color_rating_axis)
# initially de-activating all
actives = [False] * len(self.issue_list)
self.checkbox = CheckButtons(ax_checkbox,
labels=self.issue_list,
actives=actives)
self.checkbox.on_clicked(self.save_issues)
for txt_lbl in self.checkbox.labels:
txt_lbl.set(**cfg.checkbox_font_properties)
for rect in self.checkbox.rectangles:
rect.set_width(cfg.checkbox_rect_width)
rect.set_height(cfg.checkbox_rect_height)
# lines is a list of n crosses, each cross (x) defined by a tuple of lines
for x_line1, x_line2 in self.checkbox.lines:
x_line1.set_color(cfg.checkbox_cross_color)
x_line2.set_color(cfg.checkbox_cross_color)
self._index_pass = self.issue_list.index(cfg.func_mri_pass_indicator)
def initWindow(self):
if not (self.args.placeOne and self.args.placeTwo):
self.randomBB()
for i in range(100):
axes().set_aspect('equal', 'datalim')
plt.subplot(2, 2, (1, 2))
plt.subplot(2, 2, (1, 2)).set_aspect('equal')
subplots_adjust(left=0.31)
subplots_adjust(bottom=-0.7)
plt.title('QSR Visualisation ' + self.args.distribution[0])
axcolor = 'lightgoldenrodyellow'
rax = plt.axes([0.03, 0.4, 0.22, 0.45], fc=axcolor)
checkBox = CheckButtons(rax, self.qsr_type,
(False, False, False, False, False))
checkBox.on_clicked(self.EventClick)
plt.subplot(2, 2, 3)
plt.axis('off')
plt.text(1,
1, (self.__compute_qsr(self.bb1[i], self.bb2[i])),
family='serif',
style='italic',
ha='center')
if self.qsr:
self.updateWindow()
plt.show()
def initVisibilityCheckBoxes(self):
visibility = self.visibility
if kElementsKey in visibility.keys():
visibility.pop(kElementsKey)
subAxes = plt.axes([0.81, 0.4, 0.1, 0.5], frameon=False, anchor='NW')
self.checkBoxes = CheckButtons(subAxes, visibility.keys(),
visibility.values())
step = 0.15
for i, (label, rectangle, lines) in enumerate(
zip(self.checkBoxes.labels, self.checkBoxes.rectangles,
self.checkBoxes.lines)):
h = 0.85 - step * i
label.set_fontsize(11)
rectangle.set_x(0.05)
rectangle.set_y(h)
rectangle.set(width=0.12, height=0.04)
label.set_y(h + 0.02)
label.set_x(0.2)
lineA, lineB = lines
lineA.set_xdata([0.05, 0.17])
lineB.set_xdata([0.05, 0.17])
lineA.set_ydata([h, h + 0.04])
lineB.set_ydata([h + 0.04, h])
self.checkBoxes.on_clicked(self.onCheckBoxCallback)
def start_spectrum(self, *args, **kwargs):
if self.fig_spectrum is None:
self.fig_spectrum = plt.figure()
self.ax_spectrum = self.fig_spectrum.add_axes([0.1, 0.1, 0.7, 0.8])
# Widgets
ax_hanning = self.fig_spectrum.add_axes([0.825, 0.75, 0.15, 0.15])
self.checkbox_hanning = CheckButtons(ax_hanning, ["Hanning", "AC"],
[self.hanning, self.ac])
self.checkbox_hanning.on_clicked(self.ask_hanning_change)
ax_spectrum_unit = self.fig_spectrum.add_axes(
[0.825, 0.51, 0.15, 0.25])
self.radio_units = RadioButtons(
ax_spectrum_unit,
["V^2/Hz", "V/sq(Hz)", "mV/sq(Hz)", "µV/sq(Hz)", "nV/sq(Hz)"],
)
self.radio_units.on_clicked(self.ask_spectrum_units_change)
ax_restart = self.fig_spectrum.add_axes([0.825, 0.35, 0.15, 0.075])
self.btn_restart = Button(ax_restart, label="Restart")
self.btn_restart.on_clicked(self.clean_spectrum)
ax_save_hold = self.fig_spectrum.add_axes(
[0.825, 0.25, 0.15, 0.075])
self.btn_save_hold = Button(ax_save_hold, label="Hold&Save")
self.btn_save_hold.on_clicked(self.save_hold)
self.clean_spectrum()
def make_log_button(ax, box=[0.015, 0.05, 0.12, 0.15], ylims=None):
""" Make a log button
ax : the axis
ylims : None or dictionary with the ylim for 'linear' and 'log' scales
"""
f = plt.gcf()
ax_btn = f.add_axes(box, facecolor=slider_color)
labels = ['log x', 'log y']
widget = CheckButtons(ax_btn, labels,
[ax.get_xscale() == 'log',
ax.get_yscale() == 'log'])
def set_log(label):
if label == 'log x':
method = 'set_xscale'
index = 0
if label == 'log y':
method = 'set_yscale'
index = 1
state = 'log' if widget.get_status()[index] else 'linear'
getattr(ax, method)(state)
if ylims is not None:
if label == 'log y':
ax.set_ylim(ylims[state])
f.canvas.draw_idle()
widget.on_clicked(set_log)
return widget
def _recreate_show_operators_check_button(self):
axcolor = 'lightgoldenrodyellow'
if hasattr(self, 'rax_showlines_operator'):
self.rax_showlines_operator.cla()
self.rax_showlines_operator = plt.axes([self.x_start__ + self.slider_length__ \
+ self.text_length__ + 2* self.button_length__,
self.y_start__,
self.button_length__,
self.button_height__], facecolor=axcolor)
visible = self.prop_ifs.holder.get_visible()
linestyle = self.prop_ifs.holder.get_linestyle()
labels = ('F', 'G', 'H', 'J')
actives = (self.prop_ifs.hide_ifs, visible, linestyle
not in ['None', None], self.prop_ifs.show_ann)
self.holder_actives_operator = dict(zip(labels, actives))
self.w_check_components_operator = CheckButtons(
self.rax_showlines_operator, labels, actives)
self.w_check_components_operator.on_clicked(
self.update_show_components_operator)
return self.w_check_components_operator
def setup_ui():
fig, ax = plt.subplots()
plt.subplots_adjust(top=0.98, right=0.99, left=0.25, bottom=0.16)
slider_axes = plt.axes([0.08, 0.04, 0.82, 0.03])
slider = Slider(slider_axes, 'Steps', STEP_MIN, STEP_MAX, valinit=STEP)
def slider_listener(value):
global STEP
STEP = int(value)
plot(ax, value)
fig.canvas.draw_idle()
slider.on_changed(slider_listener)
checkbox_axes = plt.axes([0.01, 0.3, 0.15, 0.5])
checkbox = CheckButtons(checkbox_axes, METHODS,
[method in SELECTED_METHODS for method in METHODS])
def checkbox_listener(label):
method = [x for x in METHODS if x.name == label][0]
if method in SELECTED_METHODS:
SELECTED_METHODS.remove(method)
else:
SELECTED_METHODS.add(method)
plot(ax, STEP)
fig.canvas.draw_idle()
checkbox.on_clicked(checkbox_listener)
return ax, slider, checkbox
def render(self):
print(f'drawing with {self.plotter.n} circles')
self.fig = Figure(figsize=(13, 13), dpi=100)
self.ax = self.fig.subplots()
self.root.wm_title(f"Render - {base_name(args.file_name, True)}")
canvas = FigureCanvasTkAgg(self.fig, master=self.root)
canvas.draw()
canvas.get_tk_widget().pack(side=tkinter.TOP, fill=tkinter.BOTH, expand=1)
if not self.hide_widgets:
rax = self.fig.add_axes([0.05, 0.0, 0.1, 0.1])
rax.axis('off')
self.check = CheckButtons(rax, ('hide',), (self.plotter.hide,))
self.check.on_clicked(lambda _: self.plotter.toggle_hide())
nax = self.fig.add_axes([0.2, 0.07, 0.7, 0.02])
self.nslider = Slider(nax, 'n', 2, self.plotter.frames,
valinit=self.plotter.n, valstep=1)
self.nslider.on_changed(self._update_n)
fpsax = self.fig.add_axes([0.2, 0.03, 0.7, 0.02])
self.fpsslider = Slider(fpsax, 'fps', 1, 50,
valinit=10, valstep=1)
self.fpsslider.on_changed(self._update_fps)
self._init_animation()
if args.save:
self.save(args.out)
else:
tkinter.mainloop()
def plot_hmm_path(trajectory_objs, paths, legends=[], items=[]):
global colors
print_n_flush("Colors are:", colors)
for i, (trajectory, p) in enumerate(zip(trajectory_objs, paths)):
print_n_flush("Path:", p)
tr_colors = [colors[int(state)] for state in p]
t = trajectory.plot2d(color=tr_colors)
# t = plot_quiver2d(trajectory, color=tr_colors, path=p)
too_high = [tt for tt in trajectory if tt[1] > 400]
# print_n_flush( "Too high", too_high)
legends.append("Trajectory%i" % i)
# items.append(p)
items.append(t)
# gca().legend()
# Let's create checkboxes
rax = plt.axes([0.05, 0.4, 0.1, 0.15])
# rax = plt.gca()
from matplotlib.widgets import CheckButtons
check = CheckButtons(rax, legends, [True] * len(legends))
# plt.sca(axes)
def func(label):
widget = items[legends.index(label)]
widget.set_visible(not widget.get_visible())
plt.draw()
check.on_clicked(func)
def build_stock_gdp():
"""
Creates graphic of stock prices of 8 companies and GDP of USA
>>> build_stock_gdp()
"""
axes = []
for comp in found_years:
x = np.arange(found_years[comp], 2019)
if comp == 'LGF':
x = np.arange(found_years[comp], 2017)
lst = []
for el in database:
if database[el][1][comp] is not None:
lst.append(database[el][1][comp])
y = np.array(lst)
axes.extend([x, y])
lst = []
x = np.arange(1962, 2019)
for el in gdp_info:
if int(el) >= 1962:
lst.append(gdp_info[el] / 100)
y = np.array(lst)
axes.extend([x, y])
fig, ax = plt.subplots()
l1, = plt.plot(axes[0], axes[1], linestyle='solid')
l2, = plt.plot(axes[2], axes[3], linestyle='solid')
l3, = plt.plot(axes[4], axes[5], linestyle='solid')
l4, = plt.plot(axes[6], axes[7], linestyle='solid')
l5, = plt.plot(axes[8], axes[9], linestyle='solid')
l6, = plt.plot(axes[10], axes[11], linestyle='solid')
l7, = plt.plot(axes[12], axes[13], linestyle='solid')
l8, = plt.plot(axes[14], axes[15], linestyle='solid')
l9, = plt.plot(axes[16], axes[17], linestyle='solid')
plt.subplots_adjust(left=0.3)
lgnd = ax.legend(list(found_years.keys()),
loc='upper center',
ncol=4,
fontsize='xx-small',
shadow=True)
lgnd.get_frame().set_facecolor('#ffb19a')
rax = plt.axes([0.04, 0.5, 0.17, 0.37], facecolor='lightgoldenrodyellow')
check = CheckButtons(rax, tuple(found_years.keys()),
(True, True, True, True, True, True, True, True))
def func(label):
for line, comp in zip([l1, l2, l3, l4, l5, l6, l7, l8],
list(found_years.keys())):
if label == comp:
line.set_visible(not line.get_visible())
plt.draw()
check.on_clicked(func)
plt.show()
def plot(self):
print("plotting object of dimension " +
str(self.decomposition.dimension))
self.make_figure()
self.make_axes()
self.main()
self.label_axes()
# I would love to move this to its own method, but I
# don't think that is possible with the limiations of
# matplotlib
# can this be done with async??
# Create our check boxes
# These four coordinates specify the position of the checkboxes
rax = plt.axes([0.05, 0.4, 0.2, 0.15])
check = CheckButtons(rax,
('Vertices', 'Surface', 'Raw Surface', 'STL'),
(True, True, False, False))
# Export STL button
# exportstl = plt.axes([0.81, 0.01, 0.15, 0.075])
# bfvtostl = Button(exportstl,'Export STL')
# if(bfvtostl.on_clicked()):
# print("Export successfully")
def func(label):
if label == 'Vertices':
# works but with hardcoded axes
self.options.visibility.vertices = (
not self.options.visibility.vertices)
self.ax.clear()
self.replot()
elif label == 'Surface':
self.options.visibility.samples = (
not self.options.visibility.samples)
self.ax.clear()
self.replot()
elif label == 'Raw Surface':
self.options.visibility.raw = (not self.options.visibility.raw)
self.ax.clear()
self.replot()
elif label == 'STL':
self.fvtostl()
plt.draw()
check.on_clicked(func)
self.apply_title()
plt.show()
class PeakIdentifier:
def __click_cb(self, label):
self.all_names[label] = not self.all_names[label]
def __init_fig__(self, data_name):
self.fig = figure()
self.ax = subplot(111)
self.fig.subplots_adjust(left=0.3)
title(_path.basename(data_name))
self.button_ax = plt.axes([0.05, 0.1, 0.15, 0.8])
self.check = CheckButtons(self.button_ax, sorted(self.all_names.keys()), [False] * len(self.all_names))
self.check.on_clicked(self.__click_cb)
def __init_data__(self, data, start_i, end_i, pos, pos_s):
start_i = int(start_i)
end_i = int(end_i) + 1
self.start_i = start_i
self.end_i = end_i
self.__pos = pos
self.__pos_s = pos_s
self.d = end_i - start_i
new_start = start_i - 10 * self.d
if new_start < 0:
new_start = 0
new_end = end_i + 10 * self.d
if new_end >= len(data[2]):
new_end = len(data[2] - 1)
self.new_start = new_start
self.new_end = new_end
self.xs = r_[self.new_start : self.new_end]
y1 = data[2][new_start:new_end]
y2 = data[3][new_start:new_end]
self.y = y2 - y1
def __init_plot__(self):
self.ax.axvline(self.__pos, color="m", linewidth=2)
self.ax.axvline(self.__pos - self.__pos_s, color="g")
self.ax.axvline(self.__pos + self.__pos_s, color="g")
self.ax.plot(self.xs, self.y, color="c")
self.ax.set_ylim(min(self.y), max(self.y))
self.ax.set_xlim(self.new_start, self.new_end)
def __init_names__(self, names):
self.all_names = {}
for n in names:
self.all_names[n] = False
def __init__(self, names, data, start_i, end_i, data_name, pos, pos_s):
self.__init_names__(names)
self.__init_fig__(data_name)
self.__init_data__(data, start_i, end_i, pos, pos_s)
self.__init_plot__()
def run(self):
show()
close()
return [k for k, v in self.all_names.items() if v]
def _set_control_AS(self):
"""
Will connect the checkbutton control panel with the plotting axis.
"""
self.check_AS = CheckButtons(
self.AS_widg_ax, ('all rep', 'avg', 'fill'),
(self.plot_all_AS, self.plot_avg_AS, self.fill_between_AS))
self.check_AS.on_clicked(self._on_click_AS)
def GUI(self, figsize=(13,12)):
"""
Creates the figure, lines, texts and annotations that will be manipulated, and also the
interactive elements.
"""
# check if the GUI has already been initialized. If it is, just show the figure from the previous state
if self.GUI_initialized:
self.fig.show()
else:
# initializing GUI plot
self.static_plot(show=False, figsize=figsize)
self.spec_lim = self.init_spec_lim.copy()
# adjust the main plots to make room for the sliders
plt.subplots_adjust(bottom=self.bottom_adjust_val, top=self.top_adjust_val)
# make sliders
self.sliders_ax, self.sliders, current_right_column_pos = self.make_sliders()
# make input textboxes for wavelength limits
if current_right_column_pos[0] == self.right_x:
right_edge = self.reset_button_arg[0] - 0.03
else:
right_edge = self.right_x+self.width
textbox_width = (right_edge-current_right_column_pos[0]-self.textbox_gap)/2
self.ax_spec_min = plt.axes([current_right_column_pos[0],
current_right_column_pos[1], textbox_width, self.height])
self.spec_min_box = TextBox(self.ax_spec_min, r'$\lambda_{min}$', initial=self.init_spec_lim[0])
self.spec_min_box.on_submit(self.submit_min)
self.ax_spec_max = plt.axes([current_right_column_pos[0]+textbox_width+self.textbox_gap,
current_right_column_pos[1], textbox_width, self.height])
self.spec_max_box = TextBox(self.ax_spec_max, r'$\lambda_{max}$', initial=self.init_spec_lim[1])
self.spec_max_box.on_submit(self.submit_max)
# register the update function with each slider
for key in self.sliders.keys():
self.sliders[key].on_changed(self.update)
# Create a `matplotlib.widgets.Button` to reset all sliders to initial values.
self.resetax = plt.axes(self.reset_button_arg)
self.reset_button = Button(self.resetax, 'Reset', color=self.slider_colors['misc'], hovercolor='0.975')
self.reset_button.on_clicked(self.reset)
# Create a `matplotlib.widgets.CheckButton` to toggle show or not additional plots
self.moreplotsax = plt.axes(self.moreplots_check_arg)
self.moreplots_check = CheckButtons(self.moreplotsax, ['additional plots'])
self.moreplots_check.on_clicked(self.toggle_additional_plots)
# additional plots toggle bool
self.reset_additional_plots()
plt.show()
self.GUI_initialized = True
def showat(self, ax):
v = [self.__getattribute__(name) for name in self.names]
self.check = CheckButtons(ax, self.names, v)
def func(label):
self.__setattr__(label, not self.__getattribute__(label))
print("clicked")
self.check.on_clicked(func)
def show_cse(filename):
plt.subplots_adjust(left=0.04, top=0.99, bottom=0.03, wspace=0.12,
hspace=0.23, right=0.86)
record = reader.read_cse(filename)
with EcgInterpreter.from_record(record) as interpreter:
borders = interpreter.get_global_borders()
points = interpreter.get_points()
measures = interpreter.get_intervals()
borders = [x.sample for x in borders if x.sample > 0]
onset = int(borders[0] - 0.02 * record.rate)
offset = int(borders[-1] + 0.02 * record.rate)
empty_count = 0
local_points = []
measurements = []
name = _record_name(filename)
for i, item in enumerate(record.signals):
if 2 <= i <= 5:
empty_count += 1
continue
chunk = item[onset:offset]
plot_index = (i - empty_count) * 2
if plot_index > 7:
plot_index -= 7
plt.subplot(421 + plot_index)
plt.ylabel("Lead " + _LEADS[i] + ". Voltage, mV.")
plt.xlabel("Time, s.")
plt.plot(chunk)
measurements += _plot_borders(onset, borders)
local_points += _plot_local_points(onset, points[i], chunk)
legend_items = {
_LABEL_POINTS: local_points[0]
}
if measurements:
legend_items[_LABEL_MEASURES] = measurements[0]
plt.figlegend(legend_items.values(), legend_items.keys(), "upper right")
triggers = CheckButtons(plt.axes([0.87, 0.8, 0.12, 0.10]),
legend_items.keys(),
[True] * len(legend_items))
def switch(label):
lines = None
if label == _LABEL_MEASURES:
lines = measurements
elif label == _LABEL_POINTS:
lines = local_points
for item in lines:
item.set_visible(not item.get_visible())
plt.draw()
triggers.on_clicked(switch)
_result_table(measures, name, ((10, 15), (10, 10), (10, 10), (25, 30)),
"ref_dict.pkl")
plt.get_current_fig_manager().window.state("zoomed")
plt.gcf().canvas.set_window_title("CSE Miltilead: " + name)
plt.show()
def __init__(self, field, fieldname, halospec=None):
"""Initializes a visualization instance, that is a windows with a field
field is a 3D numpy array
fieldname is a string with the name of the field
halospec is a 2x2 array with the definition of the halo size
After this call the window is shown
"""
self.field = field
self.fieldname = fieldname
# Register halo information
if halospec is None:
halospec = [[3, 3], [3, 3]]
self.istart = halospec[0][0]
self.iend = field.shape[0] - halospec[0][1]
self.jstart = halospec[1][0]
self.jend = field.shape[1] - halospec[1][1]
self.plotHalo = True
self.plotLogLog = False
self.curklevel = 0
self.figure = plt.figure()
# Slider
slideraxes = plt.axes([0.15, 0.02, 0.5, 0.03],
axisbg='lightgoldenrodyellow')
self.slider = Slider(slideraxes,
'K level',
0,
field.shape[2] - 1,
valinit=0)
self.slider.valfmt = '%2d'
self.slider.set_val(0)
self.slider.on_changed(self.updateSlider)
# CheckButton
self.cbaxes = plt.axes([0.8, -.04, 0.12, 0.15])
self.cbaxes.set_axis_off()
self.cb = CheckButtons(self.cbaxes, ('Halo', 'Logscale'),
(self.plotHalo, self.plotLogLog))
self.cb.on_clicked(self.updateButton)
# Initial plot
self.fieldaxes = self.figure.add_axes([0.1, 0.15, 0.9, 0.75])
self.collection = plt.pcolor(self._getField(), axes=self.fieldaxes)
self.colorbar = plt.colorbar()
self.fieldaxes.set_xlim(right=self._getField().shape[1])
self.fieldaxes.set_ylim(top=self._getField().shape[0])
plt.xlabel('i')
plt.ylabel('j')
self.title = plt.title('%s - Level 0' % (fieldname, ))
plt.show(block=False)
def __init__(self, field, fieldname, halospec=None):
"""Visualization a field in a matplotlib window
Each k level is represented as a simple slice in the window.
:param field: Field to display
:type field: numpy.array
:param fieldname: Name of the field to display
:type fieldname: str
:param halospec: 2x2 array with the definition of the halo size (e.g ``[[3, 3], [3, 3]]``)
:type halospec: array[array]
"""
self.__field = field
self.__fieldname = fieldname
# Register halo information
if halospec is None:
halospec = [[3, 3], [3, 3]]
self.__istart = halospec[0][0]
self.__iend = field.shape[0] - halospec[0][1]
self.__jstart = halospec[1][0]
self.__jend = field.shape[1] - halospec[1][1]
self.__plotHalo = True
self.__plotLogLog = False
self.__curklevel = 0
self.__figure = plt.figure()
# Slider
slideraxes = plt.axes([0.15, 0.02, 0.5, 0.03], axisbg='lightgoldenrodyellow')
self.__slider = Slider(slideraxes, 'K level', 0, field.shape[2] - 1, valinit=0)
self.__slider.valfmt = '%2d'
self.__slider.set_val(0)
self.__slider.on_changed(self.__update_slider)
# CheckButton
self.__cbaxes = plt.axes([0.8, -.04, 0.12, 0.15])
self.__cbaxes.set_axis_off()
self.__cb = CheckButtons(self.__cbaxes, ('Halo', 'Logscale'),
(self.__plotHalo, self.__plotLogLog))
self.__cb.on_clicked(self.__update_button)
# Initial plot
self.__fieldaxes = self.__figure.add_axes([0.1, 0.15, 0.9, 0.75])
self.__collection = plt.pcolor(self.__get_field(), axes=self.__fieldaxes)
self.__colorbar = plt.colorbar()
self.__fieldaxes.set_xlim(right=self.__get_field().shape[1])
self.__fieldaxes.set_ylim(top=self.__get_field().shape[0])
plt.xlabel('i')
plt.ylabel('j')
self.__title = plt.title('%s - Level 0' % (fieldname,))
plt.show(block=True)
def _init_checks(self):
self.check_labels = []
self.check_display = []
for i in range(self.n):
fn = self.config['data'][i]['filename']
label = '%s' % (os.path.basename(fn))
self.check_labels.append(label)
self.check_display.append(self.mappables[i])
self.checks = CheckButtons(self.check_ax, self.check_labels, \
self.check_display)
def toggle_lines(ax=None, autoscl=True, numbering=False, txtwidth=15):
"""
Make checkbuttons to toggle visibility of each line in current plot.
autoscl : Rescale axis limits as required by currently visible lines.
numbering: Add numbering to labels.
txtwidth : Wrap labels to this length.
"""
# Get lines and their properties
if ax is None: ax = plt.gca()
lines = {'handle': list(ax.get_lines())}
for p in ['label', 'color', 'visible']:
lines[p] = [plt.getp(x, p) for x in lines['handle']]
lines = pd.DataFrame(lines)
lines = lines[~lines.label.str.startswith('_')]
if numbering:
lines['label'] = [
str(i) + ': ' + lb for i, lb in enumerate(lines['label'])
]
if txtwidth:
lines['label'] = [
textwrap.fill(n, width=txtwidth) for n in lines['label']
]
# Setup buttons
# When there's many, the box-sizing is awful, but difficult to fix.
plt.subplots_adjust(left=0.32, right=0.97)
N = len(lines)
H = min(1, 0.07 * N)
rax = plt.axes([0.05, 0.5 - H / 2, 0.22, H])
check = CheckButtons(rax, lines.label, lines.visible)
# Adjust button style
for i in range(N):
check.rectangles[i].set(lw=0, facecolor=lines.color[i])
check.labels[i].set(color=lines.color[i])
# Callback
def toggle_visible(label):
ind = lines.label == label
handle = lines[ind].handle.item()
vs = not lines[ind].visible.item()
handle.set_visible(vs)
lines.loc[ind, 'visible'] = vs
if autoscl:
autoscale_based_on(ax, lines[lines.visible].handle)
plt.draw()
check.on_clicked(toggle_visible)
# Must return (and be received) so as not to expire.
return check
def add_button ( self , ax ) :
#extend window to have some place to write the coordinate
expand_figure ('LEFT' , 2 , ax , self.fig)
ax_but = plt.axes([0.01, 0.05, 0.4, 0.8 ])
#do not show the graph of the button axis
ax_but.axis('off')
#add buttons
self.check = CheckButtons(ax_but, self.name_curves , [True for _ in self.curves] )
#link the event handler function to the button
self.check.on_clicked(self.on_click)
def genCombineResalePrices(yearEnter, monthNum, p_leaseEnter, flatType):
import numpy as np
from datetime import datetime
from matplotlib.widgets import CheckButtons
global townSelect, towndata
## To get data within the Period from parameter pass of p_yearEnter p_monthEnter to current date
dateStart = yearEnter + monthNum[-2:]
firstStartDate = yearEnter + "-" + monthNum[-2:]
dateEnd = datetime.now().strftime('%Y%m')
dataPeriod = data[data['month'] == firstStartDate]
for i in range(int(dateStart) + 1, int(dateEnd) + 1):
strDate = str(i)
last2digit = int(strDate[-2:])
if last2digit < 13:
iStrDate = str(i)
iDate = iStrDate[0:4] + "-" + iStrDate[-2:]
idata = data[data['month'] == iDate]
dataPeriod = np.append(dataPeriod, idata)
## To get data within the Flat lease period (Flat Lease > parameter p_leaseEnter)
leaseStart = int(p_leaseEnter) + 1
leaseData = dataPeriod[dataPeriod['remaining_lease'] > leaseStart]
flatTypeData = leaseData[leaseData['flat_type'] == flatType]
towndata = flatTypeData
## To get data within the Selected Checkbox Town Line
line_labels = [str(line.get_label()) for line in checkBoxLines]
line_visibility = [line.get_visible() for line in checkBoxLines]
check = CheckButtons(rax, line_labels, line_visibility)
checkButtonThemes(check)
status = check.get_status()
#index = line_labels.index(label)
#checkBoxLines[index].set_visible(not checkBoxLines[index].get_visible())
statusIndex = np.arange(0, len(status))
resale_prices = flatTypeData['resale_price']
town_resale_prices = np.zeros(len(townList), object)
for t in townIndex:
town_resale_prices[t] = resale_prices[towndata['town'] ==
townList[t]]
townSelect = np.zeros(len(townList), object)
townName = []
resale_prices_combined = []
labelIndex = -1
for s in statusIndex:
if status[s]:
labelIndex += 1
townSelect[labelIndex] = towndata[towndata['town'] ==
townList[s]]
#townSelect = np.append(townSelect,sdata)
townName.append(line_labels[s])
resale_prices_combined.append(town_resale_prices[s])
return resale_prices_combined, townName
class AppMatplotlib(App):
"""
A class that uses matplotlib to display the drawing of a graph.
"""
def __init__(self, graph_list: List[nx.PlanarEmbedding]):
App.__init__(self, graph_list)
self.fig, self.ax = plt.subplots()
self.fig.canvas.set_window_title('Planar graph drawing on a grid')
plt.subplots_adjust(left=0, right=1, top=1, bottom=0)
self.display_can_order = True
self.prev_button = None
self.next_button = None
self.dummy_edge_button = None
self.can_order_button = None
self.change_graph()
plt.show()
plt.close()
def add_widgets(self):
axprev = plt.axes([0.01, 0.9, 0.1, 0.075])
axnext = plt.axes([0.12, 0.9, 0.1, 0.075])
self.prev_button = Button(axprev, "Previous")
self.prev_button.on_clicked(lambda event: self.change_graph(-1))
self.next_button = Button(axnext, "Next")
self.next_button.on_clicked(lambda event: self.change_graph())
axcanbox = plt.axes([0.01, 0.81, 0.1, 0.1], frameon=False)
self.can_order_button = CheckButtons(axcanbox, ["Canonical order"], [self.display_can_order])
self.can_order_button.on_clicked(self.toggle_can_order)
axckeckbox = plt.axes([0.01, 0.76, 0.1, 0.1], frameon=False)
self.dummy_edge_button = CheckButtons(axckeckbox, ["Dummy edges"], [self.display_dummy_edges])
self.dummy_edge_button.on_clicked(self.toggle_dummy_edges)
axes_text_box = plt.axes([0.01, 0.69, 0.06, 0.075], frameon=False)
text_box = TextBox(axes_text_box, "", "Nodes : " + str(len(self.current_graph)))
def toggle_dummy_edges(self, event=None):
self.display_dummy_edges = not self.display_dummy_edges
self.update_display()
def toggle_can_order(self, event=None):
self.display_can_order = not self.display_can_order
self.update_display()
def update_display(self):
self.fig.clear()
self.draw_graph()
plt.axis('equal')
# plt.gca().set_aspect('equal', adjustable='box')
# plt.axis('off')
self.add_widgets()
class fogContainerTrfcStat(MplCanvas):
""" plot container traffic statistics for FOG demo"""
colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k', 'w']
def __init__(self, trfc_stats, n_points=15, parent=None):
'''
trfc_stats: a list of multiprocessing.Value, in which each
element coresponds to a container's current traffic bandwidth.
'''
super().__init__(parent=parent)
self.trfc_stats = trfc_stats
self.n_points = n_points
self.n_lines = len(trfc_stats)
self.fig.subplots_adjust(left=0.35)
self.ax_checkbuttom = self.fig.add_axes([0.04, 0.2, 0.17, 0.5])
self.compute_initial_figure()
def compute_initial_figure(self):
self.t = np.arange(0, self.n_points)
self.alldata = []
self.lines = []
for i in range(self.n_lines):
data = deque([0] * self.n_points, maxlen=self.n_points)
ls = self.axes.plot(self.t,
data,
lw=1,
color=fogContainerTrfcStat.colors[i],
label=f'Container {i}')
self.alldata.append(data)
self.lines.append(ls[0])
self.labels = [str(line.get_label()) for line in self.lines]
self.visibility = [line.get_visible() for line in self.lines]
self.checkbuttom = CheckButtons(self.ax_checkbuttom, self.labels,
self.visibility)
self.checkbuttom.on_clicked(self.checkbutton_func)
self.axes.set_ylim(0, 810000)
self.axes.legend(loc='upper right') #'lower right'
self.axes.get_xaxis().set_ticks([])
self.draw()
def checkbutton_func(self, label):
idx = self.labels.index(label)
self.lines[idx].set_visible(not self.lines[idx].get_visible())
self.draw()
def update_figure(self):
#self.axes.cla()
for i in range(self.n_lines):
self.alldata[i].append(self.trfc_stats[i].value)
self.lines[i].set_ydata(self.alldata[i])
# print(self.alldata[i])
self.draw()
def _heatmap(self, heat):
if heat is None: return
if not hasattr(self, 'chxbox'):
self.activated = True
ax = self.fig.add_axes([.77, .82, 0.3, .2], frame_on=False)
self.chxbox = CheckButtons(ax, ['heatmap'], [self.activated])
def callback(_):
self.activated = not self.activated
self.chxbox.on_clicked(callback)
if not self.activated:
if hasattr(self, 'heat'):
self.heat.remove()
delattr(self, 'heat')
return
min_, max_ = np.min(heat), np.max(heat)
heat = heat.reshape(self.x_plots, self.y_plots).T
x_w, y_w = (self.ax_img.get_array().shape)
heat = cv2.resize(heat, (x_w, y_w),
interpolation=cv2.INTER_CUBIC) # cv2.INTER_NEAREST
heat = interp1d([np.min(heat), np.max(heat)], [0., 1.])(heat)
if not hasattr(self, 'heat'):
self.heat = self.axes.imshow(
heat,
interpolation='bicubic',
cmap='jet',
alpha=.5,
)
if not self.cbar:
self.cbar = self.fig.colorbar(
self.heat,
ax=self.axes,
drawedges=False,
format='%.3f',
ticks=[0, 0.5, 1],
label='pis',
)
self.heat.set_data(heat) # update the heat map
self.cbar.draw_all()
self.cbar.set_alpha(1) # avoid lines caused by transparency
cbar_ticks = [
float(f'{x:.4f}')
for x in np.linspace(min_, max_, num=3, endpoint=True)
]
self.cbar.ax.set_yticklabels(cbar_ticks)
def main():
fmax = 0.1
t = linspace(-fmax, fmax, num=20000)
# Signals
ft = f(t) # Input
gt = g(t) # Tren de pulsos cuadrada
fg = f(t) * g(t) # Señal muestreada
sf = signal_filter(fg) # Señal recuperada con filtro
# Se dibujan en la ventana
fig, ax = plt.subplots(figsize=(13, 6))
l0, = ax.plot(t,
ft,
visible=True,
lw=1,
color='green',
label='f(t)',
dashes=[6, 2])
l1, = ax.plot(t,
gt,
visible=False,
lw=1,
color='red',
label='g(t)',
dashes=[6, 2])
l2, = ax.plot(t, fg, visible=False, lw=1, color='blue', label='f(t)*g(t)')
l3, = ax.plot(t,
sf,
visible=True,
lw=1,
color='blue',
label='Senial filtrada')
lines = [l0, l1, l2, l3]
# configuracion de la figura
fig.canvas.set_window_title("Teorema de muestreo - %s" % (__author__))
plt.subplots_adjust(left=0.2)
plt.ylim(-4, 5)
plt.xlim(-fmax, fmax)
plt.axhline(0, color="black")
plt.axvline(0, color="black")
# Se crean los chebkbuttons para las señales lines[]
rax = plt.axes([0.05, 0.4, 0.1, 0.15])
labels = [str(line.get_label()) for line in lines]
visibility = [line.get_visible() for line in lines]
check = CheckButtons(rax, labels, visibility)
def func(label):
index = labels.index(label)
lines[index].set_visible(not lines[index].get_visible())
plt.draw()
check.on_clicked(func)
plt.show()
def plot_movie(dales, num_frames):
n = len(fields) # number of fields to draw
fig, axes = plot.subplots(1, n, sharey=True)
handles = []
time_label = fig.text(0.1, 0.1, 'time')
zf = dales.get_zf().value_in(units.m)
ind = 0
colors = "bgrcmyk"
for ax in axes:
f = fields[ind]
var_name, var_unit = f[0], f[1]
ax = axes[ind]
ax.set_xlabel(var_name)
ax.set_ylabel("z")
var_prof = getattr(dales.profiles, var_name).value_in(var_unit)
line = ax.plot(var_prof[:], zf[:], '-', color=colors[ind], linewidth=2)
handles.append((ax, line))
ind += 1
# plot.tight_layout()
# draw a check box for forcing
rax = plot.axes([0.25, 0.05, 0.15, 0.08])
check = CheckButtons(rax, ('Forcing', ), (False, ))
check.on_clicked(checkbtn_clicked)
def update_plot(lines, prof):
lines[0].set_xdata(prof[:])
def update(i):
if forcingFlag:
tendency = numpy.zeros(dales.get_ktot())
print 'Applying forcing'
for i in range(-15, 15):
tendency[30 + i] = numpy.exp(-(i / 5.0)**2)
dales.set_tendency_V(tendency * .2 | units.m / units.s**2)
dales.set_tendency_QT(tendency * .000002 | units.mfu / units.s)
t = dales.get_model_time()
dales.evolve_model(t + dt)
t = dales.get_model_time()
print "updating with arg", i, 'time = ', t
time_label.set_text('time %.2f' % t.value_in(units.s))
for f, h in zip(fields, handles):
name, unit = f[0], f[1]
prof = getattr(dales.profiles, name).value_in(unit)
update_plot(h[1], prof)
a = movie.FuncAnimation(fig, update, frames=num_frames, repeat=False)
plot.show()
def plot(self, params, confidence_levels=[0.90, 0.95, 0.99], downsample=100):
from plotutils import plotutils as pu
from matplotlib import pyplot as plt
from matplotlib.widgets import CheckButtons
from itertools import cycle
lines = ['solid', 'dashed', 'dashdot', 'dotted']
linecycler = cycle(lines)
N = len(self._tracking_array)
tracking_array = self._tracking_array
param1 = tracking_array[params[0]]
param2 = tracking_array[params[1]]
pu.plot_greedy_kde_interval_2d(np.vstack([param1, param2]).T, [0.90,0.95,0.99])
ax = plt.gca()
arrows = {}
for proposal in self._proposals:
ls = next(linecycler)
sel = tracking_array['proposal'] == proposal
accepted = tracking_array['accepted'][sel]
xs = param1[sel]
ys = param2[sel]
x_ps = tracking_array[params[0]+'_p'][sel]
y_ps = tracking_array[params[1]+'_p'][sel]
dxs = x_ps - xs
dys = y_ps - ys
arrows[proposal] = []
for x, y, dx, dy, a in zip(xs,ys,dxs,dys,accepted):
if dx != 0 or dy != 0:
c = 'green' if a else 'red'
arrow = ax.arrow(x, y, dx, dy, fc=c, ec=c, alpha=0.5, visible=False, linestyle=ls)
arrows[proposal].append(arrow)
plt.subplots_adjust(left=0.5)
rax = plt.axes([0.05, 0.4, 0.4, 0.35])
check = CheckButtons(rax, self._proposals, [False for i in self._proposals])
def func(proposal):
N = len(arrows[proposal])
step = int(np.floor(N/float(downsample)))
step = step if step is not 0 else 1
for l in arrows[proposal][::step]:
l.set_visible(not l.get_visible())
plt.draw()
check.on_clicked(func)
plt.show()
def __init__(self):
self.Consistency = 'Not self consistent'
self.Bias = 0.1
self.Gate = None
self.OrbitalSel = None
self.fig, (self.axBias, self.axTrans, self.axIVCurve) = plt.subplots(3,1)
self.fig.patch.set_facecolor('ghostwhite')
plt.subplots_adjust(left=0.3)
pos = self.axBias.get_position()
self.axBias.set_position ([pos.x0, 0.55, pos.width, 0.40])
self.axTrans.set_position([pos.x0, 0.05, pos.width, 0.40])
self.axIVCurve.set_position([0.05, 0.05, 0.2, 0.3])
self.axCM = self.fig.add_axes([0.94, 0.55, 0.01, 0.35])
self.fig.canvas.mpl_connect('button_press_event', self.OnClick)
self.fig.canvas.mpl_connect('pick_event', self.OnPick)
self.fig.canvas.mpl_connect('key_press_event', self.OnPress)
self.InitMolecule()
self.InitBias()
self.InitTrans()
self.InitOrbitals()
self.InitIVCurve()
self.axSC = plt.axes([0.05, 0.85, 0.15, 0.10], axisbg='white')
self.cbSC = RadioButtons(self.axSC, ('Not self consistent', 'Hubbard', 'PPP'))
self.cbSC.on_clicked(self.UpdateConsistency)
self.axOptions1 = plt.axes([0.05, 0.7, 0.15, 0.10], axisbg='white')
self.cbOptions1 = CheckButtons(self.axOptions1, ('Overlap', 'Show Local Density','Show Local Currents'), (False, False, True))
self.cbOptions1.on_clicked(self.Options1)
self.axOptions2 = plt.axes([0.05, 0.5, 0.15, 0.15], axisbg='white')
self.cbOptions2 = CheckButtons(self.axOptions2, ('Show Transmission', 'Show Current', 'Show DOS', 'Show Orbitals', 'Show Phase'), (True, True, False, False, False))
self.cbOptions2.on_clicked(self.Options2)
c = ['seagreen', 'b', 'darkorange', 'lightsteelblue', 'm']
[rec.set_facecolor(c[i]) for i, rec in enumerate(self.cbOptions2.rectangles)]
self.axGleft = plt.axes([0.05, 0.43, 0.15, 0.02], axisbg='white')
self.axGright = plt.axes([0.05, 0.40, 0.15, 0.02], axisbg='white')
self.sGleft = Slider(self.axGleft, 'Gl', 0.0, 1.0, valinit = gLeft)
self.sGright = Slider(self.axGright, 'Gr', 0.0, 1.0, valinit = gRight)
self.sGleft.on_changed(self.UpdateG)
self.sGright.on_changed(self.UpdateG)
self.axSave = plt.axes([0.92, 0.95, 0.07, 0.04])
self.bSave = Button(self.axSave, 'Save')
self.bSave.on_clicked(self.Save)
def __init__(self, fig, rect, labels, act=None, func=None, fargs=None):
"""init function
Parameters:
fig: a matplotlib.figure.Figure instance
rect: [left, bottom, width, height]
each of which in 0-to-1-fraction i.e. 0<=x<=1
labels: array of strings
labels to be checked
act: a len(labels) array of booleans, optional, default: None
indicating whether the label is active at first
if None, all labels are inactive
func: function, optional, default: None
if not None, function called when checkbox status changes
fargs: array, optional, default: None
(optional) arguments for func
"""
self.fig = fig
self._rect = rect
self._func = func
self._fargs = fargs
self._labels = labels
self.axes = self.fig.add_axes(rect)
self.axes.set_axis_bgcolor('1.00')
inac = act if act is not None else (False,) * len(labels)
self.cb = CheckButtons(self.axes, self._labels, inac)
self.cb.on_clicked(self._onchange)
def init_ax4():
global ax4, checkService, checkPause, checkEndBen, checkDist, check
ax4 = fig.add_axes([.72, .15, .08, .12])
ax4.set_xticklabels([])
ax4.set_yticklabels([])
ax4.set_xticks([])
ax4.set_yticks([])
# Define checkboxes
check = CheckButtons(ax4, ('Service', 'Stepped', 'Pause', 'EndBen', 'Dist'),
(checkService, checkStepped, checkPause, checkEndBen, checkDist))
# Attach checkboxes to checkbox function
check.on_clicked(func)
def __init_fig__(self, data_name):
self.fig = figure()
self.ax = subplot(111)
self.fig.subplots_adjust(left=0.3)
title(_path.basename(data_name))
self.button_ax = plt.axes([0.05, 0.1, 0.15, 0.8])
self.check = CheckButtons(self.button_ax, sorted(self.all_names.keys()), [False] * len(self.all_names))
self.check.on_clicked(self.__click_cb)
def init_ax4():
global ax4, checkService, checkPause, checkEndBen, checkDist, check
ax4 = fig.add_axes([0.72, 0.15, 0.08, 0.12])
ax4.set_xticklabels([])
ax4.set_yticklabels([])
ax4.set_xticks([])
ax4.set_yticks([])
# Define checkboxes
check = CheckButtons(
ax4,
("Service", "Stepped", "Pause", "EndBen", "Dist"),
(checkService, checkStepped, checkPause, checkEndBen, checkDist),
)
# Attach checkboxes to checkbox function
check.on_clicked(func)
def initWindow(self):
if not (self.args.placeOne and self.args.placeTwo):
self.randomBB()
axes().set_aspect('equal', 'datalim')
plt.subplot(2, 2, (1, 2))
plt.subplot(2, 2, (1, 2)).set_aspect('equal')
subplots_adjust(left=0.31)
subplots_adjust(bottom=-0.7)
plt.title('QSR Visualisation')
axcolor = 'lightgoldenrodyellow'
rax = plt.axes([0.03, 0.4, 0.22, 0.45], axisbg=axcolor)
checkBox = CheckButtons(rax, self.qsr_type,(False,False,False,False,False))
checkBox.on_clicked(self.EventClick)
plt.subplot(2, 2, 3)
plt.axis('off')
plt.text(1, 1, (self.__compute_qsr(self.bb1, self.bb2)), family='serif', style='italic', ha='center')
if self.qsr:
self.updateWindow()
plt.show()
def __init__(self, data, auto_mask, savedir):
'''The constructor initializes all the variables and creates the plotting window.
**Input arguments:**
- *data*: NxM array
The background to be masked - an averaged (static) scattering image.
- *auto_mask*: NxM array
The default mask, masking all the bad pixels.
- *savedir*: string
Directory where the mask file will be saved.
'''
self.mask_saved = False
self.savedir = savedir
self.fig = figure()
title('Select ROI to mask. Press m to mask or w to save and exit ')
self.ax = self.fig.add_subplot(111)
# Check button for logscale switching
self.cbax = self.fig.add_axes([0.01, 0.8, 0.1, 0.15])
self.cb_log = CheckButtons(self.cbax, ('log',), (False,))
self.cb_log.on_clicked(self.toggle_logscale)
self.log_flag = False
self.canvas = self.ax.figure.canvas
#self.data = n.log10(data)
self.raw_data = data.copy()
self.data = data
self.lx, self.ly = shape(self.data)
self.auto_mask = auto_mask
self.mask = auto_mask
self.masked_data = n.ma.masked_array(self.data,self.mask)
self.points = []
self.key = []
self.x = 0
self.y = 0
self.xy = []
self.xx = []
self.yy = []
self.ind = 0
self.img = self.ax.imshow(self.masked_data,origin='lower',interpolation='nearest',animated=True)
self.colorbar = colorbar(self.img,ax=self.ax)
self.lc,=self.ax.plot((0,0),(0,0),'-+w',color='black',linewidth=1.5,markersize=8,markeredgewidth=1.5)
self.lm,=self.ax.plot((0,0),(0,0),'-+w',color='black',linewidth=1.5,markersize=8,markeredgewidth=1.5)
self.ax.set_xlim(0,self.lx)
self.ax.set_ylim(0,self.ly)
for i in range(self.lx):
for j in range(self.ly):
self.points.append([i,j])
cidb = connect('button_press_event', self.on_click)
cidk = connect('key_press_event',self.on_click)
cidm = connect('motion_notify_event',self.on_move)
def __init__(self, field, fieldname, halospec=None):
"""Initializes a visualization instance, that is a windows with a field
field is a 3D numpy array
fieldname is a string with the name of the field
halospec is a 2x2 array with the definition of the halo size
After this call the window is shown
"""
self.field = field
self.fieldname = fieldname
# Register halo information
if halospec is None:
halospec = [[3, 3], [3, 3]]
self.istart = halospec[0][0]
self.iend = field.shape[0] - halospec[0][1]
self.jstart = halospec[1][0]
self.jend = field.shape[1] - halospec[1][1]
self.plotHalo = True
self.plotLogLog = False
self.curklevel = 0
self.figure = plt.figure()
# Slider
slideraxes = plt.axes([0.15, 0.02, 0.5, 0.03], axisbg="lightgoldenrodyellow")
self.slider = Slider(slideraxes, "K level", 0, field.shape[2] - 1, valinit=0)
self.slider.valfmt = "%2d"
self.slider.set_val(0)
self.slider.on_changed(self.updateSlider)
# CheckButton
self.cbaxes = plt.axes([0.8, -0.04, 0.12, 0.15])
self.cbaxes.set_axis_off()
self.cb = CheckButtons(self.cbaxes, ("Halo", "Logscale"), (self.plotHalo, self.plotLogLog))
self.cb.on_clicked(self.updateButton)
# Initial plot
self.fieldaxes = self.figure.add_axes([0.1, 0.15, 0.9, 0.75])
self.collection = plt.pcolor(self._getField(), axes=self.fieldaxes)
self.colorbar = plt.colorbar()
self.fieldaxes.set_xlim(right=self._getField().shape[1])
self.fieldaxes.set_ylim(top=self._getField().shape[0])
plt.xlabel("i")
plt.ylabel("j")
self.title = plt.title("%s - Level 0" % (fieldname,))
plt.show(block=False)
def _recreate_show_lines_check_button(self):
axcolor = "lightgoldenrodyellow"
if hasattr(self, "rax_showlines"):
self.rax_showlines.cla()
self.rax_showlines = plt.axes([0.2, 0.05, self.button_length__, self.button_height__], axisbg=axcolor)
visible = self.prop_ifs.holder.get_visible()
linestyle = self.prop_ifs.holder.get_linestyle()
labels = ("hide ifs", "markers", "edges", "labels")
actives = (self.prop_ifs.hide_ifs, visible, linestyle not in ["None", None], self.prop_ifs.show_ann)
self.holder_actives = dict(zip(labels, actives))
self.w_check_components = CheckButtons(self.rax_showlines, labels, actives)
self.w_check_components.on_clicked(self.update_show_components)
return self.w_check_components
def _recreate_show_lines_check_button(self):
if self.ax_active not in self.active_lines_idx.keys():
return None
axcolor = 'lightgoldenrodyellow'
if hasattr(self, 'rax_showlines'):
self.rax_showlines.cla()
self.rax_showlines = plt.axes([0.2, 0.05,
self.button_length__,
self.button_height__], axisbg=axcolor)
prop_ifs = self._prop_idx_active[self.line_active__]
visible = prop_ifs.holder.get_visible()
linestyle = prop_ifs.holder.get_linestyle()
self.w_check_components = CheckButtons(self.rax_showlines,
('markers', 'edges', 'labels'),
(visible, linestyle not in ['None', None], prop_ifs.show_ann))
self.w_check_components.on_clicked(self.update_show_components)
return self.w_check_components
def __init__(self):
self.data = 0
self.errors = 0
self.tBins = 0
self.fig = plt.figure(1)
self.ax = self.fig.add_subplot(111,title="Pulse Display")
self.fig.subplots_adjust(left=0.3)
self.numPulse = 1
self.flcFlag = False
self.timeOffset = 0.0
self.currentModelName=[]
self.tMaxSelector = 0
self.resultAx =False
self.useSelector = False
#check if a pulseModSelector has been passed
self.pms = False
self.radio = False
ax = plt.axes([.05, 0.05, 0.12, 0.08])
self.addButton = Button(ax,'Add Pulse',color='0.95', hovercolor='0.45')
self.addButton.on_clicked(self.AddPulse)
ax2 = plt.axes([.05, 0.15, 0.12, 0.08])
self.findMaxButton = Button(ax2,'Find Max',color='0.95', hovercolor='0.45')
self.findMaxButton.on_clicked(self.FindMax)
ax3 = plt.axes([.05, 0.25, 0.12, 0.08])
self.fitButton = Button(ax3,'Fit',color='0.95', hovercolor='0.45')
self.fitButton.on_clicked(self.FitPulse)
ax4 = plt.axes([.05, 0.45, 0.12, 0.08])
self.useSelectorCheck = CheckButtons(ax4, ["Selector"], [False] )
self.useSelectorCheck.on_clicked(self.UseSelector)
def __init__(self, ax, collection, rax, raxmap, labels, labelcolors):
self.canvas = ax.figure.canvas
self.collection = collection
self.labelcolors = [colorConverter.to_rgba(c) for c in labelcolors]
self.labels = labels
self.xys = collection.get_offsets()
self.Npts = len(self.xys)
# Ensure that we have separate colors for each object
self.fc = collection.get_facecolors()
if len(self.fc) == 0:
raise ValueError('Collection must have a facecolor')
elif len(self.fc) == 1:
self.fc = np.tile(self.fc, self.Npts).reshape(self.Npts, -1)
self.sfc = self.fc.copy()
self.state = [False] * len(labels)
self.check = CheckButtons(rax,labels,self.state)
self.check.on_clicked(self.onclick)
self.raxmap = raxmap
self.ind = []
color='orange', hovercolor='orange')
bmark.on_clicked(callback.mark)
axcolor = 'lightgoldenrodyellow'
rb1_axes = plt.axes([0.70, 0.62, 0.10, 0.15], axisbg=axcolor)
radio1 = RadioButtons(rb1_axes, ('A', 'B', 'C'))
def rb_1(label):
print 'radio button 1'
radio1.on_clicked(rb_1)
rax = plt.axes([0.70, 0.3, 0.2, 0.25])
labels=['Type 1','Type 2', 'Type 4', 'Other']
check = CheckButtons(rax, (labels[0], labels[1], labels[2], labels[3]),
(False, False, False, False))
def func(label):
if label == labels[0]: check_text=labels[0]
elif label == labels[1]: check_text=labels[1]
elif label == labels[2]: check_text=labels[2]
elif label == labels[3]: check_text=labels[3]
print 'checked: ', check_text
check.on_clicked(func)
ax.text=(0.9, 0.9, 'ax.text 0.5')
fig.text=(0.9, 0.9, 'fig.text 0.5')
plt.show()
class Visualizer:
def __init__(self, field, fieldname, halospec=None):
"""Initializes a visualization instance, that is a windows with a field
field is a 3D numpy array
fieldname is a string with the name of the field
halospec is a 2x2 array with the definition of the halo size
After this call the window is shown
"""
self.field = field
self.fieldname = fieldname
# Register halo information
if halospec is None:
halospec = [[3, 3], [3, 3]]
self.istart = halospec[0][0]
self.iend = field.shape[0] - halospec[0][1]
self.jstart = halospec[1][0]
self.jend = field.shape[1] - halospec[1][1]
self.plotHalo = True
self.plotLogLog = False
self.curklevel = 0
self.figure = plt.figure()
# Slider
slideraxes = plt.axes([0.15, 0.02, 0.5, 0.03], axisbg="lightgoldenrodyellow")
self.slider = Slider(slideraxes, "K level", 0, field.shape[2] - 1, valinit=0)
self.slider.valfmt = "%2d"
self.slider.set_val(0)
self.slider.on_changed(self.updateSlider)
# CheckButton
self.cbaxes = plt.axes([0.8, -0.04, 0.12, 0.15])
self.cbaxes.set_axis_off()
self.cb = CheckButtons(self.cbaxes, ("Halo", "Logscale"), (self.plotHalo, self.plotLogLog))
self.cb.on_clicked(self.updateButton)
# Initial plot
self.fieldaxes = self.figure.add_axes([0.1, 0.15, 0.9, 0.75])
self.collection = plt.pcolor(self._getField(), axes=self.fieldaxes)
self.colorbar = plt.colorbar()
self.fieldaxes.set_xlim(right=self._getField().shape[1])
self.fieldaxes.set_ylim(top=self._getField().shape[0])
plt.xlabel("i")
plt.ylabel("j")
self.title = plt.title("%s - Level 0" % (fieldname,))
plt.show(block=False)
def updateSlider(self, val):
if val == self.curklevel:
return
self.curklevel = round(val)
self.title.set_text("%s - Level %d" % (self.fieldname, self.curklevel))
# Draw new field level
field = self._getField()
size = field.shape[0] * field.shape[1]
array = field.reshape(size)
self.collection.set_array(array)
self.colorbar.set_clim(vmin=field.min(), vmax=field.max())
self.collection.set_clim(vmin=field.min(), vmax=field.max())
self.colorbar.update_normal(self.collection)
self.figure.canvas.draw_idle()
def updateButton(self, label):
if label == "Halo":
self.plotHalo = not self.plotHalo
if label == "Logscale":
self.plotLogLog = not self.plotLogLog
self.updatePlot()
def updatePlot(self):
# Redraw field
self.collection.remove()
field = self._getField()
if self.plotLogLog:
minvalue = field.min()
norm = SymLogNorm(linthresh=1e-10)
self.collection = plt.pcolor(field, axes=self.fieldaxes, norm=norm)
self.colorbar.set_clim(vmin=minvalue, vmax=field.max())
else:
self.collection = plt.pcolor(field, axes=self.fieldaxes)
self.colorbar.set_clim(vmin=field.min(), vmax=field.max())
self.colorbar.set_norm(norm=Normalize(vmin=field.min(), vmax=field.max()))
self.fieldaxes.set_xlim(right=field.shape[1])
self.fieldaxes.set_ylim(top=field.shape[0])
self.colorbar.update_normal(self.collection)
self.figure.canvas.draw_idle()
def _getField(self):
if self.plotHalo:
return np.rot90(self.field[:, :, self.curklevel])
else:
return np.rot90(self.field[self.istart : self.iend, self.jstart : self.jend, self.curklevel])
def evaluateKineticsGroupValues(family, database, method, testSetLabels, plot, exactOnly=False, estimateOnly=False):
"""
Evaluate the kinetics group additivity values for the given reaction
`family` using the specified lists of depository components
`testSetLabels` as the test set. The already-loaded RMG database should be
given as the `database` parameter.
"""
kunits = family.getRateCoefficientUnits()
assert not (exactOnly and estimateOnly)
print 'Categorizing reactions in test sets for {0}'.format(family.label)
testSets0 = []
for testSetLabel in testSetLabels:
testSet = createDataSet(testSetLabel, family, database)
testSets0.append((testSetLabel, testSet))
for testSetLabel, testSet in testSets0:
for index in range(len(testSet)):
reaction, template, entry = testSet[index]
for reactant in reaction.reactants:
if isinstance(reactant, Species) and not reactant.label and len(reactant.molecule) > 0:
reactant.label = reactant.molecule[0].toSMILES()
for product in reaction.products:
if isinstance(product, Species) and not product.label and len(product.molecule) > 0:
product.label = product.molecule[0].toSMILES()
# For each entry in each test set, determine the kinetics as predicted by
# RMG-Py and as given by the entry in the test set
# Note that this is done on a per-site basis!
kineticsModels = []; kineticsData = []
testSets = []
for testSetLabel, testSet0 in testSets0:
testSet = []
for index in range(len(testSet0)):
reaction, template, entry = testSet0[index]
krule = family.getKineticsForTemplate(template, degeneracy=1, method='rate rules')
kgroup = family.getKineticsForTemplate(template, degeneracy=1, method='group additivity')
kdata = convertKineticsToPerSiteBasis(reaction.kinetics, reaction.degeneracy)
if exactOnly and not re.search('Exact', krule.comment):
continue
elif estimateOnly and not re.search('Estimated', krule.comment):
continue
testSet.append((reaction, template, entry, krule, kgroup, kdata))
testSets.append((testSetLabel, testSet))
# Generate parity plots at several temperatures
print 'Generating parity plots for {0}'.format(family.label)
import matplotlib.pyplot as plt
from matplotlib.widgets import CheckButtons
Tdata = [500,1000,1500,2000]
if kunits == 'm^3/(mol*s)':
kunits = 'cm$^3$/mol*s'; kfactor = 1.0e6
elif kunits == 's^-1':
kunits = 's$^{-1}$'; kfactor = 1.0
for T in Tdata:
stdev_total = 0; ci_total = 0; count_total = 0
# Initialize plot
if plot == 'interactive':
fig = pylab.figure(figsize=(10,8))
ax = plt.subplot(1, 1, 1)
else:
fig = pylab.figure(figsize=(6,5))
ax = plt.subplot(1, 1, 1)
ax = plt.subplot(1, 1, 1)
lines = []
legend = []
# Iterate through the test sets, plotting each
for testSetLabel, testSet in testSets:
kmodel = []; kdata = []
stdev = 0; ci = 0; count = 0
for reaction, template, entry, kineticsRule, kineticsGroup, kineticsData in testSet:
if method == 'rate rules':
kineticsModel = kineticsRule
elif method == 'group additivity':
kineticsModel = kineticsGroup
# Honor temperature ranges when plotting data
# Place a dummy value so that the points so that the
# interactivity is still correct
if not kineticsData.isTemperatureValid(T):
kmodel.append(0.0)
kdata.append(0.0)
continue
# Evaluate k(T) for both model and data at this temperature
if isinstance(kineticsModel, ArrheniusEP):
km = kineticsModel.getRateCoefficient(T, 0) * kfactor
else:
km = kineticsModel.getRateCoefficient(T) * kfactor
kmodel.append(km)
if isinstance(kineticsData, ArrheniusEP):
kd = kineticsData.getRateCoefficient(T, 0) * kfactor
else:
kd = kineticsData.getRateCoefficient(T) * kfactor
kdata.append(kd)
# Evaluate variance
stdev += (math.log10(km) - math.log10(kd))**2
count += 1
stdev_total += stdev
count_total += count
stdev = math.sqrt(stdev / (count - 1))
ci = scipy.stats.t.ppf(0.975, count - 1) * stdev
assert len(kmodel) == len(testSet)
assert len(kdata) == len(testSet)
print "Test set {0} contained {1} rates.".format(testSetLabel, count)
print 'Confidence interval at T = {0:g} K for test set "{1}" = 10^{2:g}'.format(T, testSetLabel, ci)
# Add this test set to the plot
lines.append(ax.loglog(kdata, kmodel, 'o', picker=5)[0])
legend.append(testSetLabel)
stdev_total = math.sqrt(stdev_total / (count_total - 1))
ci_total = scipy.stats.t.ppf(0.975, count_total - 1) * stdev_total
print 'Total confidence interval at T = {0:g} K for all test sets = 10^{1:g}'.format(T, ci_total)
# Finish plots
xlim = pylab.xlim()
ylim = pylab.ylim()
lim = (min(xlim[0], ylim[0])*0.1, max(xlim[1], ylim[1])*10)
ax.loglog(lim, lim, '-k')
ax.loglog(lim, [lim[0] * 10**ci_total, lim[1] * 10**ci_total], '--k')
ax.loglog(lim, [lim[0] / 10**ci_total, lim[1] / 10**ci_total], '--k')
pylab.xlabel('Actual rate coefficient ({0})'.format(kunits))
pylab.ylabel('Predicted rate coefficient ({0})'.format(kunits))
if len(testSets) > 1:
pylab.legend(legend, loc=4, numpoints=1)
pylab.title('%s, T = %g K' % (family.label, T))
pylab.xlim(lim)
pylab.ylim(lim)
plot_range = math.log10(lim[1] / lim[0])
if plot_range > 25:
majorLocator = matplotlib.ticker.LogLocator(1e5)
minorLocator = matplotlib.ticker.LogLocator(1e5, subs=[1, 10, 100, 1000, 10000])
elif plot_range > 20:
majorLocator = matplotlib.ticker.LogLocator(1e4)
minorLocator = matplotlib.ticker.LogLocator(1e4, subs=[1, 10, 100, 1000])
elif plot_range > 15:
majorLocator = matplotlib.ticker.LogLocator(1e3)
minorLocator = matplotlib.ticker.LogLocator(1e3, subs=[1, 10, 100])
elif plot_range > 10:
majorLocator = matplotlib.ticker.LogLocator(1e2)
minorLocator = matplotlib.ticker.LogLocator(1e2, subs=[1, 10])
else:
majorLocator = matplotlib.ticker.LogLocator(1e1)
minorLocator = None
ax.xaxis.set_major_locator(majorLocator)
ax.yaxis.set_major_locator(majorLocator)
if minorLocator:
ax.xaxis.set_minor_locator(minorLocator)
ax.yaxis.set_minor_locator(minorLocator)
def onpick(event):
index = lines.index(event.artist)
xdata = event.artist.get_xdata()
ydata = event.artist.get_ydata()
testSetLabel, testSet = testSets[index]
for ind in event.ind:
reaction, template, entry, krule, kgroup, kdata = testSet[ind]
kunits = 'm^3/(mol*s)' if len(reaction.reactants) == 2 else 's^-1'
print testSetLabel
print 'template = [{0}]'.format(', '.join([g.label for g in template]))
print 'entry = {0!r}'.format(entry)
print str(reaction)
print 'k_data = {0:9.2e} {1}'.format(xdata[ind], kunits)
print 'k_model = {0:9.2e} {1}'.format(ydata[ind], kunits)
print krule
if kgroup: print kgroup
print krule.comment
if kgroup: print kgroup.comment
print
connection_id = fig.canvas.mpl_connect('pick_event', onpick)
if plot == 'interactive':
rax = plt.axes([0.15, 0.65, 0.2, 0.2])
check = CheckButtons(rax, legend, [True for label in legend])
def func(label):
for index in range(len(lines)):
if legend[index] == label:
lines[index].set_visible(not lines[index].get_visible())
plt.draw()
check.on_clicked(func)
fig.subplots_adjust(left=0.10, bottom=0.10, right=0.97, top=0.95, wspace=0.20, hspace=0.20)
else:
fig.subplots_adjust(left=0.15, bottom=0.14, right=0.95, top=0.93, wspace=0.20, hspace=0.20)
filename = '{0}_{1:g}'.format(family.label, T)
if method == 'rate rules':
filename += '_rules'
elif method == 'group additivity':
filename += '_groups'
if exactOnly:
filename += '_exact'
elif estimateOnly:
filename += '_estimate'
pylab.savefig('{0}.pdf'.format(filename))
pylab.savefig('{0}.png'.format(filename), dpi=200)
pylab.show()
def displayResults2(self):
""" show results with time slider
geneNames have to be unique!
"""
##
## import pylab
#### from matplotlib.widgets import Slider
## mi=1
## ma=self.results.shape[1]
## print(ma)# should be 101
##
## figure=pylab.figure()
##
## ax=pylab.Axes(figure,[0.15, 0.1, 0.65, 0.03])
## slider=pylab.Slider(ax=ax,label='time slider',valmin=mi,valmax=ma,valinit=1)
##
## def update():
## pylab.plot(self.results[:,slider.val,:])
## pylab.draw()
##
## slider.on_changed(update)
## pylab.show()
import pylab
import scipy
from matplotlib.widgets import Slider, Button, RadioButtons, CheckButtons
## test = pylab.plot(self.results[:,-1,:])
## pylab.show()
# results is a data cube [cell, time, gene]
miTime=0
maTime=self.results.shape[1]-1
## xdata=range(self.results.shape[0])
##ax = pylab.subplot(111)
pylab.subplots_adjust(left=0.25, bottom=0.25)
## t = scipy.arange(0.0, 1.0, 0.001)
## a0 = 5
## f0 = 3
## s = a0*scipy.sin(2*scipy.pi*f0*t)
## plot, = pylab.plot(t,s, lw=2, color='red')
selection=scipy.ones(len(self.geneNames)).astype(bool)# select all on start
plots = pylab.plot(self.results[:,maTime,:]) # apperently returns a plot for each line...
## pylab.axis([0, 1, -10, 10])
axcolor = 'lightgoldenrodyellow'
## axfreq = pylab.axes([0.25, 0.1, 0.65, 0.03], axisbg=axcolor)
## axamp = pylab.axes([0.25, 0.15, 0.65, 0.03], axisbg=axcolor)
## sfreq = Slider(axfreq, 'Freq', 0.1, 30.0, valinit=f0)
## samp = Slider(axamp, 'Amp', 0.1, 10.0, valinit=a0)
ax=pylab.axes([0.15, 0.1, 0.65, 0.03])
slider=pylab.Slider(ax=ax,label='time slider',valmin=miTime,valmax=maTime,valinit=maTime)
def update(val):
## amp = samp.val
## freq = sfreq.val
## l.set_ydata(amp*scipy.sin(2*scipy.pi*freq*t))
for i in range(len(plots)):
plots[i].set_ydata(self.results[:,slider.val,i])
plots[i].set_visible(selection[i])
pylab.draw()
## sfreq.on_changed(update)
## samp.on_changed(update)
slider.on_changed(update)
resetax = pylab.axes([0.8, 0.025, 0.1, 0.04])
button = Button(resetax, 'Reset', color=axcolor, hovercolor='0.975')
def reset(event):
## sfreq.reset()
## samp.reset()
slider.reset()
button.on_clicked(reset)
rax = pylab.axes([0.025, 0.5, 0.15, 0.15], axisbg=axcolor)
checker=CheckButtons(rax,self.geneNames,actives=selection)
def selector(val):
## print(val)
## print(scipy.array(range(len(self.geneNames)))[self.geneNames==val][0])
geneNr=scipy.array(range(len(self.geneNames)))[self.geneNames==val][0] # its retarded to check label names... but that is the way they like it....
selection[geneNr]=not(selection[geneNr])
update(slider.val)
checker.on_clicked(selector)
## print(checker.eventson)
## print(checker.drawon)
## radio = RadioButtons(rax, ('red', 'blue', 'green'), active=0)
##
## rax = pylab.axes([0.025, 0.5, 0.15, 0.15], axisbg=axcolor)
## radio = RadioButtons(rax, ('red', 'blue', 'green'), active=0)
## def colorfunc(label):
## for i in range(len(plots)):
## plots[i].set_color(label)
#### plots.set_color(label)
## pylab.draw()
## radio.on_clicked(colorfunc)
pylab.show()
#centers = utils.canopy_clustering(0.5, 0.65, X, utils.cosine_simularity)
#print "Number of seeds: ", len(centers)
#left, bottom, width, height
pace_ax = axes([.92,.28,.05,.05])
dur_ax = axes([.92,.34,.05,.05])
cal_ax = axes([.92,.22,.05,.05])
fuel_ax = axes([.92,.16,.05,.05])
distance_ax = axes([.92,.40,.05,.05])
reset_ax = axes([.92,.75,.05,.05])
clust_ax = axes([.03,.65,.05,.05])
rax = axes([.03, .75, .05,.15])
check = CheckButtons(rax, ('dist','dur','pace','cal','fuel'),(True, True, True, True, True))
b_pace = Button(pace_ax, "Pace")
b_dist = Button(distance_ax, "Dist")
b_dur = Button(dur_ax, "Duration")
b_cal = Button(cal_ax, "Calories")
b_fuel = Button(fuel_ax, "Fuel")
b_reset = Button (reset_ax, "Reset")
b_reclust = Button(clust_ax, "Cluster")
buckets = [0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0, 8.5, 9.0, 9.5, 10.0, 10.5, 11.0, 11.5, 12.0, 12.5,13,13.5,14,14.5]
pace_buckets = [0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0, 8.5, 9.0, 9.5, 10.0, 10.5, 11.0, 11.5, 12.0, 12.5,13,13.5,14,14.5]
dur_buckets = [0.0, 5.0,10.0, 15.0, 20.0, 25.0, 30.0, 35.0, 40.0, 45.0, 50.0, 55.0, 60.0, 65.0, 70.0, 75.0, 80.0, 85.0, 90.0, 95.0, 100.0, 105.0]
cal_buckets = [0,50,100,150,200,250,300,350,400,450,500,550,600,650,700,750,800,850,900,950,1000.0,1050,1100,1150,1200,1250,1300,1350,1400,1450,1500]
def svdplot(input_data, fmax=None, hold=0):
if hold==0: pl.clf() # erase the figure, as this is a mult- axis plot
else: pl.clf() # do it anyway, trying to overcome checkbuttons problem
n_SV = len(input_data.svs)
#for chrono in input_data.chronos:
# print(peak_freq(chrono, input_data.dim1))
# define axes
ax1 = pl.subplot(221)
ax2 = pl.subplot(222)
ax3 = pl.subplot(223)
ax4 = pl.subplot(224)
# allow space for check buttons
pl.subplots_adjust(left=0.2, right=0.98)
# setup check boxes
rax = pl.axes([0.01, 0.05, 0.09, 0.9])
# CheckButtons take tuple arguments, tuples are immutable, so create lists fom svd info, and then cast as tuple
button_name_list=[]
button_setting_list=[]
for i in range(n_SV):
button_name_list.append(' '+str(i))
button_setting_list.append(False)
# have first 2 SVs on as default
for i in [0,1]:
button_setting_list[i] = True
# like "self"
check = CheckButtons(rax, tuple(button_name_list), tuple(button_setting_list))
# hack to make check buttons square
check_box_stretch = 7
for i in range(len(check.rectangles)):
#if (1 == 0): # use this line instead of for i in... to turn off the hack
check.rectangles[i].set_width(check_box_stretch*check.rectangles[i].get_height())
for j in [0,1]: # two lines of the x
orig_x_data = check.lines[i][j].get_xdata()
orig_y_data = check.lines[i][j].get_ydata()
orig_width = orig_x_data[1]-orig_x_data[0]
new_width = check_box_stretch*orig_width
new_x_data = [orig_x_data[0],orig_x_data[0]+new_width]
check.lines[i][j].set_data(new_x_data,orig_y_data)
# plot all SVs, use button_setting_list for initial visibility
# axes 1: chrono
pl.axes(ax1)
#pl.xlabel('Time -*-get units from Timebase-*-')
pl.ylabel('chronos [a.u.]') # used to be labelled amplitude, but is not really.
# you can get to the reconstructed signals via fs.signal, but chronos is better here
## python3 - all these plot_lists refactored to look nicer in python2/3
plot_list_1 = [ ax1.plot(np.arange(len(input_data.chronos[sv_i])), input_data.chronos[sv_i], visible= button_setting_list[sv_i],alpha=0.5)[0] for sv_i in range(n_SV)]
#pl.xlim(min(input_data.dim1), max(input_data.dim1))
# axes 2: SVs
pl.axes(ax2)
sv_sv = [input_data.svs[i] for i in range(n_SV)]
ax2.semilogy(np.arange(n_SV),sv_sv,'ko',markersize=3)
# avoid extreme ylim ranges - start with no more than 10^3
(ymin,ymax) = ax2.get_ylim()
ax2.set_ylim(max(ymin, ymax/1000), ymax)
entropy = input_data.H
pl.xlabel('Singular Value number')
pl.ylabel('Singular Value')
# pl.figtext(0.75,0.83,'1/H = %.2f' %(1./entropy),fontsize=12, color='r')
# pl.figtext(0.75,0.81,'H = %.2f' %(entropy),fontsize=12, color='b')
# this is done in two places - potential for inconsistency - wish I knew better -dgp
# These changes make it easier to adjust the subplot layout
# was pl.figtext(0.75,0.78, (relative to figure), make it relative to axes
# Use kwargs so that most formatting is common to all three labels.
kwargs={'fontsize':8,'transform':ax2.transAxes,
'horizontalalignment':'right', 'verticalalignment':'top'}
bsl = np.array(button_setting_list)
RMS_scale=np.sqrt(np.mean(
(input_data.scales[:,0]*bsl)**2))
### Note: amp is dodgy - calcualted roughly here
## reconsider how fs p is calculated! maybe factor scales in before sum
## Note also - a12 is calcuated differently here but looks OK
# Also, shold update these like Dave does with Energy
amp=np.sqrt(np.sum(input_data.p*bsl))*RMS_scale
print("amp=%.3g:" % (amp)),
energy = Energy(input_data.p,bsl)
energy_label = ax2.text(0.96,0.98,'E = %.1f %%' %(100.*energy.value),
color='b', **kwargs)
labstr = str('tmid = {tm:.1f} ms, 1/H = {invH:.2f}, below for 0,1, '\
'?Amp = {Amp:.2g}, a12 = {a12:.2f} '
.replace(', ','\n')
.format(tm=1e3*np.average(input_data.chrono_labels),
invH=(1./entropy),
Amp = float(np.sqrt(np.sum(input_data.p*bsl))*RMS_scale),
a12 = float(np.sqrt(input_data.p[1]/input_data.p[0]))))
ax2.text(0.96,0.85,labstr, color='r', **kwargs)
# grid('True')
plot_list_2 = []
for sv_i in range(n_SV):
col = plot_list_1[sv_i].get_color()
plot_list_2.append(ax2.semilogy([sv_i], [input_data.svs[sv_i]],
'%so' %(col),visible= button_setting_list[sv_i],
markersize=8,alpha=0.5)[0])
# axes 3: fft(chrono)
pl.axes(ax3)
pl.xlabel('Frequency [kHz]')
pl.ylabel('Power Spectrum')
pl.grid(True) # matplotlib 1.0.X wants a boolean (unquoted)
nyquist_kHz = 1.e-3*0.5/np.average(np.diff(input_data.chrono_labels))
plot_list_3 = []
for sv_i in range(n_SV):
col = plot_list_1[sv_i].get_color()
tmp_chrono = input_data.chronos[sv_i]
tmp_fft = np.fft.fft(tmp_chrono)[:len(tmp_chrono)//2]
freq_array = nyquist_kHz*np.arange(len(tmp_fft))/(len(tmp_fft)-1)
plot_list_3.append(ax3.plot(freq_array, abs(tmp_fft), col,visible= button_setting_list[sv_i],alpha=0.5)[0])
if fmax is None:
ffact = 1e3 # seems like this routine is in kHz - where does it cvt?
try:
axt = eval(pyfusion.config.get('Plots','FT_Axis'))
if axt[3]<2e3: ffact=1.0 # avoid a python3 issue
fmax = axt[3]/ffact
except:
fmax = nyquist_kHz
pl.xlim(0,fmax)
# axes 4: topo
pl.axes(ax4)
pl.xlabel('Channel')
pl.ylabel('Topo [a.u.]')
angle_array = np.arange(n_SV+1)
#channel_names = input_data.timesegment.data[input_data.diagnostic.name].ordered_channel_list
#channel_names.append(channel_names[0])
#pl.xticks(angle_array,channel_names, rotation=90)
plot_list_4 = []
for sv_i in range(n_SV):
col = plot_list_1[sv_i].get_color()
tmp_topo = join_ends(input_data.topos[sv_i])
pos,neg = posNegFill(angle_array,np.zeros(len(angle_array)),tmp_topo)
### BUG: it looks like ax4.fill doesn't work in a couple of cases, leaving sub_plot_4_list[i] as int, which raises a set_visible() bug in button_action - also has problems with draw(). other subplots all worked fine before I started with subplot 4
sub_plot_4_list = list(range(len(pos)+len(neg)+2))
for j in range(len(pos)):
sub_plot_4_list[j], = ax4.fill(pos[j][0],pos[j][1],col,visible= button_setting_list[sv_i],alpha=0.5)
for j in range(len(neg)):
sub_plot_4_list[j+len(pos)], = ax4.fill(neg[j][0],neg[j][1],col,visible= button_setting_list[sv_i],alpha=0.5)
sub_plot_4_list[len(neg)+len(pos)+0], = ax4.plot(angle_array,tmp_topo,'%so' %(col),visible= button_setting_list[sv_i],markersize=3)
# show repeated val
sub_plot_4_list[len(neg)+len(pos)+1], = ax4.plot([angle_array[-1]],[tmp_topo[-1]],'kx', visible= button_setting_list[sv_i],markersize=6)
debug_(pyfusion.DEBUG, 2, key='svdplot')
plot_list_4.append(sub_plot_4_list)
def test_action(label):
print(label)
def button_action(label):
print('action')
# this is not very clear: but basically, the label is the str() of the element of plot_list_x we want to make / unmake visible
visible_status = plot_list_1[int(label)].get_visible()
plot_list_1[int(label)].set_visible(not visible_status)
plot_list_2[int(label)].set_visible(not visible_status)
plot_list_3[int(label)].set_visible(not visible_status)
for i in range(len(plot_list_4[int(label)])):
plot_list_4[int(label)][i].set_visible(not visible_status)
# if visible_status == False, then we are adding visiblity => add to energy, vice-verca
if visible_status:
energy.sub(int(label))
else:
energy.add(int(label))
energy_label._text='E = %.2f %%' %(100.*energy.value)
pl.draw()
# action when button is clicked
check.on_clicked(button_action)
#check.on_clicked(test_action)
# show plot
pl.show(block=hold)
def initialize(A,B,C,dA,dB,dC,DJ,DJK,DK,dJ,dK,ua,ub,uc,f_lower,f_upper,T,J_max,last_times_picked_list):
#matplotlib.use('TkAggx')
global DJ_g,DJK_g,DK_g,ua_g,ub_g,uc_g,T_g,dJ_g,dK_g,f_lower_g,f_upper_g,J_max_g
J_max_g = J_max
DJ_g = DJ
DJK_g = DJK
DK_g = DK
ua_g = ua
ub_g = ub
uc_g = uc
T_g=T
dJ_g = dJ
dK_g = dK
f_lower_g = f_lower
f_upper_g = f_upper
global picked_list
picked_list = last_times_picked_list
plt.close()
global figure_h
figure_h = plt.figure(figsize=(16.5, 4))
try:
figure_h.canvas.manager.window.Move((00,00))
except AttributeError:
pass
#thismanager = pl.get_current_fig_manager()
#thismanager.window.SetPosition((00, 0))
#thismanager.window.wm_geometry("+00+0")
int_writer(ua,ub,uc,temp=T_g,freq=(f_upper_g/1000))
var_writer_uncert(A,B,C,DJ,DJK,DK,dJ_g,dK_g)
run_SPCAT()
data = cat_reader()
global t
global s
t = []
s = []
t_b = []
s_b = []
for x in data:
s.append(0.0)
s.append(str(10**float(x[1])))
s.append(0.0)
t.append(float(x[0])-0.0001)
t.append(x[0])
t.append(float(x[0])+0.0001)
for y in picked_list:
if x[2]==y[1] and x[3]==y[2]:
s_b.append(0.0)
s_b.append(str(10**float(x[1])))
s_b.append(0.0)
t_b.append(float(x[0])-0.0001)
t_b.append(x[0])
t_b.append(float(x[0])+0.0001)
ax = figure_h.add_subplot(212)
plt.subplots_adjust(left=0.25, bottom=0.25)
pl.subplots_adjust( hspace=0.0,right=0.97 )
a0 = 5
f0 = 3
global l,triples_plt
l, = plt.plot(t,s, lw=2, color='red')
triples_plt, = plt.plot([],[], '*',markersize=15,color='blue')
ax.set_xlim([f_lower_g,f_upper_g])
global picked_plt, ax2
picked_plt, = plt.plot(t_b,s_b,lw=2,color='black')
#plt.axis([0, 1, -10, 10])
#figure_h.canvas.mpl_connect('button_press_event', handle_mouse_press)
ax2 = figure_h.add_subplot(211,sharex=ax)
ax2.axes.get_xaxis().set_visible(False)
global peak_list_plt,exp_plt,trans_1_plt,trans_2_plt,trans_3_plt
peak_list_plt, = ax2.plot([],[],'o',color='red')
trans_1_plt, = ax2.plot([],[],'s',color='cyan')
trans_2_plt, = ax2.plot([],[],'s',color='magenta')
trans_3_plt, = ax2.plot([],[],'s',color='yellow')
ax2.set_xlim([f_lower_g,f_upper_g])
global locator
locator = ax2.yaxis.get_major_locator()
exp_plt, = ax2.plot([],[],lw=2,color='black')
global peak_1_uncertainty,peak_2_uncertainty,peak_3_uncertainty,peaklist,freq_low,freq_high
figure_h.canvas.mpl_connect('key_press_event', on_key)
axcolor = 'lightgoldenrodyellow'
axA = plt.axes([0.25, 0.15, 0.65, 0.03], axisbg=axcolor)
axB = plt.axes([0.25, 0.1, 0.65, 0.03], axisbg=axcolor)
axC = plt.axes([0.25, 0.05, 0.65, 0.03], axisbg=axcolor)
axua = plt.axes([0.03, 0.22, 0.1, 0.03], axisbg=axcolor)
axub = plt.axes([0.03, 0.17, 0.1, 0.03], axisbg=axcolor)
axuc = plt.axes([0.03, 0.12, 0.1, 0.03], axisbg=axcolor)
#axub = plt.axes([0.25, 0.1, 0.65, 0.03], axisbg=axcolor)
#axuc = plt.axes([0.25, 0.05, 0.65, 0.03], axisbg=axcolor)
global ua_slider
ua_slider = Slider(axua, 'mu a', ua_g-1, ua_g+1, valinit=ua_g)
ua_slider.on_changed(update)
global ub_slider
ub_slider = Slider(axub, 'mu b', ub_g-1, ub_g+1, valinit=ub_g)
ub_slider.on_changed(update)
global uc_slider
uc_slider = Slider(axuc, 'mu c', uc_g-1, uc_g+1, valinit=uc_g)
uc_slider.on_changed(update)
global A_slider
global B_slider
global C_slider
global rax
rax = plt.axes([0.0, 0.5, 0.19, 0.4])
global check
check = CheckButtons(rax, ('','','',''), (True, False, False,False))
check.on_clicked(func)
A_slider = Slider(axA, 'A', A-dA, A+dA, valinit=A)
B_slider = Slider(axB, 'B', B-dB, B+dB, valinit=B)
C_slider = Slider(axC, 'C', C-dC, C+dC, valinit=C)
A_slider.on_changed(update)
B_slider.on_changed(update)
C_slider.on_changed(update)
global button
global radio
resetax = plt.axes([0.1, 0.025, 0.1, 0.04])
button = Button(resetax, 'Reset Sliders', color=axcolor, hovercolor='0.975')
button.on_clicked(reset)
#rax = plt.axes([0.025, 0.5, 0.15, 0.15], axisbg=axcolor)
#radio = RadioButtons(rax, ('red', 'blue', 'green'), active=0)
#radio.on_clicked(colorfunc)
global text_box
#global text_box2
text_box = plt.text(-1,8, "")
text_box2 = plt.text(-1,23, "Refine Mouse Selection: Select transitions by pushing 'q' and then clicking in the predicted spectrum ")
def animate(sources, reflect=False):
fig = plt.figure()
# create reflection images of sources
if reflect:
reflectedSources = []
for src in sources:
reflSrc = deepcopy(src)
reflSrc.pos = -src.pos
reflectedSources.append(reflSrc)
# subplot for every source
lines = []
for i, src in enumerate(sources):
# pressure on left y axis
ax1 = fig.add_subplot(len(sources)+1, 1, i+1)
ax1.set_xlim([0, xMax])
yLim = src.pAmpl * 1.3 * (1+reflect)
ax1.set_ylim([-yLim, yLim])
ax1.set_ylabel('p')
line_pSrc, = plt.plot([], [], 'r--', lw=1)
line_pRefl, = plt.plot([], [], 'r:', lw=1)
line_p, = plt.plot([], [], 'r', lw=1, label='p')
ax1.legend(loc='upper left')
# velocity on right y axis
ax2 = ax1.twinx()
ax2.set_xlim([0, xMax])
yLim = src.uAmpl * 2 * (1+reflect)
ax2.set_ylim([-yLim, yLim])
ax2.set_ylabel('u')
line_uSrc, = plt.plot([], [], 'b--', lw=1)
line_uRefl, = plt.plot([], [], 'b:', lw=1)
line_u, = plt.plot([], [], 'b', lw=1, label='u')
ax2.legend(loc='upper right')
ax1.set_title(src.name)
lines.append([line_pSrc, line_uSrc, line_pRefl, line_uRefl, line_p, line_u])
# subplot for total pressure and velocity
# pressure on left y axis
ax1 = fig.add_subplot(len(sources)+1, 1, len(sources)+1)
ax1.set_xlim([0, xMax])
yLim = sum([src.pAmpl for src in sources]) * 1.3 * (1+reflect)
ax1.set_ylim([-yLim, yLim])
ax1.set_ylabel('p')
line_p, = plt.plot([], [], 'r', lw=1, label='p')
ax1.legend(loc='upper left')
# velocity on right y axis
ax2 = ax1.twinx()
ax2.set_xlim([0, xMax])
yLim = sum([src.uAmpl for src in sources]) * 2 * (1+reflect)
ax2.set_ylim([-yLim, yLim])
ax2.set_ylabel('u')
line_u, = plt.plot([], [], 'b', lw=1, label='u')
ax2.legend(loc='upper right')
ax1.set_title('Sum of all sources')
lineSum = [line_p, line_u]
# interactive plots :)
plt.subplots_adjust(left=0.15, top=0.9)
showVelocity = [not reflect] # value in list - a hack to use nonlocal in Python 2
showPressure = [True]
checkAx = plt.axes([0.05, 0.6, 0.05, 0.15])
checkButtons = CheckButtons(checkAx, ('p', 'v'), (True, showVelocity[0]))
def toggleLines(label):
if label == 'p':
showPressure[0] = not showPressure[0]
elif label == 'v':
showVelocity[0] = not showVelocity[0]
checkButtons.on_clicked(toggleLines)
resetAnimation = [False]
buttonAx = plt.axes([0.05, 0.85, 0.05, 0.04])
button = Button(buttonAx, 'Reset', hovercolor='0.975')
def reset(event):
resetAnimation[0] = True
button.on_clicked(reset)
def drawBackground():
for iSrc, src in enumerate(sources):
lines[iSrc][0].set_data([src.pos, src.pos],
[-src.pAmpl * 3, src.pAmpl * 3])
for line in lines[iSrc][1:]:
line.set_data([], [])
lineSum[0].set_data([], [])
lineSum[1].set_data([], [])
return tuple(itertools.chain(*lines)) + tuple(lineSum)
def drawFrame(i):
t = i * SPEED
x = np.linspace(0, xMax, nPOINTS)
pSum = np.zeros(nPOINTS)
uSum = np.zeros(nPOINTS)
for iSrc, src in enumerate(sources):
pDirect = np.zeros(nPOINTS)
uDirect = np.zeros(nPOINTS)
pRefl = np.zeros(nPOINTS)
uRefl = np.zeros(nPOINTS)
p = np.zeros(nPOINTS)
u = np.zeros(nPOINTS)
for i in xrange(0, nPOINTS):
pDirect[i] = src.p(pnt2x(i), t)
uDirect[i] = src.u(pnt2x(i), t)
if reflect:
pRefl[i] = reflectedSources[iSrc].p(pnt2x(i), t)
uRefl[i] = reflectedSources[iSrc].u(pnt2x(i), t)
pSum[i] += (pDirect[i] + pRefl[i])
uSum[i] += (uDirect[i] + uRefl[i])
p = pDirect + pRefl
u = uDirect + uRefl
if showPressure[0]:
lines[iSrc][0].set_data(x, pDirect)
lines[iSrc][2].set_data(x, pRefl)
lines[iSrc][4].set_data(x, p)
else:
lines[iSrc][0].set_data([], [])
lines[iSrc][2].set_data([], [])
lines[iSrc][4].set_data([], [])
if showVelocity[0]:
lines[iSrc][1].set_data(x, uDirect)
lines[iSrc][3].set_data(x, uRefl)
lines[iSrc][5].set_data(x, u)
else:
lines[iSrc][1].set_data([], [])
lines[iSrc][3].set_data([], [])
lines[iSrc][5].set_data([], [])
if showPressure[0]:
lineSum[0].set_data(x, pSum)
else:
lineSum[0].set_data([], [])
if showVelocity[0]:
lineSum[1].set_data(x, uSum)
else:
lineSum[1].set_data([], [])
return tuple(itertools.chain(*lines)) + tuple(lineSum)
def cnt():
i = 0
while True:
yield i
if resetAnimation[0]:
i = 0
resetAnimation[0] = False
else:
i += 1
anim = animation.FuncAnimation(fig, drawFrame, init_func=drawBackground,
frames=cnt, interval=40, blit=True, repeat=False)
plt.show()
def plot_data_for_station(station, available_data, event, get_data_callback,
domain_bounds):
"""
Plots all data for a station in an interactive plot.
:type station: dict
:param station: A dictionary containing the keys 'id', 'latitude',
'longitude', 'elevation_in_m', and 'local_depth_in_m' describing the
current station.
:type available_data: dict
:param available_data: The available processed and synthetic data. The raw
data is always assumed to be available.
:type event: dict
:param event: A dictionary describing the current event.
:type get_data_callback: function
:param get_data_callback: Callback function returning an ObsPy Stream
object.
get_data_callback("raw")
get_data_callback("synthetic", iteration_name)
get_data_callback("processed", processing_tag)
:type domain_bounds: dict
:param domain_bounds: The domain bounds.
"""
import datetime
import matplotlib.dates
from matplotlib.widgets import CheckButtons
from obspy.core.util.geodetics import calcVincentyInverse
import textwrap
# Setup the figure, the window and plot title.
fig = plt.figure(figsize=(14, 9))
fig.canvas.set_window_title("Data for station %s" % station["id"])
fig.text(0.5, 0.99, "Station %s" % station["id"],
verticalalignment="top", horizontalalignment="center")
# Add one axis for each component. Share all axes.
z_axis = fig.add_axes([0.30, 0.65, 0.68, 0.3])
n_axis = fig.add_axes([0.30, 0.35, 0.68, 0.3], sharex=z_axis,
sharey=z_axis)
e_axis = fig.add_axes([0.30, 0.05, 0.68, 0.3], sharex=z_axis,
sharey=z_axis)
axis = [z_axis, n_axis, e_axis]
# Set grid, autoscale and hide all tick labels (some will be made visible
# later one)
for axes in axis:
plt.setp(axes.get_xticklabels(), visible=False)
plt.setp(axes.get_yticklabels(), visible=False)
axes.grid(b=True)
axes.autoscale(enable=True)
axes.set_xlim(0.0, 12345.0)
# Axes for the data selection check boxes.
raw_check_axes = fig.add_axes([0.01, 0.8, 0.135, 0.15])
synth_check_axes = fig.add_axes([0.155, 0.8, 0.135, 0.15])
proc_check_axes = fig.add_axes([0.01, 0.5, 0.28, 0.29])
# The map axes
map_axes = fig.add_axes([0.01, 0.05, 0.28, 0.40])
# Fill the check box axes.
raw_check = CheckButtons(raw_check_axes, ["raw"], [True])
proc_check = CheckButtons(proc_check_axes, [
"\n".join(textwrap.wrap(_i, width=30))
for _i in available_data["processed"]],
[False] * len(available_data["processed"]))
synth_check = CheckButtons(synth_check_axes, available_data["synthetic"],
[False] * len(available_data["synthetic"]))
for check in [raw_check, proc_check, synth_check]:
plt.setp(check.labels, fontsize=10)
raw_check_axes.text(
0.02, 0.97, "Raw Data", transform=raw_check_axes.transAxes,
verticalalignment="top", horizontalalignment="left", fontsize=10)
proc_check_axes.text(
0.02, 0.97, "Processed Data", transform=proc_check_axes.transAxes,
verticalalignment="top", horizontalalignment="left", fontsize=10)
synth_check_axes.text(
0.02, 0.97, "Synthetic Data", transform=synth_check_axes.transAxes,
verticalalignment="top", horizontalalignment="left", fontsize=10)
bounds = domain_bounds["bounds"]
map_object = plot_domain(
bounds["minimum_latitude"], bounds["maximum_latitude"],
bounds["minimum_longitude"], bounds["maximum_longitude"],
bounds["boundary_width_in_degree"],
rotation_axis=domain_bounds["rotation_axis"],
rotation_angle_in_degree=domain_bounds["rotation_angle"],
plot_simulation_domain=False, zoom=True, ax=map_axes)
plot_stations_for_event(map_object=map_object,
station_dict={station["id"]: station},
event_info=event)
# Plot the beachball for one event.
plot_events([event], map_object=map_object, beachball_size=0.05)
dist = calcVincentyInverse(
event["latitude"], event["longitude"], station["latitude"],
station["longitude"])[0] / 1000.0
map_axes.set_title("Epicentral distance: %.1f km | Mag: %.1f %s" %
(dist, event["magnitude"], event["magnitude_type"]),
fontsize=10)
PLOT_OBJECTS = {
"raw": None,
"synthetics": {},
"processed": {}
}
def plot(plot_type, label=None):
if plot_type == "raw":
st = get_data_callback("raw")
PLOT_OBJECTS["raw"] = []
save_at = PLOT_OBJECTS["raw"]
elif plot_type == "synthetic":
st = get_data_callback("synthetic", label)
PLOT_OBJECTS["synthetics"][label] = []
save_at = PLOT_OBJECTS["synthetics"][label]
elif plot_type == "processed":
st = get_data_callback("processed", label)
PLOT_OBJECTS["processed"][label] = []
save_at = PLOT_OBJECTS["processed"][label]
# Loop over all traces.
for tr in st:
# Normalize data.
tr.data = np.require(tr.data, dtype="float32")
tr.data -= tr.data.min()
tr.data /= tr.data.max()
tr.data -= tr.data.mean()
tr.data /= np.abs(tr.data).max() * 1.1
# Figure out the correct axis.
component = tr.stats.channel[-1].upper()
if component == "N":
axis = n_axis
elif component == "E":
axis = e_axis
elif component == "Z":
axis = z_axis
else:
raise NotImplementedError
if plot_type == "synthetic":
time_axis = matplotlib.dates.drange(
event["origin_time"].datetime,
(event["origin_time"] + tr.stats.delta * (tr.stats.npts))
.datetime,
datetime.timedelta(seconds=tr.stats.delta))
zorder = 2
color = "red"
elif plot_type == "raw":
time_axis = matplotlib.dates.drange(
tr.stats.starttime.datetime,
(tr.stats.endtime + tr.stats.delta).datetime,
datetime.timedelta(seconds=tr.stats.delta))
zorder = 0
color = "0.8"
elif plot_type == "processed":
time_axis = matplotlib.dates.drange(
tr.stats.starttime.datetime,
(tr.stats.endtime + tr.stats.delta).datetime,
datetime.timedelta(seconds=tr.stats.delta))
zorder = 1
color = "0.2"
else:
msg = "Plot type '%s' not known" % plot_type
raise ValueError(msg)
save_at.append(axis.plot_date(time_axis[:len(tr.data)], tr.data,
color=color, zorder=zorder, marker="",
linestyle="-"))
axis.set_ylim(-1.0, 1.0)
axis.xaxis.set_major_formatter(
matplotlib.dates.DateFormatter("%H:%M:%S"))
if component == "E":
try:
plt.setp(axis.get_xticklabels(), visible=True)
except:
pass
if plot_type != "raw":
axis.set_xlim(time_axis[0], time_axis[-1])
# Adjust the limit only if there are no synthetics and processed if
# plotting raw data.
elif not PLOT_OBJECTS["synthetics"] and \
not PLOT_OBJECTS["processed"]:
axis.set_xlim(time_axis[0], time_axis[-1])
for label, axis in zip(("Vertical", "North", "East"),
(z_axis, n_axis, e_axis)):
axis.text(0.98, 0.95, label,
verticalalignment="top", horizontalalignment="right",
bbox=dict(facecolor="white", alpha=0.5, pad=5),
transform=axis.transAxes, fontsize=11)
def _checked_raw(label):
checked(label, "raw")
def _checked_proc(label):
checked(label, "proc")
def _checked_synth(label):
checked(label, "synth")
def checked(label, check_box):
if check_box == "raw":
if PLOT_OBJECTS["raw"] is not None:
for _i in PLOT_OBJECTS["raw"]:
for _j in _i:
_j.remove()
PLOT_OBJECTS["raw"] = None
else:
PLOT_OBJECTS["raw"] = []
plot("raw")
elif check_box == "synth":
if label in PLOT_OBJECTS["synthetics"]:
for _i in PLOT_OBJECTS["synthetics"][label]:
for _j in _i:
_j.remove()
del PLOT_OBJECTS["synthetics"][label]
else:
PLOT_OBJECTS["synthetics"][label] = []
plot("synthetic", label=label)
elif check_box == "proc":
# Previously broken up.
label = label.replace("\n", "")
if label in PLOT_OBJECTS["processed"]:
for _i in PLOT_OBJECTS["processed"][label]:
for _j in _i:
_j.remove()
del PLOT_OBJECTS["processed"][label]
else:
PLOT_OBJECTS["processed"][label] = []
plot("processed", label=label)
try:
fig.canvas.draw()
except:
pass
raw_check.on_clicked(_checked_raw)
proc_check.on_clicked(_checked_proc)
synth_check.on_clicked(_checked_synth)
# Always plot the raw data by default.
_checked_raw("raw")
try:
fig.canvas.draw()
except:
pass
# One has call plt.show() to activate the main loop of the new figure.
# Otherwise events will not work.
plt.gcf().patch.set_alpha(0.0)
plt.show()
class Graphics (GraphMolecule, GraphBias, GraphTrans, GraphOrbitals, GraphIVCurve):
""" Manages the graphical representation of the project
Attributes
fig Handle to the entire figure
Bias The selected bias
Gate The selected gate voltage
OrbitalSel The selected orbital or series of orbitals
axSC Handle to the radio buttons window for the self consistency
cbSC Handle to the radio buttons for the self consistency
axOptions1 Handle to the checkbox window with options 1
cbOptions1 Handle to the checkboxes with options 1
axOptions2 Handle to the checkbox window with options 2
cbOptions2 Handle to the checkboxes with options 2
axGleft, axGright Handle to the slider windows for selecting the lead interaction strength
sGleft, sGright Handle to the sliders for selecting the lead interaction strength
axSave Handle to the save button window
bSave Handle to the save button
Methods
init()
OnPick(event) Manages the pick events
OnClick(event) Manages the on click events
Save() Manages the input from the save button
Options1(label) Manages the input from the options 1 window
Options2(label) Manages the input from the options 2 window
SetMolecule(Mol) Sets the molecule class from which the graphics gets its information
UpdateMolecule() Updates everything that changes when the molecule has changed
UpdateConsistency(label)Updates the selected consistency method
UpdateG(val) Updates the interaction strength with the leads
UpdateBias(bias) Updates the selected bias
UpdateHamExt() Updates everything that changes when the extended hamiltonian is changed
UpdateGate(gate) Updates the selected gate voltage
UpdateAtomSel(iAtom) Updates the selected atom
UpdateOrbitalSel(iOrb) Updates the selected orbital
UpdateSelection() Updates everything that changes after one of the selections has changed
"""
def __init__(self):
self.Consistency = 'Not self consistent'
self.Bias = 0.1
self.Gate = None
self.OrbitalSel = None
self.fig, (self.axBias, self.axTrans, self.axIVCurve) = plt.subplots(3,1)
self.fig.patch.set_facecolor('ghostwhite')
plt.subplots_adjust(left=0.3)
pos = self.axBias.get_position()
self.axBias.set_position ([pos.x0, 0.55, pos.width, 0.40])
self.axTrans.set_position([pos.x0, 0.05, pos.width, 0.40])
self.axIVCurve.set_position([0.05, 0.05, 0.2, 0.3])
self.axCM = self.fig.add_axes([0.94, 0.55, 0.01, 0.35])
self.fig.canvas.mpl_connect('button_press_event', self.OnClick)
self.fig.canvas.mpl_connect('pick_event', self.OnPick)
self.fig.canvas.mpl_connect('key_press_event', self.OnPress)
self.InitMolecule()
self.InitBias()
self.InitTrans()
self.InitOrbitals()
self.InitIVCurve()
self.axSC = plt.axes([0.05, 0.85, 0.15, 0.10], axisbg='white')
self.cbSC = RadioButtons(self.axSC, ('Not self consistent', 'Hubbard', 'PPP'))
self.cbSC.on_clicked(self.UpdateConsistency)
self.axOptions1 = plt.axes([0.05, 0.7, 0.15, 0.10], axisbg='white')
self.cbOptions1 = CheckButtons(self.axOptions1, ('Overlap', 'Show Local Density','Show Local Currents'), (False, False, True))
self.cbOptions1.on_clicked(self.Options1)
self.axOptions2 = plt.axes([0.05, 0.5, 0.15, 0.15], axisbg='white')
self.cbOptions2 = CheckButtons(self.axOptions2, ('Show Transmission', 'Show Current', 'Show DOS', 'Show Orbitals', 'Show Phase'), (True, True, False, False, False))
self.cbOptions2.on_clicked(self.Options2)
c = ['seagreen', 'b', 'darkorange', 'lightsteelblue', 'm']
[rec.set_facecolor(c[i]) for i, rec in enumerate(self.cbOptions2.rectangles)]
self.axGleft = plt.axes([0.05, 0.43, 0.15, 0.02], axisbg='white')
self.axGright = plt.axes([0.05, 0.40, 0.15, 0.02], axisbg='white')
self.sGleft = Slider(self.axGleft, 'Gl', 0.0, 1.0, valinit = gLeft)
self.sGright = Slider(self.axGright, 'Gr', 0.0, 1.0, valinit = gRight)
self.sGleft.on_changed(self.UpdateG)
self.sGright.on_changed(self.UpdateG)
self.axSave = plt.axes([0.92, 0.95, 0.07, 0.04])
self.bSave = Button(self.axSave, 'Save')
self.bSave.on_clicked(self.Save)
def OnPick(self, event):
if isinstance(event.artist, Rectangle):
self.OnPickOrbital(event)
else:
self.OnPickMolecule(event)
def OnClick (self, event):
if event.inaxes==self.axMol:
if event.button==1:
self.OnClickMolecule (event)
elif event.button==3:
self.OnClickBias (event)
elif event.inaxes==self.axOrb:
if event.button==1:
return
if event.button==3:
self.OnClickTrans (event)
return
def Save(self, event):
self.Mol.Save()
def Options1(self, label):
if label == 'Overlap':
self.Mol.SetOverlap(self.cbOptions1.lines[0][0].get_visible())
self.UpdateMolecule()
elif label == 'Show Local Density':
self.DrawLocalDensity(self.cbOptions1.lines[1][0].get_visible())
elif label == 'Show Local Currents':
self.DrawLocalCurrents(self.cbOptions1.lines[2][0].get_visible())
return
def Options2(self, label):
if label == 'Show Transmission':
self.DrawTransmission()
elif label == 'Show Current':
self.DrawCurrent()
elif label == 'Show DOS':
self.DrawDOS()
elif label == 'Show Orbitals':
self.DrawOrbitals()
self.DrawSelOrbitals()
elif label == 'Show Phase':
self.DrawTransmission()
self.DrawSelTransmission()
return
def SetMolecule(self, Molecule):
self.Mol = Molecule
self.UpdateMolecule()
def UpdateMolecule (self):
self.Mol.CreateHam()
self.Mol.CreateOverlap()
self.Mol.CreateV()
self.ChangeMolecule ()
self.ChangeMoleculeOrbitals ()
self.DrawMolecule ()
return self.UpdateHamExt ()
def UpdateConsistency(self, label):
self.Consistency = label
print "Consistency set to:", self.Consistency
return self.UpdateHamExt ()
def UpdateG (self, val):
print "Interaction strengths set to:", self.sGleft.val, self.sGright.val
self.Mol.SetG (self.sGleft.val, self.sGright.val)
return self.UpdateHamExt ()
def UpdateBias(self, bias):
self.Bias = bias
print "Bias voltage set to: ", self.Bias, "V"
return self.UpdateHamExt ()
def UpdateHamExt (self):
self.Mol.CreateHamExt (self.Bias, self.Consistency)
self.Mol.CalcGamma()
self.Mol.Density(self.Bias)
self.Mol.Transmission()
self.Mol.Current(self.Bias)
self.Mol.CalcDOS()
self.DrawLocalDensity ()
self.DrawBias ()
self.DrawTransmission ()
self.DrawDOS ()
self.DrawCurrent ()
self.DrawOrbitals ()
return self.UpdateSelection()
def UpdateGate (self, gate):
self.Gate = gate
print "Gates voltage set to: ", self.Gate, "V"
self.OrbitalSel = None
self.AtomSel = None
return self.UpdateSelection()
def UpdateAtomSel(self, iAtom):
self.AtomSel = iAtom
if self.AtomSel == None:
print "No atom selected"
self.axMol.set_title('No atom selected')
self.OrbitalSel = None
elif isinstance(iAtom, int):
print "Selected atom", self.Mol.Atom[self.AtomSel][0], "at", self.AtomSel, " Local density = ", self.Mol.LD[self.AtomSel, self.AtomSel]
self.axMol.set_title('Selected atom: %c at %d. Density = %f'%(self.Mol.Atom[self.AtomSel][0],self.AtomSel, self.Mol.LD[self.AtomSel, self.AtomSel]))
Orbitals = []
for i in range(self.Mol.N):
Orbitals.append(np.real(self.Mol.eigvec[i][self.AtomSel]*np.conjugate(self.Mol.eigvec[i][self.AtomSel])))
self.OrbitalSel = Orbitals
return self.UpdateSelection()
def UpdateOrbitalSel(self, iOrbital):
self.OrbitalSel = iOrbital
if isinstance(self.OrbitalSel, int):
print "Orbital set to:", self.OrbitalSel, "with energy", self.Mol.e_arr[self.OrbitalSel], "eV"
self.AtomSel = self.Mol.eigvec[iOrbital]
self.Gate = self.Mol.e_arr[self.OrbitalSel]
return self.UpdateSelection()
def UpdateSelection (self):
self.DrawLocalCurrents ()
self.DrawGate()
self.DrawIVCurve()
self.DrawSelAtom()
self.DrawSelOrbitals()
self.DrawSelTransmission()
return
def RunSequence (self):
self.fig.set_size_inches(18.5, 13.5) #default 18.5, 10.5
N=100
for i in range(N):
e = self.Mol.Gates[0]+(self.Mol.Gates[-1]-self.Mol.Gates[0])/N*i
self.UpdateGate(e)
self.fig.savefig('Output/PN/Armchair 7,21, seq ' + str(i) + ' Energy=' + str(math.ceil(e*100)/100) + 'eV.png', dpi=self.fig.dpi)
print e
for i in iterHulls:
for j in iterHulls[i]:
if j.get_visible():
legLine.append(j)
legLabel.append(j.get_label())
font=FontProperties(size='small')
if (len(legLine) > 0):
ax.legend(legLine, legLabel, loc=options.lpos, shadow=True, prop=font)
else:
ax.legend(("",),loc=options.lpos, shadow=True, prop=font)
plt.draw()
if not options.outfile:
rax = plt.axes([0.05, 0.15, 0.15, 0.6])
check = CheckButtons(rax, ['scatter', 'all', 'config'] + itervars.keys(), (scatterPoints.get_visible(), allHull.get_visible(), options.configs) + tuple(itervarsenabled.values()))
check.on_clicked(toggleHullVisibility)
toggleHullVisibility("")
if options.zoom:
limits = options.zoom.split(",");
if len(limits) != 4:
print "-z parameter needs 4 comma-seperated float values!"
exit(-1)
ax.set_xlim(float(limits[0]), float(limits[1]))
ax.set_ylim(float(limits[2]), float(limits[3]))
plt.show()
if options.outfile:
# fig.savefig(options.outfile)
def plot_clusters_in_brain(bn,background,cluster_list,actives=[True,False,False]):
if len(cluster_list)<3:
print 'just '+str(len(cluster_list))+ ' cluster'
colors=[[1,0,0],[0,1,0],[0,0,1]]
node2voxel=bn.get_node2voxel()
img=nib.load(bn.mask)
imgB = nib.load(background)
D=img.get_data()
sh=D.shape
A=bn.affine
aspect1=abs(A[2,2]/A[0,0])
aspect2=abs(A[2,2]/A[1,1])
aspect3=abs(A[1,1]/A[0,0])
M=pylab.zeros((sh[0],sh[1],sh[2],3))
B=M
B0=imgB.get_data().astype(numpy.float32, copy=False)
#B0img.get_data().astype(numpy.float32, copy=False)
B[:,:,:,0]=(B0-B0.min())/(B0.max()-B0.min())
B[:,:,:,1]=B[:,:,:,0]
B[:,:,:,2]=B[:,:,:,0]
M=B
#M[D>0,:]=pylab.ones_like(M[D>0,:])
for ii in range(len(actives)):
if actives[ii]:
if len(cluster_list)>ii:
for node in cluster_list[ii]:
(i,j,k)=node2voxel[str(node)]
M[i,j,k,:]=[B[i,j,k,l]*colors[ii][l] for l in range(3)]
plt.figure()
plt.subplot(221)
j0 = round(M.shape[1]*0.5)
lj = plt.imshow(M[:,j0,:,:].swapaxes(0,1),interpolation='None',aspect=aspect1)
plt.axis([0, sh[0], 0, sh[2]])
plt.subplot(222)
i0 = round(M.shape[0]*0.5)
li = plt.imshow(M[i0,:,:,:].swapaxes(0,1),interpolation='None',aspect=aspect2)
plt.axis([0, sh[1], 0, sh[2]])
plt.subplot(223)
k0 = round(M.shape[2]*0.5)
lk = plt.imshow(M[:,:,k0,:].swapaxes(0,1),interpolation='None',aspect=aspect3)
plt.axis([0, sh[0], 0, sh[1]])
axcolor = 'lightgoldenrodyellow'
axi = plt.axes([0.55, 0.3, 0.4, 0.03], axisbg=axcolor)
axj = plt.axes([0.55, 0.2, 0.4, 0.03], axisbg=axcolor)
axk = plt.axes([0.55, 0.1, 0.4, 0.03], axisbg=axcolor)
si = Slider(axi, 'i', 0, sh[0]-1, valinit=i0,valfmt='%i')
sj = Slider(axj, 'j', 0, sh[1]-1, valinit=j0,valfmt='%i')
sk = Slider(axk, 'k', 0, sh[2]-1, valinit=k0,valfmt='%i')
def update(val):
i = int(si.val)
j = int(sj.val)
k = int(sk.val)
lj.set_data(M[:,j,:,:].swapaxes(0,1))
li.set_data(M[i,:,:,:].swapaxes(0,1))
lk.set_data(M[:,:,k,:].swapaxes(0,1))
plt.draw()
si.on_changed(update)
sj.on_changed(update)
sk.on_changed(update)
rax = plt.axes([0.025, 0.5, 0.1, 0.15], axisbg=axcolor)
check = CheckButtons(rax, ('0', '1', '2'), actives=actives)
def showcluster(label):
i0=int(si.val)
j0=int(sj.val)
k0=int(sk.val)
M=B
for node in cluster_list[int(label)]:
(i,j,k)=node2voxel[str(node)]
if all(M[i,j,k,:]==colors[int(label)]):
M[i,j,k,:]=[1,1,1]
else:
M[i,j,k,:]=colors[int(label)]
lj.set_data(M[:,j0,:,:].swapaxes(0,1))
li.set_data(M[i0,:,:,:].swapaxes(0,1))
lk.set_data(M[:,:,k0,:].swapaxes(0,1))
plt.draw()
check.on_clicked(showcluster)
return
class CBox(object):
"""Custom checkbox."""
def __init__(self, fig, rect, labels, act=None, func=None, fargs=None):
"""init function
Parameters:
fig: a matplotlib.figure.Figure instance
rect: [left, bottom, width, height]
each of which in 0-to-1-fraction i.e. 0<=x<=1
labels: array of strings
labels to be checked
act: a len(labels) array of booleans, optional, default: None
indicating whether the label is active at first
if None, all labels are inactive
func: function, optional, default: None
if not None, function called when checkbox status changes
fargs: array, optional, default: None
(optional) arguments for func
"""
self.fig = fig
self._rect = rect
self._func = func
self._fargs = fargs
self._labels = labels
self.axes = self.fig.add_axes(rect)
self.axes.set_axis_bgcolor('1.00')
inac = act if act is not None else (False,) * len(labels)
self.cb = CheckButtons(self.axes, self._labels, inac)
self.cb.on_clicked(self._onchange)
def _onchange(self,label):
"""Actual function called when checkbox status changes."""
if self.get_visible():
if self._func is not None:
if self._fargs is not None:
self._func(*self._fargs)
else:
self._func()
self.fig.canvas.draw()
def get_status(self):
"""Get checkbox status.
Returns: status
status: list of booleans
a len(labels) list of booleans indicating whether
label is active
NB: checkbox status changes even when it's not visible
if a click hits the checkbox!
"""
stat = []
for i in self.cb.lines:
stat.append(i[0].get_visible() or i[1].get_visible())
return stat
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()